# Regulatory Impact Analysis of the **Proposed Revisions to the National Ambient Air Quality Standards for**  **Ground-Level Ozone (This page intentionally left blank)** **EPA-452/R-07-008 July 2007** 

**Regulatory Impact Analysis of the Proposed Revisions to the**  **National Ambient Air Quality Standards**

**U.S. Environmental Protection Agency Office of Air Quality Planning and Standards**  **Health and Environmental Impact Division Air Benefit-Cost Group**  **Research Triangle Park, North Carolina (This page intentionally left blank)** Table of Contents

**Chapter Page**  1 Introduction and Background
**Synopsis.............................................................................................1-1**
**1.1 Background.................................................................................1-1**
**1.2 Role of the Regulatory Impact Analysis in the NAAQS Setting Process.........1-2 1.2.1 Legislative Roles.................................................................1-2**  **1.2.2 Role of Statutory and Executive Orders..................................... ..1-2**  **1.2.3 Market Failure or Other Social Purpose..................................... ..1-3**  **1.2.4 Illustrative Nature of the Analysis.............................................1-4**
**1.3 Overview and Design of the RIA.......................................................1-5 1.3.1 Baseline and Years of Analysis.......................................**  **.........1-5 1.3.2 Control Scenarios Considered in this RIA......**  **..............................1-6 1.3.3 Evaluating Costs and Benefits................................................**  **..1-6**

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

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

2 Characterizing Ozone and Modeling Tools Used in This Analy sis
**Synopsis...............................................................................................2-1**
**2.1 Ozone Chemistry...........................................................................2-1 2.1.1 Temporal Scale....................................................................2-2**  **2.1.2 Geographic Scale and Transport............................................ ....2-2**  **2.1.3 Effects of Ozone..................................................................2-3**

**2.2 Sources of Ozone...........................................................................2-3**
**2.3 Modeling Ozone Levels in the Future................................ ...................2-4**  **2.3.1 Emissions Inventory..............................................................2 -4**  **2.3.2 CMAQ Model......................................................................2-5**

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

3 Modeled Control Strategy: Design and Analytical Results
**Synopsis............................................................................................3-1 3.1 Establishing the Baseline...............................................................**  **...3-2**

**3.1.1 National Rules.....................................................................3-4 3.1.2 Additional Controls..............................................................**  **.3-6 3.1.3 Ozone Levels for Baseline.....................................................**  **.3-12**
**3.2 Developing the Control Strategy Analysis...................... ......................3-14** 

**3.2.1 Controls Applied for a 0.070 ppm Standard: Non-EGU an d Area**  **Sectors......................................................................................3-17 3.2.2 Controls Applied for a 0.070 ppm Standard: EGU Sect**  **or................3-19 3.2.3 Controls Applied for a 0.070 ppm Standard:**  **Onroad and Nonroad Mobile Sectors............................................................................3.21**  **3.2.4 Data Quality for this Analysis............................................... ..3-22** 
**3.3 Geographic distribution of Emissions reduction s....................................3-23** 
**3.4 Ozone Design Values for partial attainment......................... .................3-27** 

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

Appendix Chapter 3
**3a.1 Non-EGU and Area Source Controls Applied in the Ba seline and Control**  **Scenarios.................................................................................3a-1 3a.1.1 Non-EGU and Area Source Control Strategies for Ozone NAAQS**  **Proposal...................................................................................3a-1 3a.1.2 NOx Control Measures for Non-EGU Point Sou** **rces.......................3a-1**

**3a.1.3 VOC Control Measures for Non-EGU Point Sources............... ............3a-2**  **3a.1.4 NOx Control Measures for Area Sources............................ ................3a-2**  **3a.1.5 VOC Control Measures for Area Sources.................................... ....3a-2**  **3a.1.6 Supplemental Controls..............................................................3a- 3** 

**3a.2 Mobile Controls/Rules Used in Baseline and Control Scenarios...............3a-11 3a.2.1 Diesel Retrofits and Vehicle Replacement.................................3a-11**  **3a.2.2 Implement Continuous Inspection and Maintenance Us ing Remote**  **Onboard Diagnostics (OBD).........................................................3a-14 3a.2.3 Eliminating Long Duration Truck Idling..................**  **..................3a-15 3a.2.4 Commuter Programs...........................................................3a-16**  **3a.2.5 Reduce Gasoline RVP from 7.8 to 7.0 in Remaining No nattainment**  **Areas.....................................................................................3a-18 3a.2.6 Application order for Onroad and Nonroad Mobile**  **Controls............3a-18**
**3a.3 EGU Controls Used in the Control Strategy.................................... ...3a-19** 
**3a.4 Emissions Reductions by Sector..................................................... .3a-22** **3a.5 Change in Ozone Concentrations between Baseline and Post-0.070 ppm Control**  **Strategy Modeling.......................................................................3a.27**

4 Approach for Estimating Reductions for Full Attainment Scenario
**Synopsis..............................................................................................4-1**
**4.1 Development of Air Quality Impact Ratios for Determination of Extrapolated**  **Costs.........................................................................................4-1 4.1.1 Approach A: Use of Sensitivity Modeling of**  **Local Emissions Reductions...................................................................................4-1**  **4.1.2 Approach B: Use of 2020 Baseline and RIA Control Scenario............4-3**
**4.2 Results from Impact Ratio Analyses..................................................................4-5**
**4.3 Determination of Extrapolated Tons Control Areas ..................................4-7** 
**4.4 Selection of Air Quality Goal for this analysis.........................................4-9**

**4.5 National 2020 Estimates of Additional Emissions Redu ctions Needed to** 

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

**4.6 Estimates of Additional Tons Needed for Four Po tential Air Quality Targets**  **(California Only, Post-2020 Attainment)....................................... ......4-16** 

5 Cost Estimates
**Synopsis...............................................................................................5-1**

**5.1 Modeled Controls..........................................................................5.2 5.1.1 Sector Methodology. .............................................................**  **..5-2 5.1.1.1 Non-EGU Point and Area Sources: AirControlNet......**  **...........5-2 5.1.1.2 EGU Sources: the Integrated Planning Mode**  **l......................5-3 5.1.1.3 Onroad and Nonroad Mobil Sources: MOBILE Model......**  **......5-4 5.1.2 Known Controls- Cost by Sector...............................................**  **5-4 5.1.3 Limitations and Uncertainties Associated with** **Engineering Cost Estimates.....................................................................................5-6** 
**5.2 Extrapolated Costs.........................................................................5- 7**  **5.2.1 Increasing Marginal Cost Methodology................ .......................5-9**  **5.2.1.1 Marginal Cost Regions..................................................5-9 5.2.1.2 Derivation of the Marginal Cost Slopes......................**  **......5-11 5.2.1.3 Calculating Extrapolated Costs using Marg**  **inal Cost Approach.........................................................................5-12**  **5.2.2 Fixed Cost per Ton Values.....................................................5-12 5.2.3 Results............................................................................5-13** 

**5.3 Summary of Costs........................................................................5-19**
**5.4 Technology Innovation and Regulatory Cost Estimates............................5-20 5.4.1 Examples of Technological Advances in Pollution Control..............5-22**  **5.4.2 Influence on Regulatory Cost Estimates............. ........................5-23** 

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

Appendix Chapter 5

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

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

**5a.3 Cost information for Onroad and Nonroad Mobile Source s........................5a-3** 

6 Incremental Benefits of Attaining Alternative Ozone Standards Relative to the Current 8-hour Standard (0.08 ppm)
**Synopsis...............................................................................................6-1**
**6.1 Background..................................................................................6-3**
**6.2 Characterizing Uncertainty: Moving Toward a Probab ilistic Framework for**  **Benefits Assessment................................................................................6- 5** 
**6.3 Health Impact Functions..................................................................6 -6**  **6.3.1 Potentially Affected Populations................................... ............6-7**  **6.3.2 Effect Estimate Sources..........................................................6-7 6.3.2.1 Premature Mortality Effects Estimates...................**  **...........6-13 6.3.2.2 Respiratory Hospital Admissions Effect Est**  **imates...............6-14 6.3.2.3 Asthma-Related Emergency Room Visits Effect Es**  **timates......6-15 6.3.2.4 Minor Restricted Activity Days Effect Estimates.................6-16**  **6.3.2.5 School Absences Effect Estimate................................... .6-16**  **6.3.2.6 Worker Productivity...................................................6-17 6.3.2.7 Visibility Benefits.......................................................**  **6-17 6.3.2.8 Other Unquantified Effects...........................................6-**  **17 6.3.2.8.1 Direct Ozone Effects on Vegetation.....................6**  **-17 6.3.2.8.2 Nitrogen Deposition.......................................6-18**  **6.3.2.8.3 Ultraviolet Radiation.......................................6-1 9**  **6.3.2.8.4 Climate Implications of Tropospheric Ozon e..........6-21**  **6.3.3 Baseline Incidence Rates...................................................... 6-22** **6.4 Economic Values for Health Outcomes......................................... ......6-24** 

**6.4.1 Mortality Valuation.............................................................6-25 6.4.2 Hospital Admissions Valuation....................................**  **............6-25 6.4.3 Asthma-Related Emergency Room Visits Valuation...**  **...................6-25 6.4.4 Minor Restricted Activity Days Valuation...................................6-25**  **6.4.5 School Absences.................................................................6-26**
**6.5 Results and Implications............................................................. ....6-31**  **6.5.1 Glidepath Incidence and Valuation Estimates f or 0.065 ppm and**  **0.075 ppm Alternatives...................................................................6 -31**  **6.5.2 PM 2.5**  **Co-Benefits Estimates...................................................6-32 6.5.3 PM**  **2.5 Co-Benefits Resulting from Attainment of 0.070 ppm**  **incremental to 0.08 ppm.................................................................6-7 4**  **6.5.4 Estimate of Full Attainment Benefits................................ .........6-77**  **6.5.5 Discussion of Results and Uncertainties................... ..................6-98**  **6.5.6 Summary of Total Benefits...................................................6- 101** 

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

Appendix Chapter 6

6a Additional Benefits Information
**Summary..................................................................................6a-1**
**6a.1 Developing an air quality estimate of full att ainment with the alternative**  **ozone standards..................................................................6a-1**

**6a.2 Partial Attainment PM 2.5**  **Incidence and Valuation Estimates.............6a-2**

6b Health-Based Cost-Effectiveness of Reductions in Ambi ent PM 2.5 Associated with Illustrative Ozone NAAQS 0.070ppm AttainmentStrategy

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

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

**6b.3 Effectiveness Measures.........................................................6b-7**
**6b.4 Changes in Premature Death, Life Years, and Quality of Life............6b-9 6b.4.1 Calculating Reductions in Premature Deaths.....................6b-10**  **6b.4.2 Calculating Changes in Life Years from Direc t Reductions in**  **PM 2.5**  **-Related Mortality Risk................................................6b-11 6b.4.2.1 Should Life Years Gained Be Adjusted for Initial He**  **alth Status?.......................................................................................6b-14** 
**6b.5 Calculating Changes in the Quality of Life Yea rs (Morbidity) .........6b-16**  **6b.5.1 Calculating QALYs Associated with Reductions in the Incidence**  **of Chronic Bronchitis.........................................................6b-17 6b.5.2 Calculating QALYs Associated with Reductions**  **in the Incidence of Nonfatal Myocardial Infarctions.........................................6b-21** 
**6b.6 Cost-Effectiveness Analysis..................................................6b-28**

**6b.6.1 Aggregating Life Expectancy and Quality-of-Life G ains......6b-28**  **6b.6.2 Dealing with Acute Health Effects and Non-health Ef fects....6b-31**  **6b.6.3 Cost-Effectiveness Ratios...........................................6b-33**
**6b.7 Discount Rate Sensitivity Analysis..........................................6b-35**

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

**6b.9 References.......................................................................6b-38**
6c Additional Sensitivity Analyses Related To the Bene fits Analysis
**6c.1 Premature Mortality Cessation Lag Structure.......... .....................6c-1** 
**6c.2 Threshold Sensitivity Analysis........................................... ......6c-5** 
**6c.3 Income Elasticity of Willingness to Pay............. .........................6c-8** 

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

7 Discussion of Ozone Secondary Standard

8 Conclusions and Implications of the Illustrative Be nefit-Cost Anlaysis
**Synopsis.............................................................................................8-1**

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

**8.2 Discussion of Results.................................................................... .8-10** 
**8.3 What did we Learn Through this Analysis? ..........................................8-13**

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

**9.1 Executive Order 12866: Regulatory Planning and Review..........................9-1** 
**9.2 Paperwork Reduction Act.............................................................. ...9-1** 
**9.3 Regulatory Flexibility Act.................................................... ............9-2** 

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

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

**9.6 Executive Order 13175: Consultation and Coordi nation with Indian Tribal**  **Governments................................................................................9-3**

**9.7 Executive Order 13045: Protection of Children from Environmental Health**  **& Safety Risks.............................................................................9-4**
**9.8 Executive Order 13211: Actions that Significan tly Affect Energy Supply,**  **Distribution, or Use.......................................................................9-4**
**9.9 National Technology Transfer Advancement Act................ ....................9-5** 

**9.10 Executive Order 12898: Federal Actions to Addres s Environmental Justice**  **in Minority Populations and Low-Income Populations ..............................9-5** List of Tables

**Number Page** 
**2.1 Geographic Specifications of Modeling Domains............ ........................2-7** 
**3.1 National Rules and Control Measures, by Sector, Contributing to the Baseline..3-5 3.2 Controls by Sector Included in the Baseline Dete**  **rmination for 2020............3-10 3.3 Controls for Emissions Reductions, by Sector, fo**  **r the 0.070 ppm Control Strategy (Incremental to Baseline)...............................................................3-15**  **3.4 Annual Tons of Emissions Remaining after Application of the 0.070 ppm Control**  **Strategy (35 States + DC Analysis Area).............................................3-29**
**4.1 Summary of site-specific impact ratios over the five an alysis zones of**  **Approach A...........................................................................................................4-3 4.2 Summary of site-specific impact ratios over the four analys**  **is zones of Approach B.................................................................................4-4**  **4.3 The NOx impact ratios at the controlling counties for each methodology over the**  **analysis zones..............................................................................4-5 4.4 List of counties that did not reach 0.070 in the RIA control scenario and how t** **hey were aggregated into extrapolated tons control areas................................4-8**  **4.5 2020 Air Quality Glidepath Targets for LA and Kern Count y.....................4-11**  **4.6 Estimated Annual Incremental Tons Needed for an 0.065 p pm Air Quality Target**  **in 2020 (29 areas)........................................................................4-13 4.7. Estimated Annual Incremental Tons Needed for an 0.070**  **ppm air quality target in 2020 (20 areas)...........................................................................4-14**  **4.8 Estimated Annual Incremental Tons Needed for an 0.075 p pm air quality target in**  **2020 (11 areas)...........................................................................4-15 4.9 Estimated Annual Incremental Tons Needed for an 0.079 p**  **pm air quality target in 2020 (6 areas)............................................................................4-15**  **4.10 California: Estimated Tons Needed for Attainmen t of 0.065 ppm Air Quality**  **Target (beyond 2020)...................................................................4-16 4.11 California: Estimated Tons Needed for Attainmen** **t of 0.070 ppm Air Quality Target (Beyond 2020)...................................................................4-16**  **4.12 California: Estimated Tons Needed for Attainme nt of 0.075 ppm air quality target**  **(beyond 2020)............................................................................4-16 4.13 California: Estimated Tons Needed for Attainme**  **nt of 0.079 ppm air quality target (beyond 2020).....................................................................4-16** 
**5.1 Comparison of Modeled Annual Control Costs Nationwide, by sector, for a 0.070**  **ppm control scenario ($1999)............................................................5 -6**  **5.2 Regions and Slopes for Extrapolated Costs................................. .........5-10**  **5.3 Data Selection Criteria for Extrapolated Costs.................. .....................5-11**  **5.4 Extrapolated Costs of Meeting the 0.079 ppm Standa rd............................5-15**  **5.5 Extrapolated Costs of Meeting the 0.075 ppm Standa rd...........................5-16**  **5.6 Extrapolated Costs of Meeting the 0.070 ppm Standar d...........................5-17** **5.7 Extrapolated Costs of Meeting the 0.065 ppm Standa rd...........................5-18** 

**5.8 Total Costs of Attainment in 2020 for Differen t Levels of the Ozone Standard**  **(National Attainment in 2020).........................................................5-1 9**  **5.9 California Extrapolated Costs ($M)................................. ..................5-20**  **5.10 Comparison of Inflation-Adjusted Estimated Costs an d Actual Price Changes**  **for EPA Fuel Control Rules............................................................5-25**

**6.1 Human Health and Welfare Effects of Pollutants Affected by the Alternate**  **Standards...................................................................................6-9 6.2 Ozone and PM Related Health Endpoints basis for t** **he concentration-response function associated with that endpoint, and sub-populations for which they were**  **computed.................................................................................6-11 6.3 National Average Baseline Incidence Rates..........................................6-23**  **6.4 Unit Values for Economic Valuation of Health En dpoints (2000$)..............6-27**  **6.5 Illustrative Strategy to Attain 0.065 ppm: Esti mated Annual Reductions in the**  **Incidence of Premature Mortality Associated with Ozon e Exposure in 2020**  **(Incremental to Current Ozone Standard).................................... ........6-33**  **6.6 Illustrative Strategy to Attain 0.065 ppm: Esti mated Annual Reductions in the**  **Incidence of Morbidity Associated with Ozone Exposu re (Incremental to Current**  **Ozone Standard, 95% Confidence Intervals in Parentheses)...... ................6-34**  **6.7 Illustrative Strategy to Attain 0.065 ppm in Cali fornia: Estimated Annual**  **Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard)........................**  **........6-35 6.8 Illustrative Strategy to Attain 0.065 ppm in Cali**  **fornia: Estimated Annual Reductions in the Incidence of Morbidity Associated w**  **ith Ozone Exposure...6-36 6.9 Illustrative 0.065 ppm Full Attainment Scenario: Est**  **imated Annual Reductions in the Incidence of PM Premature Mortality associate with P**  **M co-benefit.........6-37 6.10 Illustrative 0.065 ppm Full Attainment Scenario: Es**  **timated Annual Reductions in the Incidence of Morbidity Associated with PM Co-benefit......**  **.................6-38 6.11 Illustrative Strategy to Attain 0.070 ppm: Est**  **imated Annual Reductions in the Incidence of Premature Mortality Associated with Ozone Exp**  **osure (Incremental to Current Ozone Standard)............................................................6-39**  **6.12 Illustrative Strategy to Attain 0.070 ppm: Es timated Annual Reductions in the**  **Incidence of Morbidity Associated with Ozone Exposu re (Incremental to Current**  **Ozone Standard, 95% Confidence Intervals in Parentheses)...... ................6-40**  **6.13 Illustrative Strategy to Attain 0.070 ppm in Cal ifornia: Estimated Annual**  **Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard)........................**  **........6-41 6.14 Illustrative Strategy to Attain 0.070 ppm in Cal**  **ifornia: Estimated Annual Reductions in the Incidence of Morbidity Associated with Ozone Exposure**  **(Incremental to Current Ozone Standard, 95% Confide nce Intervals in**  **Parentheses)..............................................................................6-42 6.15 Illustrative 0.070 ppm Full Attainment Scenario: Es**  **timated Annual Reductions in the Incidence of PM Premature Mortality associate with P**  **M co-benefit.........6-43 6.16 Illustrative 0.070 ppm Full Attainment Scenario: E**  **stimated Annual Reductions in the Incidence of Morbidity Associated with PM Co-benefit......**  **.................6-44 6.17 Illustrative Strategy to Attain 0.075 ppm: Es**  **timated Annual Reductions in the Incidence of Premature Mortality Ozone Exposures (Incre**  **mental to Current Ozone Standard).........................................................................6-45**  **6.18 Illustrative Strategy to Attain 0.075 ppm: Es timated Annual Reductions in the**  **Incidence of Morbidity Associated with Ozone Exposu re (Incremental to Current**  **Ozone Standard).........................................................................6-46 6.19 Illustrative Strategy to Attain 0.075 ppm in Ca**  **lifornia: Estimated Annual Reductions in the Incidence of Premature Mortality Associated with Ozone**  **Exposure (Incremental to Current Ozone Standard)........................ ........6-47**  **6.20 Illustrative Strategy to Attain 0.075 ppm in Ca lifornia: Estimated Annual**  **Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current Ozone Standard, 95% Confide**  **nce Intervals in Parentheses)..............................................................................6-48**  **6.21 Illustrative 0.075 ppm Full Attainment Scenario: E stimated Annual Reductions in**  **the Incidence of PM Premature Mortality associate with P M co-benefit.........6-49**  **6.22 Illustrative 0.075 ppm Full Attainment Scenario: E stimated Annual Reductions in**  **the Incidence of Morbidity Associated with PM Co-benefit...... ................6-50**  **6.23 Illustrative Strategy to Attain 0.079 ppm: Es timated Annual Reductions in the**  **Incidence of Premature Mortality Ozone Exposures (Incre mental to Current**  **Ozone Standard).........................................................................6-51 6.24 Illustrative Strategy to Attain 0.079 ppm: Es**  **timated Annual Reductions in the Incidence of Morbidity Associated with Ozone Exposu** **re (Incremental to Current Ozone Standard).........................................................................6-52**  **6.25 Illustrative Strategy to Attain 0.079 ppm in Ca lifornia: Estimated Annual**  **Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard)........................**  **........6-53 6.26 Illustrative Strategy to Attain 0.079 ppm in Cal**  **ifornia: Estimated Annual Reductions in the Incidence of Morbidity Associated with Ozone Exposure**  **(Incremental to Current Ozone Standard, 95% Confide nce Intervals in**  **Parentheses)..............................................................................6-54 6.27 Illustrative 0.079 ppm Full Attainment Scenario: E**  **stimated Annual Reductions in the Incidence of PM Premature Mortality associate with P**  **M co-benefit.........6-55 6.28 Illustrative 0.079 ppm Full Attainment Scenario: E**  **stimated Annual Reductions in the Incidence of Morbidity Associated with PM Co-benefit (9**  **5 th** **percentile confidence intervals provided in parentheses)..............................**  **.........6-56 6.29 Illustrative Strategy to Attain 0.065 ppm: Es**  **timated Annual Valuation of Reductions in the Incidence of Premature Mortality Associated with Ozone**  **Exposure (Incremental to Current Ozone Standard, Millions of 1999$).........6-57 6.30 Illustrative Strategy to Attain 0.065 ppm: Es**  **timated Annual Reductions in the Incidence of Morbidity Associated with Ozone Exposu** **re (Incremental to Current Ozone Standard, 95% Confidence Intervals in Parenth**  **eses, Millions of 1999$).......................................................................6-58** **6.31 Illustrative Strategy to Attain 0.065 ppm in Ca lifornia: Estimated Annual**  **Valuation of Reductions in the Incidence of Prematur e Mortality Associated with**  **Ozone Exposure (Incremental to Current Ozone Standard) ........................6-59**  **6.32 Illustrative Strategy to Attain 0.065 ppm in Ca lifornia: Estimated Annual**  **Valuation of Reductions in the Incidence of Morbidity Associated with Ozone**  **Exposure (Incremental to Current Ozone Standard)........................ ........6-60**  **6.33 Illustrative Strategy to Attain 0.070 ppm: Es timated Annual Valuation of**  **Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard, Millions of 1999$).........6-61**  **6.34 Illustrative Strategy to Attain 0.070 ppm: Es timated 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 1999$).......................................................6-62**  **6.35 Illustrative Strategy to Attain 0.070 ppm in Ca lifornia: Estimated Annual**  **Valuation of Reductions in the Incidence of Prematur e Mortality Associated with**  **Ozone Exposure (Incremental to Current Ozone Standard) ........................6-63**  **6.36 Illustrative Strategy to Attain 0.070 ppm in Ca lifornia: Estimated Annual**  **Valuation of Reductions in the Incidence of Morbidity Associated with Ozone**  **Exposure (Incremental to Current Ozone Standard, 95% Confi dence Intervals in**  **Parentheses)..............................................................................6-64 6.37 Illustrative Strategy to Attain 0.075 ppm: Es**  **timated Annual Monetary Value of Reductions in the Incidence of Mortality Associated with Exposure to Ozone**  **(Millions of 1999$, Incremental to Current Standard )..............................6-65**  **6.38 Illustrative Strategy to Attain 0.075 ppm: Es timated Annual Monetary Value of**  **Reductions in the Incidence of Morbidity Associated w ith Exposure to Ozone**  **(Millions of 1999$, Incremental to Current Standard )..............................6-66**  **6.39 Illustrative Strategy to Attain 0.075 ppm in Ca lifornia: Estimated Annual**  **Valuation of Reductions in the Incidence of Prematur e Mortality Associated with**  **Ozone Exposure (Incremental to Current Ozone Standard) ........................6-67**  **6.40 Illustrative Strategy to Attain 0.075 ppm in Ca lifornia: Estimated Annual**  **Valuation of Reductions in the Incidence of Morbidity Associated with Ozone**  **Exposure (Incremental to Current Ozone Standard)........................ ........6-68**  **6.41 Illustrative Strategy to Attain 0.079 ppm: Es timated Annual Monetary Value of**  **Reductions in the Incidence of Premature Mortality As sociated with Exposure to**  **Ozone (Millions of 1999$, Incremental to Current Standard)... ..................6-69**  **6.42 Illustrative Strategy to Attain 0.079 ppm: Es timated Annual Monetary Value of**  **Reductions in the Incidence of Morbidity Associated w ith Exposure to Ozone**  **(Millions of 1999$, Incremental to Current Standard )..............................6-70**  **6.43 Illustrative Strategy to Attain 0.079 ppm in Ca lifornia: Estimated Annual**  **Valuation of Reductions in the Incidence of Prematur e Mortality Associated with**  **Ozone Exposure (Incremental to Current Ozone Standard) ........................6-71**  **6.44 Illustrative Strategy to Attain 0.079 ppm in Ca lifornia: Estimated Annual**  **Valuation of Reductions in the Incidence of Morbidity Associated with Ozone**  **Exposure (Incremental to Current Ozone Standard)........................ ........6-72**  **6.45 Estimated PM 2.5**  **Co-Benefits Associated with Full Attainment of 0.070 ppm incremental to 0.08 ppm................................................................6-76** **6.46 Estimate of Total Annual Ozone and PM 2.5**  **Benefits (95% Confidence Intervals, Millions of $1999) for the 0.065 ppm Standard Alternative: National Glidepath**  **Attainment................................................................................6-78 6.47 Estimate of Total Annual Ozone and PM**  **2.5 Benefits (95% Confidence Intervals,**  **Millions of $1999) for the 0.065 ppm Standard Alter native: California**  **Attainment................................................................................6-79 6.48 Estimate of Total Annual Ozone and PM**  **2.5 Benefits (95% Confidence Intervals,**  **Millions of $1999) for the 0.070 ppm Standard Alternative: National Glidepath Attainment................................................................................6-80**  **6.49 Estimate of Total Annual Ozone and PM 2.5**  **Benefits (95% Confidence Intervals, Millions of $1999) for the 0.070 ppm Standard Alter** **native: California Attainment................................................................................6-81**  **6.50 Estimate of Total Annual Ozone and PM 2.5**  **Benefits (95% Confidence Intervals, Millions of $1999) for the 0.075 ppm Standard Alternative: National Glidepath**  **Attainment................................................................................6-82 6.51 Estimate of Total Annual Ozone and PM**  **2.5 Benefits (95% Confidence Intervals,**  **Millions of $1999) for the 0.075 ppm Standard Alter native: California**  **Attainment................................................................................6-83 6.52 Estimate of Total Annual Ozone and PM**  **2.5 Benefits (95% Confidence Intervals,**  **Millions of $1999) for the 0.079 ppm Standard Alternative: National Glidepath Attainment................................................................................6-84**  **6.53 Estimate of Total Annual Ozone and PM 2.5**  **Benefits (95% Confidence Intervals, Millions of $1999) for the 0.079 ppm Standard Alter** **native: California Attainment................................................................................6-85**  **6.54 Combined Estimate of Annual Ozone and PM 2.5**  **Benefits (95% Confidence Intervals, Millions of $1999) for the 0.065 ppm Alternat**  **ive Standard: National Glidepath Attainment...................................................................6-86**  **6.55 Combined Estimate of Annual Ozone and PM 2.5**  **Benefits (95% Confidence Intervals, Millions of $1999) for the 0.065 ppm Alternativ**  **e Standard: California Glidepath Attainment...................................................................6-87**  **6.56 Combined Estimate of Annual Ozone and PM 2.5**  **Benefits (95% Confidence Intervals, Millions of $1999) for the 0.065 ppm Alternativ**  **e Standard: Incremental Benefits of California Post 2020 Attainment.......................................**  **.6-88 6.57 Combined Estimate of Annual Ozone and PM**  **2.5 Benefits (95% Confidence**  **Intervals, Millions of $1999) for the 0.065 ppm Alternat ive Standard: Total**  **California Benefits of Post 2020 Attainment....................................... .6-89**  **6.58 Combined Estimate of Annual Ozone and PM 2.5**  **Benefits (95% Confidence Intervals, Millions of $1999) for the 0.070 ppm Alternat**  **ive Standard: National Glidepath Attainment...................................................................6-90**  **6.59 Combined Estimate of Annual Ozone and PM 2.5**  **Benefits (95% Confidence Intervals, Millions of $1999) for the 0.070 ppm Alternativ**  **e Standard: California Glidepath Attainment...................................................................6-91**  **6.60 Combined Estimate of Annual Ozone and PM 2.5**  **Benefits (95% Confidence Intervals, Millions of $1999) for the 0.070 ppm Alternativ**  **e Standard: Incremental Benefits of California Post 2020 Attainment.......................................**  **.6-92 6.61 Combined Estimate of Annual Ozone and PM**  **2.5 Benefits (95% Confidence**  **Intervals, Millions of $1999) for the 0.070 ppm Alternativ e Standard: California**  **Post 2020 Attainment...................................................................6-93 6.62 Combined Estimate of Annual Ozone and PM**  **2.5 Benefits (95% Confidence**  **Intervals, Millions of $1999) for the 0.075 ppm Alternat ive Standard: National**  **Glidepath Attainment...................................................................6-94 6.63 Combined Estimate of Annual Ozone and PM**  **2.5 Benefits (95% Confidence**  **Intervals, Millions of $1999) for the 0.075 ppm Alternativ e Standard: California**  **Post 2020 Attainment...................................................................6-94 6.64 Combined Estimate of Annual Ozone and PM**  **2.5 Benefits (95% Confidence**  **Intervals, Millions of $1999) for the 0.079 ppm Alternat ive Standard: National**  **Glidepath Attainment...................................................................6-96 6.65 Combined Estimate of Annual Ozone and PM**  **2.5 Benefits (95% Confidence**  **Intervals, Millions of $1999) for the 0.079 ppm Alternativ e Standard: California**  **Post 2020 Attainment...................................................................6-97 6.66 Summary of Total Number of Annual Ozone and PM**  **2.5 -Related Premature**  **Mortalities and Premature Morbidity Avoided: 2020 N ational Benefits.......6-102**  **6.67 Summary of Total Number of Annual Ozone and PM 2.5**  **-Related Premature Mortalities and Premature Morbidity Avoided: Califo**  **rnia Post 2020 Attainment..............................................................................6-103** 
**7.1 Comparison of number of counties exceeding vari ous W126 levels when meeting**  **various levels of the 8-hr standard for the 3-year period 2003-2005...............7.3** 

**8.1a National Annual Costs and Benefits: 0.079 ppm Standar d in 2020 (including**  **California glidepath)......................................................................8-4 8.1b National Annual Costs and Benefits: 0.075 ppm Stan**  **dard in 2020 (including California glidepath)........................................................**  **.8-4 8.1c National Annual Costs and Benefits: 0.070 ppm Standar**  **d in 2020 (including California glidepath)......................................................................8-4**  **8.1d National Annual Costs and Benefits : 0.065 ppm Standard in 2020 ( including**  **California glidepath)...........................................................................8-5 8.2: Summary of Total Number of Annual Ozone and PM**  **2.5 -Related Premature**  **Mortalities and Premature Morbidity Avoided: 2020 N ational Benefits............8-6**  **8.3a California: Annual Costs and Benefits of Att aining 0.079 ppm Standard (beyond**  **2020)........................................................................................8-7 8.3b California: Annual Costs and Benefits of Attaining 0.070 ppm Standard (beyond**  **2020)........................................................................................8-7 8.3c California: Annual Costs and Benefits of Att**  **aining 0.075 ppm Standard (beyond 2020).........................................................................................8-7**  **8.3d California: Annual Costs and Benefits of Att aining 0.065 ppm Standard (beyond**  **2020)........................................................................................8-8 8.4: Summary of Total Number of Annual Ozone and PM**  **2.5 -Related Premature**  **Mortalities and Premature Morbidity Avoided: Califo rnia Post 2020**  **Attainment.................................................................................8-9**List of Figures

**Number Page** 

**1.1 Process used to create this RIA .........................................................1-5**
**2.1 Map of the CMAQ Modeling Domains Used for Ozone NAAQS RIA ............2-6**  **3.1 Counties Where Controls for Nitrogen Oxides (NOx) W ere Included for Non**  **EGU Point and Area Sources, for the Baseline (Curre nt Standard, 0.08 ppm)....3-7**  **3.2 Counties Where Controls for Volatile Organic Chemic als (VOCs) Were Applied**  **to Non-EGU Point and Area Sources in Baseline (Current Stand ard, 0.08**  **ppm).........................................................................................3-8 3.3 Areas Where NOx and VOC Controls Were Included**  **for Mobile Onroad and Nonroad Sources in Addition to National Mobile Cont** **rols in Baseline (Current Standard, 0.08 ppm).......................................................................3-9**  **3.4 Baseline Annual Ozone Air Quality in 2020........... ...............................3-13**  **3.5 Counties Where Controls for Nitrogen Oxides (NOx) Wer e Applied to Non-EGU**  **Point and Areas Sources for RIA Control Strategy Desi gned to meet 0.070 ppm**  **(Incremental to Baseline)...............................................................3-18 3.6 Counties Where VOC Controls Were Applied to Non-EGU** **Point and Areas Sources for the Control Strategy Designed to Meet 0.070**  **ppm (Incremental to Baseline)..................................................................................3-19**  **3.7 States Where Nitrogen Oxide (NOx) Controls Were Applied to Electrical**  **Generating Units (EGUs) for the Control Strategy Designed to Meet 0.070 ppm (Incremental to Baseline)...............................................................3-21**  **3.8 Areas Where NOx and VOC Controls Were Applied to Mobile Onroad and**  **Nonroad Sources in Addition to National Mobile Contr ols for the 0.070 ppm**  **Control Strategy (incremental to Baseline)..........................................3-22 3.9 Annual Tons of Nitrogen Oxide (NOx) Emission Reduction**  **s from Controls Designed to Meet 0.070 ppm Standard...............................................**  **3-24 3.10 Percentage of Total Annual NOx Emissions Redu**  **ced from Various Sources...3-25 3.11 Annual Tons of Volatile Organic Compound (VOC) Emissi**  **on Reductions from Controls Designed to Meet 0.070 ppm Standard.................................... 3-26**  **3.12 Percentage of Total Annual VOC Emissions Reduc ed from Various Sources..3-27**  **3.13 Projected Ozone Air Quality in 2020 After Application of Known Controls...3-28 3.14 Annual NOx Emissions Remaining after PM NAAQS**  **15/35, Ozone Current Standard, and 0.070 ppm Control Strategies (35 States +**  **DC Analysis Area)..3-30 3.15 Annual VOC Emissions Remaining after PM NAAQS 15/35, O**  **zone Current Standard, and 0.070 ppm Control Strategies (35 States +**  **DC Analysis Area)..3-31**
**4.1 Nine Local Control Areas in Existing 2010 Sensitiv ity Runs........................4-2**  **4.2 The NOx impact ratios at each county (sorted from lowest to highest) for the**  **Approach B over the four analysis areas...............................................4-6**
**5.1 Extrapolated Cost Example (MC Approach)........................................ .5-12**  **5.2 Technological Innovation Reflected by Marginal Co st Shift......................5-22** Acronyms

**AHRQ Agency for Healthcare Research and Quality AQS Air Quality System**  **BenMAP Benefits Mapping and Analysis Program BWC Best workplaces for commuters**  **CAAA Clean Air Act Amendments CAIR Clean Air Interstate Rule**  **CAMR Clean Air Mercury Rule CAPMS Criteria Air Pollutant Modeling System**  **CAVR Clean Air Visibility Rule CDC Centers for Disease Control**  **CDC WONDER Centers for Disease Control Wide-Ranging Onlin e Data for** 

**Epidemiological Research CFR Code of Federal Regulations**  **CI Confidence interval CMAQ Community Multi-Scale Air Quality**  **COI Cost of illness COPD Chronic obstructive pulmonary disease**  **CPI-U Consumer price index – urban C-R concentration-response**  **DOE Department of Energy DOI Department of the Interior**  **DPF Diesel Particulate Filters EGU electric generating unit**  **EIA Economic Impact Analysis EO Executive Order**  **EPA Environmental Protection Agency ER Emergency room**  **HIS National Health Interview Survey ICD International Classification of Disease**  **IPM Integrated Planning Model km kilometer**  **kWh kilowatt hour MACT Maximum Achievable Control Technology**  **MRAD Minor restricted activity days MWRPO Mid-West Regional Planning Organization**  **NAAQS National Ambient Air Quality Standards NAS National Academy of Sciences**  **NCHS National Center for Health Statistics NEI National Emissions Inventory** **NHAMCS National Hospital Ambulatory Medical Care Survey**

**NHANES National Health and Nutrition Examination Survey NHDS National Hospital Discharge Survey**  **NMMAPS National Morbidity, Mortality and Air Pollution S tudy**  **Non-EGU Non-Electricity Generating Unit NOx nitrogen oxides**  **O&M operation and maintenance OAQPS Office of Air Quality Planning and Standards (EPA)**  **OH Hydroxide OMB Office of Management and Budget**  **ORD Office of Research and Development (EPA) OTAQ Office of Transportation and Air Quality (EPA)**  **OTC Ozone Transport Commission PM particulate matter**  **PM 10**  **Particulate matter less than or equal to 10 microns**
**PM 2.5**  **Particulate matter less than or equal to 2.5 microns POC Parameter occurrence code**  **ppb parts per billion ppm parts per million**  **RIA Regulatory Impact Analysis RVP Reid vapor pressure**  **SCR selective catalytic reduction SIP state implementation plan**  **SMR standard mortality rate SNCR selective non-catalytic reduction**  **SO 2**  **Sulfur dioxide US United States**  **USC US code USDA United States Department of Agriculture**  **USEPA United States Environmental Protection Agency USFWS United States Fish and Wildlife Service**  **USGS United States Geological Survey UV-B ultraviolet light, type B**  **VNA Voronoi neighbor averaging VOC Volatile Organic Compounds**  **VSL Value of statistical life WHO World Health Organization**  **WTP Willingness to pay ES-1** 



### Executive Summary

Overview
**EPA has performed an illustrative analysis of the p otential costs and human health benefits of**  **nationally attaining alternative ozone standards. Per E xecutive Order 12866 and the guidelines of**  **OMB Circular A-4, this Regulatory Impact Analysis ( RIA) presents analyses of the range of**  **standards proposed by the Administrator in the Notice of Proposed Rulemaking (0.070 – 0.075**  **ppm), as well as one more stringent option (0.065 p pm). The less stringent option is the**  **baseline, or the current primary standard for ozone ( 0.08 ppm, effectively 0.084 ppm due to**  **current rounding conventions). The benefit and cost estimates below are calc ulated incremental**  **to a 2020 baseline that incorporates air quality im provements 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 includes the**  **Clean Air Interstate Rule and mobile source programs, wh ich will help many areas move toward**  **attainment of the current standard.**
**We present two sets of results. The first reflects full attainment of the alternative ozone**  **standards in all locations of the U.S. except two ar eas of California in 2020. These two areas of**  **California are not planning to meet the current stand ard by 2020, so the estimated costs and**  **benefits for these areas are based on reaching an es timated attainment point in 2020 (their**  **“glidepath” targets). The second set of results, fo r California only, estimate the costs and**  **benefits from California fully attaining the alternati ve standards in a year beyond 2020 (glidepath**  **estimates, plus the increment needed to reach full attainm ent beyond 2020, added together for a**  **California total). Further explanation about these unique circumstances is provided in Chapter 4.** 
**In addition, EPA designed a two-stage approach to esti mating costs and benefits because we**  **recognized from the outset that known and reasonably ant icipated emissions controls would**  **likely be insufficient to bring many areas into att ainment with either the current, or alternative,**  **more stringent ozone standards. The individual chapte rs of the RIA present more detail**  **regarding estimated costs and benefits based on both par tial attainment (manageable with current**  **technologies) and full attainment (manageable in so me locations only with hypothetical**  **technologies). The post-2020 estimates for California are entirely based on hypothetical**  **technologies.**
**In setting primary ambient air quality standards, EP A’s responsibility under the law is to**  **establish standards that protect public health. The Clean Air Act (“Act”) requires EPA, for each**  **criteria pollutant, to set a standard that protects pub lic health with “an adequate margin of**  **safety.” As interpreted by the Agency and the courts, t he Act requires EPA to base this decision**  **on health considerations only; economic factors canno t be considered.** 
**The prohibition against the consideration of cost i n the setting of the primary air quality**  **standards, however, does not mean that costs, benefits or other economic considerations are**  **unimportant or should be ignored. The Agency believe s that consideration of costs and benefits**  **is an essential decision making tool for the effici ent implementation of these standards. The** **ES-2**

**impacts of cost, benefits, and efficiency are consider ed by the States when they make decisions**  **regarding what timelines, strategies, and policies make t he most sense.** 
**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 purposes of th is analysis, though, we assume attainment by**  **2020 for all areas except for two areas in Californ ia** 
**Because States are ultimately responsible for implem enting strategies to meet revised standards,**  **this RIA provides insights and analysis of a limited number of illustrative control strategies that**  **states might adopt to meet any revised standard. These illustrative strategies are subject to a**  **number of important assumptions, uncertainties and l imitations, which we document in the**  **relevant portions of the analysis.**

ES.1 Approach to the Analysis
**This RIA consists of multiple analyses including an ass essment of the nature and sources of**  **ambient ozone; estimates of current and future emissions of relevant precursors that contribute to**  **the problem; air quality analyses of baseline and alter native 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 alter native standards, together with an**  **examination of key uncertainties and limitations; and a series of conclusions and insights gained**  **from the analysis.**
**The air quality modeling results for the** regulatory baseline**(explained in Chapter 3) provide the starting point for developing illustrative control strate**  **gies to attain the alternative standards that are the focus of this RIA. The baseline shows that by 202**  **0, while ozone air quality would be significantly better than today under current requirements, several ea**  **stern and western states would need to develop and adopt additional controls**  **to attain the alternative standards.**
**In selecting controls, we focused more on ozone cost- effectiveness (measured as $/ ppb) than on**  **the NOx or VOC cost-effectiveness (measured as $/ton). Mo st of the overall reductions in NOx**  **achieved our illustrative control strategy were from no n-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 descr ibed here include the co-benefits of reductions**  **in fine particulate levels (PM) associated with year -round application of NOx control strategies**  **beyond those in the regulatory baseline.**
**Estimated reductions in premature mortality from red uctions in ambient ozone and PM dominate**  **the benefits estimates. For this reason, our assessment pr ovides a range of estimates for both PM**  **and ozone premature mortality. Although we note that t here are uncertainties that are not fully**  **captured by this range of estimates, and that additi onal research is needed to more fully establish**  **underlying mechanisms by which such effects occur, s uch ranges are illustrative of the extent of**  **uncertainly associated with some different modeling as sumptions.** **ES-3**

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

**1 Modeled emissions reflect the expected reductions from federal programs including the Clean Air**  **Interstate Rule, the Clean Air Mercury Rule, the Cl ean Air Visibility Rule, the Clean Air Nonroad Dies**  **el Rule, the Light-Duty Vehicle Tier 2 Rule, the Heavy** **Duty Diesel Rule, proposed rules for Locomotive and Marine Vessels and for Small Spark-Ignition Eng**  **ines, and state and local level mobile and stationa ry**  **source controls identified for additional reduction s in emissions for the purpose of attaining the cur** **rent PM 2.5 and Ozone standards.**  **2 Controls applied are illustrative. States may ch oose to apply different control strategies for**  **implementation. 3 The current standard of 0.08 ppm is effectively e**  **xpressed as 0.084 ppm when rounding conventions are applied.**  **4 Modeled design values in ppm are only interpreted up to 3 decimal places.**  **5 Map shows results from a total of 491 counties wi th projected design values. Consistent with current**  **modeling guidance, EPA did not project 2020 concentr ations for counties where 2001 base year**  **concentrations were less than recommended criterion . Such projections may not represent expected**  **future levels. ES-4** 

ES-2. Results of Benefit-Cost Analysis
**There are two sets of results presented below. The first set of national results assumes**  **attainment of revised standards by 2020 in all areas, except for two areas in Southern California.**  **It is expected that benefits and costs will begin o ccurring earlier, as states begin implementing**  **control measures to show progress towards attainment . Some areas with high ozone levels, such**  **as the two areas in Southern California, are not planning to attain even the current standard until**  **after 2020. In these locations, our national 2020 estimate includes the cost and benefits of**  **reaching an estimated progress point in 2020 (known as a “glidepath” target). The 2020 results**  **will thus not represent a true “full attainment” sc enario for the entire nation. In order to gain an**  **understanding of the possible additional costs and bene fits of fully attaining in California in a**  **year beyond 2020, we provide an additional set of res ults for California only. Tables ES-1 to**  **ES-3 present national benefits and costs in 2020, incl uding the “glidepath” targets for California;**  **companion Table ES-4 provides the national estimated reductions in premature mortality and**  **morbidity in 2020, including the “glidepath” targets for California.** 
**Tables ES-5 to ES-7 present the costs and benefits of full attainment for California (“glidepath”**  **in 2020 plus the additional increment achieved betwee n 2020 and a future year added together**  **into one California total); Table ES-8 is the compani on table showing estimated reductions in**  **premature mortality and morbidity for California. Beca use various mobile source rules, such as**  **the onroad and nonroad diesel rule, among others, w ould be expected to be implemented**  **between 2020 and a future year, the tons of emissio n reduction expected to occur as a result of**  **those rules has been taken out of the calculated costs an d benefits for the estimates of additional**  **tons of emission reduction needed in California betwee n 2020 and a future year. EPA did the**  **analysis this way because to force full attainment in an earlier year than would be required under**  **the Clean Air Act would likely lead to an overstatement of co sts because those areas might**  **benefit from these existing federal or state progra ms that would be implemented between 2020**  **and the attainment year; because additional new tec hnologies may become available between**  **2020 and the attainment year; and because the cost of existing technologies might fall over time.**  **As such, we use the best available data to estimate costs and benefits of full attainment for**  **California in a future year, while recognizing that the estimates of costs and benefits for**  **California in a year between 2020 and a future year are likely to be relatively more uncertain**  **than the national attainment estimates for 2020. It is not appropriate to add together the 2020**  **national attainment, California glidepath estimate and t he estimate of California full attainment**  **as an estimate of national full attainment in 2020. T he extra increment of attainment that is**  **estimated for California will not occur in 2020, so it is not accurate to add it to our nationwide**  **estimate of the “glidepath” benefits and costs to ar rive at a “full attainment” estimate for 2020**  **1**

**. It is also not accurate to add the two estimates to**  **gether to arrive at an estimate of future, post 2020 full attainment benefits and costs, because our**  **nationwide full attainment estimates do not**

**1 The California full attainment costs calculated usi**  **ng the offset in NOx emissions from mobile programs would understate the costs of fully attain**  **ing in 2020, however, California will not be required to attain in 2020.** **ES-5**

**allow other areas of the nation to take credit for the reductions in NOx from the mobile source**  **rules that will occur after 2020. 2** 

**In these tables, the individual row estimates reflect the different studies available to describe the**  **ozone premature mortality relationship. Ranges within the total benefits column reflect**  **variability in the studies upon which the estimates asso ciated with premature mortality were**  **derived. PM co-benefits account for between 13 and 99 percent of co-benefits, depending on the**  **standard analyzed and on the choice of ozone and PM mo rtality functions used.** 
**Ranges in the total costs column reflect different a ssumptions about the extrapolation of costs.**  **The low end of the range of net benefits is construc ted by subtracting the highest cost from the**  **lowest benefit, while the high end of the range is constructed by subtracting the lowest cost from**  **the highest benefit. The presentation of the net benefi t estimates represents the widest possible**  **range from this analysis.**

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

(including California glidepath ) Mean Total Benefits, in Billions of 1999$ **Premature Mortality**  **Function or Assumption**  **Reference** **Total Benefits* Total Costs** Net Benefits**

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

**Bell et al. 2005 $1.6 to $12 $3 to $3.3 -$1.7 to $8 .9**  **Ito et al. 2005 $1.7 to $12 $3 to $3.3 -$1.7 to $8.9 Meta-analysis**  **Levy et al. 2005 $1.6 to $12 $3 to $3.3 -$1.7 to $8 .9**  **Assumption that association is not causal** *** **$1.1 to $11 $3 to $3.3 -$2.2 to $8.4**

**2 This approach would be an overestimate of national f**  **ull attainment costs in a future year after 2020 because it would not take into account that oth**  **er states (not just California) could replace more expensive NOx reductions from other sources with** **the post-2020 reductions obtained from implementation of mobile source rules that are incl**  **uded in the regulatory baseline. ES-6** 

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

(including California glidepath ) Mean Total Benefits, in Billions of 1999$ **Premature Mortality**  **Function or Assumption**  **Reference** **Total Benefits* Total Costs** Net Benefits**

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

**Bell et al. 2005 $7.3 to $20 $5.5 to $8.8 -$1.5 to $15**  **Ito et al. 2005 $7.8 to $21 $5.5 to $8.8 -$1. to $15 Meta-analysis**  **Levy et al. 2005 $8.7 to $22 $5.5 to $8.8 -$0.1 to $16**  **Assumption that association is not causal** *** **$1.5 to $15 $5.5 to $8.8 -$7.3 to $9**

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

(including California glidepath) Mean Total Benefits, in Billions of 1999$ **Premature Mortality**  **Function or Assumption**  **Reference** **Total Benefits* Total Costs** Net Benefits**

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

**Bell et al. 2005 $9.7 to $31 $10 to $22 -$12 to $21 Ito et al. 2005 $10 to $32 $10 to $22 -$11 to $22**  **Meta-analysis Levy et al. 2005 $11 to $33 $10 to $22 -$10 to $23**  **Assumption that association is not causal** *** **$2.5 to $24 $10 to $22 -$20 to $14**

Table ES-4 National Annual Costs and Benefits : 0.065 ppm Sta ndard in 2020 (including California glidepath) Mean Total Benefits, in Billions of 1999$ **Premature Mortality**  **Function or Assumption**  **Reference** **Total Benefits* Total Costs** Net Benefits**

**NMMAPS Bell et al. 2004 $7.7 to $45 $17 to $46 -$3 8 to $28** 

**Bell et al. 2005 $18 to $55 $17 to $46 -$28 to $38 Ito et al. 2005 $19 to $56 $17 to $46 -$27 to $39**  **Meta-analysis Levy et al. 2005 $20 to $57 $17 to $46 -$27 to $40**  **Assumption that association is not causal** *** **$4.3 to $42 $17 to $46 -$42 to $25**

***Includes ozone benefits, and PM 2.5 co-benefits. Range w as developed by adding the estimate**  **from the ozone premature mortality function to both the lower and upper ends of the range of the**  **PM2.5 premature mortality functions characterized in the expert elicitation**  ****Range reflects lower and upper bound cost estimates** *****Total includes ozone morbidity benefits only ES-7**  Table ES-5: Summary of Total Number of Annual Ozone and PM 2.5 -Related Premature Mortalities and Premature Morbidity Avoided: 2020 N ational 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) 200 to 1,900 430 to 2,600 670 t o 4,300 1,200 to 7,400

Bell (2005) 260 to 2,000 1,100 to 3,300 1,500 to 5, 100 2,800 to 9,000 Ito (2005) 270 to 2,000 1,200 to 3,300 1,600 to 5,2 00 3,000 to 9,200 Meta-Analysis Levy (2005) 260 to 2,000 1,300 to 3,500 1,800 to 5, 400 3,000 to 9,200 No Causality 180 to 1,900 230 to 2,400 390 to 4,000 660 to 6,900

Combined Estimate of Morbidity

Acute Myocardial Infarction 1,100 1,400 2,300 4,000 Hospital and ER Visits 1,300 5,600 7,600 13,000 Chronic Bronchitis 370 470 780 1,300 Acute Bronchitis 950 1,200 2,000 3,500 Asthma Exacerbation 7,300 9,400 16,000 27,000 Lower Respiratory Symptoms 8,100 10,000 17,000 29,00 0 Upper Respiratory Symptoms 5,900 7,500 13,000 22,000 School Loss Days 50,000 610,000 780,000 1,300,000 Work Loss Days 51,000 65,000 110,000 190,000 Minor Restricted Activity Days 430,000 2,000,000 2,7 00,000 4,700,000**ES-8**

Table ES-6 California: Annual Costs and Benefits of Attainin g 0.079 ppm Standard (beyond 2020)* Mean Total Benefits, in Billions of 1999$ **Premature Mortality**  **Function or Assumption**  **Reference Total Benefits**  Total Costs*** Net Benefits**

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

**Bell et al. 2005 $0.2 to $0.7 $0.3 to $1.7 -$1.5 to $0.4**  **Ito et al. 2005 $0.3 to $0.7 $0.3 to $1.7 -$1.4 to $0.4 Meta-analysis**  **Levy et al. 2005 $0.2 to $0.7 $0.3 to $1.7 -$1.5 to $0.4**  **Assumption that association is not causal** **** **$0.05 to $0.5 $0.3 to $1.7 -$1.6 to $0.2**

Table ES-7 California: Annual Costs and Benefits of Attainin g 0.070 ppm Standard (beyond 2020)* Mean Total Benefits, in Billions of 1999$ **Premature Mortality**  **Function or Assumption**  **Reference Total Benefits**  Total Costs*** Net Benefits**

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

**Bell et al. 2005 $1.9 to $4.7 $2 to $13 -$11 to $2. 7**  **Ito et al. 2005 $2.1 to $4.8 $2 to $13 -$11 to $2.9 Meta-analysis**  **Levy et al. 2005 $2.1 to $4.8 $2 to $13 -$11 to $2. 9**  **Assumption that association is not causal** **** **$0.4 to $3.1 $2 to $13 -$13 to $1.2**

Table ES-8 California: Annual Costs and Benefits of Attaini ng 0.075 ppm Standard (beyond 2020)* Mean Total Benefits, in Billions of 1999$ **Premature Mortality**  **Function or Assumption**  **Reference Total Benefits**  Total Costs*** Net Benefits**

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

**Bell et al. 2005 $1.1 to $2.6 $1.1 to $6.2 -$5.1 to $1.5**  **Ito et al. 2005 $1.2 to $2.7 $1.1 to $6.2 -$5.1 to $1.6 Meta-analysis**  **Levy et al. 2005 $1.2 to $2.7 $1.1 to $6.2 -$5 to $1.6 Assumption that association is not**  **causal****** **$0.2 to $1.7 $1.1 to $6.2 -$6 to $0.6 ES-9** 

Table ES-9 California: Annual Costs and Benefits of Attainin g 0.065 ppm Standard (beyond 2020)* Mean Total Benefits, in Billions of 1999$ **Premature Mortality**  **Function or Assumption**  **Reference Total Benefits**  Total Costs*** Net Benefits**

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

**Bell et al. 2005 $3.1 to $7.2 $2.9 to $21 -$17 to $4 .3**  **Ito et al. 2005 $3.4 to $7.4 $2.9 to $21 -$17 to $4.5 Meta-analysis**  **Levy et al. 2005 $3.3 to $7.4 $2.9 to $21 -$17 to $ 4.5**  **Assumption that association is not causal** **** **$0.5 to $4.6 $2.9 to $21 -$20 to $1.7** *** Tables present the total of CA glidepath in 2020, plus the additional increment needed to reach**  **full attainment in a year beyond 2020**   Includes ozone benefits and PM 2.5 co-benefits. Rang**  **e was developed by adding the estimate from the ozone premature mortality function to bo**  **th the lower and upper ends of the range of the PM2.5 premature mortality functions char**  **acterized in the expert elicitation** *****Range reflects lower and upper bound cost estimat es**  ******Total includes ozone morbidity benefits only ES-10**  Table ES-10: Summary of Total Number of Annual Ozon e and PM 2.5 -Related Premature Mortalities and Premature Morbidity Avoided: California Post 2020 Attainment Combined Estimate of Mortality Standard Alternative and Model or Assumption Combined Range of Ozone Benefits and PM2.5 Co-Benefits 0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm NMMAPS Bell (2004) 17 to 93 61 to 310 110 to 570 1 80 to 840

Bell (2005) 42 to 120 170 to 410 300 to 760 490 to 1,200 Ito (2005) 45 to 120 180 to 430 320 to 780 530 to 1 ,200 Meta-Analysis Levy (2005) 46 to 120 180 to 430 320 to 780 520 to 1 ,200 No Causality 8.2 to 84 26 to 270 49 to 500 72 to 740

Combined Estimate of Morbidity

Acute Myocardial Infarction 49 160 290 430 Hospital and ER Visits 200 790 1,400 2,200 Chronic Bronchitis 17 53 99 150 Acute Bronchitis 43 140 260 380 Asthma Exacerbation 330 1,100 2,000 2,900 Lower Respiratory Symptoms 360 1,200 2,200 3,200 Upper Respiratory Symptoms 270 850 1,600 2,300 School Loss Days 30,000 120,000 210,000 340,000 Work Loss Days 2,300 7,400 14,000 20,000 Minor Restricted Activity Days 87,000 340,000 600,00 0 960,000

*****Range was developed by adding the estimate from the ozone premature mortality function to both the lo**  **wer and upper ends of the range of the PM**  **2.5 premature mortality functions characterized in the exp**  **ert elicitation ES-11** 

ES-3. Caveats and Conclusions
**Of critical importance to understanding these estimates of future costs and benefits is that they**  **not intended to be forecasts of the actual costs and benef its of implementing revised standards.**  **There are many challenges in estimating the costs and ben efits of attaining a tighter ozone**  **standard, which are fully discussed in Chapter 8. An alytically, the characterization of ozone**  **mortality benefits and the estimation of the costs a nd benefits of the nation fully attaining a**  **tighter standard are being subject to further review by science advisory boards.** 
**There are significant uncertainties in both cost and be nefit estimates. Below we summarize some**  **of the more significant sources of uncertainty.**

x **Benefits estimates are influenced by our ability to correctly model relationships between**  **ozone and PM and their associated health effects (e. g., premature mortality).** 

x **Benefits estimates are also heavily dependent upon the choice of statistical estimates for**  **values associated with each of the health benefits.**

x **EPA has requested advice from the National Academy of Sci ences on how best to**  **quantify uncertainty in the relationship between oz one exposure and premature mortality**  **in the context of quantifying benefits associated with altern ative ozone control strategies.** 

x **PM co-benefits are derived primarily from reductions in nitrates (associated with NOx**  **controls). As such, these estimates are strongly influe nced by the assumption that all PM**  **components are equally toxic. Co-benefit estimates are also i nfluenced by the extent to**  **which a particular area chooses to use NOx controls rather than VOC controls.** 

x **EPA employed a monitor rollback approach to estimate the benef its of attaining an**  **alternative standard of 0.079 ppm nationwide. This approa ch likely understates the**  **benefits that would occur due to implementation of actu al controls 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 standa rd in down-wind locations). The**  **estimated benefits of attaining a standard of 0.075 ppm, however, are likely overstated.**  **EPA will develop and present consistent approaches f or the alternative standards for the**  **final RIA.**

x **There are several nonquantified benefits (e.g. effec ts of reduced ozone on forest health**  **and agricultural crop production) and disbenefits (e.g. decreases in tropos pheric ozone**  **lead to reduced screening of UV-B rays and reduced n itrogen fertilization of forests and**  **cropland) discussed in this analysis in chapter 6.**

x **Changes in air quality as a result of controls are not expected to be uniform over the**  **country. In our hypothetical control scenario some increases in ozone levels occur in**  **areas already in attainment, though not enough to p ush the areas into nonattainment** 

x **As explained in chapter 5, there are several uncertainties in our cost estimates. For**  **example, the states are likely to use different approaches for reducing NOx and VOCs in**  **their state implementation plans to reach a tighter st andard. In addition, since we are**  **unable to use known controls to get all areas into at tainment, we needed to use simple**  **$/ton costs to estimate the overall national cost of meeting the tighter alternatives.** 

x **As discussed in chapter 5, recent advice from EPA’s Science Advisory Board has**  **questioned the appropriateness of an approach similar to that used here for estimating** **ES-12**

**extrapolated costs. EPA will consider this advice and other guidance as it develops the**  **methodology for analyzing the final rule.**

x **Both extrapolated costs and benefits have additional un certainty relative to modeled costs**  **and benefits. The extrapolated costs and benefits will only be realized to the extent that**  **unknown extrapolated controls are economically feasib le and are implemented.** 

x **Technological advances over time will tend to increase t he economic feasibility of**  **reducing emissions, and will tend to reduce the cos ts of reducing emissions.** 

x **These sources of uncertainty are discussed in more deta il in subsequent chapters of the**  **RIA. In addition to considering any advice which come s from advisory bodies prior to**  **the publication of the final ozone NAAQS, EPA will underta ke an updated approach with**  **improvements to emissions inventories, models and cont rol strategies for the RIA which**  **will accompany that rulemaking.**1-1

Chapter 1: Introduction and Background

Synopsis

This document estimates the incremental costs and mon etized human health and welfare benefits of attaining possible revised primary ozon e National Ambient Air Quality Standards (NAAQS) nationwide. This document contain s illustrative analyses that consider limited emission control scenarios that stat es, tribes and regional planning organizations might implement to achieve a revised ozon e NAAQS. In some cases, EPA weighed the available empirical data to make judgments regarding the proposed attainment status of certain urban areas in the future . According to the Clean Air Act, EPA must use health-based criteria in setting the NAAQS and cannot consider estimates of compliance cost. This Regulatory Impact Analysis (RIA) is intended to provide the public a sense of the benefits and costs of meeting new alternative ozone NAAQS, and to meet the requirements of Executive Order 12866 and OMB Circular A-4 (described below in Section 1.2.2).
1.1 Background
Two sections of the Clean Air Act (“Act”) govern the establishment and revision of NAAQS. Section 108 (42 U.S.C. 7408) directs the Administrator to identify pollutants which “may reasonably be anticipated to endanger pub lic health or welfare,” and to issue air quality criteria for them. These air quality criteri a are intended to “accurately reflect the latest scientific knowledge useful in indicating the kind and extent of al l identifiable effects on public health or welfare which may be expe cted from the presence of [a] pollutant in the ambient air.” Ozone is one of six pollutants for which EPA has developed air quality criteria.
Section 109 (42 U.S.C. 7409) directs the Administrator to propose and promulgate “primary” and “secondary” NAAQS for pollutants identifie d under section 108. Section 109(b)(1) defines a primary standard as “the attainment an d maintenance of which in the judgment of the Administrator, based on [the] crite ria and allowing an adequate margin of safety, [are] requisite to protect the public health.” Asecondary standard, as defined in section 109(b)(2), must “specify a level of air quality the attainment and maintenance of which in the judgment of the Administrator, based on [the] cr iteria, [are] requisite to protect the public welfare from any known or anticipated adverse effects associated with the presence of [the] pollutant in the ambient air.” Welfare effects as defined in section 302(h) [42 U.S.C 7602(h)] include but are not limited to “effects on soils, water, crops, vegetation, manmade materials, animals, wildlife, w eather, visibility and climate, damage to and deterioration of property, and hazards to tr ansportation, as well as effects on economic values and on personal comfort and well-be ing.”
Section 109(d) of the Act directs the Administrator to review existing criteria and standards at 5-year intervals. When warranted by such review, the Administrator is to1-2

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

1.2 Role of the Regulatory Impact Analysis in the NAAQS Setting Process
1.2.1 Legislative Roles In setting primary ambient air quality standards, EPA’s r esponsibility under the law is to establish standards that protect public health. Th e Clean Air 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 create standards based on health considerations only. Economic factors cannot be considered.
The prohibition against the consideration of cost in the setting of the primary air quality standard, however, does not mean that costs or other e conomic considerations are unimportant or should be ignored. The Agency believes that consideration of costs and benefits are essential to making efficient, cost eff ective decisions for implementation of these standards. The impact of cost and efficiencyare considered by states during this process, as they decide what timelines, strategies, and policies make the most sense. This RIA is intended to inform the public about the potential cost s and benefits that may result when a new ozone standard is implemented, but is not r elevant to establishing the standards themselves.
1.2.2 Role of Statutory and Executive Orders There are several statutory and executive orders th at dictate the manner in which EPA considers rulemaking and public documents. This document is separate from the NAAQS decision making process, but there are severa l statutes and executive orders that still apply to any public documentation. A summaryof the pertinent orders is included in Appendix 1. The analysis required by these statutes and ex ecutive orders is presented in Chapter 9.
EPA presents this RIA pursuant to Executive Order 12866 and the guidelines of OMB Circular A-4. 1 These documents present guidelines for EPA to assess the benefits and costs of the selected regulatory option, as well as one less stringent and one more stringent option. OMB circular A-4 also requires both a c ost-benefit, and a costeffectiveness analysis for rules where health is the pri mary effect. Within this RIA we provide a cost benefit analysis. We also provide acost-effectiveness analysis for that portion of the benefits which occur from concurrent reductio ns in particulate matter as a result of controls on NOx emissions to reduce ozone levels (see Appendix 6b). We are investigating options for conducting a cost-effecti veness analysis for the ozone portion of the benefits and expect to provide estimates based on that analysis in the final RIA.

1 U.S. Office of Management and Budget. Circular A-4, September 17, 2003. Found on the Internet at <http://www.whitehouse.gov/omb/circu lars/a004/a-4.pdf>.1-3

1.2.3 Market Failure or Other Social Purpose OMB Circular A-4 indicates that one of the reasons a regulation such as the NAAQS may one may be issued is to address market failure. The major types of market failure include: externality, market power, and inadequate or asymmetr ic information. Correcting market failures is one reason for regulation, but it is no t the only reason. Other possible justifications include improving the function of gove rnment, removing distributional unfairness, or promoting privacy and personal freedom.
An externality occurs when one party’s actions impose uncompensated benefits or costs on another party. Environmental problems are a classic ca se of externality. For example, the smoke from a factory may adversely affect the he alth of local residents while soiling the property in nearby neighborhoods. If bargaining wa s 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 fr om 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 wh at would be offered in a competitive industry in order to obtain higher price s. They may exercise market power collectively or unilaterally. Government action can b e a source of market power, such as when regulatory actions exclude low-cost imports. Generally, regulations that increase market power for selected entities should be avoided. Howe ver, there are some circumstances in which government may choose to vali date a monopoly. If a market can be served at lowest cost only when production is li mited to a single producer of local gas and electricity distribution services, a natural mo nopoly is said to exist. In such cases, the government may choose to approve the monopoly and to regu late its prices and/or production decisions. Nevertheless, it should be note d that technological advances often affect economies of scale. This can, in turn, tran sform what was once considered a natural monopoly into a market 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 exist ence of incomplete or asymmetric information. Even though the market may supply less than th e full amount of information, the amount it does supply may be reaso nably adequate and therefore not require government regulation. Sellers have an incentiv e to provide information through advertising that can increase sales by highlightingdistinctive characteristics of their products. Buyers may also obtain reasonably adequate information about product characteristics through other channels, such as a seller offering a warranty or a third party providing information.
There are justifications for regulations in addition to correcting market failures. A regulation may be appropriate when there are clearly id entified 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-effe ctive. Congress also authorizes some regulations to prohibit discrimination that conflicts with generally accepted norms within1-4

our society. Rulemaking may also be appropriate to protect p rivacy, permit more personal freedom or promote other democratic aspirations.
From an economics perspective, setting an air quality stand ard is a straightforward case of addressing an externality, in this case where firms are e mitting pollutants, which cause health and environmental problems without compensation for those suffering the problems. Although this economics perspective is reflec ted in Clean Air Act legislative history, there is also legislative history in which m embers of Congress stated that the purpose of setting national air quality standards solely ba sed on health considerations (without considering costs) is to protect a fundame ntal right of Americans to be protected from air pollution levels that adversely affect thei r health. Setting a standard with a reasonable margin of safety attempts to place the cos t of control on those who emit the pollutants and lessens the impact on those who suffe r the health and environmental problems from higher levels of pollution.
1.2.4 Illustrative Nature of the Analysis This ozone NAAQS RIA is an illustrative analysis that provides useful insights into a limited number of emissions control scenarios that states might implement to achieve a revised ozone NAAQS. Because states are ultimatelyresponsible for implementing strategies to meet any revised standard, the control scenarios in this RIA are necessarily hypothetical in nature. They are not forecasts of expected future outcomes. Important uncertainties and limitations, are documented in the rel evant portions of the analysis.
The illustrative goals of this RIA are somewhat different fr om other EPA analyses of national rules, or the implementation plans states dev elop, and the distinctions are worth brief mention. This RIA does not assess the regulatory imp act of an EPA-prescribed national or regional rule such as the Clean Air Int erstate Rule, nor does it attempt to model the specific actions that any state would take to implement a revised ozone standard. This analysis attempts to estimate the costs and human and welfare benefits of cost-effective implementation strategies which might b e undertaken to achieve national attainment of new standards. These hypothetical strategies represent a scenario where states use one set of cost-effective controls to attain arevised ozone NAAQS. Because states—not EPA—will implement any revised NAAQS, the y will ultimately determine appropriate emissions control scenarios. State imp lementation plans would likely vary from EPA’s estimates due to differences in the data and assumptions that states use to develop these plans.
The illustrative attainment scenarios presented in this RIA were constructed with the understanding that there are inherent uncertainties i n projecting emissions and controls. Furthermore, certain emissions inventory, control, mode ling and monitoring limitations and uncertainties inhibit EPA’s ability to model fu ll attainment in all areas. An additional limitation is that this analysis is carr ied out for the year 2020, before some areas are required to reach the current ozone standard. Section 1.3.1 below explains why EPA selected the analysis year of 2020. Despite these limi tations, EPA has used the best available data and methods to produce this RIA.1-5

1.3 Overview and Design of the RIA
This Regulatory Impact Analysis evaluates the costs and benefits of hypothetical national strategies to attain several potential revised prim ary ozone standards. The document is intended to be straightforward and written for the lay person with a minimal background in chemistry, economics, and/or epidemiology. Figure 1 .1 provides an illustration of the framework of this RIA.

Figure 1.1: the process used to create this RIA

1.3.1 Baseline and Years of Analysis
The analysis year for this regulatory impact analysis is 2020, which allows E PA to build the ozone RIA analysis on the previously completed PM NAAQ S RIA analysis. Many areas will reach attainment of the current ozone st andard or any alternative standard by 2020. For purposes of this analysis, we assume attainment by 2020 for all areas except for two areas in California with unique circumstances descri bed in chapter 4. Some areas for which we assume 2020 attainment may in fa ct need more time to meet one or more of the analyzed standards, while others will need less time. This analysis does not Estimate 2020 Emissions

Model 2020 Baseline Air Quality

Identify Areas Projected to Exceed Alternate Standard and Calculate Needed Reduction Targets

Select Control Strategies

Estimate Modeled Control Costs Estimate Modeled Human Health Effects and Dollar Benefits Determine Post-Control Air Quality and Compare pre- and post-control strategy air quality (partial attainment in most areas)

Extrapolate Tons to Reach Full Attainment and Compare pre-and post- extrapolation air quality

Estimate Extrapolated Control Costs Estimate Extrapolated Human Health Effects and Dollar Benefits1-6

prejudge the attainment dates that will ultimately be assigned to individual areas under the Clean Air Act, which contains a variety of potential dates and flexibility to move to later dates (up to 20 years), provided that the date is a s expeditious as practicable.
The methodology first estimates what baseline ozone levels might look like in 2020 with existing Clean Air Act programs, including application of controls to meet the current ozone standard and the newly revised PM NAAQS standard. and then models how ozone levels would be predicted to change following the ap plication of additional controls to reach a tighter standard. This allows for an analysi s of the incremental change between the current standard and an alternative standard. This timeline is also consistent with expected attainment in 2020 of the revised Particul ate Matter (PM) NAAQS covered in the PM NAAQS RIA issued in September 2006. As explained in Chapter 2, since one of the principal precursors for ozone, NOx, is also a precurso r for PM, it is important that we account for the impact on ozone concentrations o f NOx controls used in the hypothetical control scenario used in the PM NAAQS RIA , so as to avoid double counting the benefits and costs of these controls.

1.3.2 Control Scenarios Considered in this RIA A hypothetical control strategy was developed for analternative 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 ai r quality changes that would result from the application of emissions control options that are kn own to be available to different types of sources in portions of the country that were predicted to be in nonattainment with 0.070 ppm in 2020. However, given the limitations of current technology and the amount of improvement in air quality needed to reach a standard of 0.070 ppm in some areas, it was also expected that modeling these known controls would not reduce ozone concentrations sufficiently to allow al l areas to reach the more stringent standard. This required a second step to calculate th e number of tons of emission reductions that would be needed to reach full attai nment. This required calculating a conversion factor to quantify the estimated tons of emissions that needed to be reduced to generate a particular change in air quality concentrations of ozone (in ppm). This factor, coupled with the estimated remaining increment (in ppm) of o zone necessary to reach the alternative standard in each area, allowed for an extrapolation of how many tons of additional emissions reductions were estimated to be ne eded to reach the alternate standard.
1.3.3 Evaluating Costs and Benefits Applying a two step methodology for estimating emission re ductions needed to reach full attainment enabled EPA to evaluate nationwide costs and benefits of attaining a tighter ozone standard, albeit with substantial additional uncertainty regarding the s econd step estimates. Costs and benefits are presented in this RIA in the same two steps that emissions reductions were estimated. First, the costs a ssociated with applying known controls were quantified, and presented along with an estimate of their economic impact. Second, EPA estimated costs of the additional tons of extrapolated emission reductions estimated which were needed to reach full attainment. The ana lysis of the benefits of setting an alternative standard included both mortality and morbidity calculations1-7

matching the costs of applying known controls and then th e benefits of reaching full attainment. The costs and monetized benefits were then compared to provide an estimate of net benefits nationwide.
The RIA presents two sets of results for estimated costs and benefits. The first reflects full attainment in 2020 in all locations of the U.S. ex cept two areas of California. These two areas are not planning to meet the current stan dard by 2020, so the estimated costs and benefits for these areas are based on reaching an estimated progress point in 2020 (their “glidepath” targets). The second set of results for Cal ifornia only, estimate the costs and benefits from California fully attaining the alternative standards in a year beyond 2020 (glidepath estimates for 2020, plus further increments needed to reach full attainment beyond 2020, added together for Californ ia total).
To streamline this RIA, it refers to several previou sly published documents, including two technical documents EPA produced to prepare for the ozone NAAQS proposal. The first was a Criteria Document created by EPA’s Office of Res earch and Development (published in 2006), which presented the latest available pe rtinent information on atmospheric science, air quality, exposure, dosimetr y, health effects, and environmental effects of ozone. The second was a “Staff Paper” (published in 2007) that evaluated the policy implications of the key studies and scientif ic information contained in the Criteria Document, as well as presented a risk assessment fo r various standard levels. The Staff Paper also includes staff conclusions and recommendat ions to the Administrator regarding potential revisions to the standards. Inaddition to the Criteria Document and Staff Paper, this ozone RIA relies heavily on the 2006 RIA fo r particulate matter (PM). Many of the models and methodology used here are the same as in the PM NAAQS RIA. This RIA identifies methodologies used to generate data, but refers readers to the PM NAAQS RIA for many technical details. The focus of this RIA is to explain in detail how the approach or methodologies have changed from the PM NAAQS RIA analysis, and to present the results of the methodologies emp loyed in this analysis, which compares attainment of tighter levels of the ozone standard to the baseline of the current standard.

1.4 Ozone Standard Alternatives Considered
Per Executive Order 12866 and the guidelines of OMB Circular A-4, this RIA presents analyses of the range of standards proposed by the Administrator in the Notice of Proposed Rulemaking (0.070 – 0.075 ppm), as well as one mor e stringent option (0.065 ppm), and one less stringent option (0.079 ppm). EPA will also model a baseline as the current primary standard for ozone (0.08 parts per millio n (ppm), calculated as the 3-year average of the annual fourth-highest daily maximum 8 -hour average ozone concentration measured at each monitor within an area -- effectiv ely 0.084 ppm using current data rounding conventions).

