# Final Ozone NAAQS Regulatory Impact AnalysisEPA-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 Carolinaii 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 Thi s 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- 6iii

2.4 References........................................

................................................... ............... 2-10
Appendix: Chapter 2 Characterizing Ozone and Modeling T ools 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 Precurs ors ........... 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 F ull Attainment Scenario.................... 4-1
Synopsis ........................................................................................................................ 4-1
4.1 Development of Full Attainment Targets for Estimate of Ext rapolated 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 outsid e of Phase 1 and 2 Areas ......................................... .................................................. 4 -9 4.1.5 Aggregate Results / Verification Modeling of Extrapo lated Targets .... 4-11iv

4.2 Conversion of Full Attainment Percentage Targets int o Extrapolated Tons ..... 4-15
4.3 Methodology Used to Estimate the Amount of “Overcontrolle d” 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 Engin eering 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 Control s ................................. 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 Probabil istic Framework for Benefits Assessment ..............................

................................................... .... 6-5v

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................................ ................................................ 6-2 3 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 .................................... ................................................ 6-2 7
6.5 Results and Implications .........................

................................................... ...... 6-28
6.5.1 Ozone Benefit Estimates.............................. .......................................... 6-28
6.5.2 PM 2.5 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 Rela ted To the Benefits Analysis...........................................

................................................... .............. 6c-1
Appendix Chapter 6d: Exploring the Effects of Chang es in Tropospheric Ozone on UVB.............................................................................................................6d-1

Chapter 7: Conclusions and Implications of the Illus trative 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 Alter nate Baseline Scenario................................... .................................................. 7 -9vi 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 Analys is................................... 7a-1
Appendix Chapter 7b Post 2020 Attainment Analysis ........ ....................................... 7b-1

Chapter 8: Statutory and Executive Order Impact Ana lyses ............................................... .. 8-1
Synopsis ........................................................................................................................ 8-1
8.1 Executive Order 12866: Regulatory Planning and Rev iew................................ 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 Af fect 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 Env ironmental Justice in Minority Populations and Low-Income Populat ions.......................... 8-6vii LIST OF TABLES

Number Page
2.1 Geographic Specifications of Modeling Domains....

................................................... . 2-6 2.2 Control Strategies and Projection Assumptions in the 2 020 Emissions Inventory ...... 2-7

3.1 Controls for Current Ozone Standard by Sector A pplied in the Baseline Determination for 2020...............................

................................................... ............... 3-5 3.2 Controls Applied, by Sector, for the 0.070 ppm Contr ol Strategy (Incremental to Baseline) ..................................................................................................................... 3-11 3.3 Emissions and Reductions (2020) From Applying the Mod eled Control Strategy (Incremental to the Baseline)..........................

................................................... ......... 3-24

4.1 Estimated Percentage Reductions of NOx and VOC beyond t he RIA Control Scenario Necessary to Meet Various Alternate Ozone St andards in the Phase I Areas ............................................................................................................................. 4-7 4.2 Estimated Percentage Reductions of NOx beyond the RIA C ontrol 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 C ontrol Scenario Necessary to Meet the 0.070 ppm Ozone Standard in P hase 2 Areas .......................... 4-9 4.4 Estimated Percentage Reductions of NOx beyond the RIA C ontrol Case Necessary to Meet the 0.065 ppm Ozone Standard in P hase 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 Standa rds 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 Re ductions of NOx Beyond the RIA Control Scenario Necessary to Meet the Vari ous Ozone Standards in 2020............................................................................................................................. 4-16 4.7b Estimated Extrapolated Emissions Reductions of NO x + VOC Beyond the RIA Control Scenario Necessary to Meet the Various Ozone Standar ds in 2020.............. 4-17 4.8 Estimated Percentages of Modeled Control Strategy Emi ssions 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........................

................................................... .... 4-20viii LIST OF TABLES (CONTINUED)

Number Page
5.1 Annual Control Costs by Sector and Region, for th e Modeled Control Strategy (2006$).......................................................................................................................... 5-6 5.2 Marginal Cost and Average Cost Values Used in Calcul ating M .............................. 5-16 5.3 Extrapolated Emission Reductions Needed (Post Appl ication of Supplemental Controls) to Meet Various Alternate Standards in 202 0............................................. 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 A ctual Price Changes for EPA Fuel Control Rules ....................................................................................... 5-30

6.1 Human Health and Welfare Effects of Ozone and PM 2.5 6-9 6.2 Ozone and PM Related Health Endpoints Basis for th e Concentration-Response Function Associated with that Endpoint, and Sub-Populatio ns 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 End points (2006$)........................... 6-24 6-5 Illustrative Strategy to Attain 0.065 ppm: Esti mated Annual Reductions in the Incidence of Premature Mortality Associated with Ozone Exp osure in 2020 (Incremental to Current Ozone Standard, Arithmetic Me an, 95% Confidence Intervals in Parentheses) ..........................

................................................... ................ 6-34 6-6 Illustrative Strategy to Attain 0.065 ppm: Esti mated 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: Es timated Annual Reductions in the Incidence of PM Premature Mortality associate d with PM co-benefit............. 6-36 6-8 Illustrative 0.065 ppm Full Attainment Scenario: Es timated Annual Reductions in the Incidence of Morbidity Associated with PM Co-ben efit.................................. 6-37 6-9 Illustrative Strategy to Attain 0.070 ppm: Esti mated Annual Reductions in the Incidence of Premature Mortality Associated with Ozone Exp osure in 2020 (Incremental to Current Ozone Standard, Arithmetic Me an, 95% Confidence Intervals in Parentheses) ..........................

................................................... ................ 6-38ix LIST OF TABLES (CONTINUED)

Number Page
6-10 Illustrative Strategy to Attain 0.070 ppm: Est imated 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: E stimated Annual Reductions in the Incidence of PM Premature Mortality associate d with PM co-benefit............. 6-40 6-12 Illustrative 0.070 ppm Full Attainment Scenario: E stimated Annual Reductions in the Incidence of Morbidity Associated with PM Co-ben efit.................................. 6-41 6-13 Illustrative Strategy to Attain 0.075 ppm: Est imated Annual Reductions in the Incidence of Premature Mortality Associated with Ozone Exp osure in 2020 (Incremental to Current Ozone Standard, Arithmetic Me an, 95% Confidence Intervals in Parentheses) ..........................

................................................... ................ 6-42 6-14 Illustrative Strategy to Attain 0.075 ppm: Est imated 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: E stimated Annual Reductions in the Incidence of PM Premature Mortality associate d with PM co-benefit............. 6-44 6-16 Illustrative 0.075 ppm Full Attainment Scenario: E stimated Annual Reductions in the Incidence of Morbidity Associated with PM Co-ben efit.................................. 6-45 6-17 Illustrative Strategy to Attain 0.079 ppm: Est imated Annual Reductions in the Incidence of Premature Mortality Associated with Ozone Exp osure in 2020 (Incremental to Current Ozone Standard, Arithmetic Me an, 95% Confidence Intervals in Parentheses) ..........................

................................................... ................ 6-46 6-18 Illustrative Strategy to Attain 0.079 ppm: Est imated 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: E stimated Annual Reductions in the Incidence of PM Premature Mortality associate d with PM co-benefit............. 6-48 6-20 Illustrative 0.079 ppm Full Attainment Scenario: E stimated Annual Reductions in the Incidence of Morbidity Associated with PM Co-ben efit.................................. 6-49 6-21 Illustrative Strategy to Attain 0.065 ppm: Est imated Annual Valuation of Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard, Arithmet ic Mean, 95% Confidence Intervals in Parentheses, Millions of 20 06$)........................................... 6-50 6-22 Illustrative Strategy to Attain 0.065 ppm: Est imated Annual Valuation of Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current Ozone Standard, 95% Confide nce Intervals in Parentheses, Millions of 2006$) ................................................................................. 6-51 6-23 Illustrative 0.065 ppm Full Attainment Scenario: E stimated Annual Valuation of Reductions in the Incidence of PM Premature Mortalit y associated with PM cobenefit (Millions of 2006$)......................................................................................... 6-52x LIST OF TABLES (CONTINUED)

Number Page
6-24 Illustrative 0.065 ppm Full Attainment Scenario: E stimated Annual Valuation of Reductions in the Incidence of Morbidity Associated w ith PM Co-benefit (Millions of 2006$)..................................................................................................... 6-53 6-25 Illustrative Strategy to Attain 0.070 ppm: Est imated Annual Valuation of Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard, Arithmet ic Mean, 95% Confidence Intervals in Parenthes, Millions of 2006$) .............................................. 6-54 6-26 Illustrative Strategy to Attain 0.070 ppm: Est imated Annual Valuation of Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current Ozone Standard, 95% Confide nce Intervals in Parentheses, Millions of 2006$) ................................................................................. 6-55 6-27 Illustrative 0.070 ppm Full Attainment Scenario: E stimated Annual Valuation of Reductions in the Incidence of PM Premature Mortalit y associated with PM cobenefit (Millions of 2006$)......................................................................................... 6-56 6-28 Illustrative 0.070 ppm Full Attainment Scenario: E stimated Annual Valuation of Reductions in the Incidence of Morbidity Associated w ith PM Co-benefit (Millions of 2006$)..................................................................................................... 6-57 6-29 Illustrative Strategy to Attain 0.075 ppm: Est imated Annual Valuation of Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard, Arithmet ic Mean, 95% Confidence Intervals in Parenthes, Millions of 2006$) .............................................. 6-58 6-30 Illustrative Strategy to Attain 0.075 ppm: Est imated Annual Valuation of Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current Ozone Standard, 95% Confide nce Intervals in Parentheses, Millions of 2006$) ................................................................................. 6-59 6-31 Illustrative 0.075 ppm Full Attainment Scenario: E stimated Annual Valuation of Reductions in the Incidence of PM Premature Mortalit y associated with PM cobenefit (Millions of 2006$)......................................................................................... 6-60 6-32 Illustrative 0.075 ppm Full Attainment Scenario: E stimated Annual Valuation of Reductions in the Incidence of Morbidity Associated w ith PM Co-benefit (Millions of 2006$)..................................................................................................... 6-61 6-33 Illustrative Strategy to Attain 0.079 ppm: Est imated Annual Valuation of Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard, Arithmet ic Mean, 95% Confidence Intervals in Parenthes, Millions of 2006$) .............................................. 6-62 6-34 Illustrative Strategy to Attain 0.079 ppm: Est imated Annual Valuation of Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current Ozone Standard, 95% Confide nce Intervals in Parentheses, Millions of 2006$) ................................................................................. 7-63xi LIST OF TABLES (CONTINUED)

Number Page
6-35 Illustrative 0.079 ppm Full Attainment Scenario: E stimated Annual Valuation of Reductions in the Incidence of PM Premature Mortalit y associated with PM cobenefit (Millions of 2006$)......................................................................................... 6-64 6-36 Illustrative 0.079 ppm Full Attainment Scenario: E stimated Annual Valuation of Reductions in the Incidence of Morbidity Associated w ith PM Co-benefit (Millions of 2006$)..................................................................................................... 6-65 6-38 Estimate of Annual Ozone and PM 2.5 Combined Morbidity and Mortality (Millions of 2006$) for the 0.065 ppm Full Attainme nt............................................. 6-68 6-39 Estimate of Annual Ozone and PM 2.5 Combined Morbidity and Mortality (Millions of 2006$) for the 0.070 ppm Full Attainme nt............................................. 6-69 6-40 Estimate of Annual Ozone and PM 2.5 Combined Morbidity and Mortality (Millions of 2006$) for the 0.075 ppm Full Attainme nt............................................. 6-70 6-41 Estimate of Annual Ozone and PM 2.5 Combined Morbidity and Mortality (Millions of 2006$) for the 0.079 ppm Full Attainme nt............................................. 6-71 6-42 Combined Estimate of Annual Ozone and PM 2.5 Benefits (Millions of $2006, 3% Discount Rate) for the 0.065 ppm Alternative Stand ard ...................................... 6-72 6-43 Combined Estimate of Annual Ozone and PM 2.5 Benefits (Millions of $2006, 7% Discount Rate) for the 0.065 ppm Alternative Stand ard ...................................... 6-73 6-44 Combined Estimate of Annual Ozone and PM 2.5 Benefits (Millions of $2006, 3% Discount Rate) for the 0.070 ppm Alternative Stand ard ...................................... 6-74 6-45 Combined Estimate of Annual Ozone and PM 2.5 Benefits (Millions of $2006, 7% Discount Rate) for the 0.070 ppm Alternative Stand ard ...................................... 6-75 6-46 Combined Estimate of Annual Ozone and PM 2.5 Benefits (Millions of $2006, 3% Discount Rate) for the 0.075 ppm Alternative Stand ard ...................................... 6-76 6-47 Combined Estimate of Annual Ozone and PM 2.5 Benefits (Millions of $2006, 7% Discount Rate) for the 0.075 ppm Alternative Stand ard ...................................... 6-77 6-48 Combined Estimate of Annual Ozone and PM 2.5 Benefits (Millions of $2006, 3% Discount Rate) for the 0.079 ppm Alternative Stand ard ...................................... 6-78 6-49 Combined Estimate of Annual Ozone and PM 2.5 Benefits (Millions of $2006, 7% Discount Rate) for the 0.079 ppm Alternative Stand ard ...................................... 6-79 6-50 Monetary Benefits Associated with Visibility Impr ovements from the 0.070 Simulated Ozone Attainment Strategy in Selected Federal Cla ss I Areas in 2020 (in millions of 2006$) .............................

................................................... ................. 6-84 6.51 Summary of Total Number of Annual Ozone and PM 2.5 -Related Premature Mortalities and Premature Morbidity Avoided in 2020.............................................. 6-88 6.52 Regional Breakdown of Annual Ozone Benefit Resu lts by Health Endpoint in 2020 (thousands of 2006$).......................................................................................... 6-90 6.53 Regional Breakdown of Annual PM Benefit Results b y Health Endpoint in 2020 (thousands of 2006$) at 3% .............................

................................................... ........ 6-91xii LIST OF TABLES (CONTINUED)

Number Page
6.54 Regional Breakdown of Annual PM Benefit Results b y Health Endpoint in 2020 (thousands of 2006$) at 7% .............................

................................................... ........ 6-92 6.55 Regional Breakdown of Annual Ozone and PM Benefi t Results by Health Endpoint in 2020 (3% discount rate, thousands of 20 06$)......................................... 6-93 6.56 Regional Breakdown of Annual Ozone and PM Benefi t Results by Health Endpoint in 2020 (7% discount rate, thousands of 20 06$)......................................... 6-94

7.1a Estimated Range of Annual Monetized Costs and Ozone Be nefits and PM 2.5 CoBenefits: 0.075 ppm Standard in 2020 in Billions of 2006$......................................... 7-3 7.1b Estimated Range of Annual Monetized Costs and Ozone Be nefits and PM 2.5 CoBenefits: 0.079 ppm Standard in 2020 in Billions of 2006$......................................... 7-3 7.1c Estimated Range of Annual Monetized Costs and Ozone Be nefits and PM 2.5 CoBenefits: 0.070 ppm Standard in 2020 in Billions of 2006$......................................... 7-3 7.1d Estimated Range of Annual Monetized Costs and Ozone Be nefits and PM 2.5 CoBenefits: 0.065 ppm Standard in 2020 in Billions of 2006$......................................... 7-4 7.2 Summary of Total Number of Annual Ozone and PM 2.5 -Related Premature Mortalities and Premature Morbidity Avoided: 2020 N ational Benefits...................... 7-9xiii 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) W ere Included for NonEGU Point and Area Sources, for the Current Ozon e Standard in the Baseline......................................................................................................................... 3-6 3.2 Counties Where Controls for Volatile Organic Chemica ls (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 f or Mobile Onroad and Nonroad Sources in Addition to National Mobile Cont rols for the Current Ozone Standard in the Baseline .....................

................................................... ............ 3-8 3.4 Baseline Projected 8-Hour Ozone Air Quality in 202 0 ................................................ 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 (Incrementa l 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 Contr ols for the Modeled Control Strategy (incremental to Baseline).........

................................................... ..... 3-17 3.9 Annual Tons of NOx Emission Reductions for the Model ed 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 Mode led 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 App lication of Controls for the Baseline and Modeled Control Strategy ............... ................................................ 3-2 4xiv LIST OF FIGURES (CONTINUED)

Number Page

4.1 Counties within which Across-the-Board Emissions R eductions 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 Highe st 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 High est 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 High est 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 S pecific 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 th at Standard ......................... 4-12 4.3b Map of Extrapolated Cost Counties for the 0.070 pp m Alternate Standard and the Estimated Percent NOx Controls Needed to Meet th at 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 th at Standard ......................... 4-13 4.3d Map of Extrapolated Cost Counties for the 0.079 pp m Alternate Standard and the Estimated Percent NOx Controls Needed to Meet th at 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 G eographic Areas (VOC nonEGU Point and Area Source Controls Prior to Cut Points) .................................... 5-4 5.3 Total Annualized Costs by Emissions Sector and Re gion for Modeled Control Strategy in 2020 ............................................................................................................ 5-8 5.4 Ratio of Unspecified Emission Reductions to Known E mission Reductions Across Various Standards for Phase 1 Areas............... ............................................... 5-16 5.5 Ranges of Hybrid (Mid) Average Cost/Ton Values ac ross 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.......................

................................................... .......... 5-23xv 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 PM 2.5 Benefits by Standard Alternative (3% and 7% Discount R ates) .... 6-81 6.4 Example Combined Ozone and PM 2.5 Monetized Benefits Estimates by Standard Alternative (3% and 7% Discount Rates).......... .......................................... 6-82 6.5 Ozone and PM Total Benefits including all combinations o f Mortality Estimates (3% discount rate)...................................

................................................... ................. 6-83 6.6 Ozone and PM Total Benefits including all combinations o f Mortality Estimates (7% discount rate)...................................

................................................... ................. 6-84 6.7 Total Annual Ozone and PM 2.5 -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 potentia l 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 stringe**  **nt 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 improve**  **ments achieved through the projected implementation of existing regulations and full att**  **ainment 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 program**  **s, which will help many areas move toward attainment of the current ozone standard.** 
**This RIA is focused on development and analyses of i llustrative control strategies to meet these**  **alternative standards in 2020. This analysis does n ot prejudge the attainment dates that will**  **ultimately be assigned to individual areas under th e Clean Air Act, which contains a variety of**  **potential dates and flexibility for extensions. For purp oses 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 EP A plans for implementing the current ozone**  **standard which propose that these two areas of Cali fornia 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 attainmen t 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 cos ts and benefits, because we recognized that**  **some areas with significant ozone problems would need emiss ion controls beyond those**  **currently available to meet either the 1997 ozone standa rds, or alternative, more stringent**  **standards. However, as documented in Chapter 5, there ar e numerous examples of how**  **technological innovation has led to the development of n ew and improved ways of reducing air**  **pollution, often at lower cost than estimated at th e time a new NAAQS is established. The**  **individual chapters of the RIA present more detail regardin g estimated costs and benefits based**  **on both partial attainment (manageable with current techno logies) and full attainment (which in**  **some locations will require new or innovative appro aches and technology).** 
**In setting primary ambient air quality standards, EPA’s re sponsibility 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 pu blic 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 canno**  **t 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 ot her 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 effici ent 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 imple menting strategies to meet revised standards,**  **this RIA provides insights and analysis of a limited number o f illustrative control strategies that**  **states might adopt to meet any revised standard. These ill ustrative strategies are subject to a**  **number of important assumptions, uncertainties and limit ations, which we document in the**  **relevant portions of the analysis.**

ES.1 Approach to the Analysis
**This RIA consists of multiple analyses including an assessmen t of the nature and sources of**  **ambient ozone; estimates of current and future emiss ions of relevant precursors that contribute to**  **the problem; air quality analyses of baseline and alternat ive control strategies; development of**  **illustrative control strategies to attain the stand ard alternatives in future years; estimates of the**  **incremental costs and benefits of attaining the alt ernative standards, together with an**  **examination of key uncertainties and limitations; a nd 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 str**  **ategies to attain the alternative standards that are the focus of this RIA. The baseline shows that by 2020, whi**  **le ozone air quality would be significantly better than today under current require**  **ments, 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 unspec**  **ified emission reductions are applied to establish attainment. The cost of these unknown controls wa**  **s extrapolated and is included in the total cost numbers.** 
**In selecting controls, we focused more on ozone cost-effect iveness (measured as $/ppb) than on**  **the NOx or VOC cost-effectiveness (measured as $/ton). Mos t 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 als o expected to reduce ambient PM**  **2.5 levels in**  **many locations. The total benefits estimates described he re include the co-benefits of reductions**  **in fine particulate levels (PM) associated with year-round application of NOx control st rategies**  **beyond those in the regulatory baseline. In moving further down the list of cost-effe ctive known**  **and available controls, we deplete our database of available choices of known controls, and are**  **left with background emissions and remaining anthro pogenic emissions for which we do not**  **have enough knowledge to determine how, and at what cost, re ductions can be achieved in the**  **future when attainment would be required.**
**Estimated reductions in premature mortality from red uctions in ambient ozone and PM dominate**  **the benefits estimates. For this reason, our assessm ent 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 addit ional 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. The**  **se estimates only include areas assumed to meet the current standard by 2020. As mentioned earl**  **ier, 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 under**  **lying the 2020 analysis presented here, and the 2030 analysis in Appendix 7b, it is no**  **t appropriate to add the results together to get a national “full attainment” scenario.** 
**In Tables ES.1 through ES.4, the individual row est imates reflect the different studies available**  **to describe the ozone premature mortality relationshi p. Ranges within the total benefits column**  **reflect variability in the studies upon which the e stimates associated with premature mortality**  **were derived. For the 0.075ppm alternative, PM 2.5**  **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 a ssumptions about the extrapolation of costs as**  **discussed in Chapter 5. The low end of the range of n et benefits is constructed by subtracting the**  **highest cost from the lowest benefit, while the high en d of the range is constructed by subtracting**  **the lowest cost from the highest benefit. The presentation o f 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 PM 2.5 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** **Meta analysis**  **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 PM 2.5 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** **Meta analysis**  **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 PM 2.5 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 –2 0 – 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** **Meta analysis**  **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 PM 2.5 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 Ito et al. 2005 21 – 58 21 – 54 32 – 44 –23 – 26 –23 – 22**  **Meta-analysis 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 est**  **imates do not include visibility benefits. Only includes area**  **s required to meet the current standard by 2020, do es not include**  **San Joaquin and South Coast areas in California. Ap pendix 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 mort**  **ality functions characterized in the expert elicitation.**  **Tables exclude unquantified and nonmonetized benefi ts.**  *****Range reflects lower and upper bound cost estima tes. Data for calculating costs at a 3% discount ra**  **te was not available for all sectors, and therefore total annu**  **alized costs at 3% are not presented here. Addition ally, these**  **estimates assume a particular trajectory of aggressive technological change. An alternativ e storyline might**  **hypothesize a much less optimistic technological tr ajectory, with increased costs, or with decreased b**  **enefits in 2020 due to a later attainment date.**  ******Total includes ozone morbidity benefits and tot al PM co-benefits only.** 

**Table ES.5 presents the total number of estimated o zone and PM**  **2.5 -related premature mortalities**  **and morbidities avoided nationwide in 2020. ES-6** 

Table ES.5: Summary of Total Number of Annual Ozone and PM 2.5 -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 PM**  **2.5 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 2,0 00 – 6,500**  **Ito (2005) 230 – 1,300 500 – 2,300 1,100 – 4,300 2, 500 – 7,000**  **Meta-Analysis 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 17,00 0**  **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 2,10 0,000 3,500,000**  ***Only includes areas required to meet the current s tandard by 2020, does not include San Joaquin Valle**  **y 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 lowe**  **r 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 t o PM**  **2.5 reductions only** 

**The following set of graphs is included to provide the read er with a richer presentation of the**  **range of costs and benefits of the alternative standards. Th e 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 cos t methods. Each of the 140 bars in each**  **graph represents an independent and equally probabl y 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 indic ate combinations where net benefits are**  **positive. Figure ES.1 shows all of these combinations for all st andards analyzed. Figure ES.2**  **shows a close-up of the range of net benefits for the sel ected 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, andthe 2 cost methods. All combinations are treated as independent and equally probable.Figure ES.1:**Costs exceed Benefits** **Costs exceed Benefits** **Costs exceed Benefits** **Costs exceed Benefits** **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, andthe 2 cost methods. All combinations are treated as independent and equally probable.For the selected standard of 0.075 ppm, the medianvalue of all of the independent point estimates is$0.8 billion, and the majority (64%) of thecombinations 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 a nd benefits of implementing revised**  **standards. There are many challenges in estimating the co sts and benefits of attaining a tighter**  **ozone standard, which are fully discussed in Chapters 5, 6, a nd 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 fur ther review by EPA science advisory boards.** 
**There are significant uncertainties in both cost an d 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 choic e of the statistical model**  **chosen for each health benefit.**

x **EPA has requested advice from the National Academy of Scienc es on how best to**  **quantify uncertainty in the relationship between ozone e xposure 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, esp ecially in the range of PM**  **2.5 benefits. EPA has plans to ask its Science Advis ory Board for advice about how**  **to best characterize the PM mortality benefits in fut ure analyses.** 

x **PM co-benefits are derived primarily from reductions in ni trates (associated with**  **NOx controls). As such, these estimates are strongly i nfluenced 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 N Ox 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 appro ach likely understates the**  **benefits that would occur due to implementation of actual controls because controls**  **implemented to reduce ozone concentrations at the high est monitor would likely**  **result in some reductions in ozone concentrations a t 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 disbene**  **fits (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 s ome increases in ozone levels**  **occur in areas already in attainment, though not en ough to push the areas into**  **nonattainment**

x **As explained in Chapter 5, there are several uncerta inties 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 rea ch a tighter standard. In**  **addition, since our modeling of known controls does not ge t 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 geographi c areas) that are not expected to**  **attain 0.075 ppm 1**  **in 2020 2**  **, assumed costs of unspecified controls represent a substantial fraction, of the costs estimated in this ana**  **lysis ranging from 50% to 89% of total costs depending on the standard being analy**  **zed.**

x **As discussed in Chapter 5, recent advice from EPA’s Scien ce Advisory Board has**  **questioned the appropriateness of an approach simila r to one of those used here**  **for estimating extrapolated costs. For balance, EPA also ap plied a methodology**  **recommended by the Science Advisory Board in an effort t o best approximate the**  **costs of control technologies that might be developed in th e 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 co**  **ntrols are economically feasible and are implemented. Technological advances**  **over time will tend to increase the economic feasibility of reducing emiss**  **ions, and will tend to reduce the costs of reducing emissions. Our estimates of cos**  **ts 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 hypothe size a much less optimistic**  **technological change path, such that emissions reductio ns technologies for**  **industrial sources would be more expensive or would be unavailable, so that emissions reductions from many smaller sources migh**  **t be required for 2020 attainment, at a potentially greater cost per ton. Under**  **this alternative storyline, two outcomes are hypothetically possible: Under one scenar**  **io, total costs associated with full attainment might be substantially h**  **igher. Under the second scenario, states may choose to take advantage of flexibil**  **ity 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 un**  **til 2024. ES-11**  **adopt plan with later attainment dates to allow for ad ditional technologies to be**  **developed and for existing programs like EPA’s Onroad Di esel, Nonroad Diesel,**  **and Locomotive and Marine rules to be fully impleme nted. 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. How**  **ever, in this case, state decision makers seeking to maximiz**  **e economic efficiency would not impose costs, including potential opportunity co**  **sts of not meeting their attainment date, when they exceed the expected heal**  **th 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 ben**  **efits plus the costs of nonattainment. Clearly, the second stage analysis**  **is a highly speculative exercise, because it is based on estimating emission reductio**  **ns 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 standa rd 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 additio**  **nal technology changes in the onroad technology sector. If these changes do not occu**  **r, 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% d**  **iscount rate, and $330 million to $2.8 billion when using a 7% discou**  **nt 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 purel**  **y 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 ben**  **efits 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 polic**  **y contexts, including other regulatory impact analyses. Furthermore, the benefits only refl**  **ect a partial accounting of the total benefits associated with emission reductions related to the m**  **obile controls included in this sensitivity analysis.** 1-1

Chapter 1: Introduction and Background

Synopsis

This document estimates the incremental costs and m onetized human health and welfare benefits of attaining a revised primary ozone National Ambien t Air Quality Standard (NAAQS) nationwide. This document contains illustrative ana lyses that consider limited emission control scenarios that states, tribes and regional planning o rganizations 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 o f certain urban areas in the future. According to the Clean Air Act, EPA must use health-based crit eria in setting the NAAQS and cannot consider estimates of compliance cost. This Regulat ory Impact Analysis (RIA) is intended to provide the public a sense of the benefits and costs of meeting new al ternative 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 Administra tor to identify pollutants which “may reasonably be anticipated to endanger public health or we lfare,” and to issue air quality criteria for them. These air quality criteria are intended to “accu rately reflect the latest scientific knowledge useful in indicating the kind and extent of all id entifiable effects on public health or welfare which may be expected from the presence of [a] pol lutant in the ambient air.” Ozone is one of six pollutants for which EPA has developed air quality c riteria.
Section 109 (42 U.S.C. 7409) directs the Administra tor to propose and promulgate “primary” and “secondary” NAAQS for pollutants identified under sect ion 108. Section 109(b)(1) defines a primary standard as “the attainment and maintenance of whic h 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, a s defined in section 109(b)(2), must “specify a level of air quality the attainment and maintena nce of which in the judgment of the Administrator, based on [the] criteria, [are] requis ite 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 30 2(h) [42 U.S.C. 7602(h)] include but are not limited to “effects on soils, water, crops, vegetat ion, manmade materials, animals, wildlife, weather, visibility and climate, damage to and deteriorat ion 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 r eview existing criteria and standards at 5-year intervals. When warranted by such review, the Admini strator is to retain or revise the NAAQS. After promulgation or revision of the NAAQS, the s tandards 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 re sponsibility under the law is to establish standards that protect public health. The Clean Air Act requires EPA, for each crite ria pollutant, to set a standard that protects public health with “an adequate margi n of safety.” As interpreted by the Agency and the courts, the Act requires EPA t o create standards based on health considerations only. Economic factors cannot be considered.
The prohibition against the consideration of cost i n 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, a s they decide what timelines, strategies, and policies make the most sense. This RIA is intended to in form the public about the potential costs and benefits that may result when a new ozone standard i s 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 dict ate the manner in which EPA considers rulemaking and public documents. This document is separat e 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-effec tiveness analysis for rules where health is the primary effect. Within this RIA we provide a cost b enefit 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 m ay one may be issued is to address market failure. The maj or types of market failure include: externality, market power, and inadequate or asymmetric information. C orrecting market failures is one reason for regulation, but it is not the only reason . Other possible justifications include improving the function of government, removing dist ributional 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, Septemb er 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 l ocal 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 f rom 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 wha t 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. Genera lly, regulations that increase market power for selected entities should be avoided. However, t here 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 gov ernment may choose to approve the monopoly and to regulate its prices and/or production decis ions. 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 marke t where competition can flourish.
Market failures may also result from inadequate or as ymmetric information. Because information, like other goods, is costly to produce and dis seminate, an evaluation will need to do more than demonstrate the possible existence of inco mplete or asymmetric information. Even though the market may supply less than the full amo unt of information, the amount it does supply may be reasonably adequate and therefore not requi re government regulation. Sellers have an incentive to provide information through ad vertising that can increase sales by highlighting distinctive characteristics of their produc ts. 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 infor mation.
There are justifications for regulations in addition to corr ecting 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 so me regulations to prohibit discrimination that conflicts with generally accepted norms within our soc iety. Rulemaking may also be appropriate to protect privacy, permit more personal free dom or promote other democratic aspirations.
From an economics perspective, setting an air quality stan dard is a straightforward case of addressing an externality, in this case where firms a re 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 pl ace the cost of control on those who emit the pollutants and lessens the impact on those wh o 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 tha t provides useful insights into a limited number of emissions control scenarios that states might im plement 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 a re 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 f rom 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-pres cribed 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. Thi s analysis attempts to estimate the costs and human and welfare benefits of cost-effect ive implementation strategies which might be undertaken to achieve national attainment of new standard s. These hypothetical strategies represent a scenario where states use one set of co st-effective controls to attain a revised ozone NAAQS. Because states—not EPA—will implement any revised N AAQS, they will ultimately determine appropriate emissions control scenarios. Stat e implementation plans would likely vary from EPA’s estimates due to differences in the data and assumptions that s tates use to develop these plans.
The illustrative attainment scenarios presented in this RIA were constructed with the understanding that there are inherent uncertainties i n projecting emissions and controls. Furthermore, certain emissions inventory, control, model ing and monitoring limitations and uncertainties inhibit EPA’s ability to model full a ttainment in all areas. An additional limitation is that this analysis is carried out for the year 2 020, before some areas are required to reach the current ozone standard. Section 1.3.1 below explains why EPA selected the analysi s year of 2020. Despite these limitations, EPA has used the b est available data and methods to produce this RIA.

1.3 Overview and Design of the RIA
This Regulatory Impact Analysis evaluates the costs a nd benefits of hypothetical national strategies to attain several potential revised prim ary ozone standards. The document is intended to be straightforward and written for the lay person with a mi nimal background in chemistry, economics, and/or epidemiology. Figure 1.1 provides an il lustration 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 at tainment 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 wi th 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 ne ed less time. This analysis does not prejudge the attainment dates that will ultimately be assign ed to individual areas under the Clean Air Act, which contains a variety of potential dates and flexibili ty to move to later dates (up to 20 years), provided that the date is as expeditious as practicab le.
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 c ontrols to reach a tighter standard. This allows for an analysis of the incremental change between the curre nt standard and an alternative standard. This timeline is also consistent with expected attainment in 2020 of th e 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 Benefits1-6

Particulate Matter (PM) NAAQS covered in the PM NAA QS RIA issued in September 2006. As explained in Chapter 2, since one of the principal precurso rs for ozone, NOx, is also a precursor for PM, it is important that we account for the impa ct on ozone concentrations of NOx controls used in the hypothetical control scenario used in t he 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 wo uld result from the application of emissions control options that are known to be avai lable to different types of sources in portions of the country that were predicted to be in non-attai nment with 0.070 ppm in 2020. However, given the limitations of current technology and the amo unt 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 sufficien tly to allow all areas to reach the more stringent standard. We performed air quality sensit ivity 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. T his enabled us to determine, for an extrapolation analysis, the approximate number of tons of a dditional reductions, beyond those achieved by known controls that would be required t o meet the alternate standards.
1.3.3 Evaluating Costs and Benefits
Applying a two step methodology for estimating emis sion reductions needed to reach full attainment enabled EPA to evaluate nationwide costs and benefits of attaining a tighter ozone standard, albeit with substantial additional uncert ainty 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 applyin g known controls were quantified, and presented along with an estimate of their economic i mpact. 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 alte rnative primary standard included both mortality and morbidity calculations matching the c osts of applying known controls and then the benefits of reaching full attainment. The costs and mon etized 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 second ary 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 seve ral previously published documents, including two technical documents EPA produced to prepar e for the ozone NAAQS proposal. The first was a Criteria Document created by EPA’s Of fice of Research and Development (published in 2006), which presented the latest available pertinent information on atmospheric science, air quality, exposure, dosimetry, health e ffect, and environmental effects of ozone. The second was a “Staff Paper” (published in 2007) that evaluated the policy impl ications 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 a lso includes staff conclusions and1-7

recommendations to the Administrator regarding potential revisions to the standards. In addition to the Criteria Document and Staff Paper, this ozone RIA re lies heavily on the 2006 RIA for particulate matter (PM). Many of the models and meth odology used here are the same as in the PM NAAQS RIA. This RIA identifies methodologies used to ge nerate 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 i n this analysis, which compares attainment of tighter levels of the ozone standard to the baseline of the cu rrent standard.

1.4 Ozone Standard Alternatives Considered
EPA has performed an illustrative analysis of the potentia l 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 stringe nt 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 improve ments achieved through the projected implementation of existing regulations and full att ainment 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 program s, which will help many areas move toward attainment of the current ozone standard.

1.5 References

Henderson, R. 2006. October 24, 2006. Letter from C ASAC 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: PolicyAssessment of Scientific and Technical Information. OAQPS Staff Paper. North Carolina. EPA452/R-07-0032-1

Chapter 2: Characterizing Ozone and Modeling Tools Used in Th is Analysis

Synopsis
This chapter describes the chemical and physical propertie s 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 e valuate 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 prim e 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 te mperature, solar radiation, wind speed and other meteorological factors. Ultraviolet radiatio n from the sun plays a key role in initiating the processes leading to ozone formation. However, t here is little empirical evidence directly linking day-to-day variations in observed surface ultr aviolet radiation levels with variations in tropospheric ozone levels.
The rate of ozone production can be limited by eithe r VOCs or NOx. In general, ozone formation using these two precursors is reliant upon the relat ive sources of hydroxide (OH) and NOx. When the rate of OH production is greater than the ra te of production of NOx, indicating that NOx is in short supply, the rate of ozone productio n is NOx-limited. In this situation, ozone concentrations are most effectively reduced by lowering cu rrent 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, oz one 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. Ho wever ozone is relatively insensitive to marginal changes in both NOx and VOC in this situation. In urban areas with a hig h population concentration, ozone is often VOC-limited. Ozone is generally NOx-limited in rural areas and downwind suburban areas. Additional infor mation 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 th e local quantities of NOx, VOC, and ozone catalysts such as the OH and HO2 radicals. In areas d ominated by fresh emissions of NOx, ozone2-2

catalysts are removed via the production of nitric acid, which slows the ozone fo rmation 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 a nd further downwind. The terms “NOx disbenefits” or “ozone disbenefits” refer to the ozone increases that can re sult 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 differen ces 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 prec ursors from the surrounding region can also be important. Thus, in designing control strategies to reduce ozone concentration s in a local area, it is often necessary to account for regional trans port within the U.S.
In some areas, such as California, global transport of ozo ne from beyond North America can contribute to nonattainment areas. In a very limite d number of areas, including areas such as Buffalo, Detroit and El Paso, which are located nea r borders, emissions from Canada or Mexico may contribute to nonattainment. In these areas, our illus trative 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 desi gn values. The transport of ozone is determined by meteorological and chemical processes which typically extend over spatial scales of several hundred kilometers. Additionally, convec tion is capable of transporting ozone and its precursors vertically through the troposphere, with re sulting 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) su ggests that ozone transport constitutes a sizable portion of projec ted nonattainment in most eastern areas based on a 2010 analysis. A listing of Eastern states and th e 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/finalt ech02.pdf , Table VI-2.2-3

2.2 Sources of Ozone
The anthropogenic precursors of ozone originate from a wid e variety of stationary and mobile sources. In urban areas, both biogenic (natural) an d 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 contributi ons to total emissions. Emissions of VOCs from highway vehicles account for roughly two-t hirds of the transportation-related emissions. 3 By 2020, EPA emission projections show that VOC emiss ions from highway vehicles decrease significantly. Solvent use VOC decreas es as well, but by 2020 solvent use VOC is projected to be a slightly more significant VOC con tributor than mobile VOC. On the regional and global scales, emissions of VOCs from vegetat ion are much larger than those from anthropogenic sources.
Anthropogenic NOx emissions are associated with comb ustion 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 the ir relative impacts on ozone in all local areas because EGUs are more sparse than mobile sources, p articularly in the west and south (See Chapter 3 for a discussion of emission reductions projected i n 2020 for the 8-hr ozone current standard baseline and the more stringent alternative contro l scenario). Natural NOx sources include stratospheric intrusions, lightning, soils, an d wildfires. Lightning, fertilized soils, and wildfires are the major natural sources of NOx in the Unite d States. Uncertainties in natural NOx inventories are much larger than for anthropogenic N Ox emissions.
A complete list of emissions source categories, for b oth 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 202 0, it is necessary to use modeling to derive estimated air quality concentrations. The modeling a nalysis uses an emissions inventory and historical meteorological conditions to simulate poll utant concentrations. The predictions from the modeling are used to (a) project future ozone d esign values (a representation of the resultant air quality concentration in 2020 representing the 4 th highest maximum 8-hr concentration) and (b) create spatial fields of ozone and PM 2.5 for characterizing human health impacts from reducing ozone precursors, which in the case of NOx will also affect the formation of PM 2.5 . The air quality model used in this RIA is the Community Multi-Scale Air Quality (CMAQ) model 4

. The modeling for ozone and PM 2.5 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: PolicyAssessment 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 PM 2.5 co-benefits because the controls developed to reduce summe r 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 o zone standards. In addition, the modelbased projections of ozone and PM 2.5 were used as inputs to the calculation of expected incremental benefits from the alternative ozone sta ndards 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 scenar io. This modeling platform includes a number of updates and improvements to data and tools compa red to the 2001-based platform that was used for the proposal modeling. For the final rule modeli ng we used the new 2002 National Emissions Inventory along with updated versions of the mod els used to project future emissions from electric generating units (EGUs) and onroad and nonr oad 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 wh ich were used as the starting point for projecting future air quality. For proposal, we used meteo rology for 2001 for modeling the East and 2002 for modeling the West. The updates 5 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) heter ogeneous reaction involving nitrate formation; (4) an updated gas-phase ch emistry mechanism, Carbon Bond 2005 (CB05); and (5) an aqueous chemistry mechanism that p rovides 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 At mospheric Research Mesoscale Model (Grell, Dudhia, and Stauffer, 1994). This model, commonly referred to as MM5, is a limited-area, nonhydrostatic, terrain-following sys tem that solves for the full set of physical and thermodynamic equations which govern atmospheric motions. T he lateral boundary and initial species concentrations for the 36 km continental sc ale modeling domain, described below, were obtained from a three-dimensional global atmospheric chemistry model, the GEOSChem model (Yantosca, 2004). The global GEOSChem model simulate s atmospheric chemical and physical processes driven by assimilated meteorological observat ions from the NASA’s Goddard Earth Observing System (GEOS). We used GEOSChem results for 2 002 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 mod el performance methodology and results are described in the 2002-Based Modeling Platform Report (EPA, 2008). As in the evaluation for previous model applications, the “acc eptability” of model performance for the ozone RIA modeling was judged by comparing the results to t hose found in recent regional

5 Additional documentation on the updates in CMAQ ve rsion 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 i s generally within the range of these other applications.
Figure 2.1 shows the modeling domains that were used as a p art of this analysis. The geographic specifications for these domains are provided in Tab le 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 20 02 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 in itial and boundary concentrations for the 12 km domains. As indicated above, the model produce s spatial fields of gridded air quality concentrations on an hourly basis for the ent ire 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 analysi s of costs and benefits. The air quality modeling results are used in a relative sens e 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 th e starting point for projections. For the proposal, design values from 199 9 through 2003 were used. The change between proposal and final in terms of the period of des ign 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 200 2 for the final rule).
For this analysis, predictions from the East domain we re used to provide data for all areas that are east of approximately 104 degrees longitude. Model pr edictions 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 f or 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

í 121.77 18.17 SW

í 106.79 24.99 SW

í 121.65 28.29 NE

í 58.54 52.41 NE

í 65.32 47.63 NE

í 94.94 51.91

2.3.2 Emissions Inventory
The 2020 inventory, projected from the 2002 Version 3 emiss ions modeling platform (EPA, 2008), is the starting point for the baseline and control st rategy for the Final Ozone NAAQS emissions inventory. The 2002 documentation describes th e 2002 base year inventory as well as the projection methodology and controls applied to cre ate 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 assumption s 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 stra tegies 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 App roach 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, SO 2 2

Refinery Consent Decrees: plant/SCC controls NOx, PM, SO 2 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 manuf acture Large Appliances Miscellaneous Organic NESHAP (MON): Alkyd Resins, C helating 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 Pape r 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 App roach 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 Regul ations NOx, PM, SO 2 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 & SO 2 : Lime Manufacturing PM, SO 2 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 NH 3 , PM 10

Residential Wood Combustion Growth reflects increase in use of lower polluting wood sto ves, 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, SO 2 , PM 14

Onroad Mobile and Nonroad Mobile Growth and Controls Onroad and Nonroad Growth: Onroad growth is based on VMT growth from Annual En ergy Outlook (AEO) 2006 estimates of growth by vehicle type. Non road growth is based on activity increases from NONROAD model default gr owth 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 App roach 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 Ignit ion Engines and Recreational Engines (Marine and Land Based): “Pent athalon Rule” all 16

Aircraft, Locomotives, and Commercial Marine Assump tions Aircraft: Itinerant (ITN) operations at airports all 17

Locomotives: Energy Information Administration (EIA) fuel consum ption 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-fuele d vessels Clean Air Nonroad Diesel Final Rule—Tier 4 Emissions Standards for Commercial Marine Diesel En gines, 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 Federa l Register on Response to Court decisions (Federal Register, 2004) for IC Engine percent reductions an d 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 ver ified using the world wide web. 5. Used data list of plants provided by project lead from 2001-based platform; required mapping the 200 1 plants to 2002 NEI plants due to plant id changes across i nventory 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 wei ghted average across two processes ) 7. Percent reductions recommended and plants to appl y to reduction to were based on recommendations by rule lead engineer, and are consistent with the referenc e: EPA, 2007e 8. Percent reductions recommended are determined fro m the existing plant estimated baselines and estima ted reductions as shown in the Federal Register Notice for the rule. SO2 % reduction will therefore be 6147/30,783 = 20% and PM10 and PM 2.5 reductions will both be 3786/13588 = 28% 9. Same approach used in CAIR: FR notice estimates r eductions of “PM emissions by 10,538 tpy, a reducti on of about 62%.” Used same list of plants as were identi fied based on tonnage and SCC from CAIR. 10. Except for dairy cows and turkeys (no growth), b ased in animal population growth estimates from USD A and Food and Agriculture Policy and Research Institute. 11. Expected benefits of woodstoves change-out progr am: http://www.epa.gov/woodstoves/index.html 12. VOC emission ratios of year 2020 to year 2002 fr om the National Mobile Inventory Model (NMIM) resul ts for onroad refueling including activity growth from VMT, Stage II control programs at gasoline station s, and phase in of newer vehicles with onboard Stage II ve hicle controls. 13. VOC emission ratios of year 2020 to year 2002 fr om MSAT rule (EPA, 2007c, EPA, 2007d) 14. http://www.epa.gov/airmarkets/progsregs/epa-ipm/doc s/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.htm2-10

Differences between the 2020 emissions modeling pla tforms—particularly the inventories—used in the Ozone NAAQS Proposal and here in the Ozone NAAQS Fi nal are discussed in the Appendix for Chapter 2.
The development of the 2020 baseline inventory and the mode led control scenarios are discussed in Chapter 3. The 2020 baseline inventory includes the same y ear 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 M odel. pp. 7.
Byun, D.W., and K.L. Schere. 2006. “Review of the Gove rning 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 Descript ion 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. “Scien ce Algorithms of EPA Models-3 Community Multiscale Air Quality.” (CMAQ Modeling S ystem D.W. Byun and J.K.S. Ching, Eds. EPA/600/R-99/030, Office of Research and Developm ent).
U.S. Environmental Protection Agency (EPA). 2005. Clean Air Interstate Rule Emissions Inventory Technical Support Document , U.S. Environmental Protection Agency, Office of Ai r 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. Environ mental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF.
U.S. Environmental Protection Agency (EPA). 2006b. “Regul atory Impact Analysis for the Review of the Particulate Matter National Ambient Air Quali ty Standards (Chapter 2: Defining the PM 2.5 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 Sci entific and Technical Information. OAQPS Staff Paper. North Carolina. EPA-452/R-07-003.
U.S. Environmental Protection Agency (EPA). 2007b. “Guidan ce on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 , 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 Source s , 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 Locomotiv e Engines and Marine CompressionIgnition Engines Less than 30 Liters per Cylinder, Chapter 3: Emission Inventory , U.S. Environmental Protection Agency, Office of Transportatio n 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 Qu ality Modeling Platform for the Ozone National Ambient Air Quality Standard Final Ru le Regulatory Impact Assessment.
U.S. Environmental Projections Agency (EPA). 2008b. Techn ical Support Document: Preparation of Emissions Inventories For the 2002-b ased Platform, Version 3, Criteria Air Pollutants, USEPA, January, 2008.

Yantosca, B. 2004. GEOS-CHEMv7-01-02 User’s Guide, Atmos pheric Chemistry Modeling Group, Harvard University, Cambridge, MA, October 15, 2004.**2a-1**

Appendix 2: Additional Emissions Modeling Platform Info rmation

2a.1 Discussion of Similarities and Differences Bet ween 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 m odeling 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 differen**  **ces in the 2001 and 2002 emission platforms, as well as assumptions used to project emissions t**  **o the year 2020.**

2a.1.1 Similarities in the 2001 and 2002 Emissions M odeling Platforms
**The 2001 and 2002 emissions platforms share the same Canad a, Mexico, and offshore oil**  **production emissions. Both platforms also share the same w ildfire and prescribed burning**  **emissions. Most input ancillary files used in the emissio ns processor are also unchanged;**  **specifically, almost all cross-reference factors use d 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 emiss ions 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 y ear 2020 activity (growth) factors is very**  **small. Plant closures, consent decrees and settleme nts, and most national programs for stationary**  **non-EGUs are applied as consistently as possible in 2002 as in 2001, by which, we used a cross**  **reference file to match controls for plants in the 2001 t o 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 Proposa**  **l 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 emissi**  **ons 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 fo**  **r new source categories appearing in different inventory sectors; examples include farms and ai**  **rports 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 signif**  **icant change in the emissions modeling platforms is the use of a new chemical mechanism -CB**  **05 (Yarwood, 2005) versus CB IV in the proposal platform.** 
**Emissions by geographic area and by model platform in the base and future years are shown in Figure 2a.1 and Figure 2a.2, for NOx and VOC, respe**  **ctively. “Northeast” in all figures represents the full OTC (Ozone Transport Commission**  **) member states: Maine, New Hampshire, Vermont, Massachusetts, Connecticut, Rhode Island, N**  **ew York, New Jersey, Pennsylvania, Maryland, Delaware, and the District of Columbia. Em**  **issions 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, O hio, 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 2002Millions

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 2002Millions

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 invento ries 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 s een.** 
**Year 2020 emissions, projected from the 2001 and 2002 emissi on platforms show slightly less**  **NOx in 2020 in the 2002-based platform in the Northeast, Mid west, and rest of the US. Perhaps**  **most significant from an air quality modeling aspect is the relative change in emissi ons 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 s lightly 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 est imates 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 emission s 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 geo**  **graphic area for year 2020 between the 2001-based and 2002-based emission mo**  **deling platforms.**

2a.2 References
**U.S. Environmental Protection Agency (EPA). 2006. “Regula tory Impact Analysis for the**  **Review of the Particulate Matter National Ambient Air Quali ty Standards (Chapter 2: Defining**  **the PM 2.5 Air Quality Problem).” EPA-HQ-OAR-2006-08 34-0048.3.** 
**U.S. Environmental Protection Agency (EPA). 2007a.** Regulatory Impact Analysis for Final Rule: Control of Hazardous Air Pollutants from Mobile Sourc es **, 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 Qual**  **ity Modeling Platform for the Ozone National Ambient Air Quality Standard Final Ru**  **le 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-04 00675. Available at**  **http://www.camx.com/publ/pdfs/CB05_Final_Report_1208 05.pdf** 

**.**3-1

Chapter 3: Modeled Control Strategy - Design and Analytica l Results

Synopsis

In order to estimate the costs and benefits of alte rnate ozone standards, EPA has analyzed one possible hypothetical scenario to illustrate the control strategies that areas a cross 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 o f geographic areas that may be affected by a new ozone standard. Specifically, EPA has model ed the impact that additional emissions controls across numerous sectors would have on predicted ambient ozone concentrations, incremental to meeting the current PM 2.5 and ozone standards (baseline). Thus, the modeled analysis for a revised standard focuses specifi cally on incremental improvements beyond the current standards, and uses control options that might be available to states for application by 2020. The hypothetical modeled contr ol strategy presented in this RIA is one illustrative option for achieving emissions reductions to mo ve towards a national attainment of a tighter standard. It is not a recommendation for how a t ighter ozone standard should be implemented, and states will make all final decisions regar ding implementation strategies once a final NAAQS has been set.
In order to model a hypothetical control strategy inc remental to attainment of the current standard, EPA approached the analysis in stages. First, E PA identified controls to be included in the baseline. These included current state and feder al programs (see) plus controls to attain the current ozone standard (Table 3.1) and PM 2.5 standards (see http://www.epa.gov/ttnecas1/ria.html for a complete list of controls). Then, EPA applied addition al known controls within geographic areas designed to bring areas predicted to exceed 0.070 p pm in 2020 into attainment. This chapter presents the hypothetical modeled control stra tegy, the geographic areas where controls were applied, and the results of the modeling which pred icted 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 modele d control strategy did yield only partial attainment. Chapter 4 will explain how EPA used addi tional air quality modeling to estimate total annual tons/year of emissions reduct ions needed to achieve ozone concentrations for 0.075 ppm as well as the less stringent option of 0.079 ppm t he and the more stringent options of 0.070 ppm and 0.065 ppm). Chapters 5 and 6 pre sent the estimated costs and benefits of the modeled costs and benefits for partial attai nment.

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 wit h 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 curren t 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 attain3-2

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 descr ibes the methodology EPA used to estimate the amount of extrapolated tons necessary for contr ol 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 202 0, 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 o rder to develop and evaluate a control strategy for attaining a more stringent (0.070 ppm) pri mary standard, it is important to first estimate ozone levels in 2020 given the current NAA QS standards and trends (more information is provided in Chapter 1). This scenario is known as the b aseline. Establishing this baseline allows us to estimate the incremental costs and bene fits of attaining any alternate primary standard.

This focus on the assessment of the incremental costs and be nefits 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 asse ssment, risks are estimated associated with just meeting recent air quality and upon just meeting the curren t and alternative standards as well as incremental reductions in risks in going from the c urrent standard to more stringent alternative standards. When considering risk estimates remainin g upon attaining a given standard, EPA is only interested in the risks in excess of policy re levant 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 s ources, 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 sour ces outside of the U.S. are uncertain, as are the specific impacts those emissions will have on ozone c oncentrations in areas exceeding alternative standards. Our models use available informat ion 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 t hese emissions contribute a greater (lesser) proportion of ozone on high ozone days, more (less) reduction s in emissions from U.S. sources might be required to reduce ozone levels below the an alyzed alternative standards.
In contrast, the RIA only examines the incremental r eduction, not the remaining risk, which results from changes in U.S. anthropogenic emissions. The a ir quality modeling used to establish the baseline for the RIA explicitly includes contribu tions 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 alt ernative standards are clearly above the3-3

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 th at there are several areas that are not required to meet the current standard by 2020. The Clean Air Act allows areas with more significant air quality problems to take additional time to reach the current standard. Two areas in Southern California 1 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 PM 2.5 NAAQS RIA analysis. Second, since these reductions alone were not predicted to bring all areas into attainme nt with the tighter standard, EPA used a hypothetical control strategy to apply additional known c ontrols. 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), Onroa d Mobile Sources and Nonroad Mobile Sources. A fifth sector was used in the subsequent c ontrol strategy for a tighter alternative standard: Electricity Generating Unit P oint Sources (EGUs). Each of these sectors is defined below for clarity.

x NonEGU point sources are stationary sources that em it at least one criteria pollutant with emissions of 100 tons per year or higher. NonEGU point sourc es are found across a wide variety of industries, such as chemical manufacturing, cem ent manufacturing, petroleum refineries, and iron and steel mills.

x NonPoint Area Sources 3 (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 agg regated source emissions information is maintained for the entire source category instead o f 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 Sources 4 are any combustion engine that travels by other means than roadways. These sources include railroad locomotives ; marine vessels; aircraft; off-road

1 At the time of this analysis the South Coast and San Joaquin Valley air basins are expected to request a redesignation to extreme status for the c urrent ozone standard.

2 In establishing the baseline, EPA selected a set of cost-effective controls to simulate attainment of the current ozone and PM 2.5 standards. These control sets are hypothetical as stat es will ultimately determine controls as part of the SIP pr ocess.
3 Areas Sources include the nonpoint emissions sector onl y.
4 For the purposes of presentation nonroad mobile sources i ncorporates 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 elec tricity to the grid, such as fossil-fuelfired boilers and combustion turbines.
3.1.1 Control Measures Applied in the Baseline for Ozone P recursors
The purpose of identifying and modeling baseline controls for ozone precursors, NOx and VOC, is to reduce ambient ozone concentrations to meet th e current ozone standard in this analysis. Control measures were applied in the baseline to red uce ozone concentrations in addition to the control set developed for the hypothetical national attainm ent strategy presented in the PM 2.5 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 bel ow. Details regarding the individual controls are provided in Appendix 3. Due to the extensive reductions from EGUs already impl emented in CAIR/CAMR/CAVR, no additional EGU controls were inclu ded 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 contr ols in Chicago, Houston, and California were included in the baseline because these areas c ontain 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 projec ted to violate the current standard in 2020. Controls were then applied to all surrounding counti es within the same state that were completely contained within 200 km 5 of the county containing the projected violating monito r (Figure 3.1). Second, controls were applied to large nonEGU point sources 6 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 c ounties where: VOC emissions were high (>5,000 tpy or >25tpy/sq. mi), the county design va lue was projected to be  0.08 ppm in the 2020 basecase, and the area had some historical evi dence 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 o f 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 Appl ied 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 (E PA, 2005a) Elimination of Long Duration Idling Continuous Inspection and Maintenance Commuter Programs Additional Technology Changes in the Onroad Transpo rtation Sector Nonroad Mobile Diesel Retrofits and Engine Rebuilds

Reduce Gasoline Reid Vapor Pressure (RVP) to 7.0 (E PA, 2005a) Aircraft NOx International Standard EGU None None3-6

Figure 3.1: Counties Where Controls for Nitrogen Ox ides (NOx) Were Included for NonEGU Point and Area Sources, for the Current Ozon e Standard in the Baseline

For the Onroad and Nonroad Mobile source sectors, some cont rols 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 buf fer 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, contro ls with the lowest costs were generally included first. Both onroad and nonroad diesel retrofits a nd idling elimination were included in California with an assumed 75% market penetration, and in ba seline reduction areas outside of California with an assumed 25% market penetration. EPA dete rmined that 25% would have a significant impact, but was feasible to achieve and w as applied for reduction areas outside of California. EPA further determined that for souther n 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 poi nt and Area sources3-7

Figure 3.2: Counties Where Controls for Volatile Or ganic Chemicals (VOCs) Were Applied to NonEGU Point and Area Sources for the Current Ozon e Standard in the Baseline

3.1.2 Ozone Levels for Baseline
Establishing the baseline required design values (predicted concentrations) of ozone acros s the country. Because the intention of this evaluation w as 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 conc entrations for the 661 counties with ozone monitors. Projections of ozone design values were develope d 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 va lues, the guidance recommends using a criterion that there be a minimum of 5 modeled days wi th predicted base year ozone at or above 0.070 ppm. This criterion was relaxed to a mini mum 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 Sourc es3-8

Figure 3.3: Areas Where NOx and VOC Controls Were I ncluded for Mobile Onroad and Nonroad Sources in Addition to National Mobile Controls* fo r the Current Ozone Standard in the Baseline

* International Aircraft NOx Standard, national con trol measures applied as part of the PM NAAQS RIA, and Additional Technology Changes in the Onroad Tra nsportation Sector. **Onroad retrofits, elimination of long duration id ling, and lower Reid Vapor Pressure (RVP) gasoline. ***Nonroad retrofits, continuous inspection and mai ntenance, and commuter programs.

The baseline shows that 6 counties would not meet t he current ozone standard in 2020, even after inclusion of all known controls. Of these 6 counties, 5 of the m are in portions of California that have current state implementation plans that reflect an att ainment 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 for ms the foundation for the cost-benefit analysis conducted in this RIA, where EPA compares m ore stringent primary ozone standard alternatives incrementally to national attainment o f the current standard.

Statewide + Local controls*** Statewide controls**3-9 Figure 3.4: Baseline Projected 8-Hour Ozone Air Quality i n 2020 a, 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 (EP A, 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 E ngines (EPA, 2007b), and state and local level mobile and stationary source controls identified fo r additional reductions in emissions for the purpos e of attaining the current PM 2.5 and Ozone standards

. b Controls applied are illustrative. States may choo se to apply different control strategies for implementation.

c The current standard of 0.08 ppm is effectively ex pressed 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 (OM B) guidance under Executive Order 12866 defines a no-action baseline as “what the world will b e like if the proposed rule is not adopted.” The illustrative analysis in this RIA assesses th e costs and benefits of moving from this “no-action” baseline to a suite of possible new standa rds. 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 tot al of 28

6 counties that exceed 0.084

61 additional counties that exceed 0.070 ppm for a tot al of 89

142 additional counties that exceed 0.065 ppm for a tot al of 231

430 counties meet 0.065 ppm 5 counties that exceed 0.079 ppm for a total of 113-10

x changes in external factors affecting expected benef its 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 regu lations (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 me asuring benefits and costs against alternative baselines. In doing so you can analyz e the effects on benefits and costs of making different assumptions about other agencie s’ 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 differe nt 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 ad opted to reach an alternative primary standard by 2020. The stricter standard alternative of 0.070 ppm was c hosen 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 propos al resulted in 203 counties with projected design values exceeding 0.070 ppm. In the fi nal 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 fina l reflects the net effect of the updates to the air quality modeling platform, as described in Chapt er 2, and the additional emissions controls in the final rule baseline modeling compared to proposa l.
Controls for five sectors were used in developing t he control analysis, as discussed previously: nonEGU point, Area, onroad mobile and nonroad mobil e, along with EGUs. Reductions in both NOx and VOC ozone precursors were needed in all sectors to mee t a tighter standard.
As depicted in the flow diagram in Figure 1.1, the c ontrol 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. Reductio ns in both NOx and VOC ozone precursors were needed in all sectors to meet a tig hter standard. The emissions for this control strategy were input to the CMAQ model as part of the proc ess 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 t o develop those estimates and those3-11

calculations are presented in Chapter 4. Appendix 7a presents a sensitivity analysis of three mobile source control measures that could be includ ed in the control strategy to illustrate attainment.

Table 3.2: Controls Applied, by Sector, for the 0.0 70 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 Mobile a Increased Penetration of Onroad SCR and DPF from 25 % to 75%

Continuous Inspection and Maintenance (OBD) Nonroad Mobile a Increased Penetration of Nonroad SCR and DPF from 2 5% 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 a pplied for the Baseline analysis were applied to additional geographic areas in the 0.070 ppm analys is. 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 CS As, see http://www.census.gov/population/www/estimates/metr odef.html3-12

3.2.1 Controls Applied for the Modeled Control Strat egy: 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, b elow a cost cap, were applied, allowing for the largest emission reduction per source ove r 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 residu al nonattainment remained after the modeled control strateg y, 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 informati on on how we selected our cost cap and the extrapolated cost analysis.
Supplemental controls, which estimated additional e missions 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 202 0 baseline. Additional controls were applied in surrounding counties within 200 km of the county proj ected to be out of attainment (at 0.070 ppm), but not crossing state boundaries. In addition, cont rols were applied to large nonEGU point sources outside the 200km buffer zones. The crit eria 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 r eview, this software tool has undergone substantial review within EPA’s OAR and O AQPS, 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 1 0 months. See http://www.epa.gov/particles/measures/pm_control_mea sures_tables_ver1.pdf for more details on this control measures database. In addition, the control mea sure data within AirControlNET has been used by Regional Planning Organizations (RP Os) such as the Lake Michigan Air District Commission (LADCO), the Ozone Transport Commi ssion (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 las t 3 years. All of their technical reports are available on their web sites.3-13

Figure 3.5: Counties Where Controls for Nitrogen Oxi des (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 t he following criteria were met (including the counties which included VOC controls in their b aselines): VOC emissions were high (>5,000 tpy or >25tpy/sq. mi), the county design value was projec ted to be  0.070 ppm in the 2020 (See Figure 3.6), and the area had some historical evidence that VOC controls wou ld 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 Strateg y: 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, em issions reductions were targeted in the OTC and MWRPO states through lower “nested caps” and “command and control” application in the non-attain ment counties outside of the OTC and MWRPO within CAIR.
For the Final RIA, we have employed an enhanced strategy, b oth in terms of the quantity of reductions and the geographic extent of the areas cover ed. Figure 3.7 depicts the areas covered for the EGU sector emission reduction strategy.

Nitrogen oxide (NOx) controls applied to NonEGU Poi nt and area sources3-14

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

Sources for the Modeled Control (Incremental to Bas eline)

Annual and ozone season CAIR caps remained unchanged, but coal-fired units were targeted for this shifted strategy within those caps. This strategy was ap propriate 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. Calif ornia, 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 developme nt 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-ro und. The EGU sector used the Integrated Planning Model (IPM) to evaluate the reductions that are pr edicted 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). C AIR will bring significant emission reductions in NOx, and a result, ambient ozone conc entrations in the eastern U.S. by 2020. 11 A map of the CAIR region is presented in Appendix 3.4. Emission s and air quality impacts of CAIR are documented in detail in the Regulatory Imp act Analysis of the Final Clean Air Interstate Rule. 12

11 See http://www.epa.gov/airmarkets/progress/progress-rep orts.html for more information

12 See http://www.epa.gov/CAIR/technical.html

VOC controls applied to NonEGU Point and Area Sourc es3-15

Figure 3.7: Geographic Areas where NOx Controls were Applied to Electrical Generating Units (EGUs) for the Modeled Control Strategy (Increm ental to Baseline)

To address nonattainment in the CAIR region (especially th e Midwest, Mid-Atlantic,, and Northeast), and East Texas 13 lower nested ozone season caps (a limit lower than the current CAIR cap) were applied in these areas for NOx, while holding th e CAIR cap unchanged for the entire region. This provides an opportunity to redu ce 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 Organizati on (MWRPO) consisting WI, IL, IN, MI, and OH; and the Ozone Transport Commission (OTC), co nsisting 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 s tates are currently investigating ways of reducing EGU emissions further in their states a nd because most of the potential ozone nonattainment areas are found within these two regi ons. East Texas has also non-attainment areas, and the state is looking for strategies to redu ce 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 th at could be obtained by installing

13 East Texas geographic area was defined to be ident ical to the geographic area for other sectors.3-16

post-combustion controls, such as Selective Catalytic Reduction (SCR) and Sel ective NonCatalytic Reduction (SNCR), to all of the coal-fired units that were not projected to have previously installed post-combustion controls in the ba se-case. Following this, 75% of the reducti 14 on that could be obtained from these units was subtract ed 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 unchang ed.

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 remaine d unchanged in the East, and coal-fired units were targeted for this reduction. Preliminary analysis showed that most of theneeded 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-attain ment 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 nonattainm ent counties.