The EPA Administrator received recommendations to revise the current primary ozone standard from both the Clean Air Act Scientific Adv isory Council (CASAC) and EPA1-8

staff. Both CASAC and staff expressed the view that the c urrent standard was not adequately protective of human health and should be t ightened to provide additional public health protection. Specifically, CASAC recommended that “... the current primary ozone NAAQS [should] be revised and that th e level that should be considered for the revised standard be from 0.060 to 0.070 ppm .” 2 In the Staff Paper, EPA staff suggested a slightly broader range, recommending “that consideration be given to a standard level within the range of somewhat below 0.080 ppm to 0.060 ppm. ” 3 Both of these recommendations encourage maintaining the sam e averaging time and form as the current standard. Additionally, both recommendations suggested it was appropriate to specify the level of the standard out to three signific ant digits.
In the concurrent ozone NAAQS proposal, EPA propose s to revise the 8-hour standard to a level within the range of 0.070 to 0.075 ppm, and to re quest comment on a wider range of an 8-hour standard from 0.060 ppm to 0.084 ppm, to provide i ncreased protection for children and other sensitive populations against an array o f ozone-related adverse health effects that range from decreased lung function and inc reased respiratory symptoms to serious indicators of respiratory morbidity includin g emergency department visits and hospital admissions for respiratory causes, and pos sibly cardiovascular-related morbidity as well as total nonaccidental and cardiopulmonary mortality. The EPA also proposes to specify the level of the primary standard to the ne arest thousandth ppm.
This RIA presents benefit and cost estimates for both ends of the proposal range of 0.070 to 0.075 ppm. It also assesses the costs and benef its of attaining one more stringent standard option (0.065 ppm), and will include a less stringent option of 0.079 ppm.. Since EPA is not considering loosening the standard , the less stringent option to the proposed range is the current standard itself. Since this RIA presents an analysis of the costs and benefits incremental to the current standard, re taining the current standard is assumed to have no additional incremental costs or benefits.
For the secondary standard, EPA proposes to revise the current 8-hour standard with one of two options to provide increased protection agai nst ozone-related adverse impacts on vegetation and forested ecosystems. One option is to replace the current standard with a cumulative, seasonal standard expressed as an index (called the W126) of the annual sum of weighted hourly concentrations, cumulated over 12 hours per day (8:00 am to 8:00 pm) during the consecutive 3-month period within the ozone season with the maximum index value, set at a level within the range of 7 to 21 p pm-hours. The other option is to make the secondary standard identical to the propos ed primary 8-hour standard in all respects. This RIA provides a limited discussion of an alt ernative secondary standard showing the number of additional counties with monit ors that may violate the standards,

2 (Henderson, 2006c, p. 5). Henderson, R. (2006c) Letter from CASAC Chairman Rogene Henderson to EPA Administrator Stephen Johnson, Oct ober 24, 2006, EPACASAC-07-001. 3 U.S EPA. 2007. Review of the National Ambient Air QualityStandards for Ozone: Policy Assessment of Scientific and Technical Information. OAQPS Staff Paper. North Carolina. EPA-452/R-07-0031-9

depending on the level of the secondary standard. Costs and benefits of attaining the different levels of the secondary were not estimated in thi s RIA; this analysis focused on the health benefits of the primary standard. An analy sis of a separate secondary will be included in the final RIA as appropriate. Hereafter , any reference to the ozone standard will be assumed to be the primary standard, unless otherwise noted.

1.5 References:
Henderson, R. 2006. October 24, 2006. Letter from CASAC Chairman Rogene Henderson to EPA Administrator Stephen Johnson, , EPA-CASAC-07-001.

U.S. EPA. 1970. Clean Air Act. 40CFR50.
U.S. EPA. 2006 .Air Quality Criteria for Ozone and Related P hotochemical Oxidants (Final). U.S. Environmental Protection Agency, Washington , DC, EPA/600/R-05/004aFcF,
U.S EPA. 2007. Review of the National Ambient Air Quality Standards for Ozone: Policy Assessment of Scientific and Technical Informat ion. OAQPS Staff Paper. North Carolina. EPA-452/R-07-0031-10

Pertinent Statutory and Executive Orders

1. Executive Order 12866: Regulatory Planning and Review http://www.archives.gov/federal-register/executive-o rders/pdf/12866.pdf
2. Paperwork Reduction Act, 44 U.S.C. 3501 et seq http://www.archives.gov/federal-register/laws/paper work-reduction/
3. Regulatory Flexibility Act, 5 U.S.C. 601 et seq

. http://www.archives.gov/federal-register/laws/regul atory-flexibility/

4. Unfunded Mandates Reform Act, Public Law 104-4http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=104_cong_public_laws&docid=f:publ4.104.pdf

5. Executive Order 13132: Federalism, Executive Order 13132http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=1999_register&docid=fr10au99-133.pdf

6. Executive Order 13175: Consultation and Coordination with Indian Tribal Governmentshttp://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=2000_register&docid=fr09no00-167.pdf

7. Executive Order 13045: Protection of Children from Enviro nmental Health & Safety Riskshttp://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=1997_register&docid=fr23ap97-130.pdf

8. Executive Order 13211: Actions that Significantly Affect Energy Supply, Distribution or Usehttp://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=2001_register&docid=fr22my01- 133.pdf
9. National Technology Transfer Advancement Act, Public Law No. 104-113, §12(d)http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=104_cong_public_laws&docid=f:publ113.104.pdf

10. Executive Order 12898: Federal Actions to Address Environmental Justi ce in Minority Populations and Low-Income Populations http://www.archives.gov/federal-register/executive-o rders/pdf/12898.pdf**2-1**

Chapter 2: Characterizing Ozone and Modeling Tools U sed in This Analysis

Synopsis
**This chapter describes the chemical and physical propertie s of ozone, general ozone air**  **quality patterns, key health and environmental impacts a ssociated with exposure to**  **ozone, and key sources of ozone precursor emissions. In order to evaluate the health and**  **environmental impacts of trying to reach a tighter ozone standard in the year 2020, it was**  **necessary to use models to predict concentrations in the fut ure. The tools and**  **methodology used for the air quality modeling are describ ed in this chapter. Subsequent**  **chapters of this RIA rely heavily on the results of this modeling.** 

2.1 Ozone Chemistry
**Ozone occurs both naturally in the stratosphere to provide a protective layer high above**  **the earth, and at ground-level (troposphere) as the prime ingredient of smog.**  **Tropospheric ozone, which is regulated by the NAAQS, is form ed by both naturally**  **occurring and anthropogenic sources. Ozone is not emitted di rectly into the air, but is**  **created when its two primary components, volatile organic c ompounds (VOC) and oxides**  **of nitrogen (NOx), combine in the presence of sunlig ht. 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 temper ature, solar radiation, wind**  **speed and other meteorological factors. Ultraviolet radia tion from the sun plays a key**  **role in initiating the processes leading to ozone format ion. However, there is little**  **empirical evidence directly linking day-to-day variatio ns in observed surface ultraviolet**  **radiation levels with variations in tropospheric oz one levels.** 
**The rate of ozone production can be limited by eith er VOCs or NO**  **x . In general, ozone**  **formation using these two precursors is reliant upon the rela tive sources of hydroxide**  **(OH) and NOx. When the rate of OH production is greater than the rate of production of NO**  **x , indicating that NO**  **x is in short supply, the rate of ozone production i**  **s NO x**  **-limited. In this situation, ozone concentrations are most eff**  **ectively reduced by lowering current and future NO**  **x emissions, rather than lowering emissions of VOCs. When the rate of OH**  **production is less than the rate of production of N O**  **x , ozone production is VOC-limited.**  **Here, ozone is most effectively reduced by lowering VOCs. Between the NOx- and**  **VOC- limited extremes there is a transitional region where ozone is nearly equally**  **sensitive to each species. However ozone is relati vely insensitive to marginal changes in**  **both NOx and VOC in this situation. In urban areas with a high population**  **concentration, ozone is often VOC-limited. Ozone is genera lly NOx-limited in rural areas**  **and downwind suburban areas.**
**Due to the complex photochemistry of ozone production, NOx emissions lead to both the**  **formation and destruction of ozone, depending on the loc al quantities of NOx, VOC, and**  **ozone catalysts such as the OH and HO2 radicals. In areas dominated by fresh emissions 2-2** 

**of NOx, ozone catalysts are removed via the product ion of nitric acid, which slows the**  **ozone formation rate. Because NOx is generally depleted more rapidly than VOC, this**  **effect is usually short-lived and the emitted NOx c an lead to ozone formation later and**  **further downwind. The terms “NOx disbenefits” or “ ozone disbenefits” refer to the**  **ozone increases that can result from NOx emission reductio ns in these localized areas.**  **1**

2.1.1 Temporal Scale **Ground-level ozone forms readily in the atmosphere, u sually 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 in** **terannual differences in ozone concentrations. Differences in climatic regime, amount a** **nd mixture of emissions, and the extent of transport contribute to variations in ozone**  **from city to city.**
2.1.2 Geographic Scale and Transport **In many urban areas, ozone nonattainment is not caused by emissions from the local area**  **alone. Due to atmospheric transport, contributions of precursors from the surrounding**  **region can also be important. Thus, in designing c ontrol strategies to reduce ozone**  **concentrations in a local area, it is often necessa ry to account for regional transport**  **within the U.S.**
**In some areas, such as California, global transport of oz one from beyond North America**  **can contribute to nonattainment areas. In a very li mited number of areas, including**  **areas such as Buffalo, Detroit and El Paso, which are loca ted near borders, emissions**  **from Canada or Mexico may contribute to nonattainme nt. In these areas, our illustrative**  **implementation strategies may have included more controls on domestic sources than**  **would be required if cross-border transport did not occ ur. However, we have not**  **conducted formal analysis, and as such cannot determin e the contribution of non-U.S.**  **sources to ozone design values. The transport of oz one is determined by meteorological**  **and chemical processes which typically extend over spatial scales of several hundred**  **kilometers. Additionally, convection is capable of transporting ozone and its precursors**  **vertically through the troposphere, with resulting m ixing of stratospheric ozone for**  **periods of a month or more with tropospheric ozone.**
**The Technical Support Document (TSD) for the Clean Air Inter state Rule (CAIR)**  **suggests that ozone transport constitutes a sizable portion of projected nonattainment in most eastern areas based on a 2010 analysis. A list**  **ing of Eastern states and the extent of transported ozone they receive in the CAIR analysis**  **is located in the CAIR TSD. 2**  **We used this information to help guide the design of e**  **missions 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/finalte**  **ch02.pdf , table VI-2** **2-3**

2.1.3 Effects of Ozone **Exposure to ground-level ozone is associated with a w ide array of human health effects.**  **Short-term exposure to ozone can cause acute respiratory pr oblems; aggravate asthma;**  **cause significant temporary decreases in lung capacity; c ause inflammation of lung**  **tissue; lead to hospital admissions and emergency room visi ts; and impair the body's**  **immune system defenses, making people more susceptib le to respiratory illnesses,**  **including bronchitis and pneumonia. In addition, recent studies also provide evidence of**  **additional health impacts associated with exposure to ozone, including premature**  **mortality and possibly cardiac-related effects. (For a complete discussion of these effects, see Chapter 3 of the Staff Paper.)**  **Ground-level ozone is also associated with numerous enviro nmental impacts. For**  **example, ozone interferes with the ability of plants to produce and store food, so that**  **growth, reproduction and overall plant health are compromi sed. By weakening sensitive**  **vegetation, ozone makes plants more susceptible to d isease, pests, and environmental**  **stresses. Ground-level ozone has been shown to reduce agric ultural yields for many**  **economically important crops (e.g., soybeans, kidne y beans, wheat, cotton). The effects**  **of ground-level ozone on long-lived species such as tree s are believed to add up over**  **many years so that whole forests or ecosystems can be affected. For example, ozone can adversely impact ecological functions such as water move**  **ment, mineral nutrient cycling, and habitats for various animal and plant species.**  **Furthermore, one of the key components of ozone, nitrogen oxides, contributes to fis**  **h kills and algae blooms in sensitive waterways, such as the Chesapeake Bay. (For a compl**  **ete discussion of these effects, see Chapter 7 of the Staff Paper.)** 

2.2 Sources of Ozone **The anthropogenic precursors of ozone originate from a wide variety of stationary and**  **mobile sources. In urban areas, both biogenic (natural) a nd 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 m ain sources of VOCs, with roughly**  **equal contributions to total emissions. Emissions of VOC s from highway vehicles**  **account for roughly two-thirds of the transportation-rel ated emissions**  **3 By 2020, EPA**  **emission projections show that VOC emissions from highwa y vehicles decrease**  **significantly. Solvent use VOC decreases as well, but by 20 20 solvent use VOC is**  **projected to be a slightly more significant VOC contribu tor than mobile VOC. On the**  **regional and global scales, emissions of VOCs from vegeta tion are much larger than**  **those from anthropogenic sources.**
**Anthropogenic NOx emissions are associated with combu stion processes. The two**  **largest sources of NOx are electric power generation plants (EGUs) and motor vehicles.**

**3 U.S EPA. 2007. Review of the National Ambient Air**  **Quality Standards for Ozone: Policy Assessment of Scientific and Technical Infor** **mation. OAQPS Staff Paper. North Carolina. EPA-452/R-07-003** **2-4**

**EGU NOx is approximately 40% less than onroad mobile NOx in 2001. Both decrease**  **between 2001 and 2020, with onroad mobile NOx decre asing more, so that their**  **emissions are similar in 2020. It is not possible to make an overall statement about their**  **relative impacts on ozone in all local areas becaus e EGUs are more sparse than mobile**  **sources, particularly in the west and south (See ch apter 3 for a discussion of emission**  **reductions projected in 2020 for the 8-hr ozone current st andard baseline and the more**  **stringent alternative control scenario). Natural NOx sour ces include stratospheric**  **intrusions, lightning, soils, and wildfires. Lightning, fertilized soils, and w ildfires are the**  **major natural sources of NOx in the United States. Uncertain ties in natural NOx**  **inventories are much larger than for anthropogenic N Ox emissions.** 
**A complete list of emissions source categories, for bo th NOx and VOCs, is compiled in**  **the final ozone Staff Paper (EPA, 2007, pp. 2-3 to 2-6).**

2.3 Modeling Ozone Levels in the Future
**In order to evaluate the predicted air quality in 2020 , it is necessary to use modeling to**  **derive estimated air quality concentrations. The m odeling analysis uses an emissions**  **inventory and historical meteorological conditions to simulate pollutant concentrations.**  **The predictions from the modeling are used to (a) pro ject future ozone design values (a**  **representation of the resultant air quality concentration in 2020 equal to 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 o**  **zone 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 to capture the PM**  **2.5 was performed for a one year time period. Modeling**  **to calculate ozone-related benefits and the projection of ozone design values**  **was performed for the period June through August. All controls in the illustrative 0.**  **070 scenario were applied similarly to all months. There were no controls applied specific**  **ally for PM2.5 co-benefits because the controls developed to reduce summer ozone were appl**  **ied to all months (see Chapter 3).**

2.3.1 Emissions Inventory
**The 2020 inventory from the Final PM NAAQS emissions platform was used as the**  **starting point for the baseline and all subsequent analys es**  **5 . This included emissions from**  **Canada as of 2000 6**  **, and Mexico as of 1999. 7**  **As first discussed in the PM NAAQS RIA, an examination of the historical data suggests our pre**  **vious methods have over-predicted future-year emissions for stationary non-EGU point**  **and non-point sources, especially in the longer-forecast periods required for the NAAQS and other programs. To address this**  **issue, we developed an ‘interim’ emission projectio n approach that assumes no growth to** 

**4 See CMAQ references listed at end of this chapter** 

**5 Final PM NAAQS Inventory is in the public docket**  **EPA-HQ-OAR-2006-0834-0048.3 6**  **http://www.epa.gov/ttn/chief/net/canada.html#data 7**  **http://www.epa.gov/ttn/chief/net/mexico.html 2-5** 

**emissions for many stationary non-EGU sources in estimati ng future-year emissions. In**  **the future, we intend to pursue improved methods an d models that provide more**  **consistency with the historical record and reasonabl e assumptions regarding future**  **conditions. More information is provided in Appendi x D of the PM NAAQS RIA on the**  **interim approach and a sensitivity analysis of the implic ations of this method relative to**  **our previous forecasting methods. An updated 2020 in ventory based on the EPA's 2002**  **modeling platform is currently being developed and wil l be used for the final ozone**  **NAAQS RIA In addition, all national and local controls use d in the illustrative control**  **scenario for the revised PM NAAQS RIA were included in this 2020 baseline. These**  **controls were included to prevent double counting of costs a nd benefits, especially in the**  **case of NOx controls which are precursors to both PM 2.5**  **and ozone.**
2.3.2 CMAQ Model
**A national scale air quality modeling analysis was performed to estimate future year**  **attainment/nonattainment of the current and alternative ozone standards. In addition, the model-based projections of ozone and PM**  **2.5 were used as inputs to the calculation of**  **expected incremental benefits from the alternative oz one standards considered in this**  **assessment. The 2001-based CMAQ modeling platform (version 4.5) was used as the**  **basis for air quality modeling of future baseline em issions and control scenarios designed**  **to bring areas into attainment with specific standa rds. This modeling platform was used**  **in the 2006 PM NAAQS RIA. In addition to the CMAQ model, the modeling platform**  **includes the emissions, meteorology, and initial an d boundary condition data which are**  **inputs to this model. The model produces spatial fiel ds of gridded air quality**  **concentrations on an hourly basis for the entire mod eling domain. The concentrations**  **that are produced can be averaged to produce a number of air quality metrics, including**  **the 8-hr ozone design values, and can be used as inputs for the analysis of costs and**  **benefits.**
**The key inputs to the CMAQ model include emissions from anthropogenic and biogenic sources, meteorological data, and initial and boundary** **conditions. The CMAQ meteorological input files were derived from simulations o**  **f the Pennsylvania State University / National Center for Atmospheric Researc**  **h Mesoscale Model (Grell, Dudhia, and Stauffer, 1994). This model, commonly referred to as**  **MM5, is a limited-area, nonhydrostatic, terrain-following system that solve**  **s for the full set of physical and thermodynamic equations which govern atmospheric moti**  **ons. The lateral boundary and initial species concentrations were obtained from a t** **hree-dimensional global atmospheric chemistry model, the GEOS-CHEM model (Yantosca, 200**  **4). The global GEOS-CHEM model simulates atmospheric chemical and physical proc**  **esses driven by assimilated meteorological observations from the NASA’s Goddard Earth** **Observing System (GEOS).** 
**EPA performed an extensive evaluation of the 2001-based CMAQ modeling platform as**  **part of the analyses for CAIR and the PM NAAQS RIA. T hese evaluations have been**  **updated as part of the Locomotive/Marine Proposed Rule to foc us on model performance**  **for ozone from the 12 km CMAQ base year simulations in the E ast. Details of the model**  **performance methodology are described in the Locomo tive/Marine Rule Air Quality** **2-6** 36km Domain Boundary

12km East Domain Boundary 12km West Domain Boundary **Modeling Technical Support Document. For the months of Jun e, July, and August 2001,**  **which were used as the basis for the Ozone NAAQS mo deling, the 8-hour daily**  **maximum modeled concentrations underestimate the cor responding observed values by**  **10 to 15 percent in the East and West. As in the e valuation for previous model**  **applications, the “acceptability” of model performance for the ozone RIA modeling was**  **judged by comparing the results to those found in re cent regional ozone model**  **applications for other EPA and non-EPA studies. Overall, the performance for the CMAQ application is generally within the range of these**  **other applications.**
**Figure 2-1 shows the modeling domains that were used as a part of this analysis. The**  **geographic specifications for these domains are provide d in Table 2-1. All three**  **modeling domains contain 14 vertical layers with a top a t about 16,200 meters, or 100**  **mb. Two domains with 12 km horizontal resolution were used for episodic ozone**  **modeling. These domains are labeled as the Eastern and Western 12 km domains in**  **Figure 2-1. Also shown in this figure is the 36 km domain which was used for modeling**  **PM 2.5**  **concentrations. For this analysis, predictions fro m the Eastern domain were used**  **to provide data for all areas east of 100 degrees longitude. Model predictions from the**  **Western domain we used for all areas west of this longitude.**
Figure 2.1. Map of the CMAQ Modeling Domains Used for Ozone NAAQS RIA

**The selection of 12 km grid resolution for ozone modeling and 36 km resolution for**  **PM 2.5**  **modeling is consistent with recommendations on grid res olution for regional**  **analyses in EPA’s air quality modeling guidance for ozon e and PM. Specifically, the**  **guidance recommends modeling at 12 km or finer resolution, but not greater than 36 km**  **resolution for regional scale modeling. The recommendatio ns in the guidance are based** **2-7**

**largely on analyses of model performance and model response at various grid resolutions**  **which indicate that results are generally similar at thes e grid resolutions for secondarily**  **formed pollutants like ozone, nitrate, and sulfate which are components of PM**  **2.5 .**  **Another factor weighed in the selection of grid resolution is the computation requirement for modeling at 12 km versus 36 km. Specifically,**  **national modeling at 12 km resolution requires roughly 10 times more computer time compared**  **to modeling at 36 km. We were able to minimize the computer burden of modeling at 12 km**  **for ozone because we limited the duration of the ozone model simulations t**  **o just the summer months when ozone concentrations are typically at their peak. For PM**  **2.5 , however, it is useful to model**  **a full year in order to determine annual average PM 2.5**  **concentrations in a manner consistent with the annual PM NAAQS. In view of the computer r**  **equirements for modeling at 12 km, it would not have been possible**  **to model a full year at 12 km resolution for the Eastern and Western domains as p**  **art of the analysis for this proposal RIA.** 
Table 2.1. Geographic Specifications of Modeling Domains. **36 km Domain (148 x 112 Grid Cells)**  **12 km Eastern Domain (279 x 240 Grid Cells)**  **12 km Western Domain (213 x 192 Grid Cells)**  **Lon lat lon lat lon lat SW -121.77 18.17 SW -106.79 24.99 SW -121.65 28.29**  **NE -58.54 52.41 NE -65.32 47.63 NE -94.94 51.91**

2.4 References **Amar, P., R. Bornstein, H. Feldman, H. Jeffries, D. Steyn, R. Yamartino, and Y. Zhang.**  **2004. Final Report Summary: December 2003 Peer Revi ew of the CMAQ Model. 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 modeli**  **ng: 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.** 
**U.S. Environmental Protection Agency (EPA). 1999. “Scienc e Algorithms of EPA**  **Models-3 Community Multiscale Air Quality.” (CMAQ M odeling System D.W. Byun**  **and J.K.S. Ching, Eds. EPA/600/R-99/030, Office of Research and Development).** 
**U.S. Environmental Protection Agency (EPA). March 2005. CMAQ Model Evaluation Report, Office of Air Quality Planning and Standard, Resear**  **ch Triangle Park, NC. (Docket No. OAR-2005-0053-2149).** **2-8**

**U.S. Environmental Protection Agency (EPA). 2006 .Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. Enviro**  **nmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF,** 
**U.S Environmental Protection Agency (EPA). 2007. Review of the National Ambient Air**  **Quality Standards for Ozone: Policy Assessment of S cientific and Technical Information.**  **OAQPS Staff Paper. North Carolina. EPA-452/R-07-003**

**U.S. Environmental Protection Agency (EPA). 2007. “Guid ance on the Use of Models**  **and Other Analyses for Demonstrating Attainment of Ai r Quality Goals for Ozone,**  **PM2.5, and Regional Haze.” EPA-454/B-07-002, http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf** 
**U.S. Environmental Protection agency (EPA). “Technical Su pport Document for the**  **Proposed Locomotive/Marine Rule: Ozone Modeling.” EPA-454 /R-07-004.** 
**Yantosca, B. 2004. GEOS-CHEMv7-01-02 User’s Guide, Atmosp heric Chemistry**  **Modeling Group, Harvard University, Cambridge, MA, October 15, 2004.** **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 th e control strategies that areas across the**  **country might employ to attain an alternative more stringent primary standard of 0.070**  **ppm. Specifically, EPA has modeled the impact that additional emissions controls across**  **numerous sectors would have on predicted ambient ozo ne concentrations, incremental to**  **meeting the current standard (baseline). Thus, the modeled analysis for a revised**  **standard focuses specifically on incremental improve ments beyond the current standard,**  **and uses control options that might be available to states for application by 2020. The**  **hypothetical scenario presented in this RIA is one illustrative option for achieving**  **emissions reductions to move towards a national attain ment of a tighter standard. It is not**  **a recommendation for how a tighter ozone standard should be implemented, and states**  **will make all final decisions regarding implementation st rategies once a final NAAQS**  **has been set.**
**In order to model a hypothetical control strategy to achieve national attainment of 0.070**  **ppm incremental to attainment of the current standa rd, EPA approached the analysis in**  **stages. First, EPA identified controls to be included in the baseline (current state and federal programs plus controls to attain the curren** **t ozone and PM standards). Then, EPA applied additional known controls within geographic**  **areas designed to bring areas predicted to exceed 0.070 ppm in 2020 into attainme**  **nt. This chapter presents the hypothetical control strategy, the geographic areas**  **where controls were applied, and the results of the modeling which predicted ozone concentrati**  **ons in 2020 after application of the strategy. The strategy to attain a 0.070 ppm level wa**  **s the only strategy modeled by EPA. EPA did not expect the modeled control strate**  **gy to result in attainment at 0.070 ppm everywhere, so the control will result in only pa** **rtial attainment. Chapter 4 will explain how EPA used the results of the modeled control stra** **tegy for 0.070 ppm to estimate total tons of emissions reductions needed to achie**  **ve ozone concentrations for the bounds of the range of the proposed more stringent st**  **andard (0.075 and 0.070 ppm, and the more stringent option analyzed of 0.065 ppm). Chapters**  **5 and 6 present the estimated costs and benefits of the modeled costs and benefits f**  **or partial attainment.**

**Because EPA’s baseline indicated that some areas were no t likely to be in attainment**  **with the current standard by 2020 (0.08 ppm, effecti vely 0.084 ppm based on current**  **rounding conventions) – (Fig 3.4) EPA expected that known controls would not be enough to bring those areas, and likely others, int**  **o attainment with 0.070 ppm in 2020. Modeling results showed that to be the case (see Fi**  **g 3.13).**
**Because it was impossible to meet either the current or a ny tighter ozone standard**  **nationwide using only known controls, EPA conducted a se cond step in the analysis, and**  **estimated the number of further tons of emission reductions n eeded to attain 0.070 ppm**  **(presented in Chapter 4). It is uncertain what con trols States would put in place to attain** **3-2**

**a tighter standard, since additional control measures are not currently recognized as being**  **commercially available. However, existing emissio ns inventories for the areas that were**  **predicted to be in non-attainment after application of all known controls, do indicate that**  **substantial amounts of ozone precursor emissions (i.e. tons of NOx or VOC) are available**  **for control, pending future technology. Chapter 4 describes the methodology EPA used**  **to estimate the amount of tons available for control to rea ch attainment, and Chapters 5**  **and 6 present the extrapolation-based costs and benefits of achieving the reductions in**  **ozone necessary to fully attain the standards, except for a few areas in California, which**  **will be more fully explained in Chapter 4.**

3.1 Establishing the Baseline
**The regulatory impact analysis (RIA) is intended to evaluate the costs and benefits of**  **reaching attainment with potential alternative ozone sta ndards. In order to develop and**  **evaluate a control strategy for attaining a more stringent (0 .070 ppm) primary standard, it**  **is important to first estimate ozone levels in 2020 given the current ozone standard and**  **trends (more information is provided in chapter 1). T his scenario is known as the**  **baseline. Establishing this baseline allows us to estimate the incremental costs and**  **benefits of attaining any alternative standard.**

**This focus on the assessment of the incremental costs and benefits of attaining any**  **alternative standard is an important difference from the focus of the risk assessment used**  **in developing the standard. For purposes of the Staff Paper-ri sk assessment, risks are**  **estimated associated with just meeting recent air quali ty and upon just meeting the**  **current and alternative standards as well as increm ental reductions in risks in going from**  **the current standard to more stringent alternative standards. When considering risk**  **estimates remaining upon attaining a given standard, EPA is only interested in the risks in**  **excess of policy relevant background (PRB). PRB is defined in the ozone Criteria**  **Document and Staff Paper as including (1) O3 in the U.S. from natural sources of emissions in the U.S., Canada, and Mexico, and (2) O3 in th**  **e U.S. from the transport of O3 or the transport of emissions from both natural and man-m**  **ade sources, from outside of the U.S. and its neighboring countries (Staff Paper, p.2-54). Emissions of ozone**  **precursors from natural sources (e.g. isoprenes emi tted from trees) and from sources**  **outside of the U.S. are uncertain, as are the speci fic impacts those emissions will have on**  **ozone concentrations in areas exceeding alternative standards. Our models use available**  **information on these emissions in generating future projections of baseline ozone concentrations, and our modeled reductions in U.S.**  **emissions of NOx and VOC are based on these baseline levels that include the contribution of natural**  **and non-U.S. emissions. To the extent that these emissions contr**  **ibute a greater (lesser) proportion of ozone on high ozone days, more (less) reductions in emissions**  **from U.S. sources might be required to reduce ozone levels below the analyzed altern**  **ative standards.**
**In contrast, the RIA only examines the incremental reduction, not the remaining risk,**  **which results from changes in U.S. anthropogenic em issions. The air quality modeling**  **used to establish the baseline for the RIA explicitl y includes contributions from natural** **3-3**

**and anthropogenic emissions in Canada, Mexico, and other countries abroad, as well as**  **the contributions to ozone levels from natural sources in the U.S. Since the RIA does not**  **attempt to estimate the risk remaining upon meeting a given standard, and the alternative**  **standards are clearly above the Staff Paper estimates of PRB, we do not consider PRB a**  **component of the RIA costs and benefits estimates.**
**In developing the baseline it was important to recognize that there are several areas that**  **are not required to meet the current standard by 20 20. The Clean Air Act allows areas**  **with more significant air quality problems to take a dditional time to reach the current**  **standard. Two areas in Southern California, are not planning to meet the curre nt standard**  **by 2020, so the estimated emission reductions for t hese areas are based on reaching an**  **estimated progress point in 2020 (their “glidepath” t argets). We provide an estimate of**  **the additional amount of tons these areas would need to reduc e to meet the standard and**  **the additional costs and benefits of reducing those tons in those few areas.** 

**The baseline includes controls which EPA estimates need t o be included to attain the**  **current standard (0.08 ppm, effectively 0.084 ppm based on current rounding conventions) for 2020. Two steps were used to develop the baseline. First, the**  **reductions expected in national ozone concentrations from national rules in effect or proposed today were considered. Because these alon**  **e were not predicted to bring all areas into attainment with the tighter standard, EP**  **A used a hypothetical control strategy to apply additional known controls. Additional cont** **rol measures were used in four sectors to establish the baseline**  **1 : Non-Electricity Generating Unit Point Sources (No**  **n EGUs), Non-Point Area Sources (Area), Onroad Mobile Sources and Nonroad Mobile**  **Sources. A fifth sector was used in the subsequent control strategy for a tighter**  **alternative standard: Electricity Generating Unit Poin t Sources (EGUs). Each of these**  **sectors is defined below for clarity.**

x **NonEGU point sources are stationary sources that emit at least one criteria pollutant with emissions of 100 tons per year or higher. Non**  **EGU point sources are found across a wide variety of industries, such a**  **s chemical manufacturing, cement manufacturing, petroleum refineries, and iron and s**  **teel mills.**

x **Non-Point Area Sources (Area) are stationary source s that are too numerous or**  **whose emissions are too small to be individually inc luded in a stationary source**  **emissions inventory. Area sources are the activities wh ere aggregated source**  **emissions information is maintained for the entire so urce category instead of each**  **point source, and are reported at the county level.**

x **Onroad Mobile Sources are mobile sources that travel on roadways. These**  **sources include automobiles, buses, trucks, and motorcycles traveling on roads and highways.** 

**1 In establishing the baseline, EPA selected a set of cost-ef**  **fective controls to simulate attainment of the current ozone and PM**  **2.5 standards. These control sets are hypothetical**  **as states will ultimately determine controls as par t of the SIP process.** **3-4**

x **Nonroad Mobile Sources are any portable engine that travels by other means than roadways. These sources include railroad locomotives; marine vessels; aircraft;**  **off-road motorcycles; snowmobiles; pleasure craft; and farm, construction,**  **industrial and lawn/garden equipment.**

x **Electricity Generating Unit Point Sources (EGUs) are s tationary sources**  **producing electricity, such as fossil-fuel-fired boilers a nd combustion turbines.** 

3.1.1 National Rules
**To reduce ambient ozone concentrations, it was necessary to control emissions of ozone**  **precursors, NOx and VOC. Establishing the baseline required identifying the national**  **rules which were expected to contribute to reduction s in NOx and VOCs between now**  **and 2020. Some of these include the Clean Air Interstate R ule (CAIR), Clean Air**  **Mercury Rule (CAMR), and the Clean Air Visibility Rule (CAVR); and the 2007 proposed Locomotive/Marine rule. A complete listing o**  **f these rules is provided in table 3.1. In addition, EPA included the control set developed fo**  **r the hypothetical national attainment strategy presented in the PM NAAQS RIA in t** **he baseline for this ozone analysis.** 
**At the time that EPA established the regulatory base line -- to capture how existing rules**  **affect the emissions inventory over time even in the abs ence of this new NAAQS**  **standard -- EPA focused on information that was rea dily available in the emission**  **inventories and other data sources. Typically, a RIA analysis baseline includ es only**  **reductions from final rules and not reductions from regula tory proposals or other actions**  **being contemplated. However, for this analysis, EPA did not include the recently**  **promulgated Renewable Fuel Standard (RFS), due to a lac k of readily available**  **quantitative information. In addition, EPA did incl ude reductions from some upcoming**  **rules in an attempt to better characterize reduction s that we anticipate to occur in the**  **future (e.g. Ocean Going Vessel Rule). For the ana lysis to support the Final Rule, EPA**  **will be using an updated emission inventory and improved mo dels and sets of control**  **information. The starting point for the analysis will inc lude only and all promulgated**  **rules, including the Renewable Fuel Standard rule. An y potential reductions resulting**  **from proposed or upcoming rules will be discussed separate ly.** 
**The RFS RIA provides an analysis of the energy, emissions, air quality, and economic impacts of expanding the use of renewable fuels in comparis**  **on to a reference case of 4 billion gallons of renewable fuel use that represent**  **s 2004 conditions projected out to 2012. Depending on the anticipated volume of renewa**  **ble fuel usage in 2012, EPA estimates that this transition to renewable fuels wi**  **ll reduce petroleum consumption between 2.0 and 3.9 billion gallons or roughly 0.8 to 1.6 percent of the**  **petroleum that would otherwise be used by the transportation sector** **2**

**.**

**2 http;//www.epa.gov/otaq/renewablefuels** **3-5**

**With regard to emissions impacts, carbon monoxide emissio ns from gasoline-powered**  **vehicles and equipment will be reduced between 0.9 and 2.5 percent. Emissions of**  **benzene (a mobile source air toxic) will be reduced between 1.8 and 4.0 percent. Further,**  **the use of renewable fuel will reduce carbon dioxid e equivalent greenhouse gas emissions**  **between 8.0 and 13.1 million metric tons, about 0.4 t o 0.6 percent of the anticipated**  **greenhouse gas emissions from the transportation se ctor in the United States in 2012.** 
**At the same time, other vehicle emissions may increase as a result of greater renewable**  **fuel use. Nationwide, EPA estimates an increase in total emissions of volatile organic**  **compounds and nitrogen oxides (VOC + NOx) between 41,000 and 83,000 tons. However, the effects will vary significantly by region.**  **Areas that already are using ethanol will experience little or no change in emis**  **sions or air quality. In some contexts and situations, however, the use of renewable fuels may impa**  **ct compliance with a reduced ozone NAAQS standard.** 
**In addition to changes in NOx and VOC emissions resulting f rom increased use of**  **ethanol in gasoline, fugitive ethanol emissions may also increase peroxacetyl nitrate**  **(PAN) concentrations. Fugitive emissions of ethanol in a photochemical smog polluted**  **environment will generate acetaldehyde, a precursor to PAN. PAN, in turn, can lead to**  **increase ozone levels. As part of the analysis to sup port the final rule, EPA will examine**  **whether this increase in PAN will affect baseline ozone co ncentrations in some areas and**  **how this effect can be quantified and incorporated into th e baseline.** 
**For the final analysis, EPA will be using an updated emission inventory and improved**  **models and sets of control information. The starti ng point for the analysis will include all**  **promulgated rules, including the increases in regio nal VOC and NOx emissions from**  **increased combustion of ethanol.**

Table 3.1 National Rules and Control Measures, by Sector, Con tributing to the Baseline 3,4 Sources of Controls-National Sector **NOx VOC** **Non-EGUs PM 15/35* (west only) (none used)** **Area PM 15/35* (west only) (none used)** **Onroad Mobile**  **-Onroad Diesel Particulate**

**Filters and Retirement - Commuter Reduction**  **Strategies -Idling Elimination**  **-Intermodal Transfer from Trucks to Rail**  **-Onroad Diesel Particulate Filters and Retirement**  **- Commuter Reduction Strategies**

**3 References for these rules are provided at the end o**  **f this chapter. Controls are explained in Appendix 3.**  **4 0.08 ppm, effectively 0.084 ppm based on current rounding**  **conventions 3-6** 

**Nonroad Mobile**  **-Diesel Marine & Locomotives**

**Rule -Ocean-Going Vessels Rule**  **-Small Spark-Ignition Engine Rule**  **-Nonroad Diesel Particulate Filters & Engine Rebuilds**  **-Small Spark-Ignition Engine Rule -Nonroad Diesel Particulate Filters**  **& Engine Rebuilds**

**EGU -CAIR/CAMR/ CAVR**

**-PM 15/35* (West only)** **(none used)**

***** **NOx controls are included as part of the hypothetica l control scenario modeled**  **in the 2006 PM NAAQS RIA. These controls included low NOx burners and SNCR for industrial boilers. Further examples can be found in Table 3-5 (page 3-18) of the 2006**  **PM NAAQS RIA. http://www.epa.gov/ttn/ecas/ria.html**

3.1.2 Additional Controls

**Additional known controls were also included as needed in the baseline, to simulate**  **attainment with current ozone NAAQS. The applicabl e controls and their respective**  **sectors are listed in table 3.2 and described below. Deta ils regarding the individual**  **controls are provided in appendix 3. Due to the ex tensive reductions from EGUs already**  **implemented in CAIR/CAMR/CAVR, no additional EGU contro ls were included in the**  **baseline. The East was evaluated separately from th e West, due to the nature of the**  **controls available in each area and the specific fea tures of the areas needing reductions in**  **ozone, as explained in more detail below.**
**In the East, controls included in the baseline for Non-EGU and area sources came from a**  **variety of geographic areas and scales. Almost all available controls in Chicago,**  **Houston, and the Northeast Corridor were included in the baseline because these areas**  **contain counties that were projected to be nonattainment of the current ozone NAAQS in 2020 (based on air quality modeling performed as pa** **rt of the PM NAAQS RIA).**

**NOx controls from Non-EGU/Area sources were included in two ways in the East. First,**  **controls were included in 22 counties with monitors that were projected to violate the**  **current standard in 2020. Second, controls were included in all surrounding counties**  **within the same state that were completely contained within 200 km of the county**  **containing the projected violating monitor. These counti es were chosen based upon an**  **examination of previous ozone air quality modeling and emissions inventories as well as**  **existing EPA guidance. VOC controls were applied ( for area sources only) in 26**  **counties where VOC emissions were high (>5,000 tpy), and scr eening analysis indicated**  **that mean ozone concentrations were predicted to be markedly reduced by local VOC**  **controls (** **0.5 ppb) by local VOC controls of 25%. Two additi onal counties that did not**  **meet these criteria were also included. 5**  **In the West, Non-EGU and Area Controls were**

**5 Porter County, IN, was included, despite being below the**  **emissions threshold, due to its close proximity to Chicago. Harris County, TX, was in**  **cluded because of local 3-7** 

**included in the baseline only for California, where they were included state-wide. In**  **California, all controllable tons of NOx and VOC emission s were reduced using known**  **Non-EGU and Area Controls in the baseline. (See Fig 3. 1 and Fig 3.2)** 
Fig. 3.1 Counties Where Controls for Nitrogen Oxides (NOx) Were Included for

Non-EGU Point and Area Sources, for the Baseline

(Current Standard, 0.08 ppm)

Fig. 3.2 Counties Where Controls for Volatile Organ ic Chemicals (VOCs) Were

Applied to Non-EGU Point and Area Sources in Baseli ne

(Current Standard, 0.08 ppm)

**information about the benefits of VOC control, and concer ns about the screening tool**  **performance in the 36km region of which Houston is a part. 3-8** 

**In the Onroad Mobile sector, local controls were inc luded as necessary in the baseline for**  **both East and West. Counties projected to have a m onitor that exceeded the current**  **standard were surrounded by a 200km buffer zone, and controls were included in the counties within this buffer that were within the sam**  **e state as the exceeding monitor. Where some control measures overlapped for a given count** **y, controls with the lowest costs were included first. This is the only instanc**  **e in which controls were included in a certain order. For a complete list of the controls**  **and the order in which they were included, see Appendix 3. Both onroad and nonroad**  **diesel retrofits and idling elimination were included statewide in California wit** **h an assumed 75% market penetration, and elsewhere in the nation with an ass**  **umed 25% market penetration for all states with a county projected to be in nonattainme**  **nt with the current standard in 2020. EPA determined that 25% would have a significant impact, b**  **ut was reasonably easy to achieve and was applied for reduction areas outside o**  **f California. EPA further determined that for southern California a higher level of red**  **uction was required. 75% was the highest penetration rate that EPA felt could b**  **e reasonably accomplished. The remainder of mobile controls were included statewide in Ozone Transport Commission**  **(OTC) states (see section 3.2.2 for more information on OTC st ates), with the exceptions**  **of Vermont, Maine, New Hampshire, and Massachusetts, which were not projected to**  **have counties in nonattainment with the current standard in 2020. These additional**  **mobile controls were included statewide in California (See Fig. 3.3)** **3-9**

Fig. 3.3 Areas Where NOx and VOC Controls Were Included for Mo bile Onroad and Nonroad Sources in Addition to National Mobile Contro ls in Baseline

(Current Standard, 0.08 ppm)

***Onroad retrofits and elimination of long duration idl ing**  ****Onroad retrofits, elimination of long duration idli ng, nonroad retrofits,**  **Commuter Reduction Strategies and Reid Vapor Pressu re (RVP)** **3-10** Table 3.2 Controls by Sector Included in the Baseline Determ ination for 2020 Controls- East Controls- West Sector **NOx VOC NOx VOC Non-EGUs -LEC (Low Emission** 

**Combustion) -LNB (Low NOx Burner)**  **-LNB + FGR (Flu-Gas Sulfurization)**  **-LNB + SCR (Selective Catalytic Reduction)**  **-Mid-Kiln Firing -NSCR (Non-selective**  **Catalytic Reduction) -OXY-Firing**  **-SCR -SCR + Steam Injection**  **-SCR + Water Injection -SNCR (Selective Non**  **catalytic Reduction) -SNCR - Urea**  **-SNCR - Urea Based (none used) -LNB**  **-Mid-Kiln Firing -NSCR**  **-OXY-Firing -SCR**  **-SCR + Steam Injection -SNCR**  **-SNCR - Urea Based (none used)** 

**Area -RACT to 25 tpy (LNB)**

**-Water Heater + LNB Space Heaters**  **-CARB Long-Term Limits -Catalytic Oxidizer**  **-Equipment and Maintenance -Gas Collection (SCAQMD/**  **BAAQMD) -Incineration**  **-Incineration >100,000 lbs bread -Low Pressure/Vacuum Relief**  **Valve -OTC Mobile Equipment Repair**  **-RACT to 25 tpy (LNB) -Switch to Low Sulfur Fuel**  **-Water Heater + LNB Space Heaters**  **-Add-On Controls -Airtight Degreasing System**  **-Catalytic Oxidizer -Equipment and Maintenance**  **-FIP Rule (VOC content & TE) -Gas Collection**  **(SCAQMD/BAAQMD) -Incineration**  **-Incineration >100,000 lbs bread -Low Pressure/ Vacuum Relief** **3-11 and Refinishing Rule**  **-OTC Solvent Cleaning Rule -SCAQMD - Low VOC**  **-SCAQMD Limits -SCAQMD Rule 1168**  **-Switch to Emulsified Asphalts -Use of Low or No VOC Materials**  **Valve -OTC Solvent Cleaning Rule**  **-Reformulation - FIP Rule -SCAQMD Limits**  **-SCAQMD Rule 1168 -South Coast Phase III**  **-Switch to Emulsified Asphalts -Use of Low or No VOC Materials**  **Onroad Mobile**  **-Onroad Selective Catalytic Reduction (SCR) and Diesel**

**Particulate Filters (DPF) 6**  **-Reduce Gasoline Reid Vapor Pressure (RVP) -Onroad Selective Catalytic Reduction (SCR) and Diesel**  **Particulate Filters (DPF) 6**  **-Reduce Gasoline Reid Vapor Pressure (RVP) -Nonroad Selective Catalytic Reduction (SCR) and Diesel**  **Particulate Filters (DPF) 6**  **-Reduce Gasoline Reid Vapor Pressure (RVP) -Nonroad Selective Catalytic Reduction (SCR) and Diesel**  **Particulate Filters (DPF) 6**  **-Reduce Gasoline Reid Vapor Pressure (RVP) Nonroad**  **Mobile**

**-Aircraft NOx Engine**

**Standard -Ocean-Going Vessels –**  **reductions for vessels burning residual fuels**  **7 (none used for VOC only) -Aircraft NOx Engine**  **Standard (none used for VOC only)** 

**EGU (none used) (none used) (none used) (none used)**

**6 Onroad and Nonroad DPF were applied in the baseline**  **, and SCR retrofit technologies were chosen because of the need to reduce**  **NOx emissions.. 7**  **Reductions from Ocean-Going Vessels burning residu al fuels were applied in the Baseline analysis for**  **the east, but inadvertently omitted for the west. The omission was not identif**  **ied in time to include it in the initial Baseline a nalysis for the west.**  Table 3.2 Controls by Sector Included in the Baseline Determ ination for 2020 (continued)**3-12**

3.1.3 Ozone Levels for Baseline
**Establishing the baseline required design values (predicted concentrations) of ozone across the country. Because the intention of this**  **evaluation was to achieve attainment of the current ozone standard, controls were included to reduce ambient ozone**  **concentrations to 0.08 ppm (effectively 0.084 ppm based on current rounding conventions). A map of the country is presented in figure 3.4, which shows predicted**  **concentrations for the 491 counties with ozone monitors t hat were included in the**  **baseline. Modeling projections were developed for all appro priate counties according to**  **procedures outline in EPA modeling guidance 8** 

**. The baseline shows that 10 counties would not meet the current ozone standard in 2020,**  **even after inclusion of all known controls. After including known controls as described**  **above, the analysis predicted that the remaining 481 counties would attain the current standard by 2020. The baseline forms the foundatio**  **n for the cost-benefit analysis conducted in this RIA, where EPA compares more strin**  **gent primary ozone standard alternatives incrementally to national attainment o**  **f the current standard.**

**8 Available online at:**  **http://www.epa.gov/scram001/guidance/guide/final-03 -pm-rh-guidance.pdf** **3-13** Fig. 3.4 Baseline Annual Ozone Air Quality in 2020

**a Modeled emissions reflect the expected reductions f**  **rom federal programs including the Clean Air Interstate Rule, the Clean Air Mercury Rul**  **e, the Clean Air Visibility Rule, the Clean Air Nonroad Diesel Rule, the Light-Duty Vehic**  **le Tier 2 Rule, the Heavy Duty Diesel Rule, proposed rules for Locomotive and Marine Ve**  **ssels and for Small Spark Ignition Engines, and state and local level mobile and**  **stationary source controls identified for additional reductions in emissions for**  **the purpose of attaining the current PM 2.5 and Ozone standards.** 

**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 expressed**  **as 0.084 ppm when rounding conventions are applied.** 

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

**e Map shows results from a total of 491 counties with** **projected design values. Consistent with current modeling guidance, EPA did not project 2020 concentrations for counties**  **where 2001 base year concentrations were less than recom mended criterion. Such**  **projections may not represent expected future level s.** **3-14**

3.2 Developing the Control Strategy Analysis
**After developing the baseline, EPA developed a hypothetic al control strategy to illustrate**  **one possible national control strategy that could be adop ted to reach an alternative**  **primary standard of 0.070 ppm by 2020. The stricter standard alternative of 0.070 ppm**  **was chosen as being representative of the set of alterna tives being considered by EPA in**  **its notice of proposed rulemaking on the ozone NAAQS. Control s for five sectors were**  **used in developing the control analysis, as discusse d previously: non-EGU stationary,**  **Area, onroad mobile and nonroad mobile, along with EGU controls only in the East**  **(EGU controls for the West were included in the hypo thetical PM NAAQS 15/35**  **national control strategy, and were therefore already in t he ozone baseline). Reductions**  **in both NOx and VOC ozone precursors were needed in all four re maining sectors to**  **meet a tighter standard.**
**As depicted in the flow diagram in figure 1.1, the co ntrol strategy modeled in this RIA**  **first applied and exhausted nearly all known contro ls (see section 3.2.1 an explanation of**  **which controls were excluded from this analysis). After controls were identified, the expected emissions reductions were input to an air quality model that projected design**  **values for ozone in 2020. Following the control strategy, t here were some areas**  **projected not to attain 0.070 ppm in 2020 using all known cont rol measures. EPA was**  **then required to extrapolate the additional emission reduc tions required to reach**  **attainment. The methodology used to develop those estima tes and those calculations are**  **presented in Chapter 4.**
**As in the analysis for the baseline, parts of the h ypothetical national control strategy for**  **0.070 ppm focused on the Eastern (East) United States (U.S .) separately from the**  **Western U.S. (West). However, this RIA presents estimates o f the costs and benefits of**  **attaining alternative ozone standards on a national basis. Table 3.3 presents the specific control technologies that were applied within each sect**  **or for the 0.070 ppm control strategy.** **3-15** Controls- East Controls- West Sector **NOx VOC NOx VOC Non**  **EGUs -Biosolid Injection Technology** 

**-LEC (Low Emission Combustion)**  **-LNB -LNB + FGR**  **-LNB + SCR -LNB+SCR**  **-Mid-Kiln Firing -NGR**  **-NSCR -OXY-Firing**  **-SCR -SCR + Steam Injection**  **-SCR + Water Injection -SNCR**  **-SNCR - Urea -SNCR – Urea Based**  **-LDAR (Leak Detection and Repair) -Enhanced LDAR**  **-Flares Gas Recovery Monitoring Program**  **-Permanent Total Enclosure (PTE) -Wastewater Drain Control**  **-Biosolid Injection Technology -LNB**  **-LNB + FGR -LNB + SCR**  **-Mid-Kiln Firing -NSCR**  **-OXY-Firing -SCR**  **-SCR + Steam Injection -SCR + Water Injection**  **-SNCR -SNCR - Urea Based**  **(none used)**

**Area -RACT to 25 tpy (LNB)**

**-Water Heater + LNB Space Heaters**  **-CARB Long-Term Limits -Catalytic Oxidizer**  **-Equipment and Maintenance -Gas Collection (SCAQMD/BAAQMD)**  **-Incineration -Incineration >100,000 lbs bread**  **-Low Pressure/Vacuum Relief Valve -OTC Mobile Equipment Repair and**  **Refinishing Rule -OTC Portable Gas Container Rule**  **-RACT to 25 tpy (LNB) -Switch to Low Sulfur Fuel**  **-Water Heater + LNB Space Heaters**  **(none used)** Table 3.3: Controls for Emissions Reductions, by Sector, for the 0.070 ppm Control Strategy (Incremental to Baseline)**3-16**

**9 For Onroad and Nonroad Mobile Source control measu** **res, all measures applied for the Baseline analysis w ere applied to additional**  **geographic areas in the .070 analysis. 10**  **Reductions from Ocean-Going Vessels burning diesel fuel were applied in the Base Case analysis. However**  **, we inadvertently omitted the associated reductions that would occur i**  **n vessels burning residual fuels. These additional reductions were applied in the**  **Baseline analysis for the east and in the .070 anal yses for the east and west. The omission was not iden** **tified in time to include it in the initial Baseline analysis for the west, but was in**  **cluded in the Baseline national PM co-benefits analysis for the east and west.**  **-OTC Solvent Cleaning Rule -SCAQMD - Low VOC**  **-SCAQMD Limits -SCAQMD Rule 1168**  **-Switch to Emulsified Asphalts -Use of Low or No VOC Materials**  **Onroad Mobile**  **9 -Increased Penetration of Onroad SCR and DPF from 25**  **% to 75%**

**-Continuous Inspection and Maintenance (OBD) -Continuous Inspection and Maintenance (OBD)** 

**Nonroad Mobile**  **9 -Increased Penetration of Nonroad SCR and DPF from 25% to 75%**  **-Ocean-Going Vessels – reductions for vessels burning**  **residual fuels 10**  **EGU -Lower nested caps in OTC and**

**MWRPO states -Application of SCR and SNCR in**  **coal fired units in NA counties outside of OTC and MWRPO**  **(none used) (none used) (none used)**

Table 3.3 (Continued): Controls for Emissions Reductions, by Sector, for the 0.070 ppm Control Strategy (Incremental to Baseline)**3-17**

3.2.1 Controls Applied for a 0.070 ppm Standard: Non-EGU and Area Sectors
**Non-EGU and Area control measures were identified using AirControlNET 4.1. 11,12**  **To reduce NOx and VOC levels, all known control measures, wit** **hin a given cost-cap, were applied, allowing for the largest emission reduction p**  **er source over the widest geographic area. The cost-caps were pollutant specif**  **ic and applicable only in the East portion of the analysis. For reductions of NOx emiss**  **ions the cap was $16,000/ton, based upon the approximate benefit per ton of reductions. In some**  **instances, controls were too costly due to the large capital component of installing the**  **se controls. A similar process was followed for reductions from VOCs. The marginal cost cur** **ve was analyzed, and there was a clear break in the curve at approximate**  **ly $6,000/ton. Because all available controls up to the cost cap were used in counties ne**  **eding emission reductions, there was no ordering of which controls were applied first. V**  **OCs were cut at this level because approximately 75% of reductions were coming from control**  **s below that number. Additionally, the relative effectiveness of VOC contro**  **ls is not high. See Chapter 5 for more information on cost caps** 
**Additionally, controls were added that appeared in prel iminary State Implementation**  **Plans (SIPs) from States and Regional Planning Bodi es. Supplemental controls that**  **estimated near-term source controls based on similar te chnology were included in the**  **Non-EGU and Area Source sectors as well. Supplemental controls are described in further detail in Appendix 3.** 
**NOx controls were applied in the East for the 233 counties that were projected to have**  **concentrations of greater than 0.070 ppm in the 2020 baseline. Additional controls were**  **applied in surrounding counties within 200 km of the cou nty projected to be out of**  **attainment (at 0.070 ppm), but not crossing state boundaries. In the West, NOx controls were applied statewide, rather than only to counties w**  **ith violating monitors and their immediate neighbors (See Fig. 3.5). This was due t**  **o modeling methodology, in which the 200 km buffer was only validated for the East.** 

**11 See http://www.epa.gov/ttnecas1/AirControlNET.htm**  **for a description of how AirControlNET operates and what data is included in** **this tool. 12**  **While AirControlNET has not undergone a formal peer r eview, this software tool has**  **undergone substantial review within EPA's OAR and OAQPS , and by technical staff in**  **EPA's Regional offices. Much of the control measu re data has been included in a control**  **measure database that will be distributed to EPA Reg ional offices for use by States as**  **they prepare their ozone, regional haze, and PM2.5 SIPs over the next 10 months. In addition, the control measure data within AirControlNET h**  **as been used by Regional Planning Organizations (RPOs) such as the Lake Mich**  **igan Air District Commission (LADCO), the Ozone Transport Commission (OTC), and the Visib**  **ility Improvement State and Tribal Assocation of the Southeast (VISTAS) as part**  **of their technical analyses associated with SIP development over the last 3 year**  **s. All of their technical reports are available on their web sites.** **3-18**

Fig 3.5 Counties Where Controls for Nitrogen Oxides (NOx) Were Applied to NonEGU Point and Areas Sources for RIA Control StrategyDesigned to Meet 0.070 ppm (Incremental to Baseline)

**In the East, VOC controls were applied (for area sources only) in 47 counties where the**  **following criteria were met (including the 26 counti es which included VOC controls in**  **their baselines): VOC emissions within the county or an adj acent county were high (e.g.**  **>5000 tons per year of area source emissions), and screening analyses i ndicated that**  **ozone design values would be markedly reduced (> 0.5 ppb) by local VOC controls o f**  **25%, and the county design value was projected to be** **0.070 ppm in the 2020 baseline (See Fig 3.6). No VOC controls were used in the West.** **3-19**

Fig. 3.6 Counties Where VOC Controls Were Applied to Non-EGU Point and Areas Sources for the Control Strategy Designed to Meet 0 .070 ppm (Incremental to Baseline)

3.2.2 Controls Applied for a 0.070 ppm Standard: EGU Sector
**For the East only, a control strategy was applied f or the EGU sector (Fig. 3.7) (EGU**  **controls for the West were already included in the ozone baseline sin ce they were applied**  **for the hypothetical national control strategy in t he PM NAAQS RIA.) Annual and**  **ozone season CAIR caps remained unchanged, but coal-fired units were targeted for this**  **shifted strategy within those caps. This strategy wa s appropriate to consider because**  **transport of NOx pollution is more of a concern in the East, and NOx from EGUs still**  **accounts for a significant portion of emissions in this region. California, while in ne ed of**  **reductions as well, was not included in this strate gy because all known controls**  **(including EGU controls) had already been applied in the base line. The development of**  **an EGU-component to this control strategy was based exclu sively on NOx emissions**  **during the ozone season, although the hypothetical controls applied would operate year**

  ** round. The EGU sector used the Integrated Plannin** **g Model (IPM) to evaluate the reductions that are predicted from a specific contr**  **ol strategy. Details of this tool and subsequent analysis can be found in appendix 3.4.** 
**Reductions in the EGU sector are influenced significantly by the 2003 Clean Air Interstate Rule (CAIR) (see appendix 3.4 for more details on**  **CAIR). CAIR will bring significant emission reductions in NOx, and a resul**  **t, ambient ozone concentrations in the 3-20** 

**eastern U.S. by 2020 13**  **. A map of the CAIR region is presented in appendix 3.4. Emissions and air quality impacts of CAIR are documented**  **in detail in the Regulatory Impact Analysis of the Final Clean Air Interstate R**  **ule 14** 
**To address nonattainment in the CAIR region (especially th e Midwest, Mid-Atlantic, and**  **Northeast), lower nested caps (a limit lower than t he current CAIR cap) were applied in**  **these areas for NOx, while holding the CAIR cap unchanged f or the entire region. This**  **provides an opportunity to reduce emissions in a co st effective manner in targeted**  **regions. Two geographic regions were targeted for emissions reductions: the Midwest**  **Regional Planning Organization (MWRPO) consisting WI, IL, I N, MI, and OH; and the**  **Ozone Transport Commission (OTC), consisting of DC, MD, PA, DE, NJ, CT, NY, RI,**  **MA, VT, NH, and ME. These areas were chosen because the MWRPO and OTC states are currently investigating ways of reducing EGU emissions**  **further in their states and because most of the potential ozone nonattainment ar**  **eas are found within these two regions. Considering transport, as well as the local effect**  **s, reducing emissions in these areas expected to help bringing the Lake Michigan and Northeast corridor nonattainment**  **areas into attainment.**
**Lower nested caps were applied in the MWRPO and OTC states, for the ozone season only. The caps that were applied lead to reductions**  **that could be obtained by installing post-combustion controls to all of the coal-fired uni**  **ts that were not projected to have previously installed post-combustion controls in the ba**  **se-case. Following this, 75% of the reduction that could be obtained from these units**  **was subtracted from the sum of State level ozone control season NOx caps in CAIR**  **15 . The CAIR cap for the entire**  **region was kept unchanged.**
**In order to address non-attainment in the CAIR region o utside of the MWRPO and OTC,**  **a “command and control” type strategy for coal-fired units h as been designed. Annual**  **and ozone season CAIR caps remained unchanged, and coal-f ired units were targeted for**  **this reduction. Preliminary analysis showed that mo st of the needed NOx reductions in**  **the EGU sector can be achieved through application of post-combustion controls (e.g. Selective Catalytic Reductions (SCR) and Selective No**  **n-Catalytic Reductions (SNCR)) on coal units that are projected to remain without**  **controls under the CAIR/CAMR/CAVR cap-and-trade scheme.** 

**13 See**  **http://www.epa.gov/airmarkets/progress/progress-rep orts.html**  **for more information**  **14 See http://www.epa.gov/CAIR/technical.html**  **15 Detailed analysis showed that 75% reduction provides t** **he most cost-effective way of reducing emissions at the targeted non-attainment area**  **s, considering transport, with the most air quality impacts.** **3-21**

Fig 3.7 States Where Nitrogen Oxide (NOx) Controls W ere Applied to Electrical Generating Units (EGUs) for the Control Strategy Desi gned to Meet 0.070 ppm (Incremental to Baseline)

3.2.3 Controls Applied for a 0.070 ppm Standard: Onroad and Nonroad Mobile Sectors
**As in other sectors, there are several mobile source control strategies that h ave been, or**  **are expected to be, implemented through previous national or regional rules. Although many expected reductions from these rules are inclu**  **ded in the baseline, additional mobile source controls were required to illustrate attainment**  **of a 0.070 ppm standard (See Fig 3.8). Modeling of the onroad and nonroad mobile sectors wa**  **s done using MOBILE6. See Appendix 3 for more information.** 

**All of the local mobile source controls included in the ozo ne baseline were expanded for**  **the hypothetical national control strategy to attain 0.0 70 ppm standard. In the case of**  **onroad and nonroad Selective Catalytic Reduction (SCR ) and Diesel Particulate Filters**  **(DPF), the measure was applied at a greater penetra tion rate – to 75% of the equipment**  **population. 75% was the highest penetration rate that EPA felt could be reas onably**  **accomplished. All local measures were applied to sou rces in additional geographic areas.**  **Continuous inspection and maintenance, which allows for m uch more rapid identification**  **of vehicles failing their emissions standard, was added. De scriptions of the mobile**  **source rules and measures can be found in appendix 3.3.** **3-22**

**As in the baseline, onroad SCR and DPF and eliminat ion of idling were applied statewide**  **for all states with a county projected to exceed the 0.07 0 ppm standard. All other**  **controls were applied to counties within a 200 km buff er from counties projected to**  **exceed the 0.070 ppm alternative standard with the follo wing exceptions:** 

x **counties in neighboring states were omitted from th e buffer zone** 

x **controls were applied statewide to Ozone Transport Commiss ion (OTC) states,**  **with the exception of Vermont**

x **controls were applied statewide in California, Colorado, Utah, New Mexico,**  **Arizona, and Nevada .**

Fig. 3.8 Areas Where NOx and VOC Controls Were Ap plied to Mobile Onroad and Nonroad Sources in Addition to National Mobile Contro ls for the 0.070 ppm

Control Strategy (incremental to Baseline)

***Onroad retrofits and elimination of long duration idl ing**  ****Onroad retrofits, elimination of long duration idl ing, nonroad retrofits, Best Workplace**  **for Commuters programs (BWC), low Reid Vapor Pressure (RVP)**
3.2.4 Data Quality for this Analysis **The estimates of emission reductions associated with our control strategies above are subject to important limitations and uncertainties. EPA’**  **s analysis is based on its best judgment for various input assumptions that are uncert**  **ain. As a general matter, the Agency selects the best available information from**  **available engineering studies of air pollution controls and has set up what it believes is the most reasonable framework for** **3-23**

**analyzing the cost, emission changes, and other impacts of regulatory controls. EPA is working on approaches to quantify the uncertainties in thes**  **e 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 Fig ures 3.9 and 3.11, respectively.**  **Figures 3.10 and 3.12 indicate the emission reductions attr ibuted to each sector.**  **Appendix 3 contains maps of emissions reductions by sector, n ationwide.** 
**Prior to reading the maps, there is an important ca veat to consider. The control strategy**  **above focuses on reducing emissions of VOCs and NOx, the two pr ecursors to ozone**  **formation. However, in some cases, the application of the co ntrol strategy actually**  **increased the level of NOx or VOC emissions. This is due to controls that affect multiple**  **pollutants and complex interactions between air poll utants, as well as trading aspects**  **under the CAIR rule.**
**Emissions of NOx do not decrease everywhere within the CA IR region. As explained**  **earlier, the NOx EGU control strategy was designed to ach ieve emission reductions**  **specifically in the non-attainment areas, while ret aining the overall CAIR cap** 

**. Application of nested and lower (ozone season) caps**  **for the states in the MWRPO and OTC regions and local controls (SCR and SNCR) on the u** **ncontrolled coal units in the non-attainment counties outside of the OTC and MWRPO w**  **ithin CAIR region result in increase of emissions elsewhere within CAIR region. While there are substantial NOx**  **emission reductions within the OTC and MWRPO expected for the 2020 ozone season**  **(roughly 55,500 tons) as a result of cap-and-trade program with lower caps and local command-and-control reductions in other non-attainment counties where uncontrolled**  **coal units exist, there is the possibility of increase d emissions from the remainder of**  **sources within CAIR region. This approach provides a cos t effective opportunity for**  **reducing emissions where the reductions are most nee ded to help reach attainment. It is**  **important to recall that this is a hypothetical cont rol strategy, the states or other**  **authorities may take additional steps to minimize t hese increases if warranted.** **3-24**

Fig 3.9 Annual Tons of Nitrogen Oxide (NOx) Emissio n Reductions From Controls Designed to Meet 0.070 ppm Standard,* incremental to the current standard

***Reductions are negative and increases are positive**  The -99 - +100 range is shown without color becau**  **se these are small county-level NOx reductions or increases that likely had little to no impa**  **ct on ozone estimates. Most counties in this range had NOx differences less tha** **n 1 ton. 3-25** 

Fig. 3.10 Percentage of Total Annual NOx Emissions R educed from Various Sources

*** Note that on a national basis, NOx emissions are red uced by <1%. However, the EGU**  **strategy used in this analysis gains reductions in nonattainment areas, balanced by**  **increases in attainment areas (described in Section 3.3)** 

### Non**Electrical Generating**  **Unit Point (79%)**  **Electrical Generating Unit**  **Point(<1%) On-Road (16%)** 

**Non-Road (<1%) Area (3%)** **3-26**

Fig. 3.11 Annual Tons of Volatile Organic Compound (VOC) Emi ssion Reductions From Controls Designed to Meet 0.070 ppm Standard*, i ncremental to the current standard

***Reductions are negative and increases are positive**  The -99 - +57 range is shown without color becaus**  **e these are small county-level VOC reductions or increases that likely had little to no impa**  **ct on ozone estimates. Most counties in this range had VOC differences less than 1 t**  **on. 3-27** 

Fig. 3.12 Percentage of Total Annual VOC Emissions Reduced from Various Sources*

3.4 Ozone Design Values for partial attainment
**After determining the emissions reductions from NOx and VOC, we used modeling tools**  **(see section 2.3.2) to determine ozone design values for 2020. Figure 3.13 shows a map of the design values after modeling the control strategy to**  **reach 0.070 ppm. The map legend is broken out to demonstrate under this contr**  **ol strategy, with no adjustments, which counties would reach the targeted standard of 0**  **.070 ppm, the more stringent alternative standard analyzed (0.065 ppm), and the**  **other end of the proposal range (0.075 ppm). It is understood that this illustrative strategy would not be the**  **exact hypothetical strategy used to try to attain either of these alter** **native standards, due to over- and under attainment in many counties. (Chapter 4 describes EPA’s me**  **thodology for estimating tons of reductions needed to hypothetically attain these**  **other two possible alternative standards.) In addition, because ozone formation is d**  **ependent on a variety of factors, it is not possible to directly attribute changes in predict**  **ed ozone concentrations to emission reductions of a specific precursor from a specific**  **sector.**
**A full listing of the counties and their design values is provided in Appendix 3. Figures 3.14 and 3.15 show the tons of emissions reduced by the hypothetical RIA 0**  **.070 ppm control strategy, and the tons of emissions rem**  **aining after application of those controls, by sector.** 
**Using this strategy, it is possible to reach attainm ent in 365 counties. However, there are**  **still an additional 126 counties that will remain ou t of attainment with an alternative**  **standard of 0.070 ppm using this control strategy. All k nown controls were applied to**  **this scenario, but attainment was not achieved ever ywhere. Because of this partial**  **attainment outcome, it will be necessary to identif y additional reductions in NOx and**  **VOC in order to assess the costs and benefits of full attainment nationwide. Chapter 4**  **Area (45%)** 

**Non-Road (7%)**  **On-Road (46%)**  **Non-Electrical Generating**

**Unit Point (2%) 3-28** 

**will address the methodology for determining the ad ditional tons that were needed to**  **reach full attainment.** Fig. 3.13 Projected Ozone Air Quality in 2020 AfterApplication of Known Controls

**1 Modeled emissions reflect the expected reductions from federal programs including the Clean**  **Air Interstate Rule, the Clean Air Mercury Rule, th e Clean Air Visibility Rule, the Clean Air**  **Nonroad Diesel Rule, the Light-Duty Vehicle Tier 2 Rule, the Heavy Duty Diesel Rule, proposed**  **rules for Locomotive and Marine Vessels and for Sma ll Spark-Ignition Engines, and state and**  **local level mobile and stationary source controls i dentified for additional reductions in emissions**  **for the purpose of attaining the current PM 2.5 and Ozone standards.**  **2 Controls applied are illustrative. States may ch oose to apply different control strategies for**  **implementation. 3 The current standard of 0.08 ppm is effectively e**  **xpressed as 0.084 ppm when rounding conventions are applied.**  **4 Modeled design values in ppm are only interpreted up to 3 decimal places.**  **5 Map shows results from a total of 491 counties wi th projected design values. Consistent with**  **current modeling guidance, EPA did not project 2020 concentrations for counties where 2001**  **base year concentrations were less than recommended criterion. Such projections may not**  **represent expected future levels. 3-29** 

Table 3.4 Annual Tons of Emissions Remaining after Applic ation of the 0.070 ppm Control Strategy (35 States + DC Analysis Area) 16

Pollutant Sector 2020 Emissions After Controls Applied for PM2.5 15/35 (tons) 2020 Emissions

After Controls Applied for PM2.5 15/35 and Ozone 0.084 Control Strategy Baseline (tons) 0.070 ppm

Reductions (tons) 2020 Emissions

After Controls Applied for PM2.5 15/35 and Ozone 0.070 ppm Control Strategy (tons)

Area **1,200,000 1,200,000 30,000 1,200,000** Onroad **1,800,000 1,700,000 170,000 1,600,000** Nonroad **1,900,000 1,800,000 8,000 1,800,000** EGU **1,500,000 1,500,000 7,800 1,500,000** NOX

Non-EGU **2,200,000 1,900,000 800,000 1,100,000**

Area **5,800,000 5,600,000 84,000 5,500,000** Onroad **1,500,000 1,500,000 86,000 1,400,000** Nonroad **1,000,000 1,000,000 12,000 1,000,000** EGU **39,000 39,000 26 38,000** VOC

Non-EGU **1,100,000 1,100,000 3,400 1,100,000**

**16 Numbers may not add up due to rounding** **3-30**

Fig 3.14 Annual NOx Emissions Remaining after PM N AAQS 15/35, Ozone

Current Standard, and 0.070 ppm Control Strategies

(35 States + DC Analysis Area)

**1,700,000 1,**  **600, 000** 

**1,**

**900,000 1,**  **800, 000**  **1,800,000**

**1,**

**500, 000**  **1, 500,**  **00 0**  **1, 50**  **0,000**

**2,**

**200, 000**  **1,900,000 1,**  **200,000 1,**  **200, 000** 

**1,200,000 1,800,000**  **1, 10**  **0,000**

**2020 Emissions After Controls Applied for PM 2.5 15/35 and**  **Accounting for National Rules on the Books**  **2020 Emissions After Controls Applied for PM2.5 15/35 and**  **Ozone 0.084 Control Strategy**

**Baseline 2020 Emissions After Controls**  **Applied for PM2.5 15/35 and Ozone 0.070 Control Strategy** 

**Tons NOX Area**  **NOX Onroad NOX Nonroad**  **NOX EGU NOX NonEGU** **3-31**

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

Current Standard, and 0.070 ppm Control Strategies

(35 States + DC Analysis Area)

**5 ,**  **800, 000**  **5,600,000 5,**  **500, 000** 

**1 ,**  **5 0**  **0 ,**  **0 0**  **0 1**  **, 5**  **0 0**  **, 0**  **0 0**  **1 ,**  **4 0**  **0 ,**  **0 0**  **0**

**1,000,000 1,**  **000, 000** 

**1,100,000 1,000,000** 

**39,000 38,000** 

**3 9,000**  **1, 100,**  **0 00** 

**1,100, 0**  **00**

**2020 Emissions After Controls Applied for PM 2.5 15/35 and**  **Accounting for National Rules on the Books**  **2020 Emissions After Controls Applied for PM2.5 15/35 and**  **Ozone 0.084 Control Strategy**

**Baseline 2020 Emissions After Controls**  **Applied for PM2.5 15/35, O3 Current Standard and 0.070**  **Control Strategies**

**Tons VOC Area**  **VOC Onroad VOC Nonroad**  **VOC EGU VOC NonEGU** 

3.5 References
**Michigan Department of Environmental Quality and 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.**
**National Ambient Air Quality Standards for Particula te Matter, 40 CFR Part 50 (2006)** 
**Rule To Reduce Interstate Transport of Fine Particul ate Matter and Ozone (Clean Air**  **Interstate Rule); Revisions to Acid Rain Program; Revi sions to the NOX SIP Call; Final**  **Rule, 40 CFR Parts 51, 72, 73, 74, 77, 78 and 96 (2 005).** 
**Standards of Performance for New and Existing Stationary Sources: Electric Utility**  **Steam Generating Units, 40 CFR Parts 60, 63, 72, and 75 (2005) 3-32** 

**Regional Haze Regulations and Guidelines for Best Available Retrofit Technology (BART) Determinations, 40 CFR Part 51 (2005)** 

**Control of Emissions of Air Pollution from Locomotive Engines and Marine**  **Compression-Ignition Engines Less than 30 Liters per C ylinder, Proposed rule, 40 CFR**  **Parts 92, 94, 1033, 1039, 1042, 1065 and 1068 (2007)**
**Control of Emissions from Nonroad Spark-Ignition Engines and Equipment; proposed rule, 40 CFR Parts 60, 63, 85, 89, 90, 91, 1027, 1045, 1048, 1051, 1054, 1060, 1065,**  **1068, and 1074 (2007)**

**USEPA.** Guide on Federal and State Summer RVP Standards for Conventional Gasoline Only. **EPA420-B-05-012. November 2005**
**USEPA. 2007, Regulatory Announcement: EPA Proposal for More Stringent Emissions Standards for Locomotives and Marine Compression-Ignition E**  **ngines. EPA420-F-07- 015** 
**USEPA. 2007, Proposed Emission Standards for New Nonr oad Spark-Ignition Engines,**  **Equipment, and Vessels. EPA420-F-07-032 3a-1** 

Appendix- Chapter 3

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

3a.1.1 **Non-EGU and Area Source Control Strategies for Ozon e NAAQS Proposal** 
**In the Non-EGU and Area Sources portion of the control strate gy, maximum control**  **scenarios were used from the existing control measure dataset from AirControlNET 4.1**  **for 2020 (for Geographic Areas defined for each lev el of the standard being analyzed).**  **This existing control measure dataset reflects chan ges and updates made as a result of the**  **reviews performed for the final PM2.5 RIA. Following th is, an internal review was**  **performed by the OAQPS engineers in the Sector Policies and Programs Division (SPPD)**  **to examine the controls applied by AirControlNET and dec ide if these controls were**  **sufficient or could be more aggressive in their application, given the 2020 a nalysis year.**  **This review was performed for non-EGU NOx control me asures. The result of this**  **review was an increase in control efficiencies applied for many control measures, and**  **more aggressive control measures for over 80 SCC’s. For example, SPPD recommended**  **that we apply SCR to cement kilns to reduce NOx emis sions in 2020. Currently, there**  **are no SCRs in operation at cement kilns in the U.S, but t here are several SCRs in**  **operation at cement kilns in France now. Based on the SCR experience at cement kilns**  **in France, SPPD believes SCR could be applied at U.S. cement kilns by 2020. Following**  **this, it was recommended that supplemental controls could be applied to 8 additional**  **SCC’s from non-EGU NOx sources. We also looked into sources of controls for highly**  **reactive VOC non-EGU sources. Four additional controls were applied for highly**  **reactive VOC non-EGU sources not in AirControlNET.**
3a.1.2 NOx Control Measures for Non-EGU Point Sourc es. **Several types of NOx control technologies exist for non-EGU sources: SCR, selective**  **noncatalytic reduction (SNCR), natural gas reburn (NGR), coal reburn, and low-NOx**  **burners. In some cases, LNB accompanied by flue gas reci rculation (FGR) is applicable,**  **such as when fuel-borne NOx emissions are expected to be of greater importance than**  **thermal NOx emissions. When circumstances suggest that combustion controls do not**  **make sense as a control technology (e.g., sintering processes, coke oven batteries, sulfur**  **recovery plants), SNCR or SCR may be an appropriate choice. Finally, SCR can be**  **applied along with a combustion control such as LNB with overfire air (OFA) to further**  **reduce NOx emissions. All of these control measures a re available for application on**  **industrial boilers.**
**Besides industrial boilers, other non-EGU source categori es covered in this RIA include**  **petroleum refineries, kraft pulp mills, cement kilns, s tationary internal combustion**  **engines, glass manufacturing, combustion turbines, an d incinerators. NOx control**  **measures available for petroleum refineries, particu larly process heaters at these plants,**  **include LNB, SNCR, FGR, and SCR along with combinati ons of these technologies.** **3a-2**