At this time, we are in the process of improving our ability to a chieve additional reductions available in NOx emissions from EGUs and correspondi ng air quality benefits, especially on high energy demand days (HEDDs) throug h 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 Strate gy: Onroad and Nonroad Mobile Sectors

As in other sectors, there are several mobile sourc e 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 t he baseline, additional mobile source controls were required to illustrate attainment of an altern ate primary standard (See Figure 3.8). Information on mobile source control measures for t he modeled control strategy analysis were derived from various EPA studies and from running EPA’s N ational 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 f or more information on mobile source controls included in t he modeled control strategy analysis.

All of the local mobile source controls included in the oz one baseline were expanded for the hypothetical national control strategy to attain an altern ate 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 Sourc es on High Energy Demand Days with Energy Efficiency Measures. Technical Support Document for the Final Ozone NAAQS Regulatory Impact Analysis. U.S. Environmental Pro tection Agency, Office of Air and Radiation. March 2008.

15 Detailed analysis showed that 75%–90% reduction prov ides the most cost-effective way of reducing emissions at the targeted non-attainment are as, considering transport, with the most air quality impacts.3-17

the measure was applied at a greater penetration ra te—to 75% of the modeled equipment population. 75% was the highest cost-effective pene tration 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 A pplied 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 idl ing, and lower Reid Vapor Pressure (RVP) gasoline. **Nonroad retrofits, continuous inspection and main tenance, and commuter programs.
As in the baseline, some mobile source controls were applied statewide for al l states with a county projected to exceed 0.070 ppm. ‘Local’ contr ols 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 th e buffer zone

x controls were applied statewide to Ozone Transport Commis sion (OTC) states, with the exception of Vermont
As stated at the beginning of this section, additional reductions were needed to complete t he analysis of the alternate standard. In addition to the emission reductions accounted for in the extrapolation approach described in Chapter 4, Appe ndix 7a presents a sensitivity analysis of

Statewide controls* Statewide + Local controls**3-18

three mobile source control measures that could be include d 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 wit h 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 pollu tion controls and has set up what it believes is the most reasonable framework for analyz ing the cost, emission changes, and other impacts of regulatory controls. EPA is working on ap proaches 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 ea ch 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 precurso rs to ozone formation. However, in some cases, the application of the control str ategy 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 tra ding aspects under the CAIR rule.
With respect to the baseline (CAIR/CAMR/CAVR), total emiss ions 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 emissio n reductions are already being achieved through CAIR/CAMR/CAVR. This strategy focu ses reductions under trading programs where they are needed most, with the result that some areas get l ess reductions than might have been otherwise expected within the. CAIR region. As ex plained earlier, the NOx EGU control strategy was designed to achieve emission reductions specifically in the non-attain ment areas, while retaining the overall CAIR cap . Application of nested and lower (ozone season) caps (fo r 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 c ounties (and surrounding CBSA and CSA) outside of the trading regions OTC and MWRPO with in CAIR region result in emission shifts increase of emissions elsewhere wit hin or outside of CAIR region compared to the base line (CAIR/CAMR/CAVR). While there are substantia l total NOx emission reductions (roughly 53,000 tons within the OTC, and MWRPO, and East Texa s; and roughly 16,000 tons nationwide) expected for the 2020 ozone season (rou ghly 55,500 tons) compared to the base line (CAIR/CAMR/CAVR) as a result of cap-and-trade program wi th lower caps and local command-and-control reductions in other non-attainm ent 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 r egion. This approach provides a3-19

cost effective opportunity for reducing emissions w here the reductions are most needed to help reach attainment. It is important to recall that this i s a hypothetical control strategy, and the states or other authorities may take additional steps to minim ize 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

í 99– +100 range is shown without color because these are small county-level NOx reductions or increases that likely had little to no impact on oz one estimates. Most counties in this range had NOx differences less than 1 ton.3-20

Figure 3.10: Percentage of 2020 Annual NOx Emissions Reduce d by Sector Incremental to the Baseline

4% 5%

64% 21% 6%

Area EGU Point NonEGU Point Onroad Nonroad3-21

Figure 3.11: Annual Tons of VOC Emission Reductions for the M odeled Control Strategy (Incremental to the Baseline)*

* Reductions are negative and increases are positive

** The

í 99–+53 range is shown without color because these a re 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. Fig ure 3.13 shows a map of the design values after the modeled control strategy. The map lege nd is broken out to demonstrate under this control strategy, with no adjustments, which c ounties would reach the targeted standard of 0.070 ppm, the more stringent alternative standard ana lyzed (0.065 ppm), and the other end of the proposal range (0.075 ppm, and 0.079 ppm). It is underst ood that this illustrative strategy would not be the exact hypothetical strategy used to try to attain either of these a lternative standards, due to over- and under-attainment in many coun ties. (Chapter 4 describes EPA’s methodology for estimating tons of reductions needed to hypothetically attain t hese other two possible alternative standards.) In addition, becaus e ozone formation is dependent on a variety of factors, it is not possible to directly attribute cha nges in predicted ozone concentrations to emission reductions of a specific precursor from a spe cific 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 model ed control strategy, incremental to the baseline. Figure 3.14 shows the tons of emissions r emaining after application of the hypothetical modeled control strategy, by sector.
Using this strategy, it is possible to reach attainme nt in 600 counties. However, there are still 61 counties that will remain out of attainment with an alterna tive standard of 0.070 ppm using this control strategy. All known controls were applied to th is scenario, but attainment was not achieved everywhere. Because of this partial attain ment outcome, it will be necessary to identify3-23

additional reductions in NOx and VOC. Chapter 4 will a ddress the methodology for determining the additional tons that were needed to reach full atta inment.

Figure 3.13: Projected 8-Hour Ozone Air Quality in 2020 From Applying the Modeled Control Strategy a, 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 (EP A, 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 reduction s in emissions for the purpose of attaining the cur rent PM 2.5 and Ozone standards.

b Controls applied are illustrative. States may choo se to apply different control strategies for implementation.

c The current standard of 0.08 ppm is effectively ex pressed 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 tot al of 21 6 counties that exceed 0.084 ppm

40 additional counties that exceed 0.070 ppm for a tot al of 61

105 additional counties that exceed 0.065 ppm for a tot al of 166

495 counties meet 0.065 ppm standard 4 counties that exceed 0.079 ppm for a total of 103-24

Table 3.3: Emissions and Reductions (2020) From Applyin g the Modeled Control Strategy by Region (Incremental to the Baseline)

Modeled Control Strategy Emission Reductions (annua l

tons/year) Baseline Annual Emissions (annual tons/year) East West California a Emissions Sector VOC NOX VOC NOX VOC NOX VOC NOX
Area 1,700,000 7,900,000 140,000 20,000 15,000 1,10 0 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 3,3 00 19 140

a A majority of the control measures were applied fo r 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 StrategyVOC NOX

Emissions (M Tons)

Nonroad Onroad Area NonEGU EGU3-25

3.5 References
Michigan Department of Environmental Quality and Southea st Michigan Council of Governments. Proposed Revision to State of Michigan State Implementation Plan for 7.0 Low Vapor Pressure Gasoline Vapor Request for Southeast Michiga n. May 24, 2006.
National Ambient Air Quality Standards for Particula te Matter, 40 CFR Part 50 (2006).
Rule To Reduce Interstate Transport of Fine Particu late 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 S ources: 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 Locomotiv e Engines and Marine CompressionIgnition Engines Less than 30 Liters per Cylinder, P roposed 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 Su lfur 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. Avail able at http://www.epa.gov/otaq/highway-diesel/regs/exec-su m.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 Ai r 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. A vailable at http://www.epa.gov/ttn/atw/utility/emiss_inv_oar-200 2-0056-6129.pdf
U.S. Environmental Protection Agency (EPA). 2005d. Regulatory Impact Analysis for the Final Clean Air Visibility Rule or the Guidelines for Best Available Retro fit 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, Regula tory Announcement: EPA Proposal for More Stringent Emissions Standards for Locomotives a nd Marine Compression-Ignition Engines. EPA420-F-07-015.
U.S. Environmental Protection Agency (EPA). 2007b, Propose d Emission Standards for New Nonroad Spark-Ignition Engines, Equipment, and Vessel s. EPA420-F-07-032.
U.S. Environmental Protection Agency (EPA). 2008a. Air Qua lity Modeling Platform for the Ozone National Ambient Air Quality Standard Final Ru le Regulatory Impact Assessment.
U.S. Environmental Protection Agency (EPA). 2008b. Techni cal Support Document: Preparation of Emissions Inventories For the 2002-based Platform, Ver sion 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 Establi shments. 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 s trategy, maximum control scenarios were used from the existing control measure dat aset 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 m ade as a result of the reviews performed for the final PM2.5 RIA. Following this, an interna l 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 revie w 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 contro l measures for over 70 SCC’s. For example, SPPD recommended that we apply SCR to cement kil ns 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 kil ns by 2020. Following this, it was recommended that supplemental controls could be appl ied to 8 additional SCC’s from nonEGU point NOx sources. We also looked into sources of con trols for highly reactive VOC nonEGU point sources. Four additional controls were appl ied 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, se lective noncatalytic reduction (SNCR), natural gas reburn (N GR), 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 importanc e 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 alo ng with a combustion control such as LNB with overfire air (OFA) to further reduce NOx emiss ions. 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 kiln s, stationary internal combustion engines, glass manufacturing, combustion turbines, and incinerators. NOx control measures available for petroleum refineries, particularly process heaters at t hese 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 boi lers, namely LNB, SCR, SNCR, along with water injection (WI). NOx control measures availabl e for cement kilns include those available to industrial boilers, namely LNB, SCR, and SNCR. Non-sel ective catalytic reduction (NSCR) can be used on stationary internal combustion engines. OX Y-firing, a technique to modify3a-2

combustion at glass manufacturing plants, can be us ed to reduce NOx at such plants. LNB, SCR, and SCR + steam injection (SI) are available measures for comb ustion 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 me asures 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 4,8003a-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 G as 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: perman ent total enclosure (PTE) applied to paper and web coating operations and fabric operations, and inc inerators or thermal oxidizers applied to wood products and marine surface coating operations. A PTE c onfines VOC emissions to a particular area where can be destroyed or used in a way th at limits emissions to the outside atmosphere, and an incinerator or thermal oxidizer d estroys 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 publ ishing sources as well as to surface coating operations. Table 3a.2 contains the emissions reduc tions 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 r eport.3a-4

Table 3a.2: VOC NonEGU Point Emission Reductions by Control Measure

Control Measure Source Type Modeled Control Strategy Reductions (annual tons/year) Fabric Printing, Coating and Dyeing 43 Permanent Total Enclosure (PTE) Paper and Other Web Coating 490 Printing and Publishing 3,600 Petroleum and Solvent Evaporation Surface Coating 400

3a.1.4 NOx Control Measures for Area Sources
There were three control measures applied for NOx em issions from area sources. The first is RACT (reasonably available control technology) to 25 t py (LNB). This control is the addition of a low NOx burner to reduce NOx emissions. This control is ap plied to industrial oil, natural gas, and coal combustion sources. The second control is water hea ters plus LNB space heaters. This control is based on the installation of low-NOx spa ce heaters and water heaters in commercial and institutional sources for the reduction of NOx emissio ns. The third control was switching to low sulfur fuel for residential home heating. This control is primarily designed to reduce s ulfur 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 AirCon trolNET 4.1 control measures documentation report.
Table 3a.3: NOx Area Source Emission Reductions by Control M easure

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 Commercial/Institutional—NG 4,300 Water Heater + LNB Space Heaters Residential NG 6,700

3a.1.5 VOC Control Measures for Area Source.
The most frequently applied control to reduce VOC em issions from area sources was CARB Long-Term Limits. This control, which represents con trols available in VOC rules promulgated by the California Air Resources Board, applies to commercia l solvents and commercial adhesives, and depends on future technological innova tion and market incentive methods to achieve emission reductions. The next most frequentl y 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 c ategories 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 w ell as solvent volatility limits and operating practices for cold cleaners. The Low Pressure/Va cuum Relief Valve control measure is the addition of low pressure/vacuum (LP/V) relief valves to gasoline storage tanks at service3a-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 equip ment requirements. Switch to Emulsified Asphalts control is a generic control me asure replacing VOC-containing cutback asphalt with VOC-free emulsified asphalt. The equip ment and maintenance control measure applies to oil and natural gas production. The Reformulatio n—FIP Rule control measure intends to reach the VOC limits by switching to and/or enco uraging the use of low-VOC pesticides and better Integrated Pest Management (IPM) practices. Table 3 a.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 t he AirControlNET 4.1 control measures documentation report.

Table 3a.4: VOC Area Source Emission Reductions by Control Me asure

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 Produ ction 450 Gas Collection (SCAQMD/BAAQMD) Municipal Solid Wast e Landfill 1,100 Incineration >100,000 lbs bread Bakery Products 2,7 00 Stage II Service Stations 9,900 Low Pressure/Vacuum Relief Valve Stage II Service Stations—Underground Tanks 9,800

Aircraft Surface Coating 720 OTC Mobile Equipment Repair and 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 me asures database by providing the pollutant it controls and its control efficiency (CE). These controls were applied not as part of the modeled control strategy, b ut 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 AirControlN ET.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 87 NOx LEC

20200254 Internal Comb. Engines/Industrial/ Natural Gas/4-cycle Lean Burn 87

3018001- Fugitive Leaks 50 Enhanced LDAR 30600701 30600999 - Flares 98

LDAR 3018001 - Fugitive Leaks 80 Monitoring Program 30600702- Cooling towers No gene ral 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 natura l 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-c ombustion chambers, increased turbocharging or adding a turbocharger, and increased c ooling and/or adding an intercooler or aftercooler, resulting in an engine that is designe d to achieve a consistent NO x emission rate of not more than 1.5-3.0 g/bhp-hr at full capacity (usu ally 100 percent speed and 100 percent load). This type of retrofit technology is fairly widely avai lable for stationary internal combustion engines.
For CE, EPA estimates that it ranges from 82 to 91 pe rcent for LEC technology applications. The EPA believes application of LEC would achieve average NO x emission levels in the range of 1.5-3.0 g/bhp-hr. This is an 82-91 percent reduction from the average uncontrolled e mission levels reported in the ACT document. An EPA memorandum sum marizing 269 tests shows that3a-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 emissi on rates of 2.0 g/bhp-hr or less. 2

Major Uncertainties: The EPA acknowledges that specific values will vary from en gine 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 o f LEC may yield improved fuel economy and power output, both of which may affect the emis sions generated by the device.
Leak Detection and Repair (LDAR) for Fugitive Leaks

Overview: This control measure is a program to reduce leaks o f fugitive VOC emissions from chemical plants and refineries. The program includes speci al “sniffer” equipment to detect leaks, and maintenance schedules that affected facilities are t o 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 chemica l plants is always difficult to quantify given the large number of such leaks at a typical chemical manufacturing plant. There are also growing indication s based on tests conducted by TCEQ and others in Harris County, Texas that fugitive leaks h ave 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, howev er, given that most chemical plants are under some type of requirement to have an LDAR prog ram. However, as mentioned earlier, there is growing evidence that fugitive leak emissi ons 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. S eptember 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 Infor mation on NOx Emissions and Control Techniques,” Ec/R Incorporated, Chapel Hill, NC. Septemb er 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 Losse s, 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 Losse s, and Potential Policy Gaps. 2006 International Workshop. U.S. Environmental Protectio n 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 recove r 98 percent of the VOC emitted by flares that emit 20 tons per year or more of the p ollutant.

Major Uncertainties: Flare gas recovery is just gaining commercial acce ptance 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 f rom 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 meas ure; 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 measur e.
Wastewater Drains and Separators

Overview: This control measure includes an inspection and mai ntenance 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 requ irements for such sources.

Major Uncertainties: The reference for this control measures notes that the VOC emissions inventories for the five San Francisco Bay Area ref ineries 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 pra ctices (e.g., storing VOCcontaining cleaning materials in closed containers, m inimizing spills) or using coatings that have much lower VOC content. These measures, which are of relativ ely low cost compared to other VOC area source controls, can apply to a variety of processe s, 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 coa tings, and large appliance coatings published by the US EPA in July 2007. 7

5 Bay Area Air Quality Management District (BAAQMD). Propo sed Revision of Regulation 8, Rule 8: Wastewater Collection Systems. Staff Report, Mar ch 17, 2004.

6 Bay Area Air Quality Management District (BAAQMD). Propo sed Revision of Regulation 8, Rule 8: Wastewater Collection Systems. Staff Report, Mar ch 17, 2004.

7 U.S. Environmental Protection Agency. Consumer and Commer cial Products: Control Techniques Guidelines in Lieu of Regulations for Pape r, Film, and Foil Coatings; Metal3a-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 a ffected processes are implementing or already implemented these control m easures. This may be particularly true in California. Also, there are nine States that have m any of the above work practices in effect for paper, film and foil coatings processes, but the work pract ices are not meant to achieve a specific emissions limit. 8 Hence, it is uncertain how much VOC reduction is occurring fro m this control measure in this case.
In addition to the new supplemental controls presented abov e, there were a number of changes made to existing AirControlNET controls. These changes wer e made based upon an internal review performed by EPA engineers to examine the cont rols 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 p oint 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 ove r 70 SCCs. The changes apply to the control strategies performed for the Eastern US onl y. These changes are listed in Table 3a.6.

Table 3a.6: Supplemental Emission Control Measures—Chang es 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 Commer cial Products: Control Techniques Guidelines in Lieu of Regulations for Pape r, 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_ccp092 807.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 SC R 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 803a-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 contr ol 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 403a-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/Natu ral 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 reducti ons for the mobile source control measures discussed in this section.

Table 3a.8: NOx Mobile Emission Reductions by Control M easure

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 Meas ure

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

Reduce Gasoline RVP 6,300 Nonroad Diesel Retrofits and Engine Rebuilds 5,2003a-13

3a.2.1 Diesel Retrofits and Engine Rebuilds
Retrofitting heavy-duty diesel vehicles and equipme nt manufactured before stricter standards are in place—in 2007–2010 for highway engines and in 2011–2014 for most nonroad equipment— can provide NO X and HC benefits. The retrofit strategies included in the RIA retrofit measure

are:

x Installation of emissions after-treatment devices c alled selective catalytic reduction (“SCRs”)

x Rebuilding nonroad engines (“rebuild/upgrade kit”)
We chose to focus on these strategies due to their high NOx em issions reduction potential and widespread application. Additional retrofit strateg ies include, but are not limited to, lean NOx catalyst systems—which are another type of after-tr eatment device—and alternative fuels. Additionally, SCRs are currently the most likely type o f 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 s ignificantly by strategy and by the type and age of the engine and its application.
To estimate the potential emissions reductions from this me asure, 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 Yea r 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 e missions inventory from heavy-duty highway vehicles, so we did not include trucks below class 6. We chose not to includ e locomotive and marine engines in our analysis since E PA has proposed regulations to address these engines, which will significantly impact the emissio ns 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 engin es, so we did not include them in this analysis. In addition, EPA is in the process of neg otiating standards for new aircraft engines.
The lower bound in the model year range—1995 for hig hway vehicles and 1991 for nonroad engines—reflects the first model year in which emissions a fter-treatment devices can be reliably applied to the engines. Due to a variety of factors, d evices are at a higher risk of failure for earlier model years. We expect the engines manufact ured before the lower bound year that are still in existence in 2020 to be retired quickly du e to natural turnover, therefore, we have not included strategies for pre-1995/1991 engines becau se of the strategies’ relatively small impact on emissions. The upper bound in the model year range reflect s the last year before more stringent emissions standards will be fully phased-in.
We chose the type of strategy to apply to each model y ear of highway vehicles and nonroad equipment based on our technical assessment of which strategies would achieve re liable results at the lowest cost. After-treatment devices can be more cost-effective than rebuild and vice versa3a-14

depending on the emissions rate, application, usage rates , and expected life of the engine. The performance of after-treatment devices, for example, depe nds heavily upon the model year of the engine; some older engines may not be suitable for af ter-treatment devices and would be better candidates for rebuild/upgrade kit. In certain cases, non road 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 Highw ay 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 an d Later Model Year Heavy-duty Highway Engines and Vehicles published October 2000), interviews w ith component manufacturers, and EPA’s Summary of Potential Retrofit Technologies. This i nformation 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 3 a.13, respectively.

Table 3a.12: Percentage Emissions Reduction by Highway Ve hicle Retrofit Strategy

PM CO HC NOx SCR (+DPF) 90% 90% 90% 70%

Table 3a.13: Percentage Emissions Reduction by Nonroad Eq uipment 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 th e 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 percen tage reduction in emissions as for agricultural mowers). We did this in order to simpli fy 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 esti mates used in the RIA, however, reflect the3a-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 e missions (PM, NOx, HC, and CO) from the retrofit measure for each re levant engine category (source category code, or SCC) for each county in 2020. To e valuate 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 M arch 17, 2006.
For the control analysis, we applied the retrofit m easure corresponding to the percent reductions of the specified pollutants in Tables 3a.12 and 3a.13 to the s pecified model years in Tables 3a.10 and 3a.11 of the relevant SCCs. Fleet turnover rates are modeled in the NMIM, so we applied t he retrofit measure to the 2007 fleet inventory, and then evaluated the resulting e missions inventory in 2020. The timing of the application of the retrofit measur e is not a factor; retrofits only need to take place prior to the attainment date target (202 0 for this RIA). For example, if retrofit devices are installed on 1995 model year bulldozers in 2007, the o nly impact on emissions in 2020 will be from the expected inventory of 1995 model year b ulldozer emissions in 2020.
We then compared the baseline and control analyses t o determine the percent reduction in emissions we estimate from this measure for the rel evant 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 O BD systems on light-duty OBD-equipped vehicles. It involves equip ping subject vehicles with some type of transmitter that attaches to the OBD port. The devi ce transmits the status of the OBD system to receivers distributed around the I/M area. Transmission m ay be through radio-frequency, cellular or wi-fi means. Radio frequency and cellular techno logies 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 throug hout 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. Thu s, 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 truck s:

x All 1996 and newer (SCC 2201001000) Light Duty Gasoline Veh icles (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 illumin ate the malfunction indicator lamp whenever emissions of HC, CO or NOx would exceed 1.5 t imes the vehicle’s certification standard. Thus, the benefits of this measure will affect all t hree criteria pollutants. MOBILE6 was used to estimate the emission reduction benefit s of Continuous I/M, using the methodology discussed above.
3a.2.3 Eliminating Long Duration Truck Idling
Virtually all long duration truck idling—idling tha t lasts for longer than 15 minutes—from heavy-duty diesel class 8a and 8b trucks can be eli minated 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 sto ps or public rest areas and while trucks are waiting to perform a task at private dis tribution terminals. When truck spaces are electrified, truck drivers can shut down their engines and use electricity to power e quipment which supplies air conditioning, heat, and electrical powe r for on-board appliances.
MIRTs can eliminate long duration idling from truck s that are stopped away from these central sites. For a more complete list of MIRTs see EPA’s Idl e Reduction Technology page at http://www.epa.gov/otaq/smartway/idlingtechnologies .htm

. This measure demonstrates the potential emissions reduct ions 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 me asure, 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 clas s 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 o n fuel conversion factors. The inventory in the MOBILE model, however, does not fully capture long durati on idling emissions. There is evidence that idling may represent a much greater s hare 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 realistic3a-17

targets. For more information on determining baseli ne idling activity see EPA’s “Guidance for Quantifying and Using Long-Duration Truck Idling Em ission Reductions in State Implementation Plans and Transportation Conformity” avai lable at http://www.epa.gov/smartway/idle-guid.htm

. Pollutants and Source Categories Affected by Measure: NO x Table 3a.14: Class 8a and 8b Heavy Duty Diesel Truc ks (decrease NOx for all SCCs) SCC Note: All SCC Descriptions below begin with “Mob ile 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 % de crease in NOx for all SCCs affected by measure

3a.2.4 Commuter Programs
Commuter programs recognize and support employers who pro vide incentives to employees to reduce light-duty vehicle emissions. Employers impl ement a wide range of incentives to affect change in employee commuting habits including transit subsidies, bike-friendly facilit ies, telecommuting policies, and preferred parking for van pools and carpools. The commuter measure in this RIA reflects a mixed package of incentives.
This measure demonstrates the potential emissions reduct ions from providing commuter incentives to 10% and 25% of the commuter population i n 2020.
We used the findings from a recent Best Workplaces for Commute rs 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 wo rkplaces with comprehensive commuter programs emit 15% fewer emissions th an 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 Rese arch 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 progr ams measure into two program penetration rates: 10% and 25%. This was meant to p rovide 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 thi s 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 (SC C): NO x , and VOC3a-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); Rur al Interstate: Total 2201001130 Light Duty Gasoline Vehicles (LDGV); Rur al Other Principal Arterial: Total 2201001150 Light Duty Gasoline Vehicles (LDGV); Rur al Minor Arterial: Total 2201001170 Light Duty Gasoline Vehicles (LDGV); Rur al Major Collector: Total 2201001190 Light Duty Gasoline Vehicles (LDGV); Rur al Minor Collector: Total 2201001210 Light Duty Gasoline Vehicles (LDGV); Rur al Local: Total 2201001230 Light Duty Gasoline Vehicles (LDGV); Urb an Interstate: Total 2201001250 Light Duty Gasoline Vehicles (LDGV); Urb an Other Freeways and Expressways: Total 2201001270 Light Duty Gasoline Vehicles (LDGV); Urb an Other Principal Arterial: Total 2201001290 Light Duty Gasoline Vehicles (LDGV); Urb an Minor Arterial: Total 2201001310 Light Duty Gasoline Vehicles (LDGV); Urb an Collector: Total 2201001330 Light Duty Gasoline Vehicles (LDGV); Urb an 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 def ines 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 v apor 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 cont rol measure represents the use of gasoline with a RVP limit of 7.0 psi from May through September in count ies 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) t hat the plan implements. EPA can approve a state fuel requirement as necessary only if no othe r 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), Tota l: All Road Types

– 2201020000 Light Duty Gasoline Trucks 1 (LDGT1), Tot al: All Road Types

– 2201040000 Light Duty Gasoline Trucks 2 (LDGT2), Tot al: All Road Types

– 2201070000 Heavy Duty Gasoline Vehicles (HDGV), Tot al: 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 fro m 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 (

í 11%) 113

(

í 26%) 33

(

í 23%) 812

(

í 2%) 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 th e 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 t he emissions contributing to interstate transport of air pollution and available control measures , EPA determined that achieving required reductions in the identified States by con trolling emissions from power plants is highly cost effective. CAIR will permanently cap emissions of sulfur dioxide (SO 2 ) and nitrogen oxides (NO x ) in the eastern United States. CAIR achieves large reduc tions of SO 2 and/or NO x emissions across 28 eastern states and the District of Columbia.3a-21 Figure 3a.1: CAIR Affected Region

States controlled for fine particles (annual SO 2 and NOx) States not covered by CAIR

States controlled for ozone (ozone season NOx) States controlled for both fine particles (annual S O 2 and NOx) and ozone (ozone season NOx)

When fully implemented, CAIR will reduce SO 2 emissions in these states by over 70% and NO x emissions by over 60% from 2003 levels (some of which are due t o NOx SIP Call). This will result in significant environmental and health benefits and will substantially reduce premature mortality in the eastern United States. The benefit s will continue to grow each year with further implementation. CAIR was designed with current air quali ty 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 PM 2.5 standards. Some areas will need to adopt additiona l 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 SO 2 , NO x , and direct PM, will move States towards attainment in a deliberate and logical manner.

Based on the final State rules that have been submi tted 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 requirem ents 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 ozon e standard of 0.070 ppm for the power
sector.
3a.3.2 Integrated Planning Model and Background
CAIR was designed to achieve significant emissions r eductions in a highly cost-effective manner to reduce the transport of fine particles that have been found to contribute to nonattainment. EPA3a-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 analy sis 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 F ederal 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 upda tes in modeling the power sector and incorporates Federal and State rules and regula tions adopted before September 2006 and various NSR settlements. A detailed discussion of u ncertainties 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 modeli ng is based on its best judgment for various input assumptions that are uncertain, particularly assumptions for future fuel prices an d electricity demand growth. To some degree, EPA addre sses the uncertainty surrounding these two assumptions through sensitivity analyses. More detail on IPM can be found in the model documentation, which provides additional informatio n 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 SO 2 , NO x , and mercury (Hg) emission control technology opti ons for meeting existing and future federal, regional, and state, S O 2 , NO x and Hg emission limits. The NOx control technology options include Selective Catalytic Re duction (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 me eting 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 Contr ol Performance Assumptions Selective Catalytic Reduction

(SCR) Selective Non-Catalytic Reduction (SNCR) Unit Type Coal Oil/Gas a Coal Oil/Gas a 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 th is RIA. Existing coal-fired units that are retrofit with SC R have a NOx removal efficiency of 90%, with a minimum controlled NOx emission rate of 0.06 lb/m mBtu 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 a nd 0.06 lb NOx/mmBtu.
Detailed cost and performance derivations for NOx control s are discussed in detail in the EPA’s documentation of IPM ( http://www.epa.gov/airmarkets/progsregs/epa-ipm/pas tmodeling.html ).

3a.4 Emissions Reductions by Sector
Figures 3a.2–3a.6 show the NOx reductions for each sector a nd Figures 3a.7–3a.10 show the VOC reductions for each sector under the modeled control str ategy.
Figure 3a.2: Annual Tons of NOx Emissions Reduced from EG U Sources*

* Reductions are negative and increases are positive.

** The

í 99–+100 range is not shown because these are small county-level NOx reductions or increases that likely had little to no impact on ozone estima tes. Most counties in this range had NOx difference s of under 1 ton.3a-24

Figure 3a.3: Annual tons/year of Nitrogen Oxide (NO x) Emissions Reduced from NonEGU Point Sources*

* Reductions are negative and increases are positive.
** The

í 99–0 range is not shown because these are small cou nty-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 (NO x) Emissions Reduced from Area Sources *

*Reductions are negative and increases are positive
**The

í 99–0 range is not shown because these are small cou nty-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 (NO x) Emissions Reduced from Nonroad Sources*

*Reductions are negative and increases are positive
**The

í 99–0 range is not shown because these are small cou nty-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 (NO x) Emissions Reduced from Onroad Sources*

*Reductions are negative and increases are positive
**The

í 99–0 range is not shown because these are small cou nty-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

í 99–0 range is not shown because these are small cou nty-level VOC reductions or increases that likely had little to no impact on ozone estimates3a-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

í 99–0 range is not shown because these are small cou nty-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

í 99–0 range is not shown because these are small cou nty-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

í 99–0 range is not shown because these are small cou nty-level VOC reductions or increases that likely had little to no impact on ozone estimates.

3a.5 Change in Ozone Concentrations Between Baselin e and Modeled Control Strategy
Table 3a.23 provides the projected 8-hour ozone desi gn values for the 2020 baseline and 2020 control strategy scenarios for each monitored county. The ch anges 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 Bas eline 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

í 0.001 Alabama Elmore 0.055 0.055 0.001 Alabama Etowah 0.054 0.053

í 0.001 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

í 0.002 Arizona Navajo 0.058 0.058

í 0.001 Arizona Pima 0.064 0.063

í 0.001 Arizona Pinal 0.065 0.063

í 0.002 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 0.0003a-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

í 0.001 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

í 0.004 Colorado Arapahoe 0.069 0.065

í 0.005 Colorado Boulder 0.063 0.058

í 0.004 Colorado Denver 0.064 0.060

í 0.004 Colorado Douglas 0.072 0.068

í 0.005 Colorado El Paso 0.062 0.060

í 0.003 Colorado Jefferson 0.073 0.068

í 0.005 Colorado La Plata 0.052 0.051 0.000 Colorado Larimer 0.067 0.062

í 0.005 Colorado Montezuma 0.062 0.062 0.000 Colorado Weld 0.064 0.060

í 0.004 Connecticut Fairfield 0.079 0.077

í 0.002 Connecticut Hartford 0.066 0.063

í 0.003 Connecticut Litchfield 0.064 0.062

í 0.003 Connecticut Middlesex 0.073 0.071

í 0.003 Connecticut New Haven 0.076 0.074

í 0.003 Connecticut New London 0.068 0.066

í 0.002 Connecticut Tolland 0.068 0.065

í 0.003 Delaware Kent 0.069 0.067

í 0.002 Delaware New Castle 0.071 0.068

í 0.003 Delaware Sussex 0.070 0.068

í 0.002 D.C. Washington 0.069 0.065

í 0.004 Florida Alachua 0.056 0.057 0.000 Florida Baker 0.055 0.054

í 0.001 Florida Bay 0.061 0.063 0.002 Florida Brevard 0.051 0.052 0.001 Florida Broward 0.054 0.054 0.0003a-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

í 0.001 Georgia Chatham 0.053 0.052 0.000 Georgia Cherokee 0.053 0.051

í 0.002 Georgia Clarke 0.054 0.052

í 0.002 Georgia Cobb 0.063 0.061

í 0.002 Georgia Coweta 0.065 0.060

í 0.006 Georgia Dawson 0.056 0.054

í 0.002 Georgia De Kalb 0.067 0.065

í 0.002 Georgia Douglas 0.064 0.062

í 0.002 Georgia Fayette 0.062 0.060

í 0.002 Georgia Fulton 0.070 0.068

í 0.002 Georgia Glynn 0.054 0.054

í 0.001 Georgia Gwinnett 0.061 0.059

í 0.002 Georgia Henry 0.064 0.062

í 0.002 Georgia Murray 0.059 0.058

í 0.001 Georgia Muscogee 0.054 0.052

í 0.002 Georgia Paulding 0.060 0.058

í 0.002 Georgia Richmond 0.064 0.059

í 0.005 Georgia Rockdale 0.064 0.062

í 0.002 Georgia Sumter 0.054 0.053

í 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 0.060 0.056

í 0.0043a-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

í 0.001 Illinois Clark 0.053 0.053

í 0.001 Illinois Cook 0.074 0.073

í 0.001 Illinois Du Page 0.061 0.059

í 0.001 Illinois Effingham 0.057 0.056

í 0.001 Illinois Hamilton 0.059 0.057

í 0.002 Illinois Jersey 0.067 0.065

í 0.002 Illinois Kane 0.062 0.061

í 0.001 Illinois Lake 0.071 0.070

í 0.001 Illinois McHenry 0.067 0.065

í 0.001 Illinois McLean 0.057 0.056

í 0.001 Illinois Macon 0.056 0.055

í 0.001 Illinois Macoupin 0.057 0.055

í 0.002 Illinois Madison 0.066 0.064

í 0.003 Illinois Peoria 0.063 0.062

í 0.001 Illinois Randolph 0.059 0.058

í 0.001 Illinois Rock Island 0.055 0.054

í 0.001 Illinois St Clair 0.066 0.064

í 0.002 Illinois Sangamon 0.054 0.053

í 0.001 Illinois Will 0.062 0.060

í 0.001 Illinois Winnebago 0.058 0.057

í 0.001 Indiana Allen 0.067 0.065

í 0.002 Indiana Boone 0.067 0.066

í 0.002 Indiana Carroll 0.062 0.061

í 0.001 Indiana Clark 0.068 0.067

í 0.002 Indiana Delaware 0.064 0.063

í 0.002 Indiana Elkhart 0.066 0.064

í 0.002 Indiana Floyd 0.066 0.065

í 0.002 Indiana Gibson 0.051 0.050

í 0.001 Indiana Greene 0.063 0.061

í 0.001 Indiana Hamilton 0.070 0.068

í 0.002 Indiana Hancock 0.067 0.066

í 0.002 Indiana Hendricks 0.065 0.063

í 0.002 Indiana Huntington 0.064 0.062

í 0.002 Indiana Jackson 0.062 0.060

í 0.002 Indiana Johnson 0.064 0.063

í 0.002 Indiana Lake 0.078 0.077

í 0.001 Indiana La Porte 0.074 0.073

í 0.001 Indiana Madison 0.067 0.066

í 0.002 Indiana Marion 0.069 0.067

í 0.002 Indiana Morgan 0.066 0.064

í 0.002 Indiana Porter 0.075 0.074

í 0.001 Indiana Posey 0.061 0.060

í 0.002 Indiana St Joseph 0.068 0.067

í 0.002 Indiana Shelby 0.069 0.067

í 0.002 Indiana Vanderburgh 0.060 0.058

í 0.002 Indiana Vigo 0.066 0.065

í 0.002 Indiana Warrick 0.064 0.061

í 0.003 Iowa Bremer 0.059 0.059 0.000 Iowa Clinton 0.063 0.062

í 0.0013a-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

í 0.001 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

í 0.001 Iowa Story 0.049 0.048 0.000 Iowa Van Buren 0.059 0.058

í 0.001 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

í 0.001 Kentucky Boone 0.063 0.061

í 0.002 Kentucky Boyd 0.071 0.069

í 0.002 Kentucky Bullitt 0.062 0.060

í 0.002 Kentucky Campbell 0.070 0.068

í 0.003 Kentucky Carter 0.058 0.057

í 0.001 Kentucky Christian 0.058 0.058 0.000 Kentucky Daviess 0.059 0.058

í 0.001 Kentucky Edmonson 0.059 0.058

í 0.001 Kentucky Fayette 0.057 0.056

í 0.002 Kentucky Graves 0.060 0.059

í 0.001 Kentucky Greenup 0.065 0.063

í 0.001 Kentucky Hancock 0.063 0.064 0.001 Kentucky Hardin 0.058 0.056

í 0.001 Kentucky Henderson 0.060 0.058

í 0.003 Kentucky Jefferson 0.065 0.063

í 0.002 Kentucky Jessamine 0.057 0.056

í 0.001 Kentucky Kenton 0.066 0.063

í 0.003 Kentucky Livingston 0.061 0.061

í 0.001 Kentucky McCracken 0.064 0.063

í 0.001 Kentucky McLean 0.059 0.058

í 0.001 Kentucky Oldham 0.063 0.061

í 0.002 Kentucky Perry 0.055 0.055

í 0.001 Kentucky Pike 0.055 0.053

í 0.001 Kentucky Pulaski 0.059 0.061 0.002 Kentucky Scott 0.050 0.049

í 0.001 Kentucky Simpson 0.057 0.056 0.000 Kentucky Trigg 0.052 0.053 0.000 Kentucky Warren 0.060 0.059

í 0.001 Louisiana Ascension 0.069 0.065

í 0.004 Louisiana Beauregard 0.062 0.059

í 0.003 Louisiana Bossier 0.061 0.060

í 0.001 Louisiana Caddo 0.059 0.057

í 0.001 Louisiana Calcasieu 0.066 0.064

í 0.002 Louisiana East Baton Rouge 0.077 0.074

í 0.003 Louisiana Grant 0.060 0.058

í 0.0023a-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

í 0.004 Louisiana Jefferson 0.069 0.067

í 0.002 Louisiana Lafayette 0.066 0.061

í 0.005 Louisiana Lafourche 0.065 0.062

í 0.003 Louisiana Livingston 0.069 0.064

í 0.004 Louisiana Orleans 0.058 0.056

í 0.001 Louisiana Ouachita 0.061 0.060

í 0.001 Louisiana Pointe Coupee 0.064 0.057

í 0.007 Louisiana St Bernard 0.063 0.062

í 0.001 Louisiana St Charles 0.066 0.064

í 0.002 Louisiana St James 0.064 0.061

í 0.003 Louisiana St John The Baptist 0.069 0.066

í 0.003 Louisiana St Mary 0.061 0.058

í 0.004 Louisiana West Baton Rouge 0.074 0.070

í 0.004 Maine Cumberland 0.063 0.061

í 0.002 Maine Hancock 0.071 0.069

í 0.003 Maine Kennebec 0.060 0.058

í 0.002 Maine Knox 0.063 0.061

í 0.002 Maine Oxford 0.050 0.049

í 0.001 Maine Penobscot 0.064 0.062

í 0.002 Maine Sagadahoc 0.060 0.057

í 0.002 Maine York 0.067 0.064

í 0.002 Maryland Anne Arundel 0.072 0.069

í 0.003 Maryland Baltimore 0.071 0.068

í 0.003 Maryland Carroll 0.065 0.062

í 0.003 Maryland Cecil 0.071 0.068

í 0.003 Maryland Charles 0.065 0.062

í 0.003 Maryland Frederick 0.066 0.061

í 0.004 Maryland Harford 0.077 0.074

í 0.003 Maryland Kent 0.070 0.067

í 0.003 Maryland Montgomery 0.064 0.061

í 0.003 Maryland Prince Georges 0.069 0.066

í 0.003 Maryland Washington 0.064 0.061

í 0.003 Massachusetts Barnstable 0.071 0.068

í 0.002 Massachusetts Berkshire 0.069 0.067

í 0.002 Massachusetts Bristol 0.069 0.067

í 0.003 Massachusetts Essex 0.070 0.068

í 0.002 Massachusetts Hampden 0.068 0.066

í 0.003 Massachusetts Hampshire 0.066 0.064

í 0.002 Massachusetts Middlesex 0.065 0.062

í 0.003 Massachusetts Norfolk 0.074 0.072

í 0.002 Massachusetts Suffolk 0.069 0.067

í 0.002 Massachusetts Worcester 0.065 0.063

í 0.002 Michigan Allegan 0.073 0.072

í 0.001 Michigan Benzie 0.067 0.065

í 0.001 Michigan Berrien 0.071 0.069

í 0.001 Michigan Cass 0.068 0.067

í 0.002 Michigan Clinton 0.065 0.063

í 0.002 Michigan Genesee 0.066 0.065

í 0.002 Michigan Huron 0.069 0.067

í 0.0023a-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

í 0.002 Michigan Kalamazoo 0.063 0.061

í 0.002 Michigan Kent 0.065 0.063

í 0.002 Michigan Lenawee 0.067 0.065

í 0.002 Michigan Macomb 0.075 0.073

í 0.002 Michigan Mason 0.066 0.064

í 0.001 Michigan Missaukee 0.062 0.061

í 0.001 Michigan Muskegon 0.070 0.069

í 0.001 Michigan Oakland 0.072 0.071

í 0.001 Michigan Ottawa 0.067 0.065

í 0.002 Michigan St Clair 0.070 0.068

í 0.002 Michigan Schoolcraft 0.063 0.062

í 0.001 Michigan Washtenaw 0.069 0.067

í 0.002 Michigan Wayne 0.071 0.069

í 0.002 Minnesota St Louis 0.059 0.059 0.000 Mississippi Adams 0.060 0.060

í 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

í 0.001 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

í 0.001 Missouri Clay 0.065 0.064

í 0.001 Missouri Greene 0.059 0.058

í 0.001 Missouri Jefferson 0.067 0.064

í 0.003 Missouri Monroe 0.060 0.059

í 0.001 Missouri Platte 0.063 0.063

í 0.001 Missouri St Charles 0.071 0.069

í 0.002 Missouri Ste Genevieve 0.065 0.063

í 0.002 Missouri St Louis 0.070 0.068

í 0.003 Missouri St Louis City 0.071 0.068

í 0.002 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

í 0.001 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

í 0.002 New Hampshire Carroll 0.055 0.054

í 0.001 New Hampshire Cheshire 0.057 0.055

í 0.002 New Hampshire Grafton 0.058 0.057

í 0.001 New Hampshire Hillsborough 0.065 0.063

í 0.0023a-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

í 0.002 New Hampshire Rockingham 0.064 0.061

í 0.002 New Hampshire Strafford 0.060 0.058

í 0.002 New Hampshire Sullivan 0.061 0.060

í 0.001 New Jersey Atlantic 0.067 0.065

í 0.002 New Jersey Bergen 0.074 0.072

í 0.002 New Jersey Camden 0.077 0.075

í 0.003 New Jersey Cumberland 0.072 0.069

í 0.003 New Jersey Essex 0.053 0.051

í 0.002 New Jersey Gloucester 0.076 0.073

í 0.003 New Jersey Hudson 0.066 0.064

í 0.002 New Jersey Hunterdon 0.071 0.068

í 0.003 New Jersey Mercer 0.076 0.073

í 0.003 New Jersey Middlesex 0.073 0.070

í 0.003 New Jersey Monmouth 0.073 0.071

í 0.002 New Jersey Morris 0.071 0.068

í 0.003 New Jersey Ocean 0.080 0.077

í 0.003 New Jersey Passaic 0.067 0.065

í 0.003 New Mexico Bernalillo 0.065 0.065 0.000 New Mexico Dona Ana 0.069 0.068

í 0.001 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

í 0.003 New York Bronx 0.069 0.067

í 0.002 New York Chautauqua 0.073 0.070

í 0.003 New York Chemung 0.062 0.060

í 0.002 New York Dutchess 0.069 0.066

í 0.003 New York Erie 0.075 0.072

í 0.003 New York Essex 0.069 0.067

í 0.002 New York Hamilton 0.063 0.062

í 0.001 New York Herkimer 0.059 0.058

í 0.001 New York Jefferson 0.073 0.072

í 0.002 New York Madison 0.062 0.061

í 0.002 New York Monroe 0.067 0.065

í 0.002 New York Niagara 0.075 0.074

í 0.002 New York Oneida 0.063 0.061

í 0.002 New York Onondaga 0.068 0.066

í 0.002 New York Orange 0.064 0.061

í 0.003 New York Oswego 0.054 0.052

í 0.002 New York Putnam 0.071 0.068

í 0.003 New York Queens 0.070 0.068

í 0.002 New York Rensselaer 0.067 0.064

í 0.003 New York Richmond 0.074 0.071

í 0.002 New York Saratoga 0.067 0.064

í 0.003 New York Schenectady 0.062 0.059

í 0.002 New York Suffolk 0.080 0.078

í 0.002 New York Ulster 0.064 0.062

í 0.002 New York Wayne 0.066 0.064

í 0.0023a-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

í 0.003 North Carolina Alexander 0.062 0.062 0.000 North Carolina Avery 0.059 0.058

í 0.001 North Carolina Buncombe 0.061 0.060

í 0.001 North Carolina Caldwell 0.061 0.060 0.000 North Carolina Caswell 0.061 0.060

í 0.001 North Carolina Chatham 0.059 0.058

í 0.001 North Carolina Cumberland 0.062 0.060

í 0.001 North Carolina Davie 0.064 0.062

í 0.002 North Carolina Duplin 0.060 0.059

í 0.001 North Carolina Durham 0.062 0.060

í 0.001 North Carolina Edgecombe 0.063 0.062

í 0.001 North Carolina Forsyth 0.064 0.062

í 0.002 North Carolina Franklin 0.063 0.062

í 0.001 North Carolina Granville 0.065 0.063

í 0.001 North Carolina Guilford 0.060 0.059

í 0.001 North Carolina Haywood 0.065 0.064

í 0.001 North Carolina Jackson 0.064 0.063

í 0.001 North Carolina Johnston 0.060 0.059

í 0.001 North Carolina Lenoir 0.060 0.060

í 0.001 North Carolina Lincoln 0.065 0.065 0.001 North Carolina Martin 0.060 0.059

í 0.001 North Carolina Mecklenburg 0.072 0.071

í 0.001 North Carolina New Hanover 0.057 0.057 0.001 North Carolina Northampton 0.062 0.061

í 0.002 North Carolina Person 0.063 0.062

í 0.001 North Carolina Pitt 0.059 0.058

í 0.001 North Carolina Randolph 0.058 0.057

í 0.001 North Carolina Rockingham 0.062 0.061

í 0.001 North Carolina Rowan 0.069 0.067

í 0.002 North Carolina Swain 0.053 0.053

í 0.001 North Carolina Union 0.062 0.061

í 0.001 North Carolina Wake 0.064 0.063

í 0.001 North Carolina Yancey 0.063 0.062

í 0.001 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

í 0.003 Ohio Ashtabula 0.076 0.073

í 0.003 Ohio Butler 0.068 0.065

í 0.003 Ohio Clark 0.067 0.063

í 0.004 Ohio Clermont 0.069 0.066

í 0.003 Ohio Clinton 0.069 0.067

í 0.003 Ohio Cuyahoga 0.068 0.066

í 0.002 Ohio Delaware 0.067 0.064

í 0.002 Ohio Franklin 0.069 0.066

í 0.002 Ohio Geauga 0.077 0.074

í 0.0023a-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

í 0.004 Ohio Hamilton 0.069 0.066

í 0.003 Ohio Jefferson 0.064 0.062

í 0.002 Ohio Knox 0.065 0.062

í 0.002 Ohio Lake 0.073 0.070

í 0.002 Ohio Lawrence 0.065 0.064

í 0.001 Ohio Licking 0.065 0.063

í 0.002 Ohio Lorain 0.067 0.065

í 0.002 Ohio Lucas 0.070 0.068

í 0.002 Ohio Madison 0.065 0.062

í 0.003 Ohio Mahoning 0.065 0.063

í 0.002 Ohio Medina 0.067 0.065

í 0.002 Ohio Miami 0.065 0.062

í 0.003 Ohio Montgomery 0.066 0.063

í 0.003 Ohio Portage 0.069 0.066

í 0.002 Ohio Preble 0.060 0.058

í 0.003 Ohio Stark 0.066 0.063

í 0.003 Ohio Summit 0.071 0.069

í 0.003 Ohio Trumbull 0.069 0.066

í 0.003 Ohio Warren 0.069 0.065

í 0.003 Ohio Washington 0.061 0.061

í 0.001 Ohio Wood 0.068 0.065

í 0.003 Oklahoma Canadian 0.057 0.056

í 0.001 Oklahoma Cleveland 0.060 0.059

í 0.001 Oklahoma Comanche 0.061 0.060

í 0.002 Oklahoma Dewey 0.058 0.057

í 0.002 Oklahoma Kay 0.061 0.060

í 0.001 Oklahoma Mc Clain 0.062 0.060

í 0.001 Oklahoma Oklahoma 0.061 0.060

í 0.001 Oklahoma Ottawa 0.063 0.062

í 0.001 Oklahoma Pittsburg 0.061 0.060 0.000 Oklahoma Tulsa 0.066 0.066

í 0.001 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

í 0.003 Pennsylvania Allegheny 0.072 0.069

í 0.003 Pennsylvania Armstrong 0.068 0.066

í 0.003 Pennsylvania Beaver 0.071 0.069

í 0.003 Pennsylvania Berks 0.066 0.063

í 0.003 Pennsylvania Blair 0.061 0.058

í 0.002 Pennsylvania Bucks 0.078 0.075

í 0.003 Pennsylvania Cambria 0.064 0.061

í 0.003 Pennsylvania Centre 0.062 0.060

í 0.002 Pennsylvania Chester 0.071 0.068

í 0.003 Pennsylvania Clearfield 0.065 0.062

í 0.003 Pennsylvania Dauphin 0.065 0.061

í 0.005 Pennsylvania Delaware 0.071 0.068

í 0.0033a-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

í 0.003 Pennsylvania Franklin 0.067 0.064

í 0.003 Pennsylvania Greene 0.064 0.062

í 0.002 Pennsylvania Lackawanna 0.062 0.060

í 0.002 Pennsylvania Lancaster 0.068 0.063

í 0.005 Pennsylvania Lawrence 0.058 0.055

í 0.002 Pennsylvania Lehigh 0.067 0.064

í 0.003 Pennsylvania Luzerne 0.062 0.060

í 0.002 Pennsylvania Lycoming 0.061 0.059

í 0.002 Pennsylvania Mercer 0.068 0.065

í 0.003 Pennsylvania Montgomery 0.071 0.069

í 0.003 Pennsylvania Northampton 0.067 0.063

í 0.004 Pennsylvania Perry 0.062 0.059

í 0.003 Pennsylvania Philadelphia 0.077 0.075

í 0.003 Pennsylvania Tioga 0.065 0.063

í 0.002 Pennsylvania Washington 0.067 0.064

í 0.003 Pennsylvania Westmoreland 0.069 0.066

í 0.003 Pennsylvania York 0.067 0.062

í 0.005 Rhode Island Kent 0.070 0.067

í 0.003 Rhode Island Providence 0.069 0.067

í 0.003 Rhode Island Washington 0.071 0.068

í 0.003 South Carolina Abbeville 0.060 0.059

í 0.001 South Carolina Aiken 0.062 0.058

í 0.003 South Carolina Anderson 0.064 0.062

í 0.001 South Carolina Barnwell 0.059 0.057

í 0.002 South Carolina Berkeley 0.053 0.053 0.000 South Carolina Charleston 0.055 0.054

í 0.001 South Carolina Cherokee 0.061 0.060

í 0.001 South Carolina Chester 0.059 0.058

í 0.001 South Carolina Chesterfield 0.059 0.058

í 0.001 South Carolina Colleton 0.058 0.057

í 0.001 South Carolina Darlington 0.061 0.060

í 0.001 South Carolina Edgefield 0.059 0.057

í 0.002 South Carolina Oconee 0.061 0.059

í 0.001 South Carolina Pickens 0.060 0.059

í 0.001 South Carolina Richland 0.066 0.065

í 0.002 South Carolina Spartanburg 0.063 0.061

í 0.002 South Carolina Union 0.059 0.057

í 0.001 South Carolina Williamsburg 0.052 0.052

í 0.001 South Carolina York 0.060 0.059

í 0.001 South Dakota Pennington 0.062 0.062 0.000 Tennessee Anderson 0.059 0.058 0.000 Tennessee Blount 0.065 0.064

í 0.001 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

í 0.0013a-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

í 0.001 Tennessee Rutherford 0.058 0.058 0.000 Tennessee Sevier 0.066 0.065

í 0.001 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

í 0.001 Texas Brazoria 0.074 0.073

í 0.001 Texas Brewster 0.054 0.054

í 0.001 Texas Cameron 0.053 0.052

í 0.001 Texas Collin 0.070 0.068

í 0.002 Texas Dallas 0.069 0.067

í 0.002 Texas Denton 0.075 0.072

í 0.002 Texas Ellis 0.063 0.059

í 0.004 Texas El Paso 0.069 0.068

í 0.001 Texas Galveston 0.074 0.073

í 0.002 Texas Gregg 0.068 0.064

í 0.004 Texas Harris 0.089 0.088

í 0.001 Texas Harrison 0.061 0.059

í 0.003 Texas Hidalgo 0.062 0.062

í 0.001 Texas Hood 0.058 0.057

í 0.002 Texas Jefferson 0.074 0.071

í 0.003 Texas Johnson 0.066 0.063

í 0.003 Texas Kaufman 0.055 0.053

í 0.002 Texas Montgomery 0.074 0.073

í 0.001 Texas Nueces 0.065 0.064

í 0.001 Texas Orange 0.066 0.064

í 0.003 Texas Parker 0.063 0.062

í 0.002 Texas Rockwall 0.062 0.060

í 0.002 Texas Smith 0.064 0.062

í 0.002 Texas Tarrant 0.075 0.073

í 0.002 Texas Travis 0.063 0.062

í 0.001 Texas Victoria 0.061 0.060

í 0.001 Texas Webb 0.054 0.053

í 0.001 Utah Box Elder 0.064 0.062

í 0.002 Utah Cache 0.056 0.055

í 0.002 Utah Davis 0.070 0.068

í 0.003 Utah Salt Lake 0.070 0.067

í 0.002 Utah San Juan 0.064 0.064 0.000 Utah Utah 0.067 0.065

í 0.002 Utah Weber 0.065 0.063

í 0.002 Vermont Bennington 0.061 0.058

í 0.003 Vermont Chittenden 0.063 0.062

í 0.001 Virginia Arlington 0.072 0.069

í 0.004 Virginia Caroline 0.059 0.057

í 0.002 Virginia Charles City 0.069 0.067

í 0.002 Virginia Chesterfield 0.066 0.064

í 0.002 Virginia Fairfax 0.071 0.068

í 0.0043a-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

í 0.002 Virginia Frederick 0.062 0.060

í 0.002 Virginia Hanover 0.070 0.068

í 0.002 Virginia Henrico 0.068 0.066

í 0.002 Virginia Loudoun 0.067 0.063

í 0.004 Virginia Madison 0.063 0.061

í 0.002 Virginia Page 0.058 0.057

í 0.002 Virginia Prince William 0.063 0.060

í 0.003 Virginia Roanoke 0.062 0.061

í 0.001 Virginia Rockbridge 0.057 0.056

í 0.001 Virginia Stafford 0.063 0.060

í 0.002 Virginia Wythe 0.060 0.060 0.000 Virginia Alexandria City 0.067 0.063

í 0.003 Virginia Hampton City 0.071 0.070

í 0.001 Virginia Suffolk City 0.070 0.069

í 0.001 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

í 0.002 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

í 0.002 West Virginia Cabell 0.069 0.067

í 0.001 West Virginia Greenbrier 0.060 0.060

í 0.001 West Virginia Hancock 0.064 0.062

í 0.003 West Virginia Kanawha 0.062 0.062 0.000 West Virginia Monongalia 0.056 0.055

í 0.001 West Virginia Ohio 0.063 0.061

í 0.002 West Virginia Wood 0.062 0.061

í 0.001 Wisconsin Brown 0.065 0.064

í 0.001 Wisconsin Columbia 0.060 0.059

í 0.001 Wisconsin Dane 0.060 0.059

í 0.001 Wisconsin Dodge 0.063 0.062

í 0.001 Wisconsin Door 0.072 0.071

í 0.001 Wisconsin Florence 0.058 0.057

í 0.001 Wisconsin Fond Du Lac 0.061 0.060

í 0.001 Wisconsin Green 0.059 0.059

í 0.001 Wisconsin Jefferson 0.063 0.061

í 0.001 Wisconsin Kenosha 0.081 0.080

í 0.001 Wisconsin Kewaunee 0.071 0.070

í 0.001 Wisconsin Manitowoc 0.069 0.068

í 0.001 Wisconsin Marathon 0.058 0.057

í 0.001 Wisconsin Milwaukee 0.074 0.073

í 0.001 Wisconsin Oneida 0.057 0.056

í 0.001 Wisconsin Outagamie 0.061 0.060

í 0.001 Wisconsin Ozaukee 0.075 0.073

í 0.0013a-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

í 0.001 Wisconsin Rock 0.064 0.063

í 0.001 Wisconsin St Croix 0.060 0.060 0.000 Wisconsin Sauk 0.057 0.057

í 0.001 Wisconsin Sheboygan 0.077 0.076

í 0.001 Wisconsin Vernon 0.060 0.059

í 0.001 Wisconsin Vilas 0.057 0.056

í 0.001 Wisconsin Walworth 0.064 0.063

í 0.001 Wisconsin Washington 0.065 0.064

í 0.001 Wisconsin Waukesha 0.063 0.062

í 0.001 Wisconsin Winnebago 0.066 0.064

í 0.001 Wyoming Campbell 0.067 0.067 0.000 Wyoming Teton 0.063 0.063 0.0003a-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 s trategy, maximum control scenarios were used from the existing control measure dat aset 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 m ade as a result of the reviews performed for the final PM2.5 RIA. Following this, an interna l 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 revie w 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 contro l measures for over 70 SCC’s. For example, SPPD recommended that we apply SCR to cement kil ns 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 kil ns by 2020. Following this, it was recommended that supplemental controls could be appl ied to 8 additional SCC’s from nonEGU point NOx sources. We also looked into sources of con trols for highly reactive VOC nonEGU point sources. Four additional controls were appl ied 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, se lective noncatalytic reduction (SNCR), natural gas reburn (N GR), 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 importanc e 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 alo ng with a combustion control such as LNB with overfire air (OFA) to further reduce NOx emiss ions. 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 kiln s, stationary internal combustion engines, glass manufacturing, combustion turbines, and incinerators. NOx control measures available for petroleum refineries, particularly process heaters at t hese 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 boi lers, namely LNB, SCR, SNCR, along with water injection (WI). NOx control measures availabl e for cement kilns include those available to industrial boilers, namely LNB, SCR, and SNCR. Non-sel ective catalytic reduction (NSCR) can be used on stationary internal combustion engines. OX Y-firing, a technique to modify3a-2

combustion at glass manufacturing plants, can be us ed to reduce NOx at such plants. LNB, SCR, and SCR + steam injection (SI) are available measures for comb ustion 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 me asures 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 4,8003a-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 G as 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: perman ent total enclosure (PTE) applied to paper and web coating operations and fabric operations, and inc inerators or thermal oxidizers applied to wood products and marine surface coating operations. A PTE c onfines VOC emissions to a particular area where can be destroyed or used in a way th at limits emissions to the outside atmosphere, and an incinerator or thermal oxidizer d estroys 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 publ ishing sources as well as to surface coating operations. Table 3a.2 contains the emissions reduc tions 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 r eport.3a-4

Table 3a.2: VOC NonEGU Point Emission Reductions by Control Measure

Control Measure Source Type Modeled Control Strategy Reductions (annual tons/year) Fabric Printing, Coating and Dyeing 43 Permanent Total Enclosure (PTE) Paper and Other Web Coating 490 Printing and Publishing 3,600 Petroleum and Solvent Evaporation Surface Coating 400

3a.1.4 NOx Control Measures for Area Sources
There were three control measures applied for NOx em issions from area sources. The first is RACT (reasonably available control technology) to 25 t py (LNB). This control is the addition of a low NOx burner to reduce NOx emissions. This control is ap plied to industrial oil, natural gas, and coal combustion sources. The second control is water hea ters plus LNB space heaters. This control is based on the installation of low-NOx spa ce heaters and water heaters in commercial and institutional sources for the reduction of NOx emissio ns. The third control was switching to low sulfur fuel for residential home heating. This control is primarily designed to reduce s ulfur 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 AirCon trolNET 4.1 control measures documentation report.
Table 3a.3: NOx Area Source Emission Reductions by Control M easure

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 Commercial/Institutional—NG 4,300 Water Heater + LNB Space Heaters Residential NG 6,700

3a.1.5 VOC Control Measures for Area Source.
The most frequently applied control to reduce VOC em issions from area sources was CARB Long-Term Limits. This control, which represents con trols available in VOC rules promulgated by the California Air Resources Board, applies to commercia l solvents and commercial adhesives, and depends on future technological innova tion and market incentive methods to achieve emission reductions. The next most frequentl y 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 c ategories 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 w ell as solvent volatility limits and operating practices for cold cleaners. The Low Pressure/Va cuum Relief Valve control measure is the addition of low pressure/vacuum (LP/V) relief valves to gasoline storage tanks at service3a-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 equip ment requirements. Switch to Emulsified Asphalts control is a generic control me asure replacing VOC-containing cutback asphalt with VOC-free emulsified asphalt. The equip ment and maintenance control measure applies to oil and natural gas production. The Reformulatio n—FIP Rule control measure intends to reach the VOC limits by switching to and/or enco uraging the use of low-VOC pesticides and better Integrated Pest Management (IPM) practices. Table 3 a.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 t he AirControlNET 4.1 control measures documentation report.

Table 3a.4: VOC Area Source Emission Reductions by Control Me asure

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 Produ ction 450 Gas Collection (SCAQMD/BAAQMD) Municipal Solid Wast e Landfill 1,100 Incineration >100,000 lbs bread Bakery Products 2,7 00 Stage II Service Stations 9,900 Low Pressure/Vacuum Relief Valve Stage II Service Stations—Underground Tanks 9,800

Aircraft Surface Coating 720 OTC Mobile Equipment Repair and 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 me asures database by providing the pollutant it controls and its control efficiency (CE). These controls were applied not as part of the modeled control strategy, b ut 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 AirControlN ET.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 87 NOx LEC

20200254 Internal Comb. Engines/Industrial/ Natural Gas/4-cycle Lean Burn 87

3018001- Fugitive Leaks 50 Enhanced LDAR 30600701 30600999 - Flares 98

LDAR 3018001 - Fugitive Leaks 80 Monitoring Program 30600702- Cooling towers No gene ral 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 natura l 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-c ombustion chambers, increased turbocharging or adding a turbocharger, and increased c ooling and/or adding an intercooler or aftercooler, resulting in an engine that is designe d to achieve a consistent NO x emission rate of not more than 1.5-3.0 g/bhp-hr at full capacity (usu ally 100 percent speed and 100 percent load). This type of retrofit technology is fairly widely avai lable for stationary internal combustion engines.
For CE, EPA estimates that it ranges from 82 to 91 pe rcent for LEC technology applications. The EPA believes application of LEC would achieve average NO x emission levels in the range of 1.5-3.0 g/bhp-hr. This is an 82-91 percent reduction from the average uncontrolled e mission levels reported in the ACT document. An EPA memorandum sum marizing 269 tests shows that3a-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 emissi on rates of 2.0 g/bhp-hr or less. 2

Major Uncertainties: The EPA acknowledges that specific values will vary from en gine 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 o f LEC may yield improved fuel economy and power output, both of which may affect the emis sions generated by the device.
Leak Detection and Repair (LDAR) for Fugitive Leaks

Overview: This control measure is a program to reduce leaks o f fugitive VOC emissions from chemical plants and refineries. The program includes speci al “sniffer” equipment to detect leaks, and maintenance schedules that affected facilities are t o 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 chemica l plants is always difficult to quantify given the large number of such leaks at a typical chemical manufacturing plant. There are also growing indication s based on tests conducted by TCEQ and others in Harris County, Texas that fugitive leaks h ave 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, howev er, given that most chemical plants are under some type of requirement to have an LDAR prog ram. However, as mentioned earlier, there is growing evidence that fugitive leak emissi ons 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. S eptember 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 Infor mation on NOx Emissions and Control Techniques,” Ec/R Incorporated, Chapel Hill, NC. Septemb er 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 Losse s, 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 Losse s, and Potential Policy Gaps. 2006 International Workshop. U.S. Environmental Protectio n 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 recove r 98 percent of the VOC emitted by flares that emit 20 tons per year or more of the p ollutant.

Major Uncertainties: Flare gas recovery is just gaining commercial acce ptance 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 f rom 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 meas ure; 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 measur e.
Wastewater Drains and Separators

Overview: This control measure includes an inspection and mai ntenance 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 requ irements for such sources.

Major Uncertainties: The reference for this control measures notes that the VOC emissions inventories for the five San Francisco Bay Area ref ineries 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 pra ctices (e.g., storing VOCcontaining cleaning materials in closed containers, m inimizing spills) or using coatings that have much lower VOC content. These measures, which are of relativ ely low cost compared to other VOC area source controls, can apply to a variety of processe s, 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 coa tings, and large appliance coatings published by the US EPA in July 2007. 7

5 Bay Area Air Quality Management District (BAAQMD). Propo sed Revision of Regulation 8, Rule 8: Wastewater Collection Systems. Staff Report, Mar ch 17, 2004.

6 Bay Area Air Quality Management District (BAAQMD). Propo sed Revision of Regulation 8, Rule 8: Wastewater Collection Systems. Staff Report, Mar ch 17, 2004.