**NOx control measures available for kraft pulp mills include those available to industrial**  **boilers, namely LNB, SCR, SNCR, along with water in jection (WI). NOx control**  **measures available for cement kilns include those a vailable to industrial boilers, namely**  **LNB, SCR, and SNCR. Non-selective catalytic reducti on (NSCR) can be used on**  **stationary internal combustion engines. OXY-firing, a technique to modify combustion at**  **glass manufacturing plants, can be used to reduce NO x at such plants. LNB, SCR, and**  **SCR + steam injection (SI) are available measures for combustion turbines. Finally,**  **SNCR is an available control technology at incinerators. For more information on these**  **measures, please refer to the AirControlNET 4.1 control measures documentation report.** 
3a.1.3 VOC Control Measures for Non-EGU Point Sources.
**VOC controls were applied to a variety of non-EGU point sourc es as defined in the emissions**  **inventory in this RIA. These controls are: permanent tot al enclosure (PTE) applied to paper**  **and web coating operations and fabric operations, and inci nerators or thermal oxidizers**  **applied to wood products and marine surface coating operat ions. A PTE confines VOC**  **emissions to a particular area where can be destroyed or used in a way that limits emissions**  **to the outside atmosphere, and an incinerator or thermal oxi dizer destroys VOC emissions**  **through exposure to high temperatures (2,000 degrees Fahren heit or higher).**  **For more information on these measures, refer to the AirControlNET 4.1**  **control measures documentation report.** 
3a.1.4 NOx Control Measures for Area Sources
**There were two controls applied for NOx emissions fr om area sources. The first is**  **RACT (reasonably available control technology) to 25 tpy (LNB). This control is the**  **addition of a low NOx burner to reduce NOx emissions . This control is applied to**  **industrial oil, natural gas, and coal combustion so urces. The second control is water**  **heaters plus LNB space heaters. This control is ba sed on the installation of low-NOx**  **space heaters and water heaters in commercial and i nstitutional sources for the reduction**  **of NOx emissions. For additional information regard ing these controls**  **please refer to the AirControlNET 4.1 control measures documentation report.** 
3a.1.5 VOC Control Measures for Area Source.
**The most frequently applied control to reduce VOC em issions from area sources was**  **CARB Long-Term Limits. This control, which represents controls available in VOC**  **rules promulgated by the California Air Resources B oard, applies to commercial solvents**  **and commercial adhesives, and depends on future tech nological innovation and market**  **incentive methods to achieve emission reductions. The next most frequently applied**  **controls was the use of low or no VOC materials for graph ic art source categories. The**  **South Coast Air District’s SCAQMD Rule 1168 control applies to wood furniture and**  **solvent source categories sets limits for adhesive and sealant VOC content. The OTC**  **solvent cleaning rule control establishes hardware and operating requirements for**  **specified vapor cleaning machines, as well as solve nt volatility limits and operating**  **practices for cold cleaners. The Low Pressure/Vacuum Rel ief Valve control measure is**  **the addition of low pressure/vacuum (LP/V) relief v alves to gasoline storage tanks at** **3a-3**

**service stations with Stage II control systems. LP/V reli ef valves prevent breathing**  **emissions from gasoline storage tank vent pipes. S CAQMD Limits control establishes**  **VOC content limits for metal coatings along with applic ation procedures and equipment**  **requirements. Switch to Emulsified Asphalts control i s a generic control measure**  **replacing VOC-containing cutback asphalt with VOC-fre e emulsified asphalt. The**  **equipment and maintenance control measure applies to oil and natural gas production.**  **The Reformulation - FIP Rule control measure intends to reach the VOC limits by**  **switching to and/or encouraging the use of low-VOC pes ticides and better Integrated Pest**  **Management (IPM) practices. For additional informat ion regarding these controls please**  **refer to the AirControlNET 4.1 control measures doc umentation report.** 
3a.1.6 Supplemental Controls
**The table below summarizes the supplemental control measures added to our control**  **measures database by providing the pollutant it controls and its control effici ency. These**  **controls were applied in the baseline scenario to Hou ston and Chicago, and the Northeast**  **as well as in the incremental control strategy appli ed to the Eastern U.S. However, these**  **controls are not located in AirControlNET.**
Table 3a.1 Supplemental Emission Control Measures A pplied in Modeled Attainment Strategies for the Ozone NAAQS RIA – NewControl Technologies Added to the Control Measures Database
**Pollutant SCC SCC Description Control Technology**  **Percent**

**Reduction (%)**  **NOx 20200252 Internal Comb. Engines/Industrial/Natur**  **al Gas/2-cycle Lean Burn LEC (Low**  **Emission Combustion)**  **87**

**20200254 Internal Comb. Engines/Industrial/Natur**  **al Gas/4-cycle Lean Burn LEC (Low**  **Emission Combustion)**  **87**

**VOC* 3018001-**

**30600701 and**  **30600999**

**- Fugitive Leaks** 

**Flares Enhanced**  **LDAR 50** 

**98**

**3018001 - Fugitive Leaks LDAR 80 30600702** 

**- Cooling towers Monitoring**  **Program No one** 

**general estimate** **3a-4**

**30600503**

**- Wastewater Drains and** 

**Separators Inspection**  **and Maintenance**  **Program (Separators)**  **Water Seals (Drains)**  **65**

***Note: the cost of these measures are not included in our incremental annualized cost**  **estimates since these controls are found in the Har ris-Galveston-Brazoria Cos. SIP**  **(Texas), and they will be incurred by 2020 in any eve nt. We do quantify the**  **emission reductions since these controls are not accounted for in our baseline inventory for 2020, however.** 

**Low Emission Combustion (LEC)**

Overview: **LEC technology is defined as the modification of a natural gas fueled,**  **spark ignited, reciprocating internal combustion engine to reduce emissions of**  **NOx by utilizing ultra-lean air-fuel ratios, high e nergy ignition systems and/or**  **pre-combustion chambers, increased turbocharging or adding a turbocharger, and**  **increased cooling and/or adding an intercooler or aftercooler , resulting in an**  **engine that is designed to achieve a consistent NO x** **emission rate of not more than 1.5-3.0 g/bhp-hr at full capacity (usually 100 percent**  **speed and 100 percent load). This type of retrofit technology is fairly widely a** **vailable for stationary internal combustion engines.** 
**For control efficiency, EPA estimates that it ranges fr om 82 to 91 percent for LEC**  **technology applications. The EPA believes applicat ion of LEC would achieve**  **average NO x** **emission levels in the range of 1.5-3.0 g/bhp-hr. Thi s is an 82-91**  **percent reduction from the average uncontrolled emission level s reported in the**  **ACT document. An EPA memorandum summarizing 269 tests shows that 96 percent of IC engines with installed LEC technology** **achieved emission rates of less than 2.0 g/bhp-hr.**  **1 The 2000 EC/R report on IC engines summarizes 476**  **tests and shows that 97% of the IC engines with ins talled LEC technology achieve**  **emission rates of 2.0 g/bhp-hr or less. 2** 

**1 “Stationary Reciprocating Internal Combustion Engi**  **nes Technical Support Document for NOx SIP Call Proposal,” U.S. Environmental Protection** **Agency. September 5, 2000. Available on the Internet at**  **http://www.epa.gov/ttn/naaqs/ozone/rto/sip/data/tsd 9-00.pdf** 

**.**

**2 “Stationary Internal Combustion Engines: Updated Infor** **mation on NOx Emissions and Control Techniques,” Ec/R Incorporated, Chapel Hill**  **, NC. September 1, 2000. Available on the Internet at**  **http://www.epa.gov/ttn/naaqs/ozone/ozonetech/ic_engin e_nox_update_09012000.pdf** 

**. 3a-5**  Major Uncertainties: **The EPA acknowledges that specific values will var y from**  **engine to engine. The amount of control desired and number of operating hours**  **will make a difference in terms of the impact had f rom a LEC retrofit. Also, the**  **use of LEC may yield improved fuel economy and powe r output, both of which**  **may affect the emissions generated by the device.**
**Leak Detection and Repair (LDAR) for Fugitive Leaks**

Overview: **This control measure is a program to reduce leaks of fugitive VOC**  **emissions from chemical plants and refineries. The program includes special**  **“sniffer” equipment to detect leaks, and maintenance sc hedules that affected**  **facilities are to adhere to. This program is one that is contained within the**  **Houston-Galveston-Brazoria 8-hour Ozone SIP.**
Major Uncertainties: **The degree of leakage from pipes and processes at chemical plants is always difficult to quantify giv**  **en the large number of such leaks at a typical chemical manufacturing plant. Ther**  **e are also growing indications based on tests conducted by TCEQ and ot** **hers in Harris County, Texas that fugitive leaks have been underestimated from che**  **mical 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 refinerie s that presumes that an**  **existing LDAR program already is in operation.**
Major Uncertainties: **The calculations of control efficiency and cost pr esume use**  **of LDAR at a chemical plant. This should not be an unreasonable assumption,**  **however, given that most chemical plants are under s ome type of requirement to**  **have an LDAR program. However, as mentioned earlier, there is growing**  **evidence that fugitive leak emissions are underesti mated from chemical plants by**  **a factor of 6 to 20 or greater. 4** 

**3 VOC Fugitive Losses: New Monitors, Emissions Losses**  **, and Potential Policy Gaps. 2006 International Workshop. U.S. Environmental Prot**  **ection Agency, Office of Air Quality Planning and Standards and Office of Solid Waste and Emergency Re**  **sponse. October 25-27, 2006.**  **4 VOC Fugitive Losses: New Monitors, Emissions Losses**  **, and Potential Policy Gaps. 2006 International Workshop. U.S. Environmental Prot**  **ection Agency, Office of Air Quality Planning and Standards and Office of Solid Waste and Emergency Re**  **sponse. October 25-27, 2006.** **3a-6**

**Flare Gas Recovery**

Overview: **This control measure is a condenser that can reco ver 98 percent of the**  **VOC emitted by flares that emit 20 tons per year or mo re of the pollutant.** 

Major Uncertainties: **Flare gas recovery is just gaining commercial acce ptance in**  **the US and is only in use at a small number of refi neries.** 
**Cooling Towers**

Overview **: The control measure is continuous monitoring of VOC from the cooling water return to a level of 10 ppb. This mon**  **itoring is accomplished by using a continuous flow monitor at the inlet to each** **cooling tower.**
**There is not a general estimate of control efficiency for this measure; one is to**  **apply a continuous flow monitor until VOC emissions have reached a level of 1.7 tons/year for a given cooling tower.**  **5**

Major Uncertainties: **The amount of VOC leakage from each cooling tower can greatly affect the overall cost-effectiveness of th**  **is control measure.**
**Wastewater Drains and Separators**

Overview: **This control measure includes an inspection and maintenance**  **program to reduce VOC emissions from wastewater drains an d water seals on**  **drains. This measure is a more stringent version of mea sures that underlie**  **existing NESHAP requirements for such sources.**
Major Uncertainties: **The reference for this control measures notes that the VOC**  **emissions inventories for the five San Francisco Ba y Area refineries whose data**  **was a centerpiece of this report are incomplete. I n addition, not all VOC species**  **from these sources were included in the VOC data that is a basis for these**  **calculations. 6** 

**In addition to the new supplemental controls present ed above, there were a number of**  **changes made to existing AirControlNET controls. These changes were made based**  **upon an internal review performed by EPA engineers to examine the controls applied by**  **AirControlNET and determine if these controls were sufficie nt or could be more**  **aggressive in their application, given the 2020 analysis year. This rev iew was performed**  **for non-EGU NOx control measures. The result of th is review was an increase in control** 

**5 Bay Area Air Quality Management District (BAAQMD).**  **Proposed Revision of Regulation 8, Rule 8: Wastewater Collection Systems.**  **Staff Report, March 17, 2004. 6**  **Bay Area Air Quality Management District (BAAQMD). Proposed Revision of**  **Regulation 8, Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004.** **3a-7**

**efficiencies applied for many control measures, and more aggressive control measures for**  **over 70 SCCs. The changes apply to the control str ategies performed for the Eastern US**  **only. These changes are listed in the table below.**
Table 3a.2 Supplemental Emission Control Measures A pplied in Modeled Attainment Strategies for the Ozone NAAQS RIA – Cha nges to Control technologies currently in our Control Measures Data base

Pollutant SCC AirControlNET Source Description AirControlNET

Control Technology New

Control Technology New

Control Efficiency (%) Old

Control Efficiency (%) **NOX 10200104**

**10200204 10200205**  **10300207 10300209**  **10200217 10300216**  **ICI Boilers -**

**Coal-Stoker SNCR SCR 90.0 40.0** 

**NOX 10200901**

**10200902 10200903**  **10200907 10300902**  **10300903 ICI Boilers -** 

**Wood/Bark/ Waste**  **SNCR SCR 90.0 55.0**

**NOX 10200401**

**10200402 10200404**  **10200405 10300401**  **ICI Boilers -**

**Residual Oil SCR SCR 90.0 80** 

**NOX 10200501**

**10200502 10200504**  **ICI Boilers -**

**Distillate Oil SCR SCR 90.0 80** 

**NOX 10200601**

**10200602 10200603**  **10200604 10300601**  **10300602 10300603**  **10500106 10500206**  **ICI Boilers -**

**Natural Gas SCR SCR 90.0 80** 

**NOX 30500606 Cement Manufacturing -**  **Dry SCR SCR 90.0 80** **3a-8** Pollutant SCC AirControlNET Source Description AirControlNET

Control Technology New

Control Technology New

Control Efficiency (%) Old

Control Efficiency (%) **NOX 30500706 Cement Manufacturing -**  **Wet SCR SCR 90.0 80** 

**NOX 30300934 Iron & Steel Mills - Annealing**  **SCR SCR 90.0 85**

**NOX 10200701**

**10200704 10200707**  **10200710 10200799**  **10201402 10300701**  **10300799 ICI Boilers -** 

**Process Gas SCR SCR 90.0 80** 

**NOX 10200802**

**10200804 ICI Boilers -** 

**Coke SCR SCR 90.0 70** 

**NOX 10201002 ICI Boilers - LPG**  **SCR SCR 90.0 80**

**NOX 10201301**

**10201302 ICI Boilers -** 

**Liquid Waste SCR SCR 90.0 80** 
**NOX 30700110 Sulfate Pulping - Recovery**  **Furnaces SCR SCR 90.0 80** 

**NOX 30100306 Ammonia Production –**  **Pri. Reformer, Nat. Gas**  **SCR SCR 90.0 80**

**30500622**

**30500623 Cement Kilns Biosolid**  **Injection Biosolid** 

**Injection 40.0 23** 

**NOX 30590013**

**30190013 30190014**  **39990013 Industrial and** 

**Manufacturing Incinerators**  **SNCR SCR 90.0 45**

**NOX 30101301**

**30101302 Nitric Acid** 

**Manufacturing SNCR SCR 90.0 908** 

**NOX 30600201 Fluid Cat. Cracking Units**  **LNB + FGR SCR 90.0 901**

**NOX 30590003 Process Heaters -**  **Process Gas LNB + SCR LNB + SCR 90.0 88** 

**NOX 30600101** 

**30600103 30600111**  **Process Heaters -**

**Distillate Oil LNB + SCR LNB + SCR 90.0 90** **3a-9** Pollutant SCC AirControlNET Source Description AirControlNET

Control Technology New

Control Technology New

Control Efficiency (%) Old

Control Efficiency (%)

**NOX 30600106** 

**30600199 Process Heaters -** 

**Residual Oil LNB + SCR LNB + SCR 90.0 80** 

**NOX 30600102** 

**30600105 Process Heaters -** 

**Natural Gas LNB + SCR LNB + SCR 90.0 80** 
**NOX 30700104 Sulfate Pulping - Recovery**  **Furnaces SCR SCR 90.0 80** 

**NOX 30790013 Pulp and Paper - Natural Gas -**  **Incinerators SNCR SCR 90.0 45** 

**NOX 39000201 In-Process; Bituminous Coal;**  **Cement Kiln SNCR - urea** 

**based SCR 90.0 50** 

**NOX 39000203 In-Process; Bituminous Coal;**  **Lime Kiln SNCR - urea** 

**based SCR 90.0 50** 

**NOX 39000289 In-Process Fuel Use;Bituminous**  **Coal; Gen SNCR SCR 90.0 40** 

**NOX 39000489 In-Process Fuel Use; Residual**  **Oil; Gen LNB SCR 90.0 37** 

**NOX 39000689 In-Process Fuel Use; Natural Gas;**  **Gen LNB SCR 90.0 50** 

**NOX 39000701 In-Proc;Process Gas;Coke**  **Oven/Blast Furn LNB + FGR SCR 90.0 55** 

**NOX 39000789 In-Process; Process Gas;**  **Coke Oven Gas LNB SCR 90.0 50** 

**NOX 50100101**

**50100506 50200506**  **50300101 50300102**  **50300104 50300506**  **50100102 Solid Waste** 

**Disp;Gov;Other Incin;Sludge**  **SNCR SCR 90.0 45**

**The last category of supplemental controls is contr ol technologies currently in our control**  **measures database being applied to SCCs not controlled currently in AirControlNET.** **3a-10**

Table 3a.3 Supplemental Emission Control Measures A pplied in Modeled Attainment Strategies for the Ozone NAAQS RIA –Cont rol technologies currently in our Control Measures Database Applied to New Source ty pes Pollutant SCC SCC Description Control Technology Control

Efficiency **NOX 39000602 Cement Manufacturing - Dry SCR 90.0 NOX 30501401 Glass Manufacturing - General OXY-Firin**  **g 85.0 NOX 30302351** 

**30302352 30302359**  **Taconite Iron Ore Processing -**

**Induration - Coal or Gas SCR 90.0** 

**NOX 10100101 External Combustion Boilers;Electric Generation;Anthracite Coal;Pulverized**  **Coal SNCR 40.0** 

**NOX 10100202 External Combustion Boilers;Electric Generation;Bituminous/Subbituminous**  **Coal;Pulverized Coal: Dry Bottom (Bituminous Coal)**  **SNCR 40.0**

**NOX 10100204 External Combustion Boilers;Electric Generation;Bituminous/Subbituminous**  **Coal;Spreader Stoker (Bituminous Coal) SNCR 40.0** 

**NOX 10100212 External Combustion Boilers;Electric Generation;Bituminous/Subbituminous**  **Coal;Pulverized Coal: Dry Bottom (Tangential) (Bituminous Coal)**  **SNCR 40.0**

**NOX 10100401 External Combustion Boilers;Electric Generation;Residual Oil;Grade 6 Oil:**  **Normal Firing SNCR 50.0** 

**NOX 10100404 External Combustion Boilers;Electric Generation;Residual Oil;Grade 6 Oil:**  **Tangential Firing SNCR 50.0** 

**NOX 10100501 External Combustion Boilers;Electric Generation;Distillate Oil;Grades 1 and 2**  **Oil SNCR 50.0** 

**NOX 10100601 External Combustion Boilers;Electric Generation;Natural Gas;Boilers > 100**  **Million Btu/hr except Tangential NGR 50.0** 

**NOX 10100602 External Combustion Boilers;Electric Generation;Natural Gas;Boilers < 100**  **Million Btu/hr except Tangential NGR 50.0** 

**NOX 10100604 External Combustion Boilers;Electric Generation;Natural Gas;Tangentially**  **Fired Units NGR 50.0** **3a-11**

**NOX 10101202 External Combustion Boilers;Electric Generation;Solid Waste;Refuse Derived**  **Fuel SNCR 50.0** 

**NOX 20200253 Internal Comb. Engines/Industrial/Natural Gas/4-cycle**  **Rich Burn NSCR 90** 

3a.2Mobile Controls/Rules Used in Baseline and Contr ol Scenarios
3a.2.1 Diesel Retrofits and Vehicle Replacement
**Retrofitting heavy-duty diesel vehicles and equipment manufactured befo re stricter**  **standards are in place – in 2007-2010 for highway eng ines 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:** 

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

 **Rebuilding nonroad engines (“rebuild/upgrade kit”)**
**We chose to focus on these strategies due to their high NOx emissions reduction potential**  **and widespread application. Additional retrofit str ategies include, but are not limited to,**  **lean NOx catalyst systems – which are another type of after-treatment device – and**  **alternative fuels. Additionally, SCRs are currently t he most likely type of control**  **technology to be used to meet EPA’s NOx 2007-2010 r equirements for HD diesel trucks**  **and 2008-2011 requirements for nonroad equipment. A ctual emissions reductions may**  **vary significantly by strategy and by the type and ag e of the engine and its application.** 
**To estimate the potential emissions reductions from this m easure, we applied a mix of**  **two retrofit strategies (SCRs and rebuild/upgrade k its) for the 2020 inventory of:** 

 **Heavy-duty highway trucks class 6 & above, Model Year 19 95-2009** 

 **All diesel nonroad engines, Model Year 1991-2007, e xcept for locomotive,**  **marine, pleasure craft, & aircraft engines**
**Class 6 and above trucks comprise the bulk of the NO x emissions inventory from heavy**  **duty highway vehicles, so we did not include trucks below class 6. We chose not to**  **include locomotive and marine engines in our analys is since EPA has proposed**  **regulations to address these engines, which will sig nificantly impact the emissions**  **inventory and emission reduction potential from retrof its in 2020. There was also not**  **enough data available to assess retrofit strategies for existing aircraft and pleasure craft**  **engines, so we did not include them in this analysis . In addition, EPA is in the process of**  **negotiating standards for new aircraft engines.**
**The lower bound in the model year range – 1995 for highway vehicles and 1991 for**  **nonroad engines – reflects the first model year in w hich emissions after-treatment devices**  **can be reliably applied to the engines. Due to a v ariety of factors, devices are at a higher** **3a-12**

**risk of failure for earlier model years. We expect the engines manufactured before the**  **lower bound year that are still in existence in 2020 to be retired quickly due to natural**  **turnover, therefore, we have not included strategies for pre-1995/1991 eng ines because of**  **the strategies’ relatively small impact on emission s. The upper bound in the model year**  **range reflects the last year before more stringent emissi ons standards will be fully**  **phased-in.**
**We chose the type of strategy to apply to each mode l year of highway vehicles and**  **nonroad equipment based on our technical assessment o f which strategies would achieve**  **reliable results at the lowest cost. After-treatme nt devices can be more cost-effective**  **than rebuild and vice versa depending on the emissio ns rate, application, usage rates, and**  **expected life of the engine. The performance of afte r-treatment devices, for example,**  **depends heavily upon the model year of the engine; some ol der engines may not be**  **suitable for after-treatment devices and would be b etter candidates for rebuild/upgrade**  **kit. In certain cases, nonroad engines may not be suitable for either after-treatment**  **devices or rebuild, which is why we estimate that r etrofits are not suitable for 5% of the**  **nonroad fleet. The mix of strategies employed in th is RIA for highway vehicles and**  **nonroad engines are presented in Table 3a.4 and Tabl e 3a.5, respectively. The groupings**  **of model years for highway vehicles reflect changes in EP A’s published emissions**  **standards for new engines.**
Table 3a.4 Application of Retrofit Strategy for Hig hway Vehicles by Percentage of Fleet 

### Model Year SCR <1995 0% 1995-2006 100% 2007-2009 50% >2009 0%

Table 3a.5 Application of Retrofit Strategy for Non road Equipment by Percentage of Fleet 

### Model Year Rebuild/Upgrade kit SCR 1991-2007 50% 50%

**The expected emissions reductions from SCR’s are bas ed on data derived from EPA**  **regulations (Control of Emissions of Air Pollution from 2004 and Later Model Year**  **Heavy-duty Highway Engines and Vehicles published Octo ber 2000), interviews with**  **component manufacturers, and EPA’s Summary of Potenti al Retrofit Technologies. This**  **information is available at www.epa.gov/otaq/retrofit/retropotentialtech.htm**  **. The estimates for highway vehicles and nonroad engines ar**  **e presented in Table 3a.6 and Table 3a.7, respectively.** **3a-13**

Table 3a.6: Percentage Emissions Reduction by Highwa y Vehicle Retrofit Strategy



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

Table 3a.7: Percentage Emissions Reduction by Nonro ad Equipment Retrofit Strategy 

### Strategy PM CO HC NOx SCR (+DPF) 90% 90% 90% 70% Rebuild/Upgrade Kit 30% 15% 70% 40%

**It is important to note that there is a great deal of variability among types of engines**  **(especially nonroad), the applicability of retrofit str ategies, and the associated emissions**  **reductions. We applied the retrofit emissions reduction estimates to engine s across the**  **board (e.g. retrofits for bulldozers are estimated to produce the same percentag e reduction**  **in emissions as for agricultural mowers). We did this i n order to simplify model runs,**  **and, in some cases, where we did not have enough da ta to differentiate emissions**  **reductions for different types of highway vehicles and no nroad equipment. We believe**  **the estimates used in the RIA, however, reflect the best available estimates of emissions**  **reductions that can be expected from retrofitting the heavy-duty diesel fleet.** 
**Using the retrofit module in EPA’s National Mobile Inv entory Model (NMIM) available**  **at http://www.epa.gov/otaq/nmim.htm**  **, we calculated the total percentage reduction in emissions (PM, NOx, HC, and CO) from the retrofit m**  **easure for each relevant engine category (source category code, or SCC) for each cou** **nty in 2020. To evaluate this change in the emissions inventory, we conducted both**  **a baseline and control analysis. Both analyses were based on NMIM 2005 (version NMIM200**  **60310), NONROAD2005 (February 2006), and MOBILE6.2.03 which included th**  **e updated diesel PM file PMDZML.csv dated March 17, 2006.** 
**For the control analysis, we applied the retrofit me asure corresponding to the percent**  **reductions of the specified pollutants in Tables 3a.6 an d 3a.7 to the specified model years**  **in Tables 1 and 2 of the relevant SCCs. Fleet turn over rates are modeled in the NMIM,**  **so we applied the retrofit measure to the 2007 fleet inventory, and then evaluated the**  **resulting emissions inventory in 2020. The timing o f the application of the retrofit**  **measure is not a factor; retrofits only need to take place prior to the attainment date target**  **(2020 for this RIA). For example, if retrofit devi ces are installed on 1995 model year**  **bulldozers in 2007, the only impact on emissions in 2 020 will be from the expected**  **inventory of 1995 model year bulldozer emissions in 202 0.** **3a-14**

**We then compared the baseline and control analyses to deter mine the percent reduction in**  **emissions we estimate from this measure for the rel evant SCC codes in the targeted**  **nonattainment areas.**
**Pollutants and Source Categories Affected by Measur e (SCC)**  **NOx, and HC**

3a.2.2 Implement Continuous Inspection and Maintenance Using Remote Onboard Diagnostics (OBD)

**Continuous Inspection and Maintenance (I/M) is a ne w way to check the status of OBD**  **systems on light-duty OBD-equipped vehicles. It involv es equipping subject vehicles**  **with some type of transmitter that attaches to the O BD port. The device transmits the**  **status of the OBD system to receivers distributed arou nd the I/M area. Transmission may**  **be through radio-frequency, cellular or wi-fi means . Radio frequency and cellular**  **technologies are currently being used in the states of O regon, California and Maryland.** 
**Current I/M programs test light-duty vehicles on a periodic basis – either annually or**  **biennially. Emission reduction credit is assigned b ased on test frequency. Using**  **Continuous I/M, vehicles are continuously monitored as they are operated throughout the**  **non-attainment area. When a vehicle experiences an OBD f ailure, the motorist is notified**  **and is required to get repairs within the normal gra ce period – typically about a month.**  **Thus, Continuous I/M will result in repairs happeni ng essentially whenever a malfunction**  **occurs that would cause the check engine light to i lluminate.**  **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 w** **hen going from a biennial program to an annual program. The same increment of**  **credit applies going from an annual program to a continuous program.** 
**Pollutants and Source Categories Affected by Measur e (SCC):** 

x **All 1996 and newer light-duty gasoline vehicles and trucks:** 

x **All 1996 and newer 2201001000 Light Duty Gasoline Ve hicles (LDGV), Total:**  **All Road Types**

x **All 1996 and newer 2201020000 Light Duty Gasoline T rucks 1 (LDGT1), Total:**  **All Road Types**

x **All 1996 and newer 2201040000 Light Duty Gasoline T rucks 2 (LDGT2), Total:**  **All Road Types**
**OBD systems on light duty vehicles are required to il luminate the malfunction**  **indicator lamp whenever emissions of HC, CO or NOx woul d exceed 1.5 times the**  **vehicle’s certification standard. Thus, the benefits of this measure will affect al l three**  **criteria pollutants. MOBILE6 was used to estimate the em ission reduction benefits of**  **Continuous I/M, using the methodology discussed above**

**. 3a-15** 

3a.2.3 Eliminating Long Duration Truck Idling
**Virtually all long duration truck idling – idling t hat lasts for longer than 15 minutes –**  **from heavy-duty diesel class 8a and 8b trucks can b e eliminated with two strategies:** 

 **truck stop & terminal electrification (TSE)**

 **mobile idle reduction technologies (MIRTs) such as auxiliary power units,**  **generator sets, and direct-fired heaters**
**TSE can eliminate idling when trucks are resting at truc k stops or public rest areas and**  **while trucks are waiting to perform a task at priva te distribution terminals. When truck**  **spaces are electrified, truck drivers can shut down their engines and use electricity to**  **power equipment which supplies air conditioning, heat, and electrical 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 Idle Reduction Technology**  **page at http://www.epa.gov/otaq/smartway/idlingtechnologies.**  **htm**

**. This measure demonstrates the potential emissions redu**  **ctions if every class 8a and 8b truck is equipped with a MIRT or has dependable acces**  **s to sites with TSE in 2020.**
**To estimate the potential emissions reduction from thi s measure, we applied a reduction**  **equal to the full amount of the emissions attributed to long duration idling in the**  **MOBILE model, which is estimated to be 3.4% of the total NOx emissions from class 8a**  **and 8b heavy duty diesel trucks. Since the MOBILE model does not distinguish between**  **idling and operating emissions, EPA estimates idlin g emissions in the inventory based on**  **fuel conversion factors. The inventory in the MOBI LE model, however, does not fully**  **capture long duration idling emissions. There is e vidence that idling may represent a**  **much greater share than 3.4% of the real world invent ory, based on engine control**  **module data from long haul trucking companies. As such, we believe the emissions**  **reductions demonstrated from this measure in the RI A represent ambitious but realistic**  **targets. For more information on determining baseli ne idling activity see EPA’s**  **"Guidance for Quantifying and Using Long-Duration Truck I dling Emission Reductions**  **in State Implementation Plans and Transportation Conformity ” available at**  **http://www.epa.gov/smartway/idle-guid.htm**

**. Pollutants and Source Categories Affected by Measure**  **(SCC): NO x**  Table 3a.8 Class 8a and 8b heavy duty diesel trucks (decrease NOx for all SCCs)

SCC Note: All SCC Descriptions below begin with "Mobile Sources; Highway Vehicles - Diesel;" **2230074110 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rura**  **l Interstate: Total 2230074130**  **Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rura l Other Principal Arterial: Total**  **2230074150 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rura**  **l Minor Arterial: Total 2230074170**  **Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rura l Major Collector: Total**  **2230074190 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rura**  **l Minor Collector: Total 3a-16** 

**2230074210 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rura**  **l Local: Total 2230074230**  **Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Urba n Interstate: Total**  **2230074250 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Urba** **n Other Freeways and Expressways: Total 2230074270**  **Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Urba n Other Principal Arterial: Total**  **2230074290 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Urba** **n Minor Arterial: Total 2230074310**  **Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Urba n Collector: Total**  **2230074330 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Urba** **n Local: Total**

**Estimated Emissions Reduction from Measure (%): 3.4 % decrease in NOx for all SCCs**  **affected by measure**
3a.2.4 Commuter Programs
**Commuter programs recognize and support employers wh o provide incentives to**  **employees to reduce light-duty vehicle emissions. Employers implement a wide range of**  **incentives to affect change in employee commuting hab its including transit subsidies,**  **bike-friendly facilities, telecommuting policies, an d preferred parking for vanpools and**  **carpools. The commuter measure in this RIA reflects a mixed package of incentives.** 
**This measure demonstrates the potential emissions red uctions from providing commuter**  **incentives to 10% and 25% of the commuter population in 2020.** 
**We used the findings from a recent Best Workplaces for Commuters survey, which was**  **an EPA sponsored employee trip reduction program, to estimate the potential emissions**  **reductions from this measure. 7**  **The BWC survey found that, on average, employees at workplaces with comprehensive commuter programs emit 15% fewer em**  **issions than employees at workplaces that do not offer a compreh**  **ensive commuter program.**
**We believe that getting 10-25% of the workforce involve d in commuter programs is**  **realistic. For modeling purposes, we divided the commut er programs measure into two**  **program penetration rates: 10% and 25%. This was m eant to provide flexibility to model**  **a lower penetration rate for areas that need only low levels of emissions reductions to**  **achieve attainment.**
**According to the 2001 National Household Transportati on Survey (NHTS) published by**  **DOT, commute VMT represents 27% of total VMT. Based on this information, we**  **calculated that BWC would reduce light-duty gasolin e emissions by 0.4% and 1% with a**  **10% and 25% program penetration rate, respectively.**
**Pollutants and Source Categories Affected by Measure (SCC): NO**  **x , and VOC** 

**7 Herzog, E., Bricka, S., Audette, L., and Rockwell, J., 2005.**  Do Employee Commuter Benefits Reduce Vehicle Emissions and Fuel Consumption ? Results of the Fall 2004 Best Workplaces for Commuters Survey **, Transportation Research Record, Journal of the Transportation Research Board: Forthcoming.** **3a-17**

Table 3a.9 All light-duty gasoline vehicles and tru cks

SCC Note: All SCC Descriptions below begin with "Mobile Sources; Highway Vehicles - Gasoline;" **2201001110 Light Duty Gasoline Vehicles (LDGV);Rural Interstat**  **e: Total 2201001130**  **Light Duty Gasoline Vehicles (LDGV);Rural Other Pri ncipal Arterial: Total**  **2201001150 Light Duty Gasoline Vehicles (LDGV);Rural Minor Art**  **erial: Total 2201001170**  **Light Duty Gasoline Vehicles (LDGV);Rural Major Col lector: Total**  **2201001190 Light Duty Gasoline Vehicles (LDGV);Rural Minor Col**  **lector: Total 2201001210**  **Light Duty Gasoline Vehicles (LDGV);Rural Local: To tal**  **2201001230 Light Duty Gasoline Vehicles (LDGV);Urban Interstat**  **e: Total 2201001250**  **Light Duty Gasoline Vehicles (LDGV);Urban Other Fre eways and Expressways: Total**  **2201001270 Light Duty Gasoline Vehicles (LDGV);Urban Other Pri**  **ncipal Arterial: Total 2201001290**  **Light Duty Gasoline Vehicles (LDGV);Urban Minor Art erial: Total**  **2201001310 Light Duty Gasoline Vehicles (LDGV);Urban Collector**  **: Total 2201001330**  **Light Duty Gasoline Vehicles (LDGV);Urban Local: To tal**  **2201020110 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);**  **Rural Interstate: Total 2201020130**  **Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Other Principal Arterial: Total**  **2201020150 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);**  **Rural Minor Arterial: Total 2201020170**  **Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Major Collector: Total**  **2201020190 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);**  **Rural Minor Collector: Total 2201020210**  **Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Local: Total**  **2201020230 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);**  **Urban Interstate: Total 2201020250**  **Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Other Freeways and Expressways: Total**  **2201020270 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);**  **Urban Other Principal Arterial: Total 2201020290**  **Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Minor Arterial: Total**  **2201020310 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);**  **Urban Collector: Total 2201020330**  **Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Local: Total**  **2201040110 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);**  **Rural Interstate: Total 2201040130**  **Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Other Principal Arterial: Total**  **2201040150 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);**  **Rural Minor Arterial: Total 2201040170**  **Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Major Collector: Total**  **2201040190 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);**  **Rural Minor Collector: Total 2201040210**  **Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Local: Total**  **2201040230 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);**  **Urban Interstate: Total 2201040250**  **Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Other Freeways and Expressways: Total**  **2201040270 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);**  **Urban Other Principal Arterial: Total 2201040290**  **Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Minor Arterial: Total**  **2201040310 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);**  **Urban Collector: Total 2201040330**  **Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Local: Total** 
**Estimated Emissions Reduction from Measure (%): With a 10% program penetration rate: 0.4%**  **With a 25% program penetration rate: 1% 3a-18** 

3a.2.5 Reduce Gasoline RVP from 7.8 to 7.0 in Remai ning 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 me asure of gasoline volatility, as**  **well as a generic term for gasoline volatility. EPA regulates the vapor pressure of all**  **gasoline during the summer months (June 1 to September 15 at retail stations). Lower**  **RVP helps to reduce VOCs, which are a precursor to ozone formation. This control**  **measure represents the use of gasoline with a RVP limit of 7.0 psi from May through**  **September in counties with an ozone season RVP value gre ater 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 a ir quality standard (NAAQS) that the**  **plan implements. EPA can approve a state fuel requi rement as necessary only if no other**  **measures would bring about timely attainment, or if other measures exist but are**  **unreasonable or impracticable.**
**Pollutants and Source Categories Affected by Measur e (SCC):** 

x **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), To tal: All Road Types** 

x **2201040000 Light Duty Gasoline Trucks 2 (LDGT2), To tal: All Road Types** 

x **2201070000 Heavy Duty Gasoline Vehicles (HDGV), Total: A ll Road Types** 

x **2201080000 Motorcycles (MC), Total: All Road Types**

3a.2.6 Application order for Onroad and Nonroad Mobile Con trols
**Application order- 0.084 Mobile**

x **Eliminate Long Duration Idling**

x **ONRetrofit**

x **LOWRVP**

x **Best Workplaces for Commuters**
**Application order- 0.084 Nonroad**

x **Diesel C1&C2 Marine/Diesel C3 Marine - 90% Rule (addin g controls for**  **SCCs for residual fuel)**

x **ICAO Engine NOx Standards for Commercial Aircraft**

x **NRRetrofit**

x **LOWRVP**
**Application order- 0.070 Mobile**

x **Eliminate Long Duration Idling**

x **Inspection and Maintenance**

x **ONRetrofit 3a-19**  x **LOWRVP**

x **Best Workplaces for Commuters**
**Application order – 0.070 Nonroad**

x **Diesel C1&C2 Marine/Diesel C3 Marine - 90% Rule (addin g controls for**  **SCCs for residual fuel)**

x **ICAO Engine NOx Standards for Commercial Aircraft**

x **NRRetrofit**

x **LOWRVP**

3a.3 EGU Controls Used in the Control Strategy

**CAIR The data and projections presented in Section 3.2.2**  **cover the electric power sector, an industry that will achieve significant emission red**  **uctions under the Clean Air Interstate Rule (CAIR) over the next 10 to 15 years. Based on**  **an assessment of the emissions contributing to interstate transport of air pollution an**  **d available control measures, EPA determined that achieving required reductions in the**  **identified States by controlling emissions from power plants is highly cost effective. CAIR**  **will permanently cap emissions of sulfur dioxide (SO**  **2 ) and nitrogen oxides (NO**  **x ) in the eastern United States.**  **CAIR achieves large reductions of SO 2**  **and/or NO x** **emissions across 28 eastern states and the District of Columbia.** 

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)**3a-20**

**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 to NOx SIP**  **Call). This will result in significant environmenta l and health benefits and will**  **substantially reduce premature mortality in the eastern United States. The benefits will**  **continue to grow each year with further implementati on. CAIR was designed with current**  **air quality standard in mind, and requires significa nt emission reductions in the East,**  **where they are needed most and where transport of polluti on is a major concern. CAIR**  **will bring most areas in the Eastern US into attain ment with the current ozone and current**  **PM 2.5**  **standards. Some areas will need to adopt additional local control measures beyond**  **CAIR. CAIR is a regional solution to address transp ort, not a solution to all local**  **nonattainment issues. The large reductions anticipate d 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 logica**  **l 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 inte nd to use the CAIR trading programs**  **as their mechanism for meeting the emission reduction r equirements of CAIR.** 
**The analysis in this section reflects these realitie s and attempts to show, in an illustrative**  **fashion, the costs and impacts of meeting a proposed 8-hr ozone standard of 0.070 for the**  **power sector. Integrated Planning Model and Background** 
**CAIR was designed to achieve significant emissions re ductions in a highly cost-effective**  **manner to reduce the transport of fine particles th at have been found to contribute to**  **nonattainment. EPA analysis has found that the most efficient method to achieve the**  **emissions reduction targets is through a cap-and-tr ade system on the power sector that**  **States have the option of adopting. The modeling done w ith IPM assumes a region-wide**  **cap and trade system on the power sector for the St ates covered.** 
**It is important to note that the analysis herein us es the Integrated Planning Model (IPM)**  **v2.1.9 to ensure consistency with the analysis prese nted in 2006 PM NAAQS RIA and**  **report incremental results. EPA’s IPM v2.1.9 incorporates Federal and State rules and regulations adopted before March 2004 and various N**  **SR settlements. A detailed discussion of uncertainties associated with the EGU s**  **ector can be found in 2006 PM NAAQS RIA (pg. 3-50). A newer version of the model (IPM v3**  **.0) is available which includes input and model assumption updates in model**  **ing power sector. IPM v3.0 will be used in the Final Ozone NAAQS RIA as part of the**  **updated modeling platform. Additionally, other control strategies are being con**  **sidered that may be applicable to the EGU sector, which would be presented in the final Ozone**  **RIA.**
**The economic modeling using IPM presented in this and other chapters has been**  **developed for specific analyses of the power sector . EPA’s modeling is based on its best**  **judgment for various input assumptions that are uncertain, particularly assumptions for**  **future fuel prices and electricity demand growth. To some degree, EPA addresses the**  **uncertainty surrounding these two assumptions throu gh sensitivity analyses. More detail** **3a-21**

**on IPM can be found in the model documentation, which provides additional information**  **on the assumptions discussed here as well as all ot her assumptions and inputs to the**  **model ( http://www.epa.gov/airmarkets/progsregs/epa-ipm/past**  **-modeling.html ).** 
**EGU NOx Emission Control Technologies**
**The Integrated Planning Model v2.1.9 (IPM) includes SO**  **2 , NO**  **x , and mercury (Hg)**  **emission control technology options for meeting exi sting and future federal, regional, and**  **state, SO 2**  **, NO x**  **and Hg emission limits. The NOx control technology option s include**  **Selective Catalytic Reduction (SCR) system and Selec tive Non-Catalytic Reduction**  **(SNCR) systems. It is important to note that beyon d these emission control options, IPM**  **offers other compliance options for meeting emission limits. These include fuel**  **switching, re-powering, and adjustments in the dispa tching of electric generating units.** 

**Table 3a.10 summarizes retrofit NOx emission contro l performance assumptions.** 
Table 3a.10. Summary of Retrofit NOx Emission Control Pe rformance Assumptions

Selective Catalytic Reduction

(SCR) Selective Non-Catalytic Reduction (SNCR)

Unit Type Coal Oil/Gas* Coal Oil/Gas*

Percent Removal 90%

down to 0.06 lb/mmBtu 80% 35% 50%

Size Applicability Units

Қ 100 MW Units

Қ 25 MW Units

Қ 25 MW and Units < 200 MW Units

Қ 25 MW *** Controls to oil- or gas-fired EGUs are not applie d as part of the EGU control strategy**  **included in this 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/mmBtu in IPM v2.1.9..**  **Potential (new) coal-fired, combined cycle, and IGCC units ar e modeled to be**  **constructed with SCR systems and designed to have em ission rates ranging between 0.02**  **and 0.06 lb NOx/mmBtu.**
**Detailed cost and performance derivations for NOx cont rols are discussed in detail in the**  **EPA’s documentation of IPM (http://www.epa.gov/airmarke ts/progsregs/epa-ipm/past**  **modeling.html). 3a-22** 

3a.4 Emissions Reductions by Sector
**Figures 3a.2- 3a.6 show the NOx reductions for each secto r under the 0.070 ppm control**  **strategy.**

Figure 3a.2 Tons of Nitrogen Oxide (NOx) Emissions Reduced from Electrical

Generating Unit (EGU) Sources*

***Reductions are negative and increases are positive**  The -99 - +100 range is not shown because these a** **re small county-level NOx reductions or increases that likely had little to no impa**  **ct on ozone estimates. Most counties in this range had NOx differences of under 1 ton.** **3a-23**

Figure 3a.3 Tons of Nitrogen Oxide (NOx) Emissions Reduced from Non-EGU Point Sources*

***Reductions are negative and increases are positive**  The -99 - 0 range is not shown because these are smal**  **l 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 of under 1 ton.** **3a-24**

Figure 3a.4 Tons of Nitrogen Oxide (NOx) Emissions Reduced from Area Sources*

***Reductions are negative and increases are positive**  The -99 – 0 range is not shown because these are small count** **y-level NOx reductions or increases that likely had little to no impact on oz**  **one estimates. Most counties in this range had NOx differences of under 1 ton.** **3a-25**

Figure 3a.5 Tons of Nitrogen Oxide (NOx) Emissions Reduced from Nonroad Sources*

***Reductions are negative and increases are positive**  The -99 - 0 range is not shown because these are smal**  **l 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 of under 1 ton.** **3a-26**

Figure 3a.6 Tons of Nitrogen Oxide (NOx) Emissions Reduced from Onroad Sources*

***Reductions are negative and increases are positive**  The -99 - 0 range is not shown because these are smal**  **l 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 of under 1 ton.** **3a-27**

3a.5 Change in Ozone Concentrations Between Baselin e and Post-0.070 ppm Control Strategy Modeling
Table 3a.11 Changes in Ozone Concentrations between Base line and Post-0.070 ppm Control Strategy Modeling

State County Baseline 8-hour ozone DV (ppm) Control Scenario

8-hour ozone DV (ppm) Change

(ppm)

**Alabama Baldwin 0.067 0.066 0.001 Alabama Clay 0.060 0.056 0.004**  **Alabama Elmore 0.062 0.060 0.002 Alabama Jefferson 0.064 0.063 0.001**  **Alabama Madison 0.063 0.061 0.002 Alabama Mobile 0.068 0.068 0.000**  **Alabama Montgomery 0.061 0.060 0.001 Alabama Morgan 0.066 0.065 0.001**  **Alabama Shelby 0.066 0.065 0.001 Alabama Tuscaloosa 0.057 0.056 0.001**  **Arizona Maricopa 0.078 0.077 0.001 Arizona Pinal 0.072 0.071 0.001**  **Arkansas Crittenden 0.075 0.072 0.003 Arkansas Pulaski 0.069 0.068 0.001**  **California Alameda 0.067 0.067 0.000 California Amador 0.068 0.068 0.000**  **California Butte 0.069 0.069 0.000 California Calaveras 0.073 0.073 0.000**  **California Colusa 0.059 0.059 0.000 California Contra Costa 0.070 0.070 0.000**  **California El Dorado 0.080 0.080 0.000 California Fresno 0.092 0.092 0.000**  **California Glenn 0.060 0.060 0.000 California Imperial 0.072 0.072 0.000**  **California Kern 0.096 0.096 0.000 California Kings 0.079 0.079 0.000**  **California Lake 0.053 0.053 0.000 California Los Angeles 0.105 0.105 0.000**  **California Madera 0.075 0.075 0.000 California Mariposa 0.073 0.073 0.000**  **California Merced 0.080 0.080 0.000 California Monterey 0.054 0.054 0.000** **3a-28**

**California Napa 0.051 0.051 0.000 California Nevada 0.076 0.076 0.000**  **California Orange 0.066 0.065 0.001 California Placer 0.076 0.076 0.000**  **California Riverside 0.102 0.102 0.000 California Sacramento 0.076 0.076 0.000**  **California San Benito 0.067 0.067 0.000 California San Bernardino 0.129 0.129 0.000**  **California San Diego 0.077 0.077 0.000 California San Joaquin 0.067 0.067 0.000**  **California San Luis Obispo 0.053 0.053 0.000 California Santa Barbara 0.065 0.065 0.000**  **California Santa Clara 0.065 0.065 0.000 California Santa Cruz 0.054 0.055 -0.001**  **California Shasta 0.058 0.058 0.000 California Solano 0.057 0.057 0.000**  **California Sonoma 0.049 0.049 0.000 California Stanislaus 0.076 0.076 0.000**  **California Sutter 0.065 0.065 0.000 California Tehama 0.066 0.066 0.000**  **California Tulare 0.088 0.088 0.000 California Tuolumne 0.073 0.073 0.000**  **California Ventura 0.079 0.080 -0.001 California Yolo 0.064 0.064 0.000**  **Colorado Adams 0.061 0.060 0.001 Colorado Arapahoe 0.073 0.072 0.001**  **Colorado Boulder 0.066 0.064 0.002 Colorado Denver 0.068 0.067 0.001**  **Colorado Douglas 0.076 0.076 0.000 Colorado El Paso 0.064 0.063 0.001**  **Colorado Jefferson 0.078 0.076 0.002 Colorado Larimer 0.069 0.067 0.002**  **Colorado Weld 0.067 0.065 0.002 Connecticut Fairfield 0.088 0.087 0.001**  **Connecticut Hartford 0.069 0.067 0.002 Connecticut Litchfield 0.063 0.061 0.002**  **Connecticut Middlesex 0.081 0.080 0.001 Connecticut New Haven 0.084 0.083 0.001**  **Connecticut New London 0.072 0.070 0.002 Connecticut Tolland 0.071 0.069 0.002**  **D.C. Washington 0.076 0.073 0.003 3a-29** 

**Delaware Kent 0.072 0.070 0.002 Delaware New Castle 0.075 0.073 0.002**  **Delaware Sussex 0.070 0.068 0.002 Florida Bay 0.067 0.066 0.001**  **Florida Brevard 0.055 0.053 0.002 Florida Duval 0.058 0.057 0.001**  **Florida Escambia 0.069 0.069 0.000 Florida Hillsborough 0.072 0.071 0.001**  **Florida Manatee 0.067 0.065 0.002 Florida Pasco 0.061 0.060 0.001**  **Florida Pinellas 0.064 0.063 0.001 Florida Santa Rosa 0.065 0.064 0.001**  **Florida Sarasota 0.063 0.061 0.002 Georgia Bibb 0.073 0.069 0.004**  **Georgia Chatham 0.057 0.056 0.001 Georgia Cherokee 0.055 0.052 0.003**  **Georgia Cobb 0.072 0.068 0.004 Georgia Coweta 0.072 0.064 0.008**  **Georgia Dawson 0.058 0.055 0.003 Georgia De Kalb 0.076 0.072 0.004**  **Georgia Douglas 0.071 0.067 0.004 Georgia Fayette 0.069 0.066 0.003**  **Georgia Fulton 0.080 0.076 0.004 Georgia Glynn 0.058 0.057 0.001**  **Georgia Gwinnett 0.067 0.064 0.003 Georgia Henry 0.072 0.068 0.004**  **Georgia Murray 0.062 0.059 0.003 Georgia Muscogee 0.065 0.061 0.004**  **Georgia Paulding 0.068 0.065 0.003 Georgia Richmond 0.067 0.063 0.004**  **Georgia Rockdale 0.071 0.067 0.004 Illinois Adams 0.062 0.057 0.005**  **Illinois Champaign 0.064 0.062 0.002 Illinois Clark 0.057 0.056 0.001**  **Illinois Cook 0.083 0.083 0.000 Illinois Du Page 0.065 0.064 0.001**  **Illinois Effingham 0.062 0.061 0.001 Illinois Hamilton 0.066 0.064 0.002**  **Illinois Jersey 0.074 0.069 0.005 Illinois Kane 0.067 0.066 0.001**  **Illinois Lake 0.074 0.073 0.001 3a-30** 

**Illinois Macon 0.060 0.059 0.001 Illinois Macoupin 0.064 0.060 0.004**  **Illinois Madison 0.071 0.066 0.005 Illinois McHenry 0.070 0.068 0.002**  **Illinois McLean 0.063 0.061 0.002 Illinois Peoria 0.066 0.064 0.002**  **Illinois Randolph 0.065 0.062 0.003 Illinois Rock Island 0.058 0.057 0.001**  **Illinois Sangamon 0.060 0.058 0.002 Illinois St Clair 0.072 0.069 0.003**  **Illinois Will 0.068 0.067 0.001 Illinois Winnebago 0.061 0.060 0.001**  **Indiana Allen 0.070 0.068 0.002 Indiana Boone 0.072 0.069 0.003**  **Indiana Carroll 0.066 0.064 0.002 Indiana Clark 0.076 0.074 0.002**  **Indiana Delaware 0.069 0.067 0.002 Indiana Floyd 0.071 0.070 0.001**  **Indiana Gibson 0.056 0.054 0.002 Indiana Greene 0.069 0.067 0.002**  **Indiana Hamilton 0.076 0.073 0.003 Indiana Hancock 0.074 0.071 0.003**  **Indiana Hendricks 0.071 0.069 0.002 Indiana Huntington 0.067 0.065 0.002**  **Indiana Jackson 0.068 0.065 0.003 Indiana Johnson 0.070 0.068 0.002**  **Indiana La Porte 0.075 0.073 0.002 Indiana Lake 0.084 0.083 0.001**  **Indiana Madison 0.071 0.068 0.003 Indiana Marion 0.075 0.072 0.003**  **Indiana Morgan 0.070 0.066 0.004 Indiana Perry 0.071 0.071 0.000**  **Indiana Porter 0.078 0.077 0.001 Indiana Posey 0.071 0.070 0.001**  **Indiana Shelby 0.077 0.074 0.003 Indiana St Joseph 0.069 0.067 0.002**  **Indiana Vanderburgh 0.068 0.066 0.002 Indiana Vigo 0.070 0.065 0.005**  **Indiana Warrick 0.068 0.067 0.001 Iowa Clinton 0.063 0.062 0.001**  **Iowa Scott 0.066 0.065 0.001 3a-31** 

**Kansas Wyandotte 0.070 0.069 0.001 Kentucky Bell 0.063 0.062 0.001**  **Kentucky Boone 0.067 0.066 0.001 Kentucky Boyd 0.072 0.067 0.005**  **Kentucky Bullitt 0.067 0.064 0.003 Kentucky Campbell 0.077 0.073 0.004**  **Kentucky Carter 0.064 0.061 0.003 Kentucky Christian 0.066 0.065 0.001**  **Kentucky Daviess 0.062 0.062 0.000 Kentucky Edmonson 0.067 0.066 0.001**  **Kentucky Fayette 0.063 0.061 0.002 Kentucky Graves 0.068 0.066 0.002**  **Kentucky Greenup 0.068 0.064 0.004 Kentucky Hancock 0.067 0.067 0.000**  **Kentucky Hardin 0.068 0.065 0.003 Kentucky Henderson 0.066 0.065 0.001**  **Kentucky Jefferson 0.072 0.070 0.002 Kentucky Jessamine 0.062 0.063 -0.001**  **Kentucky Kenton 0.073 0.069 0.004 Kentucky Livingston 0.071 0.069 0.002**  **Kentucky McCracken 0.069 0.067 0.002 Kentucky McLean 0.065 0.064 0.001**  **Kentucky Oldham 0.072 0.070 0.002 Kentucky Pulaski 0.065 0.064 0.001**  **Kentucky Scott 0.056 0.055 0.001 Kentucky Simpson 0.066 0.065 0.001**  **Kentucky Trigg 0.060 0.058 0.002 Kentucky Warren 0.066 0.065 0.001**  **Louisiana Ascension 0.071 0.066 0.005 Louisiana Bossier 0.073 0.070 0.003**  **Louisiana Caddo 0.068 0.065 0.003 Louisiana Calcasieu 0.072 0.067 0.005**  **Louisiana East Baton Rouge 0.077 0.074 0.003 Louisiana Grant 0.063 0.059 0.004**  **Louisiana Iberville 0.076 0.072 0.004 Louisiana Jefferson 0.072 0.069 0.003**  **Louisiana Lafayette 0.070 0.064 0.006 Louisiana Lafourche 0.072 0.068 0.004**  **Louisiana Livingston 0.072 0.068 0.004 Louisiana Orleans 0.060 0.058 0.002**  **Louisiana Ouachita 0.068 0.064 0.004 3a-32** 

**Louisiana Pointe Coupee 0.064 0.060 0.004 Louisiana St Bernard 0.067 0.065 0.002**  **Louisiana St Charles 0.068 0.066 0.002 Louisiana St James 0.069 0.065 0.004** 

**Louisiana St John The** 

**Baptist 0.072 0.069 0.003 Louisiana St Mary 0.068 0.062 0.006** 

**Louisiana West Baton** 

**Rouge 0.074 0.071 0.003 Maine Cumberland 0.065 0.063 0.002**  **Maine Hancock 0.070 0.067 0.003 Maine Kennebec 0.060 0.057 0.003**  **Maine Knox 0.063 0.060 0.003 Maine Penobscot 0.062 0.059 0.003**  **Maine York 0.069 0.066 0.003 Maryland Anne Arundel 0.076 0.074 0.002**  **Maryland Baltimore 0.077 0.075 0.002 Maryland Calvert 0.065 0.063 0.002**  **Maryland Carroll 0.068 0.066 0.002 Maryland Cecil 0.078 0.075 0.003**  **Maryland Charles 0.070 0.068 0.002 Maryland Frederick 0.067 0.065 0.002**  **Maryland Harford 0.084 0.082 0.002 Maryland Kent 0.075 0.072 0.003**  **Maryland Montgomery 0.072 0.070 0.002 Maryland Prince Georges 0.075 0.072 0.003**  **Maryland Washington 0.067 0.063 0.004 Massachusetts Barnstable 0.072 0.070 0.002**  **Massachusetts Berkshire 0.067 0.066 0.001 Massachusetts Bristol 0.072 0.069 0.003**  **Massachusetts Essex 0.071 0.070 0.001 Massachusetts Hampden 0.070 0.068 0.002**  **Massachusetts Hampshire 0.068 0.066 0.002 Massachusetts Middlesex 0.067 0.064 0.003**  **Massachusetts Suffolk 0.067 0.065 0.002 Massachusetts Worcester 0.064 0.062 0.002**  **Michigan Allegan 0.075 0.072 0.003 Michigan Benzie 0.070 0.068 0.002**  **Michigan Berrien 0.072 0.070 0.002 Michigan Cass 0.069 0.067 0.002**  **Michigan Clinton 0.066 0.062 0.004 3a-33** 

**Michigan Genesee 0.067 0.064 0.003 Michigan Huron 0.070 0.067 0.003**  **Michigan Ingham 0.066 0.062 0.004 Michigan Kalamazoo 0.065 0.062 0.003**  **Michigan Kent 0.067 0.064 0.003 Michigan Lenawee 0.069 0.063 0.006**  **Michigan Macomb 0.080 0.078 0.002 Michigan Mason 0.072 0.070 0.002**  **Michigan Missaukee 0.064 0.061 0.003 Michigan Muskegon 0.074 0.071 0.003**  **Michigan Oakland 0.077 0.075 0.002 Michigan Ottawa 0.070 0.067 0.003**  **Michigan St Clair 0.073 0.071 0.002 Michigan Washtenaw 0.076 0.072 0.004**  **Michigan Wayne 0.076 0.073 0.003 Minnesota Anoka 0.058 0.057 0.001**  **Minnesota Washington 0.059 0.059 0.000 Mississippi De Soto 0.069 0.067 0.002**  **Mississippi Hancock 0.070 0.068 0.002 Mississippi Harrison 0.064 0.067 -0.003**  **Mississippi Hinds 0.055 0.053 0.002 Mississippi Jackson 0.069 0.070 -0.001**  **Mississippi Warren 0.054 0.051 0.003 Missouri Clay 0.070 0.068 0.002**  **Missouri Jefferson 0.076 0.072 0.004 Missouri Platte 0.069 0.068 0.001**  **Missouri St Charles 0.076 0.072 0.004 Missouri St Louis 0.079 0.075 0.004**  **Missouri St Louis City 0.078 0.075 0.003 Missouri Ste Genevieve 0.068 0.064 0.004**  **Nevada Clark 0.072 0.072 0.000 Nevada Washoe 0.063 0.063 0.000**  **New Hampshire Hillsborough 0.063 0.060 0.003**  **New Hampshire Rockingham 0.063 0.061 0.002**  **New Jersey Atlantic 0.071 0.069 0.002 New Jersey Bergen 0.077 0.075 0.002**  **New Jersey Camden 0.082 0.080 0.002 New Jersey Cumberland 0.073 0.071 0.002**  **New Jersey Essex 0.056 0.055 0.001 3a-34** 

**New Jersey Gloucester 0.080 0.078 0.002 New Jersey Hudson 0.074 0.073 0.001**  **New Jersey Hunterdon 0.078 0.077 0.001 New Jersey Mercer 0.083 0.081 0.002**  **New Jersey Middlesex 0.081 0.079 0.002 New Jersey Monmouth 0.078 0.077 0.001**  **New Jersey Morris 0.077 0.075 0.002 New Jersey Ocean 0.084 0.081 0.003**  **New Jersey Passaic 0.071 0.069 0.002 New Mexico Dona Ana 0.071 0.070 0.001**  **New Mexico San Juan 0.071 0.068 0.003 New York Albany 0.064 0.063 0.001**  **New York Bronx 0.069 0.068 0.001 New York Chautauqua 0.074 0.070 0.004**  **New York Dutchess 0.067 0.066 0.001 New York Erie 0.079 0.075 0.004**  **New York Jefferson 0.075 0.072 0.003 New York Monroe 0.073 0.072 0.001**  **New York Niagara 0.076 0.075 0.001 New York Orange 0.063 0.061 0.002**  **New York Putnam 0.070 0.068 0.002 New York Queens 0.068 0.067 0.001**  **New York Richmond 0.074 0.072 0.002 New York Saratoga 0.066 0.065 0.001**  **New York Suffolk 0.086 0.084 0.002 New York Ulster 0.065 0.063 0.002**  **New York Wayne 0.070 0.068 0.002 New York Westchester 0.075 0.074 0.001**  **North Carolina Alexander 0.066 0.064 0.002**  **North Carolina Buncombe 0.065 0.065 0.000**  **North Carolina Camden 0.063 0.062 0.001**  **North Carolina Caswell 0.063 0.059 0.004**  **North Carolina Chatham 0.063 0.061 0.002**  **North Carolina Cumberland 0.065 0.063 0.002**  **North Carolina Davie 0.067 0.065 0.002** **3a-35**

**North Carolina Durham 0.063 0.060 0.003**  **North Carolina Edgecombe 0.066 0.064 0.002**  **North Carolina Forsyth 0.068 0.065 0.003**  **North Carolina Franklin 0.063 0.060 0.003**  **North Carolina Granville 0.067 0.065 0.002**  **North Carolina Guilford 0.064 0.061 0.003**  **North Carolina Johnston 0.062 0.059 0.003**  **North Carolina Lincoln 0.069 0.067 0.002**  **North Carolina Mecklenburg 0.074 0.072 0.002**  **North Carolina New Hanover 0.062 0.061 0.001**  **North Carolina Northampton 0.067 0.064 0.003**  **North Carolina Person 0.071 0.068 0.003**  **North Carolina Randolph 0.063 0.060 0.003**  **North Carolina Rockingham 0.064 0.061 0.003**  **North Carolina Rowan 0.073 0.071 0.002**  **North Carolina Union 0.065 0.063 0.002**  **North Carolina Wake 0.066 0.064 0.002**  **Ohio Allen 0.071 0.067 0.004 Ohio Ashtabula 0.077 0.073 0.004**  **Ohio Butler 0.073 0.070 0.003 Ohio Clark 0.068 0.063 0.005**  **Ohio Clermont 0.071 0.068 0.003 Ohio Clinton 0.074 0.070 0.004**  **Ohio Cuyahoga 0.071 0.068 0.003 Ohio Delaware 0.071 0.068 0.003**  **Ohio Franklin 0.076 0.073 0.003 Ohio Geauga 0.080 0.076 0.004**  **Ohio Greene 0.068 0.062 0.006 3a-36** 

**Ohio Hamilton 0.074 0.070 0.004 Ohio Jefferson 0.067 0.064 0.003**  **Ohio Knox 0.069 0.065 0.004 Ohio Lake 0.076 0.073 0.003**  **Ohio Lawrence 0.069 0.065 0.004 Ohio Licking 0.069 0.066 0.003**  **Ohio Lorain 0.071 0.068 0.003 Ohio Lucas 0.072 0.069 0.003**  **Ohio Madison 0.068 0.063 0.005 Ohio Mahoning 0.071 0.068 0.003**  **Ohio Medina 0.069 0.066 0.003 Ohio Miami 0.065 0.061 0.004**  **Ohio Montgomery 0.068 0.062 0.006 Ohio Portage 0.074 0.070 0.004**  **Ohio Preble 0.061 0.058 0.003 Ohio Stark 0.071 0.068 0.003**  **Ohio Summit 0.075 0.071 0.004 Ohio Trumbull 0.073 0.070 0.003**  **Ohio Warren 0.071 0.068 0.003 Ohio Washington 0.064 0.061 0.003**  **Ohio Wood 0.070 0.067 0.003 Oklahoma Cleveland 0.065 0.064 0.001**  **Oklahoma Marshall 0.069 0.067 0.002 Oklahoma Mc Clain 0.067 0.065 0.002**  **Oklahoma Oklahoma 0.067 0.065 0.002 Oklahoma Tulsa 0.073 0.070 0.003**  **Pennsylvania Allegheny 0.079 0.076 0.003 Pennsylvania Armstrong 0.072 0.069 0.003**  **Pennsylvania Beaver 0.076 0.073 0.003 Pennsylvania Berks 0.071 0.068 0.003**  **Pennsylvania Blair 0.065 0.063 0.002 Pennsylvania Bucks 0.084 0.082 0.002**  **Pennsylvania Cambria 0.071 0.068 0.003 Pennsylvania Centre 0.066 0.064 0.002**  **Pennsylvania Chester 0.075 0.073 0.002 Pennsylvania Clearfield 0.068 0.065 0.003**  **Pennsylvania Dauphin 0.070 0.068 0.002 Pennsylvania Delaware 0.074 0.073 0.001**  **Pennsylvania Erie 0.069 0.067 0.002 Pennsylvania Franklin 0.070 0.068 0.002**  **Pennsylvania Greene 0.069 0.066 0.003 3a-37** 

**Pennsylvania Lackawanna 0.064 0.062 0.002 Pennsylvania Lancaster 0.071 0.068 0.003**  **Pennsylvania Lawrence 0.063 0.059 0.004 Pennsylvania Lehigh 0.071 0.069 0.002**  **Pennsylvania Luzerne 0.064 0.063 0.001 Pennsylvania Lycoming 0.059 0.057 0.002**  **Pennsylvania Mercer 0.073 0.069 0.004 Pennsylvania Montgomery 0.078 0.076 0.002**  **Pennsylvania Northampton 0.072 0.070 0.002 Pennsylvania Perry 0.063 0.061 0.002**  **Pennsylvania Philadelphia 0.080 0.078 0.002 Pennsylvania Washington 0.070 0.067 0.003**  **Pennsylvania Westmoreland 0.070 0.067 0.003 Pennsylvania York 0.071 0.067 0.004**  **Rhode Island Kent 0.074 0.072 0.002 Rhode Island Providence 0.071 0.068 0.003**  **Rhode Island Washington 0.075 0.072 0.003 South**  **Carolina Anderson 0.067 0.065 0.002**  **South Carolina Berkeley 0.058 0.057 0.001**  **South Carolina Charleston 0.057 0.055 0.002**  **South Carolina Cherokee 0.063 0.061 0.002**  **South Carolina Chester 0.064 0.061 0.003**  **South Carolina Edgefield 0.063 0.058 0.005**  **South Carolina Pickens 0.065 0.063 0.002**  **South Carolina Richland 0.069 0.066 0.003**  **South Carolina Spartanburg 0.066 0.063 0.003**  **South Carolina Union 0.062 0.059 0.003**  **South Carolina York 0.063 0.061 0.002**  **Tennessee Anderson 0.064 0.061 0.003 Tennessee Blount 0.071 0.067 0.004**  **Tennessee Davidson 0.064 0.063 0.001 Tennessee Hamilton 0.066 0.063 0.003** **3a-38**

**Tennessee Haywood 0.067 0.063 0.004 Tennessee Jefferson 0.068 0.065 0.003**  **Tennessee Knox 0.071 0.066 0.005 Tennessee Meigs 0.066 0.063 0.003**  **Tennessee Rutherford 0.065 0.063 0.002 Tennessee Shelby 0.072 0.069 0.003**  **Tennessee Sullivan 0.072 0.062 0.010 Tennessee Sumner 0.068 0.067 0.001**  **Tennessee Williamson 0.068 0.066 0.002 Tennessee Wilson 0.066 0.065 0.001**  **Texas Brazoria 0.078 0.076 0.002 Texas Collin 0.075 0.072 0.003**  **Texas Dallas 0.079 0.077 0.002 Texas Denton 0.078 0.075 0.003**  **Texas El Paso 0.070 0.069 0.001 Texas Ellis 0.074 0.069 0.005**  **Texas Galveston 0.078 0.075 0.003 Texas Gregg 0.079 0.073 0.006**  **Texas Harris 0.092 0.090 0.002 Texas Harrison 0.065 0.062 0.003**  **Texas Hood 0.068 0.066 0.002 Texas Jefferson 0.079 0.072 0.007**  **Texas Johnson 0.073 0.069 0.004 Texas Marion 0.069 0.065 0.004**  **Texas Montgomery 0.072 0.070 0.002 Texas Orange 0.068 0.064 0.004**  **Texas Parker 0.068 0.066 0.002 Texas Rockwall 0.067 0.063 0.004**  **Texas Smith 0.071 0.068 0.003 Texas Tarrant 0.079 0.076 0.003**  **Utah Box Elder 0.066 0.065 0.001 Utah Cache 0.055 0.054 0.001**  **Utah Davis 0.071 0.070 0.001 Utah Salt Lake 0.073 0.072 0.001**  **Utah Utah 0.070 0.069 0.001 Utah Weber 0.067 0.066 0.001**  **Vermont Bennington 0.060 0.059 0.001 Virginia Alexandria City 0.072 0.069 0.003**  **Virginia Arlington 0.078 0.075 0.003 Virginia Caroline 0.063 0.062 0.001**  **Virginia Charles City 0.074 0.073 0.001 3a-39** 

**Virginia Chesterfield 0.071 0.070 0.001 Virginia Fairfax 0.077 0.074 0.003**  **Virginia Fauquier 0.062 0.061 0.001 Virginia Frederick 0.067 0.064 0.003**  **Virginia Hampton City 0.077 0.076 0.001 Virginia Hanover 0.074 0.072 0.002**  **Virginia Henrico 0.074 0.073 0.001 Virginia Loudoun 0.070 0.068 0.002**  **Virginia Madison 0.067 0.065 0.002 Virginia Prince William 0.066 0.064 0.002**  **Virginia Roanoke 0.069 0.067 0.002 Virginia Stafford 0.064 0.062 0.002**  **Virginia Suffolk City 0.080 0.080 0.000 West Virginia Berkeley 0.068 0.063 0.005**  **West Virginia Cabell 0.073 0.069 0.004 West Virginia Hancock 0.068 0.065 0.003**  **West Virginia Kanawha 0.069 0.064 0.005 West Virginia Monongalia 0.064 0.063 0.001**  **West Virginia Ohio 0.067 0.064 0.003 West Virginia Wood 0.065 0.062 0.003**  **Wisconsin Brown 0.065 0.063 0.002 Wisconsin Columbia 0.062 0.061 0.001**  **Wisconsin Dane 0.062 0.060 0.002 Wisconsin Dodge 0.063 0.062 0.001**  **Wisconsin Door 0.074 0.072 0.002 Wisconsin Fond Du Lac 0.061 0.060 0.001**  **Wisconsin Jefferson 0.066 0.065 0.001 Wisconsin Kenosha 0.086 0.085 0.001**  **Wisconsin Kewaunee 0.074 0.072 0.002 Wisconsin Manitowoc 0.074 0.072 0.002**  **Wisconsin Milwaukee 0.075 0.073 0.002 Wisconsin Outagamie 0.059 0.057 0.002**  **Wisconsin Ozaukee 0.079 0.077 0.002 Wisconsin Racine 0.079 0.077 0.002**  **Wisconsin Rock 0.069 0.068 0.001 Wisconsin Sheboygan 0.082 0.080 0.002**  **Wisconsin Walworth 0.066 0.065 0.001 Wisconsin Washington 0.065 0.063 0.002**  **Wisconsin Waukesha 0.067 0.065 0.002 Wisconsin Winnebago 0.063 0.062 0.001** **4-1**

Chapter 4: Approach for Estimating Reductions for Full Att ainment Scenario
Synopsis
**This chapter presents the methodology used to estimate emission reductions that may be needed to reach national attainment of the proposed tig**  **hter alternate primary 8-hour ozone standard of 0.070-0.075 ppm. After applying t** **he hypothetical control strategy described in Chapter 3, there were many areas that**  **were still not projected to attain the more stringent standard modeled of 0.070 ppm. This**  **chapter presents the methodology EPA developed to determine emissions reductions needed for**  **national attainment of the alternate standards on each end of the proposed range (e.g**  **. 0.070 and 0.075 ppm). It also presents estimated emission reductions needed to attain a** **more stringent option analyzed of 0.065 ppm, and a less stringent option of 0.079 ppm.** 

4.1 Development of Air Quality Impact Ratios for Determination of Extrapolated Costs
**Table 3a.11 lists the highest projected design valu e in each monitored county for the**  **2020 baseline (current standard – effectively 0.084 ppm) and after application of the illustrative national control strategy designed to a** **ttain an alternate primary standard of 0.070 ppm. From this table one can determine the counties that**  **did not meet the target air quality levels after implementation of the national hy** **pothetical 0.070 control scenario. Because the goal of the RIA is to estimate the estima** **ted incremental costs of full attainment, some estimate of the remaining emission**  **s needed to reach these targets is required for each of these areas.** 
**It was beyond the scope of this illustrative analys is to perform detailed area-specific**  **analyses of the predicted additional emissions reductions needed to meet various air**  **quality goals. Instead, based on existing air quality sen sitivity modeling, EPA developed**  **several simple, generic relationships of the expecte d air quality improvement to be**  **achieved as a result of ozone precursor reductions. Th ese relationships are referred to**  **here as "impact ratios" and have units of ppb of ozo ne improvement per thousand tons of**  **ozone precursor emissions reduction (ppb/kton). Tw o separate approaches were used to**  **develop the impact ratios. The following paragraphs describe the development of the impact ratios used in the extrapolated cost analysis of this R** **IA**

**. Based on data presented**  **later in this chapter and considering the uncertain ties and limitations of both approaches,**  **we decided to use a single impact ratio for NOx and a single impact ratio for VOC for the**  **purposes of this illustrative analysis for all areas in the U .S. that are included in the**  **extrapolated cost analysis.**

4.1.1 Approach A: Use of Sensitivity Modeling of Local Emissions Reductions
**In this approach, the impact ratios were calculated based on modeling results from four**  **existing, 36 km CMAQ 2010 emissions sensitivity simulations and a 2010 base case**  **simulation also derived from previously completed modelin g:** **4-2 1. 90% NOx reduction in all anthropogenic sectors in**  **nine specific local areas, 2. 90% NOx reduction in all anthropogenic sectors over the rest of the U.S.,**  **3. 90% VOC reduction in all anthropogenic sectors in nin e specific local areas,**  **4. 90% VOC reduction in all anthropogenic sectors ove r the rest of the U.S.** 
**We calculated the ppb/kton ratios for five of the nine zo nes shown in Figure 4.1 that are**  **included in the extrapolated costs analysis: Dallas , Atlanta, the Lake Michigan area, the**  **Northeast Corridor, and central California. It is expected that these five zones would**  **provide a representative range of ratios, so the analysi s was not done for Denver,**  **Phoenix, and Salt Lake City. Because we were not c alculating extrapolated tons for**  **Seattle, the ratio determination was not done for t hat region. For monitoring sites in each**  **of these five geographic areas we compared the ozon e improvement in the 90% control**  **cases (simulations 1 and 3) against the tons of NOx and VOC reduced within the**  **corresponding control area. The impact ratio for each si te was calculated by dividing the**  **ozone improvement by the corresponding tons reduced . Impact ratios were calculated for**  **88 sites over the five analysis zones. The results from Approach A are summarized i n**  **Table 4.2**
Figure 4.1: Nine Local Control Areas in Existing 2010 Sensitivity Runs**4-3**

**A sample calculation for one of the sites in the five analysis zones (a monitoring site**  **located in Denton TX) is shown below:**

x **A 90% NOx reduction equals 130.4 ktons in the local Dallas area.** 

x **The ozone improvement from this reduction was 17.6 ppb (87.9 to 70.3).** 

x **This yields an impact ratio of 0.135 ppb/kton for this county**

**. The advantage to this approach is that it allows for**  **all-sector, local-only controls without consideration of transport effects. This approach is best-suited for areas in which ozone**  **transport is not a large contributor to the local o zone problem, relative to local emissions**  **(e.g., Atlanta, Dallas).**
Table 4.1: Summary of site-specific impact ratios over t he five analysis zones of Approach A. **Minimum**

**Impact Ratio Maximum** 

**Impact Ratio Average** 

**Impact Ratio Controlling County** 

**Impact Ratio Atlanta 0.051 0.187 0.123 0.187**  **Central CA 0.077 0.106 0.095 0.106 Dallas 0.118 0.138 0.130 0.135**  **Lake Michigan Area -0.022 1**  **0.052 0.010 0.032 Northeast Corridor 0.002 0.035 0.022 0.035** 
**It is important to note that we are not able to fact or in impacts of controls outside of the**  **local regions using this methodology and thus, the impa ct ratios are likely to be**  **conservative. Additionally, depending upon the source-re ceptor relationship at a**  **particular location, some impact ratios would be ex pected to be lower than others due to**  **prevailing transport direction. For instance, one wo uld not expect a location in the**  **southern portion of the Northeast Corridor to show much local air quality improvement**  **when the majority of the controls were implemented upwind. Other limitations to this**  **approach include: the assumption that response to NOx an d VOC reductions is linear**  **between 0% and 90% control, the assumption that ratio s developed from 2010 base case**  **modeling are applicable to 2020 post-strategy ozone, and the fact t hat impact ratios**  **calculated from a single month of 36 km modeling may not be appropriate for an analysis**  **of urban scale ozone.**

4.1.2 Approach B: Use of 2020 Baseline and RIA Control Scenario
**In the second approach, we used the results from the 2 020 baseline and the 2020**  **hypothetical control scenario to calculate impact r atios for Atlanta, Houston, the Lake** 

**1 The negative value of minimum impact ratio in the Lake Michig**  **an area indicates that ozone levels at one monitoring site are projected to incre**  **ase slightly with 90% local NOx control. This 'ozone disbenefit' has been projected by the mo**  **del to occur in a very few, highly localized, areas with large amounts of NOx emissions**  **. This lone negative value is not representative of regional impact ratios."** **4-4**

**Michigan Area and the Northeast Corridor. We focused on these four analysis zones**  **because they were expected to require extensive extr apolated tons to attain the air quality**  **targets. We did not use this approach in the western U.S. beca use there was very little**  **difference in the controls between those two cases in Cal ifornia. We again calculated**  **impact ratios for all monitoring sites in each zone by dividing the ozone change at each**  **site by the NOx emissions reductions that led to that ozone reductio n. For the specific**  **purpose of estimating impact ratios, we have made the unrealistic but simplifying**  **assumption that the air quality change can be fully a scribed to the total NOx emissions**  **changes within 200 km of the area. Different assumptions ab out which emissions are**  **responsible for the air quality change would yield different impact ratios.**
**A sample calculation for one of the counties (Kenosha WI) i s shown below:** 

x **The RIA control scenario resulted in a NOx reduction of 16.8 k tons in the**  **Chicago zone (including 200 km buffer).**

x **The ozone improvement from this reduction was 1.6 p pb (86.6 to 85.0).** 

x **This yields an impact ratio of 0.095 ppb/kton for this county**

**. The advantage to Approach B is that it allows for an estimate of the impact of actual**  **controls applied regionally because controls in the hypothetical scenario cover nearly the**  **entire eastern US. Thus, this approach is best suited for areas in which ozone transport is**  **a large contributor to the local ozone problem (e.g., the Lake Michigan a rea and the**  **Northeast Corridor). The primary disadvantage to this approach is that the 2020 control**  **scenario is weighted toward non-EGU point source con trols which may result in non**  **homogeneous reductions and thereby affect individual coun ty impact ratios. Impact**  **ratios were calculated for 47 counties over the four a nalysis zones. The results from**  **Approach B are summarized in Table 4.2.**
Table 4.2: Summary of site-specific impact ratios over the four analysis zones of Approach B.

**Minimum**

**Impact Ratio Maximum** 

**Impact Ratio Average** 

**Impact Ratio Controlling County** 

**Impact Ratio Atlanta 0.041 0.129 0.068 0.070**  **Houston 0.050 0.057 0.054 0.057 Lake Michigan Area -0.006 0.095 0.064 0.095**  **Northeast Corridor 0.068 0.155 0.110 0.105 4-5** 

4.2 Results from Impact Ratio Analyses
**In general, both approaches indicate that impact ra tios could range between 0.03 and 0.20**  **ppb/kton. However, the approaches did not yield co nsistent impact ratios for individual**  **analysis zones. Individual local impact ratios are likely influenced by: the importance of transport, the local NOx/VOC ratio, the meteorology** **within the region, and the location of monitors relative to specific source areas.** 
**Table 4.3 shows the impact ratios for each of the control ling counties**  **2 within the areas**  **considered. Figure 4.2 shows the range of county-sp ecific impact ratios calculated over**  **the four areas included in the calculations for Approach B. Based on these data and**  **considering the uncertainties and limitations of bot h approaches, we decided to use a**  **single impact ratio for NOx and a single impact rat io for VOC for the purposes of this**  **illustrative analysis for all areas in the U.S. that are included in the extrapolated cost**  **analysis. These general impact ratios are:**

x **NOx impact ratio = 0.100 ppb/kton**

x **VOC impact ratio = 0.025 ppb/kton**
Table 4.3. The NOx impact ratios at the controlling countie s for each methodology over the analysis zones.

**Impact Ratio at controlling county Analysis Area**  **Approach A Approach B Atlanta 0.187 0.070**  **Central California 0.106 Dallas 0.135**  **Houston 0.057 Lake Michigan area 0.032 0.095**  **Northeast Corridor 0.035 0.105**

**2 The controlling county is the county within an are**  **a whose design value is farthest away from attaining the air quality target.** **4-6**

Figure 4.2. The NOx impact ratios at each county (sorted from l owest to highest) for the Approach B over the four analysis areas 3

Range of Impact Ratios: 084-070 methodology

-0.05 0.00 0.05 0.10 0.15 0.202%6%11%15%19%23%28%32%36%40%45%49%53%57%62%66%70%74%79%83%87%91%96%100%

Percentile (Counties in the Analysis)Impact Ratio (ppb/kton)

**The selection of a 0.100 ppb/kton ratio was based on a conside ration of all estimated**  **impact ratios from the bounding exercise of both approaches and the technical limitations**  **of approaches. There were three specific reasons why we thought 0.100 ppb/kton represented the best choice for extrapolating the to**  **ns needed to attain an air quality target beyond the reductions from the RIA control scenario:** 

x **0.100 is within, and near the midpoint of, the 0.03 to 0.20 rang e** 

x **0.100 is close to the median value from Approach B (0.093 ppb/ kton)** 

x **0.100 is close to the average value at the key site s (0.091 ppb/kton).** 
**As noted above, the various methods did not generate consist ent area-specific NOx**  **impact ratios. As the impact ratios are used to estimate e xtrapolated costs, one should**  **keep in mind that higher impact ratios would yield lower esti mates of needed**  **extrapolated tons and lower impact ratios would yield hig her estimates of extrapolated**  **tons/costs.**
**As an example, if in a given area X, our impact ratio of 0.1 ppb/kton for NOx is defined**  **as equivalent to 10 extrapolated ktons of emission reductions needed to achieve a particular air quality target, then a doubling of the**  **impact ratio (thus, this ratio becomes**

**3 The lone negative value of impact ratio occurs in**  **the Lake Michigan area and indicates that ozone levels at that site are projected to increase slightly in response to the RIA**  **control scenario. This 'ozone disbenefit' has previously be en projected by the model to**  **occur in a very few, highly localized, areas with la rge amounts of NOx emissions. This**  **lone negative value is not representative of regional i mpact ratios.** **4-7**

**0.2 ppb/kton) means that 5 extrapolated ktons of NOx, or one-half the original tonnage**  **reduction needed, to achieve the same air quality target. As a further example, a**  **reduction in the NOx impact ratio by half (thus, th e ratio becomes 0.05 ppb/kton) means**  **that 20 extrapolated ktons of NOx emission reductions, or twice the original tonnage reduction needed, to achieve the same air quality targe**  **t. We intend to conduct additional sensitivity analysesfor th**  **e final RIA to improve the estimates of extrapolated tons needed to meet various targe**  **ts. While it is premature to specify the exact nature of these analyses, we expe**  **ct this will include modeling to provide more information about the non-linear responsiveness of ozone, the geographic**  **variation in ozone responsiveness, the impacts of loc al versus upwind emissions**  **reductions, and the relationship between NOx and VOC cont rols in various areas. It will**  **also include an analysis of the geographic applicati on of impact ratios.** 

4.3 Determination of Extrapolated Tons Control Areas
**The extrapolated tons analysis varied slightly from the geographic areas in which controls**  **were applied for the illustrative 0.070 control strategy de scribed in Chapter 3. In the**  **extrapolated tons analysis, we aggregated all counties t hat were above the air quality goal**  **into discrete control areas, that is, areas from which the to ns would need to be extracted**  **in order to meet the target. These control areas we re either regional, statewide, or local**  **depending upon the nature of the ozone problem within the are a. Two regional areas**  **were identified: the Ozone Transport Region and the Lake Michigan region. Both of these areas have traditionally employed multi-State contro**  **l plans to lower ozone in those regions. For states with multiple areas above the air**  **quality target, we assumed that statewide control programs would be developed to br**  **ing these areas into attainment. For example, for the 0.065 ppm target, Ohio exceeds the**  **air quality target in Cincinnati, Cleveland, and Columbus. We assumed that extrapola** **ted tons could be achieved anywhere in Ohio to meet the targets in all three a** **reas. All remaining counties were treated as places where local controls would be eff**  **ective. The only exceptions to the statewide assumption were in Texas and California. We sep**  **arated the El Paso area into its own area due to its distance (i.e., far greater**  **than 200 km, which was the distance used for the 0.070 control strategy) from the Eastern Texas are**  **as (Dallas, Houston). In California, we combined the Sacramento and San Joaqu**  **in Valley counties into a single control area, but created a separate control area f**  **or Southern California. Table 4.4 shows how the monitoring counties were aggregated into the extr**  **apolated tons control areas for the 0.070 ppm target.** **4-8**

Table 4.4 List of counties that did not reach 0.0 70 in the RIA control scenario and

how they were aggregated into extrapolated tons con trol areas.

Control Region State County Control Region State County Atlanta, GA Georgia Fulton Las Vegas, NV Nevada Clark Georgia De Kalb Los Angeles South Coast Air Basin, CA California San Bernardino Baton Rouge, LA Louisiana East Baton Rouge California Los Angeles Louisiana Iberville California Riverside Louisiana West Baton Rouge California Ventura Central Califronia California Kern California San Diego California Fresno California Imperial California Tulare Louisville, KY-IN Indiana Clark California Merced Indiana Perry California El Dorado Memphis, TN-AR Arkansas Crittenden California Kings Ozone Transport Region Connecticut Fai rfield California Stanislaus New York Suffolk California Nevada Connecticut New Haven California Placer Pennsylvania Bucks California Sacramento Maryland Harford California Madera New Jersey Ocean California Mariposa New Jersey Mercer California Tuolumne New Jersey Camden California Calaveras Connecticut Middlesex Charlotte-Gastonia-Rock Hill, NC-SC North Carolina Me cklenburg New Jersey Middlesex North Carolina Rowan New Jerse y Gloucester Cleveland-Columbus-Cincinnati Ohio Geauga Pennsylvania Philadelphia Ohio Ashtabula New Jersey Hunterdon Kentucky Campbell New Jersey Monmouth Ohio Franklin Pennsylvania Allegheny Ohio Lake Pennsylvania Montgomery Ohio Summit Maryland Cecil Denver-Boulder Colorado Jefferson New Jersey Bergen Colorado Douglas New York Erie Colorado Arapahoe Virginia Arlington Detroit-Ann Arbor, MI Michigan Macomb Maryland Baltimore Michigan Oakland New Jersey Morris Michigan Wayne New York Niagara Michigan Washtenaw New York Westchester Michigan St Clai r Virginia Fairfax Houston-Dallas Texas Harris Maryland Anne Arundel Texas Dallas D.C. Washington Texas Brazoria Delaware New Castle Texas Tarrant Pennsylvania Beaver Texas Denton Pennsylvania Chester Texas Galveston Pennsylvania Delaware Texas Gregg New Jersey Hudson Texas Jefferson Maryland Prince Georges Texas Collin Maryland Kent Indianapolis, IN Indiana Shelby New York Richmond Indiana Hamilton Rhode Island Washington Indiana Marion New York Jefferson Indiana Hancock Rhode Island Kent Lake Michigan region Wisconsin Kenosha New York Monroe Indiana Lake New Jerse y Cumberland Illinois Cook Phoenix-Mesa, AZ Arizona Maricopa Wisconsin Sheboygan Arizona Pinal Indiana Porter Richmond-Norfolk Virginia Suffolk City Wisconsin Ozaukee Virginia Hampton City Wisconsin Racine Virginia Henrico Wisconsin Milwaukee Virginia Charles City Indiana La Porte Virginia Hanover Illinois Lake Salt Lake City, UT Utah Salt Lake Wisconsin Kewaunee St Louis, MO-IL Missouri St Louis Ci ty Michigan Allegan Missouri St Louis Wisconsin Manitowoc Missouri Jefferson Wisconsin Door Missouri St Charles Michigan Muskegon Tampa Bay, FL Florida Hillsborough**4-9**

4.4 Selection of Air Quality Goal for this analysis
**Under the Clean Air Act, areas are required to reach the air quality standards as**  **expeditiously as practicable and within certain statutor ily defined time periods. In**  **advance of formal designations and ozone pollution l evel classifications, which will**  **depend upon future air quality data, it is uncertain whe n areas would be required to attain**  **a new ozone standard. In addition, states may request , and EPA must grant, a higher**  **classification which under the law provides flexibi lity for a state to justify a later**  **attainment date. (The state implementation plan must show that th e attainment date**  **selected for an area is as expeditious as practicable, and no later than the maxi mum**  **statutory date for the area's classification.) In view of these and other factors, it is beyond**  **our capability to simulate in advance the state implementat ion process to determine the**  **appropriate attainment date and required controls fo r each potential nonattainment area**  **for a new standard. Instead, we have constructed an illust rative analysis that provides a**  **level playing field for comparison of the impacts of potential new, alternative standards.** 
**An important consideration in the determination of the amou nt of air quality**  **improvement needed to reach a tighter ozone standar d is the dates by which each area**  **must come into attainment. As discussed earlier, for seve ral analytical reasons we**  **selected the year 2020 (i.e., approximately 10 years from designations), as the analytical**  **target year for this analysis. Therefore, this analy sis presents two sets of results. The**  **first reflects attainment of the alternative ozone standards in all locations of the U.S.**  **except two areas of California in 2020. These two areas o f California are not planning to**  **meet the current standard by 2020 (see discussion below), so the estimated costs and**  **benefits for these areas are based on reaching an estimate d progress point (their**  **“glidepath” targets) in 2020. The second set of resu lts, for California only, estimate the**  **costs and benefits from California fully attaining t he alternative standards in a year**  **beyond 2020 (glidepath estimates, plus the incremen t needed to reach full attainment**  **beyond 2020, added together for a California total). Howev er, as noted above, we are not**  **attempting to prejudge the attainment dates and cont rols that ultimately will be**  **determined through the SIP process, and it may turn out that attainment occurs later than**  **2020 for additional areas, particularly in areas wh ere the future SIP process shows that**  **very high-cost controls would be needed to attain by 2020 . For reasons explained below,**  **assuming longer attainment dates would reduce costs and ben efits of meeting the current**  **and alternative standards, and would reduce costs more sharply in areas assumed to employ high-cost controls to meet an artificial deadline.** 
**The South Coast (Los Angeles area) and San Joaquin A ir Quality Management Districts**  **recently have proposed for comment state implementa tion plans with the statutory**  **maximum 20-year attainment dates (June 2024, with a ttainment-level reductions by**  **2023) for meeting the current 8-hour standard, which would involve a request to**  **reclassify those two areas to the “extreme” classif ication** **4 . This presented an analytical** 

**4 Proposed State Strategy for California's State Impl**  **ementation Plan (SIP) for the New PM2.5 and 8-Hour Ozone Standard, California Air Resource**  **s Board web page, http://www.arb.ca.gov/planning/sip/2007sip/2007sip.**  **html, update May 30, 2007. 4-10** 

**dilemma for this analysis because assuming that the se areas would be classified severe or**  **extreme for purposes of a new standard, these areas would no t be required to attain any**  **new standard until after the analytical year of 2020 . If an area is initially classified**  **severe, the law still would allow the state to requ est reclassification to extreme and to**  **demonstrate that a 20-year attainment date (e.g., in 2030, if designations occurred in 2010) is as expeditious as practicable. Thus, an assum**  **ption that these areas would attain a tighter standard by 2020, significantly earlier than wo**  **uld be required under the Clean Air Act, would artificially inflate both the costs and ben**  **efits of the nation attaining the new standard in 2020 on a national level.** 
**A further reason that we believe it would be inappropriate f or the analysis to assume**  **attainment by 2020 with new, more stringent alternat ive standards in the San Joaquin and**  **South Coast areas is that existing rules, especiall y for on-road and non-road mobile**  **sources, will achieve substantial additional reducti ons in NOx and VOC after 2020 before**  **reaching their full impact in 2030. If San Joaquin and South Coast received the**  **maximum statutory 20-year attainment dates for new alterna tive standards, for example,**  **they would have an attainment date in 2030, and wou ld benefit from reductions in NOx**  **and VOC from existing rules between 2020 and 2030. By 2029, exi sting rules for onroad**  **and nonroad engines would achieve 62,000 tons 5**  **of residual emissions reductions needed for attainment at no cost beyond the baseline for this analysis. By** **contrast, assuming 2020 attainment for these areas would result in assu** **ming additional high-cost reductions from unknown control measures. Therefore, assuming**  **2020 attainment for these areas could result in a significant overestimate of costs. Likewise, the benefits wou**  **ld be overestimated because the tons are attributable to**  **these existing rules that have reductions occurring after 2020 and are not part of our hypothetical**  **control strategy.**

**5 62,000 tons was estimated by subtracting the Califo**  **rnia county level Onroad and Nonroad 2030 NOx and VOC emissions from their totals in 20**  **20. These differences were estimated for counties listed under the CA con** **trol regions (Los Angeles and Kern County) detailed in Table 4.4. (Los Angeles account**  **ed for 38,500 tons, while Kern accounted for 23,300 tons, for a total of 61,800 rou** **nded to 62,000). In order to estimate total emissions for both VOC and NOx, VOC emission reductio**  **ns for these counties were adjusted using the adjustment factor detailed earlier in** **the chapter (4 VOC tons = 1 NOx ton). Given that the San Joaquin and South Coast a**  **ir quality management districts have adopted plans allowing until June 2024 (20 years fro**  **m designation) to meet the current standard, EPA believes that it would be consistent**  **for purposes of this analysis to assume a 20-year period for attainment of a new more string**  **ent standard. If designations occurred in 2010, the 20-year attainment period would end in 2030. Significant**  **emissions reductions from implementation of mobile sour ce rules are anticipated between**  **2020 and 2030. Consistent with the non-EGU growth assumptions for the rest of the RIA, non-EGU emissions are assumed to stay constant. EGU emi**  **ssions are a small fraction of the California inventory and are assumed not**  **to significantly affect the change in the state’s emissions between 2020 and 2030. Accordingly**  **, we have used the difference between 2020 and 2030 mobile emissions to estimat**  **e the post-2020 emissions reductions that will assist the two California areas**  **in reaching attainment. 4-11** 

**Thus, for this analysis we have chosen to present the 2020 costs and benefits in a way**  **that reflects partial attainment in certain Califor nia areas, an outcome consistent with the**  **Clean Air Act. This national estimate includes full attain ment in all locations except two**  **areas of California, which do not plan to meet the current sta ndard by 2020, and so have**  **estimates for a progress point in 2020 (their “glid epath” targets). The second set of results**  **presents a total for California only, which adds the 2020 progress point to the additional**  **tons of emissions that may be needed in California to fully attain the standards in a year**  **beyond 2020.**
**The following table shows the results of the calcula tion of the glidepath targets for these**  **two areas used in this analysis 6**  **. The glidepath targets reflect the more stringent of two air quality targets: (1) the improvement assumed by 20**  **20 to meet the current standard by years specified below, or the improvement needed by 2020**  **to make linear air quality progress between 2010 and a post-2020 attainment date for**  **the more stringent, alternative standards. For Los Angeles County, the glidepath air qualit** **y targets below for all four alternative standards are set based on reductions n**  **eeded to meet the current standard, with the level of the current standard being achieved in 2021. For Kern County in the**  **San Joaquin area, the glidepath for the 0.075 ppm an d 0.079 ppm alternative standards is**  **based on achieving the level of the current standar d by 2020. For the other two**  **alternative standards, the 2020 glide path targets fo r San Joaquin are based on meeting**  **the level of the alternative, more stringent standards in 202 5.** 
Table 4-5: 2020 Air Quality Glidepath Targets for LA and Kern County
**Alternative Standard Level LA County Kern County** **0.079 ppm 86.9 ppb* 84.9 ppb** **0.075 ppm 86.9 ppb 84.9 ppb 0.070 ppm 86.9 ppb 82.9 ppb**  **0.065 ppm 86.9 ppb 79.9 ppb** *** targets are expressed in ppb for clarity of presentation**
**As noted above, since our glidepath calculations and cost-benefit estimates were made,**  **the two California districts have proposed state impleme ntation plans for the current**  **standard that allow the statutory maximum 20-year period for attainment. This in turn suggests that that it would be reasonable solely for**  **purposes of this analysis to assume a 20-year period for implementation of new standards. In part**  **because decisions regarding**

**6 Assumptions made for the purposes of this analysis were ma**  **de prior to two California areas adopting SIPs which assumed attainment by June 2024. F**  **or purposes of this analysis, San Joaquin (including Kern County) was assumed**  **to meet the current standard by 2020 (consistent with the analysis assumption for most a** **reas), and South Coast (including Los Angeles County) was assumed to meet the**  **current standard in 2021 (the maximum attainment date of a severe-17 area is in June 20**  **21). Because the San Joaquin and South Coast air districts have adopted SIPs with later**  **attainment dates for the current standard, we recognize that the assumptions used in** **this analysis are not likely to be the actual years of attainment for these two areas.** **4-12**

**our analysis were made prior to the California proposa ls, the assumed time periods in this**  **analysis for attainment by the San Joaquin and Los Angeles are as are shorter -- both for**  **the current standards, and for the potential altern ative standards. This suggests that the**  **glidepath figures above are all more stringent than li kely implementation of the Clean Air**  **Act, and that as a result our analysis applies more unknown controls than would be needed in these areas for the current and alternati**  **ve standards assuming 20-year deadlines. Thus, our estimated 2020 costs and bene**  **fits for the two California areas, which influence the total cost and benefit figures**  **in this draft RIA, are higher than would likely occur under the Clean Air Act for the current**  **and the potential alternative standards. We intend to consider this issue further**  **in the final RIA.**

4.5 National 2020 Estimates of Additional Emissions Reduc tions Needed to Meet Four Potential Air Quality Targets
**This analysis presents two sets of estimates: national 2020 estimates, and California-only**  **estimates. This section presents the national 2020 estimate s.** 

**The national 2020 estimates assume full attainment in all lo cations except two areas of**  **California, which are assumed to meet 2020 air quality glidepath targets on their way toward full attainment after 2020. These 2020 national e**  **stimates present incremental tons that may be needed to meet the four separate a**  **ir quality targets were considered as part of this analysis: a less stringent alternative**  **standard of 0.079 ppm, 0.075 ppm and 0.070 ppm, which bound the range that is being propo**  **sed, and a more stringent alternative of 0.065 ppm. After the RIA control scenario,**  **there were 24, 50, 126, and 280 counties above these four thresholds, respectively. The aggregation technique discussed**  **above grouped these counties into 6, 11, 20, and 29 extrapolated ton control areas for the**  **four targets. The calculation of additional tons n eeded does not account for the ancillary**  **effects of ozone transport reductions (e.g. the imp act of Lake Michigan region reductions**  **on the OTR). The national 2020 total estimated incremental tons that may be needed to**  **attain the four targets are summarized below and presen ted in Tables 4.6 - 4.9.** 

x **0.079 = 102,000 tons of additional NOx control**

x **0.075 = 321,000 tons of additional NOx control**

x **0.070 = 1,004,000 tons of additional NOx control**

x **0.065 = 2,239,000 tons of additional NOx control 7** 

**7 While the proposed rule takes comment on a range of altern**  **ate standards from 0.060 ppm to 0.084 ppm, the RIA analysis focused on a more limited rang**  **e 4-13** 

Table 4.6 Estimated Annual Incremental Tons Needed for an 0. 065 ppm Air

Quality Target in 2020 (29 areas)

Control Region Controlling County Post-scenario

design value (ppb) Incremental

Extrapolated NOx Tons **Lake Michigan region Kenosha WI 85.0 174,000 Ozone Transport Region Fairfield CT 87.1 173,000**  **Eastern TX areas (Houston/Dallas/Beaumont) Harris T X 90.5 166,000**  **VA areas (Norfolk/Richmond/Roanoke) Suffolk City VA 80.8 149,000**  **Detroit, MI Macomb MI 78.4 125,000 Phoenix, AZ Maricopa AZ 77.6 117,000**  **Denver, CO Jefferson CO 76.9 110,000 OH areas (Cleveland/Columbus/Cincinnati) Geauga OH**  **76.5 106,000 Atlanta, GA Fulton GA 76.0 101,000**  **St Louis, MO-IL St Louis City MO 75.7 98,000 Indiana areas (Indianapolis / Evansville) Shelby IN** **74.5 86,000 LA areas (Baton Rouge/New**  **Orleans/Shreveport) E Baton Rouge LA 74.4 85,000 KY areas (Louisville/Paducah/Bowling Green) Clark I** **N 74.0 81,000 TN areas (Knoxville/Memphis/Nashville) Crittenden A**  **R 72.9 70,000 NC areas (Charlotte / Raleigh) Mecklenburg NC 72.3**  **64,000 Salt Lake City, UT Salt Lake UT 72.2 63,000**  **Las Vegas, NV Clark NV 72.0 61,000 FL areas (Tampa / Panama City / Pensacola) Hillsbor**  **ough FL 71.4 55,000 Sacramento / San Joaquin Valley / S Fran Kern CA** ** **96.3 50,000 Jackson, MS Jackson MS 70.6 47,000**  **New Mexico areas (Farmington / Las Cruces) Dona Ana NM 70.3 44,000**  **OK areas (Tulsa, Marshall) Tulsa OK 70.3 44,000 Huntington, WV-KY Cabell WV 69.9 40,000**  **El Paso, TX El Paso TX 69.3 34,000 Kansas City, MO/KS Wyandotte KS 69.0 31,000**  **Little Rock, AR Pulaski AR 68.7 28,000 Mobile AL Mobile AL 68.6 27,000**  **Columbia, SC Richland SC 66.9 10,000 Los Angeles South Coast Air Basin, CA Los Angeles C**  **A** 105.0 0***

***** **In EPA’s illustrative analysis for the PM NAAQS RIA, there were reductions of NOx in California. The amount of reductions assumed there are s**  **ufficient for these counties to achieve their glidepath targets in 2020.** 

** **Los Angeles and Kern Counties have expected attainment dates after 2020. This analysis counts the portion of reductions assumed by this an**  **alysis by 2020 or earlier. 4-14** 

Table 4.7. Estimated Annual Incremental Tons Needed for an 0. 070 ppm air

quality target in 2020 (20 areas)

Control Region Controlling County Post-scenario

design value (ppb) Incremental

Extrapolated NOx Tons
**Lake Michigan region Kenosha WI 85.0 124,000 Ozone Transport Region Fairfield CT 87.1 123,000**  **Eastern TX areas (Houston/Dallas) Harris TX 90.5 11 6,000**  **VA areas (Norfolk/Richmond) Suffolk City VA 80.8 99 ,000**  **Detroit, MI Macomb MI 78.4 75,000 Phoenix, AZ Maricopa AZ 77.6 67,000**  **Denver, CO Jefferson CO 76.9 60,000 OH areas**  **(Cleveland/Columbus/Cincinnati) Geauga OH 76.5 56,0 00**  **Atlanta, GA Fulton GA 76.0 51,000 St Louis, MO-IL St Louis City MO 75.7 48,000**  **Indiana areas (Indianapolis) Shelby IN 74.5 36,000 LA areas (Baton Rouge) E Baton Rouge LA 74.4 35,000**  **KY areas (Louisville) Clark IN 74.0 31,000 Sacramento / San Joaquin Valley Kern CA**  96.3 20,0** **00 TN areas (Memphis) Crittenden AR 72.9 20,000**  **NC areas (Charlotte) Mecklenburg NC 72.3 14,000 Salt Lake City, UT Salt Lake UT 72.2 13,000**  **Las Vegas, NV Clark NV 72.0 11,000 FL areas (Tampa) Hillsborough FL 71.4 5,000**  **Los Angeles South Coast Air Basin, CA Los Angeles C A**  105.0 0***

* **In EPA’s illustrative analysis for the PM NAAQS RIA, the re were reductions of NOx**  **in California. The amount of reductions assumed there are sufficient for these counties to**  **achieve their glidepath targets in 2020.**

 Los Angeles and Kern Counties have expected attainment dates after 2020. This**  **analysis counts the portion of reductions assumed by this an alysis by 2020 or earlier.** **4-15**

Table 4.8. Estimated Annual Incremental Tons Needed for an 0. 075 ppm air quality target in 2020 (11 areas)

***In EPA’s illustrative analysis for the PM NAAQS RIA, there were reductions of NOx in California. The amount of reductions assumed there are s**  **ufficient for these counties to achieve their glidepath targets in 2020.**  **** Los Angeles and Kern Counties have expected attainment dates after 2020. This analysis counts the portion of reductions assumed by this an**  **alysis by 2020 or earlier.**

Table 4.9. Estimated Annual Incremental Tons Needed for an 0. 079 ppm air quality target in 2020 (6 areas)

***In EPA’s illustrative analysis for the PM NAAQS RIA, there were reductions of NOx in California. The amount of reductions assumed there are s**  **ufficient for these counties to achieve their glidepath targets in 2020.**  **** Los Angeles and Kern Counties have expected attainment dates after 2020. This analysis counts the portion of reductions assumed by this analysis by 2020 or earlier.** 

Control Region Controlling County Post-scenario

design value (ppb) Incremental

Extrapolated NOx Tons **Lake Michigan region Kenosha WI 85.0 74,000 Ozone Transport Region Fairfield CT 87.1 73,000**  **Eastern TX areas (Houston/Dallas) Harris TX 90.5 66 ,000**  **VA areas (Norfolk) Suffolk City VA 80.8 49,000 Detroit, MI Macomb MI 78.4 25,000**  **Phoenix, AZ Maricopa AZ 77.6 17,000 Denver, CO Jefferson CO 76.9 10,000**  **OH areas (Cleveland) Geauga OH 76.5 6,000 Atlanta, GA Fulton GA 76.0 1,000**  **Sacramento / San Joaquin Valley Kern CA** 96.3 0*** **Los Angeles South Coast Air Basin, CA Los Angeles C A**  105.0 0***

Control Region Controlling County Post-scenario

design value (ppb) Incremental

Extrapolated NOx Tons **Lake Michigan region Kenosha, WI 85.0 34,000**  **Ozone Transport Region Fairfield. CT 87.1 33,000**  **Eastern TX areas (Houston/Dallas) Harris, TX 90.5 26,000**  **VA areas (Norfolk) Suffolk City, VA 80.8 9,000**  **Sacramento / San Joaquin Valley Kern CA** 96.3 0*** **Los Angeles South Coast Air Basin, CA Los Angeles C A**  105.0 0*****4-16**

4.6 Estimates of Additional Tons Needed for Four Potential Air Quality Targets (California only, post-2020 Attainment)
**The second estimates presented are for California onl y. Tables 4.10 – 4.13 below,**  **present the estimated tons needed to attain Californ ia’s glidepath targets in 2020 and the**  **additional increment of tons that may be needed for f ull attainment in a year beyond 2020** 
Table 4.10 California: Estimated Tons Needed For Attainment 0.065 ppm air quality target (beyond 2020)

Table 4.11 California: Estimated Tons Needed for Attai nment of 0.070 ppm air quality target (beyond 2020)

Table 4.12 California: Estimated Tons Needed for Att ainment of 0.075 ppm air quality target (beyond 2020)

Control Region Controlling

County Postscenario design value (ppb) Total

Extrapolated NOx Tons Glidepath

Extrapolated NOx Tons Remaining

tons needed (2020- attainment) “Credit” tons

from mobile rules 2020-2030 Tons needed

after mobile reductions

**Sacramento / San Joaquin Valley**  **Kern CA 96.3 90,000 0 90,000 23,300 66,700 Los Angeles South**  **Coast Air Basin, CA Los Angeles CA 105.0 88,000 0 88,000 38,500 49,500** 

Table 4.13 California: Estimated Tons Needed for Att ainment of 0.079 ppm air quality target (beyond 2020)

Control Region Controlling

County Postscenario design value (ppb) Total

Extrapolated NOx Tons Glidepath

Extrapolated NOx Tons Remaining

tons needed (2020- attainment) “Credit” tons

from mobile rules 2020-2030 Tons needed

after mobile reductions

**Sacramento / San Joaquin Valley**  **Kern CA 96.3 50,000 0 50,000 23,300 26,700 Los Angeles South**  **Coast Air Basin, CA Los Angeles CA 105.0 48,000 0 48,000 38,500 9,500**  Control Region Controlling

County Postscenario design value (ppb) Total

Extrapolated NOx Tons Glidepath

Extrapolated NOx Tons Remaining

tons needed (2020- attainment) “Credit” tons

from mobile rules 2020-2030 Tons needed

after mobile reductions

**Sacramento / San Joaquin Valley / S**  **Fran Kern CA 96.3 190,000 50,000 140,000 23,300 116,700** 
**Los Angeles South Coast Air Basin, CA**  **Los Angeles CA 105.0 188,000 0 188,000 38,500 149,5 00** 

Control Region Controlling

County Postscenario design value (ppb) Total

Extrapolated NOx Tons Glidepath

Extrapolated NOx Tons Remaining

tons needed (2020- attainment) “Credit” tons

from mobile rules 2020-2030 Tons needed

after mobile reductions

**Sacramento / San Joaquin Valley**  **Kern CA 96.3 140,000 20,000 120,000 23,300 96,700 Los Angeles South**  **Coast Air Basin, CA Los Angeles CA 105.0 138,000 0 138,000 38,500 99,50**  **0 5-1** 

Chapter 5: Cost Estimates

Synopsis
**This chapter summarizes the data sources and methodology use d to estimate the costs of**  **attaining the alternative more stringent levels for the ozone primary standard analyzed in this**  **RIA. This chapter estimates the costs of the bounds of the proposed range, 0.075- 0.070 ppm, a**  **more stringent alternative of 0.065 ppm, as well as a less stringent option of 0.079 ppm. The**  **chapter presents cost estimates for the illustrative cont rol strategy outlined in Chapter 3 (which**  **uses currently available known controls). The control str ategy discussion is followed by a**  **presentation of estimates for the costs of the additional to ns of emissions that are needed to move**  **to full attainment of the alternate standards analyze d (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. These known controls were insufficient to bring all areas**  **into attainment with 0.070 ppm, and EPA then developed method ology to estimate additional**  **tons of emissions needed to attain the bounds of the p roposed range, 0.075 and 0.070 ppm, the**  **tighter alternative of 0.065 ppm and the less stringent alt ernative option of 0.079 ppm. This**  **chapter presents the costs associated with each portion of the control analysis, clearly identifying**  **the relative costs of modeled versus extrapolated em issions reductions as well as providing an**  **estimate of the total cost of attainment nationwide in 2020. Section 5.1 summarizes the**  **methodology and the engineering costs associated with appl ying known and supplemental**  **controls to partially attain a 0.070 ppm alternative standa rd, incremental to reaching the current**  **baseline (effectively 0.084 ppm) in 2020.**
**Section 5.2 describes the methodology used to estim ate the cost of extrapolated tons needed to**  **reach attainment of the bounds of the proposed alterna tive standard (0.070 and 0.075 ppm, the**  **less stringent alternative of 0.079ppm, as well as th e more stringent alternative of 0.065 ppm)**  **and provides estimates of how much additional cost w ill be associated with moving from the**  **modeled partial attainment scenario to the nationwi de attainment scenario (see Chapter 4 for**  **discussion of extrapolated tons needed to attain 0.079, 0. 075, 0.070, and 0.065 ppm). In general,**  **EPA increased the tons required for each area using the same i mpact/ton estimate ( 5 ppb =**  **50,000 tons) and extrapolated cost approaches in or der to estimate additional costs for reaching a**  **standard level of 0.065 ppm as well as estimate cos t savings for the 0.075 and 0.079 ppm**  **standard levels (compared to the 0.070 ppm case).**

**Section 5.3 then combines the results from Sections 5.1 and 5.2 to describe the total estimated**  **cost of full attainment in 2020, including both the costs of modeled controls for reaching partial**  **attainment (engineering costs) and the additional c osts of tons of extrapolated emissions**  **reductions needed to reach attainment. This sectio n includes two sets of costs. The first reflects**  **full attainment in 2020 in all locations of the U.S. ex cept two areas of California. These two**  **areas are not planning to meet the current standard by 2020, so the estimated costs for these areas**  **are based on reaching an estimated progress point i n 2020 (their “glidepath” targets). The**  **second set of results for California only, estimat e the costs from California fully attaining the** **5-2**

**alternative standards in a year beyond 2020 (glidep ath estimates for 2020, plus further**  **increments needed to reach full attainment beyond 20 20, added together for California total).**  **The costs described in this chapter generally include t he costs of purchasing, installing, and**  **operating the referenced technologies. For a variety of rea sons, actual control costs may vary**  **from the estimates EPA presents here. As discussed throughout this report, the technologies and**  **control strategies selected for analysis are illustrative of one way in which nonattainment areas**  **could meet a revised standard. There are numerous w ays to construct and evaluate potential**  **control programs that would bring areas into attainment with alternative standards, and EPA**  **anticipates that state and local governments will consid er programs that are best suited for local**  **conditions. Furthermore, based on past experience, EPA believes that it is reasonable to**  **anticipate that the marginal cost of control will decline over time due to technological**  **improvements and more widespread adoption of previou sly niche control technologies. Also,**  **EPA recognizes the extrapolated portion of the cost estimates reflects substantial uncertainty**  **about which sectors, and which technologies, might becom e available for cost-effective**  **application in the future. This is explained in further detail in Section 5.4.**
**It is also important to recognize that the cost est imates are limited in their scope. Because we are**  **not certain of the specific actions that states will take t o design State Implementation Plans to**  **meet the revised standards, we do not present estimated cos ts that government agencies may**  **incur for managing the requirement and implementati on of these control strategies or for offering**  **incentives that may be necessary to encourage or mo tivate the implementation of the**  **technologies, especially for technologies that are not nec essarily market driven. This analysis**  **does not assume specific control measures that woul d be required in order to implement these**  **technologies on a regional or local level.**

5.1 Modeled Controls
5.1.1 Sector methodology

**5.1.1.1 Non-EGU Point and Area Sources: AirControlNET**
**After designing a national hypothetical control strateg y to meet an alternative standard of 0.070**  **ppm using the methodology discussed in Chapter 3 (see sub-se ction 3.2.1), EPA used**  **AirControlNET to estimate engineering control costs. AirC ontrolNET calculates costs using**  **three different methods: (1) by multiplying an average annua lized cost-per-ton estimate against**  **the total tons of a pollutant reduced to derive a t otal cost estimate; (2) by calculating cost using**  **an equation that incorporates information regarding key plant information; or (3) by using both**  **cost per ton and cost equations. Most control cost information within AirControlNET has been**  **developed based on the cost-per-ton approach. This is because estimating cost using an equation**  **requires more data, and parameters used in other non-cost p er ton methods may not be readily**  **available or broadly representative across sources within the emissions inventory. The costing**  **equations used in AirControlNET require either plant ca pacity or stack flow to determine annual,**  **capital and/or operating and maintenance (O&M) costs. Capital costs are converted to annual** **5-3**

**costs, in dollars per ton, using the capital recove ry factor.**  **1 Applied controls and their respective**  **costs are provided in Ozone NAAQS RIA docket.**
**The control strategy for Non-EGU Point and Area Sources incorporated cost-per-ton caps.**  **These caps were pollutant specific and applicable o nly in the eastern U.S. portion of the analysis.**  **For reductions of NOx emissions the cap was $16,000/ton. This was based upon the approximate**  **benefit per ton of reductions in NOx, as well as an examination of the marginal cost curve for**  **NOx reductions from these sectors. There were only two controls whose cost per ton were**  **greater than this cap, and subsequently not included in this analysis, due to the lar ge capital**  **component of installing these controls. A similar process w as followed for reductions from**  **VOCs. The marginal cost curve was analyzed and the re was a clear break in the curve at**  **approximately $6,000/ton. At this cap, over sixty percent of the possible reductions are being**  **controlled at less than thirty percent of the total cost of th e VOC reductions.** 
**Supplemental controls were applied in this illustra tive analysis in order to achieve the highest**  **possible emission reduction from Non-EGU point and area sources. Supplemental co ntrol**  **measures are those controls that are 1) applied in th ese analyses but are not found in**  **AirControlNET, and 2) are in AirControlNET but whose data have been modified to better**  **approximate their applicability to source categories in 2020. The controls and associated data**  **such as control cost estimates not found in AirControlNET a re taken from technical reports**  **prepared to support preliminary 8-hour ozone State Implementation Plans (SIPs) prepared by**  **States and from various reports prepared by the sta ffs of various local air quality regulatory**  **agencies (e.g. Bay Area Air Quality Management District). The reports that are the sources of**  **additional controls data are included within footnotes in the Chapter 3 Appendi x. Modification**  **of control data, including percent reduction levels and control cost data, in AirControlNET**  **occurred as a result of a review of the nonEGU point and area NOx control measures by**  **technical staff. The changes EPA supplied are provided late r in the Chapter 3 Appendix.** 
**5.1.1.2 EGU Sources: the Integrated Planning Model**
**Costs for the electric power sector are estimated using th e Integrated Planning Model (IPM).**  **The model determines the least-cost means of meetin g energy and peak demand requirements**  **over a specified period, while complying with specif ied constraints, including air pollution**  **regulations, transmission bottlenecks, fuel market res trictions, and plant-specific operational**  **constraints. IPM is unique in its ability to provi de an assessment that integrates power,**  **environmental, and fuel markets. The model account s for key operating or regulatory constraints**  **(e.g. emission limits, transmission capabilities, renewa ble generation requirements, fuel market**  **constraints) that are placed on the power, emissions, and fuel markets. IPM is particularly well**  **suited to consider complex treatment of emission reg ulations involving trading and banking of** 

**1 For more information on this cost methodology and t** **he role of AirControlNext, see Section 6 of the 2006 PM RIA, AirControlNET 4.1 Control Measures Doc**  **umentation (Pechan, 2006b), or http://www.epa.gov/ttn/catc/products.html#cccinfo** **5-4**

**emission allowances, as well as traditional command -and-control emission policies.**  **2 Applied**  **controls and their respective costs are provided in the docket. IPM is described in further detail**  **in Appendix 3.**

**5.1.1.3 Onroad and Nonroad Mobile Sources: MOBILE model**
**Cost information for mobile source controls was taken from studies conducted by EPA for previous rulemakings and studies conducted for deve**  **lopment of voluntary and local measures that could be used by state or local programs to as**  **sist in improving air quality. Applied controls and their respective costs are provided in the docket.**  **3**
**Cost analysis of the onroad and nonroad mobile sector was performed using the MOBILE6 model. MOBILE is an EPA model for estimating pollut**  **ion from highway vehicles. MOBILE calculates emissions of hydrocarbons (HC), oxides of ni**  **trogen (NOx), and carbon monoxide (CO) from passenger cars, motorcycles, light- and hea** **vy-duty trucks. The model accounts for the emission impacts of factors such as changes in vehi**  **cle emission standards, changes in vehicle populations and activity, and variation in local co**  **nditions such as temperature, humidity and fuel quality** **4**

**.** 5.1.2 Known Controls— 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 non-EGU stationary sources, area sources, EGU s,**  **and onroad and nonroad mobile sources. Engineering costs ge nerally refer to the capital**  **equipment expense, the site preparation costs for the application, and annual operating and**  **maintenance costs.**

**The total annualized cost of control in each sector in the control scenario is provided in Table**  **5.1. These numbers reflect the engineering costs across s ectors annualized at a discount rate of**  **7% and 3%, consistent with the guidance provided in th e Office of Management and Budget’s**  **(OMB) (2003) Circular A-4. However, it is importan t to note that it is not possible to estimate** 

**2 The application of the 0.070 EGU control strategy results**  **in NOx allowance price decreasing from $1340/ton in the baseline to $715/ton. See Technical Su**  **pport Document on EGU Control Strategies for more details. Further detailed information o**  **n 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 MOBILE6 model can be**  **found at http://www.epa.gov/otaq/mobile.htm** **5-5**

**both 7% and 3% discount rates for each source (see section 5. 1.3). In Table 5.1, an annualized**  **control cost is provided to allow for comparison across sectors, and between costs and benefits.**  **A 7% discount rate was used for control measures appl ied to non-EGU point, area, and mobile**  **sources. Costs from EGU sources, which are calculated using t he IPM model and variable**  **interest rates, are captured in this table at an an nualized 7% discount rate**  **5**

**. 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. In this RIA, the non-EGU 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 figur e 5.1 is representative of the sum of**  **the non-EGU Point Source sector at a 3% discount ra te, and the EGU, mobile, and Area Source**  **sector at a 7% discount rate. It is expected that the 3% discount rate value is overestimated due**  **to the addition of cost sectors at a higher discount rate. Wit h the exception of the 3 % Total**  **Annualized Cost estimate on Table 5.1, cost estimates presented throughout this and subsequent**  **chapters are based on 7% discount rate.**
**The total annualized engineering costs associated with the application of known and**  **supplemental controls to reach a revised 0.070 ppm standard, incremental to the current standard,**  **are approximately $3.9 billion.**

**5 A different plant-specific interest rate is applied in estimating control costs within IPM. See**  **PM RIA for details. 5-6** 

Table 5.1 Comparison of Modeled Annual Control Cost s Nationwide, by sector, for a 0.070 ppm control scenario ($1999) 6

0.070 ppm Control Strategy

Source Category **Total Cost ($B 1999)**  East West **Average**

**Cost per Ton ($1999)**  **A. Electric Generating Units (EGU) Sector** Controls for NOx Cap-and-Trade Program and Local Measures in Projected Nonattainment Areas $0.20 $0 **$2,000**

Total $0.20 $0 **B. Onroad** $0.51 $0.11 **$2,300 C. Nonroad**  $0.09 $0.02 **$4,400** Total $0.60 $0.13 **D. Non-EGU Sector** Point Sources (Ex: Pulp & Paper, Iron & Steel, Cement, Chemical Manu.) $2.30 $0.34 **$3,600**

**E. Area Sector** Area Sources (Ex: Res. Woodstoves, Agriculture) $0.31 $0 .01 **$2,000** Total $2.6 $0.35 Total Annualized Costs (using a 7% interest rate) $3.90
Total Annualized Costs (using a 3% interest rate) $3.60

5.1.3 Limitations and Uncertainties Associated with Eng ineering 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-accepte d approaches that**  **are commonplace for estimating engineering costs in annual terms. However, our cost analysis**  **is subject to uncertainties and limitations.**
**There are some unquantified costs that are not 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. Addi tionally, control measure costs referred to as** 

**6 All estimates provided reflect the cost of a contro**  **l strategy for 0.070 pm, incremental to a 2020 baseline of compliance with the current standard of 0.084 ppm. Note, for the final RIA**  **we will be updating our estimates to $2006. 5-7** 

**"no cost" may require limited government agency resources for administration and oversight of**  **the program not included in this analysis; those 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 (i.e. social costs) of the cost of these modeled controls were not included**  **in this analysis. Incorporating the economic impact of the extrapolated portion of the costs was**  **too uncertain to be included as part of these estimates, and it was determined best to keep the**  **modeled and extrapolated costs on the same basis. Ho wever, incorporating any economic**  **impacts would increase the total cost of attainment i n 2020 for a revised ozone standard.** 
**The illustrative analysis does quantify the potenti al for advancements in the capabilities of**  **pollution control technologies as well as reductions i n their costs over time. This is discussed in**  **Section 5.4.**

5.2 Extrapolated Costs
**This section presents the results and methodology behind the extrapolated cost calculations of attainment of the alternate standards (the ends of the propo**  **sed range – 0.075 and 0.070 ppm, the less stringent alternative of 0.079 ppm, and the more**  **stringent alternative of 0.065 ppm). Consistent with the rest of this RIA, this section**  **presents two sets of results. The first reflects full attainment in 2020 in all locations of the U.S. ex**  **cept two areas of California. These two areas are not planning to meet the current standard**  **by 2020, so the estimated costs for these areas are based on reaching an estimated progress point i**  **n 2020 (their “glidepath” targets). The second set of results for California only, estimat**  **e the costs from California fully attaining the alternative standards in a year beyond 2020 (glidep**  **ath estimates for 2020, plus further increments needed to reach full attainment beyond 20**  **20, added together for California total).**

**As discussed in Chapter 3, the application of the 0.070 ppm co ntrol strategy was not successful**  **in reaching nationwide attainment of the alternate ozo ne standards. Many areas remained in**  **non-attainment for all three alternate standard scenar ios; therefore, the engineering costs detailed**  **in Section 5.1 represent only the costs of partial att ainment.** 

**The estimation of the costs of unidentified controls neede d to reach attainment is inherently a**  **difficult issue. The degree to which unspecified c ontrols are needed to achieve attainment**  **depends upon other variables in the analysis, such as attainment date assumptions. We will**  **better understand the true scope of the issue in the futu re as states conduct detailed area-by-area**  **analyses to determine available controls and attain ment dates that are appropriate under the**  **Clean Air Act. We do not attempt to determine specific attain ment dates in this analysis.** 
**This draft RIA used two different approaches to estimatin g the costs of unspecified control**  **measures. This reflects the difficulty in defining a “best” approach to this issue as well as the**  **uncertainty related to the extrapolated costs. One approach assumes that the marginal cost of**  **abatement increases at a constant rate. The other a pproach assumes a fixed cost for abatement** **5-8**

**tons and provides estimates for two fixed cost/ton values. Use of the fixed cost per ton approach**  **reduces the possibility of inflated extrapolated costs that would result from an infinitely increasing marginal cost curve. However it does not t**  **ake into account the probability that abatement costs would increase as an industry or state redu**  **ces a higher portion of available NOx or VOC tons. In turn, the marginal cost approach c**  **aptures this increase in abatement cost, but its lack of “cost caps” can result in overestimates of cos**  **ts.**
**Our approaches have yet to be peer reviewed and reflect a r ange of views about the likely cost of**  **future techniques and strategies that reduce air pollutan t emissions. (The higher-cost estimation**  **approaches are implicitly more pessimistic about prospect s for technological advances that avoid**  **large increases in the cost per ton of emission red uction relative to controls employed in the**  **past.) Section 5.4 discusses historical experience which has shown numerous technological**  **advances in emission reduction technologies, and pr ovides a few examples of today’s emerging**  **technologies. EPA will continue to consider these is sues between now and the publication of the**  **final RIA for the final ozone NAAQS rule.**
**This section provides the additional costs of reaching n ationwide full attainment of the alternate**  **ozone standards utilizing three values: a lower fix ed cost per ton estimate based on the majority**  **of the known cost/ton control values, an upper fixed cost p er ton estimate based on the cost of**  **the last few known control measures used and an increasing marginal cost estimate similar to**  **that used in the PM NAAQS Final RIA. In addition to presenting the full attainment cost, this**  **section will provide the methodology behind each appro ach.** 
**Prior to presenting the aforementioned full attainm ent costs, it is important to provide**  **information from EPA’s Science Advisory Board Counc il Advisory**  **7 , dated June 8, 2007, on the**  **issue of estimating costs of unidentified control measure s. In that letter, the Council advises**  **against any approach that deviates from using a fix ed cost/ton estimate such as the increasing**  **marginal cost approach provided below. This increa sing marginal cost approach ‘grows’**  **extrapolated costs that have an unquantifiable level of un certainty at a rate with an equivalent**  **level of uncertainty. This approach is presented in this Prop osal RIA in order to maintain a**  **consistency with the PM NAAQS Final RIA cost extrapolation. EPA is going to reconsider its approach to estimating the full attainment costs in**  **the final RIA, in light of this advice. Consideration of this advice will be balanced with the requi**  **rements of E.O. 12866 and OMB circular A-4, which provides guidance on the estimation of b**  **enefits and costs of regulations.**
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 relies on unidentified pollution control measures to make up the difference. Emission reductions attributed to unidentified measures appear to account for a large share of

**7 U.S. Environmental Protection Agency. June 2007. Advi**  **sory Council on Clean Air Compliance Analysis (COUNCIL), Council Advisory on O**  **AR’s Direct Cost Report and Uncertainty Analysis Plan. Washington, DC** **5-9**

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, the 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, theCouncil 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 represented using alternative fixed costs per ton of emissions avoided.”
5.2.1 Increasing Marginal Cost Methodology
**This approach stems from the assumption that each unit of inc remental reduction in non**  **attainment areas will result in an increase in cost per ton or marginal cost of abatement.**  **Therefore, similar to the approach used in the PM N AAQS RIA, EPA estimated constantly**  **increasing marginal cost curves for emission reduct ions using cost per ton values from control**  **strategy data in representative non-attainment areas. These curves were then used to estimate a**  **cost of full attainment using the emission reduction targe ts detailed in Chapter 4 of this report.** 

**5.2.1.1 Marginal Cost Regions**
**EPA grouped the non-attainment areas described in Chapte r 4 along with their emission**  **reduction targets into six regions of the country (Table 5.2) in order to acquire sufficient and**  **representative data for deriving the slopes of the marginal cost curves.**

x **Nonattainment areas in Virginia were grouped with th e Northeast due to the fact that**  **Northern Virginia is part of the Ozone Transport Region (OTR) which makes up the Northeast. Resources were not available to disaggreg**  **ate states by counties.**

x **Nonattainment areas in Louisiana were grouped with Texas and Oklahoma (Plains**  **region) due to the similarity in industry mix among those stat es.** 

x **California was separated from the rest of the west due to the severity of the ozone**  **problem in the state, the glide path targets unique to the state, and because EPA**  **determined the rest of the west was not an ideal rep resentation of California.** **5-10**

Table 5.2 Regions and Slopes for Extrapolated Costs

Region Marginal Cost Slope
**Northeast (OTR) 0.035** 

**Midwest 0.045** 

**Southeast 0.036** 
**Plains (TX/LA) 0.033** 
**West (Not CA) 0.152** 

**CA 0.211** **5-11**

**5.2.1.2 Derivation of the Marginal Cost Slopes**
**Due to the efficaciousness and efficiency of NOx controls compared to VOC controls, control strategy cost per ton data was acquired for each region using**  **a selection criteria defined in Table 5.3 and applied in Ordinary Least Squares regression equati**  **ons. Results of these equations provided the slope for the marginal cost curves. F**  **or each equation, the dependant variable (** Y**= cost/ton)**  **8 and was regressed conditional to (**  X**= cumulative emissions reductions). 9** 

Y = c + ȕ X + ɟ

**The regression equations are not intended to be use d for statistical inference in regards to the**  **relation between cost/ton and cumulative emission red uctions. They represent a rough**  **approximation of an increasing rate or slope of cost /ton which could be used to project**  **extrapolated costs. This slope would then provide an increasing cost/ton rate in the extrapolated**  **portion of the cost that was equivalent to the rate observed under the modeled costs. As can be**  **seen by the range of the goodness of fit estimates (R**  **2 = 0.5 to 0.8), there is a high level of**  **uncertainty with these slope estimates which is pro pagated through the extrapolated cost**  **estimates.**
Table 5.3 Data Selection Criteria for Extrapolated Costs **1) Determine if area has sufficient NOx emissions remaining to reach**  **attainment 2) If area has sufficient NOx remaining to reach a** **ttainment, then use NOx cost/ton data due to their cost effectiveness compare**  **d VOC controls 3) If area does not have sufficient NOx emissions to reach att**  **ainment, then include VOC controls in the data set if:** 

x **VOC controls were part of the control strategy for t he area in question** 

x **VOC control cost/ton inclusions to the regression data set would**  **significantly alter the value of the slope for the m arginal cost curve**  **derived using only NOx cost/ton data Note: Data analysis demonstrated that VOC control data woul**  **d only be needed for California. Due to lack of available ozone data, NOx controls fr**  **om California also include control cost from the PM NAAQS RIA control strategies.** 

**8 For the east regions, the full cost/ton data set was applied and had a**  **maximum value of $15,267/ton. For the west, the cost/ton data was t** **runcated at $15,267/ton in order to maintain a consistent comparison with the east regions and beca** **use the few remaining controls had costs greater $35,000/ton and were therefore judged to be**  **not economically feasible. 9**  **EPA recognizes that these regression equations may be m isspecified. As stated above, the**  **objective was not to accurately capture the relation betwe en control cost/ton and emission**  **reduction for statistical or economic inference purposes . These equations represent the most**  **statistically adequate models that could be specifi ed given the data, time, and resource**  **constraints.** c **= constant** ȕ **= slope**

ɟ **= residual 5-12** 

**5.2.1.3 Calculating Extrapolated Costs Using Marginal Cost Approach** 
**Once the slope of the marginal cost curve was derive d, the extrapolation was calculated by**  **multiplying that slope with the emission reduction target and adding that value to the highest of**  **the observed cost/ton value (Figure 5.1). For this illustrative analysis, the highest of the**  **observed cost/ton values was roughly $15,267/ton which represented the intercept of the marginal cost equation. Total costs could then be es**  **timated by adding the area under the marginal cost curve in Figure 5.1 or by taking the**  **integral of the marginal cost function and inputting the emission reduction target into the equ**  **ation for total cost. 10** 
Figure 5.1 Extrapolated Cost Example (MC Approach) 11

E x t r a p o l a t e d C o s t s

P o r t i o n

0 50 100 Reductions Needed (thousands)Cost/Ton ($)

$15,267 S l o pe = ȕ

Lower Fixed Cost/Ton Estimate

5.2.2 Fixed Cost per Ton Values
**Similar to the 1997 Ozone NAAQS RIA, a fixed cost/ton value was also applied to estimate the**  **extrapolated costs of nationwide full attainment. Tot al costs for each non-attainment area was**  **calculated by multiplying the fixed cost/ton value wit h the emission reduction targets for each**  **region. For this particular illustrative analysis, a pair of fixed cost/ton values was used to**  **calculate costs.**
**NOx control strategy data for the East and West were examine d for ‘clustering’ within their**  **individual distributions. Cost/ton data for the east an d west were stratified into thousands (Ex.**  **$0-$1000, $1000-$2000) with individual source count s aggregated within each interval.**  **California was separated from the west so source co st/ton counts were conducted separately for**  **the state. This was the result of limited ozone NOx data availability for the state, the low number** 

**10** Total Cost = $15,267x + ( ȕ **/2)x 2**  **, where x = emission reduction target 11**  **In the case of 0.075 ppm, negative reductions are needed in order to estimate extrapolated cost**  **savings. 5-13** 

**of NOx emissions remaining for CA, and the inclusion of ozone VOC controls as well as NOx**  **controls from PM NAAQS RIA control strategies which were required to resolve these data and emissions issues.** 
**For the East, 90% of the controls were below $6,000/ton. As a result, the control cost closest to $6,000/ton ($6,012) was selected to represent the c**  **ost of the majority of the modeled controls as well as the lower estimate of the Eastern fixed cos**  **t/ton approach. For the West, 94% of the controls had a cost/ton value below $4,000/ton. Therefore,**  **$4,213 was selected as the lower estimate for the western fixed cost/ton approach.**  **For California, the lower estimate was $9,035 using the same method but including VOC and PM NAAQS NOx control data from the PM**  **NAAQS RIA hypothetical control scenario. This lowe r estimate for CA captured 81% of the**  **cost/ton data below $15,267 and 65% of all cost/ton data for controls applied in the ozone and**  **PM control strategies after multiplying VOC control s by a 4 to 1 substitution factor.** 
**In addition to a lower fixed cost/ton estimate, an upper fixe d cost/ton value was used for**  **calculating extrapolated costs. This upper value a s estimated at $15,267 for all regions for the**  **following reasons.**

x **This value represented the highest, in terms of cost/ ton, of the controls applied in the**  **East. The East control strategy made up the majority of the mo deled controls for the**  **ozone standard.**

x **In the case of the West, the next highest controls were roughly $35,000 and $39,000 per**  **ton. Controls with these costs were determined to be significantly less feasible to implement compared other controls.** 

x **This value provides a consistent platform from whic h to incorporate and compare**  **marginal cost values derived using the increasing m arginal cost approach.** 

5.2.3 Results
**Tables 5.4 to 5.7 provide the extrapolated cost values for 2020 attainment of the 0.079, 0.075, 0.070, 0.065 ppm standards in each area (including the Calif**  **ornia 2020 glidepath targets) applying the increasing marginal cost value as well**  **as the two fixed cost/ton values. The reader should be aware of the following stipulations prior to makin**  **g inferences from the extrapolated costs presented in the following tables.** 

x **The two extrapolated cost approaches provide three rough estimates of potential costs**  **with the marginal cost approach providing the highest va lue. Neither result includes a**  **probability or a link to sectors where reductions w ill be attained. Therefore, there are no**  **expected values within this range of outcomes and n o assumptions made about the types**  **of controls that would be applied in 2020. Although the amount of reduction assumed to**  **occur using unknown controls increases, the uncertainty of the associated costs and benefits calculations increases.** 

x **0.070 ppm extrapolated costs were estimated using d ata from the 0.070 ppm control**  **strategy. Therefore, although the degree of uncertainty is still significant, these results can be expected to have a higher level of confidenc**  **e than results for the 0.079, 0.075, and 0.065 ppm alternate standards.** **5-14**

x **The use of the 0.070 ppm control strategy as a starting poin t for extrapolating the 0.079**  **and 0.075 ppm standard resulted in over attainment of these targets in some areas. For**  **over attaining areas, cost savings and emission increas es were extrapolated using the**  **impact/ton estimates derived in Chapter 4 and their appropriate emission targets until**  **reaching the respective alternate standard.**

x **Several new non-attainment counties were added to th e analysis as a result of moving to**  **the 0.065 ppm alternate standard. Most of these counties w ere in states within the 0.070**  **ppm control strategy region described in Chapter 3. F or the east, this region was made up**  **of counties with 0.070 ppm violating monitors and their 200 km buffers which made up most of the eastern part of the US. In the west, this region was made up of six states (**  **AZ, CA, CO, NM, NV, UT). Due to the geographic scope of the 0.070**  **control strategy, no additional controls were available and costs had to be ex**  **trapolated using the same impact/ton estimates applied in the 0.070 ppm estim**  **ates. Two new states were added to the non-attainment region (KS and AL). Since controls wer**  **e available for these states, AirControlNET was used to identify controls that would a**  **chieve the required emission reduction targets.** 

x **Consistent with OMB Circular A-4, costs are presente d 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 re presentative of actual**  **costs (see section 5.1.3) 5-15**  a **Due to the limited amount of controls in the modeled contro l strategy which had this value,**  **deducting this amount would likely result in an ove r estimate of the savings. For example,**  **estimating cost savings by multiplying changes in emissio n reduction by $15,267 would**  **result in an overestimate of cost savings since not all of the applied controls had a cost/ton as**  **high as $15,267. b**  **Los Angeles and Kern Counties have expected attainm ent dates after 2020. This analysis**  **counts the portion of reductions expected by 2020 or ear lier.** 

c**We did not calculate the cost savings which would res ult from using the upper bound**  **values. This would result in an unrealistic savings because none of these cities with less**  **significant air quality problems would be expected to be get ting**  all**their expected emission reductions at such a high cost/ton. We therefore used the lower cost estimate which**  **included roughly 90% of the controls applied.** Table 5.4 Extrapolated Costs of Meeting the 0.079 ppm Standard

MC Curve Estimate Lower Fixed

Cost/Ton Estimate Upper Fixed

Cost/Ton Estimate a Cost/Ton Estimate of

Last Control Applied on MC Curve Estimate Extrapolated Costs for 0.079 Standard ($M 1999) ($M 1999) ($M 1999) ($1,999) Extrapolated Costs

CA – Los Angeles **b** $0 $0 $0

CA – Kern County

**b** $0 $0 $0

Houston / Dallas $477 $158 $400 $18,765 Ozone Transport Region $568 $200 $504 $17,787

Lake Michigan region $571 $206 $519 $17,562
Richmond / Norfolk $139 $55 $137 $15,582

Total Cost $1,755 $619

Extrapolated Cost Savings Atlanta, GA ($568) ($236) c
Cleveland, OH ($493) ($206) c
Detroit, MI ($224) ($91) c

Control Deletions Charlotte, Memphis, Las Vegas, Salt Lake City, Tampa ($119) ($119) c

Colorado, Arizona ($676) ($676) c Baton Rouge, Indianapolis, Louisville, St. Louis ($241) ($241) c

Total Cost Savings ($2,321) ($1,569)

Total Extrapolated Cost ($566) ($950) Average cost per ton $2,113 $3,549**5-16** Table 5.5 Extrapolated Costs of Meeting the 0.075 ppm Standard

Extrapolated Costs for 075 Standard MC Curve Estimate ($M 1999) Lower Fixed

Cost/Ton Estimate ($M 1999) Upper Fixed

Cost/Ton Estimate a ($M 1999) Cost/Ton Estimate of

Last Control Applied on MC Curve Estimate ($ 1999) Extrapolated Costs CA – Los Angeles **b** $0 $0 $0
CA – Kern County

**b** $0 $0 $0

Houston / Dallas $1,254 $400 $1,008 $20,085 Ozone Transport Region $1,307 $443 $1,114 $19,187 Lake Michigan region $1,310 $449 $1,130 $19,362 Richmond / Norfolk $790 $297 $748 $16,982
Detroit $396 $152 $382 $16,392
Phoenix $282 $72 $260 $17,851
Denver $160 $42 $153 $16,787 Cleveland/Columbus/Cincinnati $92 $36 $92 $15,537
Atlanta $15 $6 $15 $15,303

Total Cost $5,606 $1,896

Extrapolated Cost Savings Baton Rouge, LA ($225) ($91) c Indianapolis, IN ($209) ($85) c
Louisville, KY-IN ($284) ($115) c
St. Louis, MO-IL ($30) ($12) c

Total Cost Savings ($748) ($303)

Total Extrapolated Cost $4,858 $1,593 Average cost per ton $23,000 $7,600

a **Due to the limited amount of controls in the modeled contro l strategy which had this value,**  **deducting this amount would likely result in an ove r estimate of the savings. For example,**  **estimating cost savings by multiplying changes in emissio n reduction by $15,267 would**  **result in an overestimate of cost savings since not all of the applied controls had a cost/ton as**  **high as $15,267. b**  **Los Angeles and Kern Counties have expected attainm ent dates after 2020. This analysis**  **counts the portion of reductions expected by 2020 or ear lier.** 

c**We did not calculate the cost savings which would res ult from using the upper bound**  **values. This would result in an unrealistic savings because none of these cities with less**  **significant air quality problems would be expected to be get ting**  all**their expected emission reductions at such a high cost/ton. We therefore used the lower cost estimate which**  **included roughly 90% of the controls applied. 5-17**  Table 5.6 Extrapolated Costs of Meeting the 0.070 p pm Standard a

Extrapolated Costs for 0.070 Standard MC Curve Estimate ($M 1999) Lower Fixed

Cost/Ton Estimate ($M 1999) Upper Fixed

Cost/Ton Estimate ($M 1999) Cost/Ton Estimate

of Last Control Applied on MC Curve Estimate ($ 1999) CA – Los Angeles b $0 $0 $0 CA – Kern County b $829 $181 $305 $43,541 Houston / Dallas $2,299 $703 $1,771 $21,735 Ozone Transport Region $2,310 $746 $1,878 $20,937 Lake Michigan region $2,334 $752 $1,893 $21,612 Richmond / Norfolk $1,683 $600 $1,511 $18,732
Detroit $1,272 $455 $1,145 $18,642
Phoenix $1,364 $282 $1,023 $25,451
Denver $1,190 $253 $916 $24,387
Cleveland/Columbus/Cincinnati $926 $339 $855 $17,787
Atlanta $825 $309 $779 $17,103
St. Louis $785 $291 $733 $17,427 Indianapolis $579 $218 $550 $16,887 Baton Rouge $555 $212 $534 $16,422
Louisville $491 $188 $473 $16,383 Memphis $313 $121 $305 $15,987
Charlotte $217 $85 $214 $15,771 Salt Lake City $211 $55 $198 $17,243 Las Vegas $177 $46 $168 $16,939 Tampa $77 $30 $76 $15,447
Total Extrapolated Cost $18,441 $5,867 $15,328 Average cost per ton $18,400 $5,900 $15,300

a**EPA was not able to estimate benefit changes when moving the st andard from 0.070 to**  **0.075 for Memphis, Charlotte, Salt Lake City, Las Vegas, an d Tampa. Therefore, in order to**  **maintain a consistent comparison, cost savings were not estimated for these locations.** 

b**Los Angeles and Kern Counties have expected attainment dates after 2020. This analysis counts the portion of reductions expected by 2020 or ear**  **lier. 5-18**  Table 5.7 Extrapolated Costs of Meeting the 0.065 ppm Standard

Extrapolated Costs for 065 Standard MC Curve Estimate ($M 1999) Lower Fixed

Cost/Ton Estimate ($M 1999) Upper

Fixed Cost/Ton Estimate ($M 1999) Cost/Ton

Estimate of Last Control Applied on MC Curve Estimate ($ 1999) CA – Los Angeles a $0 $0 $0 $15,267 CA – Kern Countya $2,230 $452 $763 $49,871 Houston / Dallas $3,427 $1,006 $2,534 $23,385 Ozone Transport Region $3,401 $1,049 $2,641 $22,687 Lake Michigan region $3,471 $1,055 $2,656 $23,862 Richmond / Norfolk $2,663 $903 $2,275 $20,482 Detroit $2,260 $758 $1,908 $20,892 Phoenix $2,827 $493 $1,786 $33,051 Denver $2,599 $463 $1,679 $31,987 Cleveland/Columbus/Cincinnati $1,871 $643 $1,618 $20,037 Atlanta $1,726 $612 $1,542 $18,903 St. Louis $1,712 $594 $1,496 $19,677 Indianapolis $1,479 $521 $1,313 $19,137 Baton Rouge $1,417 $515 $1,298 $18,072 Louisville $1,355 $491 $1,237 $18,183 Memphis $1,157 $424 $1,069 $17,787 Charlotte $1,051 $388 $977 $17,571 Salt Lake City $1,263 $265 $962 $24,843 Las Vegas $1,214 $257 $931 $24,539 Tampa $894 $333 $840 $17,247 Jackson, MS $757 $285 $718 $16,959 New Mexico areas (Farmington / Las Cruces) $819 $185 $672 $21,955 OK areas (Tulsa, Marshall) $704 $267 $672 $16,719 Huntington, WV-KY $639 $242 $611 $16,707 El Paso, TX $538 $206 $519 $16,389 Kansas City, MO/KS $325 $142 $317 $16,122 Little Rock, AR $442 $170 $427 $16,275 Mobile AL $70 $70 $70 $16,239 Columbia, SC $154 $61 $153 $15,627

Extrapolated Total $42,465 $12,851 $33,684 Average cost per ton $19,300 $5,800 $15,300

a **Los Angeles and Kern Counties have expected attainment dates after 2020. This analysis counts the portion of reductions expected by 2020 or ear**  **lier. 5-19** 

5.3 Summary of Costs
**Table 5.8 presents a summary of the total national cost of attaining 0.079, 0.075, 0.070, and**  **0.065 ppm standards in 2020 (including the California glidepath). This summary includes the**  **costs presented above from the modeled controls and the extra polated costs. The range**  **presented in the extrapolated costs and the total c osts represent the upper and lower bound cost**  **estimates. Consistent with OMB Circular A-4, costs are presented at a 7% discount rate. It is**  **more consistent to present the extrapolated costs at the same discount rate as the modeled control**  **costs, for which a 7% rate was determined to be more representative of ac tual costs (see section**  **5.1.3). Although the amount of reduction assumed to occur using unknown controls increases,**  **the uncertainty of the associated costs and benefits calcul ations increases.** 

Table 5.8 Total Costs of Attainment in 2020 for Different Lev els of the Ozone Standard (National Attainment in 2020) Level of Standard in 2020 **0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm Modeled Costs ($B) $3.9 $3.9 $3.9 $3.9**  **Extrapolated Costs ($B) $13 to $42 $5.9 to $18 $1.6 to $4.9 ($0.95) to ($0.57)** * **Total Costs ($B) $17 to $46 $10 to $22 $5.5 to $8.8 $3 to $3.3**  *** The use of the 0.070 ppm control strategy as a star ting point for extrapolating the 0.079**  **standard resulted in over attainment in some areas. For over attaining areas, cost savings were**  **applied. For the 0.079 ppm standard the cost savings from ove r attaining areas was greater than**  **the costs for areas still needing extrapolated tons (see Table 5.4).** 
**Table 5.9 presents an estimate of total costs of Cal ifornia only, for fully attaining the alternative**  **standards in a year beyond 2020 (glidepath estimates for 2020, plus further increments needed to**  **reach full attainment beyond 2020, added together f or California total).** **5-20**

*** Due to the limited amount of controls in the modeled contr ol strategy which had this value,**  **deducting this amount would likely result in an over es timate of the savings.** 
**It is not appropriate to add together the 2020 national atta inment, California glidepath estimate**  **and the estimate of California full attainment as a n estimate of national full attainment in 2020.**  **The extra increment of attainment that is estimated for California will not occur in 2020, so it is**  **not accurate to add it to our nationwide estimate of the “glidepath” benefits and costs to arrive at**  **a “full attainment” estimate for 2020 12**  **. It is also not accurate to add the two estimates together to**  **arrive at an estimate of future, post-2020 full att ainment benefits and costs, because EPA’s**  **nationwide full attainment estimates do not allow other are as of the nation to take credit for the**  **reductions in NOx from the mobile source rules that will o ccur after 2020.**  **13**

5.4 Technology Innovation and Regulatory Cost Estimates
**The history of the Clean Air Act provides many exam ples in which technological innovation and**  **“learning by doing” have made it possible to achiev e greater emissions reductions than had been**  **feasible earlier, or have reduced the costs of emis sion control in relation to original estimates.**  **Innovative companies have successfully responded to the regulatory challenges and market**  **opportunities provided by the Act, producing breakthrough technologies for multiple sectors.** 

**12 The California full attainment costs calculated usi**  **ng the offset in NOx emissions from mobile programs would understate the costs of fully attaining in 20**  **20, however, California will not be required to attain in 2020.**  **13 This approach would be an overestimate of national full attai**  **nment costs in a future year after 2020 because it would not take into account that oth**  **er states (not just California) could replace more expensive NOx reductions from other sources with the post-2020 reductions obtained from**  **implementation of mobile source rules that are included in the regulatory baseline.**  Table 5.9 California Extrapolated Costs ($M)
0.079 0.075 0.070 0.065 CA (Glidepath) Marginal Cost Approach **$0 $0**  **$829 $2,230**  Lower Estimate (fixed cost) **$0 $0**  **$181 $452**  Upper Estimate (fixed cost) ***** ***** **$305 $763** 