7 U.S. Environmental Protection Agency. Consumer and Commer cial Products: Control Techniques Guidelines in Lieu of Regulations for Pape r, Film, and Foil Coatings; Metal3a-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 a ffected processes are implementing or already implemented these control m easures. This may be particularly true in California. Also, there are nine States that have m any of the above work practices in effect for paper, film and foil coatings processes, but the work pract ices are not meant to achieve a specific emissions limit. 8 Hence, it is uncertain how much VOC reduction is occurring fro m this control measure in this case.
In addition to the new supplemental controls presented abov e, there were a number of changes made to existing AirControlNET controls. These changes wer e made based upon an internal review performed by EPA engineers to examine the cont rols 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 p oint 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 ove r 70 SCCs. The changes apply to the control strategies performed for the Eastern US onl y. These changes are listed in Table 3a.6.

Table 3a.6: Supplemental Emission Control Measures—Chang es 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 Commer cial Products: Control Techniques Guidelines in Lieu of Regulations for Pape r, 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_ccp092 807.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 SC R 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 803a-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 contr ol 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 403a-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/Natu ral 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 reducti ons for the mobile source control measures discussed in this section.

Table 3a.8: NOx Mobile Emission Reductions by Control M easure

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 Meas ure

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

Reduce Gasoline RVP 6,300 Nonroad Diesel Retrofits and Engine Rebuilds 5,2003a-13

3a.2.1 Diesel Retrofits and Engine Rebuilds
Retrofitting heavy-duty diesel vehicles and equipme nt manufactured before stricter standards are in place—in 2007–2010 for highway engines and in 2011–2014 for most nonroad equipment— can provide NO X and HC benefits. The retrofit strategies included in the RIA retrofit measure

are:

x Installation of emissions after-treatment devices c alled selective catalytic reduction (“SCRs”)

x Rebuilding nonroad engines (“rebuild/upgrade kit”)
We chose to focus on these strategies due to their high NOx em issions reduction potential and widespread application. Additional retrofit strateg ies include, but are not limited to, lean NOx catalyst systems—which are another type of after-tr eatment device—and alternative fuels. Additionally, SCRs are currently the most likely type o f 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 s ignificantly by strategy and by the type and age of the engine and its application.
To estimate the potential emissions reductions from this me asure, 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 Yea r 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 e missions inventory from heavy-duty highway vehicles, so we did not include trucks below class 6. We chose not to includ e locomotive and marine engines in our analysis since E PA has proposed regulations to address these engines, which will significantly impact the emissio ns 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 engin es, so we did not include them in this analysis. In addition, EPA is in the process of neg otiating standards for new aircraft engines.
The lower bound in the model year range—1995 for hig hway vehicles and 1991 for nonroad engines—reflects the first model year in which emissions a fter-treatment devices can be reliably applied to the engines. Due to a variety of factors, d evices are at a higher risk of failure for earlier model years. We expect the engines manufact ured before the lower bound year that are still in existence in 2020 to be retired quickly du e to natural turnover, therefore, we have not included strategies for pre-1995/1991 engines becau se of the strategies’ relatively small impact on emissions. The upper bound in the model year range reflect s the last year before more stringent emissions standards will be fully phased-in.
We chose the type of strategy to apply to each model y ear of highway vehicles and nonroad equipment based on our technical assessment of which strategies would achieve re liable results at the lowest cost. After-treatment devices can be more cost-effective than rebuild and vice versa3a-14

depending on the emissions rate, application, usage rates , and expected life of the engine. The performance of after-treatment devices, for example, depe nds heavily upon the model year of the engine; some older engines may not be suitable for af ter-treatment devices and would be better candidates for rebuild/upgrade kit. In certain cases, non road 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 Highw ay 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 an d Later Model Year Heavy-duty Highway Engines and Vehicles published October 2000), interviews w ith component manufacturers, and EPA’s Summary of Potential Retrofit Technologies. This i nformation 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 3 a.13, respectively.

Table 3a.12: Percentage Emissions Reduction by Highway Ve hicle Retrofit Strategy

PM CO HC NOx SCR (+DPF) 90% 90% 90% 70%

Table 3a.13: Percentage Emissions Reduction by Nonroad Eq uipment 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 th e 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 percen tage reduction in emissions as for agricultural mowers). We did this in order to simpli fy 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 esti mates used in the RIA, however, reflect the3a-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 e missions (PM, NOx, HC, and CO) from the retrofit measure for each re levant engine category (source category code, or SCC) for each county in 2020. To e valuate 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 M arch 17, 2006.
For the control analysis, we applied the retrofit m easure corresponding to the percent reductions of the specified pollutants in Tables 3a.12 and 3a.13 to the s pecified model years in Tables 3a.10 and 3a.11 of the relevant SCCs. Fleet turnover rates are modeled in the NMIM, so we applied t he retrofit measure to the 2007 fleet inventory, and then evaluated the resulting e missions inventory in 2020. The timing of the application of the retrofit measur e is not a factor; retrofits only need to take place prior to the attainment date target (202 0 for this RIA). For example, if retrofit devices are installed on 1995 model year bulldozers in 2007, the o nly impact on emissions in 2020 will be from the expected inventory of 1995 model year b ulldozer emissions in 2020.
We then compared the baseline and control analyses t o determine the percent reduction in emissions we estimate from this measure for the rel evant 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 O BD systems on light-duty OBD-equipped vehicles. It involves equip ping subject vehicles with some type of transmitter that attaches to the OBD port. The devi ce transmits the status of the OBD system to receivers distributed around the I/M area. Transmission m ay be through radio-frequency, cellular or wi-fi means. Radio frequency and cellular techno logies 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 throug hout 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. Thu s, 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 truck s:

x All 1996 and newer (SCC 2201001000) Light Duty Gasoline Veh icles (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 illumin ate the malfunction indicator lamp whenever emissions of HC, CO or NOx would exceed 1.5 t imes the vehicle’s certification standard. Thus, the benefits of this measure will affect all t hree criteria pollutants. MOBILE6 was used to estimate the emission reduction benefit s of Continuous I/M, using the methodology discussed above.
3a.2.3 Eliminating Long Duration Truck Idling
Virtually all long duration truck idling—idling tha t lasts for longer than 15 minutes—from heavy-duty diesel class 8a and 8b trucks can be eli minated 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 sto ps or public rest areas and while trucks are waiting to perform a task at private dis tribution terminals. When truck spaces are electrified, truck drivers can shut down their engines and use electricity to power e quipment which supplies air conditioning, heat, and electrical powe r for on-board appliances.
MIRTs can eliminate long duration idling from truck s that are stopped away from these central sites. For a more complete list of MIRTs see EPA’s Idl e Reduction Technology page at http://www.epa.gov/otaq/smartway/idlingtechnologies .htm

. This measure demonstrates the potential emissions reduct ions 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 me asure, 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 clas s 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 o n fuel conversion factors. The inventory in the MOBILE model, however, does not fully capture long durati on idling emissions. There is evidence that idling may represent a much greater s hare 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 realistic3a-17

targets. For more information on determining baseli ne idling activity see EPA’s “Guidance for Quantifying and Using Long-Duration Truck Idling Em ission Reductions in State Implementation Plans and Transportation Conformity” avai lable at http://www.epa.gov/smartway/idle-guid.htm

. Pollutants and Source Categories Affected by Measure: NO x Table 3a.14: Class 8a and 8b Heavy Duty Diesel Truc ks (decrease NOx for all SCCs) SCC Note: All SCC Descriptions below begin with “Mob ile 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 % de crease in NOx for all SCCs affected by measure

3a.2.4 Commuter Programs
Commuter programs recognize and support employers who pro vide incentives to employees to reduce light-duty vehicle emissions. Employers impl ement a wide range of incentives to affect change in employee commuting habits including transit subsidies, bike-friendly facilit ies, telecommuting policies, and preferred parking for van pools and carpools. The commuter measure in this RIA reflects a mixed package of incentives.
This measure demonstrates the potential emissions reduct ions from providing commuter incentives to 10% and 25% of the commuter population i n 2020.
We used the findings from a recent Best Workplaces for Commute rs 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 wo rkplaces with comprehensive commuter programs emit 15% fewer emissions th an 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 Rese arch 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 progr ams measure into two program penetration rates: 10% and 25%. This was meant to p rovide 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 thi s 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 (SC C): NO x , and VOC3a-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); Rur al Interstate: Total 2201001130 Light Duty Gasoline Vehicles (LDGV); Rur al Other Principal Arterial: Total 2201001150 Light Duty Gasoline Vehicles (LDGV); Rur al Minor Arterial: Total 2201001170 Light Duty Gasoline Vehicles (LDGV); Rur al Major Collector: Total 2201001190 Light Duty Gasoline Vehicles (LDGV); Rur al Minor Collector: Total 2201001210 Light Duty Gasoline Vehicles (LDGV); Rur al Local: Total 2201001230 Light Duty Gasoline Vehicles (LDGV); Urb an Interstate: Total 2201001250 Light Duty Gasoline Vehicles (LDGV); Urb an Other Freeways and Expressways: Total 2201001270 Light Duty Gasoline Vehicles (LDGV); Urb an Other Principal Arterial: Total 2201001290 Light Duty Gasoline Vehicles (LDGV); Urb an Minor Arterial: Total 2201001310 Light Duty Gasoline Vehicles (LDGV); Urb an Collector: Total 2201001330 Light Duty Gasoline Vehicles (LDGV); Urb an 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 def ines 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 v apor 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 cont rol measure represents the use of gasoline with a RVP limit of 7.0 psi from May through September in count ies 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) t hat the plan implements. EPA can approve a state fuel requirement as necessary only if no othe r 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), Tota l: All Road Types

– 2201020000 Light Duty Gasoline Trucks 1 (LDGT1), Tot al: All Road Types

– 2201040000 Light Duty Gasoline Trucks 2 (LDGT2), Tot al: All Road Types

– 2201070000 Heavy Duty Gasoline Vehicles (HDGV), Tot al: 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 fro m 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 (

í 11%) 113

(

í 26%) 33

(

í 23%) 812

(

í 2%) 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 th e 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 t he emissions contributing to interstate transport of air pollution and available control measures , EPA determined that achieving required reductions in the identified States by con trolling emissions from power plants is highly cost effective. CAIR will permanently cap emissions of sulfur dioxide (SO 2 ) and nitrogen oxides (NO x ) in the eastern United States. CAIR achieves large reduc tions of SO 2 and/or NO x emissions across 28 eastern states and the District of Columbia.3a-21 Figure 3a.1: CAIR Affected Region

States controlled for fine particles (annual SO 2 and NOx) States not covered by CAIR

States controlled for ozone (ozone season NOx) States controlled for both fine particles (annual S O 2 and NOx) and ozone (ozone season NOx)

When fully implemented, CAIR will reduce SO 2 emissions in these states by over 70% and NO x emissions by over 60% from 2003 levels (some of which are due t o NOx SIP Call). This will result in significant environmental and health benefits and will substantially reduce premature mortality in the eastern United States. The benefit s will continue to grow each year with further implementation. CAIR was designed with current air quali ty 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 PM 2.5 standards. Some areas will need to adopt additiona l 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 SO 2 , NO x , and direct PM, will move States towards attainment in a deliberate and logical manner.

Based on the final State rules that have been submi tted 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 requirem ents 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 ozon e standard of 0.070 ppm for the power
sector.
3a.3.2 Integrated Planning Model and Background
CAIR was designed to achieve significant emissions r eductions in a highly cost-effective manner to reduce the transport of fine particles that have been found to contribute to nonattainment. EPA3a-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 analy sis 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 F ederal 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 upda tes in modeling the power sector and incorporates Federal and State rules and regula tions adopted before September 2006 and various NSR settlements. A detailed discussion of u ncertainties 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 modeli ng is based on its best judgment for various input assumptions that are uncertain, particularly assumptions for future fuel prices an d electricity demand growth. To some degree, EPA addre sses the uncertainty surrounding these two assumptions through sensitivity analyses. More detail on IPM can be found in the model documentation, which provides additional informatio n 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 SO 2 , NO x , and mercury (Hg) emission control technology opti ons for meeting existing and future federal, regional, and state, S O 2 , NO x and Hg emission limits. The NOx control technology options include Selective Catalytic Re duction (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 me eting 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 Contr ol Performance Assumptions Selective Catalytic Reduction

(SCR) Selective Non-Catalytic Reduction (SNCR) Unit Type Coal Oil/Gas a Coal Oil/Gas a 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 th is RIA. Existing coal-fired units that are retrofit with SC R have a NOx removal efficiency of 90%, with a minimum controlled NOx emission rate of 0.06 lb/m mBtu 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 a nd 0.06 lb NOx/mmBtu.
Detailed cost and performance derivations for NOx control s are discussed in detail in the EPA’s documentation of IPM ( http://www.epa.gov/airmarkets/progsregs/epa-ipm/pas tmodeling.html ).

3a.4 Emissions Reductions by Sector
Figures 3a.2–3a.6 show the NOx reductions for each sector a nd Figures 3a.7–3a.10 show the VOC reductions for each sector under the modeled control str ategy.
Figure 3a.2: Annual Tons of NOx Emissions Reduced from EG U Sources*

* Reductions are negative and increases are positive.

** The

í 99–+100 range is not shown because these are small county-level NOx reductions or increases that likely had little to no impact on ozone estima tes. Most counties in this range had NOx difference s of under 1 ton.3a-24

Figure 3a.3: Annual tons/year of Nitrogen Oxide (NO x) Emissions Reduced from NonEGU Point Sources*

* Reductions are negative and increases are positive.
** The

í 99–0 range is not shown because these are small cou nty-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 (NO x) Emissions Reduced from Area Sources *

*Reductions are negative and increases are positive
**The

í 99–0 range is not shown because these are small cou nty-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 (NO x) Emissions Reduced from Nonroad Sources*

*Reductions are negative and increases are positive
**The

í 99–0 range is not shown because these are small cou nty-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 (NO x) Emissions Reduced from Onroad Sources*

*Reductions are negative and increases are positive
**The

í 99–0 range is not shown because these are small cou nty-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

í 99–0 range is not shown because these are small cou nty-level VOC reductions or increases that likely had little to no impact on ozone estimates3a-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

í 99–0 range is not shown because these are small cou nty-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

í 99–0 range is not shown because these are small cou nty-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

í 99–0 range is not shown because these are small cou nty-level VOC reductions or increases that likely had little to no impact on ozone estimates.

3a.5 Change in Ozone Concentrations Between Baselin e and Modeled Control Strategy
Table 3a.18 provides the projected 8-hour ozone desi gn values for the 2020 baseline and 2020 control strategy scenarios for each monitored county. The ch anges 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 Bas eline 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 0.0013a-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 -0.0013a-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 0.0023a-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 -0.0023a-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 -0.0023a-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 -0.0063a-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 -0.0013a-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 -0.0033a-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 -0.0013a-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 -0.0023a-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 -0.0033a-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 0.0003a-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 -0.0033a-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 -0.0013a-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 Atta inment Scenario

Synopsis
**After applying the hypothetical modeled control strategy described in Chapter 3, there were**  **multiple counties that were still not projected to a ttain potential new ozone standards. Because it**  **was impossible in some areas to meet a tighter ozone s tandard 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 pr**  **imary ozone standard. The term “extrapolated tons” will be used to refer to these additionally n**  **eeded emissions reductions. Sections 4.1 and 4.2 of this chapter present the methodology EPA develop**  **ed 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. Addit**  **ionally, in other areas, the known controls in the hypothetical strategy resulted in ozo**  **ne levels lower than one or more of the four alternate standards. Sections 4.3 and 4.4 of this chap**  **ter 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 sup plemental air quality modeling**  **analyses for the final RIA. This was intended to im prove the estimates of extrapolated tons**  **needed to meet various potential standards. These additio nal modeling scenarios were designed**  **to provide more information about the response of ozone to em issions changes in terms of non**  **linearities, geographic variations, the impacts of lo cal versus upwind emissions reductions, and**  **the relationship between NOx and VOC emissions chan ges. 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 sc enario. There were 21 counties that did not**  **meet the 0.075 ppm standard. 1**  **All 21 of these counties are in four broad geograph ic 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. 16**  **6 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-Racin e, WI; c) Sheboygan WI; d) La Porte**  **IN; and e) South Bend-Elkhart IN.**

**3 This geographic area is an aggregate of six existi**  **ng nonattainment or maintenance areas: a) Philadelphia-Wilmington-Atlantic City, PA-NJ-MD-DE; b) New York-Northern New Jersey**  **Long Island, NY-NJ-CT; c) Greater Connecticut, CT; d) Baltimore MD; e) Kent and Queen Anne counties MD; and f) Poughkeepsie NY.** **4-2**

**scenario, and therefore likely the largest extrapol ated costs, we focused on these areas within the**  **supplemental modeling analyses. We will refer to th ese four areas as “Phase 1” areas. Later, we**  **will define a second and third set of areas that al so require extrapolated emissions reductions**  **which we will refer to as “Phase 2” and “Phase 3”ar eas. 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 approa ch similar to that used to**  **define the geographic control areas for non-EGU poin t controls in the RIA control scenario**  **(discussed in Chapter 3) was also used to define the supplem ental 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 pa rt 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 or**  **ange counties for NOx; only the red and pink counties fo**  **r 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 th e hypothetical RIA control case. As in the**  **modeled control strategy, NOx controls were applied to co unties 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 rat ios”**  **4 to calculate the additional tons needed**  **to meet the air quality standard. The updated appro ach uses the supplemental modeling to**  **determine what levels of ozone precursor reductions (NOx on ly 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 tar gets for the 0.070 ppm alternative**  **standard, we conducted a “verification” model run to asse ss whether our estimated emissions**  **reductions actually resulted in attainment of 0.070 ppm in e ach area. The new estimates of**  **extrapolated tons represent a considerable improvement f rom what was done for the draft RIA.** 
**For purposes of this analysis, we assume attainment by 202 0 for all areas except San Joaquin**  **Valley and South Coast air basins in California. Th e 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 purpos es that the San Joaquin Valley and**  **South Coast air basin would attain a new standard i n 2030. There are many uncertainties**  **associated with the year 2030 analysis. Between 2020 a nd 2030 several federal air quality rules**  **are likely to further reduce emissions of NOx and V OC, such as, but not limited to National rules**  **for Diesel Locomotives, Diesel Marine Vessels, and Small N onroad 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 m odeling 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 str ategies with associated costs and benefits.**  **In order to provide a rough approximation of the co sts 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 s upplemental 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 co ntrol scenario and each of the six**  **supplemental modeling runs). These figures are inst ructive 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 grap hs.** 
**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 controllin g county. After**  **application of the RIA scenario controls, our modeli ng projects that the highest 2020 8-hour** 

**4 The units for impact ratios are ppb/kton. In the draft RIA we us**  **ed 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. Thu s, 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 sc enarios, we can see that increasing**  **the level of emissions reductions beyond the RIA case yiel ds 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 add itional NOx plus VOC emissions of**  **approximately 60 percent. The 0.075 and 0.070 ppm s tandards will require between an additional**  **60-90% NOx plus VOC reduction beyond the RIA contro l 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 stan dard. 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 H ighest 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 TXProjected 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, an d 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 cont**  **rolling county within an area can vary as the precursor emissions a**  **re 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 projecte**  **d design values that contained data four places to the right of the decimal (e.g., 0.0755 ppm). In t**  **he last step of the process however, EPA truncates all decimal places b**  **eyond 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 n ot 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 t hat in this area the addition of VOC controls**  **can have a significant impact on the projected desi gn 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 supplement al 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 H ighest 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 WIProjected 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 th e RIA Control Scenario and Each of the Six Supplemental Modeling Scenarios for the H ighest 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 NJProjected 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 no nattainment 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 nonattainm**  **ent areas with earlier attainment dates meet the standards by 2020. These additional reductions in Lo**  **s Angeles and San Joaquin Valley are considered to be part of the controls needed to meet th**  **e 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 Calif**  **ornia.**

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 Spe cific 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 CAProjected 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 neces sary 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 a nd VOC beyond the RIA Control Scenario Necessary to Meet Various Alternate Ozone St andards in the Phase I Areas

Table 4.2: Estimated Percentage Reductions of NOx bey ond the RIA Control Scenario Necessary to Meet Various Alternate Ozone Standards in t he 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 scenari o. The majority of these counties are in**  **one of the Phase 1 areas. However, there were 12 counties (9 ar eas) outside of the Phase 1 areas**  **that were also not projected to meet the 0.070 NAAQS. (All co unties outside the Phase 1 areas**  **met the 0.075 and 0.079 ppm air quality standards.) For conv enience, these nine areas will be**  **referred to Phase 2 areas. A two-step process was u sed to estimate the additional emissions**  **reductions necessary for full attainment in the Phase 2 a reas. Based on the Phase 1 modeling** **4-8**

**results, targets for these areas were only generated f or NOx-only control given the**  **preponderance of cases where the additional VOC emi ssions reductions did not reduce ozone**  **enough to consider from a cost perspective.**
**For the Phase 2 areas, the first step in estimating attainme nt was to consider whether the**  **emissions reductions needed to bring the Phase 1 ar eas into attainment of 0.070 ppm would also**  **reduce ozone transport enough to bring these additi onal 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 contr ol 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 202**  **0 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 w**  **ould 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 st**  **andard to be met in Norfolk County as well, without any additional local controls in the Bost**  **on area.**
**In Geauga OH, the projected design value after the RIA control scenario is 0.074 ppm. Thus,**  **Cleveland will need additional local emissions redu ctions to meet a revised ozone standard of**  **0.070 ppm. However, in the supplemental modeling, whi ch 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 L**  **ake Michigan region is the nearest upwind Phase 1 area to Geauga County, we believe the**  **se 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 valu**  **e 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 Phas e 1 emissions reductions. For each of the**  **Phase 2 areas that is still nonattainment after ste p 1 above, we developed a site-specific**  **relationship between the ozone improvement in the R IA control case and the percent reduction in**  **local NOx emissions in the RIA control case as compared to th e 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 inform ation it is not possible to distinguish** 

**6 The full step 1 calculation for the Geauga OH example is as foll**  **ows. A 60 percent reduction yields a design value of 0.0722 ppm. A 90 percent r**  **eduction 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 0.07**  **22 ppm, yields the transport-considered design value of 0.0717 ppm which would be truncated to 0.0**  **71 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 o r 0.070 ppm, thereby attaining the**  **standard.**
**The same two step methodology described above was u sed to estimate the extrapolated targets**  **for the 0.065 ppm standard in the Phase 2 areas. Ta ble 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 percen t. 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 bey ond the RIA Control Scenario Necessary to Meet the 0.070 ppm Ozone Standard in Phase 2 Areas 7

4.1.4 Estimating Attainment of the 0.065 ppm Standar d 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 b ut 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 o f these counties are in one of the**  **Phase 1 or Phase 2 areas. However, there were 46 counties (36 ar eas) 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 red**  **uction run were the appropriate starting point for estimating the additional emissions redu**  **ctions in the Phase 3 areas. Since the**

**7 The entry “will attain” in Tables 4.3 and 4.4 signifies that t**  **his 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 prov ide 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 reducti ons needed to attain 0.065 ppm in these**  **areas. This value was the average site-specific rel ationship calculated for the Phase 2 areas, as**  **described above. These assumptions are reasonable given t he available data and the relatively**  **small role that Phase 3 areas will play in determining the fu ll costs of meeting a 0.065 ozone**  **standard. However, the estimated emissions reductio ns needed to attain 0.065 in the Phase 3**  **areas are considered to be more uncertain than the e missions reductions calculated for attaining**  **0.070, 0.075, and/or 0.079. The results of the Phase 3 anal ysis 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 Phas e 3 Areas**4-11**

4.1.5 Aggregate Results / Verification Modeling of Ex trapolated Targets
**The complete set of NOx targets are provided in Tabl e 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 A ngeles and San Joaquin Valley areas. The**  **assumption is that if all of California reduces at t hat level then all areas aside from Los Angeles**  **and the San Joaquin Valley air basins will attain b y 2020. Areas from which reductions would be**  **required include the Los Angeles and San Joaquin Val ley 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 reduc tions for Los Angeles and San Joaquin**  **air basins are shown in Appendix 7b.**

Table 4.5a: Complete Set of Estimated Percentage Reduction s of NOx beyond the RIA Control Scenario Necessary to Meet the Various Ozone Standa rds in 2020

**In total, 33 areas were determined to need addition al emissions reductions for one or more of the**  **alternate standards. The eastern Lake Michigan region w as the only one in which NOx plus VOC**  **control targets could be substantially lower than NOx o nly control targets. Table 4.5b shows the**  **NOx + VOC targets for that area. 4-12** 

Table 4.5b: Estimated Percentage Reductions of NOx + VO C beyond the RIA Control

Scenario Necessary to Meet the Various Ozone Standard s in 2020

**Figures 4.3a through 4.3d show: 1) which counties a re part of the 33 extrapolated cost areas and**  **2) the estimated percent reduction needed beyond th e RIA control case to meet each of the four**  **alternate standards within each of those areas. The conversi on 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 CMA Q air quality simulation, called a “verification**  **run,” was completed after the extrapolated percent emissio ns reductions were estimated. The**  **purpose of this run was to determine the ozone design values t hat 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 0.07 0 ppm standard for areas outside**  **of Los Angeles and San Joaquin Valley. The results o f 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 c ontrols 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 de**  **sign 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 wer e within plus or minus 0.002 ppm**  **after application of the extrapolated emissions reductio ns. The proximity of the verification**  **design values to the 0.070 ppm target provides confidence t hat the estimates of extrapolated tons**  **are reasonable. Table 4.6 shows the results of the verifica tion modeling for the 13 areas that were**  **included in the (0.070 ppm) extrapolated cost analy sis.** **4-15** Table 4.6: Summary of the Verification Modeling Result s

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 extrapol ated tons are obtained by multiplying the NOx**  **targets in Table 4.5a by the remaining emissions for each a rea after the RIA control scenario. It**  **is important to note that the extrapolated cost area s 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 E astern Lake Michigan area extends**  **further north into Wisconsin for the 0.065 ppm standard whe re 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 E missions 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, NY**  **A 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, P**  **A 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, P**  **A cost area because it falls within the 200km Pittsburgh-Beaver Valley buffer and has a lower des**  **ign value. In total, additional emissions reductions are provided for 3**  **1 areas. As footnoted, Jamestown NY is included in the Buffalo-Niagara Falls NY area. There are th**  **ree reasons for this: 1) Jamestown is within the 200km buffer for Buffalo-Niagara Fall**  **s, 2) as seen in Table 4-5a, the NOx target is greater in Buffalo-Niagara than Jamestown for each standar**  **d, 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 b**  **e substantially lower than NOx only emissions reductions. Table 4.7b shows the additiona**  **l NOx + VOC emission reductions for this area.** **4-17**

Table 4.7b: Estimated Extrapolated Emissions Reductions o f NOx + VOC Beyond the RIA Control Scenario Necessary to Meet the Various Ozone Standa rds in 2020

NOx VOC NOx VOC NOx VOC NOx VOC **Eastern Lake Michigan, IL-IN-WI 350,000 400,000 280,00 0 330,000 100,000 120,000 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 “Overcont rolled” Emissions in the Modeled Control Strategy
**The corollary to extrapolated tons (needed tons above and beyond the modeled co ntrol strategy)**  **is “overcontrolled” tons. These are emissions reduc tions within the hypothetical control case that**  **were subsequently determined not to be needed to meet part icular 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 orde r to better estimate the costs and benefits**  **of full attainment of the standards, EPA has estimated t he “overcontrolled” emissions**  **percentages within the modeled control strategy for the f our alternate standards: 079, 075, 070 &**  **065. These percentages are to be applied to the ton s reduced between the baseline and the control** 

**case.**
**The methodology for calculating the “overcontrol” pe rcentages 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 jus t 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 d escribed in Chapter 3. Because the**  **majority of the California controls are in the baseline and b ecause 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 a**  **ggregated into 32 distinct areas for an assessment of whether that area overcontrolled to meet an a**  **lternate standard. Each area included the original nonattainment county or counties, plus all counties within 200 km of th**  **at 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 amoun t 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 Strat egy Emissions Reductions not

needed to Meet the Various Ozone Standards in 2020

**a) For each standard, we first determined if the area was bel ow that standard in the baseline**  **modeled scenario. If so, then all of the hypothetical co ntrols should be returned from the**  **control scenario. For example, the highest projected desig n 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 t o 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 abov e that standard in the modeled**  **control case. If so, then none of the hypothetical contr ols should be given back. As an**  **example, the Houston area had a projected design value of 0.0 87 ppm in the control case.**  **Therefore, all of the emissions in the modeled control strate gy (and some extrapolated**  **tons) are needed in that area.**
**c) For each standard, and for all other areas that were abo ve the standard in the baseline and**  **below in the control case, we used linear interpolation to es timate 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 baselin**  **e, and 0.0742 ppm in the 4-19** 

**control case. Linear interpolation 8**  **between the baseline and the control case indicate s that**  **74% of the controls in the Cleveland area, includin g 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 Em ission Reductions into “Overcontrolled” Tons
**The percentages of modeled control strategy emission s 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 wa s no “over-control” (“NONE” in**  **Table 4.8), emission reductions not needed for alte rnative standards in Table 4.9 are zero; that is,**  **the control scenario did not “over-control” emission s 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-co**  **ntrol and no unnecessary emission reductions.** 

**8 The calculation used to determine the 74% target for th**  **e 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 tr**  **uncation of the fourth decimal place. 4-20** 

Table 4.9: Estimated 2020 Control Case Emission Reductions n ot needed to Meet the Various Ozone Standards in 2020

Area controlled within the modeled control

Strategy 2020 Base Case 2020 Baseline 2020

Control Case B ase li ne

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-V A 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 A llegan 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 7,0 00 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 1 40,000 130,000 14,000 14,000 14,000 0 0 Cleveland-Akron-Lorain, OH 270,000 250,000 210,000 44,0 00 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, L A 400,000 350,000 230,000 110,000 110,000 81,000 0 0 Richmond-Petersburg, V A 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, V A 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 3 0,000 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, P A 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 A tlanta, G A 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 4 1,000 41,000 41,000 41,000 0 2020 Control Case Emission Reductions not needed for alternate standards Annual Emissions [tons/year]5-1

Chapter 5: Engineering Cost Estimates

Synopsis
This chapter summarizes the data sources and methodology us ed to estimate the engineering costs of attaining the alternative more stringent le vels 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 currentl y available known controls). The modeled control strategy discussion is followed by a presentation o f estimates for the engineering costs of the additional tons of emissions that are needed to move t o full attainment of the alternate standards analyzed, referred to as Extrapolated Cost s (methodology and numbers discussed in Chapter 4).
As noted in Chapter 3, EPA first modeled an illustr ative 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 af fected 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 addition al 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 reduc tions 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 curren t ozone standard by the year 2020. It does not reflect full attainment for the two areas of Califor nia, 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 partiall y attain a 0.070 ppm alternative standard, incremental to reaching the current baseline (effecti vely 0.084 ppm) in 2020.
Section 5.2 describes the methodology used to estim ate 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 engin eering 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 gen erally include the costs of purchasing, installing, and operating the referenced technologies. F or a variety of reasons, actual control costs may vary from the estimates EPA presents here. As disc ussed throughout this report, the technologies and control strategies selected for anal ysis 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 are as 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 pa st experience, EPA believes that it is reasonable to anticipate that the marginal cost of cont rol will decline over time due to5-2

technological improvements and more widespread adoption of previously niche control technologies. Also, EPA recognizes the extrapolated portion of the engineering cost estim ates reflects substantial uncertainty about which sectors, and which technologies, might become available for cost-effective application in the future. T his 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 S tate Implementation Plans to meet the revised standards, w e do not present estimated costs that government agencies may incur for managing the requ irement 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 cont rol 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 engineer ing costs estimated for the modeled control strategy to calculate its economic impa cts and social costs. Economic impacts are estimates of changes in price and output for those indust ries 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 contro ls 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 strateg y 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 esti mate against the total tons of a pollutant reduced to derive a total cost estimate; (2) by cal culating 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 o n 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 metho ds 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 stac k flow to determine annual, capital and/or operating and maintenance (O&M) costs. Capital costs are co nverted to annual costs, in dollars5-3

per ton, using the capital recovery factor. 1 Where possible cost calculations are used to calcul ate 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 ot her variable costs. The resulting TACC equation is TACC = (CRF * CC) + O&M.
Engineering costs will differ based upon quantity of emissi ons reduced, plant capacity, or stack flow which can vary by emissions inventory year. En gineering costs will also differ by the year the costs are calculated for (i.e., 1999$ versus 2006$). For c apital 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 stra tegy. Our engineering cost analysis uses the equivalent unifo rm 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 t he same whether the control measure is installed in 2019 or in 2010. We make no presump tion 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 so urces incorporated annualized engineering cost per ton caps. These caps were defin ed as the upper cost per ton for controls of nonEGU point and area sources. The caps were calcula ted by examining the marginal cost curves for each pollutant for the geographic areas (appro ximately 1,300 counties for NOx controls, see Figure 3.5 and approximately 120 coun ties for VOC controls, see Figure 3.6) being analyzed for this analysis. For reductions of NOx emission s 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 t his cap, and subsequently not included in this analysis, due to the large capital component o f 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 th is cap, forty-six percent of the possible reductions are achieved at fifteen percent of the t otal engineering cost. It is important to note that as part of the extrapolated cost analysis the VOC cap was rais ed to $15,000/ton (for geographic areas where the supplemental air quality modeling sho wed VOC control to be beneficial). At this cap (2006$) 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 Docu mentation (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 estim ated 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 wit h specified constraints, including air pollution regulations, transmission bottlenecks, fu el 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 capabili ties, renewable generation requirements, fuel market constraints) that are placed on the powe r, emissions, and fuel markets. IPM is particularly well-suited to consider complex treatme nt of emission regulations involving trading and banking of emission allowances, as well as traditiona l command-and-control emission5-5

policies. 2 Applied controls and their respective engineering costs ar e 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 progr ams to assist in improving air quality. Applied controls and their respective engineering costs are prov ided in the docket. 3

Engineering costs, in terms of dollars per ton emissions reduced, were applied to emis sion reductions calculated for the onroad and nonroad mobile sec tors that were generated using the NMIM. NMIM is an EPA model for estimating pollution f rom highway vehicles and nonroad mobile equipment. NMIM uses current versions of EPA’s model fo r 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 strateg ies identified in Chapter 3 that include control technologies on nonEGU stat ionary 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 cos ts across sectors annualized at a discount rate of 7% and 3%, consistent with the guidance pro vided in the Office of Management and Budget’s (OMB) (2003) Circular A-4. However, it is i mportant 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 co mparison 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/t on. 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 200 4 and Later Model Year Heavy-duty Highway Engines and Vehicles published October 2000), interviews with component manufacturers, and EPA’s Summary of Potential Retrofit T echnologies 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.htm5-6

and mobile sources. Engineering costs from EGU sourc es, 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 applie d in estimating control costs within IPM. See PM RIA for details.5-7 Table 5.1: Annual Control Costs by Sector and Regio n, 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. A s such, totals will not sum down columns. The modeled control strategy is that strategy applied t o reach attainment of the 0.070 alternate primary standard, and is described in detail in Chapter 3.

b All estimates provided reflect the engineering cos t of the modeled control strategy, incremental to a 2020 baseline of compliance with the current standa rd of 0.084 ppm.

c The total cost is negative in the west for the mod eled control strategy due to an electricity generat ion shift. The west generates less electricity and expo rts 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 avai lable. For this modeled control strategy, data for calculating annualized costs at a 3% discount was o nly available for NonEGU point sources. Therefore, the total annualized cost value presented in this r eferenced cell is an aggregation of engineering cos ts at 3% and 7% discount rate.

e These estimates do not reflect benefits or costs f or 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 se ason NOx reductions from EGUs from controls that operate year-round as explained in Chapter 3. By c ounting 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 pra ctically the same as that in 2020 for the final CAI R 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 avail able. In this RIA, the nonEGU point source sector was the only sector with available data to per form a sensitivity analysis of our annualized control costs to the choice of interest rat e. 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 t he 3% discount rate value is overestimated due to the addition of cost sectors at a higher discount ra te. 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 E ngineering 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 m odeling framework for analyzing the cost, emissions changes, and other impac ts of regulatory controls. The annualized cost estimates of the private compliance costs ar e meant to show the increase in production (engineering) costs to the various affec ted 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 ann ual terms. However, our engineering cost analysis is subject to uncertainties and limitations.

One of these limitations is that we do not have suf ficient 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 equipme nt life—to annualize the costs for individual control measures. For the vast majority of nonEG U 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 a ble to provide annualized cost estimates at different interest rat es for these point source control measures as we have done for the proposed ozone RIA and the PM2.5 RIA last yea r.
For area source control measures, the engineering c ost information is available only in annualized cost/ton terms. We have extremely limited capi tal cost and equipment life data for area source control measures. We know that these ann ualized cost/ton estimates reflect an5-9

interest rate of 7% because these estimates are typ ically products of technical memos and reports prepared as part of rules issued by our office (OAQPS) o ver 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 an nualizing regulatory costs. Capital cost

Figure 5.3: Total Annualized Costs by Emissions Secto r and Region for Modeled Control Strategy in 2020 a, b, c, d

-$0.10 $0.10 $0.30 $0.50 $0.70 $0.90 $1.10 $1.30 $1.50Total Costs (B 2006$)

East West CA

Area nonEGU Point EGU Onroad Nonroad

a Total costs presented above are for a seven percen t discount rate.

b All estimates provided reflect the engineering cos t of the modeled control strategy, incremental to a 2020 baseline of compliance with the current standa rd of 0.084 ppm.

c The total cost is negative in the west for the mod eled control strategy due to an electricity generat ion shift. The west generates less electricity and expo rts from the east.

d These estimates do not reflect benefits or costs f or 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 capita l cost is well known and convenient to estimate. Such area source controls can include ref ormulation 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 represe nt 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 m uch like that for our area source measures. We do not have sufficient capital cost information from wha t our mobile source office (OTAQ) has sent us to compute annualized costs for differe nt interest rates other than 7%. Finally, It should be noted that the annualized capital costs fo r EGUs are prepared at an interest rate other than 7%. Information on the annualization of EGU control cost s is presented later in this chapter.
There are some unquantified costs that are not adeq uately 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 develo ping approvable SIPs, securing EPA approval of those SIPs, and Federal/State enforcement. Additionall y, 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 cos ts 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 s trategy is included in Appendix 5b of this analysis. The illustrative analysis does quantify t he potential for advancements in the capabilities of pollution control technologies as we ll as reductions in their engineering costs over time. This is discussed in Section 5.4.
For purposes of this analysis, we assume attainment by 202 0 for all areas except San Joaquin Valley and South Coast air basins in California. Th e 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 purpos es that the San Joaquin Valley and South Coast air basin would attain a new standard i n 2030. There are many uncertainties associated with the year 2030 analysis. Between 2020 a nd 2030 several federal air quality rules are likely to further reduce emissions of NOx and V OC, such as, but not limited to National rules for Diesel Locomotives, Diesel Marine Vessels, and Small N onroad 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 m odeling 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 str ategies with associated costs and benefits. In order to provide a rough approximation of the co sts 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 s upplemental 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 three5-11

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 c ontrol strategy was not successful in reaching nationwide attainment of the alternate ozone stan dards. 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 att ainment.
The estimation of engineering costs for unspecified emissi on 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 implemen tation shows that technological advances and development of innovative strategies can make p ossible emissions reductions that are unforeseen today, and to reduce costs of emerging technologi es 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 t o the cost of control options available today is not at all clear. Availa ble, 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 i n the analysis year 2020. In the absence of technological change, the needed control for full at tainment 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 assu mptions. 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 th at are appropriate under the Clean Air Act. We do not attempt to determine specific attainment d ates in this analysis. The Clean Air Act provides flexibility for a nonattainment area to re ceive an attainment date up to 20 years after designation if earlier attainment is not practical bas ed 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 und er 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 technolo gical innovation in cleaner technologies after 2020.
Prior to presenting the methodology for estimating costs for unspecified emis sion reductions, it is important to provide information from EPA’s Scien ce Advisory Board Council Advisory, 6 dated June 8, 2007, on the issue of estimating costs of unid entified control measures.
812 Council Advisory, Direct Cost Report, Unidentified Me asures (charge question 2.a)
“The Project Team has been unable to identify measures t hat yield sufficient emission reductions to comply with the National Ambient Air Quali ty Standards (NAAQS) and

6 U.S. Environmental Protection Agency. June 2007. Adviso ry Council on Clean Air Compliance Analysis (COUNCIL), Council Advisory on OAR’s Direct Cos t 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 metropo litan areas but a relatively small share of emission reductions in other locations and nationw ide.
“The Council agrees with the Project Team that there is li ttle 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, t he components of the total cost associated with identified and unidentified measures should be clearly distinguishe d. 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 reduc tions 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 un certainty about these costs is best. Of the three approaches outlined, assuming a fixed co st/ton appears to be the simplest and most straightforward. Uncertainty might be represent ed 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 co sts of regulations.
To generate estimates of the costs and benefits of meeting alternative standards, EPA has assumed the application of unspecified future contr ols that make possible the emissions reductions needed for attainment in 2020 (excluding two C alifornia areas). By definition, there is no cost data in existence for unidentified future t echnologies or innovative strategies.
EPA used two methodologies for estimating the costs of unspe cified future controls: a new hybrid methodology and a fixed-cost methodology. Bo th approaches assume that innovative strategies and new control options make possible the emiss ions reductions needed for attainment by 2020. The fixed cost methodology was preferred b y EPA’s Science Advisory Board over two other options, including a marginal-cost-based appr oach. The hybrid approach has not yet been reviewed by the SAB.
The hybrid approach creates a marginal cost curve and an aver age 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 re ductions assumed for each area, with a higher average cost-per-ton in areas needing a higher propor tion of unknown controls relative to known modeled controls. This requires assumptions abou t 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 fu ture technological progress and innovation in emission reduction strategies.
The fixed cost methodology utilizes a national aver age cost per ton of future unspecified controls needed for attainment, as well as two sensitivity value s (presented in Appendix 5a.4.3). The5-13

range of estimates reflects different assumptions a bout the cost of additional emissions reductions beyond those in the modeled control stra tegy. The alternative estimates implicitly reflect different assumptions about the amount of technol ogical progress and innovation in emission reduction strategies.
The hybrid methodology has the advantage of using t he information about how significant the needed reductions from unspecified control technolo gy are relative to the known control measures and matching that with expected increasing per unit c ost for going beyond the modeled technology. Under this approach, the relative costs of unspecified controls in different geographic areas reflect the expectation that avera ge per-ton control costs are likely to be higher in areas needing a higher ratio of emission reducti ons from unspecified and known controls.
The fixed cost methodology reflects a view that beca use no cost data exists for unspecified future strategies, it is unclear whether approaches using h ypothetical cost curves will be more accurate or less accurate in forecasting total national costs o f unspecified controls than a fixed-cost approach that uses a range of national cost per ton values.
Technological change will provide new control possi bilities that can be employed to provide the additional unspecified control needed to reach attainmen t. These new technologies will make control possible where control has not been available for es timating our known control. An example might be the development of a new control tec hnology 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 exam ple, 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 technolog ies may change so that higher control efficiencies may be obtained without a significant increase in per unit costs o f control.
Both approaches (the hybrid and the fixed) estimate costs using national level parameters and local area information about needed emission reduct ions. 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 es timates of needed emission reductions are used in both models. The hybrid model also uses i nformation about the amount of modeled control estimated for the local area.
The hybrid approach has yet to be peer reviewed and r eflects a range of views about the likely cost of future techniques and strategies that reduce air pol lutant emissions. Section 5.4 discusses historical experience which has shown numerous technologi cal 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 kn own controls not included in the modeled control strategy. These controls include the contro ls 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 o f 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 geogra phic areas that were “over controlled” and controls were removed from the analysis. For a complete disc ussion of the supplemental and “over control” emission reductions and costs see Appendi x 5a.4.1 and 5a4.2 respectively. After the supplemental controls are applied, any remaining e mission reductions needed are classified as additional tons from unknown control measures.
Supplemental controls were applied in addition to t he known controls in this illustrative analysis in order to achieve the highest possible known emiss ion 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 AirControl NET but whose data have been modified to better approximate their applicability to s ource 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 prepa red by the staffs of various local air quality regulatory agencies (e.g., Bay Area Air Quality Man agement District). The reports that are the sources of additional controls data are inc luded within footnotes in the Chapter 3 Appendix. Modification of control data, including perc ent 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 co ntrols by attainment date. Because two areas in California require no incremental additional pro gress towards attainment by 2020 for a more stringent standard (their requirements to reach att ainment of the current standard by 2024 will be the requirement that is binding) we separated the requirem ents to attain more stringent standards for those two areas from the analysis for the rest of the nati on. A highly uncertain estimate of the extrapolated engineering cost in 2030 is provided in Append ix 5a.5.
5.2.1.2 Theoretical Model for Hybrid Approach
A simple model of how marginal costs increase with in creasing control requirements was developed. The model relies on emission estimates of unspecified emissions (E 1 ) needed to reach attainment and the modeled control emission estimates. The se unspecified emissions vary both with the area and standard being analyzed. The modeled emissions vary by area. The ratio (R) of unspecified emissions (E 1 ) to controlled emissions estimates (E 0 ) is thus unique to each area and standard being analyzed. The model of cost also incl udes two parameters developed for use that don’t vary across analyses of areas and standards. One is a national projected dolla r 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 (E 1 ) increase relative to the modeled controls (R). The following equations show how Average cost (AC), Total Cost (TC), and ma rginal (MC) are modeled in the hybrid approach. See the appendix for a more detailed explanation.

AC = N(1+RM)

TC = AC(E 1 )5-15

MC = N(1+ 2RM)

For the controlled emissions estimated in the modeled contr ol, costs increase at an increasing rate as more control is applied. The shape of the co ntrol 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 sim plicity/transparency and model richness we chose a proportional per unit cost increase. This m odel 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 dete rmine this number we calculated the marginal costs for the last control applied in all geog raphic areas for nonEGU and Area known controls 7 and averaged them for both the modeled control strat egy and an alternate primary standard of 0.065 ppm, this allowed for cons istency with the modeled control strategy marginal costs. These calculations showed a range of $1 4,500 to $16,000 per ton (2006$), with $15,000 falling in the middle. The February 2007 re port, “Direct Cost Estimates for the Clean Air Act Second Section 812,” uses $10,000 (1999$) pe r 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 $15,00 0/ton. The jumping off price (N) should be interpreted as the cost of the very first ton needed from the unknown con trol 8 . 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 m ore complete information about the available control options in each area and we would be able t o estimate what the next control to be employed (the “jumping off” control) would be for each a rea needing control beyond the modeled known control.

We have to estimate R and E information for each area and ea ch 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 c onstant multiplier (M) incorporates many different influences on the unit costs of control such a s 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 e stimate of $15,000 for N, and our highest

7 NOx NonEGU point and Area controls were used for this calcul ation due to availability of detailed data across all emission sectors.

8 Although $15,000/ton (2006$) represents the cost o f 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 R 9 (2.19)for areas meeting the current standard in 2020. Fo r the modeled control we used a maximum marginal cost of control of $23,000 dol lars/ton. At this cost 98% of the possible reductions NOx from nonEGU point and area were appl ied. To arrive at a high value we doubled the maximum marginal cost value ($46,000). A number this h igh is rarely seen in either implemented controls or other RIAs (e.g. the 1997 O zone 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 mod eled control strategy ($23,000). This leads to our estimate of M of 0.12. We calculated a n 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 0.0 65 ppm alternative standard. The R for Houston was higher, yet this value was not used when calcul ating 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 needi ng 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 Areas a, b, c

0.00 0.40 0.80 1.20 1.60 2.00 2.40Ratio of Unknown Emission Reductions toKnown 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 we re calculated for VOC.

c Houston did not meet the current standard after th e 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 wit h N and linearly increases with R. The ratio of needed unknown control to modeled control (R) can be in terpreted 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 m odeled 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 Calcul ating M a 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 th e values presented above for tighter ozone standard s. 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 geographically5-18

specific average cost/ton that is then multiplied by t he amount of unspecified emissions reductions needed to attain. These average cost per ton valu es
Figure 5.5: Ranges of Hybrid (Mid) Average Cost/Ton V alues 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 ppmAverage 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 extrapolat ed cost equation involves only unspecified emissions (E 1 ) and Fixed Cost per ton (F). Thus the total cost (TC) equation is:

TC= E 1 F
The primary estimate of F is $15,000. The $15,000 per ton amou nt 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 sensit ivity analyses found in Appendix 5a.4.3.5-19

5.2.2 Results

5.2.2.1 Emission Reductions Needed to Attain Various Stand ards
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 stan dards. 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 ap proach than controlling both NOx and VOC. As expected, more areas need extrapolated emiss ion reductions when the alternative standards are more stringent.

Table 5.3: Extrapolated Emission Reductions Needed (Post Application of Supplement al

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 49,000Boston-LawrenceWorcester, MA 8,500 Buffalo-Niagara Falls, NY 18,000 3,700 Campbell Co., WY 50Charlotte-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 81,000HuntingtonAshland, WV-KY 800 Jackson Co., MS (200) a Jefferson Co, NY 6,200 Las Vegas, NV 3,900 Memphis, TN-AR 1,1005-20 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 co sts of these emission reductions are not incurred as pa rt of meeting a new ozone standard. The difference between the emission reductions needed in Table 4.7 a 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 esti mates 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. Whe n we evaluate the portion of costs for the extrapolated costs fixed approach by supplemental ai r 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 $2 0,000/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 becaus e R goes up with the stringency of the standard, the differences in costs between cost areas i ncrease with the stringency of the5-21

Figure 5.6: Extrapolated Cost by Region to Meet Var ious 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 $40Extrapolated Cost (B 2006$)

Table 5.4: Extrapolated Cost by Region to Meet Various A lternate Standards

Using Fixed Cost Approach ($15,000/ton) a, b

Fixed Cost Approach Extrapolated Cost (M 2006$) 2020 Extrapolated Cost by

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 $1,2 00

a All estimates rounded to two significant figures. A s such, totals will not sum down columns.

b These estimates do not reflect benefits or costs f or 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 po rtion 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 all ocated 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 Var ious 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 $40Extrapolated Cost (B 2006$)

Table 5.5: Extrapolated Cost by Region to Meet Various Alt ernate Standards Using Hybrid Approach (Mid) a, b Hybrid Approach Extrapolated Cost (M 2006$) 2020 Extrapolated Cost by

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. A s such, totals will not sum down columns.

b These estimates do not reflect benefits or costs f or 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 t he engineering costs presented above from the modeled controls and the extrapolated cost s. The range presented in the extrapolated costs and the total costs represent the upper and l ower 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 cost s (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 Costs e $4,500 $3,300 $2,500 $1,100 Fixed Hybrid Fixed Hybrid Fixed Hybrid Fixed Hybri d 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 $2 8 $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 $7,6 00 $8,800 $2,400 $2,900

a All estimates rounded to two significant figures. A s such, totals will not sum down columns.

b These estimates do not reflect benefits or costs f or 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 atta inment date.

d Known control costs for 0.079 ppm and 0.075 ppm inc lude 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 o f the costs of attainment.

e Known control costs consist of modeled control str ategy costs presented in Table 5.1, as well as supplemental costs and “giveback” costs presented i n Appendix 5a.4.1 and 5a.4.2. Our estimates of costs of attainment in 2020 assume a particular trajectory o f aggressive technological change. This trajectory leads to a pa rticular level of emissions reductions and costs which we have estimated based on two differen t approaches, the fixed cost and hybrid approaches. An alternative storyline might hypothe size a much less optimistic technological change path, such that emissions reductions technol ogies for industrial sources would be more expensive or would be unavailable, so that emissions reduc tions from many smaller sources might be required for 2020 attainment, at a potentially grea ter cost per ton. Under this alternative storyline, two outcomes are hypothetically pos sible: Under one scenario, total costs associated with full attainment might be substantially hi gher. Under the second scenario, states may choose to take advantage of flexibility in the Clean Air Act to adopt plan with later5-24

attainment dates to allow for additional technologi es 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 at tainment 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, seeki ng 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 s tates 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 margi nal costs are equal to marginal benefits plus the costs of nonattainment.

Figure 5.8 shows the total costs for both the fixed and hybri d approaches broken out by region.

Figure 5.8: Annual Total Costs by Region a

$0 $5 $10 $15 $20 $25 $30 $35 $40 $45Total Cost (B 2006$)

0.

065 ppm 0.070 p p m

0. 075 ppm 0.079 ppm 0.065 p p m 0.07 0 ppm 0.075 p pm 0.079 p p m

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 atta inment 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. Thi s shows graphically the increasing portion of costs that comes from unknown controls as the stand ard 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 Extrap olated Costs for Various Standards a, b

$0 $5 $10 $15 $20 $25 $30 $35 $40 $45Total Costs (B 2006$)

0.065 pp

m

0 .0 7 0 pp m

0 .0 75ppm

0 .0 7 9 pp m 0.06

5 pp m

0 .0 7 0 pp m

0 .0 75ppm

0 .0 7 9 pp m

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 str ategy costs presented in Table 5.1, as well as supplemental costs and “giveback” costs presented i n 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 atta inment date.

Lastly, Figure 5.10 shows the total cost range by s tandard. 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 Standards a

$0 $5 $10 $15 $20 $25 $30 $35 $40 $45Total Annual Costs (B 2006$)

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 atta inment 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 t han had been feasible earlier, or have reduced the costs of emission control in relation to original estimates. Studies 10 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 technol ogical 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 f irms produce goods and services, for example, firms may use less energy to produce the sam e quantities of output. In addition, technological change may result in improvements in t he quality of health care, which can have impacts on the baseline health of the population, p otentially reducing the susceptibility of the population to the effects of air pollution. While our baseli ne mortality incidence rates account for

10 Harrington et al. (2000) and previous studies cited by Harrington.5-27

increasing life expectancy, and thus reflect projecte d 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 a s asthma, there has been an observed upward trend in prevalence, which we have not captu red 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 m arginal costs per unit of abatement, and thus deviate from the assumption of one constantly increasing marginal cost curve. In addition, elevated abatement costs may res ult in significant increases in the cost of production and would likely induce production efficiencie s, in particular those related to energy inputs, which would lower emissions from the produc tion side.

Figure 5.11: Technological Innovation Reflected by Marginal Cost Shift

MC 0 MC 1

Cumulative NOx ReductionsCost/Ton

Induced Technology Shift Sl o p e = ȕ 0

S l o p e = ȕ 1

5.4.1 Examples of Technological Advances in Pollution Con trol
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 bur ners 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 bo ilers

x Sophisticated new valve seals and leak detection eq uipment for refineries and chemical plans

x Low or zero VOC paints, consumer products and cleaning p rocesses

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 contr ol systems for light-duty vehicles; and treatment devices and retrofit technologies for heavy -duty engines

x Idle-reduction technologies for engines, including truc k stop electrification efforts

x Market penetration of gas-electric hybrid vehicles, and clean fuels
These technologies were not commercially available two d ecades 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 i mpacts” 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 imp acts have been identified to occur in a number of studies conducted for various production process es. 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 contr ol costs has been estimated for a variety of sectors as part of the cost analyses done for the Draft Direct Cost Report f or the second EPA Section 812 Prospective Analysis of the Clean Air Ac t Amendments of 1990. 12 In that report, learning curve adjustments were included for those sectors and technolog ies for which learning curve data was available. A typical learni ng 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 tech nology.

12 E.H. Pechan and Associates and Industrial Economics, Dir ect Cost Estimates for the Clean Air Act Second Section 812 Prospective Analysis: Dra ft Report, prepared for U.S. EPA, Office of Air and Radiation, February 2007. Available at http://www.epa.gov/oar/sect812/mar07/direct_cost_dr aft.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 o f 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: C N = C o * N b

,
where
N = cumulative production

C N = cost to produce N th unit of capacity

C o = 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 pe rcent, depending on the sector and technology. Learning curve adjustments were prepare d in a memo by IEc (2007) supplied to US EPA and applied for the mobile source sector (both on road and nonroad) and for application of various EGU control technologies within the Draft D irect 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 porti ons of the cost analysis for which no learning curve impact data are currently available. Exampl es 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 su ch as cumulative fuel sales or cumulative emission reductions being used when output da ta was unavailable.
For this RIA, we do not have the necessary data for cumulative out put, 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 Redu ctions for Generating Technologies in the National Energy Modeling System (NEMS), Ernest Orlando Lawrence Berkeley National Laboratory, University of California at B erkeley, Berkeley, CA. January 2004, LBNL-52559.

14 Industrial Economics, Inc. Proposed Approach for Expand ing 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 i n 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-regula tory cost estimates to be higher than later estimates, in part because of inability to predict techno logical advances. Over longer time horizons, such as the time allowed for areas with h igh levels of ozone pollution to meet the ozone NAAQS, the opportunity for technical advances is grea ter.

x Multi-rule study: Harrington et al. of Resources for the Future (2000) conduct ed 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 Administrati on (OSHA), and found a tendency for predicted costs to overstate actual implementation costs. Costs were considered accurate if they fell within the analysis error b ounds 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 w ere underestimated for only three rules. Differences can result because of quanti ty differences (e.g., overestimate of pollution reductions) or differences in per-unit cos ts (e.g., cost per unit of pollution reduction). Per-unit costs of regulations were over estimated in 14 cases, while they were underestimated in six cases. In the case of EPA rules, the age ncy overestimated per-unit costs for five regulations, underestimated them for four r egulations (three of these were relatively small pesticide rules), and accurately estima ted them for four. Based on examination of eight economic incentive rules, “for th ose rules that employed economic incentive mechanisms, overestimation of per-unit cos ts seems to be the norm,” the study said.
Based on the case study results and existing liter ature, 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, aft er 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 lim ited 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, w here a substantial fraction of the estimated cost in this analysis is incu rred.

x Acid Rain SO2 Trading Program : Recent cost estimates of the Acid Rain SO2 tradin g program by Resources for the Future (RFF) and MIT hav e 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 approxim ate 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 Ru le, the ex ante numbers in 1989 were an overestimate in part because of the limitation o f economic modeling to predict technological improvement of pollution controls and oth er 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 deregul ation 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% reli ability) than expected, and that unanticipated opportunities arose to blend low and high sulfur coal in olde r boilers up to a 40/60 mixture, compared with the 5/95 mixtu re originally estimated.

Phase 2 Cost Estimates Ex ante estimates $2.7 to $6.2 billion a 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 Quality 17 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 amou nt.” 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. Th is simple statement reflects the fact that a lot happens between a producer’s determination of manufa cturing 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 substance s. This was done more efficiently than either EPA or industry originally anticipated. The p haseout for Class I substances was implemented 4-6 years faster, included 13 more chemic als, 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 anal ysis was performed for the CFC phase-out it was not anticipated that the hydrofluor ocarbon 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 cont ained HFC-134a (a compound for which no commercial production technology was avail able in 1986) instead of CFC-12” (p.13). He cites a similar story for HCFRC-141b and 1 42b, which are currently substituting for CFC-11 in important foam-blowing applica tions.”

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 whi le the associated capital cost dropped from $25-$38/kw to $15/kw (ICF, 2005).

We can not estimate the interplay between EPA regul ation and technology improvement, but it is clear that a priori cost estimation often results in overestimation of c osts because changes in technology (whatever the cause) make less costly contr ol possible.

5.5 References
Anderson, J.F., and Sherwood, T., 2002. “Comparison of EPA a nd Other Estimates of Mobile Source Rule Costs to Actual Price Changes,” Office of Transp ortation 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 Ou tput 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.col orado.edu/papers/gtaptext.html.
Babiker, M.H., J.M. Reilly, M. Mayer, R.S. Eckaus, I.S. Win g, and R.C. Hyman. 2001. “The MIT Emissions Prediction and CO2 Policy Analysis (EPP A) Model: Revisions, Sensitivities, and Comparisons of Results.” MIT Joint Program on the Scien ce and Policy of Global Change, Report No. 71. Available at http://web.mit.edu/globalcha nge/www/eppa.html.
Bovenberg, L.A., and L.H. Goulder. 1996. “Optimal Environ mental Taxation in the Presence of Other Taxes: General Equilibrium Analysis.” American Econ omic 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. GAM S: A User’s Guide. GAMS Development Corporation. Available at http://www.gams.c om.
Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L. Palmer. 2000. “S ulfur Dioxide Control by Electric Utilities: What Are the Gains fro m Trade?” Journal of Political Economy 108(#6):1292-1326.
Ellerman, Denny. January 2003. Ex Post Evaluation of Trad able 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 Ta x Burden?” Washington, DC: The Brookings Institute. Available at http://bookst ore.brookings.edu/ book_details.asp?product%5Fid=10403.

Goulder, L.H., and R.C. Williams. 2003. “The Substa ntial Bias from Ignoring General Equilibrium Effects in Estimating Excess Burden, an d a Practical Solution.” Journal of Political Economy 111:898 927. Available at http://www.journa ls.uchicago.edu/JPE/home.html
Harrington, W., R.D. Morgenstern, and P. Nelson. 20 00. “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 environmen tal 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 Hol mstead, Assistant Administrator, Office of Air and Radiation, U.S. Environmental Protection Agency, B efore 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: S purring Innovation and Growth While Cleaning the Air. Washington, DC. Available at http://www.icfi.com/Markets/Environment/doc_files/c aaa-success.pdf.

Minnesota IMPLAN Group. 2003. State Level Data for 2000. Ava ilable from http://www.implan.com/index.html.

Nestor, D.V., and C.A. Pasurka. 1995. The U.S. Environmenta l Protection Industry: A Proposed Framework for Assessment. U.S. Environmental Protec tion 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 Administra tion. 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 Administrat ion. 2001. Manufacturing Energy Consumption Survey 1998. Washington DC. Available a t http://www.eia.doe.gov/emeu/mecs/.
U.S. Department of Energy, Energy Information Admin istration. January 2003. Annual Energy Outlook 2003. DOE/EIA 0383(2003). Washington DC. Avai lable at http://www.eia.doe.gov/oiaf/archive/aeo03/pdf/0383( 2003).pdf.
U.S. Department of Energy, Energy Information Administra tion. January 2004. Annual Energy Outlook 2004. DOE/EIA-0383(2003). Washington, DC. Ava ilable at http://www.eia.doe.gov/oiaf/aeo/pdf/0383 (2001).
U.S. Environmental Protection Agency. June 2007. Adviso ry Council on Clean Air Compliance Analysis (COUNCIL), Council Advisory on OAR’s Direct Cos t 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 a nd Area Sources
(Full details on controls can be found in Appendix Chap ter 3)
5a.1.1 Engineering Costs by Control Measure

Tables 5a.1 and 5a.2 summarize the total incrementa l annualized engineering costs in 2020 for the modeled control strategy by control measure for nonEG U 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 Commercial/Institutional—NG $7.7 Water Heater + LNB Space 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 $5.15a-2 Control Measure Source Type Total Cost (M 2006$) 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 G as $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 Produ ction $210 Gas Collection (SCAQMD/BAAQMD) Municipal Solid Wast e Landfill $1.1 Incineration >100,000 lbs bread Bakery Products $5.8 Stage II Service Stations $16 Low Pressure/Vacuum Relief Valve Stage II Service Stations—Underground Tanks $15 Aircraft Surface Coating $2 OTC Mobile Equipment Repair and Refinishing Rule Machn, Electric, Railroad Ctng $12 OTC Solvent Cleaning Rule Cold Cleaning $16 SCAQMD—Low VOC Rubber and Plastics Mfg $2.65a-3 Control Measure Source Total Cost (M 2006$) SCAQMD Limits Metal Furniture, Appliances, Parts $1 9 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 Fabric Printing, Coating and Dyeing $0.069 Permanent Total Enclosure (PTE) Paper and Other Web Coating $0.85 Printing and Publishing $4.4 Petroleum and Solvent Evaporation 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 LE C 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 projec ted 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-h r, a controlled level of 2.0 g/bhp-hr (87% decrease), and nearly continuous operation in the o zone season. The EPA believes the ACT document provides a reasonable approach to calculati ng 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 Reciprocatin g 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 .pdf5a-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 ma y 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 efficien cy for this measure; one is to apply a continuous flow monitor until VOC emissions have re ached a level of 1.7 tons/year for a given cooling tower. 4 The annualized engineering cost for a continuous fl ow 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 annualize d 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 Solve nt 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 J uly 2007. 6

5a.2 Engineering Cost Information for EGU Sources
(Full details on controls can be found in Appendix Chap ter 3)

3 MARAMA Multipollutant Rule Basis for Flares, part of “Assessment of Control Technology Options for Petroleum Refineries in the mid-Atlantic Regio n.” 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). Propo sed Revision of Regulation 8, Rule 8: Wastewater Collection Systems. Staff Report, Mar ch 17, 2004.
5 Bay Area Air Quality Management District (BAAQMD). Propo sed Revision of Regulation 8, Rule 8: Wastewater Collection Systems. Staff Report, Mar ch 17, 2004.
6 U.S. Environmental Protection Agency. Consumer and Commer cial 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 achi eve significant emission reductions under the Clean Air Interstate Rule (CAIR) over the next 10 to 15 ye ars. When fully implemented, CAIR (in conjunction with NOx SIP Call) will reduce ozon e season NOx emissions by over 60 percent from 2003 levels within the CAIR states. These redu ctions 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 s how the potential impacts of the additional controls applied (see section 3.3.3 of t his RIA) to facilitate attainment of the more stringent 8-hr ozone standard. Generally, the increment al 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 summ arizes 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 Un its) SCR 219.6 229.9 SNCR 11.8 15.0

Projected Generation Mix

. Coal-fired generation and natural gas/oil-fired genera tion are projected to remain almost unchanged. Installation o f approximately 9.4 GWs of SCR and 2.4 GWs of SNCR incremental to the base case are projec ted 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 gene ration) and gas-fired generation increases a similar amount. Hydro, nuclear, other, and r enewable based generation projected to remain the same. Projected retirements of both coal and oi l/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 bei ng compared or added to other control
measure costs.5a-6

Projected Nationwide Retail Electricity Prices. Retail electricity prices are projected to decreas e marginally, about 1%. The extension of the cap-and- trade approach in the form of lower subregional caps allows industry to meet the requireme nts of CAIR in the most cost-effective manner, thereby minimizing the costs passed on to consumer s. 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 Mob ile Sources
(Full details on controls can be found in Appendix Chap ter 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 Inc remental 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 Inc remental 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 cat alytic 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 receive d a retrofit were assumed to employ selective catalytic reduction as a retrofit technology. The avera ge 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 aver age estimated cost of this system to be $15,000 per heavy duty diesel vehicle. (Source: AirC ontrolNET Documentation, III-160). OTAQ conducted an additional assessment of current SCR c osts and calculated that for the year 2020, the cost of SCRs will be approximately $13,000 per unit. This estimate reflects an economy of5a-7

scale cost reduction of 33%, which is consistent wi th trends in other mobile source control technologies that enter large scale production 8

. The rebuild/upgrade kit is applied to nonroad equip ment. 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 Tabl es 5a.6, 5a.7, and 5a.8.