CA Increment Needed for Full Attainment Marginal Cost Approach **$4,566 $6,227**  **$12,022 $18,301**  Lower Estimate (fixed cost) **$1,187 $1,050**  **$1,773 $2,405**  Upper Estimate (fixed cost) ***** ***** **$2,995 $4,064** 
CA (Full Attainment-Later Year) Marginal Cost Approach **$4,566 $6,227**  **$12,851 $20,530**  Lower Estimate (fixed cost) **$1,187 $1,050**  **$1,953 $2,857**  Upper Estimate (fixed cost) ***** ***** **$3,301 $4,827** **5-21**

**Studies 14**  **have suggested that costs of some EPA programs have been less than originally**  **estimated due in part to inadequate inability to pr edict and account for future technological**  **innovation in regulatory impact analyses.**
**Technological change will affect baseline conditions for our analysis. This change may lead to**  **potential improvements in the efficiency with which 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 basel ine mortality incidence rates account**  **for increasing life expectancy, and thus reflect projecte d improvements in health care, our**  **baseline incidence rates for other health endpoints such as ho spital 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 endpo ints, such as asthma, there has been an**  **observed upward trend in prevalence, which we have not captured in our incidence rates. For**  **these endpoints, our estimates will potentially be unders tated. In general, for non-mortality**  **endpoints, there is increased uncertainty in our estim ates due to our use of current baseline**  **incidence and prevalence rates.**
**A constantly increasing marginal cost curve similar to the one utilized for estimating**  **extrapolated costs in this RIA is likely to induce the type of innovation that would result in lower**  **costs than estimated early in this chapter. Breakthrough technologies in control equipm ent could**  **by 2020 result in a rightward shift in the marginal co st curve (Figure 5.2)**  **15 as well as perhaps a**  **decrease in its slope, reducing marginal costs per uni t of abatement, and thus deviate from the**  **assumption of one constantly increasing marginal co st curve. In addition, elevated abatement**  **costs may result in significant increases in the co st of production and would likely induce**  **production efficiencies, in particular those related to energy inputs, which would lower emissions**  **from the production side.**

**14 Harrinton et al ,2000, and previous studies cited**  **by Harrington 15**  **Figure 5.2 shows a linear marginal abatement cost curve. It is possible tha t the shape of the**  **marginal abatement cost curve is non-linear. 5-22**  

### MC 0 

### MC 1



### Cumulative NOx ReductionsCost/Ton 

### Figure 5.4 Technological Innovation Reflected by MC Shift

Induced Technology Shift S l o pe = ȕ 0

S l o p e = ȕ 1

5.4.1 Examples of Technological Advances in Pollution Control
**There are numerous examples of low-emission technologies d eveloped 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** 

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 power-based coatings to replace petroleum- based formulations** 

x **Vehicles far cleaner than believed possible in the late 1980s due to improvements in**  **evaporative controls, catalyst design and fuel contr ol systems for light-duty vehicles; and**  **treatment devices and retrofit technologies for heavy -duty engines** 

x **Continued development of activated carbon injection (ACI) technology for control of**  **mercury from electric generating units**

x **Development of integrated gasification combined cycle (IG CC) and ultra-super critical**  **pulverized coal technologies for electricity generation**

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

x **Market penetration of gas-electric hybrid vehicles, biodi esel and other clean fuels** 

Figure 5.2 Technological Innovation Reflected by Marginal Cost Shift**5-23**

**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 pollut ion control programs,** 
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) conducted an analysis of the predicted and actual costs of 28 federal and state rules, including 21 issued**  **by EPA and the Occupational Safety and Health 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 quan** **tity 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 ove**  **restimated 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 litera ture, 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.”**  **16**
**It should be noted that many (though not all) of the EPA rules examined by Harrington**  **had compliance dates of several years, which allowed a 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 tradi ng**  **program by Resources for the Future (RFF) and MIT hav e been as much as 83 percent** 

**16 Harrington et al., 2000.** **5-24**

**lower than originally projected by EPA. 17**  **Note that the original EPA cost analysis also relied on an optimization model like IPM to approxi**  **mate the results of emissions trading. 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 of**  **economic modeling to predict technological improvement of pollution controls and oth**  **er compliance options such as fuel switching. Harrington et al report that scrubbing turne**  **d out to be more efficient (95% removal vs. 80-85% removal) and more reliable (**  **95% vs. 85% reliability) than expected, and that unanticipated opportunities arose to**  **blend low and high sulfur coal in older boilers up to a 40/60 mixture, compared with t** **he 5/95 mixture originally estimated.**

x EPA Fuel Control Rules: **A 2002 study by two economists with EPA’s Office of Transportation and Air Quality**  **19 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 smalle st amount.” The paper notes that cost**  **is not the same as price, but suggests that a comparison nonetheless can be instructive. 20**  **An example focusing on fuel rules is provided:**

**17 Carlson et al., 2000; Ellerman, 2003.**  **18 2010 Phase II cost estimate in $1995.**  **19 Anderson et al, 2002.**  **20 The paper notes: “Cost is not the same as price.**  **This simple statement reflects the fact that a lot happens between a producer’s determination of m**  **anufacturing cost and its decisions about what the market will bear in terms of price change.**  **”** Phase 2 Cost Estimates Ex ante estimates $2.7 to $6.2 billion 18 Ex post estimates $1.0 to $1.4 billion**5-25**

Table 5.10 Comparison of Inflation-Adjusted Estimated Costs and Actual Price Changes for EPA Fuel Control Rules 21
Inflation-adjusted Cost Estimates (c/gal) Actual Price Changes (c/gal) EPA DOE API Other Gasoline **Phase 2 RVP Control (7.8 RVP - Summer) (1995$) 1.1 1.8 0.5**  **Reformulated Gasoline Phase 1 (1997$)**  **3.1- 5.1 3.4-4.1**  **8.2-**

**14.0 7.4 (CRA) 2.2**

**Reformulated Gasoline Phase 2 (Summer) (2000$)**  **4.6- 6.8**  **7.6- 10.2**  **10.8- 19.4 12**  **7.2 (5.1, when**

**corrected to 5yr MTBE**  **price)**

**30 ppm sulfur gasoline (Tier 2) 1.7-**  **1.9 2.9-3.4 2.6 5.7**  **(NPRA), 3.1**  **(AIAM) N/A** Diesel **500 ppm sulfur highway diesel fuel (1997$)**  **1.9- 2.4**  **3.3 (NPRA) 2.2**  **15 ppm sulfur highway diesel fuel 4.5 4.2-6.0 6.2**  **4.2-6.1 (NPRA) N/A** 

x **Chlorofluorocarbon**( CFC) Phase-Out: **EPA used a combination of regulatory, market based (i.e., a cap-and-trade system among manufacturers), and voluntary approaches to**  **phase out the most harmful ozone depleting substance s. This was done more efficiently**  **than either EPA or industry originally anticipated. The phaseout for Class I substances**  **was implemented 4-6 years faster, included 13 more chemic als, and cost 30 percent less**  **than was predicted at the time the 1990 Clean Air Act Amendments were enacted.**  22
**The Harrington study states, “When the original cost 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.”** 

**21 Anderson et al., 2002.**  **22 Holmstead, 2002.** **5-26**

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 publis hed by the Society of Automotive**  **Engineers. SAE 2002-01-1980.**
**Babiker, M.H., and T.F. Rutherford. 1997. “Input O utput 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://d ebreu.colorado.edu/papers/gtaptext.html** 

**. Babiker, M.H., J.M. Reilly, M. Mayer, R.S. Eckaus, I.S. Wing, and R.C. Hyman. 2001. “The**  **MIT Emissions Prediction and CO2 Policy Analysis (EP PA) Model: Revisions, Sensitivities,**  **and Comparisons of Results.” MIT Joint Program on the Science a nd 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 E**  **conomic Review 86(4):985-1000. Available at <http://www.aeaweb.org/aer/>.** 
**Brooke, A., D. Kendrick, A. Meeraus, and R. Raman. 1998. GAMS: A User’s Guide. GAMS Development Corporation. Available at http://www.gams.c**  **om**

**. Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper**  **, and Karen L. Palmer. 2000. “SulfurDioxide Control by Electric Utilities: What Ar**  **e the Gains from Trade?”** Journal of Political Economy **108(#6):1292-1326.**
**Ellerman, Denny. January 2003. Ex Post Evaluation of Trad able Permits: The U.S. SO2**  **Cap-and-Trade Program. Massachusetts Institute of Tech nology Center for Energy and**  **Environmental Policy Research.**
**Feenberg, D., and E. Coutts. 1993. “An Introduction to the TAXSIM Model.” Journal of Po licy**  **Analysis and Management 12(1):189 194. Available at http://www.nber.org/~taxsim/. Fullerton, D., and D. Rogers. 1993. “Who Bears th**  **e Lifetime Tax 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 Subs tantial 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. 5-27** 

**Holmstead, Jeffrey, 2002. “Testimony of Jeffrey Ho lmstead, 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.**
**Minnesota IMPLAN Group. 2003. State Level Data fo r 2000. Available from**  **http://www.implan.com/index.html**

**. Nestor, D.V., and C.A. Pasurka. 1995. The U.S. Environmen** **tal Protection Industry: A Proposed Framework for Assessment. U.S. Environmental Pro**  **tection Agency, Office of Policy, Planning, and Evaluation. EPA 230-R-95-001. Available at**  **http://yosemite.epa.gov/ee/epa/eermfile.nsf/11f680ff78df42f585256b45007e6235/41b8b642ab93 71df852564500004b543/$FILE/EE 0217A 1.pdf.** 
**U.S. Department of Energy, Energy Information Administra tion. Undated (b). State Energy**  **Price and Expenditure Report. Washington DC. Avai lable 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 at http://www.eia.doe.gov/emeu/mecs/.**
**U.S. Department of Energy, Energy Information Administra tion. January 2003. Annual Energy**  **Outlook 2003. DOE/EIA 0383(2003). Washington DC. Availabl e 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. Availa ble at**  **http://www.eia.doe.gov/oiaf/aeo/pdf/0383 (2001).**
**U.S. Environmental Protection Agency. June 2007. Advisor y 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- Chapter 5

5a.1 Cost Information for Non-EGU and area sources **(Full details on controls can be found in Appendix Chap ter 3)** 
Low Emission Combustion (LEC) **The average cost effectiveness for large IC engines using LEC technology was estimated**  **to be $532/ton (ozone season). 1**  **The EC/R report on IC engines (Ec/R, September 1, 2000) estimates the average cost effectiveness for**  **IC engines using LEC technology to range from $420-840/ton (ozone season) for engines in the**  **2,000-8,000 bhp range. The key variables in determining average cost effective**  **ness for LEC technology are the average uncontrolled emissions at the existing sour**  **ce, the projected level of controlled emissions, annualized costs of the controls, and num** **ber of hours of operation in the ozone season. The ACT document uses an average uncont** **rolled level of 16.8 g/bhp-hr, a controlled level of 2.0 g/bhp-hr (87% decrease), and**  **nearly continuous operation in the ozone season. The EPA believes the ACT document provide**  **s a reasonable approach to calculating cost effectiveness for LEC technology.** 
Leak Detection and Repair (LDAR) for Fugitive Leaks
**The control efficiency is 80 percent reduction of VOC at an annualized cost of $4,800 per ton. We do not include the costs of this control m**  **easure in our analyses in the Houston nonattainment area since these controls are already included in the 8-hour**  **Ozone SIP for this area.** 
Enhanced LDAR for Fugitive Leaks **The control efficiency of this measure is estimated at 50 percent a t a cost of $3,050/ton of**  **VOC reduced 2** 

**.** Flare Gas Recovery **The control efficiency of this measure is 98 percent redu ction of VOC emissions at a cost**  **of $2,700/ton. Costs may become negligible as the si ze of the flare increases due to**  **recovery credit. 3** 

**1 “NOx Emissions Control Costs for Stationary Reciprocatin**  **g Internal Combustion Engines in the NOx SIP Call States,” E.H. Pechan and Associate**  **s, Inc., Springfield, VA, August 11, 2000. Available on the Internet at**  **http://www.epa.gov/ttn/ecas/regdata/cost/pechan8-11.p df** 

**2 “Suggested Short List and Evaluation of Point and A**  **rea Source Emission Control Measures for the Houston-Galveston-Brazoria 8-Hour Ozone Nonattainm**  **ent Area,” Texas Council on Environmental Quality,” Prepared by ENVIRON Internationa**  **l Corp. for Lamar Univ. June 15, 2006. Available on the Internet at**  **http://www.h gac.com/NR/rdonlyres/e4cgpdlu4wd3tiguvvxkg5ziefqy36**  **adm2o5cz5jpm36c67ksxbtfurvvwvgdq uy362skyhnsel5uh4rdkfz2rusphd/Final+Short+List+%26+**  **Evaluations.pdf**

**. 3**  **MARAMA Multipollutant Rule Basis for Flares, part of “As sessment of Control Technology**  **Options for Petroleum Refineries in the mid-Atlantic Region.” February 19, 2007. Found on the**  **Internet at http://www.marama.org/reports/021907_Refinery_Contr**  **ol_Options_TSD_Final.pdf**

**. 5a- 2** 

Cooling Towers **There is not a general estimate of control efficiency f or this measure; one is to apply a**  **continuous flow monitor until VOC emissions have rea ched a level of 1.7 tons/year for a**  **given cooling tower. 4**  **The annualized cost for a continuous flow monitor is $6 3,000 –**  **this is constant over a variety of cooling tower siz es.**  Wastewater Drains and Separators **The control efficiency is 65 percent reduction of VOC emissions at a cost of $3,050/ton.**  **This is based on actual sampling and cost data for 5 refineries in the Bay Area Air**  **Quality Management District (BAAQMD). 5** 

5a.2 Cost Information for EGU sources **(Full details on controls can be found in Appendix Chap ter 3)** 
Cost of Controls as a Result of Lower Sub-regional Caps within the MWRPO and OTC and other Local Controls outside of these Regions within CAIR
**As previously discussed, the power sector will 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 s olving nonattainment issues significantly.** 
**Power sector impacts analyzed in detail in the Final PM NAAQS RIA 15/35**  **( http://www.epa.gov/ttn/ecas/ria.html**  **) provides the baseline for this RIA. The analysis and**  **projections in this section attempt to show the pot ential impacts of the additional controls applied**  **(see section 3.3.3 of this RIA) to facilitate attai nment of the more stringent 8-hr ozone standard**  **of 0.070 ppm. Generally, the incremental impacts of these controls on the power sector are**  **marginal.**
**Projected Costs. EPA projects that the annual incremental cost of th**  **e proposed new ozone standard approach is $0.2billion in 2020. The additi**  **onal annual costs reflect additional retrofits (SCR and SNCR) and generation shifts,. Annualized cost of CAIR is projected t**  **o be $6.17 billion in 2020. The proposed approach applied in this RIA** **would add $0.2 billion incremental to this cost.** 
**Projected Generation Mix**

. **Coal-fired generation and natural gas/oil-fired generat ion are**  **projected to remain almost unchanged. Installation o f approximately 3.7 GWs of SCR and 1.1**  **GWs of SNCR incremental to the base case are projected a s a result of the lower sub-regional**  **caps. There are very small changes in the generation mi x. Coal-fired generation increases about**  **12 GWh (an increase of approximately 0.25% of the to tal generation) and gas-fired generation** 

**4 Bay Area Air Quality Management District (BAAQMD).**  **Proposed Revision of Regulation 8, Rule 8: Wastewater Collection Systems. Staff Report, M**  **arch 17, 2004. 5**  **Bay Area Air Quality Management District (BAAQMD). Proposed Revision of Regulation 8,**  **Rule 8: Wastewater Collection Systems. Staff Report, M arch 17, 2004.** **5a- 3**

**decreases a similar amount. Hydo, nuclear, other, and renewable based generation projected to**  **remain the same. Projected retirements of coal units is marginal, accounting to about 0.4 GWs**  **compared to the base case approach.**
**Projected Nationwide Retail Electricity Prices**

. **Retail electricity prices are projected to change marginally, only about 1%. The extension of the cap**  **-and-trade approach in the form of lower sub-regional caps allows industry to meet the requi**  **rements of CAIR in the most cost-effective manner, thereby minimizing the costs passed on to c**  **onsumers. Retail electricity prices are projected to increase less than 1% within the MWRPO**  **and OTC regions, and decrease about 1% in the rest of the CAIR region.** 

5a.3 Cost information for Onroad and Nonroad Mobile Sources **(Full details on controls can be found in Appendix Chap ter 3)** 
Diesel Retrofits and Vehicle Replacement **To calculate costs for the use of selective catalytic reduction as a retrofit technology, the**  **assumption was made that all relevant vehicles woul d be affected by the control. Therefore, all**  **on-road heavy duty diesel vehicles that received a retr ofit were assumed to employ selective**  **catalytic reduction as a retrofit technology. The average cost of a selec tive catalytic reduction**  **system ranges from $10,000 to $20,000 per vehicle d epending on the size of the engine, the sales**  **volume, and other factors (Pechan, 2003). For AirCont rolNET analysis, the average estimated**  **cost of this system is $15,000 per heavy duty diesel v ehicle. (Source: AirControlNET**  **Documentation, III-160). OTAQ conducted an additiona l assessment of current SCR costs and**  **calculated that for the year 2020, the cost of SCRs will be approximately $13,000 per unit. This**  **estimate reflects an economy of scale cost reduction of 33%, which is consistent with trends in**  **other mobile source control technologies that enter large scale production.** 
**The rebuild/upgrade kit is applied to nonroad equipment. OTAQ estimates the cost of this kit to**  **be $2,000 to $4,000 per vehicle. For this analysis , the average estimated cost is $3,000 per**  **vehicle.**
Table 5a.1: Summary of Cost Effectiveness for 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 $18,9 00

Excavators $1,100 $4,200 $8,100 $43,400

Crawler Tractor/Dozers $1,100 $4,200 $8,300 $43,500

Skid Steer Loaders $1,000 $1,600 $7,400 $14,800

Agricultural Tractors Rebuild/ Upgrade kit

$1,200 $4,900 $9,300 $34,300**5a- 4**

Table 5a.2: Summary of Cost Effectiveness for SCR fo r Various Nonroad Vehicles

Nonroad Vehicle Retrofit Technology Range of $/ton NOx

Emission Reduced Range of $/ton HC Emission Reduced

Tractors/Loaders/Backhoes $2,900 $5,300 $32,200 $63, 700

Excavators $2,700 $10,400 $27,400 $146,200

Crawler Tractor/Dozers $2,800 $10,400 $27,900 $146, 700

Skid Steer Loaders $2,600 $4,000 $24,900 $52,100

Agricultural Tractors SCR

$3,000 $7,600 $31,200 $115,500

Table 5a.3: Summary of Cost Effectiveness for SCR fo r Various Highway Vehicles
Highway Vehicle Retrofit

Technology Range of $/ton NOx

Emission Reduced Range of $/ton HC Emission Reduced Class 6&7 Truck $5,600 $14,100 $46,900 $126,200 Class 8b Truck SCR $1,100 $2,500 $14,900 $44,600

Implement Continuous Inspection and Maintenance Using R emote Onboard Diagnostics (OBD) **Continuous I/M can significantly lower test costs an d “convenience” costs of I/M programs.**  **Using radio frequency transmission, there is a one-ti me cost for the Continuous I/M device and**  **its installation. In the case of Oregon, this cost is $50. The unit is then good for the li fe of the**  **vehicle. Annual or biennial test fees are not requ ired beyond this initial fee to operate the system**  **but there may be additional operational costs to cov er data processing, reporting, and oversight.**  **For the proposal RIA, we present estimated cost savings o f the Continuous I/M program, but do**  **not include the cost savings in the overall cost esti mates. For the final RIA, we plan to include**  **the Continuous I/M cost savings in the overall cost s. This will result in a significant reduction of**  **overall cost.**
**We can compare the costs of periodic testing to Contin uous I/M. The cost of data processing,**  **reporting and oversight is estimated to be $2 per vehicle per year in the typical I/M area. If we assume an average vehicle life span of 14 years, wit** **h the first test at 4 years of age, vehicles 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 i**  **n some areas). Thus, in a Continuous I/M program, an additional cost of $10-$20 will be incurr**  **ed for each vehicle over its life, assuming the same costs apply in a Continuous I/M program as in a**  **tailpipe test program.**
**In addition to test costs, Continuous I/M avoids mos t of the convenience costs associated with**  **I/M – the time and fuel it takes to drive to the st ation, 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 requir**  **ed after that. So, if we assume, conservatively, that**  **the typical test cycle requires a total of two hours of time at $20 per hour and a half-gallon of**  **gas (10 miles round trip with an average fuel economy of 20 mpg) at $3 per gallon gives us a cost**  **of $41.50. Over the life of the vehicle that 5a- 5** 

**works out to $207.50 in a biennial program or $415 in an annual program. Compare this to the**  **one time trip for Continuous I/M OBD at a cost of $ 41.50 and substantial savings are realized.**  **Application of Continuous I/M resulted in NOx reduc tions of 4.2 to 6.5 percent (approximately**  **10,000 tons), depending on the geographic area, veh icle class, and type of existing I/M program.**  **Some areas have no I/M, some I/M programs require a nnual testing, some require biennial**  **testing, and some areas are piloting continuous I/M . We applied continuous I/M reductions only**  **to those areas that currently have annual or bienni al programs.** 
**Putting it all together, the table below shows the lifetime inspection and convenience costs of**  **Continuous I/M versus periodic I/M (assuming the c urrent mix of annual and biennial testing**  **and current test costs). Periodic I/M testing costs about $20 billion over a 10 year lifecycle with**  **an additional $25 billion in convenience costs for a t otal of $45 billion. By contrast, Remote**  **OBD has a test and installation cost of $4.3 billion dollars over the same 10 year period, and a**  **convenience cost of $2.5 billion for a total of $6.8 bil lion. Thus, nationwide installation of**  **Remote OBD would save the nation’s motorists about $ 38 billion in inspection and convenience**  **costs over a 10 year period.**

Table 5a.4 Lifetime Inspection and Convenience Cost s of I/M

Test/Install Cost Convenience

Cost Total

Cost **Continuous I/M**  **$20 billion $25 billion $45 billion**

**Remote I/M $4.3 billion $2.5 billion $6.8 billion** Savings $15.7 billion $22.5 billion $38.2 billion

**Given that Continuous I/M will actually reduce the cost of I/M, implementation of this measure**  **is highly cost-effective. More information on I/M can b e found at**  **http://www.epa.gov/otaq/regs/im/im-tsd.pdf and www.epa.gov/obd/regtech/inspection.htm** 
Eliminating Long Duration Truck Idling **For purposes of this RIA, we identified this measure as a no cost strategy i.e. $0/ton NOx. Both**  **TSEs and MIRTs have upfront capital costs, but these costs can be fully recovered by the fuel**  **savings. The examples below illustrate the potential rate o f return on investments in idle**  **reduction strategies.**

TSE **The average price of TSE technology is $11,500 per parking space. The average service life of**  **this technology is 15 years. Truck engines at idle consume approximately 1 gallon per hour of**  **idle. Current TSE projects are operating in environ ments where trucks are idling, on average, for**  **8 hours per day per space for 365 days per year (or about 2,920 hours per year). Since TSE**  **technology can completely eliminate long duration idling at truck spaces (i.e. a 10 0% fuel**  **savings), this translates into 2,920 gallons of fuel saved per year per space. At current diesel**  **prices ($2.90/gallon), this fuel savings translates int o $8,468. Therefore, an $11,500 capital** **5a- 6**

**investment should be recovered within about 17 mont hs. In this scenario, TSE investments offer**  **over a 70% annual rate of return over the life of th e technology.** 
**While it is technically feasible to electrify all par king spaces that support long duration idling**  **trucks, we should note that TSE technology is generally deployed at a minimum of 25-50 parking spaces per location to maximize economies o**  **f scale. The financial attractiveness of installing TSE technology will depend on the demonstrated t** **ruck idling behavior – the greater the rates of idling, the greater the potential emissions**  **reductions and associated fuel and cost savings.** 

MIRTs **The price of MIRT technologies ranges from $1,000-$ 10,000. The most popular of these**  **technologies is the auxiliary power unit (APU) becau se it provides air conditioning, heat, and**  **electrical power to operate appliances. The averag e price of an APU is $7,000. The average**  **service life of an APU is 10 years. An APU consumes two-tenths of a gallon per hour, so the net**  **fuel savings is 0.80 gallons per hour. EPA estimat es that trucks idle for 7 hours per rest period,**  **on average, and about 300 days per year (or 2,100 ho urs per year). Since idling trucks consume**  **1 gallon of fuel per hour of idle, APUs can reduce fuel consumption for truck drivers/owners by**  **approximately 1,680 gallons per year. At current diesel pr ices ($2.90/gallon), truck**  **drivers/owners would save $4,872 on fuel if they used an APU. Therefore, a $7,000 capital**  **investment should be recovered within about 18 mont hs. In this scenario, APU investments offer**  **almost a 70% annual rate of return over the life of the technology.** 

**Cost-Effectiveness of Measure: $0/ton NOx**
Commuter Programs **We used the Transportation Research Board’s (TRB) cost-effe ctiveness analysis of Congestion**  **Mitigation and Air Quality Improvement Program (CMA Q) projects to estimate the cost**  **effectiveness of this measure. 6**  **TRB conducted an extensive literature review and then synthesized the data to develop comparable estimates**  **of cost-effectiveness of a wide range of CMAQ-funded measures. We took the average of the me**  **dian cost-effectiveness of a sampling of CMAQ-funded measures and then applied this number**  **to the overarching commuter reduction measure. The CMAQ-funded measures we selected were:** 

 **regional rideshares**

 **vanpool programs**

 **park-and-ride lots**

 **regional transportation demand management**

 **employer trip reduction programs**
**We felt that these measures were a representative sam pling of commuter reduction incentive**  **programs. There is a great deal of variability, ho wever, in the type of programs and the level of**  **incentives that employers offer which can impact both the amount of emissi ons reductions and**  **the cost of commuter reduction incentive programs.**

**6 Transportation Research Board, National Research C**  **ouncil, 2002.** The Congestion Mitigation and Air Quality Improvement Program: assessing 10 years of experien ce **, Committee for the Evaluation of the Congestion Mitigation and Air Quality Improvement Program.** **5a- 7**

**We chose to apply the resulting average cost-effectiveness estimate to one pollutant – NO**  **x – in**  **order to be able to compare commuter reduction progra ms to other NO**  **x reduction strategies.**  **TRB reported the cost-effectiveness of each measure, however, as a $/ton r eduction 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 fo**  **r just NO x** **reductions, so we used the combined NO**  **x and VOC estimate. The results are presented in Table 5a.5** 

**.**

Table 5a.5 Cost-Effectiveness of Best Workplaces fo r Commuters Type Measures from the 2002 TRB Study, $/ton (2000$) 1:4 VOC:NOx **(reported in the RIA as $/ton NOx)** Low High Median **Regional Rideshare $1,200 $16,000 $7,400 Vanpool Programs $5,200 $89,000 $10,500**  **Park-and-ride lots $8,600 $70,700 $43,000 Regional TDM $2,300 $33,200 $12,500**  **Employer trip reduction programs $5,800 $175,500 $22,700** Average of All Measures **$4,620 $76,900** $19,200

**Cost-Effectiveness of Measure: $19,200/ton NOx**
Reduce Gasoline RVP from 7.8 to 7.0 in Remaining No nattainment Areas **Cost-Effectiveness of Measure: Cost per ton will be $5, 700 to $36,000 / ton VOC** 

**For more information on RVP:**

x **Michigan Department of Environmental Quality and 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 Southeast Michigan **. May 24, 2006.**

x **U.S. EPA.** Guide on Federal and State Summer RVP Standards for Conventional Gasoline Only. **EPA420-B-05-012. November 2005**6-1

Chapter 6. Incremental Benefits of Attaining Alternative Ozone Standards Relative to the Current 8-hour Standard (0.08 ppm)

Synopsis
Based on projected emissions and air quality modeling, in 2020, 203 counties in the U.S. with ozone monitors are estimated to fail to meet an alternative o zone standard of 0.070 ppm for the 4 thhighest maximum 8-hour ozone concentration. This number falls to 82 for an alternative standard of 0.075 ppm, and further to 29 for an ozone standard of 0.079 ppm and increases to 360 for an alternative standard of 0.065 ppm. We es timated the health benefits of attaining these alternative ozone standards across the U.S. using the EPA Environmental Benefits Modeling and Analysis Program (BenMAP). We performed a two-stage analysis.
In the first stage we estimated the benefits associated with changes in modeled air quality following application of control technologies known to be currently available. These control strategies were sufficient to bring some, but not all, areas into attainment withthe various standard levels. Thus, the benefits computed durin g this first stage were for partial attainment in some areas. In the second stage, we estimated the be nefits of fully attaining the standards in all areas by using a “rollback” methodology to reduce ozone concentrations at residually nonattaining monitors to a level that would just meet the standards. We deviated fromthis twostage approach when analyzing the 0.075 ppm standar d alternative, where we applied an interpolation technique that is detailed further in this c hapter. To calculate the monetary value of the adverse health outcomes potentially avoided due to these reductions in ambient ozone levels, we used health impact functions based on published epidemio logical studies, and valuation functions derived from the economics literature. 1 Key health endpoints analyzed included premature mortality, hospital and emergency room visits, school absences, and mino r restricted activity days.
There is considerable uncertainty in the magnitude o f the association between ozone and premature mortality. This analysis presents four alternat ive estimates for the association based upon different functions reported in the scientific literature.. We also note that there are uncertainties within each study that are not fully captur ed by this range of estimates. Recognizing that additional research is needed to more fully establish underlying mechanisms by which such effects occur, we also consider the poss ibility that the observed associations between ozone and mortality may not be causal in nature. U sing the National Morbidity, Mortality and Air Pollution Study (NMMAPS) that was used as the p rimary basis for the risk analysis presented in our Staff Paper and reviewed by Clean Air Science Advisory Committee (CASAC), we estimated 280 avoided premature deaths annually in 2020 from reducing ozone levels to meet a standard of 0.070 ppm, which, when added to the other projected benefits from reduced ozone, including 5,600 hospital and emergency room admissions, 780,000 school absences, and over 2,100,000 minor restricted activity days, leads to an estimated total ozone-related bene fit of $2

1 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 concentration6-2

billion/yr (1999$). Using three studies that synthesize dat a across a large number of individual studies, we estimate between 1,100 and 1,400 avoide d premature deaths annually in 2020 from reducing ozone to 0.070 ppm, leading to total monetized ozone -related benefits of between $7.4 and $9.1 billion/yr. Alternatively, if there is no caus al relationship between ozone and mortality, avoided premature deaths associated with reduced ozone exposure would be zero and to tal monetized ozone-related morbidity benefits would be $190 million/yr.
For a less stringent standard of 0.075 ppm, using the NMMAPS ozone mortality study resulted in 200 premature deaths avoided and total monetized benefits of $1.6 billion/yr. Using the three synthesis studies, estimated premature deaths avoide d for the less stringent standard are between 880 and 1,100, with total monetized ozone benefits between $5.9 and $7.3 billion/yr. Alternatively, if there is no causal relationship 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 $150 million/yr.
For a less stringent standard of 0.079 ppm, using the NMMAPS ozone mortality study resulted in 19 premature deaths avoided and total monetized ben efits of $140 million/yr. Using the three synthesis studies, estimated premature deaths avoide d for the less stringent standard are between 78 and 85, with total monetized ozone benefits between $510 and $560 million/yr. Alternatively, if there is no causal relationship between ozone an d mortality, avoided premature deaths associated with reduced ozone exposure would be zero and total monetized ozone-related morbidity benefits would be $12 million/yr.
For a more stringent standard of 0.065 ppm, using the NMMAPS ozo ne mortality study resulted in 530 premature deaths avoided and total monetized benefits of $3.7 billion/yr. Using the three synthesis studies, estimated premature deaths avoided fo r the more stringent standard are between 2,100 and 2,400, with total monetized ozone benefits between $14 and $16 billion/yr. Alternatively, if there is no causal relationship 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 $330 million/yr. These estimat es reflect EPA's interim approach to characterizing the benefits of reducing premature mortality associated with ozone exposure. EPA has requested advice from the National Academy of Scienc es on how best to quantify uncertainty in the relationship between ozone exposure and premature mortality in the context of quantifying benefits associated with alternative oz one control strategies.

In addition to the direct benefits from reduced ozon e concentrations, attainment of the standards would likely result in health and welfare benefits from the r eduction of PM 2.5 that would occur as ozone precursor emissions (NOx and VOC) are reduced. Using b oth modeled and extrapolated reductions in these precursor emissions, we estimated PM-r elated co-benefits for the four alternative standards. For each alternative standard, w e provide a range of estimated benefits based on several different PM mortality effect estimates. Th ese effect estimates were derived from two different sources: the published epidemiology li terature and an expert elicitation study conducted by EPA in 2006. For the partial attainme nt of the 0.070 ppm standard, we estimated PM co-benefits including between 220 and 2,200 premature deaths avoided, with total monetized PM co-benefits of between $1 and $9.9 billion/yr (3% discount rate, 1999$). For the 20206-3

attainment of the 0.070 ppm alternative, incremental to att ainment of the 0.08 ppm standard, we estimate total ozone and PM 2.5 -related co-benefits to be between $2.5 and $33 billion/yr; this range encompasses the expert functions and the ozon e mortality functions as well as the possibility that there is no causal relationship betw een ozone and mortality. For the 2020 attainment of the 0.065 ppm alternative, incremental to att ainment of the 0.08 ppm standard, we estimate total benefits of between $4.3 and $57 bil lion/yr; this range encompasses the expert functions and the ozone mortality functions as well as the possibility that there is no causal relationship between ozone and mortality. For the 20 20 attainment of the 0.075 ppm alternative, incremental to attainment of the 0.08 ppm standard, we estimate total ozone and PM 2.5 -related co-benefits to be between $1.5 and $22 billion/yr; this range encompasses the expert functions and the ozone mortality functions as well as the poss ibility that there is no causal relationship between ozone and mortality. For the 2020 attainment of the 0. 079 ppm alternative, incremental to attainment of the 0.08 ppm standard, we estimate to tal ozone and PM 2.5 -related co-benefits to be between $1.1 and $12 billion/yr; this range enco mpasses the expert functions and the ozone mortality functions as well as the possibility that there is no causal relationship between ozone and mortality.
6.1. Background
Our purpose for this analysis is to assess the human health benefits of attaining alternative 8-hour ozone standards, including 0.075 ppm, 0.070 ppm, and 0.065 p pm, incremental to attainment of the current 8-hour ozone standard of 0.08 ppm. 2 We applied a damage function approach similar to those used in several recent U.S. EPA regulatoryimpact 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 individu al health and welfare endpoints (specific effects that can be associated with changes in air quality) and assigns values to those changes assuming independence of the individual values. To tal benefits are calculated simply as the sum of the values for all non-overlapping health and welfare endpoints. This analysis largely builds off of both the analytical approach used in the 2006 PM NAAQS RIA and the analysis of ozone health impacts reported in Hubbell et al. (2005) an d the Clean Air Interstate Rule RIA (2005). For a more detailed discussion of the principles of b enefits analysis used here, we refer the reader to those documents, as well as to the EPA Gui delines for Economic Analysis. 3,4,5
We applied a two-stage approach to estimate the ben efits of fully attaining each alternative standard. In the first stage, we estimated the ben efits associated with changes in modeled air

2 This is effectively 0.084 ppm due to current rounding conve ntions. When calculating benefits in this chapter we followed the rounding convention and rounded to 0.084 ppm. 3 U.S. EPA. 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards for Particle Pollution, Chapter 5. Available at http://www.epa.gov/ttn/ecas/ria.html

. 4 Hubbell, B., A. Hallberg, D.R. McCubbin, and E. Post. 2005. Health-Related Benefits of Attaining the 8-Hr Ozone Standard. Environmental H ealth 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/$file/Guidelines.pdf 5 U.S. EPA. 2000. Guidelines for Preparing Economic Analyses. http://yosemite1.epa.gov/ee/epa/eed.nsf/webpages/Gu idelines.html/$file/Guidelines.pdf6-4

quality following application of control technologies known to be currently available. These control strategies were sufficient to bring some, but not all, areas into attainment with the various standard levels. Thus, the benefits computed durin g this first stage were for partial attainment in some areas (see Chapter 3 for details on these control technologies and the results of the air quality modeling). In the second stage, we estimated the benefits of fully attaini ng the standards in all areas by using a “rollback” methodology to reduce oz one concentrations at residually nonattaining monitors to a level that would just me et the standards (see Appendix 6 for details on this methodology). We conducted analyses to exami ne the sensitivity of our results to a number of different assumptions about the choice of healtheffects and effect estimates from published epidemiological studies, as well as parameters that affect the economic valuation of health effects. A quantitative assessment of non-health benefits , e.g. benefits from reduced ozonerelated crop damage, was outside of the scope of this analysis due to data and resource limitations.
For this assessment, we estimated benefits of change s in ozone and PM co-benefits resulting from application of illustrative control strategies on ozone precursor emissions to attain alternative ozone NAAQS. With the exception of ozone-related premature mortality, we use methods consistent with previous PM and ozone benef its assessments. Specifically, the analysis of PM co-benefits uses an approach identical to that used in the 2006 PM NAAQS RIA (U.S. EPA, 2006). The ozone benefits analysis for non-mortality endpoints uses an approach nearly identical to that for the Clean Air Interstate Rule RIA (U.S. EPA, 2005). 6

All ozone and PM 2.5 co-benefits estimates in this chapter are incremental to a baseline of national full attainment with 0.08 ppm. 7 This baseline incorporates emission reductions projected to be achieved as a result of an array of federal rules s uch as the Clean Air Interstate and Non-Road Diesel Rule, as well as ozone and PM 2.5 state implementation plans. Moreover, the PM 2.5 cobenefits are incremental to an assumption of full attainment of the 2006 PM 2.5 NAAQS. A complete discussion of the baseline may be found in Chapter 3. The PM co-benefits presented in this chapter are incremental to the PM benefits est imated in the 2006 PM NAAQS RIA and reflect the PM benefits from NOx reductions associated with each ozone control strategy.
The remainder of this chapter describes the data and method s used in this analysis, along with the results. Additional details of the analysis ar e provided in Appendix 6 of this RIA. Section 6.2 discusses the probabilistic framework for the benefits analysis and how key uncertainties are addressed in the analysis. Section 6.3 discusses the l iterature 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 p resents the results and implications of the analysis.

6 The one exception relates to the use of updated health impact functions for emergency department visits. These new functions are detailed further in this chapter. 7 The PM2.5 benefits presented below reflect the NOx emission reductions from the ozone control strategy. Reductions from Ocean-Going Vessels burning residual diesel fuel were included both East and West in the baseline PM co-b enefits, but not included in the ozone baseline for the west. See chapter 3 for more deta ils of this rule and its application.6-5

6.2. Characterizing Uncertainty: Moving Toward a Probabil istic 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 appreciation 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 aggregate impact of uncertainty in key modeling elements on both health incidence and benefits esti mates. Components of that process include emissions modeling, air quality modeling, health effec ts incidence estimation, and valuation.
Two aspects of OAR’s approach that have been used in several recent RIAs are employed here. 8,9,10 First, we use Monte Carlo methods for estimating characterizing random sampling error associated with the concentration response functions from epidemiological studies and economic valuation functions. Monte Carlo simulationuses random sampling from distributions of parameters to characterize the effects of uncertai nty on output variables, such as incidence of premature mortality. Specifically, we used Monte Carlo me thods to generate confidence intervals around the estimated health impact and dollar benefits . Distributions for individual effect estimates are based on the reported standard errors in the epidemiological studies. Distributions for unit values are described in Table 6-4.
Second, we use a recently completed expert elicitati on of the concentration response function describing the relationship between premature mortality and ambient PM 2.5 concentration. 11 We note that incorporating only the uncertainty from random sampling error omits important sources of uncertainty (e.g., in the functional form of the mod el—e..g., whether or not a threshold may exist). Use of the expert elicitation and incorporation of the standard errors approaches provide insights into the likelihood of different outcomes and abo ut the state of knowledge regarding the benefits estimates. Both approaches have different strengths and weaknesses, which are full described in Chapter 5 of the PM NAAQS RIA.
In benefit analyses of air pollution regulations conducted to date, the estimated impact o f reductions in premature mortality has accounted for 85% to 95% of total benefits . Therefore, in characterizing the uncertainty related to the estimates of total benefits it is particularly important to attempt to characterize the uncertainties associated with this endpoint. The health impact

8 U.S. Environmental Protection Agency, 2004a. Final Reg ulatory 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. Ava ilable at: http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--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).6-6

functions used to estimate avoided premature deaths associated with reductions in ozone have associated standard errors that represent the statistic al errors around the effect estimates in the underlying epidemiological studies. 12 In our results, we report credible intervals base d on these standard errors, reflecting the uncertainty in the e stimated change in incidence of avoided premature deaths. We also provide multiple estimat es, to reflect model uncertainty between alternative study designs. In addition, we characterize the uncertainty introduced by the inability of existing empirical studies to discern whether the relationship between ozone and pre-mature mortality is causal by providing an effect estimate preconditioned on an assumption that the effect estimate for pre-mature mortality from ozone is zero. For premature mortality associated with exposure to PM, we follow the same approach used in the RIA for 2006 PM NAAQS (U.S. EPA, 2006), presenting 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 confid ence 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 meth ods 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 concentration. Health impact functions are derived from primary epidemiology studies, meta-anal yses of multiple epidemiology studies, or expert elicitations. A standard health impact function has four components: 1) an effect estimate from a particular study; 2) a baseline incidence rate for the health effect (obtained from either the epidemiology study or a source of public health statistics such as the Centers for Disease Control); 3) the size of the potentially af fected population; and 4) the estimated change in the relevant ozone or PM summary measures.
A typical health impact function might look like:

12 Health impact functions measure the change in a health endp oint of interest, such as hospital admissions, for a given change in ambient ozone or PM concentration. 13 Industrial Economics, Inc. 2006. Expanded Expert Judgment Assessment of the Concentration-Response Relationship Between PM2.5 Expo sure and Mortality. Prepared for EPA Office of Air Quality Planning and Standards, September. Available at: http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_e e_report.pdf ' ' y y e x  

 0 1 ( ) , E6-7

where y 0 is the baseline incidence (the product of the base line incidence rate times the potentially affected population),  is the effect estimate, and x is the estimated change in the summary ozone measure. There are other functional forms, but the basic elements remain the same. Chapter 3 described the ozone and PM air quality inp uts to the health impact functions. The following subsections describe the sources for eachof the other elements: size of potentially affected populations; effect estimates; and baseline inci dence rates.
6.3.1 Potentially Affected Populations
The starting point for estimating the size of potentia lly affected populations is the 2000 U.S. Census block level dataset (Geolytics 2002). Benefits Mod eling and Analysis Program (BenMAP) incorporates 250 age/gender/race categories to match specific populations potentially affected by ozone and other air pollutants. The software constructs specific pop ulations matching the populations in each epidemiological study by accessing the appropriate agespecific populations from the overall population database. BenMAP projects populations to 2020 using growth factors based on economic project ions (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 and the World Health Organization’s 2003 and 2004 reports outline numerous health effects known or suspected to be linked to exposure to ambie nt ozone and PM (US EPA, 2006; US EPA, 2005; WHO, 2003; Anderson et al., 2004). EPA recently e valuated the PM literature for use in the benefits analysis for the 2006 PM NAAQS RIA. Becaus e we use the same literature for the PM co-benefits analysis in this RIA, we do not pr ovide 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 th ese studies investigated the impact of ozone exposure on health effects such as: changes in lung structure and biochemi stry; lung inflammation; asthma exacerbation and causation; respir atory illness-related school absence; hospital and emergency room visits for asthma and other respiratory causes; and premature death.
We were not able to separately quantify all of the PM and ozone health effects that have been reported in the ozone and PM criteria documents in this analysis for four reasons: (1) the possibility of double counting (such as hospital admissio ns for specific respiratory diseases); (2) uncertainties in applying effect relationships that are based on clinical studies to the potentially

14 U.S. Environmental Protection Agency, 2005. Regulatory Impact Analysis for the PM NAAQS. EPA Prepared by Office of Air and Radiation. Ava ilable at: http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf pp. 5-29.6-8

affected population; (3) the lack of an established concentration-response relationship; or 4) the inability to appropriately value the effect (for ex ample, changes in forced expiratory volume) in economic terms. Table 6-1 lists the human health an d welfare effects of pollutants affected by the alternate standards. Table 6-2 lists the healthendpoints included in this analysis.6-9

Table 6-1 Human Health and Welfare Effects of Polluta nts Affected by the Alternate Standards

Pollutant/Effect Quantified and Monetized in Base Estimates a Unquantified Effects - Changes in:

PM/Healthb 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 bro nchitis Nonasthma respiratory emergency room visits UVb exposure (+/-) e

PM/Welfare Visibility in Southeastern Class I areas Visibility in northeastern and Midwestern Class I areas Household soiling Visibility in western U.S. Class I areas Visibility in residential and non-Class I areas UVb exposure (+/-) e

Ozone/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 Nonasthma respiratory emergency room visits UVb exposure (+/-) e

Ozone/Welfare Decreased outdoor worker productivity Yields for commercial crops Yields for commercial forests and noncommercial cro ps Damage to urban ornamental plants Recreational demand from damaged forest aesthetics Ecosystem functions UVb exposure (+/-) e

aPrimary quantified and monetized effects are those included when determining the primary estimate of t otal monetized benefits of the proposed standards. b In addition to primary economic endpoints, there are a number of biological responses that have been associated with PM health effects including morphological chan ges and altered host defense mechanisms. The publi c health impact of these biological responses may be partly represented by our quantified endpoints.6-10

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 disc ussion of this issue). dWhile some of the effects of short-term exposure are likely to be captured by the cohort estimates, there may be additional premature mortality from short-term PM e xposure not captured in the cohort estimates includ ed in the primary analysis.

e May result in benefits or disbenefits. f In addition to primary economic endpoints, there are a number of biological responses that have been associated with ozone health including increased airway respon siveness to stimuli, inflammation in the lung, acut e inflammation and respiratory cell damage, and incre ased susceptibility to respiratory infection. The public health impact of these biological responses may be partly represented by our quantified endpoints.
gThe categorization of unquantified toxic health an d welfare effects is not exhaustive.6-11

Table 6-2. Ozone and PM Related Health Endpoints basis for the concentration-re sponse function associated with that endpoint, and sub-populations for which they were computed.

Endpoint Pollutant Study Study Population Premature Mortality Premature mortality – daily time series, non-accidental O3 (24-hour avg)

O3 (24-hour avg) O3 (1-hour max) O3 (1-hour max) Bell et al (2004) (NMMAPS study)

Meta-analyses: Bell et al (2005) Ito et al (2005) Levy et al (2005) All ages

Premature mortality—cohort study, allcause 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

Premature mortality — all-cause PM 2.5 (annual avg) Woodruff et al. (1997) Infant (<1 yea r)

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) >6 4 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 years6-12

Endpoint Pollutant Study Study Population Asthma-related ER visits O3 (8-hour max) Pooled estimate: Jaffe et al (2003) Peel et al (2005) Wilson et al (2005) 5–34 years All ages All ages Asthma-related ER visits (con’t) 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 PM 10 (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 yea rs

aThe original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998) study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB-HES), we extended the app lied population to 6 to 18, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. Science Advisory Board. 2004. Advisory Plans for Health Eff ects Analysis in the Analytical Plan for EPA’s Second Prospective Analysis –Benefit s and Costs of the Clean Air Act, 1990—2020. EPA-SAB-COUNCIL-ADV-04-004. See also Nation al 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 and 10. Chen et al. (2000) studied children 6 to 11. Based on recent advice from the National Research Council and the EPA SAB-HES, we have calculated reductions in school absences for all school-aged children based on the biological similarity between children aged 5 to 17

.

In selecting epidemiological studies as sources of effect estimates, we applied several criteria to develop a set of studies that is likely to provide the best estimates of impacts in the U.S. To account for the potential impacts of different heal th care systems or underlying health status of populations, we give preference to U.S. studies over non-U.S. studies. In additi on, due to the potential for confounding by co-pollutants, we give prefe rence to effect estimates from models6-13

including both ozone and PM over effect estimates from single-pollutant models. 15,16
A number of endpoints that are not health-related also may significantly contribute to monetized benefits. Potential welfare benefits associated with ozo ne exposure include: increased outdoor worker productivity; increased yields for commercia l and non-commercial crops; increased commercial forest productivity; reduced damage to urbanornamental plants; increased recreational demand for undamaged forest aesthetics; and reduced damage to ecosystem functions (U.S. EPA 1999, 2006). While we include estimates o f the value of increased outdoor worker productivity, estimation of other welfare impac ts is beyond the scope of this analysis.
6.3.2.1 Premature Mortality Effects Estimates
While particulate matter is the criteria pollutant m ost clearly associated with premature mortality, recent research suggests that short-term rep eated ozone exposure likely contributes to premature death. The 2006 Ozone Criteria Document s tates: “Consistent with observed ozonerelated increases in respiratory- and cardiovascular-related morbidity, several newer multi-city studies, single-city studies, and several meta-analy ses of these studies have provided relatively strong epidemiologic evidence for associations between short-term ozone exposure and all-cause mortality, even after adjustment for the influence of season and PM” (EPA, 2006: E-17). The epidemiologic data are also supported by newly avai lable experimental data from both animal and human studies which provide evidence suggestive of plau sible pathways by which risk of respiratory or cardiovascular morbidity and mortality coul d be increased by ambient ozone. With respect to short-term exposure, the ozone Criteria Do cument concludes: “This overall body of evidence is highly suggestive that ozone directl y or indirectly contributes to non-accidental and cardiopulmonary-related mortality, but additional re search 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, u pon 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-analyses by B ell 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-analyses, as well as several single- and

15 U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analysis –Ben efits and Costs of the Clean Air Act, 1990—2020. EPA-SAB-COUNCIL-ADV-04-004. 16 National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . Washington, DC: The National Academies Press. 17 For an exhaustive review of the city-specific time-s eries studies considered in the ozone staff paper, see: U.S. Environmental Protection Agency, 20 07. Review of the National Ambient Air Quality Standards for Ozone: Policy Assessment of Sci entific and Technical Information. Prepared by the Office of Air and Radiation. Availabl e at http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_01_ozone_staff_paper.pdf. pp. 5-36.6-14

multiple-city studies, indicate that co-pollutants g enerally do not appear to substantially confound the association between ozone and mortality” (p. 7 -103). However, CASAC raised questions about the implications of these time-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 other desig ns, 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 pollutant s, indicating that “findings of timeseries studies do not seem to allow us to confidently attribute observed effects to individual pollutants” (id.). They note that “not only is the interpretation of these associations complicated by the fact that the day-to-day variation in concentrations of these pollutants is, to a varying degree, determined by meteorology, the pollutants are often part of a large and highly correlated mix of pollutants, only a very few of which are measured” (id .). Even with these uncertainties, the CASAC Ozone Panel, in its review of EPA’s Staff Paper, fo und “...premature total nonaccidental and cardiorespiratory mortality for inclu sion in the quantitative risk assessment to be appropriate.”
Consistent with the methodology used in the ozone ri sk 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 Informat ion, we included ozone mortality in the primary health effects analysis, with the recognition that the exact magnitude of the effects estimate is subject to continuing uncertainty. We used effect estimates from the Bell et al. (2004) NMMAPS analysis, as well as effect estimates from the three meta-analyses. Inaddition, we include the possibility that there is not a causal association between ozone and mortality, i.e., that the effect estimate for premature mortality coul d be zero.
We estimate the change in mortality incidence and estimate d 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 abo ut 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).
6.3.2.2 Respiratory Hospital Admissions Effect Estimates
Detailed hospital admission and discharge records provide data for an extensive body of literature examining the relationship between hospital admissions and air pollution. This is especially true for the portion of the population a ged 65 and older, because of the availability of detailed Medicare records. In addition, there is one stu dy (Burnett et al., 2001) providing an effect estimate for respiratory hospital admissions in chi ldren under two.
Because the number of hospital admission studies we c onsidered is so large, we used results from a number of studies to pool some hospital admission en dpoints. Pooling is the process by which multiple study results may be combined in order to produce better estimates of the effect

18 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentis t statistics.6-15

estimate, or ȕ . For a complete discussion of the pooling process, see Abt (20 05). 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 each city. These cities included Minneapolis, Detroit, Tacoma and New Haven. To estimate total respirato ry hospital admissions for Detroit, we added the pneumonia and COPD estimates, based on the ef fect estimates in the Schwartz study (1994b). Similarly, we summed the estimated hospital admissions based on the effect estimates the Moolgavkar study reported for Minneapo lis (Moolgavkar et al., 1997). To estimate total respiratory hospital admissions for Minneapolis using the Schwartz s tudy (1994a), we simply estimated pneumonia hospital admissions based on the effect estimate. Making this assumption that pneumonia admissions represent the total impact of ozone on hospital admissions in this city will give some weight to the possi bility that there is no relationship between ozone and COPD, reflecting the equivocal evidence represented by the different studies. We then used a fixed-effects pooling procedure to co mbine the two total respiratory hospital admission estimates for Minneapolis. Finally, we used random effects pooling to combine the results for Minneapolis and Detroit with results from studies in Tacoma and New Haven from Schwartz (1995). As noted above, this pooling approach incorporates both the precisio n of the individual effect estimates and between-study variabilitycharacterizing differences across study locations.

6.3.2.3 Asthma-Related Emergency Room Visits Effect Estimates We used three studies as the source of the concentration-resp onse functions we used to estimate the effects of ozone exposure on asthma-related emergencyroom (ER) visits: Peel et al. (2005); Wilson et al. (2005); and Jaffe et al. (2003). We estimated the change in ER visits using the effect estimate(s) from each study and then pooled the res ults using the random effects pooling technique (see Abt, 2005). The study by Jaffe et al. (2003) examined the relationship between ER visits and air pollution for populations aged five to 34 in the Ohio cities of Cleveland, Columbus and Cincinnati from 1991 through 1996. In single-po llutant Poisson regression models, ozone was linked to asthma visits. We use the pool ed estimate across all three cities as reported in the study. The Peel et al. study (2005) estimated asthma-related ER vis its for all ages in Atlanta, using air quality data from 1993 to 2000. Using Poisson generalized estimating equations, the authors found a marginal association betwee n 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) examin ed the relationship between ER visits for respiratory illnesses and asthma and air pollution for all people residing in Portland, Maine from 1998-2000 and Manchester, New Hampshire from 1996-2000. For all models used in the analysis, the authors restricted the ozone data inc orporated into the model to the months ozone levels are usually measured, the spring-summer months (April through September). Using the generalized additive model, Wilson et al. (2005) fo und a significant association between the maximum daily 8-hour average ozone level and ER visits for asthm a in Portland, but found no significant association for Manchester. Similar to the ap proach used to generate effect estimates for hospital admissions, we used random effects poo ling to combine the results across the

19 Abt Associates, Incorporated. Environmental Benefits Map ping and Analysis Program, Technical Appendices. May 2005. pp. I-36-16

individual study estimates for ER visits for asthma. The Peel et al. (2005) and Wilson et al. (2005) Manchester estimates were not significant at the 95 percent level, and thus, the confidence interval for the pooled incidence estimat e based on these studies includes negative values. This is an artifact of the statistical power of the studies, and the negativ e values in the tails of the estimated effect distributions do not repres ent improvements in health as ozone concentrations are increased. Instead these should be vi ewed as a measure of uncertainty due to limitations in the statistical power of the study. Note that we included both hospital admissions and ER visits as separate endpoints associated with ozone exposure, because our estimates of hospital admission costs do not include the costs of ER visi ts, and because most asthma ER visits do not result in a hospital admission.
6.3.2.4 Minor Restricted Activity Days Effects Estimate
Minor restricted activity days (MRADs) occur when individua ls reduce most usual daily activities and replace them with less-strenuous activit ies or rest, but do not miss work or school. We estimated the effect of ozone exposure on MRADs u sing a concentration-response function derived from Ostro and Rothschild (1989). These r esearchers estimated the impact of ozone and PM 2.5 on MRAD incidence in a national sample of the adult working population (ages 18 to 65) living in metropolitan areas. We developed separat e coefficients for each year of the Ostro and Rothschild analysis (1976-1981), which we then combi ned for use in EPA’s analysis. The effect estimate used in the impact function is a weighted av erage 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 othe r acute diseases caused, or aggravated by, exposure to air pollution. Several studies have found a significant association between ozone levels and school absence rates. We use two studies (Gilliland et al., 2001; Chen et al., 2000) to estimate changes in school 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 duration 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). Thu s, each Gilliland et al. period of absence is converted into 1.6 absence days.
Following recent advice from the National Research Counci l (2002), we calculated reductions in school absences for the full population of school a ge children, ages five to 17. This is consistent with recent peer-reviewed literature on estimating the impact of ozone exposure on school absences (Hall et al. 2003). We estimated the chan ge in school absences using both Chen et al. (2000) and Gilliland et al. (2001) and then, similar t o hospital admissions and ER visits, pooled the results using the random effects pooling procedure.6-17

6.3.2.6 Worker Productivity
To monetize benefits associated with increased worker p roductivity resulting from improved ozone air quality, we used information reported in Crocker and Horst (1981). Crocker and Horst examined the impacts of ozone exposure on the productivity of outdoor citrus workers. The study measured productivity impacts. Worker producti vity is measuring the value of the loss in productivity for a worker who is at work on a partic ular 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 ch ange in income associated with a change in ozone exposure, given as the elasticity o f income with respect to ozone concentration. The reported elasticity translates a ten percent re duction in ozone to a 1.4 percent increase in income. Given the national median daily income for out door workers engaged in strenuous activity reported by the U.S. Census Bureau (2002), $68 per day (2000$), a t en 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 househol d income. No information was available for quantifying the uncertainty associated with the central valuation estimate. Therefore, no uncertainty analysis was conducted for this e ndpoint.
6.3.2.7 Visibility Benefits
Changes in the level of ambient PM 2.5 caused by the reduction in emissions associated with the proposed standards will change the level of visibility throughout the United States. Increas es in PM concentrations cause increases in light extincti on, a measure of how much the components of the atmosphere absorb light. Due to time limitations, this ben efits assessment does not consider the value of improvements in visibility associated with s imulated attainment of alternate ozone standards. We anticipate that the benefits assessment supporting the promulgated ozone standard will consider this important benefits category.
6.3.2.8 Other Unquantified Effects
6.3.2.8.1 Direct Ozone Effects on Vegetation

The Ozone Criteria Document notes that “current ambient concentrations in many areas of the country are sufficient to impair growth of numerous common and economically valuable plant and tree species.” (U.S. EPA, 2006, page 9-1). Changes in g round-level ozone resulting from the implementation of alternative ozone standards are expe cted to affect crop and forest yields throughout the affected area. Recent scientific st udies have also found the ozone negatively impacts the quality or nutritive value of crops (U.S. EPA, 2006, page 9-16).
Well-developed techniques exist to provide monetaryestimates of these benefits to agricultural producers and to consumers. These techniques use mod els of planting decisions, yield response functions, and the supply of and demand for agricult ural products. The resulting welfare measures are based on predicted changes in market prices and production costs. Models also exist to measure benefits to silvicultural producer s and consumers. However, these models have6-18

not been adapted for use in analyzing ozone-related forest impacts. Because of resource limitations, we are unable to provide agricultural or benefits estimates for the proposed rule.
An additional welfare benefit expected to accrue as a result of reductions in ambient ozone concentrations in the United States is the economic val ue the public receives from reduced aesthetic injury to forests. There is sufficient sci entific information available to reliably establish that ambient ozone levels cause visible injury to foliage and impair the growth of some sensitive plant species (U.S. EPA, 2006, page 9-19). However, present analytic tools and resources preclude EPA from quantifying the benefits of improved forest aesthetics.
Urban ornamentals (floriculture and nursery crops) repres ent an additional vegetation category likely to experience some degree of negative effect s associated with exposure to ambient ozone levels and likely to affect large economic sectors. In the absence of adequate exposure-re sponse functions and economic damage functions for the pot ential range of effects relevant to these types of vegetation, no direct quantitative economi c benefits analysis has been conducted. The farm production value of ornamental crops was estimated at o ver $14 billion in 2003 (USDA, 2004). This is therefore a potentially important wel fare effects category. However, information and valuation methods are not available to allow fo r plausible estimates of the percentage of these expenditures that may be related to impacts a ssociated with ozone exposure.
6.3.2.8.2 Nitrogen Deposition
Deposition to Estuarine and Coastal Waters
Excess nutrient loads, especially of nitrogen, cause a var iety of adverse consequences to the health of estuarine and coastal waters. These effe cts include toxic and/or noxious algal blooms such as brown and red tides, low (hypoxic) or zero (anoxic) concentrations of dissolved oxygen in bottom waters, the loss of submerged aquatic veg etation due to the light-filtering effect of thick algal mats, and fundamental shifts in phytopl ankton community structure (Bricker et al., 1999). A recent study found that for the period 1990-2002, atmospheric deposition accounted for 17 percent of nitrate loadings in the Gulf of M exico, where severe hypoxic zones have been existed over the last two decades (Booth and Campbe ll, 2007) 20

. Reductions in atmospheric deposition of NOx are expe cted 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 est uarine benefits are not available. The preferred WTP-based measure of benefits depends on the avai lability of these functions and on estimates of the value of environmental responses. Because neither appropriate functions nor sufficient information to estimate the marginal valu e of changes in water quality exist at present, calculation of a WTP measure is not possible.

20 Booth, M.S., and C. Campbell. 2007. Spring Nitrate Flux in the Mississippi River Basin: A Landscape Model with Conservation Applications. Environ. Sci. Technol.; 2007; ASAP Web Release Date: 20-Jun-2007; (Article) DOI: 10.1021/es070179e6-19

Deposition to Agricultural and Forested Land
Implementation strategies for alternative standards whichreduce NO Xemissions, will also reduce nitrogen deposition on agricultural land and forest s. There is some evidence that nitrogen deposition may have positive effects on agricultura l output through passive fertilization. Holding all other factors constant, farmers’ use of purchased fertilizers or manure may increase as deposited nitrogen is reduced. Estimates of the potential value of this possible increase in the use of purchased fertilizers are not available, but it is likely that the overall value is very small relative to other health and welfare effects. The share of nitrogen requirements provided by this deposition is small, and the marginal cost of providing thi s nitrogen from alternative sources is quite low. In some areas, agricultural lands suffer from nitrogen over-saturation due to an abundance of on-farm nitrogen production, primarily from animal manure. In these areas, reductions in atmospheric deposition of nitrogen from PM rep resent additional agricultural benefits.
Information on the effects of changes in passive nitrogen dep osition on forests and other terrestrial ecosystems is very limited. The multipli city 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 limi ting factor (US EPA, 1993). Moreover, any positive effect that nitrogen deposition has on forest productivity would enhance the level of carbon dioxide sequestration as well. 21,22,23

On the other hand, there is evidence that forest 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 occur such as soil acidification which can lead to leaching of nutrients needed for plant growth and mobilizat ion of harmful elements such as aluminum. Increased soil acidification is also linked to higher amounts of acidic runoff to streams and lakes and leaching of harmful elements i nto aquatic ecosystems.

6.3.2.8.3 Ultraviolet Radiation
Atmospheric ozone absorbs a harmful band of ultraviolet radi ation from the sun called UV-B, providing a protective shield to the Earth's surface. The majority of this protection occurs in the stratosphere where 90% of atmospheric ozone is located. The remaining 10% of the Earth's ozone is present at ground level (referred to as tr opospheric ozone) (NAS, 1991; NASA). Only a portion of the tropospheric fraction of UV-B shielding is fr om anthropogenic sources (e.g., power plants, byproducts of combustion). The portion of ground level ozone associated with

21 Peter M. Vitousek et. al., “Human Alteration of the Global Nitrogen Cycle: Causes and Consequences” Issues in Ecology No. 1 (Spring) 1997. 22 Knute J. Nadelhoffer et. al., “Nitrogen deposition makes a minor contribution to carbon sequestration in temperate forests” Nature 398, 145-148 (11 March 1999)
23 Martin Köchy and Scott D. Wilson, “Nitrogen deposi tion and forest expansion in the northern Great Pla ins Journal of Ecology Journal of Ecology89 (5), 807–8176-20

anthropogenic sources varies by locality and over t ime. Even so, it is reasonable to assume that reductions in ground level ozone would lead to incr eases in the same health effects linked to in UV-B exposures. These effects include fatal and no nfatal melanoma and non-melanoma skin cancers and cataracts. The values of $15,000 per case for non-fatal melanoma skin cancer, $5,000 per case for non-fatal non-melanoma skin cancer, and $15,000 per case of cataracts have been used in analyses of stratospheric ozone depletion (U.S. EPA, 1999). Fatal cancers are valued using the standard VSL estimate, which for 2020 is $6. 6 million (1999$). UV-B has also been linked to ecological effects including damage to crops and forest. For a more complete listing of quantified and unquantified UV-B radiatio n effects, see Table G-4 and G-7 in the Benefits and Costs of the Clean Air Act, 1990-2010 (U.S. EPA, 1999. UV-B related health effects are also discussed in the context of stratospheric ozone in a 2006 report by ICF Consulting, prepared for the U.S. EPA.
There are many factors that influence UV-B radiatio n penetration to the earth’s surface, including latitude, altitude, cloud cover, surface albed o, 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 m ay be attributable to any changes in tropospheric O3 as a result of any revision to the O3 NAAQS. Such an assessment of UV-B related health effects would also need to take into account human habits, such as outdoor activities (including age- and occupation-related exposure patterns), dress and skin care to adequately estimate UV-B exposure levels. However, littl e is known about the impact of these factors on individual exposure to UV-B.
Moreover, detailed information does not exist regarding o ther 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; d amage to the eye, such as cataracts; and immune system suppression); wavelength dependency of bi ological responses; and interindividual variability in UV-B resistance to such health outcomes. Beyond these well recognized adverse health effects associated with various wavelengths of UV radiation, the Criteria Document (section 10.2.3.6) also discusses prot ective effects of UV-B radiation. Recent reports indicate the necessity of UV-B in producing vitami n D, and that vitamin D deficiency can cause metabolic bone disease among children and adults, an d may also increase the risk of many common chronic diseases (e.g., type I diabetes and rheumat oid arthritis) as well as the risk of various types of cancers. Thus, the Criteria Document concludes that any asses sment that attempts to quantify the consequences of increased UV -B exposure on humans due to reduced ground-level O3 must include consideration of both ne gative and positive effects. However, as with other impacts of UVB on human health, this bene ficial effect of UVB radiation has not previously been studied in sufficient detail. We wil l develop approaches for estimating the effects of increased UVB exposures resulting from reductio ns in tropospheric ozone and will work to present peer-reviewed quantified estimates for the fi nal rule.6-21

6.3.2.8.4 Climate Implications of Tropospheric Ozon e Although climate and air quality are generally treated as separate issues, they are closely coupled through atmospheric processes. Ozone, itself, is a major greenhouse gas and climate directly influences ambient concentrations of ozone.
The concentration of tropospheric ozone has increased subs tantially since the pre-industrial era and has contributed to warming. Tropospheric ozone is (af ter CO2 and CH4) the third most important contributor to greenhouse gas warming. Th e National Academy of Sciences recently stated24 that regulations targeting ozone precursors would hav e combined benefits for public health and climate. As noted in the OAQPS Staff Pa per, the overall body of scientific evidence suggests that high concentrations of ozone on a regi onal scale could have a discernible influence on climate. However, the Staff Paper concludes that insufficient information is available at this time to quantitatively inform the secondary NAAQS p rocess with regard to this aspect of the ozone-climate interaction.
Climate change can affect tropospheric ozone by modifyin g emissions of precursors, chemistry, transport and removal. 25 Climate change affects the sources of ozone precursors through physical response (lightning), biological response (soils, ve getation, 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 circulation . Simulations with global climate models for the 21st century indicate a decrease in the lifetime of tropospheric ozone due to increasing water vapor which could decrease global background ozone concentrations.
The Intergovernmental Panel on Climate Change (IPCC) rece ntly released a report26 which projects, with “virtual certainty,” declining air qual ity in cities due to warmer and fewer cold days and nights and/or warmer/more frequent hot days and nights over most land areas. The report states that projected climate change-related e xposures 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.

24 National Academy of Sciences, “Radiative Forcing of C limate Change: Expanding the Concept and Addressing Uncertainties,” October 2005

. 25 Denman, K.L., G. Brasseur, A. Chidthaisong, P. Ciais, P. M. Cox, R.E. Dickinson, D. Hauglustaine, C. Heinze, E. Holland, D. Jacob, U. Lohmann, S Ramachandran, P.L. da Silva Dias, S.C. Wofsy and X. Zhang, 2007: Couplings 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. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds .)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 26 IPCC, Climate Change 2007: Climate Change Impacts, Adaptation and Vulnerability, Summary for Policymakers6-22

The IPCC also reports 27 that the current generation of tropospheric ozone m odels is generally successful in describing the principal features of the pres ent-day global ozone distribution. However, there is much less confidence in the abilityto reproduce the changes in ozone associated with perturbations of emissions or climate. T here are major discrepancies with observed long-term trends in ozone concentrations o ver the 20th century, including after 1970 when the reliability of observed ozone trends is high . Resolving these discrepancies is needed to establish confidence in the models.
The EPA is currently leading a research effort withthe 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 changes in US air quality due to climate change, which includes direct meteorological impacts on atmosphe ric 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 techno logy, land use, demographic changes, and air quality regulations to construct plausible scenar ios 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.

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 a ir quality changes to the relative risk of a health effect, rather than estimating the absolute number of avoided cases. For example, a typical result might be that a 100 ppb decrease in daily oz one levels might, in turn, decrease hospital admissions by 3 percent. The baseline incidence o f 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 inthe assessment location, as it corresponds to baseline pollutant levels in that location. To derive the total baseline inciden ce per year, this rate must be multiplied by the corresponding population number. For example, if the baseline incidence rate is the number of cases per year per 100,000 people, that number must be multiplied by the number of 100,000s in the population.
Table 6-3 summarizes the sources of baseline incidence rates and provides average incidence rates for the endpoints included in the analysis. For both baseline incidence and prevalence data, we used age-specific rates where available. We applied co ncentration-response functions to individual age groups and then summed over the relevant age range to provide an estimate of total population benefits. In most cases, we used a single nat ional incidence rate, due to a lack of more spatially disaggregated data. Whenever possible, the national rates used are national averages, because these data are most applicable to anational 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 assumed to represent typical incidence at the national level. Regional incidence rates are available for hospital admissions, and county-level

27 Denman, et al, 2007: Couplings Between Changes in the Climat e System and Biogeochemistry. In: Climate Change 2007: The Physical Science Basis.6-23

data are available for premature mortality. We have pro jected mortality rates such that future mortality rates are consistent with our projections of population growth (Abt Associates, 2005).

Table 6-3. National Average Baseline Incidence Rat es

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 11.629

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 – – – – – –

AThe following abbreviations are used to describe the national surveys conducted by the National Cente r for Health Statistics: HIS refers to the National Health Interview Survey; NHDS - National Hospital Discharge Survey; NHAMCS - National Hospital Ambulatory Medical Care Survey.

B See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/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. Addi tional details on the incidence and prevalence rates, as well as the sour ces for these rates are available upon request.6-24

Table 6-3 National Average Baseline Incidence Rates (continue d)

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, the appropriate economic measure is willingness-to-pay (WTP) for changes in risk of a health e ffect rather than WTP for a health effect that would occur with certainty (Freeman, 1993 ). Epidemiological studies generally provide estimates of the relative risks of a particular health effect that is avoided because of a reduction in air pollution. We converted those to units of avoided statistical incidence for ease of presentation. We calculated the value of avoided statistical incidences by dividing individ ual WTP for a risk reduction by the related observed ch ange in risk. For example, suppose a pollution-reduction regulation is able to reduce the ri sk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individua l WTP for this risk reduction is $100, then the WTP for an avoided statistical premature death is $1 mil lion ($100/0.0001 change in risk).
WTP estimates generally are not available for some h ealth 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 understate 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 informationon the distribution of the unit value) in Table 6-4. All values are in constant year 2000 dollars, adjusted for growth in real income out to 2020 using projections provided by Standard and Poor’s. Economic theory argues that WTP for most goods (such as environmental protection) will incre ase if real income increases. Many of the valuation studies used in this analysis were conducted in the late 1980s and early 1990s. Because real income has grown since the studies were co nducted, people’s willingness to pay for Endpoint Source Notes Rate per 100 people per year 4

Ostro et al. (2001) Incidence (and

prevalence) among asthmaticAfricanAmerican 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

0.0456-25

reductions in the risk of premature death and disease likel y has grown as well. We did not adjust cost of illness-based values because they are based on current costs. Similarly, we did not adjust the value of school absences, because that value is based on current wage rates. Table 6.4 presents the values for individual endpoints adjusted to year 2020 income levels. T he discussion below provides additional details on ozone related end points. 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 s ummary measure for the value of small changes in mortality risk for a large number of peopl e. The VSL approach applies information from several published value-of-life studies to dete rmine a reasonable monetary value of preventing premature mortality. The mean value of avoiding one statistical death is estimated to be roughly $5.5 million at 1990 income levels (2000 $), and $6.6 million at 2020 income levels. This represents an intermediate value from a variety of esti mates in the economics literature (see the 2006 PM NAAQS RIA for more details on the calcul ation of VSL).
6.4.2 Hospital Admissions Valuation
In the absence of estimates of societal WTP to avoid hospit al visits/admissions for specific illnesses, estimates of total cost of illness (tota l medical costs plus the value of lost productivity ) typically are used as conservative, or lower bound, estimat es. These estimates are biased downward, because they do not include the willingness-to-pay value of avoiding pain and suffering.
The International Classification of Diseases (ICD-9, 1979) code-specific COI estimates used in this analysis consist of estimated hospital charges and the estimated opportunity cost of time spent in the hospital (based on the average length of a hospital stay for the illness). We based all estimates of hospital charges and length of stays on stat istics provided by the Agency for Healthcare Research and Quality (AHRQ 2000). We es timated the opportunity cost of a day spent in the hospital as the value of the lost daily wage, regardless of whe ther the hospitalized individual is in the workforce. To estimate the lost d aily wage, we divided the 1990 median weekly wage by five and inflated the result to year 20 00$ using the CPI-U “all items.” The resulting estimate is $109.35. The total cost-of-illness e stimate for an ICD code-specific hospital stay lasting n days, then, was the mean hospital charge plus $109 & n. 6.4.3 Asthma-Related Emergency Room Visits Valuation
To value asthma emergency room visits, we used a simple averag e of two estimates from the health economics literature. The first estimate co mes 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 ye ar was $155; in 2000$, that cost was $311.55 (using the CPI-U for medical care to adjust to 2000$). The second estimate comes from Stanford et al. (1999), who reported the cost of an average asthma-related emergency room visit at $260.67, based on 1996-1997 data. A simple averag e of the two estimates yields a (rounded)6-26

unit value of $286.
6.4.4 Minor Restricted Activity Days Valuation
No studies are reported to have estimated WTP to avoid a minor restricted activity day. However, one of EPA’s contractors, IEc (1993) has d erived an estimate of willingness to pay to avoid a minor respiratory restricted activity day, using estimates from Tolle y et al. (1986) of WTP for avoiding a combination of coughing, throat congestion and sinusitis. The IEc estimate of WTP to avoid a minor respiratory restricted activity day is $38.37 (1990$), or about $52 ($2000).
Although Ostro and Rothschild (1989) statistically linked ozone and minor restri cted 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 acti vity day.

6.4.5 School Absences
To value a school absence, we: (1) estimated the p robability that if a school child stays home from school, a parent will have to stay home from work to care for the child; and (2) valued the lost productivity at the parent’s wage. To do this, we estimated the number of families with school-age children in which both parents work, and we val ued 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 willin gness to pay to avoid the symptoms/illness that resulted in the school absence; second, it effectively gives zero value to school abse nces 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, th is method assumes that parents are unable to work from home. If this is not a valid assumption, then there would be no lost wages.
For this valuation approach, we assumed that in a househol d with two working parents, the female parent will stay home with a sick child. Fr om the Statistical Abstract of the United States (U.S. Census Bureau, 2001), we obtained: (1) the numbe rs of single, married and “other” (widowed, divorced or separated) working women with children; and (2) the rates of participation in the workforce of single, married and “oth er” women with children. From these two sets of statistics, we calculated a weighted average participation rate of 72.85 percent.
Our estimate of daily lost wage (wages lost if a mother must stay at home with a sick child) is based on the year 2000 median weekly wage among wome n ages 25 and older (U.S. Census Bureau, 2001). This median weekly wage is $551. Div iding 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 w age she would lose by missing a work day: 72.85 percent times $103, for a total loss of $75. This valuation approach is similar to that used by Hall et al. (2003).6-27 Table 6-4. Unit Values for Economic Valuation of Health Endpoints (2000$)

Central Estimate of Value Per Statistical Incidence

Health Endpoint 1990 Income Level 2020 Income

Level Derivation of Distributions of Estimates Premature Mortality (Value of a Statistical Life) $5,500,000 $6,600,000 Point estimate is the mean of a normal distribution with a 95% confidence interv al between $1 and $10 million. Confidence interval is based o n two meta-analyses of the wage-risk VSL literature: $1 million represents the lower en d of the interquartile range from the Mrozek and Taylor (2002) meta-analysis and $10 mill ion represents the upper end of the interquartile range from the Viscusi and Aldy (2003 ) meta-analysis. The mean of the distribution is consistent with the mean estimate f rom a third meta-analysis (Kochi et al 2006). The VSL represents the value of a small chan ge in mortality risk aggregated over the affected population. Chronic Bronchitis (CB) $340,000 $420,000 The WTP t o avoid a case of pollution-related CB is calculate d as WTP WTP e
x x

  13 13 * *( ) E , where x is the severity of an average CB case, WT P 13 is the WTP for a severe case of CB, and

Қ is the parameter relating WTP to severity, based o n the regression results reported in Krupnick and Cropper (1992). The distribution of WTP for an average severity-level case of CB was generated by Monte Carlo methods, drawing from each of three distributions: (1) WTP to avoid a se vere case of CB is assigned a 1/9 probability of being each of the first nine deciles of the distribution of WTP responses in Viscusi et al. (1991); (2) the severity of a pollut ion-related case of CB (relative to the case described in the Viscusi study) is assumed to have a triangular distribution, with the most likely value at severity level 6.5 and endpoints at1.0 and 12.0; and (3) the constant in the elasticity of WTP with respect to severity is norma lly distributed with mean = 0.18 and standard deviation = 0.0669 (from Krupnick and Crop per [1992]). This process and the rationale for choosing it is described in detail in the Costs and Benefits of the Clean Air Act, 1990 to 2010

(EPA, 1999). (continued)6-28 Table 6-4: Unit Values Used for Economic Valuation of Health Endpoints (2000$) (continued)

Central Estimate of Value Per Statistical Incidence

Health Endpoint 1990 Income Level 2020 Income

Level Derivation of Distributions of Estimates Nonfatal Myocardial Infarction (heart attack) 3% discount rate Age 0–24 Age 25–44 Age 45–54 Age 55–65 Age 66 and over

7% discount rate Age 0–24 Age 25–44 Age 45–54 Age 55–65 Age 66 and over $66,902 $74,676 $78,834 $140,649 $66,902

$65,293 $73,149 $76,871 $132,214 $65,293 $66,902

$74,676 $78,834 $140,649 $66,902

$65,293 $73,149 $76,871 $132,214 $65,293 No distributional information available. Age-speci fic cost-of-illness values reflect lost

earnings and direct medical costs over a 5-year per iod following a nonfatal MI. Lost earnings estimates are based on Cropper and Krupnic k (1990). Direct medical costs are based on simple average of estimates from Russell etal. (1998) and Wittels et al. (1990). Lost earnings : Cropper and Krupnick (1990). Present discounted va lue of 5 years of lost earnings: age of onset : at 3% at 7% 25-44 $8,774 $7,855 45-54 $12,932 $11,578 55-65 $74,746 $66,920 Direct medical expenses : An average of: 1. Wittels et al. (1990) ($102,658—no discounting) 2. Russell et al. (1998), 5-year period ($22,331 at3% discount rate; $21,113 at 7% discount rate)

Hospital Admissions Chronic Obstructive Pulmonary Disease (COPD) $12,378 $12,378 No distributional information available. The COI es timates (lost earnings plus direct medical costs) are based on ICD-9 code-level information (e .g., average hospital care costs, average length of hospital stay, and weighted share of total COPD category illnesses) reported in Agency for Healthcare Research and Qual ity (2000) (www.ahrq.gov). Asthma Admissions $6,634 $6,634 No distributional information available. The COI es timates (lost earnings plus direct medical costs) are based on ICD-9 code-level information (e .g., average hospital care costs, average length of hospital stay, and weighted share of total asthma category illnesses) reported in Agency for Healthcare Research and Qual ity (2000) (www.ahrq.gov). All Cardiovascular $18,387 $18,387 No distributional information available. The COI es timates (lost earnings plus direct medical costs) are based on ICD-9 code-level information (e .g., average hospital care costs, average length of hospital stay, and weighted share of total cardiovascular category illnesses) reported in Agency for Healthcare Resear ch and Quality (2000) (www.ahrq.gov). All respiratory (ages 65+) $18,353 $18,353 No distributions available. The COI point estimates (lost earnings plus direct medical costs) are based on ICD-9 code level information (e.g., av erage hospital care costs, average length of hospital stay, and weighted share of tota l COPD category illnesses) reported in Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov).6-29 Table 6-4: Unit Values Used for Economic Valuation of Health Endpoints (2000$) (continued)

Central Estimate of Value Per Statistical Incidence

Health Endpoint 1990 Income Level 2020 Income

Level Derivation of Distributions of Estimates All respiratory (ages 0-2) $7,741 $7,741 No distributions available. The COI point estimates (lost earnings plus direct medical costs) are based on ICD-9 code level information (e.g., av erage hospital care costs, average length of hospital stay, and weighted share of tota l COPD category illnesses) reported in Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov). Emergency Room Visits for Asthma $286 $286 No distributional information available. Simple average of two unit COI values: (1) $311.55, from Smith et al. (1997) and (2) $260.67, from Stanford et al. (1999). Respiratory Ailments Not Requiring Hospitalization Upper Respiratory Symptoms (URS) $25 $27 Combinations of the three symptoms for which WTP es timates are available that closely match those listed by Pope et al. result in seven d ifferent “symptom clusters,” each describing a “type” of URS. A dollar value was der ived for each type of URS, using midrange estimates of WTP (IEc, 1994) to avoid each sy mptom in the cluster and assuming additivity of WTPs. In the absence of information surrounding the frequency with which each of the seven types of URS occurs within the UR S symptom complex, we assumed a uniform distribution between $9.2 and $43.1. Lower Respiratory Symptoms (LRS) $16 $18 Combinations of the four symptoms for which WTP esti mates are available that closely match those listed by Schwartz et al. result in 11 d ifferent “symptom clusters,” each describing a “type” of LRS. A dollar value was der ived for each type of LRS, using midrange estimates of WTP (IEc, 1994) to avoid each sy mptom in the cluster and assuming additivity of WTPs. The dollar value for LRS is th e average of the dollar values for the 11 different types of LRS. In the absence of informat ion surrounding the frequency with which each of the 11 types of LRS occurs within the LRS s ymptom complex, we assumed a uniform distribution between $6.9 and $24.46. Asthma Exacerbations $42 $45 Asthma exacerbations are valued at $45 per incidenc e, based on the mean of average WTP estimates for the four severity definitions of a “bad asthma day,” described in Rowe and Chestnut (1986). This study surveyed asthmatic s to estimate WTP for avoidance of a “bad asthma day,” as defined by the subjects. For purposes of valuation, an asthma exacerbation is assumed to be equivalent to a day i n which asthma is moderate or worse as reported in the Rowe and Chestnut (1986) study. The value is assumed have a uniform distribution between $15.6 and $70.8. (continued)6-30 Table 6-4: Unit Values Used for Economic Valuation of Health Endpoints (2000$) (continued)

Central Estimate of Value Per Statistical Incidence

Health Endpoint 1990 Income Level 2020 Income

Level Derivation of Distributions of Estimates Acute Bronchitis $360 $380 Assumes a 6-day episode, with the distribution of t he daily value specified as uniform with the low and high values based on those recommended for related respiratory symptoms in Neumann et al. (1994). The low daily estimate of $ 10 is the sum of the mid-range values recommended by IEc (1994) for two symptoms believed to be associated with acute bronchitis: coughing and chest tightness. The hig h daily estimate was taken to be twice the value of a minor respiratory restricted-activity day , or $110. Work Loss Days (WLDs) Variable (U.S. median=$110) No distribution available. Point estimate is based on county-specific median annual wages divided by 50 (assuming 2 weeks of vacation) and th en by 5—to get median daily wage. U.S. Year 2000 Census, compiled by Geolytics, Inc. Minor Restricted Activity Days (MRADs) $51 $54 Median WTP estimate to avoid one MRAD from Tolley etal. (1986). Distribution is assumed to be triangular with a minimum of $22 and a maximum of $83, with a most likely value of $52. Range is based on assumption that va lue should exceed WTP for a single mild symptom (the highest estimate for a single sym ptom—for eye irritation—is $16.00) and be less than that for a WLD. The triangular distri bution acknowledges that the actual value is likely to be closer to the point estimate than e ither extreme. School Absence Days $75 $75 No distribution availab le6-31

6.5 Results and Implications
Tables 6-5 through 6-28 summarize the reduction in inci dence for ozone- and PM-related health endpoints for each of the alternative ozone standar ds evaluated. Tables 6-29 through 6-44 summarize the ozone-related economic benefits for each of the alternative standards. 28 Note that incidence and valuation estimates for each standard alternative are broken into two sets of tables. The first set of tables summarizes incidence and va luation for simulated national attainment with the standard alternative in the East and areas outs ide of California, and “glidepath” attainment in California. The second set of tables present incide nce and valuation estimates for California post2020, to account for the additional emission reduct ions projected to occur as a result of full implementation of a series of mobile source rules. In ad dition to the mean incidence estimates, we have included 5 thand 95 thpercentile estimates, except where noted, based onthe Monte Carlo simulations described above. In the tables presenting the 0.065 ppm and 0.07 0 ppm estimates, the total change in ozone-related incidence from fully attain ing the alternative standards is broken out into the change in incidence associated with the mo deled partial attainment scenario and the sum of the change in incidence associated with achieving the partial attainment increment plus the residual attainment increment. As described in App endix 6, to calculate the change in ozone concentrations to reach full 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 understate the benefits that would occur due to implementation of actual controls to reduce ozone precursor emissions becaus e controls implemented to reduce ozone concentrations at the highest monitor would likely result i n some reductions in ozone concentrations at attaining monitors down-wind (i.e. the controls would lead to concentrations below the standard in down-wind locations). Therefore, air quality improvements and resulting health benefits from full attainment would be more widespre ad than we have estimated in our rollback analyses. The incidence and valuation resu lts for attainment of the 0.075 ppm alternative are derived through an interpolation technique desc ribed in Appendix 6. As such, these estimates are presented as full attainment only. The incidence and val uation estimates for attainment of the 0.079 ppm alternative are derived through monitor rollback s alone and thus are presented as full attainment only.
In addition to disaggregating ozone benefits between model ed and rollback for the 0.070 ppm and 0.065 ppm standard alternatives, we also provide disa ggregation by region, with separate benefits estimates for the Eastern U.S., California, and the Wester n U.S. outside of California. The estimates of ozone-related mortality and morbidity fo r California are broken into glidepath and full attainment. Certain California projected non-attainment counties are required to meet an ozone target above the actual standard (that is, a “glidepath”) b y 2020 due to the severity of nonattainment. The estimates in this column reflect the benefits of meeting this target.
6.5.1 Glidepath incidence and valuation estimates for 0.065 ppm and 0.075 ppm alternatives
This analysis includes an assessment of the benefits of reac hing the glidepath targets for each of the standard alternatives in 2020 in California. Due t o time and resource limitations, we were able

28 Note that the valuation estimates for ozone benefi ts are not discounted. Because these are short term benefits that occur the same year in which the alternate standard is met, discounting is not necessary.6-32

to perform a full scale benefits analysis of the California glidepath targets for the 0.070 ppm alternative only. Thus, we derived the glidepath benefits estimates for the 0.075 ppm and 0.065 ppm alternatives by applying a scaling factor. This scaling factor represents the ratio of the California 0.070 ppm glidepath full attainment benefits t o the California 0.070 ppm full attainment benefits. This process entailed the following steps : (1) calculate the ratio of the California 0.070 ppm glidepath target benefits to the California 0.070 ppm full attainment benefits for each incidence and valuation estimate; (2) multiply this ratio by the California full attainment 0.075 ppm and 0.065 ppm incidence and valuation estimate to deri ve glidepath estimates. Because these results are scaled, it was not possible to generate confidence intervals.
While clearly the 2020 glidepath targets for the current and alternative standards vary among the standard alternatives, the relative air quality inc rement between the glidepath base and control cases in California is nearly identical among the st andard alternatives. As such, we believe this scaling approach is a valid technique to develop screening -level estimates of 0.065 ppm and 0.075 ppm California glidepath benefits.

6.5.2 PM

2.5co-benefit estimates
As discussed further below, tables 6-9, 6-10, 6-15, 6-16, 6-21, 6-22, 6-27 and 6-28 present the PM 2.5 co-benefits associated with full attainment of the 0.065 pp m, 0.070 ppm, 0.075 ppm and 0.079 ppm alternatives. To derive estimates of inci dence and valuation for the PM 2.5 related cobenefits of full attainment of each ozone standard alternative, we applied two different scaling techniques. To estimate total valuation estimates, w e applied benefit per-ton metrics; this procedure is detailed further below. Note that the valuation estimates of the PM 2.5 -related full attainment benefits are presented at a 3% discount rate; due to time an d resource limitations it was not possible to calculate these benefits at a 7% di scount rate. Had we performed this calculation, we estimate that PM 2.5 -related full attainment co-benefits would be approximate ly 15% lower. All PM 2.5 co-benefit estimates are incremental to the 2006 PM NAAQS RIA.
To estimate total incidence estimates, we applied asimple scaling factor. To estimate PM 2.5 -related incidence associated with the attainment of each ozone alt ernative, 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 descr ibed below) to the partial attainment to the partial attainment PM 2.5 valuation estimate; (2) multiply this scaling ratio against each of the PM 2.5 partial attainment mortality and morbidity endpoints to gene rate a scaled estimate of mortality and morbidity. While there are clearly substantial uncert ainties inherent in this technique, it does produce useful screening-level estimates of PM 2.5 -related incidence6-33 ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number of lives saved are based on an assumption of a norm al 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 stati stics.

D All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns

EThis table reflects full attainment in all locatio ns of the U.S. except two areas of California. The se two areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas. Table 6-5: Illustrative Strategy to Attain 0.065 pp m: Estimated Annual Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure in 2020 (Incremental to Current Ozone Standard) D

Eastern U.S. Western U.S. Excluding California California

Modeled Partial Attainment Full Attainment Modeled Partial Attainment Full

Attainment Glidepath E National 2020 Benefits

Model or Assumption A Reference Arithmetic Mean B (95% Credible Intervals) C

NMMAPS Bell et al. 2004 130 (45--220) 480

(160—790) 0.23

(0.08--0.37) 43

(15-72) 8.5 530

Bell et al. 2005 540 (260--820) 1,900

(930—2,900) 0.86

(0.42—1.3) 180

(86—270) 34 2,100

Levy et al. 2005 780 (540--1,000) 2,100

(1,500—2,800) 31

(22--41) 190

(130—250) 32 2,400 Meta-Analysis

Ito et al. 2005 590 (360--820) 2,100

(1,300—2,900) 1

(0.6--1.4) 190

(120—270) 37 2,300 Assumption that association is not causal 0 0 0 0 0 06-34 A The negative 5 thpercentile 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 anincrease in asthma-related emergency department vi sits.

B All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns

C This table reflects full attainment in all location s of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are n ot planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas. Table 6-6: Illustrative Strategy to Attain 0.065 pp m: Estimated Annual Reductions in the Incidence of Morbidity Associated

with Ozone Exposure (Incremental to Current Ozone S tandard, 95% Confidence Intervals in Parentheses) B

Eastern U.S. Western U.S. Excluding California California
Morbidity Endpoint Modeled Partial Attainment Full Attainment Modeled Partial Attainment Full Attainment Glidepath Attainment C National 2020 Benefits Hospital Admissions (ages 0-1) 960 (410--1,500) 2,700 (1,200—4,300) 53 (23--83) 330 (150—520) 48 3,100

Hospital Admissions (ages 65-99) 1,100 (52—2,800) 3,900 (180—9,800) 3.8 (0.17--9.4) 320 (16—790) 57 4,300

Emergency Department Visits, Asthma-Related A 830 (-230--2,500) 2,500 (-680—7,700) 21 (-5.8--66) 130 (-35—400) 19 2,600

School Absences 410,000 (100,000--1,000,000) 1,200,000 (290,000—3,000,000) 20,000 (4,900--53,000) 120,000 (30,000—310,000) 19,000 1,300,000

Minor Restricted Activity Days 1,100,000 (460,000--1,800,000) 3,200,000 (1,300,000—5,000,000) 49,000 (20,000--78,000) 310,000 (130,000—490,000) 50,000 3,500,0006-35 Table 6-7: Illustrative Strategy to Attain 0.065 pp m in California: Estimated Annual Reductions in

the Incidence of Premature Mortality Associated with Ozone Exposure

(Incremental to Current Ozone Standard) E

Model or Assumption A Reference California Glidepath B CaliforniaIncremental Post2020 Benefits C California TotalD

NMMAPS Bell et al. 2004 8.5 95 100

Bell et al. 2005 34 390 420

Levy et al. 2005 32 420 450 Meta-Analysis

Ito et al. 2005 37 420 450
Assumption that association is not causal 0 0 0

ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B Two areas in California have high levels of ozone and are not planning to meet the current standard until after 2020. The estimates in the table reflec t a progress point in 2020 or "glidepath target" for th e two California areas.

C Certain mobile source programs including Tier-2 an d Non-Road Diesel are projected to generate NOx emi ssion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard pos t-2020 with mobile source emission reductions in th e baseline, incremental to 2020 glidepath attainment. D This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

EAll estimates rounded to two significant figures. As such, confidence intervals may not be symmetrica l and totals will not sum across columns6-36 Table 6-8: Illustrative Strategy to Attain 0.065 pp m in California: Estimated Annual Reductions in the Incidence of Morbidity Associated with Ozone Exposure D
Morbidity Endpoint California Glidepath A California Incremental

Post-2020 Benefits B California Total C Hospital Admissions (ages 0-1) 48 830 880

Hospital Admissions (ages 65-99) 57 620 670

Emergency Department Visits, Asthma-Related A 19 290 310

School Absences 19,000 320,000 340,000

Minor Restricted Activity Days 50,000 780,000 830,0 00

A Two areas in California have high levels of ozone and are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath targ et" for the two California areas.

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emis sion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard pos t-2020 with mobile source emission reductions in th e baseline, incremental to 2020 glidepath attainment.

C This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrica l and totals will not sum across columns6-37 Table 6-9: Illustrative 0.065 ppm Full Attainment S cenario: Estimated Annual Reductions in the Incidence of PM Premature Mortality associate with PM co-benefit C
California National + 2020 California Glidepath Benefits Glidepath Incremental Post-2020 Benefits Total Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study A 1,800 33 160 190
Harvard Six-City Study B 4,000 75 360 430
Woodruff et al 1997 (infant mortality) 4 0.1 0.34 0.41
Mortality Impact Functions Derived from Expert Elic itation Expert A 5,500 100 490 590 Expert B 4,200 78 370 450 Expert C 4,100 77 370 450 Expert D 2,900 55 260 320 Expert E 6,800 130 610 740 Expert F 3,800 71 340 410 Expert G 2,400 45 220 260 Expert H 3,100 58 280 330 Expert I 4,100 77 370 440 Expert J 3,300 62 300 360 Expert K 660 12 59 72 Expert L 3,000 57 270 330

AThe 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 estimat e 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 estimat es incremental to 2006 PM NAA Q S RIA. Estimates do not include confidence interval s because the ywere derived throu g h a scalin g techni que described above.6-38 Table 6-10: Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual Reductions in the Incidence of Morbidity Associated with PM Co-benefit A
California National + 2020 California Glidepath Benefits Glidepath Incremental Post-2020 Benefits Total Morbidity Impact Functions Derived from Epidemiolog y Literature Chronic Bronchitis (age >25 and over) 1,300 25 120 150

Nonfatal myocardial infarction (age >17) 4,000 74 350 430

Hospital admissions--respiratory (all ages) 460 9 41 50 Hospital admissions-- cardiovascular (age >17) 930 17 83 100
Emergency room visits for asthma (age <19) 2,000 35 180 210

Acute bronchitis (age 8-12) 3,500 65 310 380
Lower respiratory symptoms (age 7-14) 29,000 550 2,600 3,200
Upper respiratory symptoms (asthmatic children age 9-18) 22,000 400 1,900 2,300

Asthma exacerbation (asthmatic children age 6--18) 27,000 500 2,400 2,900

Work loss days (age 18-65) 190,000 3,500 17,000 20,000
Minor restricted activity days (age 18-65) 1,100,000 21,000 100,000 120,000

A All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals because they were deri ved through a scaling technique described above.6-39 ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number of lives saved are based on an assumption of a norm al 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 stati stics.

D 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 no t planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.

E All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns Table 6-11: Illustrative Strategy to Attain 0.070 p pm: Estimated Annual Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard) E

Eastern U.S. Western U.S. Excluding California California Modeled Partial Attainment Full

Attainment Modeled

Partial Attainment Full Attainment Glidepath Attainment D 2020 National Attainment

Model or Assumption A Reference Arithmetic Mean B (95% Credible Intervals) C

NMMAPS Bell et al. 2004 130 (45--220) 260

(88--440) 0.23

(0.08--0.37) 11 (3.8--19) 5.5 (1.8--9.1) 280 (93—470)

Bell et al. 2005 540 (260--820) 1,100

(510—1,600) 0.86

(0.42--1.3) 47

(23--71) 22 (11--34) 1,100 (540—1,700) Levy et al. 2005 780 (540--1,000) 1,300

(900—1,700) 31 (22--41) 73

(50--95) 21 (14--27) 1,400 (960—1,800) Meta-Analysis

Ito et al. 2005 590 (360--820) 1,200

(700--1,600) 1

(0.6--1.4) 50

(30--70) 24 (15--34) 1,200 (740—1,700) Assumption that association is not causal 0 0 0 0 0 06-40

Table 6-12: Illustrative Strategy to Attain 0.070 p pm: Estimated Annual Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current Ozone Standard, 95 % Confidence Intervals in Parentheses) C

Eastern U.S. Western U.S. Excluding California California

Morbidity Endpoint Modeled Partial Attainment Full Attainment Modeled Partial Attainment Full Attainment Glidepath Attainment B 2020 National Benefits
Hospital Admissions (ages 0-1) 960 (410--1,500) 1,700 (720--2,600) 53 (23--83) 130 (55--200) 33 (14--51) 1,800 (790—2,900)

Hospital Admissions (ages 65-99) 1,100 (52—2,800) 2,100 (100--5,400) 3.8 (0.17--9.4) 86 (4.2--210) 37 (1.8--92) 2,300 (110—5,700)

Emergency Department Visits, Asthma-Related A 830 (-230--2,500) 1,500 (-400--4,300) 21 (-5.8--66) 50 (-13--150) 13 (-3.5--37) 1,500 (-420—4,500)

School Absences 410,000 (100,000--1,000,000) 720,000 (170,000--1,800,000) 20,000 (4,900--53,000) 47,000 (11,000--120,000) 13,000 (3,100--33,000) 780,000 (190,000—1,900,000)

Minor Restricted Activity Days 1,100,000 (460,000--1,800,000) 1,900,000 (790,000--3,000,000) 49,000 (20,000--78,000) 120,000 (49,000--190,000) 34,000 (14,000--53,000) 2,100,000 (850,000—3,300,000)

A The negative 5 thpercentile 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 anincrease in asthma-related emergency department vi sits.

B This table reflects full attainment in all locatio ns of the U.S. except two areas of California. The se two areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.

C All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns.6-41 Table 6-13: Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual R eductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard) E

Model or Assumption A Reference California Glidepath B CaliforniaIncremental Post2020 Benefits C California TotalD

NMMAPS Bell et al. 2004 5.5 (1.8--9.1) 56 62

Bell et al. 2005 22 (11--34) 230 250

Levy et al. 2005 21 (14--27) 250 280 Meta-Analysis

Ito et al. 2005 24 (15--34) 250 270
Assumption that association is not causal 0 0 0

ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B Two areas in California have high levels of ozone, are not planning to meet the current standard unti l after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two Cal ifornia areas.

C Certain mobile source programs including Tier-2 an d Non-Road Diesel are projected to generate NOx emi ssion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard pos t-2020 with mobile source emission reductions in th e baseline, incremental to 2020 glidepath attainment. D This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

EAll estimates rounded to two significant figures. As such, confidence intervals may not be symmetrica l and totals will not sum across columns6-42 Table 6-14: Illustrative Strategy to Attain 0.070 ppm in California: Estimated Ann ual Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to

Current Ozone Standard, 95% Confidence Intervals in Parentheses) D
Morbidity Endpoint California Glidepath A California Incremental

Post-2020 Benefits B California Total C Hospital Admissions (ages 0-1) 33 (14--51) 520 560

Hospital Admissions (ages 65-99) 37 (1.8--92) 370 400

Emergency Department Visits, Asthma-Related A 13 (-3.5--37) 180 190

School Absences 13,000 (3,100--33,000) 200,000 210,000

Minor Restricted Activity Days 34,000 (14,000--53,000) 480,000 520,000

A Two areas in California have high levels of ozone and not planning to meet the current standard until after 2020. The estimates in the table reflect a p rogress point in 2020 or "glidepath target" for the two Cal ifornia areas.

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emis sion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard pos t-2020 with mobile source emission reductions in th e baseline, incremental to 2020 glidepath attainment. C This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrica l and totals will not sum across columns6-43 Table 6-15: Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual Reductions in the Incidence of PM Premature Mortality associate with PM co-benefit C
California National + 2020 California Glidepath Benefits Glidepath Incremental Post-2020 Benefits Total Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study A 1,000 13 120 130
Harvard Six-City Study B 2,300 30 270 300
Woodruff et al 1997 (infant mortality) 2 0 0.3 0.3
Mortality Impact Functions Derived from Expert Elic itation Expert A 3,200 41 360 410 Expert B 2,400 31 280 310 Expert C 2,400 31 280 310 Expert D 1,700 22 190 220 Expert E 4,000 51 450 500 Expert F 2,200 28 250 280 Expert G 1,400 18 160 180 Expert H 1,800 23 200 230 Expert I 2,400 31 270 300 Expert J 1,900 25 220 250 Expert K 390 5 44 49 Expert L 1,800 23 200 220

AThe 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 estimat e 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 estimat es incremental to 2006 PM NAA Q S RIA. Estimates do not include confidence interval s because the ywere derived throu g h a scalin g techni que described above.6-44 Table 6-16: Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual Reductions in the Incidence of Morbidity Associated with PM Co-benefit A

California National + 2020 California Glidepath Benefits Glidepath Incremental Post-2020 Benefits Total Morbidity Impact Functions Derived from Epidemiolog y Literature Chronic Bronchitis (age >25 and over) 780 10 89 99

Nonfatal myocardial infarction (age >17) 2,300 30 260 290

Hospital admissions--respiratory (all ages) 270 3 31 34 Hospital admissions-- cardiovascular (age >17) 540 7 62 69
Emergency room visits for asthma (age <19) 1,200 14 130 150

Acute bronchitis (age 8-12) 2,000 26 230 260
Lower respiratory symptoms (age 7-14) 17,000 220 2,000 2,200
Upper respiratory symptoms (asthmatic children age 9-18) 13,000 160 1,400 1,600

Asthma exacerbation (asthmatic children age 6--18) 16,000 200 1,800 2,000

Work loss days (age 18-65) 110,000 1,400 12,000 14,000
Minor restricted activity days (age 18-65) 650,000 8,300 74,000 82,000

A All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals because they were deri ved through a scaling technique described above.6-45

Table 6-17: Illustrative Strategy to Attain 0.075 p pm: Estimated Annual Reductions in the Incidence of Premature Mortality Ozone Exposures (Incremental to Current Ozone Standard) D
California
Eastern U.S. Western U.S. Excluding California Glidepath Attainment E 2020 National Benefits

Model or Assumption A Reference Arithmetic Mean B (95% Credible Intervals) C

NMMAPS Bell et al. 2004 190 8.9 0 200

Bell et al. 2005 840 40 0 880

Levy et al. 2005 1,100 65 0 1,100 Meta-Analysis

Ito et al. 2005 920 43 0 960 Assumption that association is not causal 0 0 0 0 0

ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number of lives saved are based on an assumption of a norm al 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 stati stics. Credible intervals not provided due to the fact that the inc idence estimates were derived through an interpolat ion technique (see Appendix 6) that precluded us fr om generating such estimates.

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

E This table reflects full attainment in all location s of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are n ot planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.6-46 AConfidence intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that pre cluded us from generating such estimates.

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

C This table reflects full attainment in all locatio ns of the U.S. except two areas of California. The se two areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas. Table 6-18: Illustrative Strategy to Attain 0.075 p pm: Estimated Annual Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current Ozone Standard) A,B
California Morbidity Endpoint Eastern U.S. Western U.S. Excluding California Glidepath Attainment C 2020 National Benefits

Hospital Admissions (ages 0-1) 1,300 110 0 1,400

Hospital Admissions (ages 65-99) 1,700 76 0 1,800

Emergency Department Visits, Asthma-Related 1,200 44 0 1,200

School Absences 570,000 42,000 0 610,000

Minor Restricted Activity Days 1,500,000 110,000 0 1,600,0006-47 Table 6-19: Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual R eductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard) E

Model or Assumption A Reference California Glidepath B CaliforniaIncremental Post2020 Benefits C California TotalD

NMMAPS Bell et al. 2004 0 35 35

Bell et al. 2005 0 140 140

Levy et al. 2005 0 150 150 Meta-Analysis

Ito et al. 2005 0 160 160
Assumption that association is not causal 0 0 0

ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B Two areas in California have high levels of ozone and not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two Cal ifornia areas.

C Certain mobile source programs including Tier-2 an d Non-Road Diesel are projected to generate NOx emi ssion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard pos t-2020 with mobile source emission reductions in th e baseline, incremental to 2020 glidepath attainment.

D This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

EAll estimates rounded to two significant figures. As such, confidence intervals may not be symmetrica l and totals will not sum across columns6-48 Table 6-20: Illustrative Strategy to Attain 0.075 ppm in California: Estimated Ann ual Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to

Current Ozone Standard, 95% Confidence Intervals in Parentheses) D

Morbidity Endpoint California Glidepath A California Incremental

Post-2020 Benefits B California Total C Hospital Admissions (ages 0-1) 0 320 320

Hospital Admissions (ages 65-99) 0 230 230

Emergency Department Visits, Asthma-Related A 0 110 110

School Absences 0 120,000 120,000

Minor Restricted Activity Days 0 290,000 290,000

A Two areas in California have high levels of ozone and are not planning to meet the current standard un til after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for th e two California areas.

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emis sion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard pos t-2020 with mobile source emission reductions in th e baseline, incremental to 2020 glidepath attainment. C This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrica l and totals will not sum across columns6-49 Table 6-21: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Reductions in the Incidence of PM Premature Mortality associate with PM co-benefit C
California National + 2020 California Glidepath Benefits Glidepath Incremental Post-2020 Benefits Total Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study A 620 0 70 70
Harvard Six-City Study B 1,400 0 160 160
Woodruff et al 1997 (infant mortality) 1 0 0.2 0.2
Mortality Impact Functions Derived from Expert Elic itation Expert A 1,900 0 220 220 Expert B 1,500 0 170 170 Expert C 1,400 0 160 160 Expert D 1,000 0 120 120 Expert E 2,400 0 270 270 Expert F 1,200 0 150 150 Expert G 840 0 96 96 Expert H 1,100 0 120 120 Expert I 1,400 0 160 160 Expert J 1,200 0 130 130 Expert K 230 0 26 26 Expert L 1,100 0 120 120

AThe 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 inrecent 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 estimat es incremental to 2006 PM NAAQS RIA. Estimates do not include confidence i ntervals because they were derived through a scalin g technique described above.6-50 Table 6-22: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Reductions in the Incidence of Morbidity Associated with PM Co-benefit A

California National + 2020 California Glidepath Benefits Glidepath Incremental Post-2020 Benefits Total Morbidity Impact Functions Derived from Epidemiolog y Literature Chronic Bronchitis (age >25 and over) 470 0 53 53

Nonfatal myocardial infarction (age >17) 1,400 0 160 160

Hospital admissions--respiratory (all ages) 160 0 18 18 Hospital admissions-- cardiovascular (age >17) 320 0 37 37
Emergency room visits for asthma (age <19) 690 0 78 78

Acute bronchitis (age 8-12) 1,200 0 140 140
Lower respiratory symptoms (age 7-14) 10,000 0 1,200 1,200
Upper respiratory symptoms (asthmatic children age 9-18) 7,500 0 850 850

Asthma exacerbation (asthmatic children age 6--18) 9,400 0 1,100 1,100

Work loss days (age 18-65) 65,000 0 7,400 7,400

Minor restricted activity days (age 18-65) 390,000 0 44,000 44,000

A All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals because they were deri ved through a scaling technique described above.6-51

Table 6-23: Illustrative Strategy to Attain 0.079 p pm: Estimated Annual Reductions in the Incidence of Premature Mortality Ozone Exposures (Incremental to Current Ozone Standard) D
California
Eastern U.S. Western U.S. Excluding California Glidepath Attainment E 2020 National Benefits

Model or Assumption A Reference Arithmetic Mean B (95% Credible Intervals) C

NMMAPS Bell et al. 2004 19 (7.6—31) 0 0 19 (7.6—31)

Bell et al. 2005 78 (41—120) 0 0 78 (41—120) Levy et al. 2005 78 (56—100) 0 0 78 (56—100) Meta-Analysis

Ito et al. 2005 85 (55—120) 0 0 85 (55—120) Assumption that association is not causal 0 0 0 0

ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number of lives saved are based on an assumption of a norm al 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 stati stics. Credible intervals not provided due to the fact that the inc idence estimates were derived through an interpolat ion technique (see Appendix 6) that precluded us fr om generating such estimates.

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

E This table reflects full attainment in all location s of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are n ot planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.6-52 AConfidence intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that pre cluded us from generating such estimates.

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

C This table reflects full attainment in all locatio ns of the U.S. except two areas of California. The se two areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas. Table 6-24: Illustrative Strategy to Attain 0.079 p pm: Estimated Annual Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current Ozone Standard) B
California Morbidity Endpoint Eastern U.S. Western U.S. Excluding California Glidepath Attainment A ,C 2020 National Benefits

Hospital Admissions (ages 0-1) 120 (56—180) 0 0 120 (56—180)

Hospital Admissions (ages 65-99) 160 (7.4—310) 0 0 160 (7.4—310)

Emergency Department Visits, Asthma-Related 94 (-5.7—250) 0 0 94 (-5.7—250)

School Absences 50,000 (15,000—76,000) 0 0 50,000 (15,000—76,000)

Minor Restricted Activity Days 130,000 (58,000—190,000) 0 0 130,000 (58,000—190,000)6-53 Table 6-25: Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual R eductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard) E

Model or Assumption A Reference California Glidepath B CaliforniaIncremental Post2020 Benefits C California TotalD

NMMAPS Bell et al. 2004 0 8.4 8.4

Bell et al. 2005 0 34 34

Levy et al. 2005 0 38 38 Meta-Analysis

Ito et al. 2005 0 37 37
Assumption that association is not causal 0 0 0

ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B Two areas of California that have high levels of o zone are not planning to meet the current standard until after 2020. The estimates in the table refle ct a progress point in 2020 or "glidepath target" for th e two California areas.

C Certain mobile source programs including Tier-2 an d Non-Road Diesel are projected to generate NOx emi ssion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard pos t-2020 with mobile source emission reductions in th e baseline, incremental to 2020 glidepath attainment.

D This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

EAll estimates rounded to two significant figures. As such, confidence intervals may not be symmetrica l and totals will not sum across columns6-54 Table 6-26: Illustrative Strategy to Attain 0.079 ppm in California: Estimated Ann ual Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to

Current Ozone Standard, 95% Confidence Intervals in Parentheses) D

Morbidity Endpoint California Glidepath A California Incremental

Post-2020 Benefits B California Total C Hospital Admissions (ages 0-1) 0 80 80

Hospital Admissions (ages 65-99) 0 55 55

Emergency Department Visits, Asthma-Related A 0 27 27

School Absences 0 30,000 30,000

Minor Restricted Activity Days 0 73,000 73,000

A Two areas have high levels of ozone and are not planning to meet the current standard until after 2020 . The estimates in the table reflect a progress po int in 2020 or "glidepath target" for the two California areas.

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emis sion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard pos t-2020 with mobile source emission reductions in th e baseline, incremental to 2020 glidepath attainment. C This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrica l and totals will not sum across columns6-55 Table 6-27: Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual Reductions in the Incidence of PM Premature Mortality associate with PM co-benefit C
California National + 2020 California Glidepath Benefits Glidepath Incremental Post-2020 Benefits Total Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study A 480 0 22 22
Harvard Six-City Study B 1,100 0 50 50
Woodruff et al 1997 (infant mortality) 1 0 0.05 0.05
Mortality Impact Functions Derived from Expert Elic itation Expert A 1,500 0 68 68 Expert B 1,200 0 52 52 Expert C 1,100 0 51 51 Expert D 800 0 36 36 Expert E 1,900 0 84 84 Expert F 1,000 0 47 47 Expert G 660 0 30 30 Expert H 840 0 38 38 Expert I 1,100 0 51 51 Expert J 910 0 41 41 Expert K 180 0 8.2 8.2 Expert L 830 0 37 37

AThe 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 inrecent 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 estimat es incremental to 2006 PM NAAQS RIA. Estimates do not include confidence i ntervals because they were derived through a scalin g technique described above.6-56 Table 6-28: Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual Reductions in the Incidence of Morbidity Associated with PM Co-benefit (95 th

percentile confidence intervals provided in parenth eses) A

California National + 2020 California Glidepath Benefits Glidepath Incremental Post-2020 Benefits Total Morbidity Impact Functions Derived from Epidemiolog y Literature Chronic Bronchitis (age >25 and over) 370 0 17 17

Nonfatal myocardial infarction (age >17) 1,100 0 49 49

Hospital admissions--respiratory (all ages) 130 0 5.7 5.7 Hospital admissions-- cardiovascular (age >17) 250 0 12 12
Emergency room visits for asthma (age <19) 540 0 24 24

Acute bronchitis (age 8-12) 950 0 43 43
Lower respiratory symptoms (age 7-14) 8,100 0 360 360
Upper respiratory symptoms (asthmatic children age 9-18) 5,900 0 270 270

Asthma exacerbation (asthmatic children age 6--18) 7,300 0 330 330

Work loss days (age 18-65) 51,000 0 2,300 2,300
Minor restricted activity days (age 18-65) 310,000 0 14,000 14,000

A All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals because they were deri ved through a scaling technique described above.6-57 ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number of lives saved are based on an assumption of a norm al 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 stati stics.

D All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns

E This table reflects full attainment in all location s of the U.S. except two areas of California. Thes e two areas, which have high levels of ozone, are n ot planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas. Table 6-29: Illustrative Strategy to Attain 0.065 p pm: Estimated Annual Valuation of Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard, Millions of 1999$) D

Eastern U.S. Western U.S. Excluding California California

Modeled Partial Attainment Full Attainment Modeled Partial Attainment Full Attainment Glidepath Attainment E 2020 National

Benefits

Model or Assumption A Reference Arithmetic Mean B (95% Credible Intervals) C

NMMAPS Bell et al. 2004 $850 ($120—$1,900) $3,100 ($430--$6,800) $1.4 ($0.2--$3.2) $280 ($39--$620) $54 $3,400

Bell et al. 2005 $3,500 ($550--$7,500) $12,000 ($2,000--$26,000) $5.5 ($0.9--$12) $1,100

($180--$2,400) $220 $14,000

Levy et al. 2005 $5,000 ($890--$9,600) $14,000 ($2,400--$26,000) $200 ($36--$390) $1,200

($220--$2,400) $200 $15,000MetaAnalysis

Ito et al. 2005 $3,800 ($650--$7,500) $13,000 ($2,300--$27,000) $6.3 ($1.1--$13) $1,200

($210--$2,400) $240 $15,000 Assumption that association is not causal 0 0 0 0 0 06-58 A All estimates rounded to two significant figures. A s such, 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. Thes e two areas, which have high levels of ozone, are n ot planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas. Table 6-30: Illustrative Strategy to Attain 0.065 p pm: Estimated Annual Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current Ozone Standard, 95% Confidence Interv als in Parentheses, Millions of 1999$) A

Eastern U.S. Western U.S. Excluding California California
Morbidity Endpoint Modeled Partial Attainment Full

Attainment Modeled Partial

Attainment Full Attainment Glidepath Attainment B 2020 National Benefits Hospital Admissions (ages 0-1) $7.1 ($3.1--$11) $20

($8.8--$32) $0.39

($0.17--$0.62) $2.5 ($1.1--$3.9) $0.4 $23

Hospital Admissions (ages 65-99) $19 ($0.9--$49) $68

($3.2--$170) $0.67

($0.003— $0. 2) $5.6

($0.28--$14) $1 $75

Emergency Department Visits, Asthma-Related $0.23 ($-0.06--$0.67) $0.7 ($-0.2--$2) -- $0.04 ($-0.009--$0.1) -- $0.7

School Absences $30 ($7.2-$72) $87

($21--$210) $1.5

($0.35--$3.8) $8.9

($2.1--$22) $1.4 $97

Worker Productivity $15 $38 $0.38 $3.9 $2.9 $45

Minor Restricted Activity Days $27 ($1.2--$63) $79

($3.4--$180) $1.2

($0.05--$2.8) $7.6

($0.3--$18) $1.3 $876-59 Table 6-31: Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual V aluation of Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard) E

Model or Assumption A Reference California Glidepath B CaliforniaIncremental Post2020 Benefits C California TotalD NMMAPS Bell et al. 2004 $54 $610 $660

Bell et al. 2005 $220 $2,500 $2,700

Levy et al. 2005 $200 $2,600 $2,900 Meta-Analysis

Ito et al. 2005 $240 $2,700 $2,900
Assumption that association is not causal 0 0 0

ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B This table reflects full attainment in all locatio ns of the U.S. except two areas of California. The se two areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.

C Certain mobile source programs including Tier-2 an d Non-Road Diesel are projected to generate NOx emi ssion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard pos t-2020 with mobile source emission reductions in th e baseline, incremental to 2020 glidepath attainment.

D This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

EAll estimates rounded to two significant figures. As such, totals will not sum across columns6-60 Table 6-32: Illustrative Strategy to Attain 0.065 ppm in California: Estimated Ann ual Valuation of Reductions in the Incidence of Morbidi ty Associated with Ozone Exposure (Incremental to Current Ozone Standard) D

Morbidity Endpoint California Glidepath A California Incremental

Post-2020 Benefits B California Total C Hospital Admissions (ages 0-1) $0.4 $6.2 $6.6

Hospital Admissions (ages 65-99) $1 $11 $12

Emergency Department Visits, Asthma-Related A -- $0.8 $0.9

School Absences $1.4 $23 $25

Worker Productivity $2.9 $26 $29

Minor Restricted Activity Days $1.3 $19 $21

A This table reflects full attainment in all location s of the U.S. except two areas of California. These two areas, which have high levels of ozone, are no t planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emis sion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard pos t-2020 with mobile source emission reductions in th e baseline, incremental to 2020 glidepath attainment.

C This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

D All estimates rounded to two significant figures. As such, totals will not sum across columns6-61 ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number of lives saved are based on an assumption of a norm al 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 stati stics.

D 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 no t planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.

E All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns Table 6-33: Illustrative Strategy to Attain 0.070 p pm: Estimated Annual Valuation of Reductions in the Incidence of Premature

Mortality Associated with Ozone Exposure (Increment al to Current Ozone Standard, Millions of 1999$) E

Eastern U.S. Western U.S. Excluding California California

Modeled Partial Attainment Full Attainment Modeled Partial Attainment Full

Attainment Glidepath Attainment D 2020 National Benefits

Model or Assumption A Reference Arithmetic Mean B (95% Credible Intervals) C

NMMAPS Bell et al. 2004 $850 ($120—$1,900) $1,700 ($240--$3,800) $1.4 ($0.2--$3.2) $73

($10--$160) $35 ($5--$78) $1,800 ($250--$4,000)

Bell et al. 2005 $3,500 ($550--$7,500) $6,800 ($1,100--$14,000) $5.5 ($0.9--$12) $300

($48--$630) $140 ($23--$300) $7,200 ($1,200--$15,000) Levy et al. 2005 $5,000 ($890--$9,600) $8,300 ($1,500--$16,000) $200 ($36--$390) $470

($83--$900) $130 ($24--$260) $8,900 ($1,600--$17,000) Meta-Analysis

Ito et al. 2005 $3,800 ($1,300--$9,300) $7,400 ($1,300--$15,000) $6.3 ($1.1--$13) $320

($56--$640) $150 ($27--$310) $7,900 ($1,400--$16,000) Assumption that association is not causal 0 0 0 0 0 06-62 AThis table reflects full attainment in all locatio ns of the U.S. except two areas of California. The se two areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas. B All estimates rounded to two significant figures. A s such, confidence intervals may not be symmetrical and totals will not sum across columns Table 6-34: Illustrative Strategy to Attain 0.070 p pm: Estimated Annual Valuation of Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Curr ent Ozone Standard, 95% Confidence Intervals in Par entheses, Millions of 1999$) B

Eastern U.S. Western U.S. Excluding California California

Morbidity Endpoint Modeled Partial Attainment Full

Attainment Modeled Partial Attainment Full Attainment Glidepath Attainment A 2020 National Benefits Hospital Admissions (ages 0-1) $7.1 ($3.1--$11) $12

($5.3--$19) $0.39

($0.17--$0.62) $1 ($0.41--$1.5) $0.24 ($0.11--$0.38) $14 ($5.9--$21)

Hospital Admissions (ages 65-99) $19 ($0.9--$49) $38

($1.8--$95) $0.67 ($0.003— $0. 2) $1.5

($0.074-- $3.8) $0.65 ($0.32--$1.6) $40 ($1.9--$100)

Emergency Department Visits, Asthma-Related $0.23 ($-0.06--$0.67) $0.4

($-0.1--$1.2) -- -- -- $0.5 (-$0.1--$1.2)

School Absences $30 ($7.2-$72) $52

($13--$130) $1.5

($0.35--$3.8) $3.4 ($0.8--$8.4) $0.93 ($0.2--$2.4) $56 ($14--$140)

Worker Productivity $15 $22 $0.38 $1.4 $1.9 $26

Minor Restricted Activity Days $27 ($1.2--$63) $47

($2--$110) $1.2

($0.05--$2.8) $2.9

($0.13--$6.8) $0.83 ($0.036--$1.9) $51 ($2.2--$120)6-63 Table 6-35: Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual V aluation of Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard) E

Model or Assumption A Reference California Glidepath B CaliforniaIncremental Post2020 Benefits C California TotalD

NMMAPS Bell et al. 2004 $35 ($5--$78) $360 $390

Bell et al. 2005 $140 ($23--$300) $1,500 $1,600

Levy et al. 2005 $130 ($24--$260) $1,600 $1,800 Meta-Analysis

Ito et al. 2005 $150 ($27--$310) $1,600 $1,700
Assumption that association is not causal 0 0 0

ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B This table reflects full attainment in all locatio ns of the U.S. except two areas of California. The se two areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.

C Certain mobile source programs including Tier-2 an d Non-Road Diesel are projected to generate NOx emi ssion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard pos t-2020 with mobile source emission reductions in th e baseline, incremental to 2020 glidepath attainment.

D This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

EAll estimates rounded to two significant figures. As such, confidence intervals may not be symmetrica l and totals will not sum across columns6-64 Table 6-36: Illustrative Strategy to Attain 0.070 p pm in California: Estimated Annual Valuation of Reductions in the Incidence of Morbidity Associate d with Ozone Exposure (Incremental to

Current Ozone Standard, 95% Confidence Intervals in Parentheses) D

Morbidity Endpoint California Glidepath A California Incremental

Post-2020 Benefits B California Total C Hospital Admissions (ages 0-1) $0.24 ($0.11--$0.38) $3.9 $4.2

Hospital Admissions (ages 65-99) $0.65 ($0.32--$1.6) $6.5 $7.2

Emergency Department Visits, AsthmaRelated A -- $0.05 $0.05

School Absences $0.93 ($0.2--$2.4) $15 $15

Worker Productivity $1.9 $16 $17

Minor Restricted Activity Days $0.83 ($0.036--$1.9) $12 $13

A This table reflects full attainment in all location s of the U.S. except two areas of California. These two areas, which have high levels of ozone, are no t planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emis sion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard pos t-2020 with mobile source emission reductions in th e baseline, incremental to 2020 glidepath attainment.

C This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrica l and totals will not sum across columns6-65

Table 6-37: Illustrative Strategy to Attain 0.075 p pm: Estimated Annual Monetary Value of Reductions i n the Incidence of

Mortality Associated with Exposure to Ozone (Millions of 1999$, Incremental to Cur rent Standard) B

California
Eastern U.S. Western U.S.

Excluding California Glidepath Attainment E 2020 National Benefits

Model or Assumption A Reference Arithmetic Mean B (95% Credible Intervals) C

NMMAPS Bell et al. 2004 $1,400 $66 0 $1,400

Bell et al. 2005 $5,400 $270 0 $5,700

Levy et al. 2005 $6,700 $430 0 $7,100 Meta-Analysis

Ito et al. 2005 $5,900 $290 0 $6,200
Assumption that association is not causal 0 0 0 0

AConfidence intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that pre cluded us from generating such estimates.

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

C This table reflects full attainment in all locatio ns of the U.S. except two areas of California. The se two areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.6-66

Table 6-38: Illustrative Strategy to Attain 0.075 p pm: Estimated Annual Monetary Value of Reductions i n the Incidence of

Morbidity Associated with Exposure to Ozone (Millio ns of 1999$, Incremental to Current Standard) A

California Morbidity Endpoint Eastern U.S. Western U.S. Excluding California Glidepath Attainment B 2020 National Benefits

Hospital Admissions (ages 0-1) $9.9 $0.9 0 $11

Hospital Admissions (ages 65-99) $31 $1.4 0 $32

Emergency Department Visits, Asthma-Related $0.3 $0.013 0 $0.3

School Absences $41 $3 0 $44

Worker Productivity $20 $1.3 0 $21

Minor Restricted Activity Days $38 $2.6 0 $40

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

B This table reflects full attainment in all locatio ns of the U.S. except two areas of California. The se two areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.6-67 Table 6-39: Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual V aluation of Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard) E

Model or Assumption A Reference California Glidepath B CaliforniaIncremental Post2020 Benefits C California TotalD NMMAPS Bell et al. 2004 0 $220 $220

Bell et al. 2005 0 $910 $910

Levy et al. 2005 0 $990 $990 Meta-Analysis

Ito et al. 2005 0 $990 $990
Assumption that association is not causal 0 0 0

ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B This table reflects full attainment in all locatio ns of the U.S. except two areas of California. The se two areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.

C Certain mobile source programs including Tier-2 an d Non-Road Diesel are projected to generate NOx emi ssion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard post -2020 with mobile source emission reductions in the baseline, incremental to 2020 glidepath attainment.

D This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

EAll estimates rounded to two significant figures. As such, totals will not sum across columns6-68 Table 6-40: Illustrative Strategy to Attain 0.075 ppm in California: Estimated Ann ual Valuation of Reductions in the Incidence of Morbidi ty Associated with Ozone Exposure (Incremental to Current Ozone Standard) D

Morbidity Endpoint California Glidepath A California Incremental

Post-2020 Benefits B California Total C Hospital Admissions (ages 0-1) 0 $2.4 $2.4

Hospital Admissions (ages 65-99) 0 $4 $4

Emergency Department Visits, Asthma-Related A 0 $0.03 $0.03

School Absences 0 $8.7 $8.7

Worker Productivity 0 $9 $9

Minor Restricted Activity Days 0 $7.3 $7.3

A This table reflects full attainment in all location s of the U.S. except two areas of California. These two areas, which have high levels of ozone, are no t planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emis sion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard pos t-2020 with mobile source emission reductions in th e baseline, incremental to 2020 glidepath attainment.

C This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

D All estimates rounded to two significant figures. As such, totals will not sum across columns6-69

Table 6-41: Illustrative Strategy to Attain 0.079 p pm: Estimated Annual Monetary Value of Reductions i n the Incidence of Premature Mortality Associated with Exposure to Ozo ne (Millions of 1999$, Incremental to Current Stand ard) B

California
Eastern U.S. Western U.S.

Excluding California Glidepath Attainment E 2020 National Benefits

Model or Assumption A Reference Arithmetic Mean B (95% Credible Intervals) C

NMMAPS Bell et al. 2004 $120 ($18--$280) 0 0 $120 ($18--$280)

Bell et al. 2005 $500 ($82--$1,100) 0 0 $500 ($82--$1,100) Levy et al. 2005 $500 ($89--$960) 0 0 $500 ($89--$960) Meta-Analysis

Ito et al. 2005 $550 ($94--$1,100) 0 0 $550 ($94--$1,100) Assumption that association is not causal 0 0 0 0

AConfidence intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that pre cluded us from generating such estimates.

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

C This table reflects full attainment in all locatio ns of the U.S. except two areas of California. The se two areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.6-70

Table 6-42: Illustrative Strategy to Attain 0.079 p pm: Estimated Annual Monetary Value of Reductions i n the Incidence of

Morbidity Associated with Exposure to Ozone (Millio ns of 1999$, Incremental to Current Standard) A
California Morbidity Endpoint Eastern U.S. Western U.S. Excluding California Glidepath Attainment B 2020 National Benefits
Hospital Admissions (ages 0-1) $0.9 ($0.5--$1.3) 0 0 $0.9 ($0.5--$1.3)

Hospital Admissions (ages 65-99) $2.8 ($0.4--$5.1) 0 0 $2.8 ($0.4--$5.1)

Emergency Department Visits, Asthma-Related $0.03 (0--$0.07) 0 0 $0.03 (0--$0.07)

School Absences $3.6 ($1.3--$5.3) 0 0 $3.6 ($1.3--$5.3)
Worker Productivity $1.3 0 0 $1.3

Minor Restricted Activity Days $3.1 ($0.1--$7.6) 0 0 $3.1 ($0.1--$7.6)

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

B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning to meet the current stan dard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.6-71 Table 6-43: Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual V aluation of Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard) E

Model or Assumption A Reference California Glidepath B CaliforniaIncremental Post2020 Benefits C California TotalD NMMAPS Bell et al. 2004 0 $46 $46

Bell et al. 2005 0 $190 $190

Levy et al. 2005 0 $180 $180 Meta-Analysis

Ito et al. 2005 0 $200 $200
Assumption that association is not causal 0 0 0

ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B This table reflects full attainment in all locatio ns of the U.S. except two areas of California. The se two areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.

C Certain mobile source programs including Tier-2 an d Non-Road Diesel are projected to generate NOx emi ssion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard post -2020 with mobile source emission reductions in the baseline, incremental to 2020 glidepath attainment.

D This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described above

.

EAll estimates rounded to two significant figures. As such, totals will not sum across columns6-72 Table 6-44: Illustrative Strategy to Attain 0.079 ppm in California: Estimated Ann ual Valuation of Reductions in the Incidence of Morbidi ty Associated with Ozone Exposure (Incremental to Current Ozone Standard) D

Morbidity Endpoint California Glidepath A California Incremental

Post-2020 Benefits B California Total C Hospital Admissions (ages 0-1) 0 $0.3 $0.3

Hospital Admissions (ages 65-99) 0 $1 $1

Emergency Department Visits, Asthma-Related A 0 $0.1 $0.1

School Absences 0 $1.3 $1.3

Worker Productivity 0 $0.5 $0.5

Minor Restricted Activity Days 0 $1.2 $1.2

A This table reflects full attainment in all location s of the U.S. except two areas of California. These two areas, which have high levels of ozone, are no t planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in2020 or "glidepath target" for the two California areas.

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emis sion reductions in California between 2020 and 2030. The estimates in this column are the benefits of full attainment with the alternate standard pos t-2020 with mobile source emission reductions in th e baseline, incremental to 2020 glidepath attainment.

C This column sums the glidepath and incremental post -2020 benefits. The estimates in this column do not include confidence intervals because they were derived through a scaling technique described below

.

D All estimates rounded to two significant figures. As such, totals will not sum across columns6-73

Estimated reductions in ozone mortality incidence pr ovided in Tables 6-5, 6-7, 6-11, 6-13, 6-17, 6-19, 6-23 and 6-25 represent the number of premature deaths potentially avoided due to reductions in ozone exposure in 2020 using warm season func tions from the recent ozonemortality NMMAPS analysis of 95 U.S. communities (Bell et a l., 2004) and three meta-analyses of the available published literature on ozone-mortality effects (Bell et al., 2005; Ito et al., 2005; Levy et al., 2005). These same tables also include the possibility that there is not a causal association between ozone and mortality, i.e., that the estimate for premature mortality avoided could be zero. As noted above, for each standard alternativ e we break out estimates between 2020 national glidepath and California post-2020. M odel uncertainty, including whether or not the relationship is assumed to be causal, is a key source of uncertainty. Although multiple estimates are presented in these tables, no attempt was made to quantify the likelihood of a causal relationship between short-term ozone exposure and increased mortality or to weigh the results of the various models.
The estimate of central tendency for premature morta lity is expressed as the arithmetic mean, with the assumption of a normal distribution, and represent s the central estimate of the number of premature deaths avoided in association with the proposed standard based oneach study. Statistical uncertainty associated with the model es timate for each study is characterized by the 95% credible interval 29 around the mean estimate (i.e., 2.5th and 97.5th percent int erval). Of the four available studies, the NMMAPS study by Bell et al. (20 04) is considered to be the most representative for evaluating potential mortality-relate d benefits associated with the proposed standard due to its extensive coverage (examinationof 95 large communities across the United States over an extended period of time, from 1987 to 2000) an d its specific focus on the ozonemortality relationship. Annual estimates of lives sav ed from this study are lower than those from the three meta-analyses, possibly due to more stringent adjustment for meteorological factors (Ito et al., 2005; Ostro et al., 2006), publication bias in the meta-analyses (Bell et al., 2005; Ito et al ., 2005) or other factors. Clearly, the ozone-mortali ty reduction estimates are conditional on a causal relationship.
The Ozone Criteria Document (U.S. EPA, 2006) and Staff Paper (U.S. EPA, 2007) concluded that the overall body of evidence is highly suggest ive 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). For instan ce, because results of time-series studies implicate all of the criteria air pollutants, and those who would be expected to be potentially more susceptible to ozone exposure are likely to have lower exposure to ozone due to the amount of time that they spend indoors, CASAC 30 stated that it seems unlikely that the observed associations between short-term ozone concentrations and daily mortality are due

29 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentis t statistics. 30 Clean Air Scientific Advisory Committee's Peer Review of th e Agency's 2nd Draft Ozone Staff Paper, October 24, 2006. EPA-CASAC-07-001. Available at http://www.epa.gov/sab/pdf/casac-07-001.pdf6-74

solely to ozone itself (i.e., ozone may be serving as a marker for other agents that are contributing to the short-term exposure effects on mortality). Even so, CASAC concluded that the evidence was strong enough to support a quantit ative risk assessment of the relationship between short-term exposure to ozone and premature mortality as part of the Staff Paper. EPA has asked the National Academy of Sciences 31 for their advice on how best to quantify the uncertainty about the relationship between ambient ozone exposure and premature mortality within the context of quantifying projected benefits of alternative control strate gies.
Using the NMMAPS study that was used as the basis f or the risk analysis presented in our Staff Paper, we estimate 280 avoided premature deaths annually in 2020 from reducing ozone levels to meet a proposed standard of 0.070 ppm, which, when added to the other projected ozone related benefits, leads to an estimated total benefit of $1 .8 billion/yr. Using three studies that synthesize data across a large number of individual studies, we estim ate between 1,100 and 1,400 avoided premature deaths annually in 2020, leading to total monetized benefits of between $7.2 and $8.9 billion/yr. Alternatively, if there is no causal re lationship between ozone and mortality, avoided premature deaths would be zero. For a proposed stan dard of 0.075 ppm, using the NMMAPS ozone mortality study, we estimate 200 premature deaths avoided and total monetized benefits of $1.4 billion/yr. Using the three synthesis studies, we es timate premature deaths avoided for the less stringent standard to be between 880 and 1,100, wi th total monetized ozone benefits to be between $5.7 and $7.1 billion/yr. For a proposed standard of 0 .079 ppm, using the NMMAPS ozone mortality study, we estimate 19 premature deaths avoided and total monetized benefits of $140 million/yr. Using the three synthesis studies , we estimate premature deaths avoided for the less stringent standard to be between 78 and 85, with t otal monetized ozone benefits to be between $510 and $560 million/yr. Because EPA is taking comment on alternatives as low as 0.065 ppm, we show that a more stringent standard of 0.065 ppm, using the NMMAPS ozone mortality study is estimated to result in 530 prema ture deaths avoided and total monetized benefits of $3.4 billion/yr. Using the three synthesis stud ies, estimated premature deaths avoided for the more stringent standard are between 2,100 and 2,400, w ith total monetized ozone benefits between $14 and $15 billion/yr. Including prematur e mortality in our estimates had the largest impact on the overall magnitude of benefits: Premature mort ality benefits account for more than 95 percent of the total benefits we can monetize. We no te that these estimates reflect EPA's interim approach to characterizing the benefits of reduc ing premature mortality associated with ozone exposure. EPA has requested advice from the NAS on how best to quantify uncertainty in the relationship between ozone exposure and prem ature mortality in the context of quantifying benefits associated with alternative ozone control strategies.

6.5.3 PM

2.5Co-Benefits Resulting from Attainment of 0.070 ppm incremental to 0.08 ppm
The summary of PM 2.5 related co-benefits in the tables above represent the benefits of partially attaining 0.070 ppm incremental to a partial attainment of 0 .08 ppm. Thus, these estimates overstate the benefits of 0.070 ppm partial attainme nt relative to the actual incremental benefits

31 National Academy of Sciences (2007) Project Scope. Estimating Mortality Risk Reduction Benefits from Decreasing Tropospheric Ozone Exposure. Division o n Earth and Life Studies, Board on Environmental St udies and Toxicology. Available at http://www8.nationalacademies.org/cp/projectview.as px?key=487686-75

of this scenario; this is due to the fact that the be nefits estimates in these tables include the benefits of NOx reductions that would be required to attain a baseline of 0.08 ppm. Of greater analytical value would be an estimate of the PM 2.5 co-benefits associated with fully attaining 0.070 ppm incremental to full attainment of the 0.08 ppm standard.
To generate such an estimate, we calculated a new 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 premature mort ality and premature morbidity) of reducing one ton of PM 2.5 from a specified source. EPA has used a similar technique in p revious Regulatory Impact Analyses. 32 These estimates are based on the sum of the valuation of the Pope (2002) estimates of mortality (3% discount rate, 1999$) and v aluation 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 both 0.07 0 ppm incremental to full attainment of 0.08 ppm entailed the following four st eps: 1. Estimate the number of tons of NOx necessary to at tain a baseline of 0.08 ppm . Chapter 3 described the method used to estimate the extrapolated NOx e missions reductions necessary to attain a baseline of 0.08 ppm full attainment. 2. Calculate the benefits of attaining 0.08 ppm. To estimate the benefits of fully attaining 0.08 ppm incremental to partial attainment of 0.08 ppm, th e relevant benefit per ton is simply multiplied by the total number of extrapolated NOx tons abated. 3. Calculate the benefits of partially attaining 0.070 ppm incremental to full attai nment 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 incremental 0.070 ppm partial attainment. 4. Calculate the PM 2.5benefits of fully attaining 0.070 ppm . Multiplying the estimate of the extrapolated NOx tons necessary to attain 0.070 ppm fully (f ound in chapter 3) produces an estimate of the incremental benefits of fully attaining 0. 070 ppm incremental to partial attainment of 0.070 ppm. By adding this incremental 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.
The process for estimating the PM 2.5 co-benefits of fully attaining 0.065 ppm and 0.075 ppm i s identical to the steps above, with the following exc eption; in step four we substituted the number of extrapolated tons necessary to attain 0.065 ppm and 0.075 ppm, respectively. Table 5-21 below provides the inputs to the calculation steps described above. In the example below we

32 Final Regulatory Impact Analysis: Industrial Boilers and Process Heaters . Prepared by Office of Air and Radiation. Available: http://www.epa.gov/ttn/e cas/regdata/EIAs/chapter10.pdf [accessed 18 May 2007].6-76

calculate total benefits using the Pope et al. (200 2) mortality estimate. However, in subsequent tables we present benefits using Laden et al. (2006) as we ll as the twelve expert functions described previously in this document. Note that wh ile our benefit per ton estimates are associated with broad source categories (in this case, NOx E lectrical Generating Units, Other NOx point sources and Mobile NOx sources) the extra polated tons were not. For this reason we simply assumed that the total number of extrapolated NOx tons were evenly distributed between these three source types.

Table 6-45: Estimated PM 2.5 Co-Benefits Associated with Full Attainment of 0.0 70 ppm incremental to 0.08 ppm A

Calculation Extrapolated NOx Tons Benefit per ton estimate Valuation of PM2.5 Benefits (Billions 1999$) B

Benefits of attaining 0.08 ppm partially and 0.070 ppm partially: --- --- $2.9B

NOx EGU: 45,000 $3,100 NOx Point: 45,000 $2,800 Benefits of attaining 0.08 ppm from a baseline of 0.08 ppm partial attainment: NOx Mobile: 45,000 $4,600 $0.48B

Benefits of attaining 0.070 ppm partially, incremental to attainment of 0.08 ppm --- --- = $2.9B - $0.48B
=$2.5 B

NOx EGU: 340,000 $3,100 NOx Point: 340,000 $2,800 Benefits of attaining 0.070 ppm in 2020 incremental to partial attainment of 0.070 ppm NOx Mobile: 340,000 $4,600 $3.5B

Benefits of attaining 0.070 ppm incremental to attainment of 0.08 ppm =$2.5B + 3.5B
=$6.0B

A Numbers have been rounded to two significant figure s and therefore summation may not match table estim ates. 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% discount rate, in 1999$.

The procedure for calculating the PM 2.5 benefits resulting from full attainment of 0.079 ppm, 0.075 ppm and 0.065 ppm is identical to this example, with the exception of step 4; the PM 2.5 benefits of attaining 0.065 ppm, 0.075 ppm and 0.079 ppm incr emental to partial attainment of 0.070 ppm are $7.8B, $1.1B and $0.4B respectively. Thus, the total PM 2.5 benefits of attaining 0.065 ppm, 0.075 ppm and 0.079 ppm are $10.2B, $3.6B and $2.5B respectively. The full attainment PM 2.5 benefits do not include confidence intervals. Because this f ull attainment estimate was derived by summing the modeled PM 2.5 benefits and the benefits derived using the benefit per-ton metrics—and these benefit per ton m etrics do not include confidence intervals— the resulting sum of total PM 2.5 benefits do not include confidence intervals.6-77

6.5.4 Estimate of Full Attainment Benefits
Tables 6-36 through 6-43 below summarize the estimates of full attainment and 2020 California glidepath attainment ozone benefits and PM 2.5 co-benefit estimate for each standard alternative. The presentation of ozone benefits and PM 2.5 co-benefits for each standard alternative is broke n into two tables. The first table presents the nation al glidepath ozone benefits and PM 2.5 cobenefits. The second table presents California-only gli depath and post-2020 ozone benefits and PM 2.5 co-benefits. Tables 6-44 through 6-53 summarize the combine d ozone and PM 2.5 cobenefits. The presentation of combined ozone and PM 2.5 co-benefit tables is broken into four components for each standard alternative. The first table presents national glidepath benefits. The second table presents the California-only glidepath bene fits. The third table presents the incremental benefits that accrue in California from fu ll attainment of the alternative standard after 2020. The last table presents total Californiabenefits—the sum of glidepath benefits and post-2020 benefits.6-78 Table 6-46: Estimate of Total Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.0 65 ppm Standard Alternative: National Glidepath Attainment Ozone Mortality and Morbidity Benefits of Attaining 0.065 ppm Standard Alternative and Model or Assumption A Ozone Benefits, Arithmetic Mean B NMMAPS Bell (2004) $3,700

Bell (2005) $14,000 Ito (2005) $15,000MetaAnalysis Levy (2005) $16,000 No Causality $330

PM 2.5 Mortality and Morbidity Benefits of Attaining 0.06 5 ppm Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study C $10,000 Harvard Six-City Study D $23,000 Mortality Impact Functions Derived from Expert Elicitation Expert A $33,000 Expert B $25,000 Expert C $25,000 Expert D $17,000 Expert E $41,000 Expert F $23,000 Expert G $15,000 Expert H $19,000 Expert I $25,000 Expert J $20,000 Expert K $4,000 Expert L $18,000

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B A credible interval is a posterior probability int erval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. Credible intervals for ozone estimates and confidence intervals for PM 2.5 estimates not provided due to the fact that the va luation estimates were derived through a scaling technique (see above) 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 rep orted 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 sensit ivity estimate.

EAll estimates incremental to 2006 PM NAAQS RIA. Es timates derived using benefit per ton estimates dis counted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.6-79 Table 6-47: Estimate of Total Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.0 65 ppm Standard Alternative: California Attainment Ozone Mortality and Morbidity Benefits of Attaining 0.065 ppm Ozone Benefits, Arithmetic Mean B Standard Alternative and Model or Assumption A Glidepath Incremental Post2020 Benefits Total NMMAPS Bell (2004) $61 $690 $750

Bell (2005) $230 $2,600 $2,800 Ito (2005) $210 $2,800 $3,000MetaAnalysis Levy (2005) $240 $2,700 $3,000 No Causality $6.8 $93 $100
PM 2.5 Mortality and Morbidity Benefits of Attaining 0.06 5 ppm Mortality Impact Functions Derived from Epidemiolog y Literature Glidepath Incremental Post2020 Benefits Total
ACS Study C $180 $930 $1,100 Harvard Six-City Study D $380 $2,000 $2,400 Mortality Impact Functions Derived from Expert Elicitation Expert A $570 $3,000 $3,600 Expert B $430 $2,300 $2,700 Expert C $430 $2,300 $2,700 Expert D $300 $1,600 $1,900 Expert E $710 $3,800 $4,500 Expert F $390 $2,100 $2,500 Expert G $250 $1,300 $1,600 Expert H $320 $1,700 $2,000 Expert I $420 $2,200 $2,700 Expert J $340 $1,800 $2,200 Expert K $68 $360 $430 Expert L $310 $1,700 $2,000

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B A credible interval is a posterior probability int erval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. Credible intervals for ozone estimates and confidence intervals for PM 2.5 estimates not provided due to the fact that the va luation estimates were derived through a scaling technique (see above) 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 rep orted 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 sensit ivity estimate.

EAll estimates incremental to 2006 PM NAAQS RIA. Es timates derived using benefit per ton estimates dis counted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.6-80 A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number of lives saved are based on an assum ption of a normal distribution. Confidence interval s not available for PM 2.5 valuation estimates due to the fact that they were derived through a scaling technique (see above) that preclude d us from generating such estimates.

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 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 rep orted as the primary estimate in recent RIAs

EBased 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 sensit ivity estimate.

F All estimates incremental to 2006 PM NAAQS RIA. Es timates derived using benefit per ton estimates dis counted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower. Table 6-48: Estimate of Total Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.0 70 ppm Standard Alternative: National Glidepath Attainment Ozone Mortality and Morbidity Benefits of Attaining 0.070 ppm Standard Alternative and Model or Assumption A Ozone Benefits, Arithmetic Mean B (95% Credible Intervals) C NMMAPS Bell (2004) $2,000 ($300--$4,400) Bell (2005) $7,400 ($1,200--$16,000) Ito (2005) $8,000 ($1,400--$16,000)MetaAnalysis Levy (2005) $9,100 ($1,600--$18,000)
No Causality $190 ($49--$400)
PM 2.5 Mortality and Morbidity Benefits of Attaining 0.07 0 ppm Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study D $6,000 Harvard Six-City Study E $14,000 Mortality Impact Functions Derived from Expert Elicitation Expert A $19,000 Expert B $15,000 Expert C $15,000 Expert D $10,000 Expert E $24,000 Expert F $13,000 Expert G $8,500 Expert H $11,000 Expert I $14,000 Expert J $12,000 Expert K $2,300 Expert L $11,0006-81 Table 6-49: Estimate of Total Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.0 70 ppm Standard Alternative: California Attainment Ozone Mortality and Morbidity Benefits of Attaining 0.070 ppm Ozone Benefits, Arithmetic Mean B Standard Alternative and Model or Assumption A Glidepath Incremental Post2020 Benefits Total
NMMAPS Bell (2004) $40 ($7.2--$86) $410 $450 Bell (2005) $150 ($25--$310) $1,500 $1,700 Ito (2005) $160 ($29--$320) $1,600 $1,800MetaAnalysis Levy (2005) $140 ($26--$270) $1,700 $1,800
No Causality $4.5 ($2.3--$8.2) $57 $61

PM 2.5 Mortality and Morbidity Benefits of Attaining 0.07 0 ppm Mortality Impact Functions Derived from Epidemiolog y Literature Glidepath Incremental Post2020 Benefits Total
ACS Study C $70 $690 $760 Harvard Six-City Study D $150 $1,500 $1,600 Mortality Impact Functions Derived from Expert Elicitation Expert A $230 $2,200 $2,400 Expert B $170 $1,700 $1,900 Expert C $170 $1,700 $1,900 Expert D $120 $1,200 $1,300 Expert E $280 $2,800 $3,100 Expert F $160 $1,500 $1,700 Expert G $100 $980 $1,100 Expert H $130 $1,200 $1,400 Expert I $170 $1,700 $1,800 Expert J $140 $1,300 $1,500 Expert K $27 $270 $300 Expert L $120 $1,200 $1,300

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B A credible interval is a posterior probability int erval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. Credible intervals for ozone estimates and confidence intervals for PM 2.5 estimates not provided due to the fact that the va luation estimates were derived through a scaling technique (see above) 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 rep orted 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 sensit ivity estimate.

EAll estimates incremental to 2006 PM NAAQS RIA. Es timates derived using benefit per ton estimates dis counted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.6-82 Table 6-50: Estimate of Total Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.0 75 ppm Standard Alternative: National Glidepath Attainment Ozone Mortality and Morbidity Benefits of Attaining 0.075 ppm Standard Alternative and Model or Assumption A Ozone Benefits, Arithmetic Mean B NMMAPS Bell (2004) $1,600

Bell (2005) $5,900 Ito (2005) $6,400MetaAnalysis Levy (2005) $7,300 No Causality $150

PM 2.5 Mortality and Morbidity Benefits of Attaining 0.07 5 ppm Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study C $3,600 Harvard Six-City Study D $8,600 Mortality Impact Functions Derived from Expert Elicitation Expert A $12,000 Expert B $8,800 Expert C $8,700 Expert D $6,100 Expert E $14,000 Expert F $7,900 Expert G $5,100 Expert H $6,500 Expert I $8,600 Expert J $7,000 Expert K $1,400 Expert L $6,300

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B With the exception of the assumption of no causal relationship, the arithmetic mean estimates of the annual number of lives saved are based on an assumption of a normal distribution. Credible in tervals for ozone estimates and confidence interval s 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..

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 rep orted 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 sensit ivity estimate.

F All estimates incremental to 2006 PM NAAQS RIA. Es timates derived using benefit per ton estimates dis counted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.6-83 Table 6-51: Estimate of Total Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.0 75 ppm Standard Alternative: California Attainment Ozone Mortality and Morbidity Benefits of Attaining 0.075 ppm Ozone Benefits, Arithmetic Mean B Standard Alternative and Model or Assumption A Glidepath Incremental Post2020 Benefits Total NMMAPS Bell (2004) 0 $260 $260

Bell (2005) 0 $940 $940 Ito (2005) 0 $1,000 $1,000MetaAnalysis Levy (2005) 0 $1,000 $1,000 No Causality 0 $33 $33

PM 2.5 Mortality and Morbidity Benefits of Attaining 0.07 5 ppm Mortality Impact Functions Derived from Epidemiolog y Literature Glidepath Incremental Post2020 Benefits Total
ACS Study C 0 $410 $410 Harvard Six-City Study D 0 $870 $870 Mortality Impact Functions Derived from Expert Elicitation Expert A 0 $1,300 $1,300 Expert B 0 $1,000 $1,000 Expert C 0 $990 $990 Expert D 0 $690 $690 Expert E 0 $1,600 $1,600 Expert F 0 $900 $900 Expert G 0 $580 $580 Expert H 0 $740 $740 Expert I 0 $980 $980 Expert J 0 $790 $790 Expert K 0 $160 $160 Expert L 0 $720 $720

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B A credible interval is a posterior probability int erval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. Credible intervals for ozone estimates and confidence intervals for PM 2.5 estimates not provided due to the fact that the va luation estimates were derived through a scaling technique (see above) 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 rep orted 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 sensit ivity estimate.