Table 5a.6: Summary of Cost Effectiveness for Rebuil d/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 Var ious 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 Va rious 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 a nd “convenience” costs of I/M programs. Using the radio-frequency approach as an example, the cos ts 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 200 4 and Later Model Year Heavy-duty Highway Engines and Vehicles published October 2000), interviews with component manufacturers, and EPA’s Summary of Potential Retrofit T echnologies available at www.epa.gov/otaq/retrofit/retropotentialtech.htm

.5a-8

periodic I/M and Remote OBD. In this scenario, the assumption i s that all 1996 and newer vehicles currently subject to I/M will participate i n 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 d ata 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 netwo rk 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 a n 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 incu rred for each vehicle over its life to cover data processing and reporting.

Table 5a.9: Remote OBD VID Service Cost Estimate Per Vehi cle 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 an d indirect costs associated with I/M—the time and fuel it take s 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 returni ng to their point of origin or to their next stop in a trip chain. In some centralized programs, wait tim es can be very long. In decentralized programs, motorists often drop off their vehicle (r equiring two trips to the test station). For the sake of illustrating the convenience costs associate d with I/M, a reasonable range for the typical test cycle is one to two hours. If we assign a cost of $20 per hour 11 and a half-gallon of gas (10 miles round trip with an average fuel economy of 20 m pg) 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. C ompare 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 Docume nt published along with the 1992 Enhanced I/M Rule.5a-9

For the purposes of illustrating the nationwide costs and ben efits of doing remote OBD, the following analysis assumes 100% participation. It is li kely, however, that in the short run states will gradually introduce remote OBD initially on a v oluntary basis (except possibly for fleets), and that participation rates will build over time a s motorists recognize the cost and convenience advantages. Another caveat is that those states tha t require motorists to get safety checks, the convenience costs may not be fully realized (see Discussi on of Issues, below). Table 5a.10 shows the lifetime inspection and convenience costs of a man datory, nationwide remote OBD program versus a periodic OBD program (assuming the current n ationwide 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 purpose s 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 billion 12 over a 10- year lifecycle with an additional $9 to $17 billion in conven ience 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 ye ar period.

Table 5a.10: Range of Lifetime Inspection and Convenience C osts of I/M
Periodic OBD ($B 2006) Remote OBD ($B 2006) Savings ($B 2006) Low $12 $4 $8 Test/Install Cost High $12 $5 $7

Low $9 $1 $8 Convenience Cost High $17 $2 $15

Low $21 $5 $16 Total 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 n o 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 Re search’s annual I/M summary for 2005 and updated in some cases by members of the workgro up.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 c onsume approximately 1 gallon per hour of idle. Current TSE projects are operating in environments w here trucks are idling, on average, for 8 hours per day per space for 365 days per year (or a bout 2,920 hours per year). Since TSE technology can completely eliminate long duration id ling at truck spaces (i.e., a 100% fuel savings), this translates into 2,920 gallons of fue l saved per year per space. At current diesel prices ($2.90/gallon), this fuel savings translates in to $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 dur ation idling trucks, we should note that TSE technology is genera lly 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 red uctions 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) becau se it provides air conditioning, heat, and electrical power to operate appliances. The average price o f 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 estimate s that trucks idle for 7 hours per rest period, on average, and about 300 days per year (or 2,100 h ours per year). Since idling trucks consume 1 gallon of fuel per hour of idle, APUs can reduce fu el consumption for truck drivers/owners by approximately 1,680 gallons per year. At current diesel p rices ($2.90/gallon), truck drivers/owners would save $4,872 on fuel if they used an AP U. 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) proj ects 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 med ian 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 t he 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 s ampling of commuter reduction incentive programs. There is a great deal of variability, howev er, in the type of programs and the level of incentives that employers offer which can impact bot h the amount of emissions reductions and the cost of commuter reduction incentive programs.
We chose to apply the resulting average cost-effect iveness estimate to one pollutant—NO x —in order to be able to compare commuter reduction progr ams to other NO x 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) + (NO x * 4)). There was not enough information in the TRB study to isolate the $/ton cost-effectiveness f or just NO x reductions, so we used the combined NO x 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 $2 2,700 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 co st of reducing RVP to 7.0. The analysis was undertaken as part of their proposed revision to Michig an’s SIP for their 7.0 low vapor pressure request for Southeast Michigan. According to the ir 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 Southea st Michigan Council of Governments. Proposed Revision to State of Michigan State Implem entation Plan for 7.0 Low Vapor Pressure Gasoline Vapor Request for South east Michigan . May 24, 2006.

x U.S. EPA. Guide on Federal and State Summer RVP Standards for Conventio nal 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 m akes recommendations to the ICAO for environmental standards for aircraft. ICAO is a United N ations body that sets voluntary international standards for aircraft. Manufacturers in the U.S. and other countries ge nerally comply with these standards. A few years ago, ICAO set a ne w standard (CAEP/6) for NOx emissions from commercial aircraft to reduce emissi ons 12% compared to the existing standard. Compliance with this standard is reflected in the an alysis. 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 sour ces. The 0.065 ppm standard geographic areas were broader than those for the mo deled 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 co ntrols applied to select natural gas and oil fired electric generating units. Other suppleme ntal controls applied to nonEGU point and Area sources are described in the appendix to Chapt er 3 (3a.1.6 Supplemental Controls). Lastly, for the Eastern Lake Michigan area, the cut point for applyi ng 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 co ntrol 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 Emis sion Reductions [annual tons/year] Applied for Various Standards a 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 2,600 340 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 69Charlotte-Gastonia-Rock Hill, NCSC 15,000 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 29,00 0 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 fo r 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 Tot al Annualized Costs [M 2006$]

Applied for Various Standards (ppm) a

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 $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 $4 6 $100 TOTAL COSTS $1,680 $970 $870 $146 a These estimates do not reflect benefits or costs fo r 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 redu ctions 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 remo ve 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 tha t were not able to be removed from the analysis of attainment for these standards is prese nted in Table 5a.16. it is important to note that since there was “over control” for 0.070ppm, 0.075 pp m, 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 0.070 ppm 0.075 ppm 0.079 ppm 2020 Extrapolated Cost Area 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 12,00 0 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 8 4 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 2 5,000 1,700 Providence (All RI), RI 1,500 690 1,500 690 1,500 6 90 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 62,0005a-16

Table 5a.15: Modeled Control Strategy Control Measu re Annualized Total Costs [M 2006$] Removed from Extrapolated Analysis for Various Standards 0.070 ppm 0.075 ppm 0.079 ppm 2020 Extrapolated Cost Area 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 $11 0 $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 $1,9005a-17

Table 5a.16: Emission Reductions Not Needed [annual tons/ye ar] Remaining After Removing Control Measures Not Needed to Meet Various Oz one Standards a 0.070 ppm 0.075 ppm 0.079 ppm 2020 Extrapolated Cost Area 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. A s 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 f ixed cost ($20,000/ton) is presented in Figure 5a.1. You can see that as the amount of unknown emis sions 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 prese nted in Tables 5a.17 through 5a.20.5a-18 Figure 5a.1: Fixed Cost Approach Sensitivity Analysis Re sults Ranges

$- $5 $10 $15 $20 $25 $30 $35 $40 $45 $50 $55

0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppmExtrapolated 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

Fixed Cost Approach Extrapolated Costs (M 2006$) 2020 Extrapolated Cost Area ($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 $13,0 00 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 $6,8005a-19 Fixed Cost Approach Extrapolated Costs (M 2006$) 2020 Extrapolated Cost Area ($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. A s such, totals will not sum down columns.

b These estimates do not reflect benefits or costs f or 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 Approach a, b Extrapolated Costs (M 2006$) 2020 Extrapolated Cost Area ($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 $10,0 00 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. A s such, totals will not sum down columns.

b These estimates do not reflect benefits or costs f or 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 Approach a, b Extrapolated Costs (M 2006$) 2020 Extrapolated Cost Area ($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. A s such, totals will not sum down columns.

b These estimates do not reflect benefits or costs f or 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 Approach a, b

Extrapolated Costs (Thousands 2006$) 2020 Extrapolated Cost Area ($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. A s such, totals will not sum down columns.

b These estimates do not reflect benefits or costs f or 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

cQ b MC 2 

Where ( b +2 cQ ) is a nonnegative function, and b is the intercept and 2 c 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 margi nal cost function from 0 of emission reductions from unknown controls to all emissions reducti ons 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 = dx cQ b Q 

## 3  0 2 $
Linear MC Curve

Emission reductions from unknown controlsCost/Ton of unknown controls5a-21

Evaluate dx cQ b Q 

## 3  0 2 a c b a cQ bQ 



   2 2 0 0

Where MC

is nonnegative for Q cQ b d  d 2 0 the definite integral of MC equals the area of the shaded region, which is the total cost ( TC )

TC = bQ + cQ 2

To calculate average cost ( AC ) divide TC by Q

Q

cQ bQ 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

0 E NM where M

is the multiplier, and 0

E represents the known emission

reductions from the modeled control strategy. This s lope represents; control technology changes, energy technology changes, relative price changes, tec hnological innovation, and geographic distribution of sources with uncontrolled emissions , and emission reductions from known controls. Lastly, Q is represented by 1 E (the total unknown emission reductions)

 N AC 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 b etween Q 0 and Q , where Q 0 represents the quantity of emission reductions from unknown controls to reach the current ozone standard. Therefore the quantity of emissions that are extrapolat ed is

Q - Q 0
.5a-22

Figure 5a.3: Example Extrapolated Marginal Cost for G eographic Areas Not Meeting the Baseline in the Modeled Control Strategy

Evaluate dx

cQ b Q

Q 

# 3 

0 2 a cQ bQ a cQ bQ 



   2 0 0 2

Where MC

is nonnegative for Q cQ b Q d  d 2 0 the definite integral of MC equals the area of the shaded region, which is the total cost ( TC )

2 0 2 0 cQ cQ bQ bQ

 



TC = )

( ) ( 2 0 2 0 Q Q c Q Q b






To calculate average cost ( AC ) divide TC

by ( Q - Q 0 )

) ( ) ( ) (

0 2 0 2 0 Q Q Q Q c Q Q b Q TC

 





AC = b + c ( Q + Q 0 )

Replace the intercept b with the national cost/ton jumping off point ( N ), and the slope ( c ) with

0 E NM where M

is the multiplier, and 0

E represents the known emission reductions from the

modeled control strategy. This slope represents; co ntrol technology changes, energy technology changes, relative price changes, technological innovation, and geographic distribution of sour ces with uncontrolled emissions, and emission reduction s from known controls. Lastly, Q is represented by 1

E (the total unknown emission reductions), and Q 0 is represented by

084 E (unknown emission reductions to reach the current s tandard) b

0 Q Area = dx cQ b Q

Q 

# 3 

0 2 $
Linear MC Curve

Q 0 Emission reductions from unknown controlsCost/Ton of unknown controls5a-23  N AC )

( 084 1 0 E E E NM   ̧  ̧ 1 ·  ̈  ̈ © §

If we replace

0 1 E E with R , replace

0 084 E E with R s and pull out N the equation becomes

AC = N ( 1+RM+ R s M

)

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 t he 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 Geogra phic 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 $9 10 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, TX d 1.78 $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 $3 60 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. A s such, totals will not sum down columns.

b These estimates do not reflect benefits or costs f or 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 o f 0.62.

d Houston did not reach the baseline, and therefore has an additional R to reach the current standard o f 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 NOX 1.65 $21,000 Eastern Lake Michigan, IL-IN-WI VOC 1.86 $22,000 $11,000

Houston, TX c 1.63 $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. A s such, totals will not sum down columns.
b These estimates do not reflect benefits or costs f or 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 o f 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, TX c 1.36 $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. A s such, totals will not sum down columns.
b These estimates do not reflect benefits or costs f or 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 o f 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, TX d 1.17 $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. A s such, totals will not sum down columns.
b These estimates do not reflect benefits or costs f or 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 a ggressive 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 atta inment date.5a-26 d Houston did not reach the baseline, and therefore has an additional R to reach the current standard o f 0.62.
5a.4.4.3 Hybrid Approach Sensitivity Analysis Resul ts
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 ppmExtrapolated Costs (B 2006$)

Hybrid Mid Range Hybrid Lower Bound Hybrid Upper Bound5a-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 $2 3,000 $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 $2 0,000 $400 Northeast Corridor, CT-DE-MD-NJ-NY-PA $19,000 $6,40 0 $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. A s such, totals will not sum down columns.

b These estimates do not reflect benefits or costs f or 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 a ggressive 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 atta inment date.5a-28

Table 5a.26: Extrapolated Cost by Geographic Area to Meet 0.070 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$) 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 $18 0 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 $3,80 0 $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. A s such, totals will not sum down columns.

b These estimates do not reflect benefits or costs f or 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 a ggressive 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 atta inment date.

Table 5a.27: Extrapolated Cost by Geographic Area to Meet 0.075 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$) 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 $1,00 0 $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. A s such, totals will not sum down columns.

b These estimates do not reflect benefits or costs f or 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 a ggressive 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 atta inment date.5a-295a-30

Table 5a.28: Extrapolated Cost by Geographic Area to Meet 0.079 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$) 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. A s such, totals will not sum down columns.

b These estimates do not reflect benefits or costs f or 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 a ggressive 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 atta inment 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 contr ol measures on manufacturing industries, the**  **transportation sector, electricity generators, consum ers, 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 descrip tion of EMPAX-CGE followed**  **by a presentation of the results, three points are hig hlighted below that will assist the reader in**  **interpreting the economic impacts and relating thes e impacts to the modeled control strategy**  **engineering costs presented in Chapter 5.**
**(a) The selection criteria for the modeled control strate gy, and its related compliance costs, is**  **designed to select the least cost controls, from an en gineering cost standpoint, that**  **generate the greatest ozone reductions, but not nec essarily the lowest economic impact.**  **Therefore, although the control strategy is selected to r educe ozone at the lowest**  **engineering cost, it does not necessarily represent t he lowest impact strategy from an**  **economic impact standpoint. Thus, while this economic impact analysis presents results**  **for the modeled control strategy approach detailed in Cha pter 3 of the RIA, it should not**  **be viewed as reflecting the approach with the small est economic impact. Instead, the**  **results should be viewed as guidance or useful infor mation for states preparing their**  **implementation plans. It is likely that states will design implementation plans that apply**  **alternative control strategies and in some cases desi gn plans that take into account**  **secondary impacts to industries and consumers withi n their boundaries. In such a case,**  **the end result would be a set of State Implementati on 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 appe ndix include only the modeled**  **engineering costs detailed in Chapter 5 for the alter nate 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 i nput to EMPAX in this analysis are**  **those that reflect the $2.8 billion (2006 dollars) in 20 20 for the application of known**  **controls. (c) In the interest of learning how possible change**  **s in manufactured-goods prices might affect businesses and households, along with how changes in electricity/energy prices**  **might affect industry groups that are large energy users, EPA employed the “EMPAX**  **CGE” computable general equilibrium (CGE) model, which has been peer reviewed and** **5b-2**

**used in recent analyses of the Clean Air Interstate Rule (CAIR), the Clean Air Visibility**  **Rule (CAVR), and the PM 2.5**  **NAAQS. As with similar models, EMPAX-CGE focuses on the cost-side of spillover effects on the economy.**  **This implies its estimated industry sector impacts may be overstated because EMPAX-CGE is not configured to capture the**  **beneficial economic consequences of the increased la bor availability and productivity**  **expected to result from air quality improvements. EP A continues to investigate the**  **feasibility of incorporating labor productivity gains and o ther beneficial effects of air**  **quality improvements in CGE models and will incorpo rate 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 ass ociated 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 nega tively depending on the nature of the**  **regulation, and this welfare change is a measure of the soc ial costs. Such a welfare change could**  **result from higher prices on output, which may lead to less d emand by consumers and less output**  **by producers. These changes due to the higher outpu t 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 socia l 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 estimatin**  **g social cost than one using GDP in section 5b.4.4 of this appendix . Given a substantial numbe**  **r of caveats on results generated by EMPAX CGE, we include social cost and do not compare these**  **costs to the monetized benefits estimates provided later in this RIA. We also intend to solic**  **it 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 ma nufacturing 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 imp lications 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 distr**  **ibution of economic effects across the U.S. economy. This section briefly discusses the EMPA**  **X 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 anal ysis of EPA’s maximum**  **achievable control technology (MACT) rules for comb ustion sources (reciprocating internal**  **combustion engines, boilers, and turbines). The initia l framework consisted of a national**  **multimarket partial-equilibrium model with linkages only between manufacturing industries and**  **the energy sector. Modified versions of EMPAX were subseque ntly used to analyze economic** **5b-3**

**impacts of strategies for improving air quality in the So uthern Appalachian mountain region as**  **part of efforts associated with the Southern Appala chian Mountain Initiative (SAMI).** 
**Recent work on EMPAX has extended its scope to cove r all aspects of the U.S. economy at a**  **regional level in either static or dynamic modes. A lthough 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 p atterns. Consequently, EMPAX**  **now includes economic linkages among all industrial a nd energy sectors as well as households**  **that supply factors of production such as labor and pur chase 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 pro vide critical insights to policy makers**  **evaluating the magnitude and distribution of costs associ ated with environmental policies. The**  **dynamic version of EMPAX-CGE employed n this analysis, and i ts 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 upda**  **ting the energy production and consumption data (from DOE) to allow for more up to da**  **te characterization of energy markets and revising the uncompensated labor supply elastici**  **ties used in the model.**
5b.2.2 EMPAX Modeling Methodology for the Modeled Con trol 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 t**  **he economy will influence producers and consumers across the United States. However, some t**  **ypes of policies, including the Ozone National Ambient Air Quality Standard, are difficul**  **t 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 al**  **lows 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 engine ering 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 t han assume the costs represent a**  **proportional scaling up of all inputs, Nestor and Pasurka (1995) data on purchas es made by**  **industries for environmental-protection reasons are use d to allocate these additional expenditures**  **across inputs within EMPAX-CGE. Once these expenditure s 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, l inkages are made between EMPAX** 

**1 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 r esource costs and fuel consumption in**  **electricity generation. 2** 

5b.3 EMPAX-CGE Model Description: General Model Str ucture
**This section provides additional details on the EMPAX-CGE m odel 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 200 5 to 2050. It uses the classical**  **Arrow- Debreu general equilibrium framework wherein households maximize utility subject to**  **budget constraints, and firms maximize profits subje ct to technology constraints. The model**  **structure, in which agents are assumed to have perf ect foresight and maximize utility across all**  **time periods, allows agents to modify behavior in anticipa tion 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 substituti on possibilities available to producers and**  **consumers. Figure 5b.1 illustrates this general fra mework and gives a broad characterization of**  **the model. 3**  **Along with the underlying data, these nesting structures a nd associated substitution**  **elasticities determine the effects that will be est imated for policies. These nesting structures and**  **elasticities used in EMPAX-CGE are generally based on th e Emissions Prediction and Policy**  **Analysis (EPPA) Model developed at the Massachusetts Inst itute of Technology (Paltsev et al.,**  **2005). This updated version of the EPPA model incorporates some extensions over th e EPPA**  **version documented in Babiker et al. (2001) such as s pecification 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. Alth ough the two models continue**  **to have different focuses (EPPA is a model focused o n 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 w**  **ill respond to environmental policies and as such EPPA provides a strong basis to develop the theoret**  **ical structure of EMPAX-CGE.**
**Given this basic similarity, EMPAX-CGE has adopted a compara ble structure. EMPAX-CGE is**  **programmed in the GAMS 4**  **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 additi**  **onal discussion of these IPM EMPAX linkages (**  **http://www.epa.gov/interstateairquality/technical.html ).** 

**3 Although it is not illustrated in Figure 6.1, some**  **differences across industries exist in their handling of energy inputs. In addition, the agricul**  **ture 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 des**  **cription of GAMS (http://www.gams.com/).**

**5 Solving EMPAX-CGE as a MCP problem implies that complement**  **ary slackness is a feature of the equilibrium solution. In other words, any firm in ope**  **ration will earn zero economic profits and any unprofitable firms will cease operations. S**  **imilarly, 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 St ructure in EMPAX-CGE

**Leisure Consumption** **Consumption is a Cobb Douglas composite of**  **transportation plus goods.**

**Foreign Domestic**  **Domestic goods are a CES composite of local goods and**  **goods from other U.S. regions.**

**Local Output**  **Regional**

**Output**

**Intermediates KLE**  **Most producer goods use fixed proportions of**  **intermediate inputs and KLE.**

**Energy Value Added**

**Labor**

**Capital Energy**  **(5 Types) KLE is a CES**  **composite of energy (E) and**  **value-added (KL).**

**Value added is a Cobb Douglas 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**
**p urchased trans**  **p ort.** 

**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 provide d by the Minnesota IMPLAN**  **Group 7**  **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 consumpt**  **ion in dollars, these data are replaced with EIA data since the policies being investigate**  **d 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. Bur**  **eau 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 creat e 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, governme nt 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 techni**  **ques to maintain the calculated energy statistics (in both quant**  **ity and value terms) while minimizing any changes needed in the other economic data to cr**  **eate a new balanced SAM based on EIA/IMPLAN data for the baseline model year (in essence, ind**  **ustry production functions are adjusted, if necessary, to account for discrepancies betwe**  **en 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 indu stries. Prior to solving EMPAX-CGE, the**  **states and industries are aggregated up to the cate gories to be included in the analysis.**  **Aggregated regions have been selected to capture im portant differences across the country in**  **electricity generation technologies, while industry aggregations are controlled by avai lable**  **energy consumption data.**

**7 See http://www.implan.com/index.html for a description o**  **f 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/Pr**  **oductDetail1100.htm for a description of the Global Insight U.S. State Forecasting Service.** **5b-8**

**Table 5b.1 presents the 35 industry categories incl uded in EMPAX-CGE for policy analysis.**  **Their focus is on maintaining as much detail in the energy i ntensive and manufacturing sectors**  **10 as is** 

Table 5b.1: Industries in Dynamic EMPAX-CGE EMPAX Industry NAICS Classifications Energy **Coal 2121**  **Crude Oil a**  **211111, 4861 Electricity (** 

fossil and nonfossil

**) 2211**  **Natural Gas 211112, 2212, 4862**  **Petroleum Refining c**  **324, 48691** General **Agriculture 11**  **Mining (w/o coal, crude, gas) 21**  **Construction 23**  Manufacturing **Food Products 311**  **Textiles and Apparel 313, 314, 315, 316**  **Lumber 321**  **Paper and Allied 322**  **Printing 323**  **Chemicals 325**  **Plastic & Rubber 326**  **Glass 3272**  **Cement 3273**  **Other Minerals 3271, 3274, 3279**  **Iron and Steel 3311, 3312**  **Aluminum 3313**  **Other Primary Metals 3314, 3316**  **Fabricated Metal Products 332**  **Manufacturing Equipment 333**  **Computers & Communication Equipment 334**  **Electronic Equipment 335**  **Transportation Equipment 336**  **Miscellaneous remaining 312, 337, 339**  Services **Wholesale & Retail Trade 42, 44, 45**  **Transportation b**  **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 extr**  **action, the gas component of this sector is moved to the natural gas industry.** 

**b The petroleum refining industry provided oil in del**  **ivered terms, which includes pipeline transport.**

**c Transportation does not include NAICS 4862 (natura**  **l gas distribution), which is part of the natural g as**  **industry.**

**10 Energy-intensive industry categories are based on EIA d**  **efinitions of energy-intensive manufacturers in the**  Assumptions for the Annual Energy Outlook 2007

**. 5b-9** 

**allowed by available energy consumption data and computa tional limits of dynamic CGE**  **models. In addition, the electricity industry is separa ted 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 possi ble, 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 competiti ve and have production**  **technologies that exhibit constant returns to scale, except for the agriculture and n atural resource**  **extracting sectors, which have decreasing returns t o 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 p er kilowatt hour of electricity output).** 

**11 Economic data and information on nonelectricity ene**  **rgy markets are generally available only at the state level, which necessitates an approxima**  **tion of the NERC regions that follows state boundaries.** **5b-10**

**All markets, must clear (i.e., supply must equal demand in ev ery 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 cont ains four representative households,**  **classified by income, that maximize intertemporal utilit y 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% - $5 0,000 and above.**  **13 These**  **representative households are endowed with factors of production including labor, cap ital,**  **natural resources, and land inputs to agricultural production. Factor prices are equal to the**  **marginal revenue received by firms from employing a n additional unit of labor or capital. The**  **value of factors owned by each representative household de pends on factor use implied by**  **production within each region. Income from sales of the se productive factors is allocated to**  **purchases of consumption goods to maximize welfare.**
**Within each time period, intratemporal utility rece ived by a household is formed from**  **consumption of goods and leisure. All consumption g oods are combined using a Cobb Douglas**  **structure to form an aggregate consumption good. Th is composite good is then combined with**  **leisure time to produce household utility. The elasticity o f substitution between consumption**  **goods and leisure depends on empirical estimates of labor su pply elasticities and indicates how**  **willing households are to trade off leisure time for consump tion. Over time, households consider**  **the discounted present value of utility received from all p eriods’ 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 ver**  **sion of the model. We intend to review and potentially increase the number of househ**  **old classes in future version of EMPAX CGE, and will better reflect higher income household clas**  **ses as part of that effort.**

**13** U.S. Census Bureau, Current Population Survey, 200 7 Annual Social and Economic Supplement. HINC-01. Selected Characteris tics of Households, by Total Money Income. Found on the Internet at ( http://pubdb3.census.gov/macro/032007/hhinc/new01_0 01.htm )**5b-11**

**Following standard conventions of CGE models, factors of production are assumed to be intersectorally mobile within regions, but migration of pr**  **oductive factors is not allowed across regions. This assumption is necessary to calculate welfare c**  **hanges 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 compo site, differentiated**  **“Armington” goods made up of locally manufactured commodit ies and imported goods. Output**  **of local industries is initially separated into out put destined for local consumption by producers**  **or households and output destined for export. This local out put is then combined with goods**  **from other regions in the United States using Armin gton trade elasticities that indicate agents**  **make relatively little distinction between output fr om 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 elasticiti es 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]; G oulder and Williams [2003]). For example,**  **existing labor taxes distort economic choices because they e ncourage people to work below the**  **levels they would choose in an economy without labor taxes and reduces economic efficiency**  14

**. When environmental policies raise production costs for**  **firms and the price of goods and services, people may choose to work even less; the addition**  **al economic costs from this decision has been described as the “tax interaction” effect.** 
**EMPAX-CGE considers these interaction effects by ut ilizing tax data from several sources and**  **by explicitly modeling household labor supply decision s. The IMPLAN economic database**  **provides information on taxes such as indirect busin ess taxes (all sales and excise taxes) and**  **social security taxes. However, since IMPLAN reports fact or payments for labor and capital at**  **their gross of tax values, we use additional data s ources to determine personal income and capital**  **tax rates. Information from the TAXSIM model at the N ational Bureau of Economic Research**  **(Feenberg and Coutts, 1993), along with user cost of capital calculations from Fuller ton and**  **Rogers (1993), are used to establish tax rates. Elasti city parameters describing labor supply**  **choice ultimately determine how distortionary existing taxes are in the CGE model. EMPAX**  **CGE currently uses elasticities based on the relevan t literature (i.e., 0.4 for the compensated**  **labor supply elasticity and 0.15 for the uncompensate d labor supply elasticity). These elasticity** 

**14 These efficiency losses are often expressed in ter**  **ms of overall marginal excess burden; the cost associated with raising an additional dollar of tax r**  **evenue. Estimates range from $0.10 to $0.35 per dollar (Ballard et al., 1985).** **5b-12**

**values give an overall marginal excess burden assoc iated 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: tec hnological change from**  **improvements in energy efficiency, growth in the av ailable labor supply (from both population**  **growth and changes in labor productivity), increase s 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 conser vation. These changes in energy use**  **per unit of output are modeled as AEEIs (Autonomous Energy Ef ficiency 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 tren**  **ds 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 generati on, changes in available natural**  **resources, and resource prices are also based on th e AEO forecasts.** 
**Savings provide the basis for capital formation and are motivated through people’s expectations**  **about future needs for capital. Savings and investm ent 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 capita**  **l are controlled by using quadratic adjustment costs experienced when installing new cap**  **ital, which imply that real costs are experienced to build and install new capital equipm**  **ent.**
**Prior to investigating policy scenarios, it is nece ssary 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 2 007. 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 “counterfact ual” 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 includ e**  **estimates of price and output changes by industry a nd 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 conside rs 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 m ight be partially offset.** 

**15 See Babiker et al. (2001) for a discussion of how th**  **is methodology was used in the EPPA model (EPPA assumes that AEEI parameters are the same acro**  **ss 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 e stimates of GDP impacts, depend on**  **the labor supply elasticities in the model that hav e been chosen from the CGE literature on labor**  **markets and tax distortions as discussed above. More flexib le labor supply elasticities would**  **allow additional response in labor markets to polic y impacts, potentially with both positive and**  **negative effects. Other critical assumptions in EMPAX-CGE l argely revolve around the**  **production technologies and input substitution opti ons, which are based on the MIT EPPA**  **model.**
**It is also highly uncertain as to which industries wi ll be affected in the future when moving**  **beyond where known engineering controls can currently apply. This mix of industri es 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 relat ed 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 spe cified.**  **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 depende**  **nt on the extent to which the input costs by industry are defined. With a lack of knowledge of a**  **ffected industries, there is also a lack of knowledge of affected consumers or households (thus, n**  **o way to estimate completely household welfare impacts).** 
**Results from EMPAX are strongly influenced by elements in it s baseline data set such as energy**  **production and consumption data taken from the Energy Infor mation Administration’s Annual**  **Energy Outlook (AEO). The current EMPAX version use s such energy data taken from the latest**  **AEO version available (2007). This version of the A EO does not incorporate effects on energy**  **production and consumption data associated with provisio n of the Energy Independence and**  **Security Act of 2007 (or EISA) signed by the President on Dece mber 19, 2007.**  **17 Such effects**  **include increased biofuels production, increased vehicle fuel efficiency, and new minimum energy efficiency standards for many electric appli**  **ances and products. The effects of EISA will be incorporated in a revised version of the AEO tha**  **t will be released to the public in March, 2008.**  **18**

**EMPAX keeps the location of labor constant in respo nse 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 chang es may offset the inclusion of other** 

**16 Section 182 (e) (5) of the Clean Air Act allows estimat**  **ion 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 Quali**  **ty Management Plan for Ozone and PM found at**  **http://www.aqmd.gov/aqmp/docs/2003AQMPChap6.pdf**

**. 17**  **The entire text of this legislation can be read at http://frwebgate.access.gpo.gov/cgi**  **bin/getdoc.cgi?dbname=110_cong_bills&docid=f:h6enr. txt.pdf**  **. This is found at the Government Printing Office’s official web site.** 

**18 This is noted on the Energy Information Administration web**  **site at http://www.eia.doe.gov/oiaf/aeo/index.html** 

**. 5b-14** 

**effects into EMPAX that would lead to reduced econom ic impact estimates such as how**  **improvements in air quality lead to increased labor prod uctivity.** 
**Other caveats that can typically be applied to CGE analyses, including this one, cover issues**  **such as transitional dynamics in the economy. CGE m odels 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 adaptati ons may occur more quickly than if agents**  **adopted a wait-and-see approach to new regulations. The a lternative, recursive-dynamic structure**  **used in CGE models such as MIT EPPA imply that no an ticipation or adjustments will occur**  **until the policy is in place, which tends to overst ate 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 no t able to examine firm-specific impacts on**  **profits/losses or estimate how particular types of disa dvantaged households may be affected by**  **policies. Similarly, environmental justice concerns ma y not be fully addressed.** 

5b.4 EMPAX-CGE Results for the Modeled Control Stra tegy
**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 Cl ean**  **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 legisla tion 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 cos ts can affect output and prices of**  **all industries in the EMPAX-CGE model. These effects may incr ease or decrease output and/or**  **revenue, depending on their implications for produc tion costs and technologies and shifts in**  **household demands. In general, the impacts on energy produc ers and other industries will be**  **dependent on the control strategy and follow a pattern simil ar to the stringency of the ozone**  **standard.**
**As shown in Figure 5b.3, impacts on energy and indu strial 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 le**  **ss than five one-hundredths of one percent (0.05%). Effects on coal output are som**  **ewhat higher, but impacts on other types of energy producers are low and can be positive or neg**  **ative, 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 def**  **inition 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, al though there is**  **almost no change in the quantity of services produce d, 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 inf ormation on energy impacts at a**  **nationwide level, please refer to Chapter 8 where w e 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 Chan g e 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 ten d to slightly decrease coal consumption**  **(influencing U.S. coal production) and increase natural ga s use. EMPAX-CGE uses these**  **findings on coal and gas use directly from the IPM model (as d escribed 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 o**  **ther 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 th**  **eir respective industries and any changes in energy markets. Note, however, that across energy-inte**  **nsive industries as a group, output quantities decline on average by less than a two-tent**  **hs 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 gr oups 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 AirControlN ET, 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 pattern s for electricity and**  **nonelectricity energy goods;**

x **differences in industrial composition of regional e conomies;** 

x **differences in household consumption patterns; and**

x **differences in regional growth forecasts.**
**Figure 5b.4 first presents regional impacts on indu strial 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 simila r industries are grouped together to facilitate**  **the presentation. Aside from energy-intensive manufactur ing (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.h tml**  **for additional discussion of these linkages.** **5b-18**

Figure 5b.4: Modeled Control Strategy Impacts on Reg ional 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

USAgricultureEnergy-IntensiveManufacturingOther ManufacturingServicesTransportation 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

USCoalCrude OilElectricityNatural GasPetroleum Percent Change from Baseline

**Source: EMPAX-CGE. 5b-20** 

Figure 5b.6: Modeled Control Strategy Impacts on Reg ional 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 USFood and KindredPaper and AlliedChemicalsGlassCementIron and SteelAluminum 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, a ll impacts are still less than one percent (<1.0%) a** **cross the regions with most adjustments smaller than that. Under the modeled co** **ntrol strategy, coal consumption by electric utilities tends to decrease slightly in** **2020, except in the South. Natural gas use in electricity ris es, but is offset by declines in other parts of the economy** **. Such results reflect the impacts from applying the EGU control s** **trategy discussed in Chapter 3 of the RIA. This control strategy is applying** **primarily to EGUs in the Northeast and is applied o nly to coal-fired units. This leads to the costs of power generation becoming** **relatively cheaper in the South relative to the Nor theast. 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 perce ntage and physical terms in Table 5b-2. The crude** **oil and petroleum refining industries react to the alternative standard by min** **or changes in output, although refining in some reg ions 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 (trillion** **BTU) by EGUs** **Use of Coal** **Under Modeled** **Control Strategy** **(trillion BTU)** **Percent Difference** **in Coal Use (%)** **Baseline Use of** **Natural Gas** **(trillion BTU) by** **EGUs Use of** **Natural Gas** **Under Modeled** **Control Strategy** **(trillion BTU)** **Percent Difference** **in Natural Gas Use** **(%) Electricity** **Generation in Baseline** **(millions kWh)** **Electricity Generation** **Under Modeled** **Control Strategy** **(millions kWh)** **Percent Difference** **in Electricity** **Generation (%)** **North east** **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 market s 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 sector s show more regional variation, based on**  **differences in production methods and changes in ma nufacturing costs, the majority of the**  **impacts are on the order of a few tenths of one per cent. However, there are measurable impacts**  **in the output of specific industries. Under the modeled cont rol 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 specifi**  **c control strategy assumed in this illustrative analysis. As previously stated, these results represen**  **t 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 signific antly alter these regional effects.**  **Therefore, SIPs will likely be different than the st rategy developed in this RIA and could be**  **designed to alleviate any disproportionate impacts on s ensitive industries. For example, given the**  **impact on glass and cement production, assumed with this sc enario, affected States may design**  **SIP strategies that mitigate the impact on these pa rticular industries, perhaps distributing costs**  **more uniformly among all sectors.**
5b.4.3 Projected Macroeconomic Impact: GDP
**The combination of economic interactions affecting b usiness 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 mea nt to**  **illustrate the social costs associated with the mod eled control strategy applied to attain the 0.070**  **alternate Ozone standard**
**Figure 5b.7 illustrates GDP in the EMPAX-CGE model’s basel ine forecast and the modeled**  **control strategy. As shown, the estimated GDP impac t 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 bil lion 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 alt**  **ernate primary ozone standard such as improvements in labor productivity from environmental imp**  **rovements.**

**21 Redistribution of production will also tend to occur among states in each region, with some**  **states’ increasing output to offset any declines in ne ighboring states.** **5b-23** Figure 5b.7: Change in U.S. GDP Compared to EMPAX-CGE Basel ine

**Source: Department of Energy, Energy Information Ad ministration; EMPAX-CGE** 

5b.4.4 Social Cost Approaches and Estimates

**To provide an estimate of the social costs associated with the modeled control strategy, EMPAX**  **CGE monetizes welfare changes from the general equilibrium simulation using Hicksian**  **equivalent variation (EV), which is related in conc ept 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 pro vide 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, Ch**  **ipman and Moore (1980) showed that**

**22 Hicks introduced this concept into economic theory in his book “Value and Capita**  **l: 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, e**  **tc. It is also essential when modeling interactions between tax interactions and the econo**  **my.** $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) $26,62 5

$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 envi**  **ronmental benefits (e.g., environment, public health, and labor productivity). In addition,**  **these social cost estimates from EMPAX CGE do not incorporate extrapolated costs since these c**  **osts do not have a clear link to specific industries. The general equilibrium model estimates that**  **the relative change in infinite-horizon 25**  **and average annual welfare losses are extremely sma ll (approximately 0.025%). Over the 2005-**  **2020 time horizon used in EMPAX-CGE for this analysis, the so cial costs are 93 percent of the**  **engineering costs for the illustrative modeled contr ol strategy when estimated in present value**  **terms (2006 dollars). 26**  **, 27**  **We estimate social costs using a 5 percent real interest rate t o discount**  **future production and consumption as per EPA guidanc e from the SAB provided in 2003.** 
**We use EV to provide an estimate of social costs in this anal ysis instead of a metric such as GDP**  **since changes in GDP are a poor measure of impacts on consum er welfare. Although GDP is a**  **common metric among policymakers for expressing “co sts to society,” it is a poor measure of**  **“social costs.” GDP as a measure of welfare has been c riticized 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 Levinso**  **n 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, regulation**  **s that reduce fish catch will reduce GDP in the short run, but increase long-run economic prosperit**  **y... Finally, GDP excludes non-traded benefits: environmental quality, health, workplace safe**  **ty...” 28**  **Changes in household consumption are much closer to changes in the welfar**  **e of households (ignoring leisure) than changes in GDP. For example, since consumption is a**  **round two-thirds of GDP, a ballpark estimate might be that any changes in consumption will onl**  **y be around two-thirds as large in dollar terms as changes in GDP. GDP also does not accoun**  **t for the value of leisure, which is accounted for directly in estimates of welfare impac**  **ts using an EV approach as mentioned above. Regarding exports and imports, GDP does account di**  **rectly for the effect of export and**

**24 Chipman, John S., and James C. Moore. 1980. Compens**  **ating 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 est**  **imates to this many time horizons, the model approximates an infinite horizon. Turn to p. 6-9 o**  **f the EMPAX-CGE documentation at http://www.epa.gov/ttnecas1/models/empax_model_docu**  **mentation.pdf for details.** 

**26 It should be noted that we will not compare this so**  **cial cost estimate with the benefits estimates for alternate primary standards presented later in th**  **is RIA. We do not make this comparison for two key reasons: 1) the lack of linkage between air quality changes and effect categorie**  **s 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 f**  **or the modeled control strategy of $2.8 billion (2006$) is calculated using the Equivalent Uniform Annual Cost (EUAC) method.**  **The EUAC method does** not **generate the present value of the annual costs of controls on a year by-year basis from 2005 to 2020.** 

**28 Levinson, Arik. Response to 2004 Draft Report to Co**  **ngress 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 Managemen**  **t 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 accounte d for indirectly through changes**  **in household consumption and does not account for c hanges due to exports. We conclude**  **thatThus, GDP is a poor metric for estimating welfar e impacts in comparison to the EV**  **approach, and therefore social costs. 29** 

**As part of being a dynamic, forward-looking model, EMPAX u ses an interest rate to place a**  **value on the future (including both the benefits of consumpti on and costs of production). We**  **have been using a 5% real interest rate, based on the MI T 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 so**  **cial 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 perce**  **nt, 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 an alysis 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 Ou tput and General Equilibrium Estimates of**  **Embodied CO 2**  **: A Data Set and Static Framework for Assessment.” Universit y 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. Win g, and R.C. Hyman. 2001. “The**  **MIT Emissions Prediction and CO2 Policy Analysis (EPP A) Model: Revisions, Sensitivities, and**  **Comparisons of Results.” MIT Joint Program on the Scien ce and Policy of Global Change,**  **Report No. 71. Available at <http://web.mit.edu/globalch ange/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 Econ omic Review 75(1): 128-138.** 
**Bovenberg, L.A., and L.H. Goulder. 1996. “Optimal Environ mental Taxation in the Presence of**  **Other Taxes: General Equilibrium Analysis.” American Econ omic Review 86(4):985-1000.**  **Available at <http://www.aeaweb.org/aer/>.**

**Brooke, A., D. Kendrick, A. Meeraus, and R. Raman. 1998. GAM S: A User’s Guide. GAMS**  **Development Corporation. Available at http://www.gams.c om.** 

**29 We provide estimates of the changes in GDP in 2020 fr**  **om implementation of the modeled control strategy in Chapter 6, but only to provide informa**  **tion 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 Ta x Burden?” Washington, DC:**  **The Brookings Institute. Available at http://bookst ore.brookings.edu/**  **book_details.asp?product%5Fid=10403.**
**Global Insight. 2007. “U.S. State, Metropolitan Are a, and County-level Forecasting Services.”**  **<http://www.globalinsight.com/ProductsServices/Prod uctDetail1100.htm>** 

**Goulder, L.H., and R.C. Williams. 2003. “The Substa ntial Bias from Ignoring General**  **Equilibrium Effects in Estimating Excess Burden, an d a Practical Solution.” Journal of Political**  **Economy 111:898 927. Available at <http://www.journ als.uchicago.edu/JPE/home.html>** 
**Minnesota IMPLAN Group. 2006. State Level Data for 2004. Ava ilable from**  **http://www.implan.com/index.html.**

**Nestor, D.V., and C.A. Pasurka. 1995. The U.S. Environmenta l Protection Industry: A Proposed**  **Framework for Assessment. U.S. Environmental Protec tion 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 Po licy Analysis (EPPA) Model:**  **Version 4.” MIT Joint Program on the Science and Po licy of Global Change, Report No. 125.**  **Cambridge, MA. <http://web.mit.edu/globalchange/www/epp a.html>.** 
**Rutherford, T.F. 1999. “Applied General Equilibrium Mode ling 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/solver s/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 Administra tion. Undated (a). State Energy Data**  **Report. Washington DC. Available at http://www.eia.doe.go v/emeu/states/ _use_multistate.html.** 
**U.S. Department of Energy, Energy Information Administra tion. 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 Administrat ion. 2003. Manufacturing Energy**  **Consumption Survey 2002. Washington DC. Available a t http://www.eia.doe.gov/emeu/mecs/.** **5b-27**

**U.S. Department of Energy, Energy Information Admin istration. January 2007. Annual Energy**  **Outlook 2007. DOE/EIA-0383(2007). Washington, DC. Ava ilable 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 al ternative ozone standard of 0.075 ppm for the 4 th highest maximum 8-hour ozone concentration. This n umber 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 Ch apter 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) usi ng 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 standard s. Thus, for some areas, the benefits computed during this first stage only represented partial atta inment. In the second stage, we estimated the benefits of fully attaining the standards in all areas by usi ng a “rollback” method. This method reduced ozone concentrations at nonattaining monito rs to a level that would just meet the standards. To estimate the benefits for the 0.075 ppm and 0.0 79 standards, we deviated from this two-stage approach. Instead, we used an interpolatio n 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 epidemio logical 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 o f the association between ozone and premature mortality. This analysis presents four indepe ndent 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 wit hin each study. Recognizing that additional research is necessary to clarify the underlying mechanism s causing these effects, we also consider the possibility that the observed associatio ns between ozone and mortality may not be causal in nature. Using the National Morbidity, Mor tality and Air Pollution Study (NMMAPS), which was used as the primary basis for the risk an alysis 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 level s to meet a standard of 0.070 ppm. When added to the other projected benefits from red uced ozone, including 3,000 hospital and

1 All subsequent estimates of full attainment ozone benefit s and PM 2.5 co-benefits found in this chapter exclude these two areas of California.

2 Health impact functions measure the change in a health endp oint of interest, such as hospital admissions, for a given change in ambient ozone or PM con centration6-2

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 b etween 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 NM MAPS ozone mortality study resulted in 71 premature deaths avoided and total monetized benefi ts of $620 million/yr, incremental to attainment of the 0.08 ppm standard. Using the thre e 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. Al ternatively, if there is no causal relationship between ozone and mortality, avoided premature deaths assoc iated with reduced ozone exposure would be zero and total monetized ozone-related morb idity 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 bene fits of $220 million/yr, incremental to attainment of the 0.08 ppm standard. Using the thre e 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. Alternati vely, if there is no causal relationship between ozone and mortality, avoided premature deaths assoc iated with reduced ozone exposure would be zero and total monetized ozone-related morb idity benefits would be $28 million/yr.

For a more stringent standard of 0.065 ppm, using the NMMAPS ozo ne 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 thre e 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. A lternatively, if there is no causal relationship between ozone and mortality, avoided pre mature deaths associated with reduced ozone exposure would be zero and total monetized ozo ne-related morbidity benefits would be $420 million/yr.

These estimates reflect EPA’s interim approach to c haracterizing 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 uncert ainty in the relationship between ozone exposure and premature mortality in the context of quantifying benefits associated with alternative ozone control strategies. We expect to rec eive this advice later this spring.
The monetary benefits of visibility improvements from PM 2.5 reductions associated with from the 0.070 modeled attainment strategy in selected federal Cla ss I Areas in 2020 is $160 million/yr.

In addition to the direct benefits from reducing oz one, attainment of the standards would likely result in additional health and welfare benefits be cause reducing the ozone precursors NOx and VOC will also reduce PM 2.5 . Using both modeled and extrapolated reductions in these precursor emissions, we estimated PM-related co-benefits for the four alternative standards. For each6-3

alternative standard, we provide a range of estimat ed benefits based on several different PM mortality effect estimates. These effect estimates were de rived 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, incre mental to attainment of the 0.08 ppm standard, we estimate total ozone and PM 2.5 -related co-benefits to be between $3.6 and $16 billion/yr; this range encompasses the expert functi ons and the ozone mortality functions as well as the possibility that there is no causal relationship betw een ozone and mortality.
For the 2020 attainment of the 0.079 ppm alternative, incre mental to attainment of the 0.08 ppm standard, we estimate total ozone and PM 2.5 -related co-benefits to be between $2 and $11 billion/yr; this range encompasses the expert functi ons and the ozone mortality functions as well as the possibility that there is no causal relationship betw een ozone and mortality.
For the 2020 attainment of the 0.070 ppm alternative, incre mental to attainment of the 0.08 ppm standard, we estimate total ozone and PM 2.5 -related co-benefits to be between $6.5 and $27 billion/yr (3% and 7% discount rates, 2006$); this range en compasses the expert functions and the ozone mortality functions as well as the possibi lity that there is no causal relationship between ozone and mortality.
For the 2020 attainment of the 0.065 ppm alternative, incre mental to attainment of the 0.08 ppm standard, we estimate total ozone and PM 2.5 -related co-benefits of between $11 and $42 billion/yr; this range encompasses the expert functi ons and the ozone mortality functions as well as the possibility that there is no causal relationship betw een 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 th ose 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 (s pecific 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 anal ysis largely builds on both the analytical approach used in the 2006 PM NAAQS RIA and the analysis of o zone 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 conven tions. 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 N ational 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 ben efits of fully attaining each alternative standard. In the first stage, we estimated the bene fits 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 w ith 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 attain ing 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 standa rds (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 st udies and economic valuation parameters for those health effects. A quantitative a ssessment of non-health benefits (e.g., benefits from reduced ozone-related crop damage) was beyon d 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 p recursor 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. Spe cifically, 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 bene fits as the 2007 Ozone RIA (U.S. EPA, 2007).

All estimates of ozone benefits and PM 2.5 co-benefits in this chapter are incremental to a ba seline 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 PM 2.5 state implementation plans. Moreover, the PM 2.5 co-benefits are incremental to an assumption of full attain ment of the 2006 PM 2.5 NAAQS. See Chapter 3 for a complete discussion of the base line. The PM co-benefits presented in this chapter are incremental to the PM benefits estimate d 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 Basi ns. Attainment dates will be determined in the future through the SIP process based on criteria in the CAA, future air quality data, an d 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 He alth Perspectives 113:73–82. U.S. EPA. 2000. Guidelines for Preparing Economic Analyses

. http://yosemite1.epa.gov/ee/epa/eed.nsf/webpages/Gu idelines.html/$file/Guidelines.pdf

6 U.S. EPA. 2000. Guidelines for Preparing Economic Analyses. http://yosemite1.epa.gov/ee/epa/eed.nsf/webpages/Gu idelines.html/$file/Guidelines.pdf

7 The PM 2.5 benefits presented below reflect the NOx emission reductions from the ozone control strategy. Reductions from Ocean-Going Vessels burning re sidual diesel fuel were included both East and West in the baseline PM co-benefits, but no t included in the ozone baseline for the west. See chapter 3 for more details of this rule a nd 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 Jo aquin 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 Califo rnia, we are assuming a new standard attainment date of 2030. Estimates of the costs and benefit s of attaining the 0.075 ppm standard and the alternate air quality standards for these two area s in 2030 not included in the primary benefit analysis and are provided in Appendix 7b.
For purposes of this analysis, we assume attainment by 202 0 for all areas except San Joaquin Valley and South Coast air basins in California. Th e 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 purpos es that the San Joaquin Valley and South Coast air basin would attain a new standard i n 2030. There are many uncertainties associated with the year 2030 analysis. Between 2020 a nd 2030 several federal air quality rules are likely to further reduce emissions of NOx and V OC, such as, but not limited to National rules for Diesel Locomotives, Diesel Marine Vessels, and Small N onroad 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 m odeling 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 str ategies with associated costs and benefits. In order to provide a rough approximation of the co sts and benefits of attaining 0.075 ppm and the alternate standards in San Joaquin and South Coast air ba sins, 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 s upplemental 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 Valle y and South Coast air basins, see Appendix 7b.3
The remainder of this chapter describes the data and method s used in this analysis, along with the results. Appendix 6a of this RIA provides addit ional details of the analysis. Section 6.2 discusses the probabilistic framework for the benefits ana lysis and how key uncertainties are addressed in the analysis. Section 6.3 discusses the li terature on ozone- and PM-related health effects and describes the specific set of health im pact 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 pr esents the results and implications of the analysis.

6.2 Characterizing Uncertainty: Moving Toward a Probabili stic Framework for Benefits Assessment
The National Research Council (NRC) (2002) highlight ed the need for EPA to conduct rigorous quantitative analysis of uncertainty in its benefit s estimates and to present these estimates to decision makers in ways that foster an appropriate apprecia tion of their inherent uncertainty. In response to these comments, EPA’s Office of Air and Ra diation (OAR) is developing a comprehensive strategy for characterizing the aggrega te impact of uncertainty in key modeling6-6

elements on both health incidence and benefits esti mates. Components of that strategy include emissions modeling, air quality modeling, health effec ts incidence estimation, and valuation.
Two aspects of OAR’s strategy have been used in seve ral recent RIAs and are also employed in this analysis. 8 , 9 , 10 First, we used Monte Carlo methods for estimating character izing random sampling error associated with the concentration response functions from epidemiological studies and economic valuation functions. Monte Car lo simulation uses random sampling from distributions of parameters to characterize the effects of uncertainty on output vari ables, such as incidence of premature mortality. Specifically, we used Mo nte Carlo methods to generate confidence intervals around the estimated health impact an d dollar benefits. The reported standard errors in the epidemiological studies deter mined the distributions for individual effect estimates. Table 6.4 describes the distributions fo r unit values.

Second, because characterization of random statistic al 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 am bient PM 2.5 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 kno wledge 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 us ed to estimate avoided premature deaths associated with reductions in ozone have associated standard errors that re present the statistical errors around the effect estimates in the underlyin g epidemiological studies. 12 In our results, we report credible intervals based on these standard e rrors, reflecting the uncertainty in the estimated change in incidence of avoided premature deaths. We also pro vide multiple estimates, to reflect model uncertainty between alternative study designs. In ad dition, 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 Regu latory Analysis: Control of Emissions from Nonroad Diesel Engines. EPA420-R-04-007. Pr epared 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 I mpact 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. Avai lable at: http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%20 5--Benefits.pdf

11 Expert elicitation is a formal, highly structured and wel l documented process whereby expert judgments, usually of multiple experts, are obtaine d (Ayyb, 2002).

12 Health impact functions measure the change in a health endp oint 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 mor tality 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 sever al empirical estimates of premature deaths avoided, and a set of twelve estim ates based on results of the expert elicitation study. 13 Even these multiple characterizations, including confiden ce intervals, omit the contribution to overall uncertainty of uncertainty i n air quality changes, baseline incidence rates, populations exposed and transferability of the effe ct estimate to diverse locations. Furthermore, the approach presented here does not yet include met hods for addressing correlation between input parameters and the identification of reasonable upper and lower bounds for input distributions characterizing uncertainty in additional m odel elements. As a result, the reported confidence intervals and range of estimates give an i ncomplete picture about the overall uncertainty in the estimates. This information shou ld 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 hea lth endpoint of interest, such as hospital admissions, for a given change in ambient ozone or PM conc entration. Health impact functions are derived from primary epidemiology studies, meta-anal yses of multiple epidemiology studies, or expert elicitations. A standard health impact fun ction 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 af fected 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 e y y '

E '

where y 0 is the baseline incidence (the product of the base line 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, bu t the basic elements remain the same. Chapter 3 described the ozone and PM air quality in puts 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 baselin e incidence rates.

13 Industrial Economics, Inc. 2006. Expanded Expert Ju dgment Assessment of the ConcentrationResponse Relationship Between PM 2.5 Exposure and Mortality. Prepared for EPA Office of Air Quality Planning and Standards, September. Availabl e at: http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ ee_report.pdf6-8

6.3.1 Potentially Affected Populations
The starting point for estimating the size of poten tially affected populations is the 2000 U.S. Census block level dataset (Geolytics 2002). Benefi ts Modeling and Analysis Program (BenMAP) incorporates 250 age/gender/race categories to match specific populations potentially affected by ozone and other air pollutants. The sof tware constructs specific populations matching the populations in each epidemiological study by ac cessing the appropriate age-specific populations from the overall population database. Be nMAP 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 risk s. 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 t his 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; re spiratory illness-related school absence; hospital and emergency room visits for asthma and o ther 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 ad missions 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 hum an health and welfare effects of pollutants affected by the alternative standards. T able 6.2 lists the health endpoints included in this analysis.
In order to select appropriate epidemiological stud ies to use for our effect estimates, we applied several criteria to determine the set of studies th at is likely to provide the best estimates of effec ts 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 c o-pollutants, we gave preference to effect

14 U.S. Environmental Protection Agency, 2005. Regula tory 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%20 5--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 m ay also contribute significant monetized benefits. Potential welfare benefits associated wit h ozone exposure include increased outdoor worker productivity; increased yields for commercia l and non-commercial crops; increased commercial forest productivity; reduced damage to u rban ornamental plants; increased recreational demand for undamaged forest aesthetics; and reduced damage to ecosystem functions (U.S. EPA 1999, 2006). Although we estimat e 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 Ozon e and PM 2.5

Pollutant/Effect Quantified and Monetized in Base Estimates a Unquantified Effects h —Changes in: PM/Health b Premature mortality based on both cohort

study estimates and on expert elicitation c,d Bronchitis: chronic and acute Hospital admissions: respiratory and cardiovascular Emergency room visits for asthma Nonfatal heart attacks (myocardial infarction) Lower and upper respiratory illness Minor restricted-activity days Work loss days Asthma exacerbations (asthmatic population) Respiratory symptoms (asthmatic population) Infant mortality Subchronic bronchitis cases Low birth weight Pulmonary function Chronic respiratory diseases other than chronic bronchitis Non-asthma respiratory emergency room visits UVb exposure (+/-) e

PM/Welfare Visibility in Southeastern, southwestern and

California Class I areas Visibility in northeastern and Midwestern Class I areas Household soiling Visibility in residential and non-Class I

areas UVb exposure (+/-) e Ozone/Health f

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 t otal monetized benefits of the alternative standards. b In addition to primary economic endpoints, there a re a number of biological responses that have been a ssociated 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 quantifi ed endpoints.

c Cohort estimates are designed to examine the effec ts 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 th is issue). d While some of the effects of short-term exposure a re likely to be captured by the cohort estimates, th ere may be additional premature mortality from short-term PM exposure not captured in the cohort estimates included in the p rimary analysis.

e May result in benefits or disbenefits. Appendix 6d includes a sensitivity analysis that partially qua ntifies this endpoint. This analysis was performed for the purposes of this RIA only. f In addition to primary economic endpoints, there a re a number of biological responses that have been a ssociated with ozone health including increased airway responsiveness to stimuli, inflammation in the lung, acute inflammat ion and respiratory cell damage, and increased susceptibility to respiratory infection. The public health impact of these biolo gical responses may be partly represented by our quantified endpoints.
g The categorization of unquantified toxic health an d welfare effects is not exhaustive. h Health endpoints in the unquantified benefits column include both a) those for whic h there is not consensus on causality and b) those for which causality has been determined but e mpirical data are not available to allow calculation of benefits.6-11

Table 6.2: Ozone and PM Related Health Endpoints Ba sis for the Concentration-Response Function Associated with that Endpoint, and Sub-Populatio ns for which They Were Computed Endpoint Pollutant Study Study Population Premature Mortality Premature mortality—dailytime 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 PM 2.5 (annual

avg) Pope et al. (2002)

Laden et al. (2006) >29 years >25 years

Premature mortality, total
exposures PM 2.5 (annual

avg) Expert Elicitation (IEc, 2006) >24 years

Prematuremortality—allcause PM 2.5 (annual

avg) Woodruff et al. (1997) Infant (<1 year)

Chronic Illness Chronic bronchitis PM 2.5 (annual

avg) Abbey et al. (1995) >26 years

Nonfatal heart attacks PM 2.5 (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 PM 2.5 (24-hour avg) Pooled estimate:

Moolgavkar (2003)—ICD 490–496 (COPD) Ito (2003)—ICD 490–496 (COPD) >64 years

PM 2.5 (24-hour avg) Moolgavkar (2000)—ICD 490–496 (COPD) 20–64 years

PM 2.5 (24-hour avg) Ito (2003)—ICD 480–486 (pneumonia) >64 years Respiratory

PM 2.5 (24-hour

avg) Sheppard (2003)—ICD 493 (asthma) <65 years

PM 2.5 (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) >64 years Cardiovascular

PM 2.5 (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 years6-12

Endpoint Pollutant Study Study Population Peel et al (2005) Wilson et al (2005) All ages All ages Asthma-related ER visits (con’t) PM 2.5 (24-hour

avg) Norris et al. (1999) 0–18 years

Other Health Endpoints Acute bronchitis PM 2.5 (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 PM 2.5 (24-hour

avg) Schwartz and Neas (2000) 7–14 years

Asthma exacerbations PM 2.5 (24-hour

avg) Pooled estimate:

Ostro et al. (2001) (cough, wheeze and shortness of breath) Vedal et al. (1998) (cough) 6–18 years a

Work loss days PM 2.5 (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 years b

O3 (24-hour avg) Ostro and Rothschild (1989) 18–65 years Minor Restricted Activity Days (MRADs) PM 2.5 (24-hour

avg) Ostro and Rothschild (1989) 18–65 years

a The original study populations were 8 to 13 for th e Ostro et al. (2001) study and 6 to 13 for the Ved al 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. Se e: 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 A ir Pollution Regulations . Washington, DC: The National Academies Press.

b Gilliland et al. (2001) studied children aged 9 an d 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 c hildren 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 Crit eria Document states, “Consistent with observed ozone-related increases in respiratory- an d cardiovascular-related morbidity, several newer multi-city studies, single-city studies, and several meta-analyses of these studies have provided relatively strong epidemiologic evidence f or 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 eviden ce suggestive of plausible pathways by which risk of respiratory or cardiovascular morbidi ty 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 morta lity, 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 conclu sion 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 Hou ston and the Huang et al. (2004) analysis in Los Angeles. 17 This conclusion is also based on recent meta-analy ses by Bell et al. (2005), Ito et al. (2005), and Levy et al. (2005), and a new analy sis 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-a nalyses, 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 rai sed questions about the implications of these time-seri es 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 othe r 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 confident ly attribute observed effects to individual pollutants” (id.). They note that “not only is the i nterpretation 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 ar e often part of a large and highly correlated mix of pollutants, only a very few of which are mea sured” (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 inclu sion 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, 2 007. Review of the National Ambient Air Quality Standards for Ozone: Policy Assessment of S cientific and Technical Information. Prepared by the Office of Air and Radiation. Availa ble at http://www.epa.gov/ttn/naaqs/standards/ozone/data/2 007_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 Infor mation, we included ozone mortality in the primary health effects analysis, with the recogniti on that the exact magnitude of the effects estimate is subject to continuing uncertainty. We u sed 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 b e zero. EPA expects to receive advice from the National Academy of Sciences on how best to quantif y uncertainty in the relationship between ozone exposure and premature mortality in the context of quantifying benefits associ ated with alternative ozone control strategies later this spring.
We estimate the change in mortality incidence and e stimated credible interval 18 resulting from application of the effect estimate from each study and present them separately to reflect differences in the study designs and assumptions ab out causality. However, it is important to note that this procedure only captures the uncertai nty 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 se ries 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 th at the majority of the people tend to be outdoors during the daylight hours and concentratio ns 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 t he current ozone NAAQS. Together, this means that the most biologically relevant metric, an d the one used in the ozone NAAQS since 1997 is the 8-hour maximum standard. Thus, although our analysis at proposal calculated im pact functions based on either the 24 hour average or 1- hour maximum ozone levels originally reported in the epidemiogical studies, for the fina l rule analysis, we have converted ozone mortality health impact functions that use a 24-hou r average or 1-hour maximum ozone metric to maximum 8-hour average ozone concentration using st andard 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 n ot affect the relative magnitude of the health impact function. An equivalent change in the 24-hour average, 1-hour maximum and 8-hou r maximum will provide the same overall change in inc idence 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 im pact effect estimate based on the 1-hour maximum should be half that of the effect es timate 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 int erval used in Bayesian statistics, which is similar to a confidence interval used in frequentis t statistics.6-15

In EPA’s risk analysis for the ozone NAAQS rule, mo rtality 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 s tudies. 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 conver sion 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-ana lysis 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 o n national data. However, these conversions were not specific to the ozone “warm” se ason which was the period used in the health risk assessment. As such we were able to con vert the national C-R function parameters from daily average to 8-hour average, albeit with t he 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 inap propriate to use national conversion ratios to adjust C-R functions for individual cities.
6.3.2.2 Respiratory Hospital Admissions Effect Esti mates
Detailed hospital admission and discharge records p rovide data for an extensive body of literature examining the relationship between hospi tal admissions and air pollution. This is especially true for the portion of the population aged 65 and older, because o f the availability of detailed Medicare records. In addition, there is on e 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 admi ssion endpoints. Pooling is the process by which multiple study results may be combined in ord er 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 wi th changes in ambient ozone concentrations for adults over 65, we first estimated the change in ho spital admissions for each of the different effects categories that each study provided for eac h city. These cities included Minneapolis, Detroit, Tacoma and New Haven. To estimate total re spiratory 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 ef fect estimates the Moolgavkar study reported for Minneapo lis (Moolgavkar et al., 1997). To estimate total respiratory hospital admissions for Minneapol is using the Schwartz study (1994a), we simply estimated pneumonia hospital admissions base d 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 th e possibility that there is no relationship between ozone and COPD, reflecting the equivocal ev idence represented by the different studies. We then used a fixed-effects pooling procedure to c ombine the two total respiratory hospital admission estimates for Minneapolis. Finally, we us ed random effects pooling to combine the

19 Abt Associates, Incorporated. Environmental Benefi ts Mapping and Analysis Program, Technical Appendices. May 2005. pp. I-36-16

results for Minneapolis and Detroit with results fr om studies in Tacoma and New Haven from Schwartz (1995). As noted above, this pooling approach incorporates bo th the precision of the individual effect estimates and between-study variab ility 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 concentr ation-response functions we used to estimate the effects of ozone exposure on asthma-related eme rgency room (ER) visits: Peel et al. (2005); Wilson et al. (2005); and Jaffe et al. (2003). We e stimated 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 fi ve to 34 in the Ohio cities of Cleveland, Columbus and Cincinnati from 1991 through 1996. In s ingle-pollutant Poisson regression models, ozone was linked to asthma visits. We use t he 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 200 0. 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 m oving 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 1996–2 000. For all models used in the analysis, the authors restricted the ozone data inc orporated into the model to the months ozone levels are usually measured, the spring-summer mont hs (April through September). Using the generalized additive model, Wilson et al. (2005) fou nd a significant association between the maximum daily 8-hour average ozone level and ER vis its 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 poo ling 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 l evel, and thus, the confidence interval for the pooled incidence estima te based on these studies includes negative values. This is an artifact of the statistical powe r 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 visi ts and most asthma ER visits do not result in a hospital admission.
6.3.2.4 Minor Restricted Activity Days Effects Esti mate
Minor restricted activity days (MRADs) occur when i ndividuals reduce most usual daily activities and replace them with less-strenuous activities or rest, but d o not miss work or school. We estimated the effect of ozone exposure on MRADs using a concentration-response fun ction derived from Ostro and Rothschild (1989). These res earchers estimated the impact of ozone and PM 2.5 on MRAD incidence in a national sample of the adul t working population (ages 18 to 65) living in metropolitan areas. We developed separate coefficients for each year of the Ostro and6-17

Rothschild analysis (1976–1981), which we then comb ined 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 s tudies have found a significant association between ozone levels and school absence rates. We u se two studies (Gilliland et al., 2001; Chen et al., 2000) to estimate changes in school absence s resulting from changes in ozone levels. The Gilliland et al. study estimated the incidence of n ew periods of absence, while the Chen et al. study examined daily absence rates. We converted th e Gilliland et al. estimate to days of absence by multiplying the absence periods by the average d uration of an absence. We estimated 1.6 days as the average duration of a school absence, the re sult 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 G illiland et al. period of absence is converted into 1.6 absence days.
Following recent advice from the National Research Council (2002), we calculated reductio ns in school absences for the full population of school age children, ages five to 17. This is consist ent with recent peer-reviewed literature on estimating the impact of ozone exposure on school absences (Hall et al. 2003). We estimated the chang e in school absences using both Chen et al. (2000) and Gilliland et al. (2001) and then, simila r to hospital admissions and ER visits, pooled the results using the random effects pooling proced ure.
6.3.2.6 Outdoor Worker Productivity

To monetize benefits associated with increased work er 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 produ ctivity of outdoor citrus workers. The study measured productivity impacts. Worker product ivity is measuring the value of the loss in productivity for a worker who is at work on a parti cular day, but due to ozone, cannot work as hard. It only applies to outdoor workers, like frui t and vegetable pickers, or construction workers. Here, productivity impacts are measured as the chan ge in income associated with a change in ozone exposure, given as the elasticity of income w ith respect to ozone concentration. The reported elasticity translates a ten percent reduct ion in ozone to a 1.4 percent increase in income. Given the national median daily income for outdoor workers engaged in strenuo us 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 u sing 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 PM 2.5 caused by the reduction in emissions associated wit h the alternative standards will change the level of visibility throughout the Uni ted States. Increases in PM concentrations cause increases in light extincti on, a measure of how much the components of the atmosphere absorb light. This chapter contains a n estimate of the monetized benefits of improved visibility associated with the simulated e mission control strategy to attain the 0.070 ppm ozone standard. The methodology we followed to estimate changes in visibility benefits is consistent with the PM 2.5 RIA (EPA, 2006), which is described on page 5-60 o f that document.
6.3.2.8 Other Unquantified Effects

Direct Ozone Effects on Vegetation. The Ozone Criteria Document notes that “current amb ient 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 som e 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 decis ions, yield response functions, and the supply of and demand for agricul tural products. The resulting welfare measures are based on predicted changes in market prices and production costs. Models a lso exist to measure benefits to silvicultural producer s and consumers. There is considerable uncertainty, however, in such estimates, including t he fact that the extensive management of agricultural crops may mitigate the potential O3-re lated 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 hig hly managed the potential benefits of alternative O3 st andards are uncertain. Because of these uncertainties and resource limitations, we are unab le to provide benefits estimates for the commercial production of agricultural and silvacult ure commodities.
An additional welfare benefit of reducing ambient o zone concentrations is the economic value of reduced aesthetic injury to forests. There is suffi cient 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 econo mic sectors. However, the absence of adequate exposure- response functions and economic damage functions for the potential range of effects releva nt to these types of vegetation precludes us from quantifying these direct economic benefits. The far m 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 valua tion methods are not available to allow for6-19

plausible estimates of the percentage of these expe nditures 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) concentration s of dissolved oxygen in bottom waters, the loss of submerged aquatic vegetation due to the lig ht-filtering effect of thick algal mats, and fundamental shifts in phytoplankton community structure (Bricker et al., 1 999). A recent study found that for the period 1990–2002, atmospheric de position accounted for 17 percent of nitrate loadings in the Gulf of Mexico, where severe hypoxi c zones have been existed over the last two decades (Booth and Campbell, 2007). 20

Reductions in atmospheric deposition of NOx are exp ected to reduce the adverse impacts associated with nitrogen deposition to estuarine an d 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 t he availability of these functions and on estimates of the value of environmental responses. B ecause neither appropriate functions nor sufficient information to estimate the marginal val ue 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 NO X emissions will also reduce nitrogen deposition on agricultural land and forests. There is some evidence that nitrogen d eposition 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 th e use of purchased fertilizers are not available, but it is likely that the overall value is very sma ll relative to other health and welfare effects. Th e share of nitrogen requirements provided by this dep osition 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 abund ance of on-farm nitrogen production, primarily from animal manure. In these areas, reduc tions in atmospheric deposition of nitrogen from PM represent additional agricultural benefits.
Information on the effects of changes in passive ni trogen deposition on forests and other terrestrial ecosystems is very limited. The multipl icity of factors affecting forests, including other potential stressors such as ozone, and limiting fac tors 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. Env iron. Sci. Technol.; 2007; ASAP Web Release Date: 20-Jun-2007; (Article) DOI: 10.1021/e s070179e6-20

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 ec osystems 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 occ ur such as soil acidification, which can lead to leaching of nutrients needed for plant growth and m obilization of harmful elements such as aluminum. Increased soil acidification is also link ed 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 ultrav iolet radiation from the sun called UV-B, thus providing a protective sh ield 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 gr ound level (referred to as tropospheric ozone) (NAS, 1991; NASA). Only a portion of the tropospher ic fraction of UV-B shielding is from anthropogenic sources (e.g., power plants, byproduc ts of combustion). The portion of ground level ozone associated with anthropogenic sources va ries 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 ef fects include fatal and nonfatal melanoma and non-melanoma skin cancers and cataracts. The va lues 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 str atospheric 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 ef fects including damage to crops and forest. For a more complete listing of quantified and unquantified UV-B radiation effects, se e 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 co ntext of stratospheric ozone in a 2006 report by ICF Consulting, prepared for the U.S. EPA.
There are many factors that influence UV-B radiatio n 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 al titude 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 t he Ozone NAAQS. Such an assessment of UVB related health effects would also need to take int o account human habits, such as outdoor activities (including age- and occupation-related e xposure 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 deposi tion 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 regar ding 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 expo sures related to various adverse health outcomes (e.g., damage to the skin, including skin cancer; dam age to the eye, such as cataracts; and immune system suppression); wavelength dependency o f biological responses; and interindividual variability in UV-B resistance to s uch health outcomes. Beyond these wellrecognized adverse health effects associated with v arious 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 defici ency can cause metabolic bone disease among children and adu lts, 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 n egative and positive effects. However, as with other impacts of UVB on human health, this ben eficial 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 expo sures. This analysis is based on the air quality modeling conducted for the proposed Ozone N AAQS RIA, and is described in Appendix 6d to the this RIA. The screening analysis has been peer-reviewed and a summary of the p eerreview 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 couple d through atmospheric processes. Ozone, itself, is a major greenhouse gas and climate directly infl uences ambient concentrations of ozone.
The concentration of tropospheric ozone has increas ed 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 stated 24 that regulations targeting ozone precursors would have combined benefits for public health and climate. As noted in the OAQPS St aff 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 informatio n 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 o f Climate Change: Expanding the Concept and Addressing Uncertainties,” October 2005.6-22

Climate change can affect tropospheric ozone by mod ifying emissions of precursors, chemistry, transport and removal. 25 Climate change affects the sources of ozone precur sors through physical response (lightning), biological response (soils, v egetation, and biomass burning) and human response (energy generation, land use, and agricult ure). Increases in regional ozone pollution are expected due to higher temperatures and weaker circ ulation. 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 report 26 that projects, with “virtual certainty,” declining air q uality in cities due to warmer and fewer cold days and nights and/or warmer/more frequent hot day s 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 conce ntrations of ground level ozone related to climate change.
The IPCC also reports 27 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 abili ty to reproduce the changes in ozone associated with perturbations of emissions or climate. There are major discrepancies wi th observed long-term trends in ozone concentrations o ver the 20th century, including after 1970 when the reliability of observed ozone trends is hi gh. 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) cl imate, focusing on fine particles and ozone. The research builds first on an assessment of chang es in US air quality due to climate change, which includes direct meteorological impacts on atm ospheric chemistry and transport and the effect of temperature changes on air pollution emis sions. Further research will result in an assessment that adds the emission impacts from techn ology, land use, demographic changes, and air quality regulations to construct plausible scen arios of US air quality 50 years into the future. As noted in the Staff Paper, results from these eff orts are expected to be available for consideration in the next review of the ozone NAAQS.