EAll estimates incremental to 2006 PM NAAQS RIA. Es timates derived using benefit per ton estimates dis counted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.6-84 Table 6-52: Estimate of Total Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.0 79 ppm Standard Alternative: National Glidepath Attainment Ozone Mortality and Morbidity Benefits of Attaining 0.075 ppm Standard Alternative and Model or Assumption A Ozone Benefits, Arithmetic Mean B

NMMAPS Bell (2004) $140 ($22--$300) Bell (2005) $510 ($86--$1,100) Ito (2005) $560 ($98--$1,100)MetaAnalysis Levy (2005) $510 ($93--$980)
No Causality $12 ($3.5--$21)

PM 2.5 Mortality and Morbidity Benefits of Attaining 0.07 5 ppm Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study C $2,800 Harvard Six-City Study D $7,000 Mortality Impact Functions Derived from Expert Elicitation Expert A $9,100 Expert B $6,900 Expert C $6,800 Expert D $4,800 Expert E $11,000 Expert F $6,200 Expert G $4,000 Expert H $5,100 Expert I $6,800 Expert J $5,500 Expert K $1,100 Expert L $5,000

ADoes not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B With the exception of the assumption of no causal relationship, the arithmetic mean estimates of the annual number of lives saved are based on an assumption of a normal distribution. Credible in tervals for ozone estimates and confidence interval s 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..

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 rep orted 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 sensit ivity estimate.

F All estimates incremental to 2006 PM NAAQS RIA. Es timates derived using benefit per ton estimates dis counted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.6-85 Table 6-53: Estimate of Total Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.0 79 ppm Standard Alternative: California Attainment Ozone Mortality and Morbidity Benefits of Attaining 0.075 ppm Ozone Benefits, Arithmetic Mean B Standard Alternative and Model or Assumption A Glidepath Incremental Post2020 Benefits Total NMMAPS Bell (2004) 0 $50 $50

Bell (2005) 0 $190 $190 Ito (2005) 0 $210 $210MetaAnalysis Levy (2005) 0 $190 $190 No Causality 0 $4.3 $4.3

PM 2.5 Mortality and Morbidity Benefits of Attaining 0.07 5 ppm Mortality Impact Functions Derived from Epidemiolog y Literature Glidepath Incremental Post2020 Benefits Total
ACS Study C 0 $130 $130 Harvard Six-City Study D 0 $270 $270 Mortality Impact Functions Derived from Expert Elicitation Expert A 0 $410 $410 Expert B 0 $310 $310 Expert C 0 $310 $310 Expert D 0 $220 $220 Expert E 0 $510 $510 Expert F 0 $280 $280 Expert G 0 $180 $180 Expert H 0 $230 $230 Expert I 0 $310 $310 Expert J 0 $250 $250 Expert K 0 $49 $49 Expert L 0 $220 $220

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B A credible interval is a posterior probability int erval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. Credible intervals for ozone estimates and confidence intervals for PM 2.5 estimates not provided due to the fact that the va luation estimates were derived through a scaling technique (see above) 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 rep orted 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 sensit ivity estimate.

EAll estimates incremental to 2006 PM NAAQS RIA. Es timates derived using benefit per ton estimates dis counted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.6-86 Table 6-54: Combined Estimate of Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.065 ppm Alt ernative Standard: National Glidepath Attainment Alternative Standard and Model or Assumption A
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $14,000 $24,000 $25,000 $26,000 $11,000 Harvard Six-City Study C $27,000 $37,000 $38,000 $38,000 $23,000 Mortality Impact Functions Derived from Expert Elicitation Expert A $37,000 $47,000 $48,000 $49,000 $33,000 Expert B $29,000 $39,000 $40,000 $41,000 $26,000 Expert C $29,000 $39,000 $40,000 $40,000 $25,000 Expert D $21,000 $32,000 $33,000 $33,000 $18,000 Expert E $45,000 $55,000 $56,000 $57,000 $42,000 Expert F $26,000 $37,000 $38,000 $38,000 $23,000 Expert G $18,000 $29,000 $30,000 $30,000 $15,000 Expert H $22,000 $33,000 $34,000 $34,000 $19,000 Expert I $28,000 $39,000 $40,000 $40,000 $25,000 Expert J $24,000 $34,000 $35,000 $35,000 $20,000 Expert K $7,700 $18,000 $19,000 $20,000 $4,300 Expert L $22,000 $32,000 $33,000 $34,000 $19,000

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B The estimate is based on the concentration-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 has previously been a sensitivity estimat e.

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 va luation estimates were derived through a scaling te chnique (see above) that precluded us from generating such estimates. Estimates derived using benefit per ton estimates discoun ted at 3%.Estimates derived using a 7% discount rat e would be approximately 15% lower.6-87 Table 6-55: Combined Estimate of Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.065 ppm Alt ernative Standard: California Glidepath Attainment Alternative Standard and Model or Assumption A
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $240 $400 $390 $420 $180 Harvard Six-City Study C $440 $600 $590 $620 $380 Mortality Impact Functions Derived from Expert Elicitation Expert A $630 $790 $780 $810 $570 Expert B $490 $660 $650 $670 $440 Expert C $490 $650 $640 $670 $430 Expert D $360 $520 $510 $540 $300 Expert E $770 $930 $920 $950 $710 Expert F $450 $620 $600 $630 $400 Expert G $310 $480 $460 $490 $260 Expert H $380 $540 $530 $560 $320 Expert I $480 $650 $630 $660 $430 Expert J $400 $570 $550 $580 $350 Expert K $130 $300 $280 $310 $75 Expert L $370 $540 $520 $550 $320

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B The estimate is based on the concentration-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 has previously been a sensitivity estimat e.

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 va luation estimates were derived through a scaling te chnique (see above) that precluded us from generating such estimates. Estimates derived using benefit per ton estimates discoun ted at 3%.Estimates derived using a 7% discount rat e would be approximately 15% lower.6-88 Table 6-56: Combined Estimate of Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.065 ppm Alt ernative Standard: Incremental Benefits of California Post 2020 Attainment Alternative Standard and Model or Assumption A
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $1,600 $3,400 $3,600 $3,600 $1,000 Harvard Six-City Study C $2,600 $4,500 $4,700 $4,700 $2,100 Mortality Impact Functions Derived from Expert Elicitation Expert A $3,600 $5,500 $5,700 $5,700 $3,100 Expert B $2,900 $4,800 $5,000 $5,000 $2,400 Expert C $2,900 $4,800 $5,000 $4,900 $2,400 Expert D $2,200 $4,100 $4,300 $4,300 $1,700 Expert E $4,400 $6,200 $6,500 $6,400 $3,800 Expert F $2,700 $4,600 $4,800 $4,700 $2,200 Expert G $2,000 $3,800 $4,000 $4,000 $1,400 Expert H $2,300 $4,200 $4,400 $4,400 $1,800 Expert I $2,900 $4,700 $5,000 $4,900 $2,300 Expert J $2,500 $4,300 $4,500 $4,500 $1,900 Expert K $1,000 $2,900 $3,100 $3,000 $450 Expert L $2,300 $4,100 $4,400 $4,300 $1,700

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B The estimate is based on the concentration-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 has previously been a sensitivity estimat e.

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 va luation estimates were derived through a scaling te chnique (see above) that precluded us from generating such estimates. Estimates derived using benefit per ton estimates discoun ted at 3%.Estimates derived using a 7% discount rat e would be approximately 15% lower.6-89 Table 6-57: Combined Estimate of Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.065 ppm Alternativ e Standard: Total California Benefits of Post 2020 Attainment Alternative Standard and Model or Assumption A
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $1,800 $3,800 $4,000 $4,000 $1,200 Harvard Six-City Study C $3,100 $5,100 $5,300 $5,300 $2,500 Mortality Impact Functions Derived from Expert Elicitation Expert A $4,300 $6,300 $6,500 $6,500 $3,700 Expert B $3,400 $5,400 $5,700 $5,600 $2,800 Expert C $3,400 $5,400 $5,600 $5,600 $2,800 Expert D $2,600 $4,600 $4,800 $4,800 $2,000 Expert E $5,200 $7,200 $7,400 $7,400 $4,600 Expert F $3,200 $5,200 $5,400 $5,400 $2,600 Expert G $2,300 $4,300 $4,500 $4,500 $1,700 Expert H $2,700 $4,700 $4,900 $4,900 $2,100 Expert I $3,400 $5,400 $5,600 $5,600 $2,800 Expert J $2,900 $4,900 $5,100 $5,100 $2,300 Expert K $1,100 $3,100 $3,400 $3,300 $520 Expert L $2,700 $4,700 $4,900 $4,900 $2,100

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B The estimate is based on the concentration-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 has previously been a sensitivity estimat e.

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 va luation estimates were derived through a scaling te chnique (see above) that precluded us from generating such estimates. Estimates derived using benefit per ton estimates discoun ted at 3%.Estimates derived using a 7% discount rat e would be approximately 15% lower.6-90 Table 6-58: Combined Estimate of Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.070 ppm Alt ernative Standard: National Glidepath Attainment Alternative Standard and Model or Assumption A
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $7,900 $13,000 $14,000 $15,000 $6,200 Harvard Six-City Study C $16,000 $21,000 $22,000 $23,000 $14,000 Mortality Impact Functions Derived from Expert Elicitation Expert A $21,000 $27,000 $27,000 $28,000 $19,000 Expert B $17,000 $22,000 $23,000 $24,000 $15,000 Expert C $17,000 $22,000 $23,000 $24,000 $15,000 Expert D $12,000 $18,000 $18,000 $19,000 $10,000 Expert E $26,000 $31,000 $32,000 $33,000 $24,000 Expert F $15,000 $21,000 $21,000 $22,000 $13,000 Expert G $11,000 $16,000 $17,000 $18,000 $8,700 Expert H $13,000 $18,000 $19,000 $20,000 $11,000 Expert I $16,000 $22,000 $22,000 $23,000 $15,000 Expert J $14,000 $19,000 $20,000 $21,000 $12,000 Expert K $4,300 $9,700 $10,000 $11,000 $2,500 Expert L $13,000 $18,000 $19,000 $20,000 $11,000

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B The estimate is based on the concentration-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 has previously been a sensitivity estimat e.

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 va luation estimates were derived through a scaling te chnique (see above) that precluded us from generating such estimates. Estimates derived using benefit per ton estimates discoun ted at 3%.Estimates derived using a 7% discount rat e would be approximately 15% lower.6-91 Table 6-59: Combined Estimate of Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.070 ppm Alt ernative Standard: California Glidepath Attainment Alternative Standard and Model or Assumption A
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $110 $220 $230 $210 $75 Harvard Six-City Study C $190 $300 $310 $290 $150 Mortality Impact Functions Derived from Expert Elicitation Expert A $270 $370 $380 $360 $230 Expert B $210 $320 $330 $310 $180 Expert C $210 $320 $330 $310 $180 Expert D $160 $270 $280 $260 $120 Expert E $320 $430 $440 $420 $290 Expert F $200 $300 $310 $290 $160 Expert G $140 $250 $260 $240 $100 Expert H $170 $270 $290 $260 $130 Expert I $210 $320 $330 $310 $170 Expert J $180 $280 $300 $270 $140 Expert K $67 $170 $190 $170 $32 Expert L $160 $270 $280 $260 $130

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B The estimate is based on the concentration-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 has previously been a sensitivity estimat e.

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 va luation estimates were derived through a scaling te chnique (see above) that precluded us from generating such estimates. Estimates derived using benefit per ton estimates discoun ted at 3%.Estimates derived using a 7% discount rat e would be approximately 15% lower.6-92 Table 6-60: Combined Estimate of Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.070 ppm Alt ernative Standard: Incremental Benefits of California Post 2020 Attainment Alternative Standard and Model or Assumption A
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $1,100 $2,200 $2,300 $2,300 $740 Harvard Six-City Study C $1,900 $3,000 $3,100 $3,100 $1,500 Mortality Impact Functions Derived from Expert Elicitation Expert A $2,600 $3,700 $3,800 $3,900 $2,300 Expert B $2,100 $3,200 $3,300 $3,300 $1,800 Expert C $2,100 $3,200 $3,300 $3,300 $1,700 Expert D $1,600 $2,700 $2,800 $2,800 $1,200 Expert E $3,200 $4,300 $4,400 $4,400 $2,800 Expert F $1,900 $3,000 $3,100 $3,200 $1,600 Expert G $1,400 $2,500 $2,600 $2,600 $1,000 Expert H $1,600 $2,700 $2,900 $2,900 $1,300 Expert I $2,000 $3,200 $3,300 $3,300 $1,700 Expert J $1,700 $2,800 $3,000 $3,000 $1,400 Expert K $650 $1,800 $1,900 $1,900 $320 Expert L $1,600 $2,700 $2,800 $2,900 $1,300

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B The estimate is based on the concentration-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 has previously been a sensitivity estimat e.

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 va luation estimates were derived through a scaling te chnique (see above) that precluded us from generating such estimates. Estimates derived using benefit per ton estimates discoun ted at 3%.Estimates derived using a 7% discount rat e would be approximately 15% lower.6-93 Table 6-61: Combined Estimate of Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.070 ppm Alt ernative Standard: California Post 2020 Attainment Alternative Standard and Model or Assumption A
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $1,200 $2,400 $2,500 $2,300 $810 Harvard Six-City Study C $2,100 $3,300 $3,400 $3,400 $1,700 Mortality Impact Functions Derived from Expert Elicitation Expert A $2,900 $4,100 $4,200 $4,200 $2,500 Expert B $2,300 $3,500 $3,600 $3,700 $1,900 Expert C $2,300 $3,500 $3,600 $3,600 $1,900 Expert D $1,700 $2,900 $3,100 $3,100 $1,300 Expert E $3,500 $4,700 $4,800 $4,800 $3,100 Expert F $2,100 $3,300 $3,400 $3,500 $1,700 Expert G $1,500 $2,700 $2,800 $2,900 $1,100 Expert H $1,800 $3,000 $3,100 $3,200 $1,400 Expert I $2,300 $3,500 $3,600 $3,600 $1,900 Expert J $1,900 $3,100 $3,200 $3,300 $1,500 Expert K $720 $1,900 $2,100 $2,100 $350 Expert L $1,800 $3,000 $3,100 $3,100 $1,400

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B The estimate is based on the concentration-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 has previously been a sensitivity estimat e.

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 va luation estimates were derived through a scaling te chnique (see above) that precluded us from generating such estimates. Estimat es derived using benefit per ton estimates discount ed at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.6-94 Table 6-62: Combined Estimate of Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.075 ppm Alt ernative Standard: National Glidepath Attainment Alternative Standard and Model or Assumption A
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $5,100 $9,400 $10,000 $11,000 $3,700 Harvard Six-City Study C $10,000 $15,000 $15,000 $16,000 $8,800 Mortality Impact Functions Derived from Expert Elicitation Expert A $13,000 $17,000 $18,000 $19,000 $12,000 Expert B $10,000 $15,000 $15,000 $16,000 $9,000 Expert C $10,000 $15,000 $15,000 $16,000 $8,900 Expert D $7,600 $12,000 $13,000 $13,000 $6,200 Expert E $16,000 $20,000 $21,000 $22,000 $15,000 Expert F $9,500 $14,000 $14,000 $15,000 $8,100 Expert G $6,700 $11,000 $12,000 $12,000 $5,200 Expert H $8,000 $12,000 $13,000 $14,000 $6,600 Expert I $10,000 $15,000 $15,000 $16,000 $8,800 Expert J $8,500 $13,000 $13,000 $14,000 $7,100 Expert K $3,000 $7,300 $7,800 $8,700 $1,500 Expert L $7,900 $12,000 $13,000 $14,000 $6,500

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B The estimate is based on the concentration-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 has previously been a sensitivity estimat e.

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 va luation estimates were derived through a scaling te chnique (see above) that precluded us from generating such estimates. Estimates derived using benefit per ton estimates discoun ted at 3%.Estimates derived using a 7% discount rat e would be approximately 15% lower.6-95 Table 6-63: Combined Estimate of Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.075 ppm Alt ernative Standard: California Post 2020 Attainment Alternative Standard and Model or Assumption A
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $660 $1,400 $1,400 $1,400 $440 Harvard Six-City Study C $1,100 $1,800 $1,900 $1,900 $900 Mortality Impact Functions Derived from Expert Elicitation Expert A $1,600 $2,300 $2,300 $2,300 $1,400 Expert B $1,300 $2,000 $2,000 $2,000 $1,000 Expert C $1,300 $1,900 $2,000 $2,000 $1,300 Expert D $950 $1,600 $1,700 $1,700 $950 Expert E $1,900 $2,600 $2,700 $2,700 $1,900 Expert F $1,200 $1,900 $1,900 $1,900 $1,200 Expert G $830 $1,500 $1,600 $1,600 $830 Expert H $990 $1,700 $1,800 $1,800 $990 Expert I $1,200 $1,900 $2,000 $2,000 $1,200 Expert J $1,100 $1,700 $1,800 $1,800 $1,100 Expert K $410 $1,100 $1,200 $1,200 $410 Expert L $980 $1,700 $1,700 $1,700 $980

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B The estimate is based on the concentration-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 has previously been a sensitivity estimat e.

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 va luation estimates were derived through a scaling te chnique (see above) that precluded us from generating such estimates. Estimates derived using benefit per ton estimates discoun ted at 3%.Estimates derived using a 7% discount rat e would be approximately 15% lower.6-96 Table 6-64: Combined Estimate of Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.079 ppm Alt ernative Standard: National Glidepath Attainment Alternative Standard and Model or Assumption A
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $3,100 $4,000 $4,000 $4,000 $3,100 Harvard Six-City Study C $7,300 $8,100 $8,200 $8,200 $7,000 Mortality Impact Functions Derived from Expert Elicitation Expert A $9,200 $9,600 $9,600 $9,600 $9,100 Expert B $7,100 $7,400 $7,500 $7,400 $6,900 Expert C $7,000 $7,400 $7,400 $7,400 $6,900 Expert D $4,900 $5,300 $5,300 $5,300 $4,800 Expert E $11,000 $12,000 $12,000 $12,000 $11,000 Expert F $6,400 $6,700 $6,800 $6,700 $6,200 Expert G $4,100 $4,500 $4,600 $4,500 $4,000 Expert H $5,200 $5,600 $5,600 $5,600 $5,100 Expert I $6,900 $7,300 $7,300 $7,300 $6,800 Expert J $5,600 $6,000 $6,000 $6,000 $5,500 Expert K $1,200 $1,600 $1,700 $1,600 $1,100 Expert L $5,100 $5,500 $5,500 $5,500 $5,000

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B The estimate is based on the concentration-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 has previously been a sensitivity estimat e.

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 va luation estimates were derived through a scaling te chnique (see above) that precluded us from generating such estimates. Estimates derived using benefit per ton estimates discoun ted at 3%.Estimates derived using a 7% discount rat e would be approximately 15% lower.6-97 Table 6-65: Combined Estimate of Annual Ozone and PM 2.5 Benefits (95% Confidence Intervals, Millions of $1999) for the 0.079 ppm Alt ernative Standard: California Post 2020 Attainment Alternative Standard and Model or Assumption A
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study B $180 $320 $330 $320 $130 Harvard Six-City Study C $320 $460 $480 $460 $280 Mortality Impact Functions Derived from Expert Elicitation Expert A $460 $600 $620 $600 $410 Expert B $360 $500 $520 $500 $320 Expert C $360 $500 $520 $500 $310 Expert D $270 $400 $420 $400 $220 Expert E $560 $700 $720 $700 $520 Expert F $330 $470 $490 $470 $290 Expert G $230 $370 $390 $370 $190 Expert H $280 $420 $440 $420 $230 Expert I $360 $500 $510 $490 $310 Expert J $300 $440 $450 $440 $250 Expert K $100 $240 $260 $240 $54 Expert L $270 $410 $430 $410 $230

A Does not represent equal weighting among models or between assumption of causality vs. no causality ( see text on page 63).

B The estimate is based on the concentration-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 has previously been a sensitivity estimat e.

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 va luation estimates were derived through a scaling te chnique (see above) that precluded us from generating such estimates. Estimates derived using benefit per ton estimates discoun ted at 3%.Estimates derived using a 7% discount rat e would be approximately 15% lower.6-98

6.5.5 Discussion of Results and Uncertainties
This analysis has estimated the health and welfare benefit s of reductions in ambient concentrations of ozone and particulate matter resulting f rom a set of illustrative control strategies to reduce emissions of ozone. The result s suggest there will be significant additional health and welfare benefits arising from reducing e missions from a variety of sources in and around projected nonattaining counties in 2020. While 2020 is the expected date that states would need to demonstrate attainment with the revised stand ard, it is expected that benefits (and costs) will begin occurring much earlier, as states begin implementing control measures to show reasonable progress towards attainment. Using the fu ll range of benefits (including the results of the expert elicitation), we estimate that total ozone and PM 2.5 benefits would be between and $2.5 and $33 billion annually for the 0.070 ppm alt ernative when the emissions reductions from implementing the new standard is fully realized prov ides additional evidence of the important role that implementation of the standards plays in reducing the health risks associated with exceeding the standard.
There are several important factors to consider when eva luating the relative benefits of the attainment strategies for each of the alternative ozone stan dards.

1. California accounts for a substantial share of the total benefits for each of the evaluated standards. Benefits are most uncertain for Californ ia given the unique challenge of modeling attainment with the standards due to the high le vels of ozone, difficulties of modeling the impacts of emissions controls on air quality, and the very large proportion of California benefits that were derived through extrapolation are very large r elative to other areas of the U.S. for each standard alternative. On the one hand, these California benefits are likely to understate the actual benefi ts of attainment strategies, because we applied an estimation approach that reduced concentr ations only at the specific violating monitors and not surrounding monitors that did not violat e the standards. The magnitude of this underestimate is unknown. On the other hand, it is pos sible that new technologies might not meet the specifications, development time lines, or cost estimates provided in this analysis, thereby increasing the uncertainty inwhen and if such benefits would be truly achieved.

2. There are substantial uncertainties associated with the estimated benefits of the 0.065 ppm, 0.075 ppm, and 0.079 ppm alternatives, which were derived through extrapolation and interpolation, respectively. The great majorityof benefits estimated for the 0.065 ppm standard alternative were derived through extrapolati on. As noted above, these benefits are likely to be more uncertain than the m odeled benefits. The 0.075 ppm benefits were derived through an interpolation technique (described in Appendix 6) which scaled-down the benefits of the 0.070 ppm bene fits analysis. A key assumption in this approach is that the control strategy to attain 0 .075 ppm would share the same characteristics of the 0.070 ppm strategy—namely, regiona l emission controls on electrical generating units and emission controls applied to counties within 200km of projected non-attainment monitors. To the extent that states utilized fewer regional emission controls, total benefits for the 0.075 ppmstrategy may be smaller.6-99

EPA employed a monitor rollback approach to estimate the benefits of attaining an alternative standard of 0.079 ppm nationwide. This appro ach likely understates the benefits that would occur due to implementation of actual controls because controls implemented to reduce ozone concentrations at the hig hest monitor would likely result in some reductions in ozone concentrations at attaining mo nitors down-wind (i.e. the controls would lead to concentrations below the standard in down-wind locations). Therefore, air quality improvements and resulting health b enefits from full attainment would be more widespread than we have estimated in our rollback analysis.
EPA calculated 0.075 ppm benefits by interpolating the 0.070 ppm benefits estimates. 33 This interpolation approach may overestimate benefits rel ative to a modeled control scenario developed specifically to attain the 0.075 ppm alternative. The interpolation method scales down benefits only at the monitors we p roject to exceed 0.075 ppm—but it still captures the benefits achieved by the 0.070 ppm regional control strategy that occur outside of these projected non-attainment areas. To the extent that a modeled emission control strategy to attain 0.075 ppm does not include these broader regional emission reductions, total benefits would be lower than those we have estimated in this RIA.
Interpolation and monitor rollback methods of benef its estimation are inherently different. As described above, for the purposes of reviewing this analysis, the reader should understand that the benefits described for attaining a standard of 0.079 ppm are likely understated, whereas the estimated benefits o f attaining a standard of 0.075 ppm are likely overstated. We will develop and present consistent approaches for the alternative standards for the final RIA.
3. There are a variety of uncertainties associated with the health impact functions used in this modeling effort. These include: within study variability, which is the precision with which a given study estimates the relationship between air quality changes and health effects; across study variation, which refers to the fact that different published studies of the same pollutant/health effect relationship typicallydo 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; extra polation of impact functions across population, in which we assumed that certain health impac t functions applied to age ranges broader than that considered in the original e pidemiological study; and, finally, there are various uncertainties in the C-R function , including causality, the correlation among multiple pollutants, the shape of the C-R function and the relative toxicity of PM component species, and the lag between exposure and the onset of the health effect.
4. There are a variety of uncertainties associated with the economic valuation of the health endpoints estimated in this analysis. Uncertainties specific to the valuation of premature mortality include across study variation; the assumption that WTP for mortality risk

33 This procedure is detailed in Appendix 6A.6-100

reduction is linear; assuming that voluntary and involuntary mortality risk will be valued equally; assuming that premature mortality from air poll ution risk, which tend to involve longer periods of time, will be valued the same as s hort 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 derived from the Pope et. al (2002 ) mortality estimate. These benefit per-ton estimates represent regional averages. As s uch, they do not reflect any local variability in the incremental PM 2.5 benefits per ton of NOx abated. As discussed in th e PM NAAQS RIA (Table 5.5), there are a large number of uncertainties associated with these PM benefits.
6. For the 0.070 ppm alternative, we estimate co-benefits fro m PM to be between 20% and 99% of total benefits, depending on the PM2.5 and ozone mort ality functions used. In our calculation of PM 2.5 co-benefits we assume that states will pursue an o zone strategy that reduces NOx emissions. As such, these estimates are strongl y influenced by the assumption that all PM components are equally toxic. We also a cknowledge that when implementing any new standard, states may elect to pursue adifferent ozone strategy, which would in turn affect the level of PM 2.5 co-benefits.
7. Inherent in any analysis of future regulatory prog rams are uncertainties in projecting atmospheric conditions and source-level emissions, as well as population, health baselines, incomes, technology, and other factors. Inaddition, data limitations prevent an overall quantitative estimate of the uncertainty as sociated with estimates of total economic benefits. If one is mindful of these limitations, the magnitude of the benefits estimates presented here can be useful information in expan ding the understanding of the public health impacts of reducing ozone precursor emission s. 8. There are certain unquantified effects not considered in this benefits analysis due to lack of data, time and resources. These unquantified endpoints i nclude the direct effects of of ozone on vegetation, the deposition of nitrogen to estuarine and coastal waters and agricultural and forested land, and the changes in the level of exposure to ultraviolet radiation from ground level ozone.

EPA will continue to evaluate new methods and model s and select those most appropriate for estimating the health benefits of reductions in air pollution. It is important to continue improving benefits transfer methods in terms of transferring economi c values and transferring estimated impact functions. The development of both better models o f current health outcomes and new models for additional health effects such as asthma, high blood pressure, and adverse birth outcomes (such as low birth weight) will be essenti al to future improvements in the accuracy and reliability of benefits analyses (Guo et al., 1999; Ibald -Mulli et al., 2001). Enhanced collaboration between air quality modelers, epidemi ologists, toxicologists, and economists should result in a more tightly integrated analytical frame work for measuring health benefits of6-101

air pollution policies. Readers interested in a more exte nsive discussion of the sources of uncertainty in human health benefits analyses shoul d consult the PM NAAQS RIA. 34

6.5.6 Summary of Total Benefits
Tables 6-54 presents the total number of estimated ozone and PM 2.5 -related premature mortalities and morbidities avoided nationwide in 2020. Table 6- 55 presents these estimates for California, post 2020.

34 U.S. Environmental Protection Agency, 2006. Regulatory Impact Analysis for the PM NAAQS. EPA Prepared by Office of Air and Radiation. Ava ilable at: http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf6-102 Table 6-66: 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 A 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) 200 to 1,900 430 to 2,600 670 t o 4,300 1,200 to 7,400

Bell (2005) 260 to 2,000 1,100 to 3,300 1,500 to 5, 100 2,800 to 9,000 Ito (2005) 270 to 2,000 1,200 to 3,300 1,600 to 5,2 00 3,000 to 9,200 Meta-Analysis Levy (2005) 260 to 2,000 1,300 to 3,500 1,800 to 5, 400 3,000 to 9,200 No Causality 180 to 1,900 230 to 2,400 390 to 4,000 660 to 6,900

Combined Estimate of Morbidity

Acute Myocardial Infarction 1,100 1,400 2,300 4,000 Hospital and ER Visits 1,300 5,600 7,600 13,000 Chronic Bronchitis 370 470 780 1,300 Acute Bronchitis 950 1,200 2,000 3,500 Asthma Exacerbation 7,300 9,400 16,000 27,000 Lower Respiratory Symptoms 8,100 10,000 17,000 29,00 0 Upper Respiratory Symptoms 5,900 7,500 13,000 22,000 School Loss Days 50,000 610,000 780,000 1,300,000 Work Loss Days 51,000 65,000 110,000 190,000 Minor Restricted Activity Days 430,000 2,000,000 2,7 00,000 4,700,0006-103 Table 6-67: Summary of Total Number of Annual Ozone and PM 2.5 -Related Premature Mortalities and Premature Morbidity Avoided: California Post 2020 Attainment Combined Estimate of Mortality Standard Alternative and Model or Assumption A 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) 17 to 93 61 to 310 110 to 570 1 80 to 840

Bell (2005) 42 to 120 170 to 410 300 to 760 490 to 1,200 Ito (2005) 45 to 120 180 to 430 320 to 780 530 to 1 ,200 Meta-Analysis Levy (2005) 46 to 120 180 to 430 320 to 780 520 to 1 ,200 No Causality 8.2 to 84 26 to 270 49 to 500 72 to 740

Combined Estimate of Morbidity

Acute Myocardial Infarction 49 160 290 430 Hospital and ER Visits 200 790 1,400 2,200 Chronic Bronchitis 17 53 99 150 Acute Bronchitis 43 140 260 380 Asthma Exacerbation 330 1,100 2,000 2,900 Lower Respiratory Symptoms 360 1,200 2,200 3,200 Upper Respiratory Symptoms 270 850 1,600 2,300 School Loss Days 30,000 120,000 210,000 340,000 Work Loss Days 2,300 7,400 14,000 20,000 Minor Restricted Activity Days 87,000 340,000 600,00 0 960,0006-104

6.6 References:
Abt Associates, Inc. October 2005. Methodology for County-level Mortality Rate Projections. Memorandum to Bryan Hubbell and Zachary Pekar, U.S. EPA.
Adams PF, Hendershot GE, Marano MA. 1999. Current Estimate s from the National Health Interview Survey, 1996. Vital Health Stat 10(200):1-212.
Agency for Healthcare Research and Quality. 2000. H CUPnet, Healthcare Cost and Utilization Project. Rockville, Maryland. Available: http://www.ahr q.gov/HCUPnet/ [accessed 29 September 2004].
Anderson HR, Atkinson RW, Peacock JL, Marston L, Konstantin ou K. 2004. Meta-analysis of time-series studies and panel studies of Particulate Matter (PM) and Ozone (O3): Report of a WHO task group. Copenhagen, Denmark: World Health Organizati on. Bell, M.L., et al. Ozone and short-term mortality in95 US urban communities, 1987-2000. Jama, 2004. 292(19): p. 2372-8.
Bell, M.L., F. Dominici, and J.M. Samet. A meta-analysis of t ime-series studies of ozone and mortality with comparison to the national morbidity, mort ality, and air pollution study. Epidemiology, 2005. 16(4): p. 436-45.
Berger MC, Blomquist GC, Kenkel D, Tolley GS. 1987 . Valuing Changes in Health Risks: A Comparison of Alternative Measures. The Southern Economic J ournal 53:977-984.
Burnett RT, Smith-Doiron M, Stieb D, Raizenne ME, B rook JR, Dales RE, et al. 2001. Association between ozone and hospitalization for a cute respiratory diseases in children less than 2 years of age. Am J Epidemiol 153(5):444-452.
Chen L, Jennison BL, Yang W, Omaye ST. 2000. Elementary school absenteeism and air pollution. Inhal Toxicol 12(11):997-1016.
Crocker, T.D. and R.L. Horst, Jr. Hours of Work, Lab or Productivity, and Environmental Conditions: A Case Study. The Review of Economics and Statistics, 1981. 63: p. 361-368.
Freeman(III), AM. 1993. The Measurement of Environmental and Resource Values: Theory and Methods. Washington, DC: Resources for the Fut ure. Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Av ol E, Gauderman WJ, et al. 2001. The effects of ambient air pollution on school absenteeism due t o respiratory illnesses. Epidemiology 12(1):43-54.
GeoLytics Inc. 2002. Geolytics CensusCD® 2000 Short Form B locks. CD-ROM Release 1.0. GeoLytics, Inc. East Brunswick, NJ. Available: http://www.geolytics.com/ [accessed 29 September 2004].6-105

Hall JV, Brajer V, Lurmann FW. 2003. Economic Valuation of Oz one-related School Absences in the South Coast Air Basin of California. Contempo rary Economic Policy 21(4):407-417.
Harrington W, Portney PR. 1987. Valuing the Benef its of Health and Safety Regulation. Journal of Urban Economics 22:101-112.
Industrial Economics Incorporated (IEc). 1993. Memoran dum to Jim DeMocker, U.S. Environmental Protection Agency, Office of Air and Radiatio n, Office of Policy Analysis and Review. September 30. Washington, D.C.
International Classification of Diseases, Ninth Revisio n. 1979. Hyattsville, MD: National Center for Health Statistics. Available: http://www.cdc.gov/nch s/about/major/dvs/icd9des.htm [accessed 29 September 2004].
Ito, K., S.F. De Leon, and M. Lippmann. Associations betweenozone and daily mortality: analysis and meta-analysis. Epidemiology, 2005. 16(4 ): p. 446-57.
Jaffe DH, Singer ME, Rimm AA. 2003. Air pollution and emergency department visits for asthma among Ohio Medicaid recipients, 1991-1996. Env iron Res 91(1):21-28.
Köchy, Martin and Scott D. Wilson, “Nitrogen deposition an d forest expansion in the northern Great Plains Journal of Ecology Journal of Ecology89 (5), 807–817
Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr, and M. Studnicka. 2001. “Assessment of Deaths Attributable to Air Pollution: Should We U se Risk Estimates Based on Time Series or on Cohort Studies?” American Journal of Epidemiology 153(11):1050-55.
Levy JI, Carrothers TJ, Tuomisto JT, Hammitt JK, Ev ans JS. 2001. Assessing the Public Health Benefits of Reduced Ozone Concentrations. Environ Heal th Perspect 109(12):1215-1226
Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. Ozone exposure and mortality: an empiric bayes metaregression analysis. Epidemiology, 2005. 16(4): p. 458-68.
Moolgavkar SH, Luebeck EG, Anderson EL. 1997. Air pollutio n and hospital admissions for respiratory causes in Minneapolis St. Paul and Birmingham. Epidemiology 8(4 ):364-370.
National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. The National Academies Press: W ashington, D.C.
Ostro BD, Rothschild S. 1989. Air Pollution and Acute Respiratory Morbidity - an Observational Study of Multiple Pollutants. Environ Res 50 (2):238-247.
Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens, and M. White. 2001. “Air Pollution and Exacerbation of Asthma in African-American Children in Los A ngeles.” Epidemiology 12(2):200-208.6-106

Nadelhoffer, Knute J. et. al., “Nitrogen depositionmakes a minor contribution to carbon sequestration in temperate forests” Nature 398, 145-148 (11 March 1999)
Peel, J. L., P. E. Tolbert, M. Klein, et al. 2005. Ambient air pollution and respiratory emergency department visits. Epidemiology. Vol. 16 (2): 164-74.
Schwartz J. 1994a. PM(10) Ozone, and Hospital Adm issions For the Elderly in Minneapolis St Paul, Minnesota

. Arch Environ Health 49(5):366-374.
Schwartz J. 1994b. Air Pollution and Hospital Adm issions For the Elderly in Detroit, Michigan. Am J Respir Crit Care Med 150(3):648-655.

Schwartz J. 1995. Short term fluctuations in air pollution and hospital admissions of the elderly for respiratory disease. Thorax 50(5):531-538.
Smith DH, Malone DC, Lawson KA, Okamoto LJ, Battist a C, Saunders WB. 1997. A national estimate of the economic costs of asthma. Am J Respir Crit Care Med. 156(3 Pt 1):787-793.
Stanford R, McLaughlin T, Okamoto LJ. 1999. The cost of asthm a in the emergency department and hospital. Am J Respir Crit Care Med 160(1) :211-215.
Thurston GD, Ito K. 2001. Epidemiological studies of acute ozone exposures and mortality. J Expo Anal Environ Epidemiol 11(4):286-294.
Tolley GS, Babcock L, Berger M, Bilotti A, Blomquist G, BrienM, et al. 1986. Valuation of Reductions in Human Health Symptoms and Risks. Prepared f or U.S. Environmental Protection Agency. January. Washington, D.C.

U.S. Bureau of the Census. 2001. Statistical Abstract of the United States, 2001, Section 12: Labor Force, Employment, and Earnings, Table No. 521. Washi ngton, DC.
U.S. Department of Education. 1996. The Conditionof Education 1996, Indicator 42: Student Absenteeism and Tardiness. National Center for Education S tatistics. Washington DC.

U.S. EPA. 1970. Clean Air Act. 40CFR50.
U.S. Environmental Protection Agency. 1999. The Benefits and Costs of the Clean Air Act, 1990-2010. Prepared for U.S. Congress by U.S. EPA, Office of Air and Radiation/Office of Policy Analysis and Review, Washington, DC, November; EPA report no. EPA-410-R-99-001.
U.S. Environmental Protection Agency, 2004. Final Regulatory Analysis: Control of Emissions from Nonroad Diesel Engines. EPA420-R-04-007. Prepared by O ffice of Air and Radiation. Available: http://www.epa.gov/nonroad-diesel/2004fr/420r04007.pdf [accessed 29 September 2004].6-107

Vedal, S., J. Petkau, R. White, and J. Blair. 1998. “Acute Eff ects of Ambient Inhalable Particles in Asthmatic and Nonasthmatic Children.” American Journal of Respiratory and Critical Care Medicine157(4):1034-1043.
Vitousek, Peter M. et. al., “Human Alteration of the Global Ni trogen Cycle: Causes and Consequences” Issues in Ecology No. 1 (Spring) 1997.
Wilson, A. M., C. P. Wake, T. Kelly, et al. 2005. Air pollution, weather, and respiratory emergency room visits in two northern New England ci ties: an ecological time-series study. Environ Res. Vol. 97 (3): 312-21.
Woods & Poole Economics Inc. 2001. Population by Single Year of Age CD. CD-ROM. Woods & Poole Economics, Inc. Washington, D.C.
World Health Organization (WHO). 2003. Health Aspe cts of Air Pollution with Particulate Matter, Ozone and Nitrogen Dioxide: Report on a WHO Worki ng Group. World Health Organization. Bonn, Germany. EUR/03/5042688.**6a-1**

Appendix Chapter 6a: Additional Benefits Information

Summary
**This appendix provides additional information regar ding the benefits analysis, including (1)**  **methods for developing estimate of full attainment air qu ality; (2) the process for interpolating**  **the 0.075 ppm benefits estimate; (3) the partial attain ment PM**  **2.5 incidence and valuation**  **estimates.**
6a.1 Developing an air quality estimate of full att ainment 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 va lues into the BenMAP model**  **2. Perform a spatial interpolation of these design values us ing 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 hourly ozone concentrations into**  **BenMAP. Create air quality grid in BenMAP using spa tial and temporal scaling** **6a-2**

**technique. 1**  **This procedure creates grid cell level summer season ozone metrics (1 hour**  **maximum, 5 hour average, 8 hour maximum, 8 hour average and 24 hour average).**  **4. Calculate grid cell-level ratio of each ozone metric t o 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 attainmen t design value to just equal the 0.08**  **ppm standard. This process creates a new baseline design value grid. 2. Scale the design value grid cell values to ozone metric**  **grid cell values by using ratios described above.**  **3. Create new 0.084 ppm baseline air quality grid from gr id cell-level ozone metrics.**  **4. Roll back the calculate calculated 0.070 ppm and 0.065 ppm partial attainment design**  **values at each monitor to just each the 0.070 ppm and 0 .065 ppm standards, respectively.**  **5. Scale the calculated full attainment design value to grid cell-level ozone metric using**  **ratios described above. 6. Create new 0.070 ppm and 0.065 ppm air quality gri**  **ds from grid cell-level ozone metrics. 7. Perform benefits analysis with baseline and control grids.** 
**To develop a 0.075 ppm full attainment air quality grid we performed an interpolation of the**  **0.070 ppm full attainment air quality grid, rather than a monitor rollback. This interpolation**  **entailed the following steps:**
**1. We identified any monitors that were projected to not att ain 0.075 ppm alternative in the**  **0.084 ppm base case air quality grid. 2. For these monitors we calculated an adjustment fac**  **tor 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.**  **3. We then interpolated these monitor-specific ratios to the grid cell-level in BenMAP.**  **4. Finally, we used these grid cell-level ratios as the basis for scaling down the grid cell**  **level estimates of incidence and valuation from the 0. 070 ppm analysis.** 

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.**

**1 BenMAP Technical Appendices, Abt Associates: May 2005. Pag**  **e C-12. 6a-3**  Table 6a-1: Illustrative 0.070 ppm Partial Attainment Scenario: Estimated Reducti ons in PM Premature Mortality associate with PM co-benefit (95 th percentile confidence intervals provided in parent heses)

Eastern U.S. Western U.S. Excluding California California National PM co-benefits Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study A 510 (170--840) 0.17 (0.06--0.27) 47 (16—77) 550 (190--920) Harvard Six-City Study B 1,100 (570--1,700) 0.4 (0.18--0.6) 110 (53--160) 1,300 (630--1,900) Woodruff et al 1997 (infant mortality) 1.1 (0.5—1.7) 0.04 (0.02--0.06) 0.14 (0.07--0.2) 1.3 (0.6—2) Mortality Impact Functions Derived from Expert Elic itation Expert A 1,600 (170--3,000) 77 (8.2--150) 140 (15--270) 1,800 (190--3,400) Expert B 1,200 (140--2,600) 56 (4--130) 110 (13--240) 1,400 (160--3,000) Expert C 1,200 (140--2,700) 58 (6.6--130) 110 (12--250) 1,400 (150--3,000) Expert D 820 (85--1,400) 40 (4.1—68) 76 (7.8--130) 940 (96—1,600) Expert E 2,000 (890—3,000) 95 (44--150) 180 (82--280) 2,200 (1,000--3,500) Expert F 1,100 (740--1,600) 51 (33—74) 99 (67--150) 1,200 (840—1,800) Expert G 690 (0—1,300) 34 (0—65) 63 (0--120) 790 (0--1,500) Expert H 880 (-46--2,100) 43 (-2.2—100) 80 (-4.2--200) 1,000 (-52—2,400) Expert I 1,200 (60--2,200) 57 (3--110) 110 (5.5--200) 1,300 (69—2,500) Expert J 950 (230--2,200) 46 (11—110) 87 (21--200) 1,100 (260--2,500) Expert K 190 (0--970) 8.8 (0—47) 19 (0—95) 220 (0--1,100) Expert L 860 (120--1,600) 33 (0.04--81) 79 (11--150) 970 (130—1,900)

**A The estimate is based on the concentration-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 incorpora** **ting what has previously been a sensitivity estimat e.**  **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 6a-4**  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)

Eastern U.S. Western U.S. Excluding California California National PM co-benefits Morbidity Impact Functions Derived from Epidemiolog y Literature
Chronic Bronchitis (age >25 and over) 380 (42-720) 12 (1.3—21) 43 (4.6—81) 440 (47—820)
Nonfatal myocardial infarction (age >17) 1,100 (560—1,700) 0.4 (0.2—0.6) 94 (47—140) 1,200 (610—1,800)
Hospital admissions--respiratory (all ages) 130 (59—200) -- 10 (4.4-15) 140 (63—220) Hospital admissions-- cardiovascular (age >17) 270 (160—370) -- 20 (12—28) 290 (170—400) Emergency room visits for asthma (age <19) 560 (310—820) -- 22 (12—32) 590 (320—850)

Acute bronchitis (age 8-12) 990 (-130—2,100) 32 (-4.1—67) 130 (-17—270) 1,200 (-150—2,400)
Lower respiratory symptoms (age 7-14) 8,400 (3,600—13,000) 3.6 (1.6—5.5) 1,200 (520—1,900) 9,600 (4,200—15,000) Upper respiratory symptoms (asthmatic children age 9-18) 6,100 (1,500—11,000) 2.6 (0.7—4.6) 870 (220—1,500) 7,000 (1,800—12,000) Asthma exacerbation (asthmatic children age 6--18) 7,700 (550—24,000) 3.4 (0.24—11) 1,100 (77—3,400) 8,700 (620—28,000)
Work loss days (age 18-65) 53,000 (46,000—61,000) 20 (17—22) 7,200 (6,100—8,200) 61,000 (52,000—69,000) Minor restricted activity days (age 18-65) 320,000 (260,000—370,000) 120 (100—140) 42,000 (35,000—49,000) 360,000 (300,000—420,000)

**A All estimates rounded to two significant figures. 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 6a-5** 

Table 6a-3: Illustrative Strategy to Partially Atta in 0.070 ppm: Estimated Partial Attainment Value of Reductions in PM 2.5 -Related Premature Mortality Associated with PM co-benefit (3 percent discount rate, in millions of 1999$) 95 th Percentile Confidence Intervals Provided in Parent heses

Eastern U.S. Western U.S. Excluding California California National PM cobenefits Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study A $2,900 ($410--$6,700) $1 ($0.14--$2.2) $270 ($38--$610) $3,200 ($450--$7,300) Harvard Six-City Study B $6,600 ($1,100--$14,000) $2.1 ($0.35--$4.6) $610 ($98--$1,300) $7,200 ($1,200--$15,000) Woodruff et al 1997 (infant mortality) $6.3 ($1--$14) $0.2 ($0.03--$0.5) $0.8 ($0.12--$1.7) $7.3 ($1.1--$16) Mortality Impact Functions Derived from Expert Elic itation Expert A $9,100 ($810--$23,000) $440 ($51--$1,100) $830 ($75--$2,100) $10,000 ($930--$26,000) Expert B $6,900 ($470--$21,000) $320 ($16--$1,000) $630 ($42--$2,000) $7,900 ($520--$24,000) Expert C $6,800 ($620--$21,000) $340 ($38--$990) $630 ($57--$1,900) $7,800 ($710--$23,000) Expert D $4,800 ($500--$11,000) $230 ($30--$550) $440 ($46--$1,000) $5,400 ($570--$13,000) Expert E $11,000 ($1,800--$25,000) $550 ($120--$1,200) $1,000 ($160--$2,300) $13,000 ($2,000--$28,000) Expert F $6,200 ($1,100--$14,000) $290 ($67--$630) $570 (100--$1,200) $7,100 ($1,300--$15,000) Expert G $4,000 (0--$11,000) $200 (0--$530) $370 (0--$990) $4,600 (0--$12,000) Expert H $5,100 ($11--$16,000) $250 (0.7--$770) $470 ($1--$1,500) $5,800 ($12--$18,000) Expert I $6,800 ($570--$17,000) $330 ($35--$840) $620 ($53--$1,600) $7,700 ($650--$19,000) Expert J $5,500 ($700--$17,000) $270 ($44--$810) $500 ($64--$1,500) $6,200 ($790--$19,000) Expert K $1,100 (0--$6,500) $51 (0--$320) $110 (0--$650) $1,300 (0--$7,500) Expert L $5,000 ($440--$13,000) $190 ($0.2--$640) $460 ($39--$1,200) $5,700 ($480--$15,000)

**A The estimate is based on the concentration-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 incorpora** **ting what has previously been a sensitivity estimate.**  **C All estimates rounded to two si**  **g nificant fi**  **g ures. As such**  **, confidence intervals ma**  **y not be s**  **y mmetrical and totals will not sum across columns. A**  **ll estimates incremental to 2006 PM NAA Q**  **S RIA 6a-6** 

Table 6a-4: Illustrative Strategy to Partially Atta in 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 1999$) 95 th Percentile Confidence Intervals Provided in Parent heses

Eastern U.S. Western U.S. Excluding California California National PM cobenefits Mortality Impact Functions Derived from Epidemiolog y Literature ACS Study A $2,500 ($350--$5,600) $0.8 (0.1--$1.9) $230 ($32--$520) $2,700 ($380--$6,100) Harvard Six-City Study B $5,600 ($890--$12,000) $1.8 ($0.3--$3.9) $510 ($82--$1,100) $6,100 ($980--$13,000) Woodruff et al 1997 (infant mortality) $5.3 ($0.81--$12) $0.2 ($0.03--$0.38) $0.7 ($0.1--$1.4) $6.2 ($0.9--$14) Mortality Impact Functions Derived from Expert Elic itation Expert A $7,700 ($880--$19,000) $370 ($43--$940) $700 ($81--$1,800) $8,700 ($780--$22,000) Expert B $5,800 ($510--$18,000) $270 ($13--$870) $530 ($46--$1,700) $6,600 ($440--$21,000) Expert C $5,800 ($660--$17,000) $280 ($32--$830) $530 ($61--$1,600) $6,600 ($600--$19,400) Expert D $4,000 ($520--$9,500) $200 ($26--$470) $370 ($48--$880) $4,600 ($480--$11,000) Expert E $9,500 ($2,000--$21,000) $470 ($98--$1,000) $870 ($180--$1,900) $11,000 ($1,700--$24,000) Expert F $5,300 ($1,300--$11,000) $250 ($57--$530) $480 ($120--$1,000) $6,000 ($1,100--$13,000) Expert G $3,400 (0--$9,100) $160 (0--$440) $310 (0--$830) $3,800 (0--$10,00) Expert H $4,300 ($12--$13,000) $210 ($0.6--$650) $390 ($1.1--$1,200) $4,900 ($10--$15,000) Expert I $5,900 ($600--$14,000) $290 ($30--$700) $540 ($55--$1,300) $6,700 ($550--$16,000) Expert J $4,600 ($750--$14,000) $230 ($37--$680) $420 ($69--$1,300) $5,300 ($670--$16,000) Expert K $920 (0--$5,500) $43 (0--$270) $93 (0--$550) $1,100 (0--$6,300) Expert L $4,200 ($460--$11,000) $160 ($0.16--$540) $380 ($42--$1,000) $4,700 ($400--$13,000)**6a-7**

Table 6a-5: Illustrative Strategy to Partially Attain 0.070 ppm: Estimated Partial Attainment Monetary Value of Reductions in Risk of PM 2.5 - Related Morbidity Reductions Associated with PM co-benefit (in millions of 1999$) 95 th Percentile Confidence Intervals Provided in Parentheses

Eastern U.S. Western U.S. Excluding California California National PM co-benefits Morbidity Impact Functions Derived from Epidemiolog y Literature
Chronic Bronchitis (age >25 and over) $160 ($8.7--$720) $4.7 ($0.3--$22) $17 ($1--$80) $180 ($10--$820)
Nonfatal myocardial infarction (age >17) $93 ($25--$200) $0.03 ($0.01--$0.7) $8 ($2.1--$17) $100 ($27--$220)
Hospital admissions--respiratory (all ages) $90 ($23--$200) $0.03 ($0.01--$0.7) $7.7 ($2--$17) $98 ($25--$220) Hospital admissions-- cardiovascular (age >17) $2.1 ($1--$3.1) --- $0.2 ($0.08--$0.2) $2.3 ($1.1--$3.4) Emergency room visits for asthma (age <19) $5.5 ($3.4--$7.5) --- $0.4 ($0.3--$0.57) $5.9 ($3.7--$8.1)

Acute bronchitis (age 8-12) $0.2 ($0.04--$0.3) --- --- $0.2 ($0.09--$0.26)
Lower respiratory symptoms (age 7-14) $0.4 ($-0.02--$1) --- $0.05 ($-0.002--$0.1) $0.4 ($-0.02--$1.2)
Upper respiratory symptoms (asthmatic children age 9-18) $0.1 ($0.04--$0.27) --- $0.02 ($0.006--$0.04) $0.15 ($0.05--$0.3) Asthma exacerbation (asthmatic children age 6--18) $0.2 ($0.03—0.38) --- $0.022 ($0.005--$0.05) $0.2 ($0.04--$0.44)
Work loss days (age 18-65) $0.34 ($0.03--$1.3) --- $0.05 ($0.004--$0.18) $0.4 ($0.03--$1.5) Minor restricted activity days (age 18-65) $5.3 ($4.6--$6) --- $0.9 ($0.7--$1) $7.4 ($6.4--$8.3) **A All estimates rounded to two significant figures. As such, co**  **nfidence intervals may not be symmetrical and totals wil l not sum across**  **columns. All estimates incremental to 2006 PM NAAQS**6b-1

Appendix Chapter 6b: Health-Based Cost-Effectivenes s of Reductions in Ambient PM 2.5 Associated with Illustrative Ozone NAAQS 0.070ppm Attainment Strategy

6b.1 Summary
Health-based cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) have been used to analyze numerous health interventions but have n ot been widely adopted as tools to analyze environmental policies. The Office of Management and Budget (OMB) recently issued Circular A-4 guidance on regulatory analyses, requiring fede ral agencies to “prepare a CEA for all major rulemakings for which the primary benefits are impr oved public health and safety to the extent that a valid effectiveness measure can be developed to represent expected health and safety outcomes.” Environmental quality improvements may have multiple health and ecological benefits, making application of CEA more difficult and less straightforward. For the Ozone NAAQS, CEA may provide a useful framework for evalu ation: non-health benefits are substantial, but the majority of quantified benefit s come from health effects. Therefore, EPA is including in the Ozone NAAQS RIA a preliminary and experimental application of one type of CEA—a modified quality-adjusted life-years (QALYs) approach.
This cost effectiveness analysis considers the PM 2.5 benefits resulting from the illustrative ozone control strategies only. Estimation of QALY or Morb idity Inclusive Life Year (MILY, discussed below) impacts associated with reducing ozone conce ntrations is difficult for several reasons. First, with the exception of premature death, the s et of ozone-related health endpoints includes only acute diseases and impacts. As discussed belo w, there are a number of reasons that the QALY method is not appropriate for valuing acute he alth effects. Second, calculation of QALY or MILY impacts for premature mortality is complicated by a lack of information, including the change in life expectancy associated with the risk reduction (for MILYs and QALYs) and the baseline quality of life for individuals experienci ng the risk reduction (for QALY calculations). The EPA has recently asked the National Academies o f Sciences 1 for advice on characterizing the mortality risk reduction benefits of reducing o zone concentrations. In their evaluation, the NAS Committee on Estimating Mortality Risk Reductio n Benefits from Decreasing Tropospheric Ozone Exposure will provide advice on, among other topics, the adequacy of a basis for estimating the likely impact on life expe ctancy from reductions in short-term daily exposures to ozone. If there is an adequate basis, they will, to the extent practicable, estimate the magnitude and associated uncertainties of this impa ct. While awaiting the recommendations of the NAS committee, EPA is electing to not calculate QALY or MILY impacts for ozone related health effects for this proposal RIA. EPA will inv estigate the feasibility of performing such an analysis for the final RIA. As a result, the overa ll $/MILY estimates for attainment of alternative ozone NAAQS reported in this appendix will overstat e the expected $/MILY incorporating ozone effects.

1 National Academy of Sciences (2007) Project Scope . Estimating Mortality Risk Reduction Benefits from Decreasing Tropospheric Ozo ne Exposure. Division on Earth and Life Studies, Board on Environmental Studies and Toxicol ogy. Available at: http://www8.nationalacademies.org/cp/projectview.as px?key=487686b-2

QALYs were developed to evaluate the effectiveness of individual medical treatments, and EPA is still evaluating the appropriate methods for CEAfor environmental regulations. Agency concerns with the standard QALY methodology include the treatment of people with fewer years to live (the elderly); fairness to people with pree xisting conditions that may lead to reduced life expectancy and reduced quality of life; and how the analysis should best account for non-health benefits, such as improved visibility.
The Institute of Medicine (a member institution of the National Academies of Science) established the Committee to Evaluate Measures of H ealth 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 p roposed in CEA. This committee prepared a report titled “Valuing Health for Regulatory Cost-E ffectiveness Analysis” which concluded that CEA is a useful tool for assessing regulatory interventions to promote human health and safety, although not sufficient for informed regulatory dec isions (Miller, Robinson, and Lawrence, 2006). They emphasized the need for additional dat a and methodological improvements for CEA analyses, and urged greater consistency in the reporting of assumptions, data elements, and analytic methods. They also provided a number of r ecommendations for the conduct of regulatory CEA analyses. EPA is evaluating these r ecommendations and will determine a response for upcoming analyses. For this analysis, we use the same approach that was applied in the CEA that accompanied the RIA’s for the Clean Ai r Interstate Rule and the PM NAAQS.
The methodology presented in this appendix is not i ntended to stand as precedent either for future air pollution regulations or for other EPA r egulations where it may be inappropriate. It is intended solely to demonstrate one particular appro ach to estimating the cost-effectiveness of reductions in ambient PM 2.5 in achieving improvements in public health. Reduc tions in ambient PM 2.5 likely will have other health and environmental be nefits that will not be reflected in this CEA. Other EPA regulations affecting other aspects of environmental quality and public health may require additional data and models that may pre clude the development of similar healthbased CEAs. A number of additional methodological issues must be considered when conducting CEAs for environmental policies, includi ng treatment of nonhealth effects, aggregation of acute and long-term health impacts, and aggregation of life extensions and quality-of-life improvements in different populatio ns. The appropriateness of health-based CEA should be evaluated on a case-by-case basis subject to the availability of appropriate data and models, among other factors.
Attainment of the revised Ozone NAAQS is expected t o result in substantial reductions in potential population exposure to ambient concentrat ions of PM by 2020. The benefit-cost analysis presented in the RIA shows that partial attainment of the revised 0.070 ppm ozone standard achieves substantial health benefits whose monetized value is roughly equal to costs (net benefits are between -$6B and $4.1B). Despite the risk of oversimplifying benefits, cautiously-interpreted cost-effectiveness calculati ons may provide further evidence of whether the costs associated with attainment strategies for the Ozone NAAQS are a reasonable health investment for the nation.
This analysis provides estimates of commonly used h ealth-based effectiveness measures, including lives saved, life years saved (from reduc tions in mortality risk), and QALYs saved (from reductions in morbidity risk) associated withthe reduction of ambient PM 2.5 due to6b-3

illustrative attainment strategies for a more strin gent annual ozone standard. In addition, we use an alternative aggregate effectiveness metric, Morb idity Inclusive Life Years (MILY) to address some of the concerns about aggregation of life exte nsion and quality-of-life impacts. It represents the sum of life years gained due to redu ctions in premature mortality and the QALY gained due to reductions in chronic morbidity. Thi s measure may be preferred to existing QALY aggregation approaches because it does not devalue life extensions in individuals with preexisting illnesses that reduce quality of life. However, the MILY measure is still based on life years and thus still inherently gives more weight t o interventions that reduce mortality and morbidity impacts for younger populations with high er remaining life expectancy. This analysis focuses on life extensions and improvements in qual ity of life through reductions in two diseases with chronic impacts: chronic bronchitis (CB) and nonfatal acute myocardial infarctions. Monte Carlo simulations are used to propagate uncertaintyin several analytical parameters and characterize the distribution of estimated impacts. While the benefit-cost analysis presented in the RIA characterizes mortality impacts using a number of different sources for the PM mortality effect estimate, for this analysis, we focus on the mortality results generated using the effect estimates derived from the Pope et al. (2002) and L aden et al. (2005) studies.
Presented in three different metrics, the analysis suggests the following:

x In 2020 the illustrative attainment strategy for th e revised 0.070 ppm standards will result in:

– Between 550 (95% CI: 215 – 890) and 1,300 (95% CI: 680 – 1,800) premature deaths avoided using the Pope (2002) and Laden (200 6) studies, respectively, or

– Between 6,100 (95% CI: 2,400 – 9,800) and 14,000 ( 95% CI: 7,500 – 20,000) life years gained (discounted at 3 percent) using the Pope (2002) and Laden (2006) studies, respectively, or

– Between 9,100 (95% CI: 3,100 – 16,000) and 17,000 (95% CI: 8,200 – 27,000) MILYs gained (discounted at 3 percent) using the Po pe (2002) and Laden (2006) studies, respectively.

x Using a 7 percent discount rate, mean discounted li fe years gained are between 4,600 and 10,400 using the Pope (2002) and Laden (2006) studi es, respectively; mean MILYs gained are 6,800 and 13,000 using the two studies ( The estimates of premature deaths avoided are not affected by the discount rate.)

x The associated reductions in CB and nonfatal acute myocardial infarctions will reduce medical costs by approximately $140 million based o n a 3 or 7 percent discount rate.
Direct private compliance costs for the 0.070 ppm p artial attainment strategy, are $3.9 billion in 2020. Based on these costs, the incremental cost ef fectiveness (net of cost of illness and other health and visibility benefits) of the 0.070 ppm partial attainment strategy is $430,000/MILY using a 3 percent discount rate and $580,000/MILY u sing a 7 percent discount rate if one calculates MILY’s using the Pope (2002) mortality e stimate. The incremental cost effectiveness (again, net of cost of illness and other health and visibility benefits) of the 0.070 ppm partial attainment strategy is $230,000/MILY using a 3 perc ent discount rate and $310,000/MILY using6b-4

a 7 percent discount rate if one calculates MILY’s using the Laden (2006) mortality estimate. See Chapters 3 and 5 of this RIA for more discussio n of the control strategies and cost estimates.

6b.2 Introduction
Analyses of environmental regulations have typicall y used benefit-cost analysis to characterize impacts on social welfare. Benefit-cost analyses a llow for aggregation of the benefits of reducing mortality risks with other monetized benef its of reducing air pollution, including acute and chronic morbidity, and nonhealth benefits such as improved visibility. One of the great advantages of the benefit-cost paradigm is that a w ide range of quantifiable benefits can be compared to costs to evaluate the economic efficien cy 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 benefi t (e.g., lives or life years saved). CUA is a special type of CEA using preference-based measures of effectiveness, such as QALYs.
CEA and CUA are most useful for comparing programs that have similar goals, for example, alternative medical interventions or treatments that can save a life or cure a disease. They are less readily applicable to programs with multiple c ategories of benefits, such as those reducing ambient air pollution, because the cost-effectivene ss calculation is based on the quantity of a single benefit category. In other words, we cannot readily convert improvements in nonhealth benefits such as visibility to a health metric suchas 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 accept ed in the environmental economics literature because of concerns about the theoretica l consistency of QALYs with individual preferences (Hammitt, 2002), treatment of nonhuman health benefits, and a number of other factors (Freeman, Hammitt, and De Civita, 2002). F or environmental regulations, benefit-cost analysis has been the preferred method of choosing among regulatory alternatives in terms of economic efficiency. Recently several academic ana lyses have proposed the use of life yearsbased benefit-cost or CEAs of air pollution regulat ions (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).
Recently, the U.S. OMB (Circular A-4, 2003) issued new guidance requiring federal agencies to provide both CEA and benefit-cost analyses for majo r regulations. The OMB Circular A-4 directs agencies to “prepare a CEA for all major ru lemakings for which the primary benefits are improved public health and safety to the extent that a valid effectiveness measure can be developed to represent expected health and safety o utcomes.” We are including a CEA for the illustrative PM NAAQS attainment strategies to illu strate one potential approach for conducting a CEA. EPA is still evaluating the appropriate methods for CEA for environmental regulations with multiple outcomes.6b-5

The methodology presented in this appendix is not i ntended to stand as precedent either for future air pollution regulations or for other EPA r egulations governing water, solid waste, or other regulatory objectives. It is intended solelyto demonstrate one particular approach to estimating the effectiveness of reductions in ambie nt PM 2.5 in achieving improvements in public health. This analysis focuses on effectiveness mea sured by improvements in life expectancy and reductions in the incidence of two diseases with chronic impacts on quality of life: CB and nonfatal acute myocardial infarctions. Other EPA r egulations affecting other aspects of environmental quality and public health may require additional data and models that may preclude the development of similar QALY-based anal yses. The appropriateness of QALYbased CEA should be evaluated on a case-by-case bas is subject to the availability of appropriate data and models.
Preparation of a CEA requires identification of an appropriate measure of rule effectiveness. Given the significant impact of reductions in ambie nt PM 2.5 on reductions in the risk of mortality, lives saved is an important measure of e ffectiveness. However, one of the ongoing controversies in health impact assessment regards whether reductions in mortality risk should be reported and valued in terms of statistical lives s aved 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, un der the life years approach, older individuals will gain fewer life years than younger individuals for the same reduction in mortality risk during a given time period, making interventions that bene fit older individuals seem less beneficial relative to similar interventions benefiting younge r individuals. A further complication in the debate is whether to apply quality adjustments to l ife years lost. Under this approach, individuals with preexisting health conditions woul d have fewer QALYs lost relative to healthy individuals for the same loss in life expectancy, m aking interventions that primarily benefit individuals with poor health seem less beneficial t o similar interventions affecting primarily healthy individuals.
In addition to substantial mortality risk reductionbenefits, strategies for attaining the revised PM NAAQS will also result in significant reductions inchronic and acute morbidity. Several approaches have been developed to incorporate both morbidity and mortality into a single effectiveness metric. The most common of these is the QALY approach, which expresses all morbidity and mortality impacts in terms of qualityof life multiplied by the duration of time with that quality of life. The QALY approach has some appealing characteristics. For example, it can account for morbidity effects as well as losses in life expectancy without requiring the assignment of dollar values to calculate total bene fits. By doing so it provides an alternative framework to benefit-cost analysis for aggregating quantitative measures of health impacts.
While used extensively in the economic evaluation o f medical interventions (Gold et al., 1996), QALYs have not been widely used in evaluating envir onmental health regulations. A number of specific issues arise with the use of QALYs in eval uating environmental programs that affect a broad and heterogeneous population and that provide both health and nonhealth benefits. The U.S. Public Health Service report on cost-effective ness in health and medicine notes the following:
For decisions that involve greater diversity in in terventions and the people to whom they apply, cost-effectiveness ratios continue to p rovide essential information, but6b-6 that information must, to a greater degree, be evaluated in light of circumstances and values that cannot be included in the analysis. In dividuals in the population will differ widely in their health and disability beforethe intervention, or in age, wealth, or other characteristics, raising questions about h ow society values gains for the more and less health, for young and old, for rich a nd poor, and so on. The assumption that all QALYs are of equal value is les s likely to be reasonable in this context. (Gold et al., 1996, p. 11) Use of QALYs as a measure of effectiveness for envi ronmental regulations is still developing, and while this analysis provides one framework for using QALYs to evaluate environmental regulations, there are clearly many issues, both sc ientific and ethical, that need to be addressed with additional research. The Institute of Medicin e panel evaluating QALYs and other effectiveness measures prepared a report titled “Va luing Health for Regulatory CostEffectiveness Analysis” which concluded that “the Q ALY is the best measure at present on which to standardize Health Adjusted Life Year esti mation because of its widespread use, flexibility, and relative simplicity” (Miller, Robi nson, and Lawrence, 2006). EPA is evaluating this recommendation and will determine a response f or upcoming analyses. For this analysis, for reasons discussed in the text, we use the same MILYapproach that was applied in the CEA that accompanied the RIA for the Clean Air Interstate Ru le.
This appendix presents cost-effectiveness methodolo gies for evaluating programs such as attainment strategies for the revised Ozone NAAQS that are intended to reduce both ozone and PM 2.5 precursors, such as NOx and VOCs, starting from th e standard QALY literature and seeking a parallel structure to benefit-cost analys is in the use of air quality and health inputs (see Hubbell [2004a] for a discussion of some of the iss ues that arise in comparing QALY and benefit-cost frameworks in analyzing air pollution impacts). For the purposes of this analysis, we calculate effectiveness using several different metrics, including lives prolonged, life years gained, and modified QALYs. For the life years and QALY-type approaches, we use life table methods to calculate the change in life expectancy expected to result from changes in mortality risk from PM. We use existing estimates of prefere nces for different health states to obtain QALY weights for morbidity endpoints associated with air pollution. In general, consistent with the Gold et al. (1996) recommendations, we use weig hts obtained from a societal perspective when available. We explore several different sourc es for these weights to characterize some of the potential uncertainty in the QALY estimates. W e follow many of the principles of the reference case analysis as defined in Gold et al. ( 1996), although in some cases we depart from the reference case approach when data limitations r equire us to do so (primarily in the selection of quality-of-life weights for morbidity endpoints) . We also depart from the reference case (and the recommendations of the IOM report) in the metho d of combining life expectancy and quality-of-life gains.
Results in most tables are presented only at a disc ount rate of 3 percent, rather than at both 3 percent and 7 percent as recommended in EPA and OMB guidance. This is strictly for ease of presentation. Aggregate results at 7 percent are p resented in the summary, and the impact of using a 7 percent discount rate instead of 3 percent rate is summarized in a sensitivity analysis.
Monte Carlo simulation methods are used to propagat e uncertainty in several of the model parameters throughout the analysis. We characteriz e overall uncertainty in the results with 956b-7

percent confidence intervals based on the Monte Car lo simulations. In addition, we examine the impacts of changing key parameters, such as the dis count rate, on the effectiveness measures and the cost-effectiveness metrics.
The remainder of this appendix provides an overviewof the key issues involved in life year- and QALY-based approaches for evaluating the health imp acts of air pollution regulations, provides detailed discussions of the steps required for eachtype of effectiveness calculation, and presents the CEA for the PM NAAQS illustrative attainment strategies. Section G.3 introduces the various effectiveness measures and discusses some o f the assumptions required for each. Section G.4 details the methodology used to calculate chang es in life years and quality adjustments for mortality and morbidity endpoints. Section G.5 pro vides the results for the illustrative attainment strategies for the revised and more stri ngent alternative PM NAAQS and discusses their implications for cost-effectiveness of these attainment strategies.