25 Denman, K.L., G. Brasseur, A. Chidthaisong, P. Ciai s, P.M. Cox, R.E. Dickinson, D. Hauglustaine, C. Heinze, E. Holland, D. Jacob, U. L ohmann, S Ramachandran, P.L. da Silva Dias, S.C. Wofsy and X. Zhang, 2007: Couplings Betw een Changes in the Climate System and Biogeochemistry. In: Climate Change 2007: The Physical Science Basis. Co ntribution 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. Marqui s, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Ca mbridge, 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 chan ges to the relative risk of a health effect, rather than estimating the absolut e number of avoided cases. For example, a typical result might be that a 10 ppb decrease in d aily ozone levels might, in turn, decrease hospital admissions by 3 percent. The baseline inci dence 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 i n the assessment location, as it corresponds to baseline pollutant levels in that location. To deri ve 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 inciden ce rates and provides average incidence rates for the endpoints included in the analysis. F or both baseline incidence and prevalence data, we used age-specific rates where available. We appl ied concentration-response functions to individual age groups and then summed over the rele vant 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 possibl e, 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 info rmation comes from the studies themselves; in these cases, incidence in the study population is a ssumed to represent typical incidence at the national level. Regional incidence rates are availab le for hospital admissions, and county-level data are available for premature mortality. We have projected mortality rates such th at 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 polluti on generally lower the risk of future adverse health effects for a large population. Therefore, t he 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 reduc tion in air pollution. We converted those to units of avoided statistical incidence for ease of presentation. We calculated the value of avoided statistical inciden ces by dividing individual WTP for a risk reduction by the related observed change in risk. F or 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 th is 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 year d by Age Group

Endpoint Source Notes <18 18– 24 25– 34 35– 44 45– 54 55– 64 65+ Mortality CDC Compressed

Mortality File, accessed through CDC Wonder (1996–1998) nonaccidental 0.025 0.022 0.057 0.150 0.383 1.006 4.937

Respiratory Hospital Admissions 1999 NHDS public use

data files b incidence 0.043 0.084 0.206 0.678 1.926 4.389 11.62 9

Asthma ER visits 2000 NHAMCS public

use data files c ; 1999 NHDS public use data files b 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 t he national surveys conducted by the National Cente r for Health Statistics: HIS refers to the National H ealth Interview Survey; NHDS—National Hospital Discharge Survey; NHAMCS—National Hospital Ambulato ry Medical Care Survey.

b See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datase ts/NHDS/

c See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datase ts/NHAMCS/

d All of the rates reported here are population-weig hted 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 mit igating the effect as a primary estimate. These cost-of-illness (COI) estimates generally understat e the true value of reducing the risk of a health effect, because they reflect the direct expenditure s 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 dol lars, adjusted for growth in real income out to 2020 using projections provided by Standard and Poo r’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 co nducted in the late 1980s and early 1990s. Because real income has grown since the studies wer e conducted, people’s willingness to pay for reductions in the risk of premature death and disea se likely has grown as well. We did not adjust cost of illness-based values because they are based on current costs. Similarly, we did n ot adjust the value of school absences, because that value is based on current wage rates. Table 6.4 presents the values for individual endpoints adjust ed 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 ri sk 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 peo ple. The VSL approach applies information from several published value-of-life studies to det ermine a reasonable monetary value of preventing premature mortality. The mean value of a voiding 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 detai ls on the calculation of VSL).
6.4.2 Hospital Admissions Valuation

In the absence of estimates of societal WTP to avoi d hospital visits/admissions for specific illnesses, estimates of total cost of illness (tota l 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 willingne ss-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 o n statistics provided by the Agency for Healthcare Research and Quality (AHRQ 2000). We est imated the opportunity cost of a day spent in the hospital as the value of the lost dail y wage, regardless of whether the hospitalized individual is in the workforce. To estimate the los t 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-il lness estimate for an ICD code-specific hospital stay lasting n days, then, was the mean hospital charge plus $109 multiplied by n.6-26Table 6.4: Unit Values for Economic Valuation of Health Endpoints (2006$)Central Estimate of ValuePer Statistical IncidenceHealth Endpoint1990 IncomeLevel2020 IncomeLevel Derivation of Distributions of EstimatesPremature Mortality(Value of a StatisticalLife)$6,600,000 $7,900,000 Point estimate is the mean ofa 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: $1million 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 andAldy (2003) meta-analysis. The mean of the distribution is consistent with the mean estimate from athird meta-analysis (Kochi et al 2006). The VSL represents the value of a small change in mortalityrisk aggregated over the affected population.Chronic Bronchitis(CB)$410,000 $500,000 The WTP to avoid a case of pollution-related CB is calculated asWTP WTP exx 1313**( )

E, where x isthe severity of an average CB case, WTP13 is the WTP for a severe case of CB, andҚis theparameter relating WTP to severity, based on the regression results reported in Krupnick andCropper (1992). The distribution of WTP for an average severity-level case of CB was generated byMonte Carlo methods, drawing from each of three distributions: (1) WTP to avoid a severe case ofCB is assigned a 1/9 probability of being each of the first nine deciles of the distribution of WTPresponses in Viscusi et al. (1991); (2) the severity of a pollution-related case of CB (relative to thecase described in the Viscusi study) is assumed tohave a triangular distribution, with the mostlikely value at severity level 6.5 and endpoints at1.0 and 12.0; and (3) the constant in the elasticityof 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 isdescribed in detail in the Costs and Benefits of the Clean Air Act, 1990 to 2010 (EPA, 1999).Nonfatal MyocardialInfarction (heartattack)3% discount rateAge 0–24Age 25–44Age 45–54Age 55–65Age 66 and over7% discount rateAge 0–24Age 25–44Age 45–54Age 55–65Age 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$77,769No distributional information available. Age-specific cost-of-illness values reflect lost earnings anddirect medical costs over a 5-year period followinga nonfatal MI. Lost earnings estimates are basedon Cropper and Krupnick (1990). Direct medical costs are based on simple average of estimatesfrom 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 $7,85545–54 $12,932 $11,57855–65 $74,746 $66,920Direct medical expenses: An average of:1. Wittels et al. (1990) ($102,658—no discounting)2. Russell et al. (1998), 5-year period ($22,331 at3% discount rate; $21,113 at 7% discount rate)6-27Central Estimate of ValuePer Statistical IncidenceHealth Endpoint1990 IncomeLevel2020 IncomeLevel Derivation of Distributions of EstimatesHospital AdmissionsChronicObstructivePulmonary Disease(COPD)$16,606 $16,606No 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 ofhospital stay, and weighted share of total COPD category illnesses) reported in Agency forHealthcare Research and Quality (2000) (www.ahrq.gov).AsthmaAdmissions$8,900 $8,900No 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 ofhospital stay, and weighted share of total asthma category illnesses) reported in Agency forHealthcare Research and Quality (2000) (www.ahrq.gov).All Cardiovascular$24,668 $24,668No 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 ofhospital stay, and weighted share of total cardiovascular category illnesses) reported in Agency forHealthcare Research and Quality (2000) (www.ahrq.gov).All respiratory(ages 65+)$24,622 $24,622No distributions available. The COI point estimates(lost earnings plus direct medical costs) arebased on ICD-9 code level information (e.g., average hospital care costs, average length of hospitalstay, and weighted share of total COPD category illnesses) reported in Agency for HealthcareResearch and Quality, 2000 (www.ahrq.gov).All respiratory(ages 0–2)$10,385 $10,385No distributions available. The COI point estimates(lost earnings plus direct medical costs) arebased on ICD-9 code level information (e.g., average hospital care costs, average length of hospitalstay, and weighted share of total COPD category illnesses) reported in Agency for HealthcareResearch and Quality, 2000 (www.ahrq.gov).Emergency RoomVisits for Asthma$384 $384No 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 HospitalizationUpper RespiratorySymptoms (URS)$30 $30 Combinations of the three symptoms for which WTP estimates are available that closely matchthose listed by Pope et al. result in seven different “symptom clusters,” each describing a “type” ofURS. 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 ofinformation surrounding the frequency with which each of the seven types of URS occurs within theURS symptom complex, we assumed a uniform distribution between $9.2 and $43.1.6-28Central Estimate of ValuePer Statistical IncidenceHealth Endpoint1990 IncomeLevel2020 IncomeLevel Derivation of Distributions of EstimatesLower RespiratorySymptoms (LRS)$19 $21Combinations of the four symptoms for which WTP estimates are available that closely match thoselisted 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) toavoid each symptom in the cluster and assuming additivity of WTPs. The dollar value for LRS isthe average of the dollar values for the 11 different types of LRS. In the absence of informationsurrounding the frequency with which each of the 11 types of LRS occurs within the LRS symptomcomplex, we assumed a uniform distribution between$6.9 and $24.46.AsthmaExacerbations$50 $54Asthma exacerbations are valued at $45 per incidence, based on the mean of average WTPestimates for the four severity definitions of a “bad asthma day,” described in Rowe and Chestnut(1986). This study surveyed asthmatics to estimate WTPfor avoidance of a “bad asthma day,” asdefined by the subjects. For purposes of valuation,an asthma exacerbation is assumed to beequivalent to a day in which asthma is moderate orworse 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 $453Assumes a 6-day episode, with the distribution of the daily value specified as uniform with the lowand high values based on those recommended for related respiratory symptoms in Neumann et al.(1994). The low daily estimate of $10 is the sum ofthe mid-range values recommended by IEc(1994) for two symptoms believed to be associated with acute bronchitis: coughing and chesttightness. The high daily estimate was taken to betwice 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 basedon county-specific median annual wages dividedby 50 (assuming 2 weeks of vacation) and then by 5—to get median daily wage. U.S. Year 2000Census, compiled by Geolytics, Inc.Minor RestrictedActivity Days(MRADs)$61 $64 Median WTP estimate to avoid one MRAD fromTolley et al. (1986). Distribution is assumed to betriangular with a minimum of $22 and a maximum of $83, with a most likely value of $52. Range isbased on assumption that value should exceed WTP for a single mild symptom (the highest estimatefor a single symptom—for eye irritation—is $16.00) and be less than that for a WLD. The triangulardistribution acknowledges that the actual value islikely to be closer to the point estimate than eitherextreme.School AbsenceDays$89 $89 No distribution available6-29

6.4.3 Asthma-Related Emergency Room Visits Valuatio n
To value asthma emergency room visits, we used a si mple average of two estimates from the health economics literature. The first estimate com es 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 co st 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 avera ge 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 acti vity 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 valuin g this health endpoint, we used the estimate of mean WTP to avoid a minor respiratory restricted ac tivity day.

6.4.5 School Absences
To value a school absence, we: (1) estimated the pr obability that if a school child stays home from school, a parent will have to stay home from work to care for the ch ild; and (2) valued the lost productivity at the parent’s wage. To do this, we estimated the number of fam ilies 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 schoo l-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 par ents 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 ho usehold with two working parents, the female parent will stay home with a sick child. Fro m 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 a nd “other” women with children. From these two sets of statistics, we calculated a weighted av erage participation rate of 72.85 percent.6-30

Our estimate of daily lost wage (wages lost if a mo ther must stay at home with a sick child) is based on the year 2000 median weekly wage among wom en 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 w ages on a day when a mother has to stay home with a school-age child, we first estimated th e 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 benefi ts. Tables 6.6 through 6.21 summarize the reduction in incidence for ozone- and PM-related he alth 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 5 th and 95 th 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 fr om 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 p artial 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 fu ll attainment, we rolled back the ozone monitor data so that the 4 th highest daily maximum 8-hour average just met the level required to attain the alternative standard. This approach will likely und erstate the benefits that would occur due to implementation of actual controls to reduce ozone p recursor emissions because controls implemented to reduce ozone concentrations at the h ighest 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 l ocations); 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 improvement s and resulting health benefits from full attainment woul d be more widespread than we have estimated in our rollback analyses. The incidence and valuati on results for attainment of the 0.075 ppm and 0.079 ppm alternatives are derived through an inter polation technique described in Appendix 6a. As such, these estimates are presented as full atta inment only.
We model all ozone-related premature mortality and m orbidity 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 fo r PM. Therefore, we do not discount ozone estimates.

28 Note that the valuation estimates for ozone benefits are not discounted due to the fact th at there is no lag between changes in exposure and premature mortality, as is calculated for PM 2.5 benefits.6-31

Figure 6.1: Valuation of Ozone Morbidity and Mortality Benef its Results by Standard Alternative*

* This figure reflects full attainment in all locat ions of the U.S. except two areas of California. Th ese two areas, which have high levels of ozone, are not pla nning to meet the current standard until after 2020

. The estimates in the figure do not reflect benefits for the San Joaquin and South Coast Air Basins.

6.5.2 PM

2.5 Co-Benefit Estimation Methodology
Figure 6.2 summarizes the valuation of PM benefits at a 3% and 7% discounted rate, respectively. A series of tables below present the PM 2.5 co-benefits associated with full attainment of the 0.065 ppm, 0.070 ppm, 0.075 ppm a nd 0.079 ppm alternatives. To derive estimates of incidence and valuation for the PM 2.5 related co-benefits of full attainment of each ozone standard alternative, we applied a scaling te chnique described below. To estimate total valuation estimates, we applied benefit per-ton met rics; this procedure is detailed further below. Valuation estimates of the PM 2.5 -related full attainment benefits are presented at a 3% discount rate and at a 7% discount rate. All PM 2.5 co-benefit estimates are incremental to the 2006 P M 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 locat ions of the U.S. except two areas of California. Th ese 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 be nefits for the San Joaquin and South Coast Air Basins.6-33

Estimating PM 2.5 Co-Benefits Resulting from Full Attainment of the Selected Standard and Each Standard Alternative

The modeled PM 2.5 air quality scenario reflects the PM 2.5 changes associated with partially attaining 0.070 ppm incremental to a partial attain ment of 0.08 ppm; due to analytical limitations it was not possible to model a full-attainment PM 2.5 scenario for the selected standard or each standard alternative. Thus, using this projected ai r quality change to estimate PM 2.5 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 qualit y changes from NOx reductions needed to attain a baseline of 0.08 ppm. Of greater analytical value would be an estimate of the PM 2.5 co-benefits associated with fully attaining 0.070 ppm increment al to full attainment of the 0.08 ppm standard.
To generate such an estimate, we calculated a new P M 2.5 baseline that established the PM 2.5 air quality associated with full attainment of 0.08 ppm . To create such a baseline, EPA utilized benefit PM 2.5 per-ton estimates. These PM 2.5 benefit per-ton estimates provide the total monetized human health benefits (the sum of prematu re mortality and premature morbidity) of reducing one ton of PM 2.5 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 valuat ion of the Pope (2002) estimates of mortality (3% discount rate, 200 6$) and valuation of the morbidity incidence. Readers interested in reviewing the comp lete methodology for creating the benefit per-ton estimates used in this analysis can consult the Technical Support Document accompanying this RIA.
Estimating the PM 2.5 benefits that represented the full attainment of b oth 0.070 ppm incremental to full attainment of 0.08 ppm entailed the followi ng four steps:

1. Estimate the number of tons of NOx necessary to att ain a baseline of 0.08 ppm . Chapter 4 described the method used to estimate the extrapola ted NOx emissions reductions necessary to attain a baseline of 0.08 ppm full att ainment.

2. Calculate the benefits of attaining 0.08 ppm increm ental to partial attainment of 0.08
ppm. To estimate the benefits of fully attaining 0.08 pp m incremental to partial attainment of 0.08 ppm, the relevant benefit per to n is simply multiplied by the total number of extrapolated NOx tons abated. Note that t his 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 PM 2.5 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 attai ning 0.070 ppm incremental to partial attainment of 0.070 ppm. By adding this incremental benefit estimate to the benef its generated in step 3, we derived a total benefit esti mate 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 necess ary 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 PM 2.5 co-benefits.
The process for estimating the PM 2.5 co-benefits of fully attaining 0.065 ppm, 0.075 pp m, and 0.079 ppm is identical to the steps above, with the following exception; in step four we substituted the number of extrapolated tons necessar y to attain 0.065 ppm, 0.075 ppm, and 0.079 ppm respectively. Table 7-5 below provides the inpu ts to the calculation steps described above. In the example below, we calculate total benefits using the Pope et al. (2002) mortality es timate. However, in subsequent tables we present benefits u sing Laden et al. (2006) as well as the twelve expert functions described previously in this docum ent. Note that while our benefit per ton estimates are associated with broad source categori es (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 simp ly assumed that the total number of extrapolated NOx tons were evenly distributed betwe en these three source types.

The PM 2.5 benefits of attaining 0.065 ppm, 0.075 ppm and 0.0 79 ppm incremental to partial attainment of 0.070 ppm are $7.5 billion, $0.6 bill ion and -$1 billion respectively. Simulated attainment of the 0.79 ppm alternative required few er emission reductions than were modeled in the emissions control strategy to simulate attainme nt with 0.070 ppm. For this reason, we “netted out” the benefits of the incremental NOx emission r eductions 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 PM 2.5 -related incidence associated with the attainment of each ozone altern ative, we calculated a separate scaling factor as follows: (1) we calculated the ratio of the full attainment PM 2.5 valuation estimate (calculated using the benefit per ton metrics described below) to the partial attainment to the partial attainment PM 2.5 valuation estimate; (2) multiply this scaling rati o against each of the PM 2.5 partial attainment mortality and morbidity endpoint s 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 PM 2.5 -related incidence.

The total PM 2.5 benefits of attaining 0.065 ppm, 0.075 ppm and 0.07 9 ppm are $11 billion, $3.6 billion and $2 billion respectively. The full attai nment PM 2.5 benefits do not include confidence intervals. Because this full attainment estimate wa s derived by summing the modeled PM 2.56-35

benefits and the benefits derived using the benefit per-ton metrics—and these benefit per ton metrics do not include confidence intervals—the res ulting sum of total PM 2.5 benefits do not include confidence intervals.

Table 6.5: Estimated PM 2.5 Co-Benefits Associated with Full Attainment of 0.070 ppm Incremental to 0.08 ppm a

Calculation Extrapolated NOx Tons Benefit per Ton

Estimate Valuation of PM 2.5

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 figure s and therefore summation may not match table estimates. PM 2.5 benefit estimates do not include confidence interv als 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 l ocations of the U.S. except two areas of California

. These two areas, which have high levels of ozone, a re not planning to meet the current standard until after 2020. The estimates in the table do not refle ct benefits for the San Joaquin and South Coast Air Basins.

Estimated reductions in ozone mortality incidence pr ovided in Tables 6.6, 6.10, 6.14, and 6.18 represent the number of premature deaths potentiall y avoided due to reductions in ozone exposure in 2020 using warm season functions from th e 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 (Be ll et al., 2005; Ito et al., 2005; Levy et al., 2005). These same tables also include the possibili ty that there is not a causal association between ozone and mortality, i.e., that the estimat e for premature mortality avoided could be zero. Model uncertainty, including whether or not t he relationship is assumed to be causal, is a key source of uncertainty. Although multiple estima tes are presented in these tables, no attempt was made to quantify the likelihood of a causal rel ationship between short-term ozone exposure and increased mortality or to weigh the results of the various models.
The estimate of central tendency for premature mort ality is expressed as the arithmetic mean, with the assumption of a normal distribution, and r epresents the central estimate of the number of premature deaths avoided in association with the al ternative standards based on each study.6-36

Statistical uncertainty associated with the model e stimate for each study is characterized by the 95% credible interval 30 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-r elated benefits associated with the alternative standards due to its extensive coverage (examinatio n of 95 large communities across the United States over an extended period of time, from 1987 t o 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 strin gent 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-mortalit y reduction estimates are conditional on a causal relationship.
The Ozone Criteria Document (U.S. EPA, 2006) and St aff Paper (U.S. EPA, 2007) concluded that the overall body of evidence is highly suggesti ve that (short-term exposure to) ozone directly or indirectly contributes to non-accidental cardiop ulmonary-related mortality. However, various sources of uncertainty remain, including the possib ility that there is no causal relationship between ozone and mortality (i.e., zero effect). Fo r instance, because results of time-series studies implicate all of the criteria air pollutant s, 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, CASAC 31 stated that it seems unlikely that the observed associations between short-term ozone conc entrations 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 mo rtality). Even so, CASAC concluded that the evidence was strong enough to support a quantit ative risk assessment of the relationship between short-term exposure to ozone and premature m ortality as part of the Staff Paper. EPA has asked the National Academy of Sciences 32 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 benefit s 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 St aff Paper, we estimate 71 avoided premature deaths annu ally 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 $6 20 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, av oided premature deaths would be zero. For a

30 A credible interval is a posterior probability int erval used in Bayesian statistics, which is similar to a confidence interval used in frequentis t statistics.

31 Clean Air Scientific Advisory Committee’s Peer Rev iew of the Agency’s 2nd Draft Ozone Staff Paper, October 24, 2006. EPA-CASAC-07-001. Av ailable 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 Exposur e. Division on Earth and Life Studies, Board on Environmental Studies and Toxicology. Avai lable at http://www8.nationalacademies.org/cp/projectview.as px?key=487686-37

standard of 0.079 ppm, using the NMMAPS ozone morta lity 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 s tringent 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 billio n/yr. Using the three synthesis studies, we estimate premature deaths avoided for the less stri ngent 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, esti mated premature deaths avoided for the more stringent standard are between 1,500 and 2,100, wit h total monetized ozone benefits between $12 and $16 billion/yr. Including premature mortality i n 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 th ese estimates reflect EPA’s interim approach to characterizing the benefits of reducing premature m ortality associated with ozone exposure. As mentioned above, EPA has requested advice from t he NAS on this issue.
6.5.3 Estimate of Full Attainment Benefits
Tables 6.38 through 6.41 below summarize the estima tes of full attainment and PM 2.5 co-benefit estimate for each standard alternative. The present ation of ozone benefits and PM 2.5 co-benefits for each standard alternative is broken into two tables. The first table presents the nat ional ozone benefits and PM 2.5 co-benefits. Tables 6.42 through 6.49 summarize the combined ozone and PM 2.5 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 Ozon e Exposure in 2020 (Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confide nce Intervals in Parentheses) B, C, D, E
Model or Assumption A 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 l ives saved are based on an assumption of a normal distribution.

C A credible interval is a posterior probability int erval 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 symmetrica l and totals will not sum across columns.

E This table reflects full attainment in all locatio ns of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in 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 Exposu re (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) 550 1900 Emergency Department Visits, Asthma-Related C (-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. A s such, confidence intervals may not be symmetrical and totals will not sum across columns.

B This table reflects full attainment in all location s of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in and South Coast Air Basins.

C The negative 5 th percentile incidence estimates for this health end point 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 increas e 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 Mortali ty associated with PM co-benefit C

Mortality Endpoint National 2020 Benefits Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study A 1,000 Harvard Six-City Study B 2,300 Woodruff et al 1997 (infant mortality) 2.9 Mortality Impact Functions Derived from Expert Elic itation 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-respons e (C-R) function developed from the study of the Am erican 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 exten ded 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. A ll estimates incremental to 2006 PM NAAQS RIA. Esti mates do not include confidence intervals because they we re derived through a scaling technique described ab ove. 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 estimat es in the table do not reflect benefits for the San Joaquin a nd 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-benefit A, 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) 2 70 Work loss days (age 18-65) 550 Minor restricted activity days (age 18-65) 2,300

A All estimates rounded to two significant figures. A ll estimates incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals becau se they were derived through a scaling technique described above. This table reflects full attainmen t in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, a re not planning to meet the current standard until after 2020. The estimates in the table do not reflect ben efits for the San Joaquin and South Coast Air Basin s.

B Morbidity Impact Functions Derived from Epidemiolog y Literature6-42

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 Assumption A 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 l ives saved are based on an assumption of a normal distribution.

C A credible interval is a posterior probability int erval 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 symmetrica l and totals will not sum across columns.

E This table reflects full attainment in all locatio ns of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in 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 Parenth eses) 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) 550 1000 Emergency Department Visits, Asthma-Related C (-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. A s such, confidence intervals may not be symmetrical and totals will not sum across columns.

B This table reflects full attainment in all location s of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in and South Coast Air Basins.

C The negative 5 th percentile incidence estimates for this health end point 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 increas e 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 Mortali ty associated with PM co-benefit C

Mortality Endpoint National 2020 Benefits Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study A 650 Harvard Six-City Study B 1,500 Woodruff et al 1997 (infant mortality) 1.9 Mortality Impact Functions Derived from Expert Elic itation 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-respons e (C-R) function developed from the study of the Am erican 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 exten ded 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. A ll estimates incremental to 2006 PM NAAQS RIA. Esti mates do not include confidence intervals because they we re derived through a scaling technique described ab ove. 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 estimat es in the table do not reflect benefits for the San Joaquin a nd 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-benefit A, 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) 1 80 Work loss days (age 18-65) 350 Minor restricted activity days (age 18-65) 1,500

A All estimates rounded to two significant figures. A ll estimates incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals becau se they were derived through a scaling technique described above. This table reflects full attainmen t in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, a re not planning to meet the current standard until after 2020. The estimates in the table do not reflect ben efits for the San Joaquin and South Coast Air Basin s.

B Morbidity Impact Functions Derived from Epidemiolog y Literature6-46

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 Assumption A 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 l ives saved are based on an assumption of a normal distribution.

C A credible interval is a posterior probability int erval 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 symmetrica l.

E This table reflects full attainment in all locatio ns of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in 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 Parenth eses) A,B

Morbidity Endpoint National Full Attainment 480 Hospital Admissions (ages 0-1) (230--730) 470 Hospital Admissions (ages 65-99) (-5.1--930) 280 Emergency Department Visits, Asthma-Related C (-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. A s such, confidence intervals may not be symmetrical

. B This table reflects full attainment in all location s of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in and South Coast Air Basins.

C The negative 5 th percentile incidence estimates for this health end point 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 increas e 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 Mortali ty associated with PM co-benefit C

Mortality Endpoint National 2020 Benefits Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study A 390 Harvard Six-City Study B 880 Woodruff et al 1997 (infant mortality) 1.1 Mortality Impact Functions Derived from Expert Elic itation 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-respons e (C-R) function developed from the study of the Am erican 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 exten ded 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. A ll estimates incremental to 2006 PM NAAQS RIA. Esti mates do not include confidence intervals because they we re derived through a scaling technique described ab ove. 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 estimat es in the table do not reflect benefits for the San Joaquin a nd 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-benefit A, 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. A ll estimates incremental to 2006 PM NAAQS RIA. Esti mates do not include confidence intervals because they we re derived through a scaling technique described ab ove. 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 estimat es in the table do not reflect benefits for the San Joaquin a nd South Coast Air Basins.

B Morbidity Impact Functions Derived from Epidemiolog y Literature6-50

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 Assumption A 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 l ives saved are based on an assumption of a normal distribution.

C A credible interval is a posterior probability int erval 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 symmetrica l.

E This table reflects full attainment in all locatio ns of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in 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 Parenth eses) A,B

Morbidity Endpoint National Full Attainment 190 Hospital Admissions (ages 0-1) (9.0--350) 190 Hospital Admissions (ages 65-99) (90--280) 87 Emergency Department Visits, Asthma-Related C (-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. A s such, confidence intervals may not be symmetrical

. B This table reflects full attainment in all locatio ns of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in and South Coast Air Basins.

C The negative 5 th percentile incidence estimates for this health end point 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 increas e 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 Mortali ty associated with PM co-benefit C

Mortality Endpoint National 2020 Benefits Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study A 250 Harvard Six-City Study B 560 Woodruff et al 1997 (infant mortality) 0.71 Mortality Impact Functions Derived from Expert Elic itation 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-respons e (C-R) function developed from the study of the Am erican 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 exten ded 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. A ll estimates incremental to 2006 PM NAAQS RIA. Esti mates do not include confidence intervals because they we re derived through a scaling technique described ab ove. 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 estimat es in the table do not reflect benefits for the San Joaquin a nd South Coast Air Basins.6-53 Table 7-21: Illustrative 0.079 ppm Full Attainment Sce nario: Estimated Annual Reductions in the Incidence of Morbidity Associated

with PM Co-benefit A, 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) 4,20 0 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) 6 7 Work loss days (age 18-65) 140 Minor restricted activity days (age 18-65) 570

A All estimates rounded to two significant figures. A ll estimates incremental to 2006 PM NAAQS RIA. Estimates do not include confidence inte rvals because they were derived through a scaling technique described above.
B Morbidity Impact Functions Derived from Epidemiolog y Literature
C This table reflects full attainment in all locatio ns 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 Associ ated with Ozone Exposure (Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence

Intervals in Parentheses, Millions of 2006$) B,C,D,E

Model or Assumption A Reference National Modeled Partial Attainment National Rolled Back Full Attainment $960 $3,500 NMMAPS Bell et al. 2004 ($140--$2,200) ($510--$7,800) $3,100 $11,000 Bell et al. 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 nu mber of lives saved are based on an assumption of a normal distribution.

C A credible interval is a posterior probability int erval 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 symmetrica l and totals will not sum across columns.

E This table reflects full attainment in all locatio ns of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not pla nning 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 O zone Exposure (Incremental to Current Ozone Standard, 95% Confidence Intervals in Parent heses, 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) $9.9 $74 Hospital Admissions (ages 65- 99) C (-$3.3--$24) ($8.40--$140) $0.20 $0.69 Emergency Department Visits, Asthma-Related C ($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. A s such, confidence intervals may not be symmetrical and totals will not sum across columns. This table refl ects full attainment in all locations of the U.S. e xcept two areas of California. These two areas, which have high lev els 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 Coa st Air Basins.

B This table reflects full attainment in all location s of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in and South Coast Air Basins.

C The negative 5 th percentile incidence estimates for this health end point 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 increas e 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 Mo rtality 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 Epidemiolog y Literature ACS Study A $9,700 $8,800 Harvard Six-City Study B $22,000 $20,000 Woodruff et al 1997 (infant mortality) $20 $16 Mortality Impact Functions Derived from Expert Elic itation 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-respons e (C-R) function developed from the study of the Am erican 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 exten ded 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. A ll estimates incremental to 2006 PM NAAQS RIA. Esti mates do not include confidence intervals because they we re derived through a scaling technique described ab ove. 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 estimat es in the table do not reflect benefits for the San Joaquin a nd 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 Asso ciated 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. A ll estimates incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals becau se they were derived through a scaling technique de scribed above.
B Morbidity Impact Functions Derived from Epidemiolog y Literature
C This table reflects full attainment in all locatio ns of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in 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 Associa ted with Ozone Exposure (Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals in Parentheses, Millions of 2006$) B,C,D,E

Model or Assumption A Reference National Modeled Partial Attainment National Rolled Back Full Attainment $960 $1,900 NMMAPS Bell et al. 2004 ($140--$2,200) ($280--$4,300) $3,100 $6,200 Bell et al. 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 l ives saved are based on an assumption of a normal distribution.

C A credible interval is a posterior probability int erval 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 locatio ns of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in 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 O zone Exposure (Incremental to Current Ozone Standard, 95% Confidence Intervals in Parent heses, 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) $0.20 $0.37 Emergency Department Visits, 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. A s such, confidence intervals may not be symmetrical and totals will not sum across columns. This table refl ects full attainment in all locations of the U.S. e xcept two areas of California. These two areas, which have high lev els 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 Coa st Air Basins.

B This table reflects full attainment in all location s of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in 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 Mo rtality 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 Epidemiolog y Literature ACS Study A $6,000 $5,400 Harvard Six-City Study B $13,000 $12,000 Woodruff et al 1997 (infant mortality) $13 $11 Mortality Impact Functions Derived from Expert Elic itation 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-respons e (C-R) function developed from the study of the Am erican 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 exten ded 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. A ll estimates incremental to 2006 PM NAAQS RIA. Esti mates do not include confidence intervals because they we re derived through a scaling technique described ab ove. 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 estimat es in the table do not reflect benefits for the San Joaquin a nd South Coast Air Basins.6-61

Table 7-298: Illustrative 0.070 ppm Full Attainment Scenar io: Estimated Annual Valuation of Reductions in the Incidence of Morbidity Assoc iated 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. A ll estimates incremental to 2006 PM NAAQS RIA. Esti mates do not include confidence intervals because they we re derived through a scaling technique described ab ove. B Morbidity Impact Functions Derived from Epidemiolog y Literature
C This table reflects full attainment in all locatio ns of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in 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 Associa ted with Ozone Exposure (Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals in Parentheses, Millions of 2006$) B,C,D,E

Model or Assumption A 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 l ives saved are based on an assumption of a normal distribution.

C A credible interval is a posterior probability int erval 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 symmetrica l.

E This table reflects full attainment in all locatio ns of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in 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 O zone Exposure (Incremental to Current Ozone Standard, 95% Confidence Intervals in Parent heses, 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) $0.10 Emergency Department Visits, 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. A s such, confidence intervals may not be symmetrical

. B This table reflects full attainment in all location s of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in 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 Mo rtality 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 Epidemiolog y Literature ACS Study A $3,300 $3,000 Harvard Six-City Study B $7,400 $6,600 Woodruff et al 1997 (infant mortality) $8 $6 Mortality Impact Functions Derived from Expert Elic itation 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-respons e (C-R) function developed from the study of the Am erican 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 exten ded 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. A s such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates i ncremental to 2006 PM NAAQS RIA. Estimates do not i nclude confidence intervals because they were derived thro ugh 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 ha ve high levels of ozone, are not planning to meet the current stan dard until after 2020. The estimates in the table d o not reflect benefits for the San Joaquin and South Coast Air Ba sins.6-65

Table 7-33: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Valuation of Reductions in the Incidence of Morbidity Assoc iated 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. A ll estimates incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals becau se they were derived through a scaling technique de scribed above.
B Morbidity Impact Functions Derived from Epidemiolog y Literature
C This table reflects full attainment in all locatio ns of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Jo aquin 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 Associa ted with Ozone Exposure (Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals in Parentheses, Millions of 2006$) B,C,D,E

Model or Assumption A 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 l ives saved are based on an assumption of a normal distribution.

C A credible interval is a posterior probability int erval 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 symmetrica l.

E This table reflects full attainment in all locatio ns of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in 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 O zone Exposure (Incremental to Current Ozone Standard, 95% Confidence Intervals in Parent heses, 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) $0.03 Emergency Department Visits, 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. A s such, confidence intervals may not be symmetrical

. B This table reflects full attainment in all location s of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in 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 Mo rtality 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 Epidemiolog y Literature ACS Study A $1,800 $1,600 Harvard Six-City Study B $4,100 $3,700 Woodruff et al 1997 (infant mortality) $5.0 $4.0 Mortality Impact Functions Derived from Expert Elic itation 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-respons e (C-R) function developed from the study of the Am erican 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 exten ded 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. A s such, confidence intervals may not be symmetrical . All estimates incremental to 2006 PM NAAQS RIA. Estimat es do not include confidence intervals because they were derived through a scaling technique described above . This table reflects full attainment in all locati ons of the U.S. except two areas of California. These two areas, wh ich 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 Joaq uin 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 Assoc iated 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. A ll estimates incremental to 2006 PM NAAQS RIA. Esti mates do not include confidence intervals because they we re derived through a scaling technique described ab ove. B Morbidity Impact Functions Derived from Epidemiolog y Literature
C This table reflects full attainment in all locatio ns of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in and South Coast Air Basins.6-70

Table 7-38: Estimate of Annual Ozone and PM 2.5 Combined Morbidity and Mortality

(Millions of 2006$) for the 0.065 ppm Full Attainme nt

Ozone Mortality and Morbidity Benefits of Attaining 0.065 ppm Total NMMAPS Bell (2004) $3,900

Bell (2005) $12,000 Ito (2005) $16,000 Meta-Analysis Levy (2005) $16,000 No Causality $420

PM 2.5 Mortality and Morbidity Benefits of Attaining 0.06 5 ppm Total (3% Discount Rate) Total (7%

Discount Rate) Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study C $11,000 $9,600 Harvard Six-City Study D $23,000 $20,000 Mortality Impact Functions Derived from Expert Elic itation 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 int erval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. Credible i ntervals for ozone estimates and confidence interva ls for PM 2.5 estimates not provided because the valuation estima tes were derived through a scaling technique (see a bove) that precluded us from generating such estimates.

C The estimate is based on the concentration-respons e (C-R) function developed from the study of the Am erican 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 exten ded 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. Es timates derived using benefit per ton estimates discounted at 3% and 7%. This table reflects full a ttainment in all locations of the U.S. except two a reas of California. These two areas, which have high levels of ozone, are not planning to meet the current sta ndard until after 2020. The estimates in the table do not refle ct benefits for the San Joaquin and South Coast Air Basins.6-71

Table 7-39: Estimate of Annual Ozone and PM 2.5 Combined Morbidity and Mortality

(Millions of 2006$) for the 0.070 ppm Full Attainme nt

Ozone Mortality and Morbidity Benefits of Attaining 0.070 ppm Total NMMAPS Bell (2004) $2,200

Bell (2005) $6,500 Ito (2005) $8,800 Meta-Analysis Levy (2005) $9,000 No Causality $230

PM 2.5 Mortality and Morbidity Benefits of Attaining 0.07 0 ppm Total (3% Discount Rate) Total (7%

Discount Rate) Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study C $6,500 $5,900 Harvard Six-City Study D $14,000 $13,000 Mortality Impact Functions Derived from Expert Elic itation 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 int erval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. Credible i ntervals for ozone estimates and confidence interva ls for PM 2.5 estimates not provided because the valuation estima tes were derived through a scaling technique (see a bove) that precluded us from generating such estimates.
C The estimate is based on the concentration-respons e (C-R) function developed from the study of the Am erican 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 exten ded 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. Es timates derived using benefit per ton estimates discounted at 3% and 7%. This table reflects full a ttainment in all locations of the U.S. except two a reas of California. These two areas, which have high levels of ozone, are not planning to meet the current sta ndard until after 2020. The estimates in the table do not refle ct benefits for the San Joaquin and South Coast Air Basins.6-72

Table 7-40: Estimate of Annual Ozone and PM 2.5 Combined Morbidity and Mortality

(Millions of 2006$) for the 0.075 ppm Full Attainme nt

Ozone Mortality and Morbidity Benefits of Attaining 0.075 ppm Total NMMAPS Bell (2004) $620

Bell (2005) $1,900 Ito (2005) $2,500 Meta-Analysis Levy (2005) $2,600 No Causality $73

PM 2.5 Mortality and Morbidity Benefits of Attaining 0.07 5 ppm Total (3% Discount Rate) Total (7%

Discount Rate) Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study C $3,600 $3,300 Harvard Six-City Study D $7,700 $7,000 Mortality Impact Functions Derived from Expert Elic itation 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 int erval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. Credible i ntervals for ozone estimates and confidence interva ls for PM 2.5 estimates not provided because the valuation estima tes were derived through a scaling technique (see a bove) that precluded us from generating such estimates.
C The estimate is based on the concentration-respons e (C-R) function developed from the study of the Am erican 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 exten ded 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. Es timates derived using benefit per ton estimates discounted at 3% and 7%. This table reflects full a ttainment in all locations of the U.S. except two a reas of California. These two areas, which have high levels of ozone, are not planning to meet the current sta ndard until after 2020. The estimates in the table do not refle ct benefits for the San Joaquin and South Coast Air Basins.6-73

Table 7-41: Estimate of Annual Ozone and PM 2.5 Combined Morbidity and Mortality

(Millions of 2006$) for the 0.079 ppm Full Attainme nt

Ozone Mortality and Morbidity Benefits of Attaining 0.079 ppm Total NMMAPS Bell (2004) $220

Bell (2005) $640 Ito (2005) $860 Meta-Analysis Levy (2005) $890 No Causality $28

PM 2.5 Mortality and Morbidity Benefits of Attaining 0.07 9 ppm Total (3% Discount Rate) Total (7%

Discount Rate) Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study C $2,000 $1,800 Harvard Six-City Study D $4,300 $3,900 Mortality Impact Functions Derived from Expert Elic itation 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 int erval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. Credible i ntervals for ozone estimates and confidence interva ls for PM 2.5 estimates not provided because the valuation estima tes were derived through a scaling technique (see a bove) that precluded us from generating such estimates.
C The estimate is based on the concentration-respons e (C-R) function developed from the study of the Am erican 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 exten ded 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. Es timates derived using benefit per ton estimates discounted at 3% and 7%. This table reflects full a ttainment in all locations of the U.S. except two a reas of California. These two areas, which have high levels of ozone, are not planning to meet the current sta ndard until after 2020. The estimates in the table do not refle ct benefits for the San Joaquin and South Coast Air Basins.6-74

Table 7-42: Combined Estimate of Annual Ozone and P M 2.5 Benefits (Millions of $2006,

3% Discount Rate) for the 0.065 ppm Alternative Standard
Alternative Standard and Model or Assumption A

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causa lity

Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $14,000 $22,000 $26,000 $27,000 $11,000 Harvard Six-City Study C $26,000 $34,000 $38,000 $39,000 $23,000 Mortality Impact Functions Derived from Expert Elic itation Expert A $34,000 $38,000 $46,000 $50,000 $5 0,000 Expert B $26,000 $30,000 $38,000 $42,000 $4 2,000 Expert C $26,000 $30,000 $38,000 $42,000 $4 2,000 Expert D $19,000 $22,000 $30,000 $34,000 $3 5,000 Expert E $42,000 $46,000 $54,000 $58,000 $5 8,000 Expert F $24,000 $27,000 $35,000 $39,000 $4 0,000 Expert G $16,000 $19,000 $27,000 $31,000 $3 2,000 Expert H $20,000 $23,000 $31,000 $35,000 $3 6,000 Expert I $26,000 $29,000 $37,000 $41,000 $4 2,000 Expert J $21,000 $25,000 $33,000 $37,000 $3 7,000 Expert K $5,500 $9,000 $17,000 $21,000 $21,0 00 Expert L $19,000 $23,000 $30,000 $35,000 $3 5,000

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-respons e (C-R) function developed from the study of the Am erican 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 exten ded Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha s previously been a sensitivity estimate.

D All estimates incremental to 2006 PM NAAQS RIA. Co nfidence intervals for PM 2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that preclu ded 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 location s 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 estimat es in the table do not reflect benefits for the San Joaquin and Sou th Coast Air Basins.6-75

Table 7-43: Combined Estimate of Annual Ozone and P M 2.5 Benefits (Millions of $2006,

7% Discount Rate) for the 0.065 ppm Alternative Standard
Alternative Standard and Model or Assumption A

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causa lity

Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $13,000 $21,000 $25,000 $26,000 $10,000 Harvard Six-City Study C $24,000 $32,000 $36,000 $37,000 $21,000 Mortality Impact Functions Derived from Expert Elic itation Expert A $34,000 $42,000 $46,000 $47,000 $3 1,000 Expert B $27,000 $35,000 $39,000 $40,000 $2 4,000 Expert C $27,000 $35,000 $39,000 $40,000 $2 4,000 Expert D $20,000 $28,000 $32,000 $33,000 $1 7,000 Expert E $42,000 $50,000 $54,000 $54,000 $3 8,000 Expert F $25,000 $33,000 $37,000 $38,000 $2 2,000 Expert G $18,000 $26,000 $30,000 $30,000 $1 4,000 Expert H $21,000 $29,000 $33,000 $34,000 $1 8,000 Expert I $27,000 $35,000 $39,000 $39,000 $2 3,000 Expert J $23,000 $31,000 $35,000 $35,000 $1 9,000 Expert K $8,600 $16,000 $21,000 $21,000 $5,1 00 Expert L $21,000 $29,000 $33,000 $33,000 $1 7,000

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-respons e (C-R) function developed from the study of the Am erican 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 exten ded Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha s previously been a sensitivity estimate.

D All estimates incremental to 2006 PM NAAQS RIA. Co nfidence intervals for PM 2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that preclu ded 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 location s 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 estimat es in the table do not reflect benefits for the San Joaquin and Sou th Coast Air Basins.6-76

Table 7-44: Combined Estimate of Annual Ozone and P M 2.5 Benefits (Millions of $2006,

3% Discount Rate) for the 0.070 ppm Alternative Standard
Alternative Standard and Model or Assumption A

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causa lity

Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $8,700 $13,000 $15,000 $16,000 $6,700 Harvard Six-City Study C $16,000 $20,000 $23,000 $23,000 $14,000 Mortality Impact Functions Derived from Expert Elic itation Expert A $24,000 $28,000 $31,000 $31,000 $2 2,000 Expert B $19,000 $23,000 $26,000 $26,000 $1 7,000 Expert C $19,000 $23,000 $25,000 $26,000 $1 7,000 Expert D $14,000 $18,000 $21,000 $21,000 $1 2,000 Expert E $29,000 $34,000 $36,000 $36,000 $2 7,000 Expert F $17,000 $22,000 $24,000 $24,000 $1 5,000 Expert G $12,000 $16,000 $19,000 $19,000 $1 0,000 Expert H $15,000 $19,000 $21,000 $22,000 $1 3,000 Expert I $19,000 $23,000 $25,000 $26,000 $1 7,000 Expert J $16,000 $20,000 $22,000 $22,000 $1 4,000 Expert K $5,400 $9,700 $12,000 $12,000 $3,500 Expert L $14,000 $19,000 $21,000 $21,000 $1 2,000

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-respons e (C-R) function developed from the study of the Am erican 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 exten ded Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha s previously been a sensitivity estimate.

D All estimates incremental to 2006 PM NAAQS RIA. Co nfidence intervals for PM 2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that preclu ded 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 location s 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 estimat es in the table do not reflect benefits for the San Joaquin and Sou th Coast Air Basins.6-77

Table 7-45: Combined Estimate of Annual Ozone and P M 2.5 Benefits (Millions of $2006,

7% Discount Rate) for the 0.070 ppm Alternative Standard
Alternative Standard and Model or Assumption A

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causa lity

Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $8,100 $12,000 $15,000 $15,000 $6,100 Harvard Six-City Study C $15,000 $19,000 $21,000 $22,000 $13,000 Mortality Impact Functions Derived from Expert Elic itation Expert A $22,000 $26,000 $29,000 $29,000 $2 0,000 Expert B $17,000 $22,000 $24,000 $24,000 $1 5,000 Expert C $17,000 $22,000 $24,000 $24,000 $1 5,000 Expert D $13,000 $17,000 $19,000 $20,000 $1 1,000 Expert E $27,000 $31,000 $33,000 $33,000 $2 5,000 Expert F $16,000 $20,000 $23,000 $23,000 $1 4,000 Expert G $11,000 $16,000 $18,000 $18,000 $9 ,300 Expert H $14,000 $18,000 $20,000 $20,000 $1 2,000 Expert I $17,000 $21,000 $24,000 $24,000 $1 5,000 Expert J $14,000 $19,000 $21,000 $21,000 $1 2,000 Expert K $5,100 $9,500 $12,000 $12,000 $3,200 Expert L $13,000 $17,000 $20,000 $20,000 $1 1,000

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-respons e (C-R) function developed from the study of the Am erican 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 exten ded Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha s previously been a sensitivity estimate.

D All estimates incremental to 2006 PM NAAQS RIA. Co nfidence intervals for PM 2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that preclu ded 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 location s 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 estimat es in the table do not reflect benefits for the San Joaquin and Sou th Coast Air Basins.6-78

Table 7-46: Combined Estimate of Annual Ozone and P M 2.5 Benefits (Millions of $2006,

3% Discount Rate) for the 0.075 ppm Alternative Standard
Alternative Standard and Model or Assumption A

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causa lity

Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $4,200 $5,500 $6,100 $6,200 $3,700 Harvard Six-City Study C $8,300 $9,500 $10,000 $10,000 $7,800 Mortality Impact Functions Derived from Expert Elic itation Expert A $14,000 $15,000 $16,000 $16,000 $1 3,000 Expert B $11,000 $12,000 $13,000 $13,000 $1 0,000 Expert C $11,000 $12,000 $13,000 $13,000 $1 0,000 Expert D $7,800 $9,000 $9,700 $9,800 $7,300 Expert E $17,000 $18,000 $19,000 $19,000 $1 7,000 Expert F $9,900 $11,000 $12,000 $12,000 $9,3 00 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 $1 0,000 Expert J $8,800 $10,000 $11,000 $11,000 $8,3 00 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-respons e (C-R) function developed from the study of the Am erican 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 exten ded Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha s previously been a sensitivity estimate.

D All estimates incremental to 2006 PM NAAQS RIA. Co nfidence intervals for PM 2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that preclu ded 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 location s 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 estimat es in the table do not reflect benefits for the San Joaquin and Sou th Coast Air Basins.6-79

Table 7-47: Combined Estimate of Annual Ozone and P M 2.5 Benefits (Millions of $2006,

7% Discount Rate) for the 0.075 ppm Alternative Standard
Alternative Standard and Model or Assumption A

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causa lity

Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $3,900 $5,100 $5,800 $5,900 $3,400 Harvard Six-City Study C $7,600 $8,800 $9,500 $9,500 $7,000 Mortality Impact Functions Derived from Expert Elic itation Expert A $13,000 $14,000 $15,000 $15,000 $1 2,000 Expert B $9,800 $11,000 $12,000 $12,000 $9,3 00 Expert C $9,800 $11,000 $12,000 $12,000 $9,2 00 Expert D $7,100 $8,400 $9,000 $9,100 $6,600 Expert E $16,000 $17,000 $17,000 $17,000 $1 5,000 Expert F $9,000 $10,000 $11,000 $11,000 $8,5 00 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 $9,1 00 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-respons e (C-R) function developed from the study of the Am erican 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 exten ded Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha s previously been a sensitivity estimate.

D All estimates incremental to 2006 PM NAAQS RIA. Co nfidence intervals for PM 2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that preclu ded 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 location s 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 estimat es in the table do not reflect benefits for the San Joaquin and Sou th Coast Air Basins.6-80

Table 7-48: Combined Estimate of Annual Ozone and P M 2.5 Benefits (Millions of $2006,

3% Discount Rate) for the 0.079 ppm Alternative Standard
Alternative Standard and Model or Assumption A

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causa lity

Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $2,200 $2,700 $2,900 $2,900 $2,100 Harvard Six-City Study C $4,500 $4,900 $5,200 $5,200 $4,300 Mortality Impact Functions Derived from Expert Elic itation 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 $1 1,000 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-respons e (C-R) function developed from the study of the Am erican 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 exten ded Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha s previously been a sensitivity estimate.

D All estimates incremental to 2006 PM NAAQS RIA. Co nfidence intervals for PM 2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that preclu ded 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 location s 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 estimat es in the table do not reflect benefits for the San Joaquin and Sou th Coast Air Basins.6-81

Table 7-49: Combined Estimate of Annual Ozone and P M 2.5 Benefits (Millions of $2006,

7% Discount Rate) for the 0.079 ppm Alternative Standard
Alternative Standard and Model or Assumption A

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causa lity

Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $2,100 $2,500 $2,700 $2,700 $1,900 Harvard Six-City Study C $4,100 $4,500 $4,800 $4,800 $3,900 Mortality Impact Functions Derived from Expert Elic itation 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 $9,7 00 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-respons e (C-R) function developed from the study of the Am erican 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 exten ded Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha s previously been a sensitivity estimate.

D All estimates incremental to 2006 PM NAAQS RIA. Co nfidence intervals for PM 2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that preclu ded 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 location s of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning t o meet the current standard until after 2020. The e stimates in the table do not reflect benefits for the San Joaqu in and South Coast Air Basins.6-82

Figure 6.3: Ozone and PM 2.5 Benefits by Standard Alternative (3% and 7% Discount Rates)

Figure 7.3 graphically shows the breakdown between ozone and PM morbidity and mor tality 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 PM 2.5 Monetized Benefits Estimates by

Standard Alternative (3% and 7% Discount Rates)*

Figure 7.4 graphically shows four combinations of o zone and PM benefits estimates. These intermediate combinations represent reference point s:

x Bell 2004 is the epidemiological study that underli es the ozone NAAQS risk assessment and Pope is the PM mortality function that was in several EP A RIAs, and

x Bell 2005 is one of three ozone meta-analyses and L aden is a more recent PM epidemiological study that was used as an alternative in the PM NAA QS RIA6-84

Figure 6.5 and Figure 6.6 show the complete range o f 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 functi ons and the fourteen different PM functions, for each standard alternative. Each of the 70 bars repr esents an independent and equally probably point estimate of benefits under a certain combinati on of ozone and PM functions. Thus it is not possible to infer the likelihood of any single bene fit estimate.

Figure 6.5:*

079 0 7 5 070 065 0 10000 20000 30000 40000 50000 60000Millions of 2006$ Ozone and PM Total Benefits including all combinati ons of mortality estimates (3% Discount Rate)

N o C au s a l i t y & E xp e r t K

B

el l 2 0 0 4 & P o pe

B

el l 2 0 0 5 & L ad en

L ev y & E xp er t E

* These figures reflect full attainment in all loca tions of the U.S. except two areas of California. These two areas, which have high levels of ozone, a re not planning to meet the current standard until after 2020. The estimates in the tab le do not reflect benefits for the San Joaquin and South Coast Air Basins. No causality, Bell, a nd Levy represent ozone estimates. Expert K, Pope, Laden, and Expert E represent PM estimates

.6-85 Figure 6.6*:

0 7 9 075 070 0

6 5 0 10000 20000 30000 40000 50000 60000Millions of 2006$ Ozone and PM Total Benefits including all combinati ons of mortality estimates (7% Discount Rate)

N o C au s a l i t y & E x p e r t K

B

e l l 2 0 0 4 & P o p e

B

e l l 2 0 0 5 & L a d e n

L e v y & E x p er t E

* These figures reflect full attainment in all loca tions of the U.S. except two areas of California. These two areas, which have high levels of ozone, a re not planning to meet the current standard until after 2020. The estimates in the tab le 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 distributi on of visibility benefits in Class I areas in 2020. Note that these estimates represent the monet ized 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 t his 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 PM 2.5 RIA (EPA, 2006)

Table 7-50: Monetary Benefits Associated with Visib ility Improvements from the 0.070 Simulated Ozone Attainment Strategy in Select ed 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 unround ed estimates. As such, totals will not sum across colu mns.

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 s ources in and around projected nonattaining counties in 2020. While 2020 is the expected date t hat states would need to demonstrate attainment with the revised standard, it is expecte d that benefits (and costs) will begin occurring much earlier, as states begin implementing control m easures to show reasonable progress towards attainment. Using the full range of benefit s (including the results of the expert elicitation), we estimate that total ozone benefits and PM 2.5 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 realiz ed. The magnitude of these estimated benefits provide additional evidence of the importa nt role that implementation of the standards plays in reducing the health risks associated with exceeding the standard.
There are several important factors to consider whe n 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 evaluat ed standards. Benefits are most uncertain for California due to the unique challeng e of modeling attainment with the standards in this state. These challenges include h igh levels of ozone, difficulties in modeling the impacts of emissions controls on air q uality, and the very large proportion of California benefits that were derived through ext rapolation. On the one hand, these California benefits are likely to understate the ac tual benefits of attainment strategies, because we applied an estimation approach that redu ced concentrations only at the specific violating monitors and not surrounding monitors that did not violate the6-87

standards. The magnitude of this underestimate is unknown. On the other hand, it is possible that new technologies might not meet the sp ecifications, development timelines, or cost estimates provided in this analysis, thereb y 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 standa rds 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 previo usly in this chapter, these benefits are likely to be more uncertain than the modeled benefi ts. The 0.075 ppm and 0.079 ppm benefits were derived by interpolating the full att ainment benefits of the 0.070 ppm alternative (a process which is described in Append ix 6a). This approach may under- or over-estimate benefits if the actual geographic dis tribution of air quality changes is different than that assumed in the interpolation.

3. There are a variety of uncertainties associated wit h 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 th e fact that different published studies of the same pollutant/health effect relationship typic ally 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 relation ship between region and health effect, to the extent that such a relationship exists; extr apolation 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 re lative 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 wit h the economic valuation of the health endpoints estimated in this analysis . Uncertainties specific to the valuation of premat ure mortality include across study variation; the assum ption that WTP for mortality risk reduction is linear; assuming that voluntary and in voluntary 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, wh ich may bias WTP estimates upward.

5. This analysis includes estimates of PM 2.5 co-benefits that were derived through benefit per-ton estimates . These benefit per-ton estimates represent regiona l averages. As such, they do not reflect any local variability in the in cremental PM 2.5 benefits per ton of NOx abated. As discussed in the PM 2.5 NAAQS RIA (Table 5.5), there are a variety of uncertainties associated with these PM benefits.

6. PM

2.5 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 PM 2.5 and ozone mortality functions used. When calculati ng PM 2.5 co-benefits we assume that states will pursue an o zone strategy that reduces NOx6-88

emissions. As such, these estimates are strongly in fluenced by the assumption that all PM components are equally toxic. We also acknowledge t hat when implementing any new standard, states may elect to pursue a different ozone strategy, which would in turn af fect the level of PM 2.5 co-benefits.

7. Projecting key variables introduces uncertainty . Inherent in any analysis of future regulatory programs are uncertainties in projecting atmospheric conditions and source

level emissions, as well as population, health base lines, incomes, technology, and other factors. In addition, data limitations prevent an o verall quantitative estimate of the uncertainty associated with estimates of total econ omic benefits. If one is mindful of these limitations, the magnitude of the benefits es timates presented here can be useful information in expanding the understanding of the p ublic health impacts of reducing ozone precursor emissions.

8. This analysis omits certain unquantified effects du e to lack of data, time and resources

. These unquantified endpoints include the direct eff ects of ozone on vegetation, the deposition of nitrogen to estuarine and coastal wat ers and agricultural and forested land, and the changes in the level of exposure to ultravi olet radiation from ground level ozone. EPA will continue to evaluate new methods and models and select those most appropria te for estimating the health benefits of reductions in air pollution. It is important to continue improving benefits transfer methods in terms of tra nsferring economic values and transferring estimated impact functions. The development of both better mod els of current health outcomes and new models for additiona l health effects such as asthma, high blood pressure, and adverse birth outcomes (su ch as low birth weight) will be essential to future improvements in the accuracy an d reliability of benefits analyses (Guo et al., 1999; Ibald-Mulli et al., 2001). Enhanced c ollaboration between air quality modelers, epidemiologists, toxicologists, and econo mists should result in a more tightly integrated analytical framework for measuring healt h benefits of air pollution policies. Readers interested in a more extensive discussion o f the sources of uncertainty in human health benefits analyses should consult the PM NAAQ S RIA.
6.5.6 Summary of Total Benefits
Table 6.51 presents the total number of estimated o zone and PM 2.5 -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 derive d mortality functions. Figure 6.7 graphically presents the total number of estimated ozone and PM 2.5 -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.6-89Table 6.51: Summary of Total Number of Annual Ozone and PM2.5-Related Premature Mortalities and Premature Morbidity Avoided in2020ACombined Estimate of MortalityCombined Range of Ozone Benefits and PM2.5Co-benefits by Standard AlternativeDModel or Assumption0.079 ppm 0.075 ppm 0.070 ppm 0.65 ppmNMAPS Bell (2004) 140 to 1,300 260 to 2,000560 to 3,500 940 to 5,500Bell (2005) 200 to 1,300 420 to 2,200 1,100to 4,100 2,000 to 6,500Meta-analysis Ito 230 to 1,400 500 to 2,3001,400 to 4,300 2,500 to 7,000Levy 230 to 1,400 510 to 2,300 1,400 to 4,400 2,500 to 7,100No Causality 120 to 1,200 190 to 2,000 310to 3,200 490 to 5,000Combined Estimate of MorbidityCombined Ozone Benefits and PM2.5Co-benefits by Standard AlternativeAcute Myocardial InfarctionB570 890 1,500 2,300Upper Respiratory SymptomsB3,100 4,900 8,100 13,000Lower Respiratory SymptomsB4,200 6,700 11,000 17,000Chronic BronchitisB240 380 630 970Acute BronchitisB640 1,000 1,700 2,600Asthma ExacerbationB3,900 6,100 10,000 16,000Work Loss DaysB28,000 43,000 72,000 110,000School Loss DaysC72,000 200,000 640,000 1,100,000Hospital and ER Visits 890 1,900 5,100 9,400Minor Restricted Activity Days 340,000 750,0002,100,000 3,500,000ADoes not reflect estimates for the San Joaquin andSouth Coast Air BasinsBPM-related benefits onlyCOzone-related benefits onlyDIncludes ozone benefits, and PM2.5co-benefits. Range was developed by adding the estimate from the ozone premature mortality function to both thelower and upper ends of the range of the PM2.5premature mortality functions characterized in theexpert elicitation.6-90Figure 6.7: Total Annual Ozone and PM2.5-Related Premature Mortalities Avoided in 2020 by StandardAlternative6-91Table 6.52: Regional Breakdown of Annual Ozone Benefit Results by Health Endpoint in 2020 (thousands of 2006$)*Endpoint Group Author Year079Valuation079Incidence075 Valuation075Incidence070 Valuation070Incidence065 Valuation065IncidenceHospital Admissions, Respiratory (0-1) 470$ 472,200$ 2208,800$ 88015,000$ 1,500Hospital Admissions, Respiratory (65+) 1,300$ 545,200$ 22020,000$ 87050,000$ 2,100Emergency Room Visits, Respiratory 13$ 3568$ 190290$ 770530$ 1,400School Loss Days 1,700$ 19,0008,500$ 95,00035,000$ 390,00061,000$ 690,000Worker Productivity 430$ 370,0002,100$ 1,800,0008,700$ 7,500,00016,000$ 14,000,000Acute Respiratory Symptoms 2,800$ 47,00015,000$ 250,00061,000$ 1,000,000110,000$ 1,800,000Mortality Bell et al. 2004 50,000$ 7290,000$ 381,300,000$ 1702,400,000$ 300Mortality Bell et al. 2005 160,000$ 21940,000$ 1204,100,000$ 5307,600,000$ 980Mortality Ito et al. 220,000$ 291,300,000$ 1705,600,000$ 73010,000,000$ 1,300Mortality Levy et al. 230,000$ 301,300,000$ 1705,800,000$ 75011,000,000$ 1,400Hospital Admissions, Respiratory (0-1) 10$ 1.018$ 1.8820$ 832,000$ 200Emergency Room Visits, Respiratory 0.14$ 0.390.27$ 0.749.4$ 2627$ 74School Loss Days 33$ 37060$ 6702,600$ 29,0006,500$ 72,000Worker Productivity 6.3$ 5,50011$ 9,900360$ 310,0001,900$ 1,600,000Acute Respiratory Symptoms 61$ 1,000110$ 1,8004,500$ 76,00011,000$ 180,000Hospital Admissions, Respiratory (65+) 30$ 1.358$ 2.5(39)$ (1.6)3,200$ 140Mortality Bell et al. 2004 1,500$ 0.202,700$ 0.3569,000$ 9.0200,000$ 26Mortality Bell et al. 2005 5,100$ 0.658,900$ 1.2230,000$ 30670,000$ 87Mortality Ito et al. 6,800$ 0.8812,000$ 1.6310,000$ 40900,000$ 120Mortality Levy et al. 7,100$ 0.9213,000$ 1.6320,000$ 42950,000$ 120Hospital Admissions, Respiratory (0-1) 1,400$ 1402,600$ 2605,800$ 5809,100$ 910Emergency Room Visits, Respiratory 19$ 5136$ 9779$ 220130$ 340School Loss Days 4,700$ 53,0009,000$ 100,00020,000$ 220,00031,000$ 350,000Worker Productivity 4,300$ 3,800,0008,000$ 7,100,00018,000$ 16,000,00031,000$ 26,000,000Acute Respiratory Symptoms 7,800$ 130,00015,000$ 250,00033,000$ 550,00052,000$ 880,000Hospital Admissions, Respiratory (65+) 3,100$ 1305,800$ 24013,000$ 56022,000$ 910Mortality Bell et al. 2004 140,000$ 18260,000$ 33580,000$ 75940,000$ 120Mortality Bell et al. 2005 450,000$ 58840,000$ 1101,900,000$ 2503,100,000$ 400Mortality Ito et al. 610,000$ 781,100,000$ 1502,600,000$ 3304,200,000$ 540Mortality Levy et al. 630,000$ 811,200,000$ 1502,700,000$ 3404,300,000$ 560Hospital Admissions, Respiratory (0-1) 1,900$ 1904,800$ 48015,000$ 1,50026,000$ 2,700Hospital Admissions, Respiratory (65+) 4,400$ 19011,000$ 47034,000$ 1,40074,000$ 3,200Emergency Room Visits, Respiratory 32$ 87100$ 280370$ 1,000690$ 1,900School Loss Days 6,400$ 72,00018,000$ 200,00057,000$ 640,00099,000$ 1,100,000Worker Productivity 4,700$ 4,200,00010,000$ 9,000,00027,000$ 23,000,00049,000$ 42,000,000Acute Respiratory Symptoms 11,000$ 180,00029,000$ 500,00098,000$ 1,700,000170,000$ 2,900,000Mortality Bell et al. 2004 190,000$ 24550,000$ 711,900,000$ 2503,500,000$ 450Mortality Bell et al. 2005 620,000$ 801,800,000$ 2306,200,000$ 81011,000,000$ 1,500Mortality Ito et al. 830,000$ 1102,400,000$ 3108,500,000$ 1,10015,000,000$ 2,000Mortality Levy et al. 860,000$ 1102,500,000$ 3208,800,000$ 1,10016,000,000$ 2,100

National Total East Rest of West California* National Total does not reflect benefits for theSouth Coast and San Joaquin Air Basins. Confidenceintervals not available for PM estimates. All estimates rounded totwo significant figures. Valuation results for mortality and nonfatal myocardial infarctions are shownat a 3% discount rate. Does not include visibilitybenefits.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 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 87 $ 170

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 21 $ 170

21

$ 170

290

$ 2,400

780

$ 6,300 Acute Bronchitis 2.0 $ 4.0

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 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 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 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 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

$ 490National TotalEastRest of WestCalifornia

* 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 sig nificant figures. Valuation results for mortality an d nonfatal myocardial infarctions are shown at a 3% discount rate. PM inc idence and other PM morbidity incidence and valuati on estimates are identical to Table 6.54 because these are not disco unted. 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 87 $ 170

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 21 $ 170

21

$ 170

290

$ 2,400

780

$ 6,300 Acute Bronchitis 2.0 $ 4.0

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

$ 490National TotalEastRest of WestCalifornia

* 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 sig nificant figures. Valuation results for mortality a nd nonfatal myocardial infarctions are shown at a 7% discount rate. PM inc idence and other PM morbidity incidence and valuati on estimates are identical to Table 6.53 because these are not disco unted. Does not include visibility benefits.6-94

Table 6.55: Regional Breakdown of Annual Ozone and PM Ben efit Results by Health

Endpoint in 2020 (3% discount rate, thousands of 20 06$)*
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 $1,300,0 00 $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 $5,80 0,000 $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 $8,300,0 00 $14,000,000 PM Mortality Expert E $2,800,000 $8,000,000 $16,0 00,000 $26,000,000East

PM Mortality Expert K $270,000 $800,000 $1,700,00 0 $2,700,000 Ozone Morbidity (non-causal) $140 $260 $8,200 $2 4,000 Ozone Mortality Bell 2004 $1,500 $2,700 $69,000 $2 00,000 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 $1,2 00,000 PM Mortality Expert E $66,000 $65,000 $880,000 $ 2,300,000Rest 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 $580,00 0 $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 $2,70 0,000 $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 $2,100,00 0 $3,000,000 PM Mortality Laden $3,000,000 $3,700,000 $4,700,0 00 $6,700,000 PM Mortality Expert E $7,600,000 $8,100,000 $9,20 0,000 $13,000,000California

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 $6,20 0,000 $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 $8,80 0,000 $16,000,000 PM Infant Mortality Woodruff $5,000 $7,800 $13,00 0 $20,000 PM Morbidity $190,000 $300,000 $500,000 $780,000 PM Mortality Pope $1,800,000 $3,300,000 $6,000,00 0 $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 $41,000,000National Total

PM Mortality Expert K $1,000,000 $1,600,000 $2,70 0,000 $4,300,000 * Totals do not reflect benefits for the South Coas t and San Joaquin Air Basins. Confidence intervals not available for PM estimates. All estimates rounded t o two significant figures. Valuation results for mo rtality and nonfatal myocardial infarctions are shown at a 3% d iscount rate. Does not include visibility benefits.6-95

Table 6.56: Regional Breakdown of Annual Ozone and PM Ben efit Results by Health

Endpoint in 2020 (7% discount rate, thousands of 20 06$)*
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 $1,300,0 00 $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 $5,80 0,000 $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 $15,0 00,000 $23,000,000East

PM Mortality Expert K $240,000 $720,000 $1,500,00 0 $2,500,000 Ozone Morbidity (non-causal) $140 $260 $8,200 $2 4,000 Ozone Mortality Bell 2004 $1,500 $2,700 $69,000 $2 00,000 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 $1,1 00,000 PM Mortality Expert E $59,000 $58,000 $790,000 $ 2,100,000Rest of West

PM Mortality Expert K $5,700 $5,800 $81,000 $220 ,000Ozone Morbidity (noncausal) $21,000 $40,000 $90,000 $140,000 Ozone Mortality Bell 2004 $140,000 $260,000 $580,00 0 $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 $2,70 0,000 $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 $1,900,00 0 $2,700,000 PM Mortality Laden $2,700,000 $3,300,000 $4,200,0 00 $6,000,000 PM Mortality Expert E $6,900,000 $7,300,000 $8,30 0,000 $11,000,000California

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 $6,20 0,000 $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 $8,80 0,000 $16,000,000 PM Infant Mortality Woodruff $4,000 $6,300 $11,00 0 $16,000 PM Morbidity $190,000 $300,000 $500,000 $780,000 PM Mortality Pope $1,600,000 $3,000,000 $5,400,00 0 $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 $37,000,000National Total

PM Mortality Expert K $910,000 $1,500,000 $2,500, 000 $3,900,000 * Totals do not reflect benefits for the South Coas t and San Joaquin Air Basins. Confidence intervals not available for PM estimates. All estimates rounded t o two significant figures. Valuation results for mo rtality and nonfatal myocardial infarctions are shown at a 7% d iscount rate. Does not include visibility benefits.6-96

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Appendix 6a: Additional Benefits Information

Summary
This appendix provides additional information regar ding the benefits analysis, including (1) methods for developing estimate of full attainm ent air quality; (2) the process for interpolating the 0.075 ppm and 0.079 ppm benefits estimat es; (3) the partial attainment PM2.5 incidence and valuation estimates.

6a.1 Developing an Air Quality Estimate of Full Attainmen t with the Alternative Ozone Standards
As discussed in chapter 3, the modeled attainment s cenarios were not sufficient to simulate full attainment with each of the three alternative ozone standa rds 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 deriv e 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 moni tor 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 dow nward 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 cha nge 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 effe cts 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 m etrics based on the distribution of CMAQ-modeled hourly ozone concentrations for the ozon e season. However, because we were performing a benefits assessment based on monitor va lues that have been rolled-back, it was necessary to derive each of these metrics outside o f the BenMAP model. Thus, we first developed a scaling ratio that related the calculat ed 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 interpo lated to the CMAQ grid cell.
3. Import distribution of air quality modeled daily and hour ly ozone concentrations into BenMAP. Create air quality grid in BenMAP using spa tial and temporal scaling6a-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 m etric to design value that can then be subsequently used to scale the calculated design value an d thus derive each of the metrics.
After having calculated these scaling ratios we the n performed the monitor rollback as follows:
1. Roll back the calculated 0.08 ppm partial attainme nt 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 metr ic 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 t o grid cell-level ozone metric using ratios described above.
6. Create new 0.070 ppm and 0.065 ppm air quality g rids from grid cell-level ozone metrics.
7. Perform benefits analysis with baseline and control gri ds.
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 inc orporating control strategies was only available for 0.070 ppm. This interpolation for 0.0 75 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 f actor 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 t he 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 the y would from attaining 0.070
ppm.

1 BenMAP Technical Appendices, Abt Associates: May 2005. Pag e C-12.6a-3

3. We then interpolated these monitor-specific ratios t o 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 interp olation.

6a.2 Partial Attainment PM 2.5 Incidence and Valuation Estimates
Tables 6a.1 through 6a.5 below summarize the estima tes of PM 2.5 incidence and valuation resulting from the 0.070 ppm partial attainment scenario . These estimates provided the basis for the full attainment PM 2.5 co-benefit estimates found in Chapter 6 of this RIA. D etails about the methodology for this approach can also be found in Chapter 6.6a-4

Table 6a.1: Illustrative 0.070 ppm Partial Attainme nt Scenario: Estimated Reductions in PM Premature Mortality associate with PM Co-Benefit (9 5 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 Epidemiolog y Literature 420 6.3 5.4 430 ACS Study a (110--730) (2--10) (2--9) (110--750) 950 14 12 980 Harvard Six-City Study b (420--1,500) (7--21) (6--18) (440--1,500) 1.1 0.15 0.02 1.3 Woodruff et al. 1997 (infant mortality) (0.34--1.8) (0.07--0.23) (0.01-- 0.04) (0.42--2.1) Mortality Impact Functions Derived from Expert Elic itation 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.

b Based on Laden et al. (2006) reporting of the exten ded Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporat ing what has previously been a sensitivity estimate

.

c All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimat es 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 Attainme nt Scenario: Estimated Reductions in Morbidity Associated with PM Co-Benefit (95 th percentile confidence intervals provided in parentheses) a

Eastern U.S. Western U.S. Excluding California California National PM CoBenefits Morbidity Impact Functions Derived from Epidemiolog y Literature 380 38 8.7 420 Chronic Bronchitis (age >25 and over) (-11--760) (4--72) (1--17) (-6--850) 970 12 11 1,000 Nonfatal myocardial infarction (age >17) (440--1,500) (6--18) (5--16) ( 450--1,500) 120 1.3 1.1 120 Hospital admissions— respiratory (all ages) (46--184) (1--2) (1--2) (46- -186) 230 2.8 2.3 240 Hospital admissions— cardiovascular (age >17) (127--340) (2--4) (1--3) ( 130--340) 400 3.6 2.4 410 Emergency room visits for 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) 7,100 150 130 7,400 Lower respiratory symptoms (age 7–14) (2,600--12,000) (63--230) (57--210) (2,8 00--12,000) 5,200 110 95 5,400 Upper respiratory symptoms (asthmatic children age 9–18) (880--9,500) (27--190 ) (24--170) (930--9,900) 6,500 130 120 6,800 Asthma exacerbation (asthmatic children age 6–18) (-78--21,000) (10--42 0) (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) 280,000 4,800 4,700 290,000 Minor restricted activity days (age 18–65) (220,000--330,000) (4,000--5,700) (3,900--5,500) (2 30,000--340,000)

a All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimat es 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 Attai n 0.070 ppm: Estimated Partial Attainment Value of Reductions in PM2.5-Related Premature M ortality Associated with PM Co-Benefit (3 percent discount rate, in millions of 2006$) 95th Percentile Confidence Intervals Provided in Parentheses c

Eastern U.S. Western U.S.

Excluding California California National PM CoBenefits Mortality Impact Functions Derived from Epidemiolog y Literature $3,000 $44 $38 $3,000 ACS Study a ($380--$7,000) ($6.8--$110) ($5.8--$95) ($440--$7,2 00) $6,700 $99 $85 $6,900 Harvard Six-City Study b ($1,000--$14,000) ($16--$210) ($14--$180) ($1,000-- $15,000) $7.5 $1.0 $0.17 $8.8 Woodruff et al., 1997 (infant mortality) ($1.0--$17) ($0.16--$2.3) ($0.03 --$0.36) ($1.2--$20) Mortality Impact Functions Derived from Expert Elic itation $11,000 $1,100 $220 $12,000 Expert A ($200--$30,000) ($55--$2,800) ($20--$560) ($280--$3 3,000) $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--$1 6,000) $14,000 $1,300 $280 $15,000 Expert E ($2,000--$32,000) ($200--$3,000) ($43--$600) ($2,30 0--$35,000) $7,600 $740 $150 $8,500 Expert F ($1,400--$17,000) ($130--$1,600) ($27--$330) ($1,40 0--$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--$2 4,000) $6,700 $650 $130 $7,400 Expert J ($430--$22,000) ($61--$2,100) ($17--$410) ($520--$2 4,000) $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.

b Based on Laden et al. (2006) reporting of the exten ded Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporat ing what has previously been a sensitivity estimate

.

c All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimat es 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 Attai n 0.070 ppm: Estimated Partial Attainment Value of Reductions in PM 2.5 -Related Premature Mortality Associated with PM Co-Benefit (7 percent discount rate, in millions of 2006$) 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 Epidemiolog y Literature $2,700 $40 $34 $2,700 ACS Study a ($340--$6,300) ($6.8--$110) ($5.8--$95) ($360--$6,4 00) $6,000 $89 $77 $6,200 Harvard Six-City Study b ($920--$13,000) ($14--$190) ($12--$160) ($940--$13 ,000) $6.8 $0.94 $0.15 $7.9 Woodruff et al., 1997 (infant mortality) ($0.90--$16) ($0.14--$2.0) ($0.0 2--$0.33) ($1.1--$18) Mortality Impact Functions Derived from Expert Elic itation $9,900 $970 $200 $11,000 Expert A ($180--$27,000) ($50--$2,500) ($18--$510) ($250--$3 0,000) $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--$1 5,000) $12,000 $1,200 $250 $14,000 Expert E ($1,800--$29,000) ($180--$2,700) ($39--$540) ($2,00 0--$32,000) $6,800 $660 $140 $7,600 Expert F ($1,200--$16,000) ($110--$1,500) ($24--$300) ($1,30 0--$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--$2 2,000) $6,000 $590 $120 $6,700 Expert J ($390--$19,000) ($55--$1,900) ($16--$370) ($470--$2 2,000) $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.

b Based on Laden et al. (2006) reporting of the exten ded Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporat ing what has previously been a sensitivity estimate

.

c All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimat es 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 Attai n 0.070 ppm: Estimated Partial Attainment Monetary Value of Reductions in Risk of PM 2.5 -Related Morbidity Reductions Associated with PM Co-Benefit (in millions of 2006$) 95 th Percentile Confidence Intervals Provided in Parentheses a

Eastern U.S. Western U.S. Excluding California California National PM CoBenefits Morbidity Impact Functions Derived from Epidemiolog y Literature $180 $19 $4.3 $210 Chronic Bronchitis (age >25 and over) ($4.0--$870) ($1.0--$86) ($0.24--$20) ($5.2--$980) $210 $2.6 $2.3 $210 Nonfatal myocardial infarction (age >17) ($50--$480) ($0.65--$5.8) ($0. 61--$5.2) ($50--$490) $2.5 $0.03 $0.02 $ 2.5 Hospital admissions— respiratory (all ages) ($1.10--$3.80) ($0.01--$0.04 ) ($0.01--$0.04) ($1.1--$3.8) $6.5 $0.08 $0.06 $6.6 Hospital admissions— cardiovascular (age >17) ($3.80--$9.10) ($0.05--$0. 11) ($0.04--$0.09) ($3.9--$9.3) $0.15 $0.00 $0.00 $0.15 Emergency room visits for asthma (age <19) ($0.07--$0.25) ($0.00--$0.00) ($0.00--$0.00) ($0.07--$0.25) $0.50 $0.06 $0.01 $0.57 Acute bronchitis (age 8– 12) (-$0.14--$1.50) ($0.00--$0.17) ($0.00--$0.03) ( -$0.14--$1.7) $ 0.14 $0.00 $0.00 $0.14 Lower respiratory symptoms (age 7–14) ($0.04--$0.29) ($0.00--$0.01) ( $0.00--$0.01) ($0.04--$0.30) $0.16 $0.00 $0.00 $0.17 Upper respiratory symptoms (asthmatic children age 9–18) ($0.03--$0.41) ($0.00--$0.01) ($0.00--$0.01) ($0.03 --$0.42)

$0.35 $0.01 $0.01 $0.36 Asthma exacerbation (asthmatic children age 6–18) ($0.01--$1.30) ($0.00--$0.03) ($0.00--$0.02) ($0.01 --$1.4)

$5.7 $0.10 $0.12 $6.0 Work loss days (age 18– 65) ($4.9--$6.6) ($0.09--$0.11) ($0.10--$0.13) ($5. 1--$6.8) $7.8 $0.14 $0.13 $8.1 Minor restricted activity days (age 18–65) ($0.39--$16) ($0.01--$0) ($0.01--$0) ($0.40--$17)

a All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimat es 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 Am bient O 3 

### and PM 2.5



### Associated with Illustrative O 3 

### 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, 20087b-1



### Appendix Chapter 7b: Health-Based Cost-Effectivenes s of Reductions in Ambient O 3 

### and PM 2.5 

### Associated with Illustrative O 3 

### NAAQS Attainment Strategies

7b.1 Summary
Health-based cost-effectiveness analysis (CEA) and co st-utility analysis (CUA) have been used to analyze numerous health interventions but have not bee n widely adopted as tools to analyze environmental policies. Analyses of environmental r egulations have typically used benefit-cost analysis to characterize impacts on social welfare. Be nefit-cost analyses allow for aggregation of the benefits of reducing mortality risks with other mon etized 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 w ide range of quantifiable benefits can be compared to costs to evaluate the economic efficienc y 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, s uch as quality-adjusted life years (QALYs). QALYs were developed to evaluate the effectiveness of individual medical treatments, and EPA is still evaluating the appropr iate methods for CEA for environmental regulations.

In this CEA, we estimated statistical lives saved, statisti cal 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 l ife extension and quality-of-life impacts. MILYs represent t he sum of life years gained due to reductions in premature mortality and the QALYs gained du e to reductions in chronic morbidity. This measure may be preferred to existing QALY aggre gation approaches because it does not devalue life extensions in individuals with preexis ting illnesses that reduce quality of life. However, the MILY measure is still based on life ye ars and thus still inherently gives more weight to interventions that reduce mortality and m orbidity 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 QA LYs gained from mortality avoided. Put another way, we assumed weig hts 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 b enefit received in the future). We used discount rates of 3 percent and 7 percent.

For each illustrative O 3 NAAQS attainment strategy, we present several metr ics: 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 PM 2.5 -related co-benefits associated with all the illustrative O 3 NAAQS attainment strategies considered, we were able to model the changes in PM 2.5 occurring7b-2

as a result of only one illustrative O 3 NAAQS attainment strategy 1 . Therefore PM 2.5 -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 O 3 NAAQS attainment strategies omit the PM 2.5 -related co-benefits and are therefore likely to un derstate the cost effectiveness of those strategies.

For the illustrative O 3 NAAQS attainment strategy for which we were able to incl ude both direct O 3 -related health benefits and indirect PM 2.5 -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) pe r MILY gained.
The results of the analysis are summarized as follo ws:

x Estimates of O 3 -related lives saved were substantially affected by the underlying O 3 - mortality study used and, to a greater extent, by the a ttainment scenario considered. Because all O 3 -related mortality was assumed to occur in 2020, we did not discount O 3 - related lives saved. Non-zero estimates of O 3 -related lives saved based on Bell et al. (2004) ranged from 36 (95% CI: 12 – 60), under ful l 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 O 3 -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 alterna tive standards, respectively.

x Non-zero estimates of O 3 -related life years saved also depended substantially o n the underlying mortality study used and the attainment s cenario considered. In addition, we hypothesized several alternative possible sets of life expectancies associated with agespecific O 3 -related deaths avoided, and the choice of life expectancies had a large impact on the estimates of O 3 -related life years saved. Using a 3 percent discount rate, the smallest non-zero estimate of O 3 -related life years saved was 160 (95% CI: 54 – 27 0), under full attainment of the alternative standard of 0.07 9 ppm, based on Bell et al. (2004), and assuming that O 3 -related mortality occurs only in the subpopulation with severe preexisting conditions (and thus the shortest life expectancies). The largest estimate of O 3 -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 O 3 -related mortality occurs in the general populatio n.

x Using a 7 percent discount rate, the smallest non-zero e stimate of O 3 -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 th at O 3 -related mortality occurs only in the subpopulation with severe preexisting conditions (and thus the shortest l ife expectancies). The largest estimate of O 3 -related life years saved was 19,000 (95% CI: 13,000 – 25,000), under full attainment of the alte rnative standard of 0.065 ppm, based

1 This illustrative attainment strategy has a baseli ne of partial attainment of the current standard of 0.084 ppm and a control scenario of partial attainment of a n alternative standard of 0.070
ppm.7b-3

on Levy et al. (2004), and assuming that O 3 -related mortality occurs in the general population.

x The estimate of PM 2.5 -related lives saved under the single illustrative attainment strategy for which we were able to model the indirect changes in PM 2.5 concentrations and thus include PM 2.5 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 O 3 -related mortality, PM 2.5 -related mortality was not all assumed to occur in the year of exposure. Estimates of PM 2.5 -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, PM 2.5 -related life years saved was estimated to be 4,400 (95% CI: 1,700 – 7000), based on Pope e t 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 Po pe 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 PM 2.5 concentrations and thus include PM 2.5 co-benefits, we estimated PM 2.5 -related reductions in chronic bronchitis (CB) and non-fatal acute myocardial i nfarction (AMI) and the corresponding improvements in quality of life as QALYs gain ed. QALYs gained from PM 2.5 -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 PM 2.5 -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 O 3 -related life saved (in 2006 $) (in those illustrati ve attainment strategies for which we incorporated only O 3 -related benefits) were greatest in the illustrative attainment strategy of full attainment of a 0.075 ppm stand ard. Even under this one strategy, however, cost effectiveness estimates varie d substantially, depending on the underlying mortality study used and the discount ra te (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 O 3 -related benefits would tend to overstate the cost per life saved – i.e., understate cost effectiveness – becau se PM 2.5 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 O 3 -related and PM 2.5 -related lives saved were included, ranging from $1 .8 million (95% CI: $1.3 million – $2.6 million), using Levy e t al. (2005) and Laden et al. (2006),7b-4

to $5.4 million (95% CI: $3.2 million – $9.9 milli on), using Bell et al. (2004) and Pope et al. (2002).

x The pattern seen for cost per life year saved was s imilar to that seen for cost per life saved. Net costs per O 3 -related life year saved were greatest in the illustra tive attainment strategy of full attainment of a 0.075 ppm standard. Howev er, there was substantial variability in cost effectiveness estimates across these illustrative attainment strategies. The lowest cost per life year saved was estimated t o 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 li fe expectancies of the general population. The highest cost per life year saved w as estimated to be $29 million (95% CI: $15 million – $75 million), under full attainment of a 0.0 75 ppm standard, using Bell et al. (2004) and a 7 percent discount rate, and as suming 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 O 3 -related and PM 2.5 -related benefits were substantially smaller than for the other scenarios. This is not surprising, since the cost effecti veness of those other scenarios was understated – and thus the cost per life year saved wa s overstated – because of the omission of PM 2.5 -related live years saved. The lowest estimate of ne t 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 O 3 -related mortality avoided, assuming life expectancies of the general population, and using a 3 percent discount rate. The highest estimate was $0.79 mil lion (95% CI: $0.44 million – $1.6 million), based on Bell et al. (2004) and Pope e t al. (2002), and, for O 3 -related mortality avoided, assuming life expectancies of a subpopul ation with severe preexisting conditions, and using a 7 percent discount rate.

x Finally, under the single illustrative strategy for which we included both O 3 -related and PM 2.5 -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 O 3 -related mortality avoided, assuming life expectancies of the general population; the h ighest estimate was $0.30 million (95% CI: $0.19 million – $0.53 million), b ased on Bell et al. (2004) and Pope et al. (2002) and, for O 3 -related mortality avoided, assuming life expectancies of a subpopulation with severe preexisting conditions.

x Using a 7 percent discount rate, the lowest estimat e of net costs per MILY gained was $0.18 million (95% CI: $0.14 million – $0.26 milli on), based on Levy et al. (2005) and Laden et al. (2006) and, for O 3 -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 O 3 -related mortality avoided, assuming life expectancies of a subpopul ation with severe preexisting conditions.7b-5

7b.2 Introduction
Health-based cost-effectiveness analysis (CEA) and co st-utility analysis (CUA) have been used to analyze numerous health interventions but have not bee n widely adopted as tools to analyze environmental policies. Analyses of environmental r egulations have typically used benefit-cost analysis to characterize impacts on social welfare. Be nefit-cost analyses allow for aggregation of the benefits of reducing mortality risks with other mon etized 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 w ide range of quantifiable benefits can be compared to costs to evaluate the economic efficienc y 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, s uch as quality-adjusted life years (QALYs).

QALYs were developed to evaluate the effectiveness of i ndividual medical treatments, and EPA is still evaluating the appropriate methods for CEA for environmental regulations. Agency concerns with the standard QALY methodology include t he treatment of people with fewer years to live (the elderly); fairness to people with preexisting conditions that may lea d to reduced life expectancy and reduced quality of life; and how the anal ysis 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 heal th 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 st raightforward.

The Institute of Medicine (a member institution of t he National Academies of Science) established the Committee to Evaluate Measures of He alth Benefits for Environmental, Health, and Safety Regulation to assess the scientific vali dity, 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 healt h and safety, although not sufficient for informed regulatory decisions (Miller, Robinson, and Lawrence, 2006). They emphasized the need for additional data and meth odological improvements for CEA analyses, and urged greater consistency in the repo rting of assumptions, data elements, and analytic methods. They also provided a number of recomme ndations for the conduct of regulatory CEA analyses. EPA is evaluating these recomme ndations and will determine a response for upcoming analyses.

CEA and CUA are most useful for comparing programs that h ave similar goals, for example, alternative medical interventions or treatments tha t can save a life or cure a disease. They are less readily applicable to programs with multiple categor ies 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 as7b-6

visibility improvements associated with reductions in PM 2.5 or increases in worker productivity associated with reductions in O 3 , 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 analy sis of public health interventions (US FDA, 2004). QALY-based analyses have not been as accepted in th e environmental economics literature because of concerns about the theoretical consi stency of QALYs with individual preferences (Hammitt, 2002), treatment of nonhuman h ealth 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 anal yses 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 Gr aham, 2002). In addition, the World Health Organization has adopted the use of disability-adju sted life years, a variant on QALYs, to assess the global burden of disease due to different cause s, including environmental pollution (Murray et al., 2002; de Hollander et al., 1999).

One of the ongoing controversies in health impact asses sment regards whether reductions in mortality risk should be reported and valued in term s 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 y ounger 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 i nterventions 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 lo ss in life expectancy, making interventions that primarily benefit individuals with poor health seem less ben eficial 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 re ceived in the future). We used discount rates of 3 percent and 7 percent.

Where possible, benefits that could not be quantified in t he denominator of our cost-effectiveness ratios were monetized and subtracted from the cost o f the regulation in the numerator. For example, developing QALYs for acute health effects is probl ematic (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 val ue of all avoided acute morbidity effects from the cost of the regulation, in the num erator of the cost-effectiveness ratios. The7b-7

monetized benefits of non-health improvements, where th ey were estimated, were similarly subtracted from the cost of the regulation. Finall y, although QALY estimates were derived for the (PM 2.5 -related) chronic morbidity endpoints, the medical and oppo rtunity costs associated with these chronic illnesses were also subtracted fr om the cost of the regulation.

Although there are indirect PM 2.5 -related co-benefits associated with all the illustrative O 3 NAAQS attainment strategies, we were able to model the changes in PM 2.5 occurring as a result of only one illustrative O 3 NAAQS attainment strategy (see Chapter 7 for a full dis cussion of this issue). Therefore PM 2.5 -related co-benefits are included in the cost effec tiveness metrics presented only for that one strategy. The cost eff ectiveness metrics presented for all of the other illustrative O 3 NAAQS attainment strategies omit the PM 2.5 -related co-benefits and are therefore likely to understate the cost effectiveness of thos e strategies.

The indirect PM 2.5 -related co-benefits derive not only from avoided c ases of premature mortality and acute morbidity, but from avoided cases of chronic morbi dity (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 (MIL Ys), 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 indiv iduals with preexisting illnesses that reduce quality of life. However, the MILY measure is still base d on life years and thus still inherently gives more weight to interventions that reduce morta lity and morbidity impacts for younger populations with higher remaining life expectancy.

For each illustrative O 3 NAAQS attainment strategy, we present several metr ics: lives saved, life years saved, and cost of the regulation (net of the moneti zed benefits not included in the denominator) per life saved and per life year saved

.

For the illustrative O 3 NAAQS attainment strategy for which we were able to incl ude both direct O 3 -related health benefits and indirect PM 2.5 -related co-benefits, in addition to the cost effectiveness metrics listed above we also calculated MILY s and the cost of the regulation (net of the monetized benefits not included in the denominator) p er MILY gained.

Note that, like future life years saved, future QALY s 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 pa rameters throughout the analysis. In particular, we incorporated uncertainty surrounding the co efficients in the concentrationresponse (C-R) functions, the unit values for the vari ous morbidity endpoints included in the7b-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 additi on, we examined the impacts on the cost effectiveness metrics of changing key parameters and/or as sumptions, including

x the discount rate (for the cost of the regulation i n the numerator and future lives or life years saved and QALYs gained in the denominator);

x the C-R functions for O 3 -related and PM 2.5 -related mortality ; and

x the life expectancies (and therefore years of potential life lost) of individuals who die as a result of exposure to O 3 (as explained in Section 7b.5 below).

The methodology presented in this appendix is not i ntended 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 appro ach to estimating the cost-effectiveness of direct reductions in ambient O 3 (and indirect reductions in PM 2.5 , where possible) in achieving improvements in public health. Reductions in ambient O 3 and PM 2.5 are estimated to have other health and environmental benefits that will not be reflected in this CEA. Other EPA r egulations affecting other aspects of environmental quality and publ ic health may require additional data and models that may preclude the development of similar hea lth-based CEAs. A number of additional methodological issues must be considered when c onducting 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-o f-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 t he resulting metrics. Section 7b.3 provides an overview of effectiveness measures. Sect ion 7b.4 discusses general issues in constructing cost-effectiveness ratios. Section 7b.5 pr esents the methods and results for those illustrative O 3 NAAQS attainment strategies for which we were able to incorpo rate only the O 3 - related benefits; and Section 7b.6 presents the methods an d results for the single illustrative O 3 NAAQS attainment strategy for which we were able to inclu de both the O 3 -related benefits and PM 2.5 -related co-benefits. Finally, Section 7b.7 presents concluding remarks.

7b.3 Effectiveness Measures
For the purposes of CEA, we focus the effectiveness measures o n the quantifiable health impacts of the reductions in O 3 and, where possible, PM 2.5 , estimated to result from each illustrative O 3 NAAQS attainment strategy considered. If the main impact of interest is reductions in mortality risk from air pollution, the effectiveness measures are r elatively straightforward to develop. Mortality impacts can be characterized similar to the benef its analysis, by counting the number of premature deaths avoided, or can be characterize d in terms of increases in life expectancy or7b-9

life years. 2 Estimates of premature mortality have the benefit of being r elatively simple to calculate, are consistent with the benefit-cost ana lysis, 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 effecti veness of an intervention is the gain in life expectancy or life years (Rabl, 2003; Miller and Hurl ey, 2003).
Calculations of changes in life years and life expectan cy can be accomplished using standard life table methods (Miller and Hurley, 2003). However, the cal culations require assumptions about the baseline mortality risks for each age cohort af fected 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 b een raised that air pollution affects mainly those individuals with preexisting cardiovascular and re spiratory disease, who may have reduced life expectancy relative to the general population. T his issue is explored in more detail below.
Air pollution is also associated with a number of si gnificant chronic and acute morbidity endpoints. Failure to consider these morbidity eff ects may understate the cost-effectiveness of air pollution regulations or give too little weight to r eductions 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 f rom 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 ye ar spent in perfect health is equivalent to 2 years spent with quality of life half that of perfect h ealth. QALYs can be used to evaluate environmental rules under certain circumstances, altho ugh 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 mortalit y and morbidity (Gold et al., 1996). Although there are significant non-health benefits assoc iated with air pollution regulations, over 90 percent of quantifiable monetized benefits are health-r elated. Thus, it can be argued that QALYs are more applicable for these types of regulations th an 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 t he 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 re maining, before knowing which individuals will be affected. Life expectancy thus incorporate s 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 tha n 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 t he 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 rest rictive assumptions, including independence between longevity and quality of life in the ut ility function, risk neutrality with respect to years of life (which implies that the ut ility function is linear), and constant proportionality in trade-offs between quality and quan tity of life (Pliskin, Shepard, and Weinstein, 1980; Bleichrodt, Wakker, and Johannesson, 199 6). To the extent that these assumptions do not represent actual preferences, the QALY ap proach will not provide results that are consistent with a benefit-cost analysis based on th e Kaldor-Hicks criterion. 3 Even if the assumptions are reasonably consistent with reality, beca use QALYs represent an average valuation of health states rather than the sum of s ocietal WTP, there are no guarantees that the option with the highest QALY per dollar of cost wil l 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 unde rlying structures as well, incorporating ability to pay (and thus the pot ential 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 m ost part, WTP allows parameters to be determined empirically, while the QALY approach imp oses 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 Empiric ally determined Proportionality of duration/ quality trade-off Cons tant Variable Treatment of time/age in utility function Utility l inear in time Empirically determined

Preferences Community/Individual Individual Source of preference data Stated Revealed and state d Treatment of income and prices Not explicitly consi dered Constrains choices

7b.4 Construction of Cost-Effectiveness Ratios: Genera l Issues
7b.4.1 Dealing with Morbidity Health Effects and Non-h ealth Effects
Health effects from exposure to O 3 and PM 2.5 air pollution encompass a wide array of chronic and acute conditions in addition to premature mortality. EPA’s Ozone and PM Cr iteria Documents outline numerous health effects known or s uspected 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 tha t total societal welfare improves. In this case, it is sufficient that total benefits exceed t otal costs of the regulation. This is also known a s 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 of f.7b-11

benefits, acute symptoms can affect a broad populatio n 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, inc reased agricultural and forestry yields, reduced acid damage to buildings, and a host of other impac ts. 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 measur e 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 p roblems 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 a nalysis to get healthy-utility time equivalences that can be compared across acute effec ts, but it is not clear how these can be combined with QALYs for chronic effects and loss of life expe ctancy. 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 we ight 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 QAL Ys; 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 sy mptoms is equivalent to 0.0019 QALY gained). This is troubling from both a conceptual basis and a presentation bas is. An alternative approach is simply to treat acute healt h effects like non-health benefits and subtract the dollar value (based on WTP or COI) from complia nce costs in the CEA.
To address the issues of incorporating acute morbidi ty and non-health benefits, OMB suggests that agencies “subtract the monetary estimate of th e ancillary benefits from the gross cost estimate to yield an estimated net cost.” As with b enefit-cost analysis, any unquantified benefits and/or costs should be noted and an indication of h ow 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 tha n reduced mortality and improved quality of life from avoided chronic illness – in p articular, the monetized benefits of acute morbidity avoided, the medical and opportunity cost s (“cost of illness”) of avoided chronic illness, and the benefits of non-health improvement s, including increases in worker productivity associated with reductions in O 3 and visibility improvements at national parks associ ated with reductions in PM 2.5 (see Chapter 7 for more details on these benefit categorie s).
7b.4.2 Should Life Years Gained Be Adjusted for Ini tial Health Status?
The methods outlined below in Sections 7b.5 and 7b.6 provid e estimates of the total number of life years gained in a population, regardless of the quality of those life years, or equiv alently, assuming that all life years gained are in perfect health. In some CEAs (Cohen, Hammitt, and Levy, 2003; Coyle et al., 2003), analysts have adjust ed the number of life years gained to reflect7b-12

the fact that 1) the general public is not in perfe ct 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 ma ny “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 improveme nts in quality of life (e.g., an improvement from a score of 0.2 to 0.4 is equivalent to an i mprovement from 0.8 to 1.0), so the starting health status does not affect the evaluation of interventions that improve quali ty of life. However, for life-extending interventions, the gains in QA LYs 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 a n 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 s uffering from disabling conditions. Brock (2002) notes that “the problem of disability discriminatio n 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 conte xts, 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 e xecution of effectiveness measures. For example, if QALYs are used to evaluat e 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 lif e years saved should not necessarily be diminished simply because the rule sav es 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 relevan t age cohorts. More generally, when numeric adjustments are made for life expectancy or quality of life, ana lysts should prefer use of population averages rather than inf ormation derived from subgroups dominated by a particular demographic or inco me group. (p. 13) This suggests two adjustments to the standard QALY method ology: one adjusting the relevant life expectancy of the affected population, and the oth er 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 qu ality-of-life adjustments to life years gained due to air pollution reductions. A number of epidemiological and toxicological stu dies 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 indivi duals 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” invo lved in determining the correct baseline for assessing QALYs lost to air pollution (see Singer et al. [1995] for a broa der 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 re ducing air pollution. Individuals exposed to chronically elevated levels of air pollution may reali ze 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 fro m that point forward have both reduced life expectancy and reduced quality of life. The benefit to t hat 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 expecta ncy. If the QALY loss is determined based on the underlying chronic condition and life expectanc y without regard to the fact that the person would never have been in that state without long-term ex posure 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 t o injury, and potentially downplaying the importance of life expectancy losses due to air pollutio n. If the risk of chronic disease and risk of death are considered together, then there is no c onceptual problem with measuring QALYs, but this has not been the case in recent applicatio ns 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 a nd mortality jointly, rather than treating each as a separate endpoint (this is an issue for curren t 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 year s. Thus, we maintain the assumption that all direct gains in life years resulting f rom mortality risk reductions will be assigned a weight of 1.0. The U.S. Public Health S ervice Panel on Cost Effectiveness in Health and Medicine recommends that “since lives saved or exte nded 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 adjustment s to life years gained to reflect the less than perfect health of the general population. Gains in quality o f life will be addressed as they accrue because of reductions in the incidence of chronic d iseases. This is an explicit equity choice in the treatment of issues associated with quality-of-life ad justments for increases in life expectancy that still capitalizes on the ability of QALYs to c apture 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 num erator should include both the direct costs of the controls necessary to achieve the redu ction in ambient concentrations of the air pollutant and the avoided costs (cost savings) associated with the reductions in morbidit y (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 affect ed individuals (aside from the overall increases in prices that might be expected to occur as control costs are passed on by affected7b-14

industries). Likewise, because individuals do not eng age in any specific actions to realize the health benefit of the pollution reduction, there ar e no decreases in utility (as might occur from a medical intervention) that need to be adjusted for i n the denominator. Thus, the elements of the numerator are direct costs of controls minus the av oided 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 o ut the monetized value of these benefits from the numerator to yield a “net cost” estimate.

The denominators of the cost-effectiveness ratios we calcul ated are either lives saved, life years saved, or, for the single scenario in which we were able to include both O 3 -related and PM 2.5 - related benefits, MILYs gained. For the MILY aggreg ate effectiveness measure, the denominator is simply the sum of life years gained from increased life ex pectancy and QALYs gained from the reductions in incidence of chronic illnesses associated with PM 2.5 – chronic bronchitis (CB) and nonfatal acute myocardial infarction (AMI).

7b.5 Cost Effectiveness Metrics Incorporating Only O 3 -Related Benefits
In this section we describe the development of cost effectiveness metrics for those illustrative O 3 NAAQS attainment strategies for which we were able to incorporate only O 3 -related benefits. This includes the scenarios in which the baseline i s full attainment of the current O 3 standard of 0.084 ppm and the control scenarios are full attain ment 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 fo r the benefit-cost analysis described in Chapter 8. For convenience, we summari ze 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 i n the incidence of health effects. Health impact functions are derived from the C-R fu nctions reported in the epidemiology literature. A standard health impact function has four components: an effect estimate from a particular epidemiological study, a baseline inciden ce rate for the health effect (obtained from either the epidemiology study or a source of public he alth statistics, such as CDC), the affected population, and the estimated change in the relevant p ollutant summary measure.
A typical health impact function might look like this:

' ' y y e x  

 0 1 ( ) , E

where y 0 is the baseline incidence, equal to the baseline in cidence rate times the potentially affected population;  is the effect estimate; x is the estimated change in the pollutant (e.g., O 3 or PM 2.5 ) and y is the estimated change in incidence of the health effec t (e.g., the number of deaths avoided) associated with the change in the poll utant, x. There are other functional forms, but the basic elements remain the same.7b-15

7b.5.1 Reductions in O 3 -Related Premature Deaths

To calculate O 3 -related life years saved under a given illustrative O 3 NAAQS attainment strategy, we first calculated the numbers of O 3 -related statistical lives saved within 5-year age groups, using BenMAP. (For more details on the calc ulation 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 O 3 NAAQS RIA – Bell et al. (2004) and Levy et al. (2005). Bo th studies report estimated C-R functions of the association between premature mortalit y and short-term exposures to ambient O 3 . Bell et al. (2004) is a multi-city study of 95 ci ties, and as such may avoid the potential for publication bias that may be inherent in si ngle-city studies or meta-analyses of single-city studies. This study provides the lowest estimate of O 3 -related premature deaths among the mortality studies included in the O 3 NAAQS RIA benefit analysis. An upper bound estimate of O 3 -related premature deaths in the O 3 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 O 3 NAAQS attainment strategy, the total number of O 3 - related statistical lives saved, summed across all age gr oups, equals the corresponding number calculated in the benefit analysis. Age group-spec ific O 3 -related premature deaths avoided under the illustrative O 3 NAAQS attainment strategies for which we considered only O 3 -related benefits are given in Table 7b-2.7b-16

Table 7b-2. Estimated Reduction in Incidence of O 3 -Related Premature Mortality Associated with

Illustrative O 3 NAAQS Attainment Strategies in 2020

Baseline of Full Attainment of Current (0.084 ppm) S tandard 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 - 4 5) (41 - 79) 1 2 7 13 5 12 35 62 (0 - 2) (1 - 4) (2 - 12) (4 - 21) (3 - 6) (8 - 15) (24 - 4 7) (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) (1 10 - 210) (190 - 360) 3 9 28 49 15 38 120 220 (1 - 6) (3 - 15) (9 - 47) (16 - 83) (10 - 19) (27 - 50) (8 4 - 160) (150 - 290) 5 14 45 80 23 61 200 350 (2 - 9) (5 - 23) (14 - 75) (26 - 130) (16 - 30) (42 - 80) (1 30 - 260) (240 - 460) 3 9 29 51 15 39 130 220 (1 - 6) (3 - 15) (9 - 49) (17 - 85) (10 - 19) (27 - 51) (8 6 - 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 O 3 -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 i n O 3 -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 stat istical 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 th ese 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 O 3 NAAQS RIA is if the population at risk from exposure to O 3 was limited solely or disproportionately to individuals with preexisting chronic illness, whose life expectancies were, on a verage, shorter than those of the general population (unless all of those individuals had preex isting chronic illness because of long-term exposure to O 3 ).

It is reasonable to assume that someone who dies fr om 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 i n the absence of that high concentration he could be expected to recover from the illness and go on t o live the expected number of years for someone his age – i.e., he would have the age-specific life ex pectancy of the general population.
If an individual has a chronic illness that makes h im 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 lev els of the air pollutant. We can categorize individuals who are at risk of dy ing because of exposure to an air pollutant into three groups:

x those who are vulnerable because of a preexisting ac ute condition;

x those who are vulnerable because of a preexisting ch ronic 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 ch ronic condition that they would have had even in the absence of long term exposure to hig h levels of the air pollutant. The age-specific life expectancies of the general p opulation should apply to the first two groups, and the age-specific life expectancies of the subpopul ation with the relevant chronic condition(s) should apply to the third group. If we knew the pr oportions of people who die from exposure to O 3 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:

) * * * (* * 3 2 1 i i i i i i i i LE p LE p LE p M saved years life Total   

# ¦

where

M

i denotes the number of O 3 -related deaths of individuals age i,

LE i denotes the general population life expectancy for age i,7b-18

LE i * denotes the life expectancy for age i of the subpopulation with the relevant chronic condition(s) – i.e., the third group;

p 1i denotes the proportion of the M i O 3 -related deaths that are in the first group;

p 2i denotes the proportion of the M i O 3 -related deaths that are in the second group; and

p 3i denotes the proportion of the M i O 3 -related deaths that are in the third group.

Unlike for PM 2.5 (discussed below in Section 7b.6), we currently lack inform ation that would allow us to estimate the relevant proportions neces sary to estimate the set of life expectancies that would be appropriate to apply to O 3 -related deaths. Although there is substantial evidenc e linking premature mortality to short-term exposures t o O 3 , there is currently not similar evidence for long-term exposures. We therefore do not know if th e second group above is relevant in the case of O 3 -related mortality. Nor do we know what proportion of O 3 -related deaths can be attributed to preexisting acute conditions (the fir st group) versus preexisting chronic conditions that these individuals would have had even in the a bsence of long term exposure to O 3 (the third group).
Because we currently lack the necessary information t o determine the appropriate set of life expectancies to use in calculating life years saved associated with O 3 -related premature mortality avoided, we calculated life years saved based on fo ur different underlying assumptions:

x A lower bound assumption of zero life years saved, based on the hypothesis that the observed statistical association between premature mortal ity and short-term exposures to O 3 is not actually a causal relationship;

x An upper bound assumption that an O 3 -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 O 3 -related premature deaths in the three groups delineated above ( p 1i , p 2i , and p 3i ) are such that, on average, the agespecific life expectancies among people who die O 3 -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 ran ge of severities, whose life expectancies are somewhat shorter than those of the gen eral population.

Life years saved based on the upper bound assumptio n 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, Dec ember 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 prob abilities that yielded life expectancies that were close to the life expectancies derived using the more c omplicated method employed by the

4 http://www.cdc.gov/nchs/data/nvsr/nvsr56/nvsr56_09.pdf7b-19

CDC. 5 In particular, starting with a cohort of size 1,000,000 at bi rth, 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 R eport (see above) and assuming that all deaths that occurred between ages x and ( x +1) occurred midway through the year (i.e., we assi gned 0.5 life-year to each year of death). The life expecta ncy 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 O 3 -related premature mortality of an individual age n

. To estimate life years saved under the two intermediate assum ptions about the life years lost as a result of O 3 -related premature mortality, we turned to the epidemiol ogical evidence of a statistically significant association between short-t erm exposures to O 3 and respiratory hospital admissions. This evidence suggests that these shor t-term exposures may exacerbate respiratory conditions that were preexisting. It is reasonable to suppose that some of these hospi talizations for respiratory illnesses on days of relatively high O 3 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 expectanci es 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 similarl y 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 obstructi ve 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. T he cohort subjects were selected from the larger cohort of the At herosclerosis Risk in Communities (ARIC) study, which selected its subjects from the po pulation of four U.S. communities by probability sampling. 6 The subjects in the Mannino study were limited to the ARIC par ticipants 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 Glob al 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 sym ptoms 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 partici pants (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 “avera ge” COPD by adjusting the agespecific mortality probabilities and then calculating life e xpectancies 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 ac count several covariates (including, among others, age, sex, race, smoking status, and educati on 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 (GO LD) 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. CO PD is a progressive disease, so it would be expected that the proportion of older individuals w ould 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 individua ls with (1) severe COPD and (2) COPD of “average” severity. In particular, to derive age-specifi c mortality probabilities for the subpopulation with severe COPD, we multiplied each ag e-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 a ge-specificmortality probability for the general population by a weighted average of the GOLD categoryspecific hazard ratios, where the weight for a GOLD category w as 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 the se adjusted mortality probabilities and the method for calculating life expect ancies described above.

Once an appropriate set of life expectancies has be en 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 individu al who dies at a given age. This information can then be combined with the estimated number of O 3 -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 ei ther 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 inte rvals. We illustrate the first approach in7b-21

calculating O 3 -related life years saved and the second approach in c alculating PM 2.5 -related life years saved (see Section 7b.6).

Total O 3 -related life years gained was calculated as the sum o f life years gained at each age:

i N

i i M LE gained years life Total u



# ¦

0

where LE i is the remaining life expectancy for age i
, M i 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. Standar d economic theory suggests that benefits occurring in future years should be discounted rela tive 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 recommendation s 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 O 3 -related premature mortality is associated only with short-term exposures, all O 3 -related premature deaths are assumed to occur in the year of exposure. We therefore did not discount O 3 -related premature deaths avoided.

Undiscounted age-specific life expectancies, and age-sp ecific 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 O 3 -related (discounted) life years saved, based on each of the two O 3 -mortality studies and each of the assumptions about r elevant life expectancies, are given, using 3 percent and 7 per cent discount rates, in Tables 7b-7 and 7b8, respectively. The O 3 -related (discounted) life years saved, under the f irst assumption – that the observed statistical association between premature mor tality and short-term exposures to O 3 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 sho wn 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 13.87b-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 pop ulation: 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. pdf7b-24

Table 7b-5. Undiscounted and Discounted Age-Specifi c 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 10.77b-25

Table 7b-5. Undiscounted and Discounted Age-Specifi c 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 pro babilities for the general population by multiplyin g by the hazard ratio (5.7) for GOLD 3 or 4, from Man nino et al. (2006).7b-26

Table 7b-6. Undiscounted and Discounted Age-Specifi c 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 13.07b-27

Table 7b-6. Undiscounted and Discounted Age-Specifi c 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 pro babilities for the general population (see Table 2) by multiplying by the weighted average of hazard ratios for the GO LD severity categories (1.906) from Mannino et al. (2006).7b-28Table 7b-7. Estimated Discounted O3-Related Life Years Saved Under Alternative Illustrative O3NAAQS Attainment Strategies in 2020,Using a 3 Percent Discount Rate0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm 0.079 ppm 0.075ppm 0.070 ppm 0.065 ppm380 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 coefficientin the mortality C-R functions. All estimates rounded totwo significant figures.Bell et al. (2004)Levy et al. (2005)Assuming Life Expectancies of the General PopulationAssuming Life Expectancies of the Sub-Population with COPD of Average SeverityAssuming 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 O3is not actually a causal relationship – is zero inall cases (i.e., regardless of the mortality studyused and the scenario considered, andis therefore not shown.Baseline: Full Attainment of Current (0.084 ppm) Standard; Control Scenario: Full Attainment ofAlternative Standard of:Estimated O3-Related Life Years Saved*(95% CI)**7b-29Table 7b-8. Estimated Discounted O3-Related Life Years Saved Under Alternative Illustrative O3NAAQS Attainment Strategies in 2020,Using a 7 Percent Discount Rate0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm 0.079 ppm 0.075ppm 0.070 ppm 0.065 ppm290 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 PopulationAssuming Life Expectancies of the Sub-Population with COPD of Average SeverityBaseline: Full Attainment of Current (0.084 ppm) Standard; Control Scenario: Full Attainment ofAlternative 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 coefficientin the mortality C-R functions. All estimates rounded totwo significant figures.*The O3-related (discounted) life years saved, under the first assumption – that the observed statistical association between premature mortality and shortterm exposures to O3is not actually a causal relationship – is zero inall cases (i.e., regardless of the mortality studyused and the scenario considered, andis therefore not shown.7b-30

7b.5.3 Cost-Effectiveness Ratios

For each illustrative O 3 NAAQS attainment strategy for which we considered only O 3 -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 net ted out the monetized benefits of avoided cases of O 3 -related acute morbidity (respiratory hospital admissions , asthma-related ER visits, school absence days, and minor restricted activit y days) as well as avoided O 3 -related worker productivity losses from the direct costs of the controls necessary to achieve the reductions in ambient concentrations of O 3 in the numerator. Incidences of avoided acute morbidity are given in Chapter 8.
We used Monte Carlo procedures to incorporate the unce rtainty surrounding the O 3 coefficient in each of the C-R functions (including C-R functions f or each of the acute morbidity endpoints as well as the C-R function for mortality) as well as the unc ertainty surrounding the unit value (monetized benefit of an avoided case) of each acute morbid ity 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 mortalit y 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, O 3 -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, resp ectively. Both the costs of the regulation and the lives saved are discounted.

As noted in Section 1, these cost-effectiveness rat ios omit the PM 2.5 -related co-benefits of these illustrative O 3 NAAQS strategies and are therefore likely to under state 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 concen trations of O 3 , 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-effective ness ratios, similarly increase with the stringency of the alternative standards. It is therefore n ot surprising that we do not see a monotonic trend in these ratios across the increasi ngly more stringent alternative standards.7b-31Table 7b-9. Estimated Net Cost (2006$) per O3-Related Life Saved Under Alternative IllustrativeO3NAAQS Attainment Strategies in 20200.079 ppm 0.075 ppm 0.070 ppm 0.065 ppmEstimated 3% discounted cost of the regulation (inBillion $):***$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 ofthe 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 ofthe 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 $) per Life Saved**Because PM2.5-related benefits are not incorporated in these cost effectiveness ratios, the cost effectiveness of full attainment ofeach alternative O3standard shown in this table will tend to be understated.(95% CI)**Change From Full Attainment of the Current (0.084 ppm) Std. To Full Attainment of Alternative Std. of:Mortality Study**95 percent confidence or credible intervals (CIs)incorporate uncertainty surrounding the O3coefficients in the mortality andmorbidity endpoints as well as the uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to twosignificant figures.Bell et al. (2004)Bell et al. (2004)Levy et al. (2005)Levy et al. (2005)Bell et al. (2004)Levy et al. (2005)7b-32Table 7b-10. Estimated Net Cost (2006$) per O3-Related Life Year Saved Under Alternative Illustrative O3NAAQS AttainmentStrategies in 2020, Using a 3 Percent Discount Rate0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppmEstimated 3% discounted costof 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 O3standard shown inthis table will tend to be understated.Cost Effectiveness Ratio: Net Cost (in Million $) per Life Year Saved*(95% CI)**Change From Full Attainment of the Current (0.084 ppm) Std. To Full Attainment of Alternative Std. of:Life Expectancy AssumptionLevy et al. (2005) Subpopulation with Average COPDLevy et al. (2005) Subpopulation with Severe COPDMortality Study**95 percent confidence or credible intervals (CIs)incorporate uncertainty surrounding the O3coefficients in the mortality and morbidity C-R functions as well as theuncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significantfigures.Bell et al. (2004) General PopulationBell et al. (2004) Subpopulation with Average COPDBell et al. (2004) Subpopulation with Severe COPDLevy et al. (2005) General Population7b-33Table 7b-11. Estimated Net Cost (2006$) per O3-Related Life Year Saved Under Alternative Illustrative O3NAAQS Attainment Strategiesin 2020, Using a 7 Percent Discount Rate0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppmUsing lower bound estimate of 7% discounted costof 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 costof 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 functionsas well as theuncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significantfigures.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 AssumptionLevy et al. (2005) Subpopulation with Average COPDLevy et al. (2005) Subpopulation with Severe COPDBell et al. (2004) Subpopulation with Severe COPDLevy et al. (2005) General PopulationBell et al. (2004) General PopulationBell et al. (2004) Subpopulation with Average COPDLevy et al. (2005) Subpopulation with Average COPDLevy et al. (2005) Subpopulation with Severe COPDMortality Study*Because PM2.5-related benefits are not incorporated in these cost effectiveness ratios, the cost effectiveness of full attainment of each alternative O3standard shown inthis table will tend to be understated.Bell et al. (2004) General PopulationBell et al. (2004) Subpopulation with Average COPDBell et al. (2004) Subpopulation with Severe COPDLevy et al. (2005) General Population7b-34

7b.6 Cost-Effectiveness Metrics Incorporating Both O 3 -Related and PM 2.5 -Related Benefits
In this section we describe the development of cost -effectiveness metrics for the single illustrative O 3 NAAQS attainment strategy for which we were able to incorporate both O 3 - related benefits and PM 2.5 -related co-benefits, in which the baseline is parti al attainment of the current O 3 standard of 0.084 ppm and the control scenario is partial attainment of an al ternative standard of 0.070 ppm.

7b.6.1 O 3 -related Lives Saved and Life Years Saved

The methods used to calculate O 3 -related lives saved and O 3 -related life years saved under this scenario are the same as those described above in Sectio n 7b.5. Estimated numbers of O 3 -related premature deaths avoided are shown in Table 7b-12. The corresponding O 3 -related life years saved, discounted using 3 percent and 7 percent dis count rates, are shown in Tables 7b-13 and 7b-14, respectively.7b-35

Table 7b-12. Estimated Reduction in Incidence of O 3 -Related Premature Mortality Associated with

Illustrative O 3 NAAQS Attainment Strategy in 2020: Changing from P artial Attainment of the Current O 3 NAAQS to Partial Attainment of an Alternative O 3 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. Al l estimates rounded to two significant figures. Reduction in O 3 -Related Premature Mortality (95% CI)* Baseline of Partial Attainment of Current (0.084 pp m) 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 - 797b-36

Table 7b-13. Estimated O 3 -Related Life Years Saved Associated with Illustrat ive O 3 NAAQS

Attainment Strategy in 2020: Changing from Partial Attainment of the Current O 3 NAAQS to Partial Attainment of an Alternative O 3 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 O 3 -Related Life Years Saved

(95% CI)*

*95 percent confidence or credible intervals are ba sed 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 Populatio n

Assuming Life Expectancies of the Sub-Population wi th COPD of Average Severity
Assuming Life Expectancies of the Sub-Population wi th Severe COPD

Table 7b-14. Estimated O 3 -Related Life Years Saved Associated with Illustrat ive O 3 NAAQS

Attainment Strategy in 2020: Changing from Partial Attainment of the Current O 3 NAAQS to Partial Attainment of an Alternative O 3 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 O 3 -Related Life Years Saved

(95% CI)*

*95 percent confidence or credible intervals are ba sed 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 Populatio n

Assuming Life Expectancies of the Sub-Population wi th COPD of Average Severity
Assuming Life Expectancies of the Sub-Population wi th Severe COPD

7b.6.2 Reductions in PM 2.5 -Related Premature Deaths

To generate PM 2.5 -related health outcomes, we used the same framework as for t he benefit-cost analysis described in Chapter 8 and briefly summari zed above in the introductory portion of Section 8.4.7b-37

As in several recent air pollution health impact ass essments (e.g., Kunzli et al., 2000; EPA, 2004), we focused on the prospective cohort long-te rm exposure studies in deriving the health impact function for the estimate of premature mortal ity. Cohort analyses are better able to capture the full public health impact of exposure to a ir 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 unce rtainties in the benefits estimates, and the weight that was placed on both the ACS and Harvard Six-city studies by ex perts participating in the PM 2.5 mortality expert elicitation, we elected to provide est imates derived from both Pope et al. (2002) and Laden et al. (2006).
This latest re-analysis of the ACS cohort data (Pop e et al, 2002) provides additional refinements to the analysis of PM-related mortality by (a) extending the f ollow-up period for the ACS study subjects to 16 years, which triples the size of the mort ality data set; (b) substantially increasing exposure data, including consideration for cohort ex posure to PM 2.5 following implementation of PM 2.5 standard in 1999; (c) controlling for a variety of p ersonal risk factors including occupational exposure and diet; and (d) using advanced sta tistical 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 PM 2.5 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 peri od, calculated as the average of 1979–1984 and 1999–2000 PM 2.5 levels. The effect estimate from this study is 0.0058, whic h is equivalent to a relative risk of 1.06 for a 10  g change in PM 2.5
. 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 confir mation that reductions in PM 2.5 directly result in reductions in the risk of premature death. This add itional evidence stems from the observed reductions in PM 2.5 in each city during the extended follow-up period. Laden et al. (2006) found that mortality rates consistently went down at a rat e proportionate to the observed reductions in PM 2.5 . 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 ȝ g/m 3 change in PM 2.5
. Age, cause, and county-specific mortality rates were obtained from CDC for the years 1996 through 1998. CDC maintains an online data repository of h ealth 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 est imates. When estimating rates for age groups that differed from the CDC Wonder groupings, we as sumed that rates were uniform across all ages in the reported age group. For example, t o estimate mortality rates for individuals ages 30 and up, we scaled the 25- to 34-year old dea th count and population by one-half and then generated a population-weighted mortality rate usin g data for the older age groups.

The reductions in incidence of PM 2.5 -related premature mortality within each age group associated with the illustrative 0.07 ppm partial attainm ent strategy in 2020 are summarized in Table 7b-15.7b-38

Table 7b-15: Estimated Reduction in Incidence of PM 2.5 -Related All-Cause Premature Mortality

Under an Illustrative Strategy of Changing from Par tial Attainment of the Current (0.084 ppm) O 3 NAAQS to Partial Attainment of an Alternative 0.07 0 ppm O 3 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 t wo significant figures.

7b.6.3 Life Years Saved as a Result of Reductions i n PM 2.5 -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 reductio ns in premature mortality. We began with the complete unabridged life table for the United St ates 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 durin g the interval, person years lived in the interval, and remaining life expectancy. From this unabr idged life table, we constructed an abridged life table to match the age intervals for w hich we have predictions of changes in incidence of premature mortality. We used the abri dgement 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 inclu des one 5-year interval, covering adults 30 to 34, with the remain ing 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 obta ined 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 be7b-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:



### Total Life Years LE M i i i N


### u 

# ¦

1 

### ,

where LE i is the remaining life expectancy for age interval i
, M i 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 cos t-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 discounte d relative to benefits occurring in the present. OMB and EPA guidance suggest discount rat es of three and seven percent. Selection of a 3 percent discount rate is also consistent with r ecommendations from the U.S. Public Health Service Panel on Cost Effectiveness in Health and M edicine (Gold et al., 1996).

Table 7b-16. Abridged Life Table for the Total Popu lation, 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 q x

I x

d x

L x

T x

e x 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 O 3 -related premature deaths, PM 2.5 -related premature deaths are associated with longterm exposures. We therefore did not assume that these dea ths all occur in 2020. The PM 2.5 - related premature deaths avoided and associated lif e years saved are thus further discounted to account for the lag between the reduction in ambien t PM 2.5 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 PM 2.5 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 PM 2.5 concentrations averaged over the periods 1979–1983 and 1999–2000. Use of all-cause mortality is appropriate if there are no differences in the l ife expectancy of individuals dying from air pollution-related causes and those dying from other causes. The argument that long-term exposure to PM 2.5 may affect mainly individuals with serious preexisting illnesses is not supported by current empirical studies. For example, the Kr ewski et al. (2000) ACS reanalysis suggests that the mortality risk is no greater for th ose with preexisting illness at time of enrollment in the study. Life expectancy for the g eneral 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 preexist ing disease. Also, note that the OMB Circular A-4 notes that “if QALYs are used to evaluate a lifes aving 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 nece ssarily be diminished simply because the rule saves lives of people with life-shortening disab ilities. Both analytic simplicity and fairness suggest that the estimate number of life y ears 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 disab ility-specific life expectancies. Our primary life years calculations are thus consistent with the concept of not penalizing individuals with disabling chronic health conditions by assessi ng them reduced benefits of mortality risk reductions. PM 2.5 -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 PM 2.5 -Related Life Years Saved Associated with Illustrat ive O 3 NAAQS

Attainment Strategy in 2020: Changing from Partial Attainment of the Current O 3 NAAQS to Partial Attainment of an Alternative O 3 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 PM 2.5 -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 mea sured only through the estimated change in mortality risk based on the Pope et al. (2 002) C-R function. The SAB-HES has advised against including additional gains in life exp ectancy due to reductions in incidence of chronic disease or nonfatal heart attacks (EPA-SAB-COU NCIL-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 lon g follow-up period in the Pope et al. study7b-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 c alculation as years lived with improved quality of life. The met hods for calculating this benefit are discussed below.
7b.6.4 Calculating Changes in the Quality of Life Years (PM 2.5 -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 PM 2.5 also lead to reductions in serious illnesses that a ffect quality of life. These include chronic bronchitis (CB) and cardiovascular disease, for which we are able to quantify cha nges in the incidence of nonfatal heart attacks. To capture these important benefits in the measure of effectiveness, they must first be converted into a l ife-year equivalent so that they can be combined with the direct gains in life expectancy.
For the cost effectiveness analysis for the PM NAAQS R IA, we developed estimates of the QALYs gained from reductions in the incidence of CB an d nonfatal heart attacks associated with reductions in ambient PM 2.5 . In general, QALY calculations require four elements:
1. the estimated change in incidence of the health condi tion, 2. the duration of the health condition, 3. the quality-of-life weight with the health condition, a nd 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 functi on approach. The second element is based on the medical literature for each health cond ition. The third and fourth elements are derived from the medical cost-effectiveness and cost-uti lity literature. In the following two subsections, we discuss the choices of elements for C B 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., 199 6). Several methods are used to estimate qualityof-life weights. These include rating scale, stand ard 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 indivi dual specific health states or for a more general set of activity restrictions and health stat es 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 ti me 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 discu ssed in more detail below. Table 7b-18 summarizes the key inputs for calculatin g 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 w ith upper bound at 0.9 (Vos, 1999a) (slightly better than a mild/moder ate case) and a lower bound at 0.5 (average weight for a seve re 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 aver age

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., 1 996). Both studies used the time trade-off elicitation me thod. 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 a t 0.85 (Kuntz et al., 1996). Both studies used the time t rade-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 Kunt z et al. (1996).

7b.6.4.1 Calculating QALYs Associated with Reductio ns 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. po pulation (American Lung Association, 1999). For gains in quality of life result ing from reduced incidences of PMinduced CB, discounted QALYs are calculated as



### DISCOUNTED QALY GAINED CB D w w i i i i CB

i 

### u u  

# ¦ ' *

where CB i is the number of incidences of CB avoided in age interval i, w i is the average QALY weight for the i th age interval, CB i w is the QALY weight associated with CB in the i th age7b-43

interval, and * i D is the discounted duration of life with CB for individuals with onset of disease in
the i th age interval, equal to 

## 3

 i D

rt dt e

0 , where D i is the duration of life with CB for individuals
with onset of disease the i th age interval.

A limited number of studies have estimated the impa ct 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 relati onship between PM 2.5 and new incidences of CB. The number of cases of CB in each age interval was deriv ed by applying the impact function from Abbey et al. (1995) to the population in e ach 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 ri sk of 1.15 for a 10  g change in PM 2.5 . Table 7b-19 presents the estimated reduction in new incidences of CB associated with the 0.070 ppm partial attainment strate gy.
CB is assumed to persist for the remainder of an affe cted individual’s lifespan. Duration of CB will thus equal life expectancy conditioned on havi ng 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 group s 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 N ational Health Interview Survey (American Lung Association, 2002). Prevalence rates we re 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 inciden ce 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 Ch ronic Bronchitis Under an Illustrative

Strategy of Changing from Partial Attainment of the Current (0.084 ppm) O 3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O 3 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 uncertai nty surrounding the effect estimate (coefficient) i n the CB C-R function. All estimates rounded to two significant figures.

Quality of life with chronic lung diseases has been examine d in several studies. In an analysis of the impacts of environmental exposures to contamina nts, 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, sta ndard gamble, and person trade-off techniques applied to a convenience sample of health profession als. The Harvard Center for Risk Analysis catalog of preference scores reports a weight of 0.40 for severe COPD, with a ra nge 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 intervent ions 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 a re based on community preferences or judgments of health professionals. The Harvard catalog sc ore is clearly identified as based on author judgment. Given the lack of a clear preferred we ight, we selected a triangular distribution centered at 0.7 with an upper bound at 0.9 (slightly bet ter than a mild/moderate case defined by the Victoria BoD study) and a lower bound at 0.5 bas ed on the Victoria BoD study. We will need additional empirical data on quality of life with chro nic respiratory diseases based on community preferences to improve our estimates.7b-45

Selection of a reference weight for the general pop ulation without CB is somewhat uncertain. It is clear that the general population is not in perfect h ealth; however, there is some uncertainty as to whether individuals’ ratings of health states are in r eference to a perfect health state or to a generally achievable “normal” health state given age and g eneral health status. The U.S. Public Health Service Panel on Cost Effectiveness in Health and Medicine re commends that “since lives saved or extended by an intervention will not be i n 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 gene ral population without CB is 0.95. To allow for uncertainty in this parameter, we assigned a triangular distributio n around this weight, bounded by 0.9 and 1.0. Note that the reference we ight 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 exa mple, 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 weig ht is 0.95, then the incremental qualityof-life improvement is 0.25. As noted above, the populatio n is assumed to have a reference weight of 1.0 for all life years gained due to mortalit y risk reductions.
We present discounted QALYs over the duration of the lif espan 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 softwar e 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 i ncidence of CB for each age interval along with the 95 percent confidence interval resul ting 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 ra te.

8 Monte Carlo simulation uses random sampling from distributio ns 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 Reductio ns 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 he art attacks result in sudden death. However, for survivors, the long-term impacts of ad vanced coronary heart disease (CHD) are potentially of long duration and can result in signi ficant losses in quality of life and life expectancy.
In this phase of the analysis, we did not independently esti mate the gains in life expectancy associated with reductions in nonfatal heart attack s. Based on recommendations from the SABHES, we assumed that all gains in life expectancy a re captured in the estimates of reduced mortality risk provided by the Pope et al. (2002) analys is. 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 attac ks 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 o f life during the heart attack and the possible health states following the heart attack. Figure 7 b-1 shows the heart attack QALY model in diagrammatic form.
The total gain in QALYs is calculated as:7b-47

D ISCOUNTED 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 AMI i is the number of nonfatal acute myocardial infarctions av oided in age interval i
,

w i AMI is the QALY weight associated with the acute phase o f the AMI, p j is the probability of being in the j th post-AMI status, w ij postAMI is the QALY weight associated with post-AMI health

status j , w i is the average QALY weight for age interval i, D e dt i AMI rt

t D i AMI
*

 

# 3

1 , the discounted

value of D

i AMI , the duration of the acute phase of the AMI, and D e dt i postAMI rt

t D i postAMI
*

 

# 3

1 , is the

discounted value of D ij PostAMI , the duration of post-AMI health status j

.7b-48

Figure 7b-1. Decision Tree Used in Modeling Gains i n QALYs from Reduced Incidence of Nonfatal Acute Myocardial Infarctions

Nonfatal heart attacks have been linked with short-term exp osures to PM 2.5 in the United States (Peters et al., 2001) and other countries (Polonieck i et al., 1997). We used a recent study by Peters et al. (2001) as the basis for the impact functi on estimating the relationship between PM 2.5 and nonfatal heart attacks. Peters et al. is the o nly 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 f or nonfatal heart attacks based on the single available U.S. effect estimate. The finding of a sp ecific impact on heart attacks is consistent with hospital admission and other studie s 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 epidemi ologic 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 Y e s

N o Angina

Angina Y e s

N o

N o Y e s 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 Stage7b-49

the heart is able to speed up or slow down in response to momentary stresses) is nega tively 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 a re consistent with an increased risk of heart attacks.
The number of avoided nonfatal AMI in each age inte rval was derived by applying the impact function from Peters et al. (2001) to the populatio n 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 equiv alent to a relative risk of 1.27 for a 10  g change in PM 2.5 . Table 7b-21 presents the estimated reduction in no nfatal 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) O 3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O 3 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 uncertai nty surrounding the effect estimate (coefficient) i n the AMI C-R function.

9 Daily nonfatal myocardial infarction incidence rate s per person were obtained from the 1999 National Hospital Discharge Survey (assuming all di agnosed nonfatal AMI visit the hospital). Age-specific rates for four regions are used in the analys is. Regional averages for populations 18 and older are 0.0000159 for the Northeast, 0.000013 5 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 relativel y 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 f or 0.06 years, or around 22 days. An alternative assumption is the acute phase is characterized b y the average length of hospital stay for an AMI in the United States, which is 5.5 days, based on dat a from the Agency for Healthcare Research and Quality’s Healthcare Cost a nd Utilization Project (HCUP). 10 We assumed a distribution of acute phase duration characteri zed by a uniform distribution between 5.5 and 22 days, noting that due to earlier discharg es and in-home therapy available in the United States, duration of reduced quality of life may continu e after discharge from the hospital. In the period during and directly following an AMI (the acute phas e), we assigned a quality of life weight equal to 0.605, consistent with the weight for the per iod in treatment during and immediately after an attack (Vos, 1999b).
During the post-AMI period, a number of different health sta tes 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 m akes 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 per centage of the population have been diagnosed with angina or CHF with no occurrence of an AMI. Nor d o we have information on what proportion of the heart attacks occurring due to PM e xposure are first heart attacks versus repeat attacks. Probabilities for the four post-AMI hea lth 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 calcula ted 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 th en 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 percen t, so the probability of angina is 0.51.
Combining these factors leads to the probabilities f or 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 a ll discharges, including those due to death. As such, the 5.5-day average length o f stay is likely an underestimate of the average length of stay for AMI admissions where the p atient 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 conditi ons. Based on the model used for established market economies (EME) in the WHO Global B urden 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 u ndiscounted) of CHF assumed for postAMI cases by age interval.

Table 7b-22. Assumed Duration of Congestive Heart F ailure

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 b e 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 ch ance of dying of CHD that is unrelated to recurrent myocardial infarction (for example, arrhy thmia), this cohort has a higher risk for death from causes other than myocardial infarction or stro ke 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 ra tio is relative to all-cause mortality for an age-matched unselected population (i.e., general popu lation). We adopted the same ratios and applied them to each age-specific all-cause mortality r ate 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 du ration of non-CHF post-AMI health states (III and IV).7b-52

Table 7b-23. Assumed Duration of Non-CHF Post-AMI H ealth 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 w eights based on preferences for the combined conditions characterizing each health state. A number of estima tes of QALY weights are available for post-AMI health conditions.
The first two health states are characterized by th e presence of CHF, with or without angina. The Harvard Center for Risk Analysis catalog of pre ference 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 a ssumed that most cases of angina will be treated and thus kept at a mild to moderate state. We thus focu sed our selection on QALY weights for mild to moderate angina. The Harvard database i ncludes two sets of community preference-based scores for CHF (Stinnett et al., 1996; K untz et al., 1996). The scores for CHF with angina range from 0.736 to 0.85. The lower of the two s cores 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 a verage 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 witho ut 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, w ithout 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 seve rity 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 ti me trade-off method of elicitation. The Pliskin, Stason, and Weinstein score is based on the standar d gamble elicitation method. The scores for the nonspecific severity angina fall within the rang e of the two scores for mild angina specifically. Thus, we used the range of mild angina scores a s 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 abse nce of CHF and/or angina, there is only one relevant community preference score available from t he Harvard catalog. This score is 0.93, derived from a time trade-off elicitation (Kuntz et al., 1996). Insufficient info rmation 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 f or the general population without AMI is 0.95. To allow for uncertainty in this parameter, we assigned a triangular dis tribution around this weight, bounded by 0.9 and 1.0.
Based on the assumptions defined above, we used Monte Carl o 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 i nterval. 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 Myoca rdial Infarction

Age Interval QALYs Gained per Incidence a 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% co nfidence interval presented in parentheses.

7b.6.5 Aggregating Life Expectancy and Quality-of-L ife Gains
Given the estimates of changes in life expectancy a nd quality of life, the next step is to aggregate life expectancy and quality-of-life gains to form an effec tiveness measure that can be compared to costs to develop cost-effectiveness ratios. Thi s section discusses the proper characterization of the combined effectiveness measure for the denominator of th e 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 interv al 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 denomina tor 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 he alth status (quality of life). It is however, an effectiveness measure that adds a scaled morbidi ty equivalent to the standard life years calculation. Thus, we term the aggregate measure morb idity 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 b e a “fair” treatment of those with preexisting disabilities, the effectiveness measure might be ter med “fair QALY” gained. However, this implies that all aspects of fairness have been addr essed, and there are clearly other issues with the fairness of QALYs (or other effectiveness measur es) that are not addressed in this simple adjustment. The MILY measure violates some of the propert ies used in deriving QALY weights, such as linear substitution between quality of life and quan tity of life. However, in aggregating life expectancy and quality-of-life gains, it merely repre sents an alternative social weighting that is consistent with the spirit of the recent OMB guid ance on CEA. The guidance notes that “fairness is important in the choice and execution of e ffectiveness measures” (OMB, 2003). The resulting aggregate measure of effectiveness will not be co nsistent with a strict utility interpretation of QALYs; however, it may still be a us eful index of effectiveness.
Applying the life expectancies and distributions of QA LYs per incidence for CB and AMI to estimated distributions of incidences yields distribut ions of life expectancy and QALYs gained under the illustrative attainment strategy with a b aseline of partial attainment of the current (0.084 ppm) O 3 NAAQS and a control scenario of partial attainment of an alt ernative 0.070 ppm O 3 NAAQS. These distributions reflect both the quanti fied uncertainty in estimates of avoided incidence and the quantified uncertainty in QALYs gaine d per incidence avoided.
Tables 7b-25 and 7b-26 present the discounted life years, QALYs, and MILYs gained, based on each combination of O 3 -mortality study, PM 2.5 -mortality study, and life expectancy assumption for O 3 -related life years saved used for the analysis of this attainment strategy, using a 3 p ercent and 7 percent discount rate, respectively.7b-55Table 7b-25. Estimated Gains in Discounted MILYs,Using a 3 Percent Discount Rate, Under an Illustrative Strategy of Changing fromPartial Attainment of the Current (0.084 ppm) O3NAAQS to Partial Attainment of an Alternative 0.070 ppm O3NAAQS in2020(95% CI) (95% CI) (95% CI) (95% CI)(95% CI)1,3008,500(400 - 2,200)(4,700 - 12,000)1,0008,200(300 - 1,700)(4,500 - 12,000)5007,700(200 - 900)(4,100 - 12,000)6,10013,000(4,100 - 8,100)(9,100 - 18,000)4,70012,000(3,200 - 6,300)(7,900 - 16,000)2,7009,900(1,800 - 3,500)(6,200 - 14,000)1,30014,000(400 - 2,200)(8,500 - 20,000)1,00014,000(300 - 1,700)(8,200 - 19,000)50013,000(200 - 900)(7,800 - 19,000)6,10019,000(4,100 - 8,100)(13,000 - 25,000)4,70017,000(3,200 - 6,300)(12,000 - 23,000)2,70015,000(1,800 - 3,500)(9,900 - 21,000)Total MILYsGained1,970(270 - 4,700)870(220 - 1,800)4,400(1,700 - 7,000)9,900(5,400 - 14,000)O3-Related Life YearsGained from MortalityRiskReductionsPM2.5-Related LifeYears Gained fromMortality RiskReductionsQALYs Gained fromReductions in PM2.5-Related ChronicBronchitisQALYs Gained fromReductions in PM2.5-Related Non-FatalMyocardial InfarctionLevy et al. (2005) Laden et al. (2006) Subpopulationwith Average COPDLevy et al. (2005) Laden et al. (2006) Subpopulationwith 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 effectestimates (coefficients) in the C-R functions and,for QALYs and MILYs, the uncertainty surrounding the quality of life weights. All estimates roundedto two significant figures.Bell et al. (2004) Laden et al. (2006) Subpopulationwith Average COPDBell et al. (2004) Laden et al. (2006) Subpopulationwith 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 COPDO3Mortality StudyPM2.5MortalityStudyLife Expectancy Assumption for O3-Related MortalityBell et al. (2004) Pope et al. (2002) General Population7b-56Table 7b-26. Estimated Gains in Discounted MILYs,Using a 7 Percent Discount Rate, Under an Illustrative Strategy of Changing fromPartial Attainment of the Current (0.084 ppm) O3NAAQS to Partial Attainment of an Alternative 0.070 ppm O3NAAQS in2020(95% CI) (95% CI) (95% CI) (95% CI)(95% CI)9905,900(280 - 1,700)(3,300 - 8,700)7905,700(230 - 1,400)(3,100 - 8,500)4505,400(130 - 780)(2,800 - 8,100)4,6009,500(3,100 - 6,100)(6,600 - 13,000)3,7008,600(2,500 - 4,900)(5,800 - 12,000)2,2007,100(1,500 - 2,900)(4,400 - 10,000)9909,700(280 - 1,700)(5,900 - 13,000)7909,500(230 - 1,400)(5,700 - 13,000)4509,200(130 - 780)(5,400 - 13,000)4,60013,000(3,100 - 6,100)(9,400 - 17,000)3,70012,000(2,500 - 4,900)(8,600 - 16,000)2,20011,000(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 effectestimates (coefficients) in the C-R functions and,for QALYs and MILYs, the uncertainty surrounding the quality of life weights. All estimates roundedto two significant figures.Levy et al. (2005) Laden et al. (2006) Subpopulationwith Average COPDLevy et al. (2005) Laden et al. (2006) Subpopulationwith Severe COPDBell et al. (2004) Laden et al. (2006) Subpopulationwith 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) Subpopulationwith 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 COPDLevy et al. (2005) Pope et al. (2002) General PopulationBell et al. (2004) Pope et al. (2002) General PopulationBell et al. (2004) Pope et al. (2002) Subpopulation with Average COPDTotal MILYsGainedO3Mortality StudyPM2.5MortalityStudyLife Expectancy Assumption for O3-Related MortalityO3-Related Life YearsGained from MortalityRiskReductionsPM2.5-Related LifeYears Gained fromMortality RiskReductionsQALYs Gained fromReductions in PM2.5-Related ChronicBronchitisQALYs Gained fromReductions in PM2.5-Related Non-FatalMyocardial Infarction7b-57

7b.6.6 Estimating the Avoided Costs of Chronic Illn ess
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 Sec tion 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 subt racted from the direct costs of controls in the numerator are the avoided costs of illness (COI) as sociated with PM 2.5 -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 cos ts 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 o f 27 and 44; $121,177 for someone between the ages of 45 and 64; and $14,293 for someone over 65. The corresponding age-specific estimates of lifetime present discount ed value (in 2006$) using a 7 percent discount rate are $105,974, $89,506, and $11,641, respective ly. These estimates assumed that 1) lost earnings continue only until age 65, 2) medical expe nditures are incurred until death, and 3) life expectancy is unchanged by CB.
Because the costs associated with a myocardial infa rction extend beyond the initial event itself, we consider costs incurred over several years. Using age-specific annual lost earnings estimated by Cropper and Krupnick (1990) and a 3 percent disc ount rate, we estimated a present discounted value in lost earnings (in 2006$) 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 $91,647 for someone between the ages of 55 and 65. The corresponding agespecific estimates of lost earnings (in 2006$) usin g a 7 percent discount rate are $9,631, $14,195, and $82,051, respectively. Cropper and Krupnick (1 990) 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 myocard ial infarction are used. The first estimate is from Wittels, Hay, and Gotto (1990), which estimate d 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 discountin g used). Using the CPI-U for medical care, the Wittels estimate is $141,124 in year 2006$. T his estimated cost is based on a medical cost model, which incorporated therapeutic options, p rojected outcomes, and prices (using “knowledgeable cardiologists” as consultants). The model used medical data and m edical

11 Gold et al. (1996) recommend not including lost ea rnings in the cost-of-illness estimates, suggesting that in some cases, they may be already b e counted in the effectiveness measures. However, this requires that individuals fully incorp orate the value of lost earnings and reduced labor force participation opportunities into their responses to time-tradeof f or standard-gamble questions. For the purposes of this analysis and f or consistency with the way costs-of-illness are calculated for the benefit-cost analysis, we have assumed th at individuals do not incorporate lost earnings in responses to these questions. This ass umption can be relaxed in future analyses with improved understanding of how lost earnings are tre ated in preference elicitations.7b-58

decision algorithms to estimate the probabilities of cert ain 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 infarc tion of $15,540 (in 1995$), and $1,051 annually thereafter. Converting to year 2006$, tha t 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 estimate s. Because the wage-related opportunity cost estimates from Cropper and Krupnick (1990) cover a 5-year pe riod, we used estimates for medical costs that similarly cover a 5-year period. We us ed a simple average of the two 5-year estimates, or $84,956, and add it to the 5-year opp ortunity 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 Cost a Total Cost 0 – 24 $0 $84,956 $84,956 25-44 $10,757b $84,956 $95,714 45 – 54 $15,856 b $84,956 $100,812 55 – 65 $91,647 b $84,956 $176,603 >65 $0 $84,956 $84,956

a An average of the 5-year costs estimated by Wittel s, Hay, and Gotto (1990) and Russell et al. (1998).

b From Cropper and Krupnick (1990), using a 3 percen t discount rate.
The total avoided COI by age group associated with t he 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 attai nment 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 in clude 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 attai nment strategy.7b-59

Table 7b-28. Avoided Costs of Illness Associated wi th Reductions in Chronic Bronchitis and

Nonfatal Acute Myocardial Infarctions Under an Illu strative Strategy of Changing from Partial Attainment of the Current (0.084 ppm) O 3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O 3 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 Sect ion 7b.4.1), the estimate of costs in the numerator should include both the direct costs of t he controls necessary to achieve the reduction in ambient O 3 (and, indirectly, PM 2.5 ) and the avoided costs (cost savings) associated with t he reductions in morbidity (Gold et al., 1996). In gen eral, 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 pr ices that might be expected to occur as control costs are passed on by affected industries). Likewise, beca use individuals do not engage in any specific actions to realize the health benefit of t he 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 a re direct costs of controls minus the avoided COI associated with CB and nonfatal AMI. In addition, to ac count for the value of reductions in O 3 - and PM 2.5 -related acute health impacts and non-health benefi ts, we netted out the monetized value of these benefits from the numerator to yield a “net co st” estimate. For the MILY aggregate effectiveness measure, the denominator is simply the sum of (O 3 - and PM 2.5 -related) life years gained from increased life expectancy and QA LYs gained from the (PM 2.5 -related) reductions in CB and nonfatal AMI. The separate O 3 - and PM 2.5 -related inputs to the denominators of the cost-effectiveness ratios are summa rized above in Tables 7b-25 through 7b26. The cost-effectiveness ratios and 95 percent confid ence (credible) intervals resulting from all of the sources of uncertainty considered, using Monte Carl o procedures as implemented in the Crystal BallTM software program and incorporating bo th the O 3 - and PM 2.5 -related benefits are shown in the tables below. Tables 7b-29 and 7b-30 show cost per life saved, using a 3 percent7b-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 O 3 - and PM 2.5 -Related Life Saved Under an Illustrative

Strategy of Changing from Partial Attainment of the Current (0.084 ppm) O 3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O 3 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) PM 2.5 Mortality Study O 3 Mortality Study

*The 3 percent discounted cost of the regulation is estimated to be $2.6 billion. PM 2.5 -related avoided deaths are discounted back to 2020. O 3 -related deaths are assumed to occur in 2020. **95 percent confidence or credible intervals incor porate uncertainty surrounding the O 3 and PM 2.5 coefficients in the mortality and morbidity C-R functions as wel l as the uncertainty surrounding unit values of mor bidity endpoints. All estimates rounded to two significan t figures. Laden et al. (2006) Pope et al. (2002)

Laden et al. (2006)

Pope et al. (2002) Levy et al. (2005)

Levy et al. (2005) Bell et al. (2004)

Bell et al. (2004)

Table 7b-30. Estimated Net Cost (2006$) per O 3 - and PM 2.5 -Related Life Saved Under an Illustrative

Strategy of Changing from Partial Attainment of the Current (0.084 ppm) O 3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O 3 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 incor porate uncertainty surrounding the O 3 and PM 2.5 coefficients in the mortality and morbidity C-R functions as wel l as the uncertainty surrounding unit values of mor bidity endpoints. All estimates rounded to two significan t figures. Laden et al. (2006) Pope et al. (2002)

Laden et al. (2006)

Pope et al. (2002) Levy et al. (2005)

Levy et al. (2005) Bell et al. (2004)

Bell et al. (2004) PM 2.5 Mortality Study O 3 Mortality Study

*The 7 percent discounted cost of the regulation is estimated to be $2.8 billion. PM 2.5 -related avoided deaths are discounted back to 2020. O 3 -related deaths are assumed to occur in 2020.7b-61Table 7b-31. Estimated Net Cost (2006$) per O3- and PM2.5-Related Life Year Saved Under an Illustrative Strategy of Changing fromPartial Attainment of the Current (0.084 ppm) O3NAAQS to Partial Attainment of an Alternative 0.070 ppm O3NAAQS in2020, Using a 3 Percent Discount RateCost 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 isestimated to be $2.6 billion. All life years arediscounted back to the year of death. PM2.5-related avoideddeaths are discounted back to 2020. O3-related deaths are assumed to occur in 2020.**95 percent confidence or credible intervals (CIs)incorporate uncertainty surrounding the O3and PM2.5coefficients in the mortality and morbidity C-R functionsas well as the uncertainty surrounding unit valuesof morbidity endpoints. All estimates rounded to two significant figures.Laden et al. (2006) Subpopulation with Average COPDLaden et al. (2006) Subpopulation with Severe COPDBell et al. (2004)Bell et al. (2004)Bell et al. (2004)Levy et al. (2005)Bell et al. (2004)Levy et al. (2005)Levy et al. (2005)Levy et al. (2005)Laden et al. (2006) Subpopulation with Severe COPDLaden et al. (2006) General PopulationLaden et al. (2006) General PopulationLaden et al. (2006) Subpopulation with Average COPDPope et al. (2002) Subpopulation with Average COPDPope et al. (2002) Subpopulation with Severe COPDPope et al. (2002) Subpopulation with Severe COPDPope et al. (2002) General PopulationPope et al. (2002) General PopulationPope et al. (2002) Subpopulation with Average COPDLife Expectancy Assumption for O3-RelatedMortalityPM2.5Mortality StudyO3Mortality StudyBell et al. (2004)Bell et al. (2004)7b-62Table 7b-32. Estimated Net Cost (2006$) per O3- and PM2.5-Related Life Year Saved Under an Illustrative Strategy of Changing fromPartial Attainment of the Current (0.084 ppm) O3NAAQS to Partial Attainment of an Alternative 0.070 ppm O3NAAQS in2020, Using a 7 Percent Discount RateCost 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 COPDLife Expectancy Assumption for O3-RelatedMortalityPM2.5Mortality StudyO3Mortality StudyBell 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 PopulationBell 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 isestimated to be $2.8 billion. All life years arediscounted back to the year of death. PM2.5-related avoideddeaths are discounted back to 2020. O3-related deaths are assumed to occur in 2020.**95 percent confidence or credible intervals (CIs)incorporate uncertainty surrounding the O3and PM2.5coefficients in the mortality and morbidity C-R functionsas well as the uncertainty surrounding unit valuesof morbidity endpoints. All estimates rounded to two significant figures.Laden et al. (2006) Subpopulation with Average COPDLaden et al. (2006) Subpopulation with Severe COPD7b-63Table 7b-33. Estimated Net Cost (2006$) per O3- and PM2.5-Related MILY Gained Under an Illustrative Strategyof Changing from PartialAttainment of the Current (0.084 ppm) O3NAAQS to Partial Attainment of an Alternative 0.070 ppm O3NAAQS in 2020,Using a 3 Percent Discount RateCost 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)Subpopulation with Severe COPDGeneral PopulationSubpopulation with Average COPDSubpopulation with Severe COPDSubpopulation with Average COPDSubpopulation with Severe COPDGeneral PopulationSubpopulation with Average COPDGeneral PopulationSubpopulation with Average COPDSubpopulation with Severe COPDGeneral PopulationBell et al. (2004)Levy et al. (2005)Levy et al. (2005)Levy et al. (2005)Levy et al. (2005)Levy et al. (2005)Bell et al. (2004)Bell et al. (2004)Bell et al. (2004)Bell et al. (2004)Bell et al. (2004)Levy et al. (2005)*The 3 percent discounted cost of the regulation isestimated to be $2.6 billion. All life years arediscounted back to the year of death. PM2.5-related avoided deaths are discounted back to 2020.All QALYs are discounted back to 2020. O3-related deaths are assumed to occur in 2020.**95 percent confidence or credible intervals (CIs)incorporate uncertainty surrounding the O3and PM2.5coefficients in the mortality and morbidityC-R functions as well as the uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significant figures.Pope et al. (2002)Pope et al. (2002)Pope et al. (2002)Pope et al. (2002)Pope et al. (2002)Pope et al. (2002)Laden et al. (2006)Laden et al. (2006)Laden et al. (2006)Laden et al. (2006)Laden et al. (2006)Laden et al. (2006)Life Expectancy Assumption for O3-RelatedMortalityPM2.5Mortality StudyO3Mortality Study7b-64Table 7b-34. Estimated Net Cost (2006$) per O3- and PM2.5-Related MILY Gained Under an Illustrative Strategyof Changing from PartialAttainment of the Current (0.084 ppm) O3NAAQS to Partial Attainment of an Alternative 0.070 ppm O3NAAQS in 2020,Using a 7 Percent Discount RateCost 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-RelatedMortalityPM2.5Mortality StudyO3Mortality StudyLaden et al. (2006)Laden et al. (2006)Laden et al. (2006)Laden et al. (2006)*The 7 percent discounted cost of the regulation isestimated to be $2.8 billion. All life years arediscounted 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 O3and PM2.5coefficients in the mortality and morbidityC-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 COPD7b-65

7b.7 Conclusions

We estimated the effectiveness of several illustrative O 3 NAAQS attainment strategies based on reductions in premature deaths and, in the case of t he one strategy for which we were able to estimate both direct O 3 -related benefits and indirect PM 2.5 -related co-benefits, incidence of chronic disease. We measured effectiveness using s everal 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 li fe years (MILY) which assumes that society assigns a weight of one to years of life ext ended regardless of preexisting disabilities or chronic health conditions. As noted above, however , the cost effectiveness metrics presented for all but one of the illustrative O 3 NAAQS attainment strategies omit the PM 2.5 -related co-benefits and are therefore likely to understate the cost effe ctiveness of those strategies
CEA of environmental regulations that have substantial pub lic health impacts may be informative in identifying programs that have achieved c ost-effective reductions in health impacts and can suggest areas where additional cont rols may be justified. However, the overall efficiency of a regulatory action can only be judge d through a complete benefit-cost analysis that takes into account all benefits and costs, including b oth health and non-health effects. The benefit-cost analysis for the O 3 NAAQS attainment strategies, provided in Chapter 9, sh ows that the attainment strategies we modeled have potentially lar ge net benefits, indicating that implementation of the revised O 3 NAAQS will likely result in improvements in overal l public welfare.7b-66

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Appendix 6c: Additional Sensitivity Analyses Related To th e Benefits Analysis

The analysis presented in Chapter 6 is based on our current interpretation of the scientific and economic literature. That interpretation requires judgm ents 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 o f the analytical assumptions used to develop the primary estimates of benefits have been reviewed and app roved by EPA’s SAB. Both EPA and the SAB recognize that data and modeling limita tions 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 benefi ts with three additional sensitivity calculations. These supplemental estimates examine sens itivity to both valuation issues (e.g., the appropriate income elasticity) and for physical eff ects issues (e.g., the structure of the cessation lag and the sensitivity of the premature mortality estimate t o the presence of a presumed threshold). These supplemental estimates are not me ant to be comprehensive. Rather, they reflect some of the key issues identified by EPA or comment ors as likely to have a significant impact on total benefits. The individual adjustments in the tables sh ould not simply be added together because 1) there may be overlap among the alternati ve 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 discussi on and evolving advice regarding the timing of changes in health effects following changes in am bient air pollution. It has been hypothesized that some reductions in premature mortality f rom exposure to ambient PM 2.5 will occur over short periods of time in individuals with c ompromised health status, but other effects are likely to occur among individuals who, at baseli ne, have reasonably good health that will deteriorate because of continued exposure. No animal mode ls have yet been developed to quantify these cumulative effects, nor are there epidem iologic 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 observ ed in the cohort studies will be analyzed as immediate effects, and this will result in an overe stimate of the health benefits of improved air quality. Thus some time lag is appropriate for dist ributing the cumulative mortality effect of PM in the population” (EPA-SAB-COUNCIL-ADV-00-001, 199 9, p. 9). In recent advice, the SABHES suggests that appropriate lag structures may be dev eloped based on the distribution of cause-specific deaths within the overall all-cause estim ate (EPA-SAB-COUNCIL-ADV-04-002, 2004). They suggest that diseases with longer progressions should be characteri zed by longerterm lag structures, while air pollution impacts occurrin g 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 cardiopulmona ry deaths, which include both respiratory and cardiovascular causes. Some respiratory diseases may h ave 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 whet her air pollution is causing premature death in individuals with preexisting heart disease, which wo uld 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 indica ted 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 k nowledge 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 relat ive 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 t his 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 cha racterized by 30 percent of mortality reductions occurring in the first year, 50 percent occurring evenly ove r years 2 to 5 after the reduction in PM 2.5 , and 20 percent occurring evenly over the years 6 to 20 after the reduction in PM 2.5 (EPA-COUNCIL-LTR-05-001, 2004). The distribution of deaths over the latency period is intended to reflect the contribution of sh ort-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 str uctures. In this appendix, we investigate the sensitivity of premature mortality-reduction rel ated benefits to alternative cessation lag structures, noting that ongoing and future research may r esult in changes to the lag structure used for the primary analysis.
In previous advice from the SAB-HES, they recommended an ana lysis 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 th e 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 fr om PM exposure. In fact, the Pope et al. and Dockery et al. analyses do not supporting or refute the existence of a lag. Therefo re, any lag structure applied to the avoided incidences estimat ed from either of these studies will be an

1 Although these studies were conducted for 8 and 15 years, respectively, the choice of the duration of the study by the authors was not likely du e to observations of a lag in effects but is more likely due to the expense of conducting long-t erm exposure studies or the amount of satisfactory data that could be collected during th is time period.6c-3

assumed structure. The 8- and 15-year lags implicit ly 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 allocati ons of mortality to the segmented lag of the type suggested by the SAB-HES. The first sensitivity analy sis 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 struc ture 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 PM 2.5 , and 30 percent occurring evenly over the years 6 to 20 after the reduction in PM 2.5 . 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-ye ar segment, and 0 percent in the 6- to 20-year segment. The third sensitivity analysis assumes a neg ative exponential relationship between reduction in exposure and reduction in mortality ris k. This structure is based on an analysis by Röösli et al. (2004), which estimates the percentag e of total mortality impact in each period t as

(C.1)

The Röösli et al. (2004) analysis derives the lag s tructure by calculating the rate constant (–0.5) for the exponential lag structure that is consistent with both the relat ive 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 a re based on the value of statistical lives saved approach (i.e., $6.6 million per incidence in 2 006$) and are presented for both a 3 and 7 percent discount rate over the lag period.
The results of this sensitivity analyses demonstrat e that because of discounting of delayed benefits, the lag structure may also have a large impact on mo netized 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 overa ll benefits. For example, the overall impact ofmoving 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 approximatel y 5 percent when a 3 percent discount rate is used and 17 percent w hen a 7 percent discount rate is used. If no lag is assumed, benefits are increased by appro ximately 10 percent relative to the segmented lag with a 3 percent discount rate and 22 percent with a 7 percent discount ra te. 

# > @ > @ % .

. 

### Mortality Reduction (t)   

   

 f 

# ¦ RR e

RR e t

t

t 

### 1 1 1

1 1 1 0 5

0 5

16c-4

Table 6c-1: Sensitivity of Benefits of Premature Mo rtality 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 3% discount rate $3.4 10.4% None 7% discount rate $3.4 22.5%

Incidences all occur in the 8 th year 3% discount rate $2.8 -10.3% 8-year 7% discount rate $2.1 -23.7%

Incidences all occur in the 15 th year 3% discount rate $2.2 -27.0% 15-year 7% discount rate $1.3 -52.5%

20 percent of incidences occur in 1 st 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 locatio ns of the U.S. except two areas of California. These two area s, which have high levels of ozone, are not plannin g 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 PM 2.5 premature mortality benefits analysis based on an assumed cutpoint in the long-term mortality concentratio n-response function at 10 μg/m 3 , and an assumed cutpoint in the short-term morbidity concentr ation-response functions at 10 μg/m 3

. There is ongoing debate as to whether there exists a threshold below which there would be no benefit to further reductions in PM 2.5 . 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 PM 2.5 Criteria Document concludes that “the available evidence does not either support or refut e the existence of thresholds for the effects of PM on mortality across the range of concentrations in the s tudies” (U.S. EPA, 2004b, p. 9-44).6c-5

EPA’s Science Advisory Board (SAB) that provides advi ce on benefits analysis methods 2 has been to model premature mortality associated with PM ex posure as a non-threshold effect, that is, with harmful effects to exposed populations regardless o f the absolute level of ambient PM concentrations.
For these reasons we provide the results of a sensit ivity analysis in which we estimate the change in reduction in incidence of PM 2.5 -related premature mortality resulting from changes in the presumed threshold. We also provide a corresponding estima te 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 Adjustmen t for Thresholds Above 7.5 ug) 95th

Percentile Confidence Intervals Provided in Parenth eses a

East Western U.S. Excluding CA California Total 580 56 12 640 Less Certain That Benefits 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) 1.0 0.00 2.9 4.0 More Certain That Benefits 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 unrounde d estimates. As such, totals will not sum across colu mns. Estimates do not include South Coast and San Joaquin Air Basins.

2 The advice from the 2004 SAB-HES (U.S. EPA-SAB, 200 4b) is characterized by the following: “For the studies of long-term exposure, the HES n otes that Krewski et al. (2000) have conducted the most careful work on this issue. They r eport that the associations between PM 2.5 and both all-cause and cardiopulmonary mortality wer e 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 Red uctions in Mortality Risk to Assumed Thresholds for 0.070 ppm Partial Attainment Scenari o (Using Pope et al., 2002 Effect Estimate with Slope Adjustment for Thresholds Above 7.5 ug, 9 5th 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 unrounde d estimates. As such, totals will not sum across colu mns. 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 endpoin ts 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 exa mined how sensitive the estimate of total benefits is to alternative estimates of the income e lasticities. Table 6c.3 lists the ranges of elasticity values used to calculate the income adju stment 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 Growth a Benefit Category Lower Sensitivity Bound Upper Sensit ivity Bound Minor Health Effect 0.04 0.30 Severe and Chronic Health Effects 0.25 0.60 Premature Mortality 0.08 1.00 Visibility b

— —

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 ran ges were available in the current published literat ure.

Table 6c-5. Ranges of Adjustment Factors Used to Acco unt for Projected Real Income Growth a Benefit Category Lower Sensitivity Bound Upper Sensit ivity Bound Minor Health Effect 1.018 1.147 Severe and Chronic Health Effects 1.121 1.317 Premature Mortality 1.037 1.591 Visibility b

— —

a Based on elasticity values reported in Table C-4, U .S. Census population projections, and projections of real GDP per capita.

b No range was applied for visibility because no rang es were available in the current published literatu re.

Table 6c-6. Sensitivity of Monetized Benefits to Alter native Income Elasticities a 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 Mortality b $340 $520 $2,600 $4,000
Total Benefits b $380 $560 $2,800 $4,200

a All estimates rounded to two significant digits. Al l Benefits Incremental to 080 ppm Partial Attainmen t Strategy (Millions of 2006$). This table reflects f ull attainment in all locations of the U.S. except two areas of California. These two areas, which have hi gh 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 Joaq uin and South Coast Air Basins.

b Using mortality effect estimate from Bell (2004) an d mortality effect estimate from Pope et al (2002) to estimate PM 2.5 mortality at a 3% discount rate.

c No range was applied for visibility because no rang es were available in the current published literatu re.
Consistent with the impact of mortality on total ben efits, 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 Est imating 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 St andards, U.S. Environmental Protection Agency, Research Triangle Pa rk, 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 Estim ates.” In Visibility and Fine Particles , Transactions of an AWMA/EPA International Specialty Conference, C.V. Mathai, ed. Air and Waste Management Asso ciation, 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 Horizon s 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 Assessme nts 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): Advisor y by the Health and Ecological Effects Subcommittee on Initial Assessments of Health and E cological 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 o n 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 S ubcommittee of the Advisory Council on Clean Air Compliance Analysis.

Kleckner, N., and J. Neumann. June 3, 1999. “Recomm ended 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. “Yea rs of life lost attributable to air pollution in Switzerland: dynamic exposure-response m odel.” International Journal of Epidemiology 34(5):1029-35.
U.S. Environmental Protection Agency (EPA). 2004. Air Quality Criteria for Part iculate Matter, Volume II. Office of Research and Development. EPA/60 0/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 radia tion (UV-B), thereby reducing the amount of UV-B reaching the Earth’s surface. The maj ority of ozone—about 90%—is located in the stratosphere, and the stratospheric ozone layer pr ovides most of this protective filtration. Tropospheric ozone, located at ground level, accoun ts for the remaining 10% of atmospheric ozone. Although only a portion of ground level ozon e can be attributed to anthropogenic sources, it is reasonable to assume that reducing ground lev el 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., catar acts), and immune system.
The attached preliminary report entitled “Analysis of the Impact of Emissions Changes on Tropospheric Ozone” represents the EPA’s first atte mpt to develop a methodology for capturing the changes in skin cancers and their economic value th at might be associated with changes in tropospheric ozone. This initial effort was designed as a sc oping 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 analysi s focuses on a scenario reflecting the likely distribution of ground level ozone in the Easter n United States domain under an illustrative set of controls intended to reduce ozone conce ntrations towards attainment of an ozone standard of 70 parts per billion (ppb), as compared to t he 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 associa ted with skin cancers – specifically, basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and cutan eous malignant melanoma (CMM).
The general methodology developed for this draft sc oping analysis was applied in four steps. First, changes in ground level UV radiation (for ge ographical extent) were estimated using the Community Multiscale Air Quality model results as an input to the Tropospheric Ul traviolet – 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 hourl y 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, popul ation-weighted exposure estimates were derived using county based population projections develo ped using a cohort-component methodology. Third, the resulting estimates were used in the Atmospheric Health Effects Framework model to quantify expected changes in inc idence in and mortality from basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and cutaneo us malignant melanoma (CMM) associated with the given change in ground level ozone. Fo urth, the resulting health effects were monetized using a combination of estimates of the value of st atistical life and willingness to pay to avoid a case of skin cancer.

This research makes use of results from the CMAQ, T UV 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 Pr oposal and does not match runs produced for the Ozo ne NAAQS Final.6d-2

skin cancer effects associated with changes in trop ospheric ozone. 2 While all of these models have been extensively peer reviewed and validated in diff erent 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 Univ ersity; 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 in corporate 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 unc ertainty, we recognize that others may remain including, but not limited to, the appli cability 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 vari ation in activity patterns and other factors that determine population exposures and sen sitivities to UV-B radiation; as well as the effects of aerosols. These uncertainties have been recognized by the Agency and discusse d in Chapter 10 of the most recent Ozone Criteria Docume nt (U.S. EPA, 2006). The Agency will consider whether to conduct additional exploratory ana lyses 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 scopin g analysis do not match those used for the Ozone NAAQS Final Regulatory Impact Assessment (RIA), dir ect comparisons of the monetized skin cancer effects associated with reduced UV-B fi ltration 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 bene fits presented in the proposal RIA, provides a general sense of the order of magnitude of t he 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 approxi mately 0.3 to 0.6 percent of the monetized health benefits associated with the modeled set of ozone prec ursor control strategies reported in the proposal RIA.

2 TUV and AHEF were developed to estimate health eff ects associated with changes in stratospheric ozone

. 3 The individual reports from each of the peer revie wers 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 InternationalHealth 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 Heal th 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 R elationships........................ 31 4.5.2. Behavioral Uncertainties....................

................................................... ................ 32 4.5.3. Latency.................................................................................................................. 32 4.6. Uncertainty in Valuation of Human Health Effec ts................................................. ..... 33 4.7. Unquantified Sources of Uncertainty............................................................................ 33 4.8. Summary of Quantified and Unquantified Sources o f Uncertainty.............................. 36

References.........................................

................................................... ........................................ 37 Appendix A: Ground Level SCUP-h UV with 70 and 84 p pb by Day ................................... 41 Appendix B: Overview of Evaluation Methodology...........

................................................... .. 49 Glossary ...........................................

................................................... ......................................... 50Health Impacts from Ozone Changes

Draft report 6d-5 

# Introduction
Atmospheric ozone filters harmful solar ultraviolet radia tion (UV-B), thereby reducing the amount of the UV-B reaching the Earth’s surface. The majorit y of ozone—about 90%—is located in the stratosphere, and the stratospheric ozone la yer 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 o zone can be attributed to anthropogenic sources, it is reasonable to assume that red ucing ground level ozone would reduce the UV-B filtration provided, and thus would lead to increas es in health effects normally associated with reductions in stratospheric ozone. UV radi ation-induced health effects are primarily related to the skin (e.g., melanoma and n onmelanoma 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 Ambien t 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 ra diation model (TUV) and the U.S. EPA’s Atmospheric and Health Effects Framework (AH EF);

x Section 3 presents the results of the analysis, inc luding changes in ground level UV and health effects; and

x Section 4 addresses the uncertainties associated wi th modeling undertaken for this analysis. 

# Methodology 1.1. CMAQ Modeling
The inputs for this analysis were generated through Co mmunity Multiscale Air Quality (CMAQ) ozone modeling runs. The CMAQ model produced spatial f ields 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 mete rs. 

## 1.2. TUV Modeling
1.2.1. Tropospheric Ozone Scenarios The CMAQ model provided ozone concentrations in par ts 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 in tervals for 24 hours (UT) of each day from 1Health 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 inc remental to attainment of the current standard (84 ppb), EPA approached the analysis in s tages. First, EPA identified controls to be included in the baseline. These included current s tate and federal programs plus controls to attain the current ozone standard and PM2.5 PM stan dards (see http://www.epa.gov/ttnecas1/ria.html for a complete li st of controls). Then, EPA applied additional known controls within geographic areas desi gned 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. Th e 14 layers are bounded by 15 levels defined on unequally spaced modified normalized pressure coordinate s (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 s ensitivity 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 col umn 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 cm í 2 )

0 1.000 0 1000

— 1 0.995 38 995 9.67 × 10 22

2 0.990 77 991 9.89 × 10 22

3 0.980 154 982 1.94 × 10 23

4 0.960 310 964 3.89 × 10 23

5 0.940 469 946 3.90 × 10 23

6 0.910 712 919 5.85 × 10 23
7 0.860 1,130 874 9.73 × 10 23
8 0.800 1,657 820 1.17 × 10 24
9 0.740 2,212 766 1.17 × 10 24
10 0.650 3,108 685 1.75 × 10 24
11 0.550 4,212 595 1.95 × 10 24Health Impacts from Ozone Changes

Draft report 6d-7

Layer Number Sigma Approximate

Height (m) Approximate

Pressure (mb) Air column between

levels (molecules cm í 2 )

12 0.400 6,153 460 2.91 × 10 24
13 0.200 9,625 280 3.85 × 10 24
14 0.000 15,674 100 3.58 × 10 24

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 ov er the entire geographic domain and over hours of all days of each month. Also shown are the values f or 15 July (the mid-time of the simulation), since this date will be used in some sen sitivity 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 laye rs.
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 numberppb 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 eachHealth Impacts from Ozone Changes

Draft report 6d-8 corresponding layer (from Table 1), and converting to Dobson Units (DU) by dividing by 2.687 × 10 16 molecules cm -3 DU -1 . The total ozone column change is the sum of the contributions in each layer, and is shown in the fig ure 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 decr ease 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 numberDobson 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 diff erent locations and times. For example, they include night-time values when the UV radia tion 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) smal ler 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 format ion near sources, and coincident with times of highest surface biologically active irradia nces. Mid-tropospheric values have a weaker dependence on sza, consistent with long-range trans port 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 < 45 o high sun 426,139 0.69 0.11 0.22 45 o < sza < 90 o low sun 538,529 0.60 0.09 0.19 sza > 90 o night 640,932 0.50 0.11 0.19

An independent albeit rough estimate of the ozone co lumn (DU) change can be obtained from the concentrations given in Table 2. Considering onl y the values for sza < 45 o (c.f., the simple rule that UV exposure should be avoided when a pers on’s shadow is shorter than a person’s height), even a simple linear average of the ozone changes at t he surface and 500 mb yields ~ 0.4 ppb, and can be taken as applicable to the lo wer half of the atmosphere (below 500 mb). The total atmospheric column of air is about 2.0 × 10 25 molecules cm -2 , so taking half of this and 0.4 ppb ozone yields 4 × 10 15 molecules cm -2 of ozone (1.0 × 10 25 × 0.4 × 10 -9 ), or

§ 0.15 Dobson Units. This is reasonably close to th e column value in the table, 0.22 DU, which was calculated within TUV from the full vertical variation o f ozone and air concentrations, and included changes above 500 mb as well as an exponent ial 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 wi th the Tropospheric Ultraviolet-Visible (TUV) model developed by Madronich and co-workers at the Nati onal Center for Atmospheric Research (NCAR). The TUV model is widely used for the calculation of atmospheric and surfac e UV radiation including international assessments of th e environmental effects of stratospheric ozone depletion (e.g., Madronich et al., 1998), and has been ev aluated in numerous modelmeasurement intercomparison studies (e.g., Koepke et al., 1 998; Bais et al., 2003). An early version of TUV, of similar accuracy but lesser flex ibility, 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 Port al (http://cdp.ucar.edu).

Several modifications to the TUV model were made for the pr esent 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 reporte d 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 repre sent altitude levels, while layers (to which the ozone concentrations are applie d) are the volume between these levels. The TUV model used the U.S. Standard Atmosphere (USSA, 197 6) vertical profiles of temperatureHealth 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 g rid described above. Because the CMAQ model has layers that are both smaller and larger t han the standard USSA 1 km grid, some attention was given to proper vertical interpolation of air density. Specifically, the logarithm of the USSA air number density (molecules cm í 3 ) was interpolated linearly to obtain the logarithm of the air density at the CMAQ levels. Then, the ve rtical air column (molecules cm í 2 ) of each layer was obtained by logarithmic integration:

Air column in layer k

= d z [ y ( k +1) – y ( k ) ] / ln[ y ( k

+1) / y ( k ) ]

where d z = 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 × 10 í 9 ) to yield the ozone column in each layer (molecules cm í 2 ), so overwriting the USSA ozone values for these alt itudes. For altitudes above the highest level of Table 1, the interpolated USSA ozo ne values were used.

For each wavelength interval (see below), the radia tive 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 wav elength interval, for each location, for each hour (on the half-hour) of each day of June, July, and August fo r 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 no n-zero contributions, to obtain the surface biological exposure (biologicall y effective irradiance) I bio (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 o f 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). T he latter spectrum has been used extensively in the assessments of ozone depletion, andis 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 spe ctrum (as shown by Madronich et al., 1998). For brevity, biologically effective irradiance s computed from these two spectra are hereafter called I ERY and I SCUP . Values of I SCUP 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, t o 350 by 5 nm, and to 400 by 10 nm. The higher resolution at the shorter wav elengths 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 calculatio ns, including O 3 + h Q Æ O 2 + O( 1 D) 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 value s of I ERY and I SCUP were integrated over 24 hours to provide daily integrated doses, and over each mont h (June, July, and August) to provide monthly integrated values. Otherwise identical calculatio ns were performed for the 070 and 084 scenarios, and the difference between scenarios was comp uted 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. Th is is because of coupling between molecular (Rayleigh) scattering and ozone absorption: Scatter ing increases the tropospheric photon path lengths and therefore increases the probability of absorption by tropospheric absor bers 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 normali zed sensitivity (also called the Radiation Amplification Factor, RAF) is the % incre ase 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( UV

2 / UV 1 )/ln( DU 2 / DU 1 )

where the subscripts 2 and 1 refer to the perturbed and r eference calculations (Micheletti et al., 2003).

Figure 3: Normalized sensitivity (% for %) of UV-SCUP chang es 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 O 3 perturbation, kmNormalized sensitivity 15 30 60Health Impacts from Ozone Changes

Draft report 6d-12 For example, a 0.22 DU increment (from Table 1, 15 Ju ly, high sun) represents about 0.06% change in the ozone column (349 DU for the USSA, but ac tually 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 t roposphere and relatively high sun when UV matters. Multiplying (0.06 % × 1.65), the surface U V-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 ve rtical distributions of tropospheric ozone given by the CMAQ, and fully coupled scatteri ng-absorption multi-layer radiative transfer. Therefore they are expected to be more acc urate, 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-av eraged UV-SCUP percent changes for each day. They range from 0.05 % to 0.16 %, with most values near 0.1 % or slightly highe r (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. Th e most common value is near zero, and few values above 0.3%. A few negative values were note d. Finally, it should be clear that the data in these figures are not yet weighted by the affected popul ations, and therefore should be viewed as changes in the physical state of the atmosphere, not a s measures of population exposure.

Figure 4: Domain-averaged percent changes in SCUP-weight ed 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 monthPercent JUN
JUL AUGHealth Impacts from Ozone Changes

Draft report 6d-13 Figure 5: Frequency distribution of percent changes in er ythemal surface UV radiation



### Distrbution of percent changes in erythemal (CIE) s urface 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 

### PercentNumber

JUN

JUL

AUG



## 1.3. Population Adjustments
This analysis required county-based population project ions for two purposes: to calculate the population-weighted changes in ground level UV for each la titude 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 t hey cover the entire United States, while the area analyzed by CMA Q 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 techni que for projecting population changes over time. In this case, three independent components of popu lation change were used: fertility, mortality, and net international migration (i.e., migrat ions 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 pro ject population changes over time, theHealth 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 ove r 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: P t = Population in year t P t

í 1 = Population in the previous year B t = Births in year t D t = Deaths in year t NIM t = Net International Migration in year t

Beginning with an initial set of populations, annual co mponents of change were applied in the following process, which were repeated annually until th e 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 P t = P t

í 1 + B t í D t

+ NIM t **Equation 1**Health Impacts from Ozone Changes

Draft report 6d-15 In the following sections, the methods and data used for t he 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 p rovided 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 i n 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 a ges × 4 bridged race groups). County populations using bridged race and one-year age coh orts 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 o f women in a given cohort times the average number of children born annually to ever y 1000 women in that cohort divided by 1000. Because virtually all births occur to women between th e ages of 10 and 49, only those cohorts were considered in this model. These birth s are summed by race and used to create a new age zero cohort. To allocate births between males and f emales, a historic ratio of 1046 males born for every 1000 females born was calculated and i t 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 Hamil ton 2005). Similarly, mortality was estimated by multiplying th e 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 survivors hip 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 Bu reau’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 proj ections. These components of change are associated with the 1990 National Projections and w ere 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 no t yet been released.

For both fertility and mortality, the so-called Mid dle Series of component information was used. Fertility rates were provided in a single file; mortality r ates 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 begin ning with 2010, mortality rates wereHealth 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 utilize d a simple method based on the Census Bureau’s international migration projections for the enti re 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 a re 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 “Forei gn-born Net Migration to the United States” contain only national level data, it was necessary to al locate 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 cohor t of immigrants among the nation’s counties. The estimated number of immigrants in each cohort was then a dded to the existing county population of each cohort. This method assumes a c onstant distribution of recent immigrants based on Year 2000 immigration patterns. While it is likel y that new settlement patterns for immigrants will develop in the future, this is the sa me method the Census Bureau uses for assigning immigrants to states in its state project ions (U.S. Census Bureau 2005). The Census Bureau provides a low, medium, and high series for n et international immigration. In the base case, the middle series was used.


### Domestic Migration Although domestic migration is also a major compone nt of local population change, it could not be accurately modeled here. The Census Bureau’s met hodology for state estimates does contain data about state-to-state migration rates based on the ob served 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 o nly available to 2030. Developing a method for estimating future migrations was beyond the req uirements 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 add resses uncertainty in the population adjustments. 

### 1.3.3. Use of these Projections The population projections developed using the above meth odology were used for two purposes in this analysis. First, they were used to calculate the population-weighte d change in UV exposure based on the CMAQ and TUV modeling discuss ed 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 e xposure was calculated for each county. In calculating the average for any given c ounty, 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 wa s aggregated into male and female, 18 age cohorts (0–4 years, 5–9 years, 10–14 years, and...85-plus year s), and threeHealth 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 e ach 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 eac h 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 popul ation groups. Because the CMAQ model did not cover the entire United States, those co unties that were not included in the CMAQ modeling area were not included in the aggregated p opulations. 

## 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) in cidences and mortalities. 

### 1.4.1. Overview of Methodology to Estimate Changes in H ealth Effects To yield health effects estimates, the AHEF first projecte d future baseline skin cancer incidence and mortality; this calculation was based on the fut ure population estimates derived in Section 2.3 and baseline incidence and mortality rates for e ach health effect (based on a scenario of compliance with the Montreal Adjustments to the Montreal Protocol). Then the AHEF multiplied the population-weighted percentage chang es in UV exposure in a future year by the appropriate dose-response relationship (described i n Section 4.2.2 below) to yield the percentage change in future skin cancer incidence/mortality attribut able to the proposed change in the NAAQS ozone standard (from 84 ppb to 70 ppb). These percent ages were then multiplied by the baseline incidence and/or mortality for that health e ffect 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 ef fect.

These calculations were performed for each health e ffect and for each future population group 5 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 expos ure by the biological amplification factor (BAF) an d the underlying baseline incidence or mortality is the s ame as that used by other researchers to estimate c hanges in health effects based on changes in ozone concentrations (e .g., Madronich and de Gruijl 1994, Pitcher and Long streth 1991).
5 The future population group is a subset of the tot al U.S. population, calculated specifically for thi s analysis, as described in Section Error! Reference source not found. above. (Cumulative Percentage Increase in UV Exposure) × (Bio logical Amplification Factor for BCC) × (Baseline Incidence of BCC for the Populatio n 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 perc entage increase in UV exposure associated with the tightening of the NAAQS standard from 84 p pb to 70 ppb is being used as the environmental input in the AHEF, only the incremental numbe r of health effects associated with the standard change were modeled. The absolute number of h ealth 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 amplificatio n 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 spect rum—an experimentally derived function that describes the relative effectiveness of eac h UV wavelength in the induction of skin cancers. The AHEF relies on action spectra for eac h health effect because action spectra provide information regarding which wavelengths of the tota l UV spectrum are most effective at causing the particular health effect. Based on the availab le 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 s elected for modeling SCC, BCC, and CMM in the AHEF. 6

Based on the action spectrum selected for each heal th 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 measurement s or estimates of UV exposure received for a specific action spectrum and given health effect at various locations, and t he level of incidence or mortality for that health effect at those same loc ations. In the AHEF, statistical regression analyses were used to estimate the dose-response re lationship, 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 mea sured in Watts/m 2 ) 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 th e intensity of UV radiation results in a 1.5 percent change in BCC incidence. For each health effect, th e 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 re mains 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 car cinoma (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 unre lated care, such as increased costs for treating other medical conditions later in life that might h ave 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 h ealth 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 publica tion (Kyle et al. forthcoming).

Table 4: Total Cost per Case of Non-fatal Skin Canc er 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 m edical 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% confid ence 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) t o 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 calc ulated 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. EP A 1988). The cost per case was calculated by multiplying EPA’s estimates of the loss of work due to illne ss and care giving performed by others for the patient for BCC and SCC by the nation al 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 mill ion is the mean of a normal distribution with a 95 % confidence interval betwee n $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 interquarti le range from the Viscusi and Aldy (2003) meta-analysis. The VSL represents the value of a small chan ge 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., i ncremental 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 ac hieving an ozone standard of 70 ppb (down from 84 ppb) in the summer months of June, July, and August.Health Impacts from Ozone ChangesDraft report6d-21Figure 7: Ground Level UV Percent Change between 70 and 84 ppb, JuneHealth Impacts from Ozone ChangesDraft report6d-22Figure 8: Ground Level SCUP-h UV Percent Change between 70 and 84 ppb, JulyHealth Impacts from Ozone ChangesDraft report6d-23Figure 9: Ground Level SCUP-h UV Percent Change between 70 and 84 ppb, AugustHealth Impacts from Ozone Changes

Draft report 6d-24 

## 1.7. Changes in Human Health Effects
This section presents results in terms of the change s in skin cancer incidence and mortality associated with a one-year change in the ozone stan dard in 2020. Table 5 below gives the cumulative change in incidence and premature mortali ty 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 additiona l mortalities are expected. For the age cohorts relevant to this analysis (those populations p otentially 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 milli on cases and about 2.6 million mortalities. Thus, the additional cases and mortal ities 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 th ose 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 calc ulated in Section 1.15, to the central estimate.

Table 6: Monetized Summary Table (3% discount rate, discounted to 2020 with price s

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 calc ulated in Section 1.15, to the central estimate.

Table 7: Monetized Summary Table (7% discount rate, discounted to 2020 with price s

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 calc ulated in Section 1.15, to the central estimate. 

# Uncertainty
1.8. Uncertainty in estimated impacts
Uncertainty in the estimation of human health impac ts arising from a tightening of the NAAQS standards from 84 ppb to 70 ppb arise from various sources. T hese 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 change s 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 Uncertai nty

The sources and magnitudes of the uncertainties ass ociated with each step of the analysis were identified and are discussed in the relevant sections b elow.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 versio n 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 Stat es, 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. Simula tions 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 normal ized mean error = 18.3 % and 19.6 % respectively). No performance evaluation could be ass essed for the provided scenarios; however, analysis for the CMAQ review process (see ht tp://www.cmascenter.org/index.cfm) typically returns normalized mean errors for ozone § 20%.

As described in Section 2.2, the CMAQ ozone concent rations 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, primari ly from the approximate 2- stream (delta-Eddington) solution of the radiative transfe r equation, and the discretization of altitudes and wavelength and related interpolatio ns. These uncertainties have been shown in many earlier studies to be negligible, on th e order of 5% or less, when compared to higher stream models and higher vertical and spectral resolution.

2) Uncertainties in the input parameters that describ e atmospheric composition (vertical profiles of air, ozone, other absorbing gases, aerosols, an d 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 ac curate to a few percent, which is also the accuracy of the best instruments for measu ring 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 ge nerally not well known in any specific situation, and are highly variable spatially and temporally, with long-term trend s also a possibility. For the purposes of these calculation s, we adopt the principle that UV changesHealth 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 reflectiv ity. This is consistent with the approach used in calculations relating stratospheric ozone changes to s urface 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-a veraged to give the values in the third column of the table. The reference model (test number 0) wou ld be used in the AHEF estimates of skin cancer changes. The other entries in the tab le (tests number 1–6), show the UV-SCUP % change between scenarios, if other atmospheric condition s are changed individually and equally for both scenarios, as described in the second colu mn. 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 a erosols are removed from the model (test 1), the UV-SCUP increase between scenarios is only 0.112, whic h 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 an d 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 O 3 ) 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 O 3 (above

16 km) 0.123 4.2 (*) Elterman continental aerosol vertical profile, with to tal optical depth (at 550 nm) = 0.235, Angstrom alpha = 1.0, single scattering albedo = 0.99, a symmetry factor = 0.61.

1. Aerosols increase the photons’ pathlengths, and therefore increase the probability of absorption by tropospheric ozone. By removing aerosols fr om 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 atmosph ere, and a fraction of this can be scattered back toward the surface, effectively increasing the p hotons’ path-lengths for absorption by tropospheric ozone. If the surface is not reflectin g (albedo = 0%), these photon reflections do not occur and the interaction with tropospheric ozone is sm aller.

3. High clouds (e.g., cirrus) make the incident (down-welli ng) light more diffuse and therefore more slanted as it passes the troposphere. They al so reflect a fraction of the up-welling radiation (up-scattered by tropospheric molecules), back to the low er troposphere (much like surface albedo, but in the opposite direction). Both effects i ncrease tropospheric photon pathlengths and therefore the probability of absorption from any ad ditional 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 lar gest. In-cloud increases of ozone are particularly significant because of the long in-clo ud 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 (Na ck and Green, 1974).

5. Decreases in atmospheric pressure reduce, in dir ect proportion according to the ideal gas law, the conversion factor between ozone molar mixing rat ios (ppb, specified by CMAQ) and the ozone number density (molecules cm í 3 , which is integrated to obtain the ozone column in Dobson Units) used for atmospheric transmission. Als o, 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 (varyin g the surface elevation directly is possible within the TUV code, but would have created some ambiguity be tween the nominal CMAQ altitudes and the TUV geometric grid).

6. Reductions in stratospheric ozone imply that any tropo spheric 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):

UV bio v ( DU ) í RAF

for which the theoretical basis is described by Micheletti et al. (2003).

The sensitivity studies (cases 1-6) show that how the baseli ne environmental conditions, under which the difference between the two tropospheric ozone scen arios was assessed, could contribute to the uncertainties of the TUV-calculate d 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 stem ming from the changes in tropospheric ozone, by about 43%. This extreme case is patently unrealistic . Conservatively, if it is assumedHealth 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 follo ws:

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 ar e 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 consid ered a better metric for UV exposure and was therefore used in this analysis. A small uncertaint y is introduced over incidence on a horizontal plane, the previous standard. The percent chan ge in UV is reduced by about 8 % by taking the spherical output in preference to the plan ar 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 outpu t. 

### 1.10.2. Comparison with UV Changes Due to Other Fact ors In Section 2.2.3, the UV-SCUP change resulting from tropo spheric ozone change between the two CMAQ scenarios was calculated and shown to be of orde r ~ 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 s hows the UV changes, calculated for the CMAQ 084 tropospheric ozone scenario, when other en vironmental conditions, rather than tropospheric ozone, are changed relative to the ref erence 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 change s in tropospheric ozone, but rather with direct changes in the other environme ntal conditions.Health Impacts from Ozone Changes

Draft report 6d-30 Table 9: Effect on surface SCUP-UV radiation of varying env ironmental conditions

other than tropospheric O 3 .

Test number Description Domainaveraged % change in UV-SCUP

0 Reference (Elterman aerosols, 10% surface

albedo, no clouds, sea level, USSA stratospheric O 3 ), tropospheric O 3 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 O 3 (above

16 km) 7.6

Should the baseline environmental conditions actually change between the two CMAQ tropospheric ozone scenarios (084 and 070), the SCU P-UV changes could be far larger. Of course, there is no solid scientific basis for expecting suc h 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 a nd secondary organic aerosols, which can affect radiation directly as well as change cloud nucleatio n 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 an alysis. 

## 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 dome stic 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 adjacen t areas. When aggregated across broad latitude bands with hundreds of counties, small differe nces from one county to the next due to migration are likely to cancel each other out.

Interregional migration—such as the observed historic migr ations from the Northeast and upper Midwest to the Sun Belt states—is a potential sourc e 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 toHealth Impacts from Ozone Changes

Draft report 6d-31 other parts of the country are overrepresented in this an alysis, while the populations of fastgrowing regions attracting these migrants are underrepr esented. 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 t he 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 ass ociated 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 uncertain ty associated with the population projections is relatively small.

The CMAQ model area also has population implication s. 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 include d in the aggregated populations (26.2 % of the total population). It would be reasonable to assume, gi ven 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., C alifornia counties, especially Los Angeles), that this input alone would introduce a dis proportional large, unquantifiable uncertainty if the estimated health effects from th e 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 continu ous United States (e.g., Lutter and Wolz, 1997). 

## 1.12. Uncertainty in AHEF Modeling
AHEF modeling contributes uncertainties to the estimates o f human health effects—resulting from a change in NAAQS standards—in two major areas:

1) the dose-response relationships (expressed as a BAF) fo r the three endpoints of concern (i.e., BCC, SCC, and CMM), and

2) the future size, behavior, and distribution of the popula tions that will be affected (see Section 4.4. Uncertainty in Population Adjustments).

It should be noted that for this analysis, only est imated uncertainty in the BAF parameter is quantifiable. 

### 1.12.1. Uncertainties in Selected Derived Dose-Respo nse Relationships The AHEF model (described in Section 2.4) incorporates info rmation on the dose-response relationships for BCC, SCC, and CMM through the use o f a BAF (i.e., the slope of the doseresponse relationship). . The estimate of BAF and a ssociated 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 approxima tely 3 % for changes in these healthHealth 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 associat ed 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 approx imately 30% for changes in these health effects estimates.



### 1.12.2. Behavioral Uncertainties While the AHEF assumes that human exposure behavior remain s 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 socioeco nomic profiles that impact travel to areas where high UV exposure can be expected (i.e., the b each), or (3) in the use and/or efficacy of sun protection technologies such as sunglasses and suns creens can impact the extent of UV exposure received.

A number of recent studies have examined UV exposure behavi ors 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 ov erall did not practice any recommended sun protection behaviors. 

### 1.12.3. Latency Another source of uncertainty in the AHEF health eff ects estimate is associated with the exposure period over a person’s lifetime that is mo st likely to be the cause of UV-related health effects. This is especially relevant for CMM, since it has b een hypothesized that CMM is largely the product of intense exposures early in life (e.g., th rough age 20) rather than cumulative lifetime exposure. The AHEF uses whole life exposure fo r all skin cancer types as the default assumption. Using early life exposure for CMM is not the sa me 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 b y § 10 percent when the exposure assumptions (early life versus whole life) are changed (U.S. EPA, 2003), with uncertai nty concerning the appropriate exposure dose manifesting itself less in the total incremental risks predicted, than in when those incremental effects are predicted to occur, a nd who will bear them (i.e., shifting the risk to future generations). Mod eling this lag time further is difficult given the current state of knowledge about latency and its mechani sms (Madronich, 1999).Health Impacts from Ozone Changes

Draft report 6d-33 Figure 10: Excess CMM mortality for the Montreal Adjustmen t 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 det ermine the best medical cost estimates for NMSC for the Economic Evaluation of the U.S. Environmental Pr otection 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 ava ilable source of data for these health endpoints; however, the authors did not include uncertainty bands around their cen tral estimates.

The national mean annual wage for 2005 (U.S. BLS, 2 007) 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 i n the analysis’ health effects predictions. Some of these sources of uncertainty are possible to quantif y, but are not central to the structure of the analysis. Others cannot be quantified because a ny assumptions or estimates would be simply speculative. These other sources of uncertain ty 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 derivin g 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 imp act of ozone depletion from chlorine and bromine radical species generated from ODS would ch ange. In addition, long-term systematic changes in atmospheric opacity (e.g., clouds, aeroso ls, 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 c ould 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 th e ozone column in response to the phaseout of ODS. Some comp uter models predict that the phaseout of ODS will slow and eventually stop the ra te of ozone depletion, and suggest that natural ozone-making processes will enable stratospheric ozone to retur n to 1979–1980 ozone conditions. These models also predict that the reco very will eventually result in increased concentrations beyond 1979–1980 levels 7 (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 i mposes a limit on future ozone recovery to the conditions observed in 1979–1980.


### Effect of Climate Change The effects of global climate variations on stratos pheric 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 mode led NAAQS policy scenarios. To the extent that these limitations are not adhered to, fu ture ozone column conditions could be different.


### Laboratory Techniques and Instrumentation Additional uncertainty can be contributed by the la boratory techniques and instrumentation used for deriving the action spectra used to weight UV e xposure. Discrepancies between the wavelengths of UV radiation intended to be administered an d the wavelengths actually received by the test organism can result in orders of magnitude diffe rences in the measured response. In

7 Whether this recovery scenario, called “ozone supe rabundance,” is likely to occur is open to debate, particularly because of the potential for complex interactions b etween global climate change and stratospheric ozone dynamics. Model computations have predicted both higher and l ower amounts of ozone in the future.Health Impacts from Ozone Changes

Draft report 6d-35 addition, many action spectra are derived using mon ochromatic light sources that do not fully simulate the polychromatic light received directly fr om the sun.


### Improvements in medical care/increased longevity Improvements in medical care and predictions of increase d longevity for many population subgroups could affect estimates of future skin cancer inci dence and mortality significantly.


### Changes in socioeconomic factors Changes in socioeconomic factors (e.g., demographics and hu man behavioral changes) that could affect the accuracy of the analysis include:

 Changes in human UV exposure behavior: This evaluati on assumes that human exposure behavior remains constant through time, and does no t take into account innovations in sun protection technology (e.g., improved sunglasses and su nscreens), 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 whe re high UV exposure is expected (i.e., the beach), to the types of skin cancer most com monly observed.

 Changes in population composition and size: Populat ion 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 c ancer 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 th e analysis (e.g., improvements in medical care), and are therefore not addressed further in th is 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 esti mates). With disease data, under-, over-, and misreporting are not uncommon. For examp le, a studies have revealed that the incidence of CMM has been systematically under-repo rted in the SEER data (Clegg et al. 2002). 8 The original SEER data indicated that CMM rates in whit e 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 l argely attributable to diagnosis in doctors’ offices, as opposed to hospitals and other treat ment centers with better reporting accuracy. However, the AHEF results are not significantly affected by this underreporting because CMM incidence estimates in th e 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 CM M incidence under-reporting extends to the NCI-base d 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 incr emental 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 analy sis, 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 ¥ (5 2 + 30 2 + 10 2 ) § 32 %

There are a variety of other unquantified sources of uncertainty that may cont ribute to overall analytical uncertainty associated with modeled ozone chan ges, 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 uncert ainty

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 derivin g 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 (includi ng 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. I n addition, direct measurements (e.g., of future UV levels or skin cancer incidence) cannot at tribute explicitly observed changes to any specific factor, unless that factor is far more impo rtant than all the others combined. However, the principle of superposition can be used to exami ne the NAAQS impact (i.e., one effect in isolation) under the assumption that the other fact ors remain constant at current conditions. The validity of this principle is based on the assumpti on that the NAAQS impacts are independent of the other factors (e.g., behavioral changes will occur reg ardless of whether a new NAAQS standard is in place).


# References
American Cancer Society: Cancer Facts and Figures 2007. Atl anta: American Cancer Society; 2007. Bais, A., S. Madronich, J. Crawford, S. R. Hall, B. Mayer, M. VanWeele, J. Lenoble, J. G. Calvert, C. A. Cantrell, R. E. Shetter, A. Hofzumahaus, P. Koep ke, P. S. Monks, G. Frost, R. McKenzie, N. Krotkov, A. Kylling, S. Lloyd, W. H. Swartz, G. Pfister, T. J . Martin, E.-P. Roeth, E. Griffioen, A. Ruggaber, M. Krol, A. Kraus, G. D. Edwards, M. Mueller, B. L. Lefer, P. Johnston, H. Schwander, D. Flittner, B. G. Gardiner, J. Barrick, R. Schmitt , International Photolysis Frequency Measurement and Model Intercomparison: Spectral actinic solar flux measurements and modeling, J. Geophys. Res. , 108, 8543, doi:1029/2002/JD002891, 2003.Health Impacts from Ozone Changes

Draft report 6d-38 Bruhl, C. and P. J. Crutzen , On the disproportionate role o f tropospheric ozone as a filter against solar UV-B radiation, Geophys. Res. Lett. , 16, 703-706, 1989. Chen J.G., A. B. Fleischer, Jr, E. D. Smith, .C. Kancler, N. D. Goldman, P.M. Williford, S. R. Feldman. Cost of nonmelanoma skin cancer treatment in the United State s. Dermatol Surg. 2001; 27(12): 1035-8 Cokkinides VE, Johnston-Davis K, Weinstock M, O'Con nell MC, Kalsbeek W, Thun MJ, Wingo PA. Sun exposure and sun-protection behaviors and attit udes among US youth 11 to 18 years of age. Preventive Medicine, 2001, 33:141-151. de Gruijl F.R. and J.C. van der Leun, Estimate of the waveleng th dependency of ultraviolet carcinogenesis and its relevance to the risk assessment o f a stratospheric ozone depletion, Health Phys
,. 4 , 317–323, 1994. de Gruijl, F.R. and P.D. Forbes (1995), "UV-induced skin cancer in a hairless mouse mod el," BioEssays 17:651-660. Eder, B, and S. Yu (2006). A performance evaluation of the 2004 release of Model-3 CMAQ. Atmospheric Environment 40(26):4811–4824. Godar DE, Urbach F, Gasparro FP, van der Leun JC. UV d oses of young adults. Photochemistry and Photobiology, 2003, 77(4):43-457. Joseph, J. H., W. J. Wiscombe and J. A. Weinman, The d elta-Eddington approximation for radiative flux transfer, J. Atmos. Sci. 33 , 2452-2459, 1976. Koepke et al., Comparison of models used for UV Index calculations, Photochemistry and Photobiology, 67 (6) 657-662, 1998. Kyle JW, Hammitt JK, Lim HW, Geller AC, Hall-Jordan LH, Maibach EW, De Fabo EC, Wagner MC. Economic Evaluation of the U.S. Environmental Protection Agency’s SunWise Program: Sun Protection Education for Young Children. Forthcomi ng in Pediatrics Electronic Pages, May 2008. Longstreth J.D. (1998), “Melanoma genesis: putative caus es and possible mechanisms,” in Cutaneous Melanoma , C.M. Balch, A.N. Houghton, A.J. Sober, and S-J. Soong (eds.), St. Louis: Quality Medical Publishing, Inc. Madronich, S. and F. R. de Gruijl (1994), “Stratospheric ozo ne depletion between 1979 and 1992: Implications for biologically-active ultraviolet-B ra diation and non-melanoma skin cancer incidence,” Photochemistry and Photobiology 59:541-546. Madronich, S. and G. Weller, Numerical integration errors in calculated tropospheric photodissociation rate coefficients, J. Atmos. Chem., 10, 289-300, 19 90. Madronich, S. and S. Flocke. The role of solar radia tion in atmospheric chemistry. In Handbook of Environmental Chemistry, Volume 2 Part L: Environmenta l Photochemistry (Edited by P. Boule), pp. 1-26. Springer-Verlag, Heidelberg, 1999. Madronich, S., R. E. McKenzie, L. O Björn, and M. M . Caldwell, Changes in biologically active ultraviolet radiation reaching the Earth’s surface, i n Environmental Effects of Stratospheric Ozone Depletion - 1998 Update (J. van der Leun, M. Tevini, and X. Tan g, eds.), United Nations Environmental Programme, Nairobi, November 1998.Health Impacts from Ozone Changes

Draft report 6d-39 Madronich, S. (1999). Some comments on the methods us ed to estimate the effects of HSCTs on atmospheric ozone, surface UV radiation, and skin ca ncer incidence. Paper presented at a workshop held by EPA, “Meeting on Next Generational Supersonic Trans port Risk Assessment Methods,” November 18–19, 1999 at ICF Consulting, Washington, DC. Matthews, T.J. and B.E. Hamilton. (2005). Trend Anal ysis the Sex Ratio at Birth in the United States. National Vital Statistics Reports 53:20. Available online at: http://www.cdc.gov/nchs/data/nvsr/nvsr53/nvsr53_20. pdf McKinlay, A. F. and Diffey, B. L., A reference action spectrum for ultraviolet indu ced 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, Am sterdam, 1987. Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Va lue of Life? A Meta-Analysis.” Journal of Policy Analysis and Management 21(2):253-270. Nack, M. L. and A. E. S. Green, Influence of clouds , haze, and smog on the middle ultraviolet reaching the ground., 12, 2405-2415, 1974. National Center for Health Statistics (2007). Bridged-race Vintage 2006 Postcensal Population Estimates for July 1, 2000–July 1, 2006 . Available online at: http://www.cdc.gov/nchs/about/major/dvs/popbridge/d atadoc.htm Petropavlovskikh, I. Evaluation of Photodissociation Coefficient Calculations for Use in Atmospheric Chemical Models , Ph.D. thesis, U. of Brussels/NCAR Cooperative Thes is No. 159, National Center for Atmospheric Research, Boulder, 1995. Pitcher, H.M., and J.D. Longstreth (1991), "Melanom a 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. Occup ational 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: 2004–20 30

. Available online at: http://www.census.gov/population/www/projections/projectionsa gesex.html U.S. Environmental Protection Agency. EPA Guidelines for P reparing Economic Analyses. Washington, DC: Office of the Administrator, U.S. Environm ental Protection Agency; 2001. Report No. 240-R-00-003. U.S. EPA (2003). Human Health Benefits of Stratospheric Ozo ne 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 Deple tion 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, Nationa l Aeronautics and Space Administration, United States A ir Force, Washington D.C., 1976. Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market Estimates Throughout the World.” Journal of Risk and Un certainty 27(1):5-76.Health Impacts from Ozone ChangesDraft report6d-41Appendix A: Ground Level SCUP-h UV with 70 and 84 ppb by DayThis Appendix will provide a series of maps showing ground level SCUP-h UV levels under 70 and 84 ppb NAAQS for ozone for severalspecific 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 ScenarioHealth Impacts from Ozone ChangesDraft report6d-42Figure A-2: Ground Level SCUP UV, June 1; 84 ppb ScenarioHealth Impacts from Ozone ChangesDraft report6d-43Figure A-3: Ground Level SCUP UV, June 20; 70 ppb ScenarioHealth Impacts from Ozone ChangesDraft report6d-44Figure A-4: Ground Level SCUP UV, June 20; 84 ppb ScenarioHealth Impacts from Ozone ChangesDraft report6d-45Figure A-5: Ground Level SCUP UV, July 1; 70 ppb ScenarioHealth Impacts from Ozone ChangesDraft report6d-46Figure A-6: Ground Level SCUP UV, July 1; 85 ppb ScenarioHealth Impacts from Ozone ChangesDraft report6d-47Figure A-7: Ground Level SCUP UV, August 1; 70 ppb ScenarioHealth Impacts from Ozone ChangesDraft report6d-48Figure A-8: Ground Level SCUP UV, August 1; 84 ppb ScenarioHealth Impacts from Ozone Changes

6d-47 

# Appendix B: Overview of Evaluation Methodology
The schematic presented below provides a graphical summary of the m ethod used in this evaluation. Atmospheric inputs to the process are li sted 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 Atmosphere7-1

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

7.1 Synopsis
EPA has performed an illustrative analysis to estim ate 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 s tandard: 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 att aining alternative ozone standards. The benefit and cost estimates below are calculated incremental to a 2 020 baseline that incorporates air quality improvements achieved through the projected impl ementation of existing regulations and full attainment of the current standards for ozone and PM NA AQS (including the hypothetical control strategy developed in the RIA for full attainment o f 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 pres ented 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 assu me that almost all areas of the country will meet each alternate primary standard in 2020 through the dev elopment of technologies at least as effective as the hypothetical strategies used in this illu stration. 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 an alysis beyond 2020 involves the use of highly speculative assumptions that introduce a much h igher level of uncertainty to the results. Thus, in these locations, we provide estimates of th e costs and benefits of fully attaining the alternate primary standards at a later date (2030) in Appendix 7b. It is import ant 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 t he appendix, it is not appropriate to add the results together to get a national “full attain ment” scenario. Finally, Appendix 6b contains a health-based cost effectiveness analysis that complements t he 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 excep t San Joaquin Valley and South Coast air basins in California. The state has submitted p lans to EPA for implementing the current ozone standard which propose that these two areas of Cali fornia meet that standard by 2024. We have assumed for analytical purposes that the San Joaquin Valley and South Coast air basin would7-2

attain a new standard in 2030. There are many unce rtainties 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 Nationa l rules for Diesel Locomotives, Diesel Marine Vessels, and Small Nonroad Gasoline En gines. 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 associat ed costs and benefits. In order to provide a rough approximation of the costs and benefits of at taining 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 mode ling. This data was used to develop extrapolated costs and benefits of 2030 attainment. To v iew 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 p rimarily 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 c hanges 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 PM 2.5

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 Ito et al. 2005 4.4 – 19 4.3 – 17 7.6 – 8.8 –4.4 – 11 –4.5 – 9.8 Meta-analysis 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 PM 2.5

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 Ito et al. 2005 2.1 – 12 2.0 – 11 2.4 – 2.9 –0.83 – 9.2 –0.9 – 8.1 Meta-analysis 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 Cos ts and Ozone Benefits and PM 2.5

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 –2 0 – 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 Meta-analysis 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 – 5.77-4

Table 7.1d: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM 2.5

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 –3 5 – 14 –35 – 9.7

Bell et al. 2005 17 – 54 16 – 50 32 – 44 –27 – 22 –28 – 18 Ito et al. 2005 21 – 58 21 – 54 32 – 44 –23 – 26 –23 – 22 Meta-analysis 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 est imates do not include visibility benefits. Only includes area s required to meet the current standard by 2020, do es not include San Joaquin and South Coast areas in California. Ap pendix 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 mort ality functions characterized in the expert elicitation. Tables exclude unquantified and nonmonetized benefi ts. ***Range reflects lower and upper bound cost estima tes. Data for calculating costs at a 3% discount ra te was not available for all sectors, and therefore total annu alized costs at 3% are not presented here. Addition ally, t hese estimates assume a particular trajectory of aggress ive technological change. An alternative storyline might hypothesize a much less optimistic technologi cal trajectory, with increased costs, or with decreased benefits in 2020 due to a later attainmen t date.  Total includes ozone morbidity benefits and tot al PM co-benefits only.

The individual row estimates for benefits reflect the vari ability 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 th e 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 lo west 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 estima tes, as well as the uncertainties and limitations that should be considered in interpreti ng the estimates. These tables do not include visibility benefits, which are estimated at $160 milli on/yr.
Below are three graphs illustrating the net benefits of the se lected 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 est imates for each alternative. These figures depict the richness and variability in the estimate s of costs and benefits that may not be captured by the truncated summary tables above.
Figure 7.1 displays all possible combinations of net be nefits, utilizing the five different ozone functions, the fourteen different PM functions, and th e two cost methods. Each of the 140 bars in7-5

each graph represents an independent and equally pr obability point estimate of net benefits under a certain combination of cost and benefit estimation method s. 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 indic ate 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 combinati ons indicate positive net benefits for this standard.
Figure 7.3 illustrates a subset of the net benefit e stimates shown in Figure 7.1. While we treat each combination of costs and benefit estimates as being equal ly probable in our model, here we select a series of combinations of an ozone benefits esti mate, a PM 2.5 co-benefit estimate, and a cost estimate. Consistent with the distribution shown i n Figure 7.1 above, the net benefits estimate is very sensitive to the choice of ozone mortality f unction, PM 2.5 mortality function, and cost estimation approach. These intermediate combin ations (which are discussed more completely in the benefits chapter) represent reference points:

x Bell 2004 is the epidemiological study that underli es the ozone NAAQS risk assessment and Pope is the PM mortality function that was in several EP A RIAs, and

x Bell 2005 is one of three ozone meta-analyses and L aden is a more recent PM epidemiological study that was used as an alternative in the PM NAA QS 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 th at benefits are either significantly higher than costs (over $10 billion in net benefits ) or that the benefits are significantly lower than costs (roughly negative $6 billion in net benefits).7-6Figure 7.1: Range of Net Benefits (2006$) 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. Allcombinations are treated as independent and equallyprobable. These estimates do not include visibility benefits, which are estimated at $160 million/yr.Only includes areasrequired to meet the current standard by 2020, doesnot include San Joaquin and South Coast areas in California.Costs exceed BenefitsCosts exceed BenefitsCosts exceedBenefitsBenefits exceed CostsBenefits exceed CostsBenefits exceedCostsBenefits exceedCostsCosts exceedBenefits7-7Figure 7.2: Range of Net Benefits (2006$) 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. Allcombinations are treated as independent and equallyprobable.For the selected standard of 0.075 ppm, the medianvalue of all of the independent point estimates is$0.8 billion, and the majority (64%) of the combinations indicate positive netbenefits 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 by2020, does not includeSan Joaquin and South Coast areas in California.*Benefits exceed CostsCosts exceed Benefits7-8

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 prematu re mortality estimates. See Section 5.2 for discussion of the hybrid and fixed cost estima tes.7-9

Table 7.2: Summary of Total Number of Annual Ozone and PM 2.5 -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 PM 2.5 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 2,0 00 – 6,500 Ito (2005) 230 – 1,300 500 – 2,300 1,100 – 4,300 2, 500 – 7,000 Meta-Analysis 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 17,00 0 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 2,10 0,000 3,500,000 *Only includes areas required to meet the current s tandard by 2020, does not include San Joaquin Valle y 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 lowe r 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 (OM B) guidance under Executive Order 12866 defines a no-action baseline as “what the world will b e like if the proposed rule is not adopted”. The illustrative analysis in this RIA assesses th e costs and benefits of moving from this “no-action” baseline to a suite of possible new standar ds. 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 benef its 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 reg ulations. (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 me asuring benefits and costs against alternative baselines. In doing so you can analyz e the effects on benefits and costs of making different assumptions about other agencie s‘ 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 techno logies. It also assesses nationally what the change would be to costs and benefits of all standards. Cost for all sta ndards would increase by $1.8 1 billion and benefits for all standards would increas e 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 c ontrols characterized as additional technology changes in the onroad transportation sec tor. 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 alter nate primary standards would change if these technology changes wer e not implemented to meet the current standard, but were instead implemented as part of the strate gy 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 st arting point for assessing the costs and benefits of the standard of 0.075 and the alternate primary standard s 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 improvemen ts would be accomplished by including additional onroad transportation control measu res in the control scenario, equivalent to the reductions ‘removed’ from the alternate baseline. The va lue 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 p artial accounting of the total benefits associated with the mobile controls included in this sensitivi ty analysis. The sensitivity analysis does not estimate the benefits of other co-controlled emissio n 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. Further more, these estimates are highly uncertain and are purely illustrative estimates of the potential cost s 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 in spections of OBD-equipped vehicles by car owners. EPA estimates that the nationwide installation of Rem ote OBD would save the nation’s motorists about $16 to $22 billion in insp ection and convenience costs over a 10 year period.7-11

control strategies. We present them only as screeni ng-level estimates to provide a bounding estimate of the costs and benefits of including these emis sions 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 a nalyses. For more details on the baseline sensitivity analysis, please reference Appendix 7a.
7.3.2 Relative Contribution of PM Benefits to Total Benefi ts
Because of the relatively strong relationship between PM 2.5 concentrations and premature mortality, PM co-benefits resulting from reductions in NO x 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 benefit s, which is dependent on the specific ozone mortality function assumed. PM co-benefits based on daily average concentrations are calcula ted over the entire year, while ozone related benefits a re 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 f or between 42 and 99 percent of total benefits. The lower end of the range assumes a combi nation of Levy et al. (2005) & Expert K. The upper end of the range assumes a combination of the assumpt ion of no causality & Expert E.
7.3.3 Challenges to Modeling Full Attainment in All Ar eas
Because of relatively higher ozone levels in several large urban areas (Southern California, Chicago, Houston, and the Northeastern urban corridor) an d because of limitations on the available database of currently known emissions control te chnologies, EPA recognized from the outset that known and reasonably anticipated emission s controls would likely be insufficient to bring some areas into attainment with either the cu rrent 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 throug h application of documented, wellcharacterized emissions controls, and the costs and benef its 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 exercis e, 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 anal ytical 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 cu rrently available to develop reasonable approximations of the costs and benefits of the extrapolat ed portion of the emissions reductions necessary to reach attainment. However, due to the h igh level of uncertainty in all aspects of the extrapolation, we judged it appropriate to provide separat e estimates of the costs and benefits for the modeled stage and the extrapolated stage, as we ll as an overall estimate for reaching full attainment. There is a single chapter on benefits, b ecause the methodology for estimating benefits does not change between stages. However, in that chapter, we again provide separate7-12

estimates of the benefits associated with the model ed control scenario which provides the foundation upon which benefits for full attainment are extrapolated for all four alternate p rimary standards (0.079, 0.075, 0.070, and 0.065 ppm).
In both stages of the analysis, it should be recognized that a ll estimates of future costs and benefits are not intended to be forecasts of the ac tual costs and benefits of implementing revised standards. Ultimately, states and urban areas will be respo nsible for developing and implementing emissions control programs to reach attainm ent 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 magn itude 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 c osts of those controls, the levels of emissions reductions that may be achieved with these controls, the a ir quality impact that can be expected to result from reducing emissions, and the public h ealth 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 a ttainment, 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 mu ltiple pollutants with overlapping precursors (e.g., the recent tightening of the PM 2.5 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 emissio ns and remaining anthropogenic emissions for which we do not have enough knowledge to determ ine how and at what cost reductions can be achieved in the future when attainment would be req uired. With the more stringent NAAQS, more areas will need to find ways of reducing emissi ons, 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 redu ced 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 developmen t 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 ozoneNAAQS and the resulting costs and benefits. Studies indicate that it is not uncommon for preregulatory cost estimates to be higher than later estima tes, 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, t he opportunity for technical advances is greater (see Chapter 5 for details).
Our estimates of costs of attainment in 2020 assume a particular trajectory o f aggressive technological change. This trajectory leads to a pa rticular level of emissions reductions and costs which we have estimated based on two differen t approaches, the fixed cost and hybrid approaches. An alternative storyline might hypothe size a much less optimistic technological change path, such that emissions reductions technol ogies for industrial sources would be more7-13

expensive or would be unavailable, so that emissions reduc tions from many smaller sources might be required for 2020 attainment, at a potentially grea ter cost per ton. Under this alternative storyline, two outcomes are hypothetically pos sible: Under one scenario, total costs associated with full attainment might be substantially hi gher. 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 technologi es 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 at tainment 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, seeki ng 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 s tates 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 margi nal 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 under state 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 wo uld 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 est imates provided in this analysis and because the analysis assumes there are quantifiable benefit s to reducing ambient ozone below each of the alternative standards.
Estimated benefits and costs may reflect both bias and uncer tainty. While we strive to avoid bias and characterize uncertainty to the extent possible, we not e 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 ben efits 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 rela tive to the modeled emissions and cost estimates, because of the lack of refined info rmation 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 thi s 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 sele cting controls, we focused more on the ozone cost-effectiveness (measured as $/ppb) th an on the NO x or VOC costeffectiveness (measured as $/ton). When compared on a $/ton b asis, many VOC controls appear cost-effective relative to NO x reductions (see Figures 5.1 and 5.2). However, the air quality sensitivity analysis showed that NOx reduct ions were more effective than7-14

VOC reductions in reducing Ozone concentrations exc ept in urban areas which are VOC limited. In those locations, NO x reductions can actually result in increases in ozone, and as such, VOC reductions can be cost-effective relat ive to NO x on a $/ppb basis.
2. Our knowledge of technologies that might achieve NO x 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 reduc ed overall NO x emissions nationwide by 6 percent and VOC by 2 percent. After these redu ctions, remaining emissions were still substantial, with over 9 million tons of NO x and 12 million tons of VOCs remaining nationwide. The large remaining inventories of NO x and VOC emissions suggests that additional control measures need to be developed, wit h appropriate consideration of the relative effectiveness of NO x and VOC in achieving ozone reductions.

3. Most of the overall reductions in NO x achieved in our illustrative control strategy were from nonEGU point sources. This was due to the fact that: 1) EGUs have been h eavily controlled under the recent NO x 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 NO x beyond the CAIR caps, but instead to obtain NO x 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 NO x emissions were much greater in the nonEGU point sector than in the mo bile or EGU sectors. However, the remaining uncontrolled NO x emissions from EGU and mobile sectors are still greater than nonEGU point sources 2 , and additional reductions from these sectors may need to be considered in developing strategies to achieve full attainment. Exploratory analyses indicate that there are opportuni ties 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 signific ant, 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 be nefits of implementing a tighter standard will likely result i n 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 PM 2.5 ) levels increase. This is due to two reasons. The first reason is that the complexities involved in the atmospheric p rocesses which govern the transformation of emissions into ozone result in some lo cations and times when reducing NO x emissions can actually increase ozone levels on some d ays (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 effe ct of implementing NO x controls

2 NonEGU point source emission projections currently do no t include estimated activity or economic growth.7-15

is to increase overall ozone levels and increase the health e ffects associated with ozone. This same phenomenon results in some areas also seeing incre ases in PM 2.5 formation. The second reason is that the particular control st rategy that we modeled for EGU sources is a modification to controls on sources within the o verall 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 shif ts in power generation and emissions at other sources. Because under our chosen EGU contr ol 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. Thi s 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 t o benefit the most from EGU NO x emissions reductions, with emissions increases largely occurring in areas in attainment with the ozone NAAQS. However, this necessarily means that i n 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 pos itive. 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 are a 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 speci al uncertainties. EPA interpolated the benefits of the 0.070 ppm alternative t o estimate the full attainment benefits of the less stringent 0.075 ppm and 0.079 ppm altern atives. These two interpolated benefits estimates are subject to two sources of uncertainty: (1) the uncertainties inherent in the original 0.070 ppm benef its 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 benef its. This approach likely understates the benefits that would result from state i mplementation of emissions controls because controls implemented to reduce ozone concentr ations at the highest monitor would likely result in some reductions in ozone concentra tions at nearby attaining monitors. Therefore, air quality improvements and result ing 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 it s own uncertainties. We made a reasonable judgment regarding the geographic area wit hin which to interpolate benefits. However, this area may not match the ultimate geogr aphic distribution of air quality improvements under a state-implemented control stra tegy to attain either the 0.075 ppm or 0.079 ppm alternative; this could result in an under- o r over-estimate of benefits. The complexity of the various uncertainties makes it challeng ing 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 NAAQS7-16

known control costs ($3.3 billion per year 3 (2006$)) 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 15/3 5 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 an d areas is highly uncertain. Our estimates of costs for a set of modeled NO x and VOC controls comprise only a small part of the estimated costs of full attainment. These estimated c osts 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 extrapola tion 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 NO x 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 improvemen ts were derived from extrapolated emissions reductions that go well beyond the a rea of the known controls, the uncertainty associated with costs increases.

7. NonEGU point source controls dominate the estimated cos ts. These costs account for about 54 percent of modeled control costs. The avera ge 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 timeframe s. States will ultimately select the specific timelines for implementation as p art of their State Implementation Plans. To the extent that states seek classification as extreme nonattain ment areas, the timeline for implementation may be extended beyond 2020, meaning that the amount of emissions reductions that will be required in 2020 w ill 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 Establi shments. Washington, DC. http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf

.

3 Known controls include the modeled control strateg y ($2.8 billion dollars per year (2006$)) as well as any supplemental and giveback controls appl ied (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 (OM B) guidance under Executive Order 12866 defines a no-action baseline as “wha t the world will be like if the proposed rule is not adopted.” The illustrative ana lysis 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 base line may require consideration of a wide range of potential factors, including:

x evolution of the market,

x changes in external factors affecting expected benef its and costs,

x changes in regulations promulgated by the agency or o ther government entities, and

x the degree of compliance by regulated entities with other reg ulations. (OMB 2003)

Circular A-4 also recommends that...

When more than one baseline is reasonable and the choice o f baseline will significantly affect estimated benefits and costs, you shou ld consider measuring benefits and costs against alternative baselines. I n doing so you can analyze the effects on benefits and costs of making different assumption s about other agencies’ regulations, or the degree of compliance with your own existing rules. (OMB, 2003)
This sensitivity analysis is intended to provide informa tion about how the no-action baseline would differ under different assumptions about m obile technologies. It also assesses nationally what the change would be to cos ts 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 billion 1 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 diagnosti c device inspection in Inspection and Maintenance programs. As explained in the Appe ndix to Chapter 3, Remote On Board Diagnostics (OBD) eliminates the need for per iodic inspections of OBD-equipped vehicles by car owners. EPA estimates that the nat ionwide installation of Remote OBD would save the nation’s motorists about $16 to $22 billion in inspectio n 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% d iscount rate, and $330 million to $2.8 billion when using a 7% discount rate. 2

The process of analysis of costs and benefits of atta ining 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 decre es, existing promulgated programs) and layers onto that illustrative control strategies from p revious NAAQS RIA analyses, and finally, a simulated control strategy for attain ing the current NAAQS in question (O3 at 0.084 ppm). This is the point at which the “no-act ion 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 b aseline affect the starting point of the assessment of costs and benefits, and thus affect t he total incremental cost and benefit estimates.
The primary analysis baseline included some mobile c ontrols characterized as additional technology changes in the onroad transportation sec tor. The application of these controls to the baseline assumes an optimistic future where reductio ns in emissions are achieved through the implementation nationally of cutting-edge mob ile 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 instea d 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 start ing point for assessing the costs and benefits of the standard of 0.075 and the alter nate 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 addi tional onroad transportation control measures in the control scenario, equivalen t to the reductions ‘removed’ from the alternate baseline. The value in benefits of those improv ements is estimated on a $/ton emissions reduced basis derived from the Locomotive M arine Diesel Rule.
A description of the control measures added to the alternat e control scenario for this sensitivity analysis follows.

2 These estimates are highly uncertain and are purel y illustrative estimates of the potential costs and benefits of these mobile control strategies. We pr esent them only as screeninglevel estimates to provide a bounding estimate of t he 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 esti mates to other policy contexts, including other regulatory impact analyses. Furthermo re, the benefits only reflect a partial accounting of the total benefits associated wit h emission reductions related to the mobile controls included in this sensitivity analys is.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 technolo gies 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 2 3 emission levels (see Table 7a.1) for a program starting in 2013 and fully phased in by 201 5.

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 2 020 and nearly 3 million tons in 2030, a 74% reduction.

The above results are modeled relative to a Bin 5 baselin e

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 resul t in lower total costs.

3 For information on Bin emission levels, see: http://www.epa.gov/greenvehicles/summarychart.pdf7a-4

The technology is described as follows:

x The technology relies on catalyst improvements—addi ng Rhodium, improved substrate/washcoat, and 900 cpsi density (all vehicle s are assumed to need these changes)

x All vehicles are assumed to have close-coupled cata lysts (1 or 2)

x Increased use of electronically controlled air injectio n—100% implementation on everything except 4-cylinder engines
Engineering costs for this program are estimated to be appro ximately $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 R hodium, 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 i ntended to achieve rough estimates. An in-depth bottom-up detailed cost anal ysis would need to be done to support an actual Improved Catalyst Design regulatory program.

x Most of the costs are for catalyst improvements—add ing Rhodium, improved substrate/washcoat, and 900 cpsi density (all vehicle s 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, t hese numbers can vary +/- 30%.
7a.2.2 Plug-In Hybrid Electric Vehicles
Plug-In Hybrid Electric Vehicles (PHEVs) are very s imilar to Hybrid Electric Vehicles, but with three significant functional differences. The fi rst is the addition of a means to charge the battery pack from an outside source of e lectricity (usually the electric grid). Second, a PHEV would have a larger battery pack with m ore 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 poll utants 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 power7a-5

generation plant rather than from the vehicle tailpi pe, which may provide health benefits for residents of the more densely populated urban area s. Unlike most other oil-saving technologies, PHEVs also use existing infrastructure for fueling with gasoline and electricity so large investments in fueling infrastr ucture are not required. Since emissions from utilities are capped by existing programs, inc reases in power generation are generally not expected to impact attainment of air quality s tandards.
For this analysis, we assumed that PHEVs would be available as passenger cars and as light trucks in all light truck weight classes by 2 012. 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 wh at could be feasible for the market penetration of PHEVs based on reductions that are n eeded for attainment of the revised ozone NAAQS and EPA’s internal expertise and judgment. R ecent announcements by Toyota and General Motors that they plan to introduc e 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 li kely to be the ones who can take best advantage of the PHEV while still operating on an o vernight charge, i.e., urban and suburban residents with shorter commutes. We also a ssume 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 direc t exhaust emissions. 4 We used that estimate, and the assumptions of vehicle sales given abo ve, 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), Tota l: All Road Types

4 Note that this assumption is different than the ass umption 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), Tot al: All Road Types

x 2201040000 Light Duty Gasoline Trucks 2 (LDGT2), Tot al: All Road Types
Using the assumptions and methods described above, we e stimated that HC emissions would be reduced by a range of 2.4% to 3.9% for passenger car s and light trucks (reductions vary by vehicle class). For NOx, we est imate 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 proj ected cost of the vehicle package 5 . Using a retail price markup factor of 1.26 from the projecte d 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 b ase 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 OB DII 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 standar d 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 Proje cted Costs . U.S. E.P.A., October 2005. http://epa.gov/otaq/technology/420r05012.pdf7a-7

found that substantial emission reductions can be had by upgrading the quality of aftermarket catalysts.
Applying the proposed aftermarket catalyst requirements t o the national fleet would bring about nationwide reductions. According to the Manufacturers of Emission Co ntrols Association (MECA), approximately 3 million afterma rket catalysts are sold each year.
Estimated benefits are derived by comparing performance of existing replacement catalysts to that of the proposed catalysts. The di fference is applied to the 3 million vehicles in the fleet that get aftermarket replaceme nt catalysts.
All light-duty gasoline vehicles and trucks: Affected SCC:

x 2201001000 Light Duty Gasoline Vehicles (LDGV), Tota l: All Road Types

x 2201020000 Light Duty Gasoline Trucks 1 (LDGT1), Tot al: All Road Types

x 2201040000 Light Duty Gasoline Trucks 2 (LDGT2), Tot al: All Road Types
The table below (Table 7a.5) shows the emissions of the curre nt aftermarket catalysts at 25,000 miles and the performance of the OBDII-type aftermarket catalysts at the same mileage. The emission reductions from improved aftermarke t 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 r equired the 3 million replacement catalysts installed each year to meet these standar ds, 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 emissio n reductions we calculate are as follows:7a-8 Table 7a.6: Emission Reductions from Replacement Catalys ts
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 OB DII 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 standar d 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 c atalyst is $275, compared to approximately $100 for current replacement catalysts. Th ese 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 Ma rket 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 reduc ed.
7a.2.3 Summary of Emission Reductions and Costs

Total emission reductions and costs for the 3 control measur es included in the alternative baseline analysis are presented in Table 7a.8:

Table 7a.8: NOx Emission Reductions and Costs for Alternati ve 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.com7a-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 mobil e source NOx emission reductions in our baseline through a benefit per ton appro ach. Because NOx is both an ozone and PM2.5 precursor, these reductions will yield bo th reductions in the ambient levels of these pollutants as well as monetized bene fits. Because these reductions occur in the mobile source sector, we decided to estimate to tal ozone benefits by imputing an ozone benefit per-ton estimate from the soon-to-be-p romulgated Locomotive and Marine Diesel Rule. While this rule does not affect an identi cal set of sources, it is a reasonable representation of the benefits of emission reductions in mobile source emission s, 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 a nalysis. The process for deriving these estimates can be found in the same TSD.
The range of total combined ozone and PM2.5-related 2020 ben efits associated with the emission reductions are between $360 million to $3.1 bill ion in 2006$ using a 3% discount rate. The lower-end of this range represent s 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 account ing 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 th erefore an underestimate of total benefits. Furthermore, these estimates are highly uncertain and are purely illustrative estimates of the potential costs and benefits of these mobil e control strategies. We present them only as screening-level estimates to provide a boundi ng estimate of the costs and benefits of including these emissions controls in t he ozone NAAQS control case for all standards. As such, it would be inappropriate to apply thes e benefit per-ton estimates to other policy contexts, including other regulatory i mpact analyses.

7a.4 References
U.S. Office of Management and Budget. September 2003. Ci rcular A-4, Regulatory Analysis Guidance sent to the Heads of Executive Age ncies and Establishments. Washington, DC. http://www.whitehouse.gov/omb/circulars/a004/a-4.pd f

.
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.pdf7a-10

U.S. Environmental Protection Agency Science Advisory B oard. 2005. EPA’s Review of the National Ambient Air Quality Standards for Particulate M atter (Second Draft PM Staff Paper, January 2005). EPA-SAB-CASAC-05-007. June.
U.S. Environmental Protection Agency, 2006. Air Quali ty Criteria for Ozone and Related Photochemical Oxidants Volume I of III. Na tional 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 PM 2.5 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 attainme nt 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 l ater than 2020. For these two areas in California, we are assuming a new standard attainment d ate of 2030. Estimates of the benefits and costs of attaining .075 and the alternate air qual ity 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 fed eral air quality rules are expected to further reduce emissions of NOx and VOC. Because mobile sou rce rules affect new vehicles and equipment, they reduce inventories over a long peri od of time, as older vehicles and equipment are gradually scrapped and are replaced by new, reg ulated, 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 e ffect in 2011. Major nonroad rules also contribute to this decline, including the Locomoti ve 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 Pollutio n 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 ex pected inventory decreases between 2020 and 2030. The emission reductions from these programs should lo wer ambient levels of ozone between 2020 and 2030 across the state of California; t his would facilitate the process of reaching attainment with a revised ozone standard in San Joa quin 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 th e only sectors projected to 2030 in our emission inventories; we do not have 2030 inventor ies for any stationary sources and therefore do not have a comprehensive estimate of Oz one precursor emissions around which to craft control strategies to determine costs. All stationar y source emissions are held at year 2020 levels because of uncertainties in how to project stationa ry 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). Witho ut a complete set of future 2030 emission inventories and control strategies, it is not possi ble 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 sop histicated benefits analysis. In order to provide some idea of costs and benefits of attaining 0.075 and the alt ernate standards in San Joaquin and7b-2

South Coast air basins, we’ve relied on the availab le 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 model ing 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 Val ley areas into attainment by 2030. The supplemental air quality modeling showed (see Fig4-2d) t hat there was a sharp dropoff in ozone between the 60% and 90% additional NOx contro l 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 be yond the RIA Control Scenario Necessary to Meet the Various Ozone Standards in Los A ngeles 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% 8 3% 79% 75% San Joaquin Valley, C A 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 ar e based on the NOx targets for Los Angeles South Coast Air Basin in Table 7b.1. The higher red uctions for Los Angeles compared to the San Joaquin Valley should enable all of California to attain, e ven after transport effects. Inventory reductions in 2030 from the onroad mobile, nonroad m obile, 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 t he majority of emission reductions are needed for these areas to reach the current ozone s tandard The reductions also include the Final Loco-Marine controls for 2030 (EPA, 2008). Overall, the l oco-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 o f 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, CA A 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 Joaq uin Valley are included in the Sacramento Metro buffer.7b-3

To calculate the incremental costs of attainment fo r 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 reductio ns 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

0.065 A 0.070 0.075 0.079 Los Angeles-San Joaquin Valley, CA B 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 inc remental to the reductions achieved by Sacramento in 2020 (see Table 4.6a).

b The Los Angeles South Coast Air Basin and San Joaq uin Valley are included in the Sacramento Metro buffer.
The additional tons of reductions needed to attain the var ious 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 S acramento. Additionally, between 2020 and 2030 other rules are expected to reduce emissions. Am ong 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 Rulemak ing (EPA, 1998), the LocomotiveMarine Final Rule (EPA, 2007c), the Clean Air Nonroad Dies el Final Rule—Tier 4 (EPA, 2004), and Control of Air Pollution from Aircraft (EPA, 2005), amon g others. California has also regulated most of these same categories, often more stringently than the Federal government, resulting in substantial expected inventory decrease s between 2020 and 2030. A final factor that influences the total number of tons needed to attain in 2030 is the relatively greate r effectiveness in California of NOx reductions that happen in the high er range of percentage reduced from the total NOx inventory. For example, a ton reduced when 80% o f 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 nationa l 2020 analysis. There is even more uncertainty associated with this analys is 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 Valle y 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 increm ent needed for California to meet the 0.065 ppm standard..7b-4

Joaquin Valley cost area has historically had a diffic ult 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 co sts 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 i mportant 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 alter nate ozone standards.
7b.2.2.1 Fixed Cost Approach Results Table 7b.4 shows the estimated costs using the fixed cost me thodology 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

Fixed Cost Approach Extrapolated Cost (M 2006$). All 2030 Extrapolated Cost Areas

(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm Los Angeles-San Joaquin Valley, CA $1,200 $1,100 $6 80 $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 met hodology 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 a nd San Joaquin Valley use the same formulas presented in the Appendix 5a. There a re large uncertainties when extrapolating to 2030, therefore keeping the approach consistent yield ed the average cost numbers seen in Table 7b.5.

Table 7b.5: Hybrid Approach (Mid) Parameter Values for Vario us 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 a ggressive 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 atta inment 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 1.1 3 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

Hybrid Approach Extrapolated Cost (M 2006$). All 2030 Extrapolated Cost Areas

(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 atta inment 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 o f M (0.47 for the high and 0.12 for the low in place of the 0.24 used in the mid estimate). The t otal 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

Fixed Cost Approach Extrapolated Cost (M 2006$). All 2030 Extrapolated Cost Areas

(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

Fixed Cost Approach Extrapolated Cost (M 2006$). All 2030 Extrapolated Cost Areas

(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm Los Angeles-San Joaquin Valley, CA $1,600 $1,500 $9 00 $450

a All estimates rounded to two significant figures.
Table 7b.9: Hybrid Approach (Low) Average Cost/Ton for Various Standards a, b Hybrid Approach Average Cost/Ton (2006$) All 2030 Extrapolated Cost Areas

(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 a ggressive 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 atta inment date.7b-6 Table 7b.10: Extrapolated Cost to Meet Various Stan dards Using

Hybrid Approach (Low) a, b

Hybrid Approach Extrapolated Cost (M 2006$) All 2030 Extrapolated Cost Areas

(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm Los Angeles-San Joaquin Valley, CA $1,500 $1,400 $8 60 $430

a All estimates rounded to two significant figures.

b These estimates assume a particular trajectory of a ggressive 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 atta inment date.

Table 7b.11: Hybrid Approach (High) Average Cost/Ton for Va rious Standards a, b Hybrid Approach Average Cost/Ton (2006$) All 2030 Extrapolated Cost Areas

(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm Los Angeles-San Joaquin Valley, CA A $33,000 $33,000 $32,000 $31,000

a All estimates rounded to two significant figures.

b These estimates assume a particular trajectory of a ggressive 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 atta inment date.

Table 7b.12: Extrapolated Cost to Meet Various Stan dards Using

Hybrid Approach (High) a, b

Hybrid Approach Extrapolated Cost (M 2006$) All 2030 Extrapolated Cost Areas

(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm Los Angeles-San Joaquin Valley, CA A $2,600 $2,400 $1,400 $700

a All estimates rounded to two significant figures.

b These estimates assume a particular trajectory of a ggressive 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 atta inment date.

7b.2.3 Benefits

The Estimated Benefits of 2030 Attainment with Alte rnate Ozone Standards The ozone analysis for San Joaquin and South Coast applies th e 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 benefi ts 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 PM 2.5 Precursor Reduced The NOx emission reductions necessary to reach atta inment with an alternate revised standard would also reduce levels of PM 2.5 . The process for estimating the PM 2.5 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 foll ows:

1. Estimate the number of tons of NOx necessary to att ain a baseline of 0.08 ppm . As noted above, Table 7b.2 includes the estimate of extrapolated NOx t ons necessary to attain each standard alternative.

2. Calculate the benefits of attaining 0.08 ppm incrementa l to partial attainment of 0.08
ppm. To estimate the benefits of fully attaining 0.08 ppm incremen tal 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 th is calculation step allows us to net7b-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 attain ment of 0.08 ppm. Subtract the benefits of fully attaining 0.080 ppm incre mental to the partial attainment of 0.08 ppm to create a new estimate of incremen tal 0.070 ppm partial attainment.

4. Calculate the PM 2.5 benefits of fully attaining 0.070 ppm . Multiplying the estimate of the extrapolated NOx tons necessary to attain 0.070 ppm fully (T able 7b.3) produces an estimate of the incremental benefits of fully attai ning 0.070 ppm incremental to partial attainment of 0.070 ppm. By adding this incremental benefi t estimate to the benefits generated in step 3, we derived a total benefit estim ate 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 necess ary 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 PM 2.5 co-benefits.
Because this analysis estimates the PM 2.5 co-benefits of full attainment for these two airshe ds in 2030, it was necessary to apply a PM 2.5 benefit per ton estimate that incorporates this population year. The Technical Support Document for this RIA describ es 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 PM 2.5 co-benefits associated with attainment of each standard alternative.

Total Estimate of Combined Ozone Benefits and PM 2.5 Co-Benefits Table 7b.15 summarizes the total combined benefits for each standard alternative.
The following tables summarize the costs, benefits, and n et 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 Attainin g 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 ppmȱ ȱ ACS Study $120,000,000 $110,000,000 Harvard Six

Ȭ City Study $260,000,000 $240,000,000 Expert K $54,000,000 $50,000,000 Expert E $450,000,000 $410,000,000ȱ ȱȱ ȱ 0.075 ppmȱ ȱ ACS Study $240,000,000 $220,000,000 Harvard Six

Ȭ City Study $530,000,000 $480,000,000 Expert K $110,000,000 $100,000,000 Expert E $900,000,000 $820,000,000ȱ ȱȱ ȱ 0.070 ppmȱ ȱ ACS Study $400,000,000 $360,000,000 Harvard Six

Ȭ City Study $860,000,000 $780,000,000 Expert K $180,000,000 $160,000,000 Expert E $1,500,000,000 $1,300,000,000ȱ ȱȱ ȱ 0.065 ppmȱ ȱ ACS Study $420,000,000 $380,000,000 Harvard Six

Ȭ City Study $910,000,000 $820,000,000 Expert K $190,000,000 $170,000,000 Expert E $1,600,000,000 $1,400,000,0007b-10Table 7b.15: Total Combined Ozone Benefits and PM2.5 Co-Benefits of Attaining Alternate Ozone Standards in 2030 in SanJoaquin and South Coast (2006$, 3% Discount Rate)AlternativeStandardandModelorAssumptionBelletal.(2004)Belletal.(2005)Itoetal.(2005)Levyetal.(2005)AssumptionofNoCausality**0.079 ppm** **Alternative**ACSStudy$250,000,000$510,000,000$640,000,000$650,000,000$130,000,000HarvardSixȬCityStudy$390,000,000$650,000,000$780,000,000$800,000,000$280,000,000ExpertK$180,000,000$440,000,000$570,000,000$590,000,000$67,000,000ExpertE$580,000,000$840,000,000$970,000,000$990,000,000$460,000,000**0.075 ppm** **Selected Alternative** ACSStudy$500,000,000$1,000,000,000$1,300,000,000$1,300,000,000$270,000,000HarvardSixȬCityStudy$780,000,000$1,300,000,000$1,600,000,000$1,600,000,000$550,000,000ExpertK$360,000,000$870,000,000$1,100,000,000$1,200,000,000$130,000,000ExpertE$1,200,000,000$1,700,000,000$1,900,000,000$2,000,000,000$930,000,000**0.070 ppm** **Alternative**ACSStudy$930,000,000$2,000,000,000$2,500,000,000$2,600,000,000$460,000,000HarvardSixȬCityStudy$1,400,000,000$2,400,000,000$3,000,000,000$3,100,000,000$920,000,000ExpertK$710,000,000$1,800,000,000$2,300,000,000$2,400,000,000$240,000,000ExpertE$2,000,000,000$3,100,000,000$3,600,000,000$3,700,000,000$1,500,000,000**0.065 ppm** **Alternative**ACSStudy$1,200,000,000$2,800,000,000$3,500,000,000$3,700,000,000$520,000,000HarvardSixȬCityStudy$1,700,000,000$3,300,000,000$4,000,000,000$4,200,000,000$1,000,000,000ExpertK$990,000,000$2,600,000,000$3,300,000,000$3,500,000,000$280,000,000ExpertE$2,400,000,000$3,900,000,000$4,700,000,000$4,900,000,000$1,700,000,0007b-11Table 7b.16: Total Combined Ozone Benefits and PM2.5 Co-Benefits of Attaining Alternate Ozone Standards in 2030 in SanJoaquin and South Coast (2006$, 7% Discount Rate)AlternativeStandardandModelorAssumptionBelletal.(2004)Belletal.(2005)Itoetal.(2005)Levyetal.(2005)AssumptionofNoCausality**0.079 ppm** **Alternative**ACSStudy$240,000,000$490,000,000$630,000,000$640,000,000$120,000,000HarvardSixȬCityStudy$370,000,000$620,000,000$760,000,000$770,000,000$250,000,000ExpertK$180,000,000$430,000,000$570,000,000$580,000,000$63,000,000ExpertE$540,000,000$790,000,000$930,000,000$940,000,000$420,000,000**0.075 ppm** **Selected Alternative** ACSStudy$480,000,000$990,000,000$1,300,000,000$1,300,000,000$250,000,000HarvardSixȬCityStudy$730,000,000$1,200,000,000$1,500,000,000$1,500,000,000$500,000,000ExpertK$350,000,000$860,000,000$1,100,000,000$1,200,000,000$130,000,000ExpertE$1,100,000,000$1,600,000,000$1,900,000,000$1,900,000,000$840,000,000**0.070 ppm** **Alternative**ACSStudyHarvardSixȬCityStudy$1,300,000,000$2,400,000,000$2,900,000,000$3,000,000,000$840,000,000ExpertK$700,000,000$1,700,000,000$2,300,000,000$2,400,000,000$230,000,000ExpertE$1,900,000,000$2,900,000,000$3,500,000,000$3,500,000,000$1,400,000,000**0.065 ppm** **Alternative**ACSStudy$1,200,000,000$2,800,000,000$3,500,000,000$3,700,000,000$480,000,000HarvardSixȬCityStudy$1,600,000,000$3,200,000,000$3,900,000,000$4,100,000,000$920,000,000ExpertK$970,000,000$2,600,000,000$3,300,000,000$3,500,000,000$270,000,000ExpertE$2,200,000,000$3,800,000,000$4,500,000,000$4,700,000,000$1,500,000,0007b-12

Table 7b.17: Annual Monetized Costs and Benefits in 2030 in S an 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 0.1 7 - 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 0.0 7 - 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 - 0.0 8

Table 7b.19: Annual Monetized Costs and Benefits in 2030 in S an 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 - 0.297b-13

Table 7b.20: Annual Monetized Costs and Benefits in 2030 in S an 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. R ange was developed by adding the estimate from the ozone premature mortality function to both the lowe r and upper ends of the range of the PM2.5 premature mortality functions characterized in the expert elicitation. Tables exclude unquantified an d nonmonetized benefits. All estimates rounded to two significant figures, so totals may not sum across columns. **Range reflects lower and upper bound cost estimat es. Data for calculating costs at a 3% discount ra te was not available for all sectors, and therefore to tal 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 Transport ation and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105. Available at http://www.epa.gov/otaq/regs/nonroad/locomotv/frm/lo corsd.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 Anal ysis 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. Avai lable 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 Source s , 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 Locomotiv e Engines and Marine CompressionIgnition Engines Less than 30 Liters per Cylinder, Chapter 3: Emission Inventory , U.S. Environmental Protection Agency, Office of Transportatio n 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. Tech nical Support Document: Preparation of Emissions Inventories For the 2002-based Platform, Ver sion 3, Criteria Air Pollutants, USEPA, January, 2008.**8-1**



### Chapter 8: Statutory and Executive Order Impact Ana lyses

Synopsis
**This chapter summarizes the Statutory and Executive Order (EO) impact analyses relevant for**  **the ozone NAAQS RIA. In general, because this RIA a nalyzes an illustrative attainment strategy**  **to meet the revised NAAQS, and because States will ultimat ely implement the new NAAQS, the**  **Statutory and Executive Orders below did not requir e 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 N AAQS proposal and its impact on these**  **statutory and executive orders are found in section VII o f the NAAQS preamble.** 

8.1 Executive Order 12866: Regulatory Planning and Review
**Under section 3(f)(1) of Executive Order (EO) 12866 (58 FR 51 735, October 4, 1993), the ozone**  **NAAQS action is an “economically significant regulat ory 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 benefi**  **ts of attaining three alternative ozone NAAQS nationwide. Specifically, the RIA exami**  **nes the alternatives of 0.079 0.075 ppm, 0.070 ppm, and 0.065 ppm. The RIA contains illustrati**  **ve analyses that consider a limited number of emissions control scenarios that States and Regio**  **nal Planning Organizations might implement to achieve these alternative ozone NAAQS. However, the Clea**  **n Air Act (CAA) and judicial decisions make clear that the economic and technical feasib**  **ility of attaining ambient standards are not to be considered in setting or revising NAAQS, although such factors may be consider**  **ed 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. Ther**  **e 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 fina ncial resources expended by persons to**  **generate, maintain, retain, or disclose or provide info rmation to or for a Federal agency. This**  **includes the time needed to review instructions; deve lop, acquire, install, and utilize technology**  **and systems for the purposes of collecting, validating, and verifying information, processing and**  **maintaining information, and disclosing and providi ng information; adjust the existing ways to**  **comply with any previously applicable instructions and re quirements; train personnel to be able**  **to respond to a collection of information; search d ata sources; complete and review the collection**  **of information; and transmit or otherwise disclose the info rmation.** **8-2**

**An agency may not conduct or sponsor information collectio n, and a person is not required to**  **respond to a collection of information unless it displays a c urrently valid OMB control number.**  **The OMB control numbers for EPA’s regulations in 40 CFR a re listed in 40 CFR part 9.** 

8.3 Regulatory Flexibility Act
**The EPA has determined that it is not necessary to p repare 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 th at is a small industrial entity as defined by the**  **Small Business Administration’s (SBA) regulations at 13 CF R 121.201; (2) a small**  **governmental jurisdiction that is a government of a city, county, town, school district or special**  **district with a population of less than 50,000; and (3) a small organization that is any not-for**  **profit enterprise which is independently owned and operated and is not dominant in its field.** 
**After considering the economic impacts of today’s rule on small entities, EPA has concluded that**  **this action will not have a significant economic im pact on a substantial number of small entities.**  **This rule will not impose any requirements on small entitie s. This rule establishes national**  **standards for allowable concentrations of ozone in ambi ent 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), P ublic Law 104-4, establishes**  **requirements for Federal agencies to assess the eff ects of their regulatory actions on State, local,**  **and Tribal governments and the private sector. Under sectio n 202 of the UMRA, EPA generally**  **must prepare a written statement, including a cost-bene fit analysis, for proposed and final rules**  **with “Federal mandates” that may result in expendit ures 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 statem ent 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 lea**  **st burdensome alternative that achieves the objectives of the rule. The provisions of section 205 do not a**  **pply when they are inconsistent with applicable law. Moreover, section 205 allows EPA to adop**  **t an alternative other than the least costly, most cost-effective or least burdensome alternati**  **ve 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 uni**  **quely affect small governments, including Tribal governments, it must have developed under section 20**  **3 of the UMRA a small government agency plan. The plan must provide for notifying potential**  **ly 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 t he regulatory provisions of Title II of the**  **UMRA) for State, local, or Tribal governments or th e 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 re gulatory requirements that might**  **significantly or uniquely affect small governments. Furthermore, as indicated prev iously, in**  **setting a NAAQS, EPA cannot consider the economic or tec hnological feasibility of attaining**  **ambient air quality standards, although such factors m ay be considered to a degree in the**  **development of State plans to implement the standards. S ee also**  ATA I **at 1043 (noting that because EPA is precluded from considering costs of implem**  **entation in establishing NAAQS, preparation of a Regulatory Impact Analysis pursuan**  **t to the Unfunded Mandates Reform Act would not furnish any information which the court co**  **uld consider in reviewing the NAAQS). Accordingly, EPA has determined that the provisions of sect**  **ions 202, 203, and 205 of the UMRA do not apply to this final decision. The EPA ackn**  **owledges, however, that any corresponding revisions to associated SIP requirement**  **s 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 a**  **nnounces 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 4 3255, August 10, 1999), requires EPA to**  **develop an accountable process to ensure “meaningfu l 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 Exec utive Order to include regulations that**  **have “substantial direct effects on the States, on t he relationship between the national**  **government and the States, or on the distribution of power a nd responsibilities among the various**  **levels of government.”**
**At the time of the proposal, EPA concluded that the p roposed rule would not have substantial**  **direct effects on the States, on the relationship between t he 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 Co ordination with Indian Tribal**  **Governments” (65 FR 67249, November 9, 2000), requires EP A to develop an accountable**  **process to ensure “meaningful and timely input by t ribal 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 Tr**  **ibe whether to develop these programs and which programs, or appro**  **priate elements of a program, they will adopt.** **8-4**

**This rule does not have Tribal implications, as speci fied in Executive Order 13175. It does not**  **have a substantial direct effect on one or more Indi an 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 fr om 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 th at: (1) is determined to be**  **“economically significant” as defined under Executive O rder 12866, and (2) concerns an**  **environmental health or safety risk that EPA has re ason 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 explai n why the regulation is**  **preferable to other potentially effective and reasonably f easible alternatives considered by the**  **Agency. This rule is subject to Executive Order 130 45 because it is an economically significant**  **regulatory action as defined by Executive Order 12866, an d we believe that the environmental**  **health risk addressed by this action may have a dis proportionate 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 popula tion subgroups such as the elderly and people**  **with existing heart or lung disease, are potentiall y susceptible to health effects resulting from**  **ozone exposure. Because children are considered a potent ially susceptible population, we have**  **carefully evaluated the environmental health effects o f exposure to ozone pollution to this sub**  **population. These effects and the size of the popul ation 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 sect**  **ions 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 o f the Office of Information and Regulatory**  **Affairs, Office of Management and Budget, for certain actio ns identified as “significant energy**  **actions.” Section 4(b) of Executive Order 13211 def ines “significant energy actions” as “any**  **action by an agency (normally published in the Fede ral Register) that promulgates or is expected**  **to lead to the promulgation of a final rule or regu lation, including notices of inquiry, advance**  **notices of proposed rulemaking, and notices of proposed rul emaking: (1)(i) that is a significant**  **regulatory action under Executive Order 12866 or an y successor order, and (ii) is likely to have a**  **significant adverse effect on the supply, distribution, or u se of energy; or (2) that is designated by**  **the Administrator of the Office of Information and R egulatory Affairs as a significant energy** **8-5**

**action.” OMB has designated this rulemaking as a significan t 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 nationw**  **ide 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 inc**  **rease 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 t**  **he estimates of output changes. For more details on how energy impacts are modeled in this anal**  **ysis and the caveats and limitations that should be understood in interpreting these impacts, please refer to Appen**  **dix 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 E**  **GU control strategy mentioned earlier in this RIA. There are v**  **ery small changes expected in the mix of electricity generation (i.e., the number of coal-fired EGU**  **s compared to the number of natural gas-fired and oil-fired EGUs) as a response to the il**  **lustrative EGU control strategy. Hydro, nuclear, other, and renewable based generation are proj**  **ected 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 e**  **stimated 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 cert ainly 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 photochem ical air quality modeling (i.e., CMAQ) to**  **assess where precursor emission reductions are most n eeded to attain a particular alternate**  **primary ozone standard. Since such CMAQ modeling was no t applied for these other alternate**  **primary ozone standards, we thus have a differing degree o f 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 caveat s 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 incorpor ate the extrapolated costs estimated for**  **Chapter 5 of this RIA. The proportion of the engineering cos ts 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 Advance ment 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 standard**  **s are technical standards (e.g., materials specifications, test methods, sampling procedures, and bus**  **iness 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 a**  **ny of the monitoring requirements as part of this proposal, there are no impacts associat**  **ed with the NTTAA. 8-6** 

8.10 Executive Order 12898: Federal Actions to Address Environmenta l Justice in Minority Populations and Low-Income Populations

**Executive Order 12898, “Federal Actions to Address En vironmental Justice in Minority**  **Populations and Low-Income Populations,” requires Federal agencies to consider the impact of**  **programs, policies, and activities on minority popu lations and low-income populations.**  **According to EPA guidance, agencies are to assess wh ether minority or low-income populations**  **face a risk or a rate of exposure to hazards that are signifi cant and that “appreciably exceeds or is**  **likely to appreciably exceed the risk or rate to th e general population or to the appropriate**  **comparison group” (EPA, 1998).**
**In accordance with Executive Order 12898, the Agenc y has considered whether these decisions**  **may have disproportionate negative impacts on minor ity or low-income populations. This rule**  **establishes uniform, national ambient air quality sta ndards for ozone, and is not expected to have**  **disproportionate negative impacts on minority or low incom e populations. In this NAAQS**  **proposal, the Administrator considered the availabl e 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 disp roportionate negative**  **impacts on minority or low income populations.**