6b.3 Effectiveness Measures
Three major classes of benefits are associated withreductions in air pollution: mortality, morbidity, and nonhealth (welfare). For the purpos es of benefit-cost analysis, EPA has presented mortality-related benefits using estimates of avoid ed premature mortalities, representing the cumulative result of reducing the risk of premature mortality from long-term exposure to PM 2.5 for a large portion of the U.S. population. Morbid ity benefits have been characterized by numbers of new incidences avoided for chronic disea ses such as CB, avoided admissions for hospitalizations associated with acute and chronic conditions, and avoided days with symptoms for minor illnesses. Nonhealth benefits are charac terized by the monetary value of reducing the impact (e.g., the dollar value of improvements in v isibility at national parks).
For the purposes of CEA, we focus the effectiveness measure on the quantifiable health impacts of the reduction in PM 2.5 . Treatment of nonhealth benefits is important and is discussed in some detail later in this section. If the main impact o f interest is reductions in mortality risk from air pollution, the effectiveness measures are relativel y straightforward to develop. Mortality impacts can be characterized similar to the benefit s analysis, by counting the number of premature mortalities avoided, or can be characteri zed in terms of increases in life expectancy or life years. 2 Estimates of premature mortality have the benefit of being relatively simple to calculate, are consistent with the benefit-cost ana lysis, and do not impose additional assumptions on the degree of life shortening. However, some ha ve argued that counts of premature mortalities avoided are problematic because a gain in life of only a few months would be

2 Life expectancy is an ex ante concept, indicating the impact on an entire population’s expectation of the number of life years they have r emaining, 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 p ollution. Changes in population life expectancy will always be substantially smaller than changes in life years per premature mortality avoided, although the total life years ga ined in the population will be the same. This is because life expectancy gains average expected life years gained over the entire population, while life years gained measures life years gained only for those experiencing the life extension.6b-8

considered equivalent to a gain of a many life year s, and the true effectiveness of an intervention is the gain in life expectancy or life years (Rabl, 2003; Miller and Hurley, 2003).
Calculations of changes in life years and life expe ctancy can be accomplished using standard life table methods (Miller and Hurley, 2003). However, the calculations require assumptions about the baseline mortality risks for each age cohort af fected by air pollution. A general assumption may be that air pollution mortality risks affect th e 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 respiratory disease, who may have reduced life expectancy relative to the general population. This issue is explored in more detail below.
Air pollution is also associated with a number of s ignificant 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 reductions in particular pollutants that have large morbidity impacts but no effect on life expec tancy. The QALY approach explicitly incorporates morbidity impacts into measures of lif e years gained and is often used in health economics to assess the cost-effectiveness of medic al 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 year spent in perfect health is equivalent to 2 years spent with quality of life half that of perfe ct health. QALYs can be used to evaluate environmental rules under certain circumstances, al though 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 us ing QALYs when evaluating medical and public health programs that primarily reduce both m ortality and morbidity (Gold et al., 1996). Although there are significant nonhealth benefits a ssociated with air pollution regulations, over 90 percent of quantifiable monetized benefits are h ealth-related, as is the case with the attainment strategies for the PM NAAQS. Thus, it c an be argued that QALYs are more applicable for these types of regulations than for other environmental policies. However, the value of nonhealth benefits should not be ignored. As discussed below, we have chosen to subtract the value of nonhealth benefits from the c osts in the numerator of the cost-effectiveness ratio.
In the following sections, we lay out a phased appr oach to describing effectiveness. We begin by discussing how the life-extending benefits of air p ollution reductions are calculated, and then we incorporate morbidity effects using the QALY approa ch. We also introduce an alternative aggregated health metric, Morbidity Inclusive Life Years (MILY) to address some of the ethical concerns about aggregating life extension impacts i n populations with preexisting disabling conditions.
The use of QALYs is predicated on the assumptions e mbedded in the QALY analytical framework. As noted in the QALY literature, QALYs are consistent with the utility theory that underlies most of economics only if one imposes seve ral restrictive assumptions, including independence between longevity and quality of life in the utility function, risk neutrality with respect to years of life (which implies that the ut ility function is linear), and constant proportionality in trade-offs between quality and quantity of life (Pliskin, Shepard, and6b-9

Weinstein, 1980; Bleichrodt, Wakker, and Johannesso n, 1996). To the extent that these assumptions do not represent actual preferences, th e QALY approach will not provide results that are consistent with a benefit-cost analysis ba sed on the Kaldor-Hicks criterion. 3 Even if the assumptions are reasonably consistent with reality, because QALYs represent an average valuation of health states rather than the sum of 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 p otentially troubling underlying structures as well, incorporating ability to pay (and thus the po tential for equity concerns) and the notion of consumer sovereignty (which emphasizes wealth effec ts). Table G-1 compares the two approaches across a number of parameters. For the most part, WTP allows parameters to be determined empirically, while the QALY approach imp oses some conditions a priori

. Table 6b-1: Comparison of QALY and WTP Approaches

Parameter QALY WTP Risk aversion Risk neutral Empirically determined Relation of duration and quality Independent Empiri cally 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

6b.4 Changes in Premature Death, Life Years, and Qu ality of Life
To generate health outcomes, we used the same frame work as for the benefit-cost analysis described in Chapter 6. For convenience, we summar ize the basic methodologies here. For more details, see Chapter 6 and the BenMAP user’s m anual (http://www.epa.gov/ttn/ecas/benmodels.html).
BenMAP uses health impact functions to generate changes in the incidence of health effects. Health impact functions are derived from the epidem iology literature. A standard health impact function has four components: an effect estimate f rom a particular epidemiological study, a baseline incidence rate for the health effect (obta ined from either the epidemiology study or a

3 The Kaldor-Hicks efficiency criterion requires that the “winners” in a particular case be potentially able to compensate the “losers” such th at 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 w orse off.6b-10

source of public health statistics like CDC), the a ffected population, and the estimated change in the relevant PM summary measure.
A typical health impact function might look like th is:

' ' y y e x  

 0 1 ( ) , E

where y 0 is the baseline incidence, equal to the baseline i ncidence rate times the potentially affected population;  is the effect estimate; and x is the estimated change in PM 2.5 . There are other functional forms, but the basic elements rema in the same.
6b.4.1 Calculating Reductions in Premature Deaths
As in several recent air pollution health impact as sessments (e.g., Kunzli et al., 2000; EPA, 2004), we focus on the prospective cohort long-termexposure studies in deriving the health impact function for the estimate of premature morta lity. Cohort analyses are better able to capture the full public health impact of exposure t o air pollution over time (Kunzli et al., 2001; NRC, 2002). We selected effects estimate from the extended analysis of the ACS cohort (Pope et al., 2002) as well as from the Harvard Six City Study (Laden et al., 2006). Given the focus in this analysis on developing a broader expression of uncertainties in the benefits estimates, and the weight that was placed on both the ACS and Harv ard Six-city studies by experts participating in the PM 2.5 mortality expert elicitation, we have elected to p rovide estimates 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) exte nding the follow-up period for the ACS study subjects to 16 years, which triples the size of the mortality data set; (b) substantially increasing exposure data, including consideration for cohort e xposure to PM 2.5 following implementation of PM 2.5 standard in 1999; (c) controlling for a variety of personal risk factors including occupational exposure and diet; and (d) using advan ced statistical methods to evaluate specific issues that can adversely affect risk estimates, in cluding 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 m ean 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 pe riod, calculated as the average of 1979–1984 and 1999–2000 PM 2.5 levels. The effect estimate from this study is 0. 0058, which is equivalent to a relative risk of 1.06 for a 10  g change in PM 2.5 . Very recently, a follow up to the Harvard 6-city study was published (Laden et al., 2006), that both confirmed the effect size from the first studyand provided additional confirmation that reductions in PM 2.5 directly result in reductions in the risk of premature death. This additional 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 r ates consistently went down at a rate proportionate to the observed reductions in PM 2.5 . The effect estimate obtained from the Laden et al is 0.0148, which is equivalent to a relative risk of 1.16 for a 10 ȝ g/m 3 change in PM 2.5 .6b-11

Age, cause, and county-specific mortality rates wer e obtained from CDC for the years 1996 through 1998. CDC maintains an online data reposit ory of health statistics, CDC Wonder, accessible at http://wonder.cdc.gov/. The mortality rates provided are derived from U.S. death records and U.S. Census Bureau postcensal populatio n estimates. Mortality rates were averaged across 3 years (1996 through 1998) to provide more stable estimates. When estimating rates for age groups that differed from the CDC Wonder groupi ngs, we assumed that rates were uniform across all ages in the reported age group. For exa mple, to estimate mortality rates for individuals ages 30 and up, we scaled the 25- to 34-year old de ath 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 premature mortality within each age group associated with the illustrative attainment strategies for the revised and more stringent alternative Ozone NAAQS in 2020 are summarized in Table G-2.
6b.4.2 Calculating Changes in Life Years from Direc t Reductions in PM 2.5 -Related Mortality Risk
To calculate changes in life years associated with a given change in air pollution, we used a life table approach coupled with age-specific estimates of reductions in premature mortality. We began with the complete unabridged life table for the United States in 2000, obtained from CDC (CDC, 2002). For each 1-year age interval (e.g., z ero to one, one to two) the life table provides estimates of the baseline probability of dying duri ng the interval, person years lived in the interval, and remaining life expectancy. From this unabridged life table, we constructed an abridged life table to match the age intervals for which we have predictions of changes in incidence of premature mortality. We used the abri dgement method described in CDC (2002). Table G-3 presents the abridged life table for 10-y ear age intervals for adults over 30 (to match the Pope et al. [2002] study population). Note that the abridgement actually includes one 5-year interval, covering adults 30 to 34, with the remain ing age intervals covering 10 years each. This is to provide conformity with the age intervals ava ilable for mortality rates.6b-12

Table 6b-2: Estimated Reduction in Incidence of All -cause Premature Mortality Associated

with Illustrative Attainment Strategies for the Rev ised and More Stringent Alternative Ozone NAAQS in 2020

Reduction in All-Cause Premature Mortality (95% CI) Age Interval Pope (2002) Laden (2006) 30 – 34 4 (2 – 6) 9 (5 – 13) 35 – 44 12 (5 – 20) 28 (15 – 40) 45 – 54 26 (10 – 42) 60 (32 – 87) 55 – 64 70 (27 – 110) 160 (86 – 230) 65 – 74 120 (48 – 200) 280 (150 – 400) 75 – 84 140 (56 – 230) 320 (180 – 470) 85+ 170 (68 – 280) 390 (210 – 570) Total 550 (220 – 890) 1,300 (680 – 1,800)

From the abridged life table (Table 6b-3), we obtai ned the remaining life expectancy for each age cohort, conditional on surviving to that age. This is then the number of life years lost for an individual in the general population dying during that age interval. This information can then be combined with the estimated number of premature deaths in each age interval calculated with BenMAP (see previous subsection). Total life years gained will then be the sum of life years gained in each age interval:

TotalLife Years LE M i i i N
u 

## ¦

1 ,

where LE iis the remaining life expectancy for age interval i , M iis the change in incidence of mortality in age interval i , and N is the number of age intervals.
For the purposes of determining cost-effectiveness, it is also necessary to consider the timedependent nature of the gains in life years. Stand ard economic theory suggests that benefits occurring in future years should be discounted relative to benefits occurring in the present. OMB and EPA guidance suggest discount rates of three an d seven percent. As noted earlier, we present gains in future life years discounted at 3 percent. Results based on 7 percent are included in the summary and the overall impact of a 7 percent rate is summarized in Table 6b-16. Selection of a 3 percent discount rate is also cons istent with recommendations from the U.S. Public Health Service Panel on Cost Effectiveness i n Health and Medicine (Gold et al., 1996).6b-13

Table 6b-3: Abridged Life Table for the Total Popul ation, United States, 2000

Age Interval Probability of

Dying Between Ages x to x+1 Number

Surviving to Age x Number

Dying Between Ages x to x+1 Person

Years Lived Between Ages x to x+1 Total

Number of Person Years Lived Above Age x Expectation

of Life at Age x

Start Age End

Age qx

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

Discounted total life years gained is calculated as follows:

Discounted LY e dt rt LE

 

## 3

, 0 where r is the discount rate, equal to 0.03 in this case, t indicates time, and LE is the life expectancy at the time when the premature death wou ld have occurred. Life years are further discounted to account for the lag between the reduc tion in ambient PM 2.5 and the reduction in mortality risk. We use the same 20-year segmented lag structure that is used in the benefit-cost analysis (see Chapter 6).
The most complete estimate of the impacts of 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 preexis ting illnesses is not supported by current empirical studies. For exampl e, the Krewski et al. (2000) ACS reanalysis suggests that the mortality risk is no greater for those with preexisting illness at time of6b-14

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 prevale nce of chronic disease increases.
The only reason one might use a lower life expectan cy 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 lif esaving rule aimed at a population that happens to experience a high rate of disability (i. e., where the rule is not designed to affect the disability), the number of life years saved should not necessarily be diminished simply because the rule saves lives of people with life-shorteningdisabilities. 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 ove r disability-specific life expectancies. Our primary life years calculations are thus consistentwith the concept of not penalizing individuals with disabling chronic health conditions by assessi ng them reduced benefits of mortality risk reductions.
For this analysis, direct impacts on life expectanc y are measured only through the estimated change in mortality risk based on the Pope et al. ( 2002) C-R function. The SAB-HES has advised against including additional gains in life expectancy due to reductions in incidence of chronic disease or nonfatal heart attacks (EPA-SAB- COUNCIL-ADV-04-002). Although reductions in these endpoints are likely to result in increased life expectancy, the HES has suggested that the cohort design and relatively lon g follow-up period in the Pope et al. study should capture any life-prolonging impacts associat ed with those endpoints. Impacts of CB and nonfatal heart attacks on quality of life will be c aptured separately in the QALY calculation as years lived with improved quality of life. The methods for calculating this benefit are discussed below.
6b.4.2.1 Should Life Years Gained Be Adjusted for I nitial Health Status?
The methods outlined above provide estimates of the total number of life years gained in a population, regardless of the quality of those life years, or equivalently, assuming that all life years gained are in perfect health. In some CEAs ( Cohen, Hammitt, and Levy, 2003; Coyle et al., 2003), analysts have adjusted the number of li fe years gained to reflect the fact that 1) the general public is not in perfect health and thus “h ealthy” life years are less than total life years gained and 2) those affected by air pollution may b e in a worse health state than the general population and therefore will not gain as many “hea lthy” life years adjusted for quality, from an air pollution reduction. This adjustment, which co nverts life years gained into QALYs, raises a number of serious ethical issues. Proponents of QA LYs have promoted the nondiscriminatory nature of QALYs in evaluating improvements in quali ty of life (e.g., an improvement from a score of 0.2 to 0.4 is equivalent to an improvementfrom 0.8 to 1.0), so the starting health status does not affect the evaluation of interventions that improve quality of life. However, for lifeextending interventions, the gains in QALY will be directly proportional to the baseline health state (e.g., an individual with a 30-year life expe ctancy and a starting health status of 0.5 will gain exactly half the QALYs of an individual with the same life expectancy and a starting health status of 1.0 for a similar life-extending intervention). This is troubling because it imposes an additional penalty for those already suffering fromdisabling conditions. Brock (2002) notes that6b-15

“the problem of disability discrimination represent s a deep and unresolved problem for resource prioritization.”
OMB (2003) has recognized this issue in their Circu lar 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 treatme nt of all segments of the population. Fairness is important in the choice an d execution of effectiveness measures. For example, if QALYs are used to evalua te a lifesaving rule aimed at a population that happens to experience a high rate o f disability (i.e., where the rule is not designed to affect the disability), the number of life years saved should not necessarily be diminished simply because the rule s aves 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 expectan cy or quality of life, analysts should prefer use of population averages rather tha n information derived from subgroups dominated by a particular demographic or income group. (p. 13) This suggests two adjustments to the standard QALY methodology: one adjusting the relevant life expectancy of the affected population, and the other affecting the baseline quality of life for the affected population.
In addition to the issue of fairness, potential mea surement 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 epide miological and toxicological studies link exposure to air pollution with chronic diseases, su ch as CB and atherosclerosis (Abbey et al., 1995; Schwartz, 1993; Suwa et al., 2002). If these same individuals with chronic disease caused by exposure to air pollution are then at increased risk of premature death from air pollution, there is an important dimension of “double jeopardy” invo lved in determining the correct baseline for assessing QALYs lost to air pollution (see Singer e t al. [1995] for a broader discussion of the double-jeopardy argument).
Analyses estimating mortality from acute exposures that ignore the effects of long-term exposure on morbidity may understate the health impacts of r educing air pollution. Individuals exposed to chronically elevated levels of air pollution may re alize an increased risk of death and chronic disease throughout life. If at some age they contr act heart (or some other chronic) disease as a result of the exposure to air pollution, they will from that point forward have both reduced life expectancy and reduced quality of life. The benefi t to that individual from reducing lifetime exposure to air pollution would be the increase in life expectancy plus the increase in quality of life over the full period of increased life 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-te rm exposure to elevated air pollution, then the person is placed in double jeopardy. In other word s, 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 pol lution. If the risk of chronic disease and risk6b-16

of death are considered together, then there is no conceptual 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 current benefit-cost approaches as well).

Because of the fairness and measurement concerns di scussed above, for the purposes of this analysis, we do not reduce the number of life years gained to reflect any differences in underlying health status that might reduce quality of life in remaining years. Thus, we maintain the assumption that all direct gains in life years resulting from mortality risk reductions will be assigned a weight of 1.0. The U.S. Public Health S ervice Panel on Cost Effectiveness in Health and Medicine recommends that “since lives saved or extended by an intervention will not be in perfect health, a saved life year will count as les s than 1 full QALY” (Gold et al., 1996). However, for the purposes of this analysis, we prop ose 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 inquality of 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 adjustments 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.

6b.5 Calculating Changes in the Quality of Life Yea rs (Morbidity)
In addition to directly measuring the quantity of l ife gained, measured by life years, it may also be informative to measure gains in the quality of l ife. Reducing air pollution also leads to reductions in serious illnesses that affect qualityof life. These include CB and cardiovascular disease, for which we are able to quantify changes in the incidence of nonfatal heart attacks. To capture these important benefits in the measure of effectiveness, they must first be converted into a life-year equivalent so that they can be combined with the direct gains in life expectancy.
For this analysis, we developed estimates of the QA LYs gained from reductions in the incidence of CB and nonfatal heart attacks associated with re ductions in ambient PM 2.5 . In general, QALY calculations require four elements:
1. the estimated change in incidence of the health condition, 2. the duration of the health condition, 3. the quality-of-life weight with the health condi tion, and 4. the quality-of-life weight without the health co ndition (i.e., the baseline health state). The first element is derived using the health impac t function approach. The second element is based on the medical literature for each health con dition. The third and fourth elements are derived from the medical cost-effectiveness and cos t-utility literature. In the following two subsections, we discuss the choices of elements for CB and nonfatal heart attacks.6b-17

The preferred source of quality-of-life weights are those based on community preferences, rather than patient or clinician ratings (Gold et al., 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). O nly 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 fo r individual specific health states or for a more general set of activity restrictions and health states that can then be used to construct QALY weights for specific conditions (Horsman et al., 20 03; Kind, 1996). For this analysis, we used weights based on community-based preferences, using time trade-off or standard gamble when available. In some cases, we used patient or clini cian ratings when no community preferencebased weights were available. Sources for weights are discussed in more detail below. Table G-4 summarizes the key inputs for calculating QALYs ass ociated with chronic health endpoints.
6b.5.1 Calculating QALYs Associated with Reductions in the Incidence of Chronic Bronchitis
CB is characterized by mucus in the lungs and a per sistent wet cough for at least 3 months a year for several years in a row. CB affects an estimate d 5 percent of the U.S. population (American Lung Association, 1999). For gains in quality of l ife resulting 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 iis the number of incidences of CB avoided in age i nterval i, w iis the average QALY weight for age interval i, w i CB is the QALY weight associated with CB, D i * is the discounted
duration of life with CB for individuals with onset of disease in age interval i, equal to 

### e d t rt

t D i

 

# 3

1 , where D i is the duration of life with CB for individuals with onset of disease in age
interval i.
A limited number of studies have estimated the impa ct of air pollution on new incidences of CB. Schwartz (1993) and Abbey et al. (1995) provide evi dence that long-term PM exposure gives rise to the development of CB in the United States. Because this analysis focuses on the impacts of reducing ambient PM 2.5 , only the Abbey et al. (1995) study is used, becau se it is the only study focusing on the relationship between PM 2.5 and new incidences of CB. The number of cases of CB in each age interval is derived from ap plying the impact function from Abbey et al. (1995), to the population in each age interval withthe appropriate baseline incidence rate. 4 The effect estimate from the Abbey et al. (1995) study is 0.0137, which, based on the logistic

4 Prevalence rates for CB were obtained from the 199 9 National Health Interview Survey (American Lung Association, 2002). Prevalence rate s were available for three age groups: 18– 44, 45–64, and 65 and older. Prevalence rates per person for these groups were 0.0367 for 18– 44, 0.0505 for 45–64, and 0.0587 for 65 and older. The incidence rate for new cases of CB (0.00378 per person) was taken directly from Abbey et al. (1995).6b-18

specification of the model, is equivalent to a relative risk of 1.15 for a 10  g change in PM 2.5 . Table G-5 presents the estimated reduction in new i ncidences of CB associated with the illustrative PM NAAQS attainment strategies.
Table 6b-4: Summary of Key Parameters Used in QALY Calculations for Chronic Disease

Endpoints

Parameter Value(s) Source(s) Discount rate 0.03 (0.07 sensitivity analysis) Gold et al. (1996), U.S. EPA (2000), U.S. OMB (2003 )

Quality of life preference score for chronic bronchitis 0.5 – 0.7 Triangular distribution centered at 0.7 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 bo und based on Vos (1999b). Probability of CHF post AMI 0.2 Vos, 1999a (WHO Burden of Disease Study, based o n Cowie et al., 1997) Probability of angina post AMI 0.51 American Heart Association, 2003 (Calculated as the population with angina divided b y 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 a t 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 a t 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 at0.7, 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).

CB is assumed to persist for the remainder of an af fected 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 subtract 4.26 from the remaining life expectancy for each age group, up to age 75. For age groups over 75, we apply the ratio of 4.26 to the life expectancy for the 65 to 74 year group (0.237) to the life expectancy6b-19

for the 75 to 84 and 85 and up age groups to estimate potential life years lost and then subtract that value from the base life expectancy.

Table 6b-5: Estimated Reduction in Incidence of Chr onic Bronchitis Associated with Illustrative

Attainment Strategies for the Revised and More Stri ngent Alternative PM NAAQS in 2020

Reduction in Incidence (95% Confidence Interval) Age Interval 070 ppm Partial Attainment Strategy 25 – 34 74 (14 – 140) 35 – 44 85 (16 – 160) 45 – 54 82 (15 – 150) 55 – 64 88 (16 – 160) 65 – 74 62 (12 – 640) 75 – 84 31 (6 – 56) 85+ 14 (3 – 25) Total 440 (80 – 790)

Quality of life with chronic lung diseases has beenexamined in several studies. In an analysis of the impacts of environmental exposures to contaminants, de Hollander et al. (1999) assigned a weight of 0.69 to years lived with CB. This weight was based on physicians’ evaluations of health states similar to CB. Salomon and Murray (2 003) estimated a pooled weight of 0.77 based on visual analogue scale, time trade-off, standard gamble, and person trade-off techniques applied to a convenience sample of health professio nals. The Harvard Center for Risk Analysis catalog of preference scores reports a weight of 0. 40 for severe COPD, with a range from 0.2 to 0.8, based on the judgments of the study’s authors (Bell et al., 2001). The Victoria Burden of Disease (BoD) study used a weight of 0.47 for sever e COPD and 0.83 for mild to moderate COPD, based on an analysis by Stouthard et al. (199 7) 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 i nterventions affecting broad populations. Use of weights based on health professionals is notrecommended. It is not clear from the Victoria BoD study whether the weights used for COP D are based on community preferences or judgments of health professionals. The Harvard cat alog score is clearly identified as based on author judgment. Given the lack of a clear preferr ed weight, we select a triangular distribution centered at 0.7 with an upper bound at 0.9 (slightl y better than a mild/moderate case defined by the Victoria BoD study) and a lower bound at 0.5 ba sed on the Victoria BoD study. We will need additional empirical data on quality of life w ith chronic respiratory diseases based on community preferences to improve our estimates.6b-20

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 perf ect health; however, there is some uncertainty as to whether individuals’ ratings of health states ar e in reference to a perfect health state or to a generally achievable “normal” health state given ag e and general health status. The U.S. Public Health Service Panel on Cost Effectiveness in Health and Medicine recommends that “since lives saved or extended by an intervention will not be in perfect health, a saved life year will count as less than 1 full QALY” (Gold et al., 1996) . Following Carrothers, Evans, and Graham (2002), we assumed that the reference weight for th e general population without CB is 0.95. To allow for uncertainty in this parameter, we assigne d a triangular distribution 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 improv ement applied to the duration of life that would have been lived with the chronic disease. Fo r example, if CB has a quality-of-life weight of 0.7 relative to a reference quality-of-life weig ht of 0.9, then the incremental quality-of-life improvement in 0.2. If the reference quality-of-li fe weight is 0.95, then the incremental qualityof-life improvement is 0.25. As noted above, the p opulation is assumed to have a reference weight of 1.0 for all life years gained due to mort ality risk reductions.
We present discounted QALYs over the duration of th e lifespan with CB using a 3 percent discount rate. Based on the assumptions defined ab ove, 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. 5 Based on the assumptions defined above, the mean 3 percent discounted QALY gained pe r incidence of CB for each age interval along with the 95 percent confidence interval resul ting from the Monte Carlo simulation is presented in Table G-6. Table G-6 presents both th e undiscounted and discounted QALYs gained per incidence.

5 Monte Carlo simulation uses random sampling from d istributions of parameters to characterize the effects of uncertainty on output variables. Fo r more details, see Gentile (1998).6b-21

Table 6b-6: QALYs Gained per Avoided Incidence of C B

Age Interval QALYs Gained per Incidence Start Age End Age Undiscounted Discounted (3%) 25 34 12.15 (4.40-19.95) 6.52 (2.36-10.71) 35 44 9.91 (3.54-16.10) 5.94 (2.12-9.66) 45 54 7.49 (2.71-12.34) 5.03 (1.82-8.29) 55 64 5.36 (1.95-8.80) 4.03 (1.47-6.61) 65 74 3.40 (1.22-5.64) 2.84 (1.02-4.71) 75 84 2.15 (0.77-3.49) 1.92 (0.69-3.13) 85+ 0.79 (0.27-1.29) 0.77 (0.26-1.25)

6b.5.2 Calculating QALYs Associated with Reductions in the Incidence of Nonfatal Myocardial Infarctions
Nonfatal heart attacks, or acute myocardial infarct ions, require more complicated calculations to derive estimates of QALY impacts. The actual heartattack, 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. M any heart attacks result in sudden death. However, for survivors, the long-term impacts of advanced CHD are potentially of long duration and can result in significant losses in quality of life and life expectancy.
In this phase of the analysis, we did not independe ntly estimate 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 are captured in the estimates of reduced mortality risk provided by the Pope et al. (2002) analysis. We only estimate the change in quality of life over the period of life affected bythe 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 pote ntial life-year gains.
Our approach adapts a CHD model developed for the V ictoria 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 G -1 shows the heart attack QALY model in diagrammatic form.
The total gain in QALYs is calculated as:6b-22

D ISCOUNTED A M

I 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 iis the number of nonfatal acute myocardial infarct ions avoided in age interval i ,

w i AMI is the QALY weight associated with the acute phase of the AMI, p j is the probability of being in the jth post-AMI status, w ij postAMI is the QALY weight associated with post-AMI health

status j , w iis the average QALY weight for age interval i, D e dt i AMI rt

t D i AMI *

 

## 3

1 , the discounted
value of Di

AMI , the duration of the acute phase of the AMI, and D e dt i postAMI rt

t D i postAMI *

 

## 3

1 , is the
discounted value of Dij PostAMI , the duration of post-AMI health status j

.

Figure 6b-1. Decision Tree Used in Modeling Gains i n QALYs from Reduced Incidence of

Nonfatal Acute Myocardial Infarctions 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 Stage Deleted: <sp>6b-23

Nonfatal heart attacks have been linked with short- term exposures to PM 2.5 in the United States (Peters et al., 2001) and other countries (Poloniec ki et al., 1997). We used a recent study by Peters et al. (2001) as the basis for the impact function 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 cardiova scular hospital admissions, including for nonfatal heart attacks, and PM. Given the lasting impact of a heart attack on longer-term health costs and earnings, we chose to provide a separate estimate for nonfatal heart attacks based on the single available U.S. effect estimate. The fin ding of a specific 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. Severa l epidemiologic studies (Liao et al., 1999; Gold et al., 2000; Magari et al., 2001) have shown that heart rate variability (an indicator of how much the heart is able to speed up or slow down in respo nse to momentary stresses) is negatively related to PM levels. Heart rate variability is a risk factor for heart attacks and other CHDs (Carthenon et al., 2002; Dekker et al., 2000; Liao et al., 1997, Tsuji et al., 1996). As such, significant impacts of PM on heart rate variabilityare consistent with an increased risk of heart attacks.
The number of avoided nonfatal AMI in each age inte rval is derived from applying the impact function from Peters et al. (2001) to the populatio n in each age interval with the appropriate baseline incidence rate. 6 The effect estimate from the Peters et al. (2001) study is 0.0241, which, based on the logistic specification of the model, i s equivalent to a relative risk of 1.27 for a 10  g change in PM 2.5 . Table 6b-7 presents the estimated reduction in n onfatal AMI associated with the illustrative Ozone NAAQS attainment strategies.

6 Daily nonfatal myocardial infarction incidence rat es 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 analysis. 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.6b-24

Table 6b-7: Estimated Reduction in Nonfatal Acute M yocardial Infarctions Associated with

Illustrative Attainment Strategies for the Revised and More Stringent Alternative PM NAAQS in 2020

Reduction in Incidence*(95% Confidence Interval) Age Interval 070 ppm Attainment Strategy 18 – 24 1 (1 – 2) 25 – 34 4 (3 – 6) 35 – 44 37 (20 – 53) 45 – 54 110 (61 – 170) 55 – 64 290 (160 – 430) 65 – 74 350 (190 – 500) 75 – 84 280 (150 – 410) 85+ 150 (80 – 220) Total 1,200 (660 – 1,800)

Acute myocardial infarction results in significant loss of quality of life for a relatively short duration. The WHO Global Burden of Disease study, as reported in Vos (1999b), assumes that the acute phase of an acute myocardial infarction l asts for 0.06 years, or around 22 days. An alternative assumption is the acute phase is charac terized by the average length of hospital stay for an AMI in the United States, which is 5.5 days, based on data from the Agency for Healthcare Research and Quality’s Healthcare Cost a nd Utilization Project (HCUP). 7 We assumed a distribution of acute phase duration char acterized by a uniform distribution between 5.5 and 22 days, noting that due to earlier dischar ges and in-home therapy available in the United States, duration of reduced quality of life may continue after discharge from the hospital. In the period during and directly following an AMI (the ac ute phase), we assigned a quality of life weight equal to 0.605, consistent with the weight f or the period in treatment during and immediately after an attack (Vos, 1999b).
During the post-AMI period, a number of different h ealth states can determine the loss in quality of life. We chose to classify post-AMI health status into four states defined by the presence or absence of angina and congestive heart failure (CHF ). This makes a very explicit assumption that without the occurrence of an AMI, individuals would not experience either angina or CHF.

7 Average length of stay estimated from the HCUP dat a includes all discharges, including those due to death. As such, the 5.5-day average length of stay is likely an underestimate of the average length of stay for AMI admissions where the patient is discharged alive.6b-25

If in fact individuals already have CHF or angina, then the quality of life gained will be overstated. We do not have information about the p ercentage of the population have been diagnosed with angina or CHF with no occurrence of an AMI. Nor do we have information on what proportion of the heart attacks occurring due to PM exposure are first heart attacks versus repeat attacks. Probabilities for the four post-AM I health states sum to one.
Given the occurrence of a nonfatal AMI, the probabi lity 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 prevalenc e rate of angina in the U.S. population. Using data from the American Heart Association, we calcul ated 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 p revalence of angina in the total population with CHD. The estimated prevalence rate is 51 perc ent, so the probability of angina is 0.51.
Combining these factors leads to the probabilities for each of the four health states as follows:
I. Post AMI with CHF and angina = 0.102 II. Post AMI with CHF without angina = 0.098 III. Post AMI with angina without CHF = 0.408 IV. Post AMI without angina or CHF = 0.392 Duration of post-AMI health states varies, based inpart on assumptions regarding life expectancy with post-AMI complicating health condit ions. Based on the model used for established market economies (EME) in the WHO Globa l Burden of Disease study, as reported in Vos (1999b), we assumed that individuals with CH F 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 cohortestimates of reduced mortality risk). Table 6b-8 provides the duration (both discounted a nd undiscounted) of CHF assumed for postAMI cases by age interval.6b-26

Table 6b-8: Assumed Duration of Congestive Heart Fa ilure

Age Interval Duration of Heart Failure (years) Start Age End Age Undiscounted Discounted (3%) 18 24 7.11 6.51 25 34 6.98 6.40 35 44 6.49 6.00 45 54 5.31 4.99 55 64 1.96 1.93 65 74 1.71 1.69 75 84 1.52 1.50 85+ 1.52 1.50

Duration of health states without CHF is assumed to be equal to the life expectancy of individuals conditional on surviving an AMI. Ganz et al. (2000) note that “Because patients with a history of myocardial infarction have a higher ch ance of dying of CHD that is unrelated to recurrent myocardial infarction (for example, arrhythmia), this cohort has a higher risk for death from causes other than myocardial infarction or str oke than does an unselected population.” They go on to specify a mortality risk ratio of 1.5 2 for mortality from other causes for the cohort of individuals with a previous (nonfatal) AMI. The risk ratio is relative to all-cause mortality for an age-matched unselected population (i.e., general population). We adopted the same ratios and applied them to each age-specific all-cause mortali ty rate to derive life expectancies (both discounted and undiscounted) for each age group aft er an AMI, presented in Table 6b-9. These life expectancies are then used to represent the duration of non-CHF post-AMI health states (III and IV).

Table 6b-9: Assumed Duration of Non-CHF Post-AMI He alth 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 stat e. A number of estimates of QALY weights are available for post-AMI health conditions.6b-27

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 ang ina and one set specific to post-AMI CHF. Following the Victoria Burden of Disease model, we assumed that most cases of angina will be treated and thus kept at a mild to moderate state. We thus focused our selection on QALY weights for mild to moderate angina. The Harvard d atabase includes two sets of community preference-based scores for CHF (Stinnett et al., 1 996; Kuntz et al., 1996). The scores for CHF with angina range from 0.736 to 0.85. The lower of the two scores is based on angina in general with no delineation by severity. Based on the rang e 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 amidpoint of 0.81. The same two studies in the Harvard catalog also provide weights for CHF wi thout angina. These scores range from 0.801 to 0.89. We selected a uniform distribution over this range, with a midpoint of 0.85.
The third health state is characterized by angina, without the presence of CHF. The Harvard catalog includes five sets of community preference- based scores for angina, one that specifies scores for both mild and severe angina (Kuntz et al ., 1996), one that specifies mild angina only (Pliskin, Stason, and Weinstein, 1981), one that sp ecifies 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 ran ge of the two scores for mild angina specifically. Thus, we used the range of mild angi na scores as the endpoints of a uniform distribution. The range of mild angina scores is f rom 0.7 to 0.89, with a midpoint of 0.80.
For the fourth health state, characterized by the absence of CHF and/or angina, there is only one relevant community preference score available from the Harvard catalog. This score is 0.93, derived from a time trade-off elicitation (Kuntz et al., 1996). Insufficient information is available to provide a distribution for this weight ; therefore, it is treated as a fixed value.
Similar to CB, we assumed that the reference weight for the general population without AMI is 0.95. To allow for uncertainty in this parameter, we assigned a triangular distribution around this weight, bounded by 0.9 and 1.0.
Based on the assumptions defined above, we used Monte Carlo simulation methods as implemented in the Crystal BallTM software program t o 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 6b-10, along with the 95 percent confidence interval resulting from the Monte Carlo simulation. Table 6b-10 presents both the undiscounted and discounted QALYs gained per incide nce.6b-28

Table 6b-10: QALYs Gained per Avoided Nonfatal Myoc ardial 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)

aMean of Monte Carlo generated distribution; 95% co nfidence interval presented in parentheses.

6b.6 Cost-Effectiveness Analysis
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 effectiveness measure that can be compared to costs to develop cost-effectiveness ratios. Thi s section discusses the proper characterization of the combined effectiveness measure and the appropri ate calculation of the numerator of the costeffectiveness ratio.
6b.6.1 Aggregating Life Expectancy and Quality-of-L ife Gains
To develop an integrated measure of changes in heal th, we simply sum together the gains in life years from reduced mortality risk in each age interval with the gains in QALYs from reductions in incidence of CB and acute myocardial infarctions . The resulting measure of effectiveness then forms the denominator in the cost-effectivenes s ratio. What is this combined measure of effectiveness? It is not a QALY measure in a stric t sense, because we have not adjusted lifeexpectancy gains for preexisting health status (qua lity of life). It is however, an effectiveness measure that adds to the standard life years calcul ation a scaled morbidity equivalent. Thus, we term the aggregate measure morbidity inclusive life years, or MILYs. Alternatively, the combined measure could be considered as QALYs with an assumption that the community preference weight for all life-expectancy gains is 1.0. If one considers that this weight might be considered to be a “fair” treatment of those with p reexisting disabilities, the effectiveness measure might be termed “fair QALY” gained. Howeve r, this implies that all aspects of fairness have been addressed, and there are clearly other is sues with the fairness of QALYs (or other effectiveness measures) that are not addressed in this simple adjustment. The MILY measure6b-29

violates some of the properties used in deriving QA LY weights, such as linear substitution between quality of life and quantity of life. Howe ver, in aggregating life expectancy and qualityof-life gains, it merely represents an alternative social weighting that is consistent with the spirit of the recent OMB guidance on CEA. The guidance no tes that “fairness is important in the choice and execution of effectiveness measures” (OM B, 2003). The resulting aggregate measure of effectiveness will not be consistent with a stri ct utility interpretation of QALYs; however, it may still be a useful index of effectiveness.
Applying the life expectancies and distributions of QALYs per incidence for CB and AMI to estimated distributions of incidences yields distri butions of life expectancy and QALYs gained due to the Ozone NAAQS illustrative attainment strategies. These distributions reflect both the quantified uncertainty in incidence estimates and the quantified uncertainty in QALYs gained per incidence.
For the attainment strategy for the revised 070 ppm standards, Table 6b-11 presents the mean 3 percent discounted MILYs gained for each age interv al, broken out by life expectancy and quality-of-life categories. Note that quality-of-l ife gains occur from age 18 and up, while life expectancy gains accrue only after age 29. This is based on the ages of the study populations in the underlying epidemiological studies. It is unli kely that such discontinuities exist in reality, but to avoid overstating effectiveness, we chose to lim it the life-expectancy gains to those occurring in the population 30 and over and the morbidity gai ns to the specific adult populations examined in the studies.
It is worth noting that around a third of mortality -related benefits are due to reductions in premature deaths among those 75 and older, while on ly 7 percent of morbidity benefits occur in this age group. This is due to two factors: (1) the relatively low baseline mortality rates in populations under 75, and (2) the relatively constant baseline rates of chronic disease coupled with the relatively long period of life that is liv ed with increased quality of life without CB and advanced heart disease.
The relationship between age and the distribution o f MILYs gained from mortality and morbidity is shown for the 070 ppm attainment strategy in Fi gure _-2. Because the baseline mortality rate is increasing in age at a much faster rate than the prevalence rate for CB, the share of MILYs gained accounted for by mortality is proportional t o age. At the oldest age interval, avoiding incidences of CB leads to only a few MILYs gained, due to the lower number of years lived with CB. MILYs gained from avoided premature mortality is low in the youngest age intervals because of the low overall mortality rates in these intervals, although the number of MILYs per incidence is high. In later years, even though the MILYs gained per incidence avoided is low, the number of cases is very high due to higher base line mortality rates.6b-30

Table 6b-11. Estimated Gains in 3 Percent Discounte d MILYs Associated with Illustrative

Attainment Strategies for the Revised Ozone NAAQS (0 .070 ppm) in 2020: Pope (2002) Estimate of Mortality a

Age Life Years Gained

from Mortality Risk Reductions (95% CI) QALY Gained from

Reductions in Chronic Bronchitis (95% CI) QALY Gained from

Reductions in Acute Myocardial Infarctions (95% CI) Total Gain in MILYs (95% CI) 18–24 — — 3 (1 – 5) 3 (1 – 5) 25–34 100

(41 – 170) 490 (91 – 1,100) 8 (3 – 15) 600

(130 – 1,200) 35–44 310

(120 – 490) 500 (93 – 1,100) 70 (23 – 120) 870

(240 – 1,700) 45–54 580

(230 – 930) 420 (77 – 890) 170 (60 – 320) 1,200

(360 – 2,100) 55–64 1,300

(500 – 2,100) 350 (64 – 760) 330 (110 – 620) 2,000 (680 – 3,400) 65–74 1,700

(680 – 2,800) 180 (33 – 380) 280 (100 – 530) 2,200 (810 – 3,700) 75–84 1,400

(540 – 2,200) 60 (11 – 130) 150 (54 – 280) 1,600

(610 – 2,600) 85+ 690

(270 – 1,100) 10 (2 – 22) 43 (16 – 80) 740

(290 – 1,200) Total 6,100

(2,400 – 9,800) 2,000 (370 – 4,300) 1,100 (370 – 2,000) 9,100

(3,100 – 16,000)

a Note that all estimates have been rounded to two s ignificant digits.6b-31

Table 6b-12. Estimated Gains in 3 Percent Discounte d MILYs Associated with Illustrative

Attainment Strategies for the Revised Ozone NAAQS ( 0.070 ppm) in 2020: Laden (2006) Estimate of Mortality a

Age Life Years Gained

from Mortality Risk Reductions (95% CI) QALY Gained from

Reductions in Chronic Bronchitis (95% CI) QALY Gained from

Reductions in Acute Myocardial Infarctions (95% CI) Total Gain in MILYs (95% CI) 18–24 — — 3 (1 – 5) 3 (1 – 5) 25–34 240

(130 – 340) 490 (91 – 1,100) 8 (3 – 15) 730

(220 – 1,400) 35–44 690

(380 – 1,000) 500 (93 – 1,100) 70 (23 – 120) 1,300

(490 – 2,200) 45–54 1,400

(710 – 1,900) 420 (77 – 890) 170 (60 – 320) 1,900

(850 – 3,100) 55–64 2,900

(1,600 – 4,200) 350 (64 – 760) 330 (110 – 620) 3,600

(1,800 – 5,600) 65–74 3,900

(2,100 – 5,700) 180 (33 – 380) 280 (100 – 530) 4,400

(2,300 – 6,600) 75–84 3,100

(1,700 – 4,600) 60 (11 – 130) 150 (54 – 280) 3,300

(1,800 – 5,000) 85+ 1,600

(840 – 2,300) 10 (2 – 22) 43 (16 – 80) 1,600

(860 – 2,400) Total 14,000

(7,500 – 20,000) 2,000 (370 – 4,300) 1,100 (370 – 2,000) 17,000

(8,200 – 26,000)

a Note that all estimates have been rounded to two s ignificant digits.

Summing over the age intervals provides estimates o f total MILYs gained for the Ozone NAAQS illustrative attainment strategies. The tota l number of discounted (3 percent) MILYs gained for the 070 ppm attainment strategy using th e Pope (2002) estimate is 9,100 (95% CI: 3,100 – 16,000). Using the Laden (2006) estimate, the total number of discounted (3 percent) MILYs is 17,000 (95% CI: 8,200 – 26,000).
6b.6.2 Dealing with Acute Health Effects and Non-he alth Effects
Health effects from exposure to particulate air pol lution encompass a wide array of chronic and acute conditions in addition to premature mortality(EPA, 1996). Although chronic conditions and premature mortality generally account for the m ajority of monetized benefits, acute symptoms can affect a broad population or sensitive populations (e.g., asthma exacerbations in asthmatic children. In addition, reductions in air pollution may result in a broad set of nonhealth environmental benefits, including improved visibili ty in national parks, increased agricultural and forestry yields, reduced acid damage to buildin gs, and a host of other impacts. QALYs address only health impacts, and the OMB guidance n otes that “where regulation may yield several different beneficial outcomes, a cost-effec tiveness comparison becomes more difficult to interpret because there is more than one measure of effectiveness to incorporate in the analysis.”6b-32

With regard to acute health impacts, Bala and Zarki n (2000) suggest that QALYs are not appropriate for valuing acute symptoms, because of problems with both measuring utility for acute health states and applying QALYs in a linear fashion to very short duration health states. Johnson and Lievense (2000) suggest using conjoint analysis to get healthy-utility time equivalences that can be compared across acute effe cts, but it is not clear how these can be combined with QALYs for chronic effects and loss of life expectancy. There is also a class of effects that EPA has traditionally treated as acute , such as hospital admissions, which may also result in a loss of quality of life for a period of time following the effect. For example, life afte r asthma hospitalization has been estimated with a ut ility weight of 0.93 (Bell et al., 2001; Kerridge, Glasziou, and Hillman, 1995).
How should these effects be combined with QALYs for chronic and mortality effects? One method would be to convert the acute effects to 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 as thma symptoms is equivalent to 0.0019 QALY gained). This is troubling from both a concep tual basis and a presentation basis. An alternative approach is simply to treat acute health effects like nonhealth benefits and subtract the dollar value (based on WTP or COI) from compliance costs in the CEA.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

25-34 35-44 45-54 55-64 65-74 75-84 85+ Age GroupPercent of Total MILY

Morbidity Mortality

Figure 6b-2. Distribution of Mortality and Morbidit y Related MILY Across Age Groups

for Illustrative Attainment Strategy for the Revise d PM NAAQS (3 percent Discount Rate)
To address the issues of incorporating acute morbid ity and nonhealth 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 benefit-cost analysis, any unquantified benefits and/or costs should be noted and an indication of h ow they might affect the cost-effectiveness6b-33

ratio should be described. We will follow this rec ommended “net cost” approach in the illustrative exercise, specifically in netting out the benefits of health improvements other than reduced mortality and chronic morbidity, and the be nefits of improvements in visibility at national parks (see Chapter 5 for more details on these benefit categories).
6b.6.3 Cost-Effectiveness Ratios
Construction of cost-effectiveness ratios requires estimates of effectiveness (in this case measured by lives saved, life years gained, or MILY s gained) in the denominator and estimates of costs in the numerator. The estimate of costs i n the numerator should include both the direct costs of the controls necessary to achieve the redu ction in ambient PM 2.5 and the avoided costs (cost savings) associated with the reductions in mo rbidity (Gold et al., 1996). In general, because reductions in air pollution do not require direct actions by the affected populations, there are no specific costs to affected individuals (asid e from the overall increases in prices that might be expected to occur as control costs are passed onby affected industries). Likewise, because individuals do not engage in any specific actions t o realize the health benefit of the pollution reduction, there are no decreases in utility (as mi ght occur from a medical intervention) that need to be adjusted for in the denominator. Thus, the e lements of the numerator are direct costs of controls minus the avoided COI associated with CB and nonfatal AMI. In addition, to account for the value of reductions in acute health impacts and nonhealth benefits, we net out the monetized value of these benefits from the numerato r to yield a “net cost” estimate. For the MILY aggregate effectiveness measure, the denominat or is simply the sum of life years gained from increased life expectancy and the sum of QALYs gained from the reductions in CB and nonfatal AMI.

Avoided costs for CB and nonfatal AMI are based on estimates of lost earnings and medical costs. 8 Using age-specific annual lost earnings and medic al costs estimated by Cropper and Krupnick (1990) and a 3 percent discount rate, we e stimated a lifetime present discounted value (in 2000$) due to CB of $150,542 for someone betwee n the ages of 27 and 44; $97,610 for someone between the ages of 45 and 64; and $11,088 for someone over 65. The corresponding age-specific estimates of lifetime present discount ed value (in 2000$) using a 7 percent discount rate are $86,026, $72,261, and $9,030, respectively . These estimates assumed that 1) lost earnings continue only until age 65, 2) medical exp enditures 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. Usi ng age-specific annual lost earnings estimated by Cropper and Krupnick (1990) and a 3 percent disc ount rate, we estimated a present

8 Gold et al. (1996) recommend not including lost earnings in the cost-of-illness estimates, suggesting that in some cases, they may be already be counted in the effectiveness measures. However, this requires that individuals fully incor porate the value of lost earnings and reduced labor force participation opportunities into their responses to time-tradeoff or standard-gamble questions. For the purposes of this analysis and f or consistency with the way costs-of-illness are calculated for the benefit-cost analysis, we have a ssumed that 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.6b-34

discounted value in lost earnings (in 2000$) over 5 years due to a myocardial infarction of $8,774 for someone between the ages of 25 and 44, $ 12,932 for someone between the ages of 45 and 54, and $74,746 for someone between the ages of55 and 65. The corresponding agespecific estimates of lost earnings (in 2000$) usin g a 7 percent discount rate are $7,855, $11,578, and $66,920, respectively. Cropper and Krupnick (1 990) do not provide lost earnings estimates for populations under 25 or over 65. Thus, we do n ot include lost earnings in the cost estimates for these age groups.
Two estimates of the direct medical costs of myocar dial 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 di scounting used). Using the CPI-U for medical care, the Wittels estimate is $109,474 in year 2000 $. This estimated cost is based on a medical cost model, which incorporated therapeutic options, projected outcomes, and prices (using “knowledgeable cardiologists” as consultants). The model used medical data and medical decision algorithms to estimate the probabilities o f certain events and/or medical procedures being used. The second estimate is from Russell et al. (1998), which estimated first-year direct medical costs of treating nonfatal myocardial infar ction of $15,540 (in 1995$), and $1,051 annually thereafter. Converting to year 2000$, that would be $23,353 for a 5-year period (without discounting).

The two estimates from these studies are substantia lly 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 period, we used estimates for medical costs that similarly cover a 5-year period. We used a simple average of the two 5-year estimates, or $65,902, and add it to the 5-year opp ortunity cost estimate. The resulting estimates are given in Table 6b-13.
Table 6b-13: Estimated Costs Over a 5-Year Period (i n 2000$) of a Nonfatal Myocardial Infarction

Age Group Opportunity Cost Medical Cost a Total Cost 0 – 24 $0 $65,902 $65,902 25-44 $8,774 b $65,902 $74,676 45 – 54 $12,253 b $65,902 $78,834 55 – 65 $70,619 b $65,902 $140,649 >65 $0 $65,902 $65,902

aAn 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 percent discount rate. The total avoided COI by age group associated with the reductions in CB and nonfatal acute myocardial infarctions is provided in Table 6b-14. Note that the total avoided COI associated with the revised PM NAAQS is $520 million and is $1 ,200 million for the more stringent alternative. Note that this does not include any d irect avoided medical costs associated with premature mortality. Nor does it include any medic al costs that occur more than 5 years from the onset of a nonfatal AMI. Therefore, this is likelyan underestimate of the true avoided COI associated with strategies for attainment of the PM NAAQS.6b-35

Table 6b-14: Avoided Costs of Illness Associated wit h Reductions in Chronic Bronchitis and

Nonfatal Acute Myocardial Infarctions Associated wi th Attainment Strategies for the 0.070 ppm alternative Ozone NAAQS in 2020

Avoided Cost of Illness (in millions of 1999$) Age Range Chronic Bronchitis Nonfatal Acute Myocardial Infarction 18-24 — $0.07 25-34 $11 $0.3 35-44 $13 $2.6 45-54 $7 $8.6 55-64 $8 $40 65-74 $0.7 $22 75-84 $0.3 $18 85+ $0.1 $9.4 Total $41 $100

6b.7 Discount Rate Sensitivity Analysis
A large number of parameters and assumptions are ne cessary in conducting a CEA. Where appropriate and supported by data, we have included distributions of parameter values that were used in generating the reported confidence interval s. For the assumed discount rate, we felt it more appropriate to examine the impact of the assumption using a sensitivity analysis rather than through the integrated probabilistic uncertainty an alysis.
The choice of a discount rate, and its associated c onceptual basis, is a topic of ongoing discussion within the academic community. OMB and EPA guidance require using both a 7 percent rate and a 3 percent rate. In the most rec ent benefit-cost analyses of air pollution regulations, a 3 and 7 percent discount rate have b een adopted in the primary analysis. A 3 percent discount rate reflects a “social rate of ti me preference” discounting concept. A 3 percent discount rate is also consistent with the recommend ations of the NAS panel on CEA (Gold et al., 1996), which suggests that “a real annual (riskless ) rate of 3 percent should be used in the Reference Case analysis.” We have also calculated MILYs and the implicit cost thresholds using a 7 percent rate consistent with an “opportunity co st of capital” concept to reflect the time value of resources directed to meet regulatory requirements. Further discussion of this topic appears in Chapter 7 of Gold et al. (1996), in Chapter 6 of th e EPA Guidelines for Economic Analysis , and in OMB Circular A-4.6b-36

Table 6b-15: Summary of Results for the Illustrativ e Partial Attainment Strategies for the

Alternative Ozone Standard of 0.070 ppm in 2020 a

Result Using 3% Discount Rate (95% Confidence Inte rval) 070 ppm Partial Attainment Strategy Life years gained from mortality risk reductions Pope et al. (2002) 6,100 (2,400 – 9,800) Laden et al. (2006) 14,000 (7,500 – 20,000 QALY gained from reductions in chronic bronchitis 2,000 (370 – 4,300) QALY gained from reductions in acute myocardial infarctions 1,100 (370 – 2,000) Total gain in MILYs Pope et al. (2002) 9,100 (3,100 – 16,000) Laden et al. (2006) 17,000 (8,200 – 26,000) Avoided cost of illness Chronic bronchitis $41 million ($7.6 million – $75 million)
Nonfatal AMI $100 million ($63 million – $220 million) Implementation strategy costs b $3.9 billion Net cost per MILY Pope et al. (2002) $410,000 ($220,000 – $1,300,000) Laden et al. (2006) $220,000 ($140,000 – $470,000)

aConsistent with recommendations of Gold et al. (19 96), all summary results are reported at a precisio n level of two significant digits to reflect limits in the prec ision of the underlying elements.
b Costs are the private firm costs of control, as di scussed in Chapter 6, and reflect discounting using firm specific costs of capital.

Table 6b-16 presents a summary of results using the 7 percent discount rate and the percentage difference between the 7 percent results and the ba se case 3 percent results. Adoption of a 7 percent discount rate decreases the estimated life years and QALYs gained from implementing the PM NAAQS. Adopting a discount rate of 7 percent results in a 35 percent reduction in the estimated total MILYs gained in each year, while th e cost per MILY increases by approximately 60 percent.6b-37

Table 6b-16: Impacts of Using a 7 Percent Discount Rat e on Cost Effectiveness Analysis for the

Illustrative Attainment Strategies for the Revised and More Stringent PM NAAQS in 2020

6b.8 Conclusions
We calculated the effectiveness of PM NAAQS attainm ent strategies based on reductions in premature deaths and incidence of chronic disease. We measured effectiveness using several different metrics, including lives saved, life year s saved, and QALYs (for improvements in quality of life due to reductions in incidence of c hronic disease). We suggested a new metric for aggregating life years saved and improvements in qu ality of life, morbidity inclusive life years (MILY) which assumes that society assigns a weight of one to years of life extended regardless of preexisting disabilities or chronic health condi tions.
CEA of environmental regulations that have substant ial public health impacts may be informative in identifying programs that have achie ved cost-effective reductions in health impacts and can suggest areas where additional controls may be justified. However, the overall efficiency of a regulatory action can only be judge d through a complete benefit-cost analysis that takes into account all benefits and costs, includin g both health and nonhealth effects. The benefit-cost analysis for the PM NAAQS attainment s trategies, provided in Chapter 9, shows that the attainment strategies we modeled have potential ly large net benefits, indicating that implementation of the revised PM NAAQS will likely result in improvements in overall public welfare. Result Using 7 Percent Discount Rate Percentage Change Relative to Result Using 3 Percent Discount Rate Life years gained from mortality risk reductions 4,600 –24% 10,000 -24% QALY gained from reductions in chronic bronchitis 1,300 –35%

QALY gained from reductions in acute myocardial infarctions 830 –21%

Total gain in MILYs Pope et al. (2002) 6,500 -26% Laden et al. (2006) 12,000 -25% Avoided cost of illness Chronic bronchitis $27 million –36% Nonfatal AMI $111 million +10% Net cost per MILY Pope et al. (2002) $550,000 +36% Laden et al. (2006) $300,000 +33%6b-38

6b.9 References
Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancure n, and P.K. Mills. 1995. “Estimated Long-Term Ambient Concentrations of PM(10) and Deve lopment of Respiratory Symptoms in a Nonsmoking Population.” Archives of Environmental Health 50(2): 139-152.
Agency for Healthcare Research and Quality. 2000. HCUPnet, Healthcare Cost and Utilization Project.
American Lung Association. 2002. Trends in Chronic Bronchitis and Emphysema: Morbid ity and Mortality . American Lung Association, Best Practices and Pr ogram Services, Epidemiology and Statistics Unit.

American Heart Association. 2003. Heart Disease and Stroke Statistics—2003 Update. Dallas, Texas: American Heart Association.
Bala, M.V., and G.A. Zarkin. 2000. “Are QALYs an Appropriate Measure for Valuing Morbidity in Acute Diseases?” Health Economics 9:177-180.
Bell, C.M., R.H. Chapman, P.W. Stone, E.A. Sandberg , and P.J. Neumann. 2001. “An Off-theShelf Help List: A Comprehensive Catalog of Prefer ence Scores from Published Cost-Utility Analyses.” Medical Decision Making 21:288-94.
Bleichrodt, H., P.P. Wakker, and M. Johannesson. 1 996. “Characterizing QALYs by Risk Neutrality.” Journal of Risk and Uncertainty15:107-114.
Brock, D.W. 2002. “Chapter 14.3: Fairness and He alth.” In Summary Measures of Population Health: Concepts, Ethics, Measurement and Applicat ions , C.J.L. Murray, J.A. Salomon, C.D. Mathers, and A.D. Lopez (eds.). Geneva: World Hea lth Organization.
Carrothers, T. J., J.S. Evans, and J.D. Graham. 20 02. “The Lifesaving Benefits of Enhanced Air Quality.” Harvard Center for Risk Analysis Working Paper.
Carnethon, M.R., D. Liao, G.W. Evans, W.E. Cascio, L.E. Chambless, W.D. Rosamond, and G. Heiss. 2002. “Does the Cardiac Autonomic Response to Postural Change Predict Incident Coronary Heart Disease and Mortality? The Atherosc lerosis Risk in Communities Study.” American Journal of Epidemiology155(1):48-56.
Centers for Disease Control and Prevention (CDC) He alth, United States. 2003. Table 30. Years of Potential Life Lost before Age 75 for Sele cted Causes of Death, According to Sex, Race, and Hispanic Origin: United States, Selected Years 1980–2000.
Centers for Disease Control and Prevention (CDC). 2002. National Vital Statistics Reports 51(3).
Cohen, D.J., J.A. Breall, and K.K. Ho. 1994. “Eva luating the Potential Cost-Effectiveness of Stenting as a Treatment for Symptomatic Single-Vess el Coronary Disease: Use of a DecisionAnalytic Model.” Circulation 89(4):1859-1874.6b-39

Cohen, J.T., J.K. Hammitt, and J.I. Levy. 2003. “ Fuels for Urban Transit Buses: A Costeffectiveness Analysis.” Environmental Science and Technology37:1477-1484.
Cowie, M.R., A. Mosterd, D.A. Wood, et al. 1997. “The Epidemiology of Heart Failure.” European Heart Journal

18:208-25.
Coyle, D., D. Stieb, R.T. Burnett, P. DeCivita, D. Krewski, Y. Chen, and M.J. Thun. 2003. “Impact of Particulate Air Pollution on Quality Adj usted Life Expectancy in Canada.” Journal of Toxicology and Environmental Health, Part A 66:1847-1863.
Cropper, M.L., and A.J. Krupnick. 1990. “The Soci al Costs of Chronic Heart and Lung Disease.” Discussion Paper QE 89-16-REV. Washingt on, DC: Resources for the Future.
de Hollander, A.E.M., J.M. Melse, E. Lebret, and P. G.N. Kramers. 1999. “An Aggregate Public Health Indicator to Represent the Impact of Multipl e Environmental Exposures.” Epidemiology 10:606-617.
Dekker, J.M., R.S. Crow, A.R. Folsom, P.J. Hannan, D. Liao, C.A. Swenne, and E.G. Schouten. 2000. “Low Heart Rate Variability in a 2-Minute Rhythm Strip Predicts Risk of Coronary Heart Disease and Mortality From Several Causes: The ARI C Study.” Circulation 2000102:1239- 1244.
Freeman, A.M., J.K. Hammitt, and P. De Civita. 200 2. “On Quality Adjusted Life Years (QALYs) and Environmental/Consumer Safety Valuation .” AERE Newsletter 22(1):7-12.
Ganz, D.A., K.M. Kuntz, G.A. Jacobson, and J. Avorn . 2000. “Cost-Effectiveness of 3- Hydroxy-3-Methylglutaryl Coenzyme A Reductase Inhib itor Therapy in Older Patients with Myocardial Infarction.” Annals of Internal Medicine 132:780-787.
Garber, A.M., and C.E. Phelps. 1997. “Economic Fo undations of Cost-Effectiveness Analysis.” Journal of Health Economics 16:1-31.
Gentile, J.E. 1998. Random Number Generation and Monte Carlo Methods . New York, NY: Springer Verlag.
Gold, M.R., J.E. Siegel, L.B. Russell, and M.C. Wei nstein. 1996. Cost-effectiveness in Health and Medicine . New York: Oxford University Press.
Gold, D.R., A. Litonjua, J. Schwartz, E. Lovett, A. Larson, B. Nearing, G. Allen, M. Verrier, R. Cherry, and R. Verrier. 2000. “Ambient Pollution and Heart Rate Variability.” Circulation 101(11):1267-1273.
Gold, M.R., D. Stevenson, and D.G. Fryback. 2002. “HALYS and QALYS and DALYS, Oh My: Similarities and Differences in Summary Measur es of Population Health.” Annual Review of Public Health 23:115–34.
Hammitt, J.K. 2002. “QALYs versus WTP.” Risk Analysis 22(5):985-1001.6b-40

Harvard Center for Risk Analysis, Catalog of Prefer ence Scores. Available at: http://www.hcra.harvard.edu/pdf/preferencescores.pd f.
Horsman, J., W. Furlong, D. Feeny, and G. Torrance. 2003. “The Health Utilities Index (HUI): Concepts, Measurement Properties, and Applications. ” Health and Quality of Life Outcomes 1:54.
Johnson, F.R., and K. Lievense. 2000. Stated-Pref erence Indirect Utility and Quality-Adjusted Life Years. Report prepared for Health Canada, Jul y. Kerridge, R.K., P.P. Glasziou, and K.M. Hillman. 1 995. “The Use of “Quality-Adjusted Life Years” (QALYs) to Evaluate Treatment in Intensive C are.” Anaesthia and Intensive Care 23:322-31.
Kind, P. 1996. The EuroQoL Instrument: An Index of Health-Related Quality of Life. Quality of Life and Pharmacoeconomics in Clinical Trials, S econd Edition, B. Spilker (ed.), pp. 191-201. Philadelphia, PA: Lippincott-Raven Publishers.
Krewski, D., R.T. Burnett, M.S. Goldbert, K. Hoover , J. Siemiatycki, M. Jerrett, M. Abrahamowicz, and W.H. White. 2000. Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air Po llution and Mortality . Special Report to the Health Effects Institute, Cambridge, MA.
Kuntz, K.M, J. Tsevant, L. Goldman, and M.C. Weinst ein. 1996. “Cost-Effectiveness of Routine Coronary Angiography after Acute Myocardial Infarction.” Circulation 94(5):957-65.
Kunzli, N., R. Kaiser, S. Medina, M. Studnicka, O. Chanel, P. Filliger, M. Herry, F. Horak Jr., V. Puybonnieux-Texier, P. Quenel, J. Schneider, R. See thaler, J-C Vergnaud, and H. Sommer. 2000. “Public-Health Impact of Outdoor and Traffic -Related Air Pollution: A European Assessment.” The Lancet 356:795-801.
Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Hor ak Jr, and M. Studnicka. 2001. “Assessment of Deaths Attributable to Air Pollution: Should We Use Risk Estimates Based on Time Series or on Cohort Studies?” American Journal of Epidemiology153(11):1050-55.
Liao, D., J. Cai, W.D. Rosamond, R.W. Barnes, R.G. Hutchinson, E.A. Whitsel, P. Rautaharju, and G. Heiss. 1997. “Cardiac Autonomic Function and Incident Coronary Heart Disease: A Population-Based Case-Cohort Study. The ARIC Study . Atherosclerosis Risk in Communities Study.” American Journal of Epidemiology145(8):696-706.
Liao, D., J. Creason, C. Shy, R. Williams, R. Watts , and R. Zweidinger. 1999. “Daily Variation of Particulate Air Pollution and Poor Cardiac Auton omic Control in the Elderly.” Environmental Health Perspectives 107:521-5.
Magari, S.R., R. Hauser, J. Schwartz, P.L. Williams , T.J. Smith, and D.C. Christiani. 2001. “Association of Heart Rate Variability with Occupat ional and Environmental Exposure to Particulate Air Pollution.” Circulation 104(9):986-91.6b-41

Miller, B.G., and J.F. Hurley. 2003. “Life Table Methods for Quantitative Impact Assessments in Chronic Mortality.” Journal of Epidemiology and Community Health 57:200-206.
Moolgavkar, S.H. 2000. “Air Pollution and Hospita l Admissions for Diseases of the Circulatory System in Three U.S. Metropolitan Areas.” Journal of the Air Waste Management Assoc iation 50:1199-206.
Murray, C.J.L., J.A. Salomon, C.D. Mathers, and A.D . Lopez. 2002. “Summary Measures of Population Health: Concepts, Ethics, Measurement a nd Applications.” Geneva: World Health Organization.
National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed A ir Pollution Regulations . Washington, DC: The National Academies Press.
Neumann, P. 2003. “A Web-based Registry of Cost-utility Analyses.” Risk in Perspective 11(3).
Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mittleman. 2001. “Increased Particulate Air Pollution and the Triggering of Myocardial Infarcti on.” Circulation 103:2810-2815.
Pliskin, J.S., D.S. Shepard, and M.C. Weinstein. 1 980. “Utility Functions for Life Years and Health Status.” Operations Research 28:206-224.
Pliskin, J.S., W.B. Stason, and M.C. Weinstein. 19 81. “Coronary Artery Bypass Graft Surgery: Clinical Decision Making and Cost-Effective Analysi s.” Medical Decision Making 1(1):10-28.
Poloniecki, J.D., R.W. Atkinson, A.P. de Leon, and H.R. Anderson. 1997. “Daily Time Series for Cardiovascular Hospital Admissions and Previous Day’s Air Pollution in London, UK.” Occupational and Environmental Medicine54(8):535-40.
Pope, D.A., R.T. Burnett, M.J. Thun, E.E. Cale, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-T erm Exposure to Fine Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141.
Rabl, A. 2003. “Interpretation of Air Pollution M ortality: Number of Deaths or Years of Life Lost?” Journal of the Air and Waste Management Association 53:41-50.
Russell, M.W., D.M. Huse, S. Drowns, E.C. Hamel, an d S.C. Hartz. 1998. “Direct Medical Costs of Coronary Artery Disease in the United Stat es.” American Journal of Cardiology 81(9):1110-5.
Salkeld, G., P. Phongsavan, and B. Oldenburg. 1997 . “The Cost-Effectiveness of a Cardiovascular Risk Reduction Program in General Pr actice.” Health Policy41:105-19.
Salomon, J.A., and C.J.L. Murray. 2003. “A Multi- Method Approach to Measuring HealthState Valuations.” Health Economics , online.6b-42

Samet, J.M., S.L. Zeger, F. Dominici, F. Curriero, I. Coursac, D.W. Dockery, J. Schwartz, and A. Zanobetti. June 2000. The National Morbidity, Mortality and Air Pollution Study: Part II: Morbidity, Mortality and Air Pollution in the Unite d States. Research Report No. 94, Part II. Cambridge, MA: Health Effects Institute.
Schwartz, J. 1993. “Particulate Air Pollution and Chronic Respiratory Disease.” Environmental Research 62:7-13.
Singer, P., J. McKie, H. Kuhse, J. Richardson, and J. Harris. 1995. “Double Jeopardy and the Use of QALYs in Health Care Allocation.” Journal of Medical Ethics 21:144-157.
Smith, K.J., and R.R. Pesce. 1994. “Pulmonary Art ery Catheterization in Exacerbations of COPD Requiring Mechanical Ventilation: A Cost-Effe ctiveness Analysis.” Respiratory Care 39:961-7.
Stinnett, A.A., M.A. Mittleman, M.C. Weinstein, K.M . Kuntz, D.J. Cohen, L.W. Williams, P.A. Goldman, D.O. Staiger, M.G.M. Hunink, J. Tsevat, A. N.A. Tosteson, and L. Goldman. 1996. “The Cost-effectiveness of Dietary and Pharmacologi c Therapy for Cholesterol Reduction in Adults.” In Cost-effectiveness in Health and Medicine , Gold, M.E., J. E. Siegel, L.B. Russell, and M.C. Weinstein (eds.). New York: Oxford Unive rsity Press.
Stouthard, M.E.A., J.L. Essink-Bot, G.J. Bonsel, J. J. Barendregt, P.G.N. Kramers, H.P.A. Van de Water, and L.J. Gunning-Schepers. 1997. “Disabili ty Weights for Diseases in the Netherlands.” Rotterdam: Department of Public Health, Erasmus Un iversity Rotterdam.
Suwa, T. J.C. Hogg, K.B. Quinlan, A. Ohgami, R. Vin cent, and S.F. van Eeden. 2002. “Particulate Air Pollution Induces Progression of A therosclerosis.” Journal of the American College of Cardiology39:935-942.
Tsuji, H., M.G. Larson, F.J. Venditti, Jr., E.S. Manders, J.C. Evans, C.L. Feldman, and D. Levy. 1996. “Impact of Reduced Heart Rate Variability onRisk for Cardiac Events. The Framingham Heart Study.” Circulation 94(11):2850-2855.
U.S. Environmental Protection Agency (EPA). 1996. Review of the National Ambient Air Quality Standards for Particulate Matter: Assessme nt of Scientific and Technical Information. Report no. EPA/4521R-96-013. Research Triangle Park, NC: Office of Air Quality Planning and Standards.
U.S. Environmental Protection Agency (EPA). 2000. Guidelines for Preparing Economic Analyses . EPA 240-R-00-003.
U.S. Environmental Protection Agency (EPA). 2004. F inal Regulatory Analysis: Control of Emissions from Nonroad Diesel Engines.6b-43

U.S. Environmental Protection Agency (EPA) Science Advisory Board (SAB). EPA-SABCOUNCIL-ADV-04-002. March 2004. Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analys is—Benefits and Costs of the Clean Air Act, 1990-2020: Advisory by the Health Effects Sub committee of the Advisory Council on Clean Air Compliance Analysis.
U.S. Food and Drug Administration (FDA). 2004. Bar Code Label Requirements for Human Drug Products and Biological Products; Final Rule. Federal Register: February 26, 2004 Volume 69 (38): 9119-9171.
U.S. Office of Management and Budget (OMB). 2003. Circular A-4: Regulatory Analysis, September 17, 2003. Available at: http://www.whit ehouse.gov/omb/ circulars/a004/a-4.pdf.
Vos, T. 1999a. Final worksheet VM1 COPD.xls, prep ared for Victoria Burden of Disease Study. Available at: http://www.dhs.vic.gov.au/ph d/bod/daly.htm.
Vos, T. 1999b. Final worksheet VL2 IHD.xls, prepa red for Victoria Burden of Disease Study. Available at: http://www.dhs.vic.gov.au/phd/bod/da ly.htm.
Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. 1990. “Medical Costs of Coronary Artery Disease in the United States.” American Journal of Cardiology65(7):432-40.6c-1

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

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 th at are most appropriate to adopt in the face of important uncertainties. The majori ty of the analytical assumptions used to develop the primary estimates of benefits have been rev iewed and approved by EPA’s SAB. Both EPA and the SAB recognize that data and modeling limitations as well as simplifying assumptions can introduce significant uncertainty into the benefit results and that alternative choices exist for some inputs to the analysis, suchas the mortality C-R functions.
This appendix supplements our primary analysis of benefi ts with three additional sensitivity calculations. These supplemental estimates examine sensi tivity to both valuation issues (e.g., the appropriate income elasticity) and for physical effect s issues (e.g., the structure of the cessation lag and the sensitivity of the premature mortalityestimate to the presence of a presumed threshold). These supplemental estimates are not meant to be comprehensive. Rather, they reflect some of the key issues identified by EPA or commentors as likely to have a significant impact on total benefits. The individual adju stments in the tables should not simply be added together because 1) there may be overlap am ong the alternative assumptions and 2) the joint probability among certain sets of alternative assumptions may be low.
6c.1 Premature Mortality Cessation Lag Structure
Over the last ten years, there has been a continuing discussi on and evolving advice regarding the timing of changes in health effects following changes in ambient air pollution. It has been hypothesized that some reductions in premature mortality from exposure to ambient PM 2.5 will occur over short periods of time in individuals with com promised health status, but other effects are likely to occur among individuals who, at baseline, have reasonably good health that will deteriorate because of continued exposure. No animal models have yet been developed to quantify these cumulative effects, nor are there epidemiologic studies bearing on this question. The SAB-HES has recogniz ed this lack of direct evidence. However, in early advice, they also note that “alth ough there is substantial evidence that a portion of the mortality effect of PM is manifest wi thin a short period of time, i.e., less than one year, it can be argued that, if no lag assumptionis made, the entire mortality excess observed in the cohort studies will be analyzed as immediate effects, and this will result in an overestimate of the health benefits of improved air quality . Thus some time lag is appropriate for distributing the cumulative mortalityeffect of PM in the population” (EPASAB-COUNCIL-ADV-00-001, 1999, p. 9). In recent advice, th e SAB-HES suggests that appropriate lag structures may be developed based on the d istribution of cause-specific deaths within the overall all-cause estimate (EPA-SAB -COUNCIL-ADV-04-002, 2004). They suggest that diseases with longer progressions should b e characterized by longer-term lag structures, while air pollution impacts occurring in populations with existing disease may be characterized by shorter-term lags.6c-2

A key question is the distribution of causes of death within the relatively broad categories analyzed in the long-term cohort studies. Although it may be reasonable to assume the cessation lag for lung cancer deaths mirrors the lo ng latency of the disease, it is not at all clear what the appropriate lag structure should be for card iopulmonary deaths, which include both respiratory and cardiovascular causes. Some respirat ory diseases may have a long period of progression, while others, such as pneumon ia, have a very short duration. In the case of cardiovascular disease, there is an important question of whether air pollution is causing the disease, which would imply a relatively long cessation lag, or whether air pollution is causing premature death in individuals with preexisting heart disease, which would imply very short cessation lags. The SAB-HES provide s several recommendations for future research that could support the development of d efensible lag structures, including using disease-specific lag models and constructing asegmented lag distribution to combine differential lags across causes of death (EPA-SAB-COUNCIL -ADV-04-002, 2004). The SAB-HES indicated support for using “a Weibull distributi on or a simpler distributional form made up of several segments to cover the response mechanisms o utlined above, given our lack of knowledge on the specific form of the distributions” ( EPA-SAB-COUNCIL-ADV04-002, 2004, p. 24). However, they noted that “animportant question to be resolved is what the relative magnitudes of these segments should be, and how many of the acute effects are assumed to be included in the cohort effect estimat e” (EPA-SAB-COUNCIL-ADV-04-002, 2004, p. 24-25). Since the publication of that report in March 2004, EPA has sought additional clarification from this committee. In its foll owup advice provided in December 2004, this SAB suggested that until additional rese arch has been completed, EPA should assume a segmented lag structure characterized by 30 p ercent of mortality reductions occurring in the first year, 50 percent occurring e venly over 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 short-term exp osures in the first year, cardiopulmonary deaths in the 2- to 5-year period, and long-term lung disease and lung cancer in the 6- to 20- year period. Furthermore, in their advisory letter, the SAB-HES recommended that EPA include sensitivity analyses on other possible lag structures. In this appendix, we investigate the sensitivity of premature mortality-reduction rel ated benefits to alternative cessation lag structures, noting that ongoing and future research mayresult 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 mortalityallocated to each segment in the segmented lag structure (EPA-SAB-COUNCIL-ADV-00-001 , 1999, (EPA-COUNCIL-LTR05-001, 2004). The 0-year lag is representative of EPA’s assumption in previous RIAs. The 8- and 15-year lags are based on the study periods from the Pope et al. (1995) and Dockery et al. (1993) studies, respectively. 1 However, neither the Pope et al. nor Dockery et al. stu dies assumed any lag structure when estimating the relative ris ks from PM exposure. In fact, the

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 due to observat ions of a lag in effects but is more likely due to the expense of conducting long-term exposure studies or the amount of satisfactory data that could be coll ected during this time period.6c-3

Pope et al. and Dockery et al. analyses do not supp orting or refute the existence of a lag. Therefore, any lag structure applied to the avoided inciden ces estimated from either of these studies will be an assumed structure. The 8- and 1 5-year lags implicitly assume that all premature mortalities occur at the end of the study per iods (i.e., at 8 and 15 years). In addition to the simple 8- and 15-year lags, we have added three additional sensitivity analyses examining the impact of assuming different allocations of mortal ity to the segmented lag of the type suggested by the SAB-HES. The first sensitivity analysis assumes that more of the mortality impact is associated with chronic lung diseases or lung cancer and less with acute cardiopulmonary causes. This illustrat ive lag structure is characterized by 20 percent of mortality reductions occurring in the first y ear, 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 perce nt in the second 3-year segment, and 0 percent in the 6- to 20-year segment . The third sensitivity analysis assumes a negative exponential relationship between reduction in exposure and reduction in mortality risk. This structure is based on an analysis by Röösli et al. (2004), which estimate s the percentage 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 inexposure and changes in mortality.
The estimated impacts of alternative lag structures on the monetary benefits associated with reductions in PM-related premature mortality (estimated with the Pope et al. ACS impact function) are presented in Table J-1. These estimates are based on the value of statistical lives saved approach (i.e., $5.5 million per incidenc e) and are presented for both a 3 and 7 percent discount rate over the lag period. 

# > @ > @ % .

. 

### Mortality Reduction (t)   

   

 f 

# ¦ RR e

RR e t

t

t 

### 1 1 1

1 1 1 0 5

0 5

16c-4

Table 6c-1. Sensitivity of Benefits of Premature M ortality Reductions to Alternative Cessation Lag Struc tures, Using Pope et al (2002) Effect Estimate Alternative Lag Structures for PM-Related Premature

Mortality Value (billion 1999$) a.b Percent

Difference from Base Estimate None Incidences all occur in the first year 3% discount rate $3.5 10.4% 7% discount rate $3.5 31.2% 8-year Incidences all occur in the 8 th year 3% discount rate $2.9 -10.3% 7% discount rate $2.2 -18.3% 15-year Incidences all occur in the 15 th year 3% discount rate $2.3 -27.0% 7% discount rate $1.4 -49.1% Alternative Segmented 20 percent of incidences occur in 1 styear, 50

percent in years 2 to 5, and 30 percent in years 6 to 20 3% discount rate $3.1 -3.2% 7% discount rate $2.5 -8.7% 5-Year Distributed 50 percent of incidences occur in years 1

and 2 and 50 percent in years 2 to 5 3% discount rate $3.4 4.9% 7% discount rate $3.1 17.1% Exponential Incidences occur at an exponentially

declining rate following year of change in exposure 3% discount rate $3.4 5.6% 7% discount rate $3.1 14.8%

aDollar values rounded to two significant digits.6c-5

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 m onetized 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 impac ts on overall benefits. For example, the overall impact of moving from the previous 5-year distrib uted lag to the segmented lag recommended by the SAB-HES in 2004 in the primary estimate is relatively modest, reducing benefits by approximately 5 percent when a 3 percent discount rate is used and 15 percent when a 7 percent discount rate is used. If no lag is as sumed, benefits are increased by around 10 percent relative to the segmented lag with a 3 percent discount rate and 30 percent with a 7 percent discount rate.
6c. 2 Threshold Sensitivity Analysis
Chapter 6 presents the results of the 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 conce ntration-response functions at 10 μg/m 3 . There is ongoing debate as to whether there exists a thre shold 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 o f the hypothesized relationship is the possibility that there exists a PM concentration level be low 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 r efute the existence of thresholds for the effects of PM on mortalityacross the range of concentrations in the studies” (U.S. EPA, 2004b, p. 9-44). EPA’s Science Adv isory Board (SAB) that provides advice on benefits analysis methods 2 has been to model premature mortality associated with PM exposure as a non-threshold effect, that is, with harmful effects to exposed populations regardless of the absolute level of amb ient 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 PM2.5-related pre mature mortality resulting from changes in the presumed threshold. We also provide a c orresponding estimate of the valuation of these changes in incidence.

2 The advice from the 2004 SAB-HES (U.S. EPA-SAB, 20 04b) is characterized by the following: “For the studies of long-term exposure, the HES notes that Krewski et al. (2000) have conducted the most carefu l work on this issue. They report that the associations b etween PM 2.5 and both all-cause and cardiopulmonary mortality were near linear within the relevant ranges, with n o 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 effec ts down to lower levels. Therefore, it is reasonable for EPA to assume a no threshold model down to, at least, t he low end of the concentrations reported in the studies.”6c-6

Table 6c-2: Mortality Threshold Sensitivity Analys is for 0.070 ppm Ozone Scenario (Using Pope et al., 2002 Effect Estimate with Slope Adjustment for Thresholds Abo ve 7.5 ug) 90 th Percentile Confidence Intervals Provided in Parenth eses a

East Western U.S. Excluding CA California Total
Less Certainty That Benefits Are at Least as Large No

Threshold 570

(230—920) 28
(11—45) 53

(21—85) 650

(260—1,100)

Threshold

at 7.5 μg 580

(230—930) 15
(6—24) 51

(20—85) 650

(250—1,000)

Threshold

at 10 μg 510

(200—810) 0.2
(0.07—0.3) 47

(18—75) 550

(220—890)

Threshold

at 12 μg 100

(40—160) 0.03

(0.01—0.04) 42

(16—67) 140

(56—230)
More Certainty That Benefits are at Least as Large Threshold

at 14 μg --- --- 36
(14—59) 36

(14—59)

a All estimates are rounded to 2 significant digits . All rounding occurs after final summing of unrounded estimates. As such, totals will not
sum across columns.6c-7

Table 6c-3: Sensitivity of Monetized Benefits of R eductions 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 0 th Percentile Confidence Intervals Provided in Parentheses a

Eastern U.S. Western U.S. Excluding CA California Total Nationwide Attainment

3% $3,300

($830-- $6,900) $160

($41--$340) $310

($77--$630) $3,800

($950--$7,900) Less Certain that Benefits Are at Least as Large No

Threshold 7% $2,800 ($700-- $5,800) $140

($34--$280) $260

($64--$530) $3,200

($800--$6,200)

3% $3,400

($840-- $7,000) $86

($22--$$180) $300

($74--$620) $3,700

($940--$7,800)

Threshold at 7.5 ug
7% $2,800 ($710-- $5,900) $72

($18--$150) $250

($63--$520) $3,200

($790--$6,600)

3% $2,900

($730-- $6,100) $1

($0.2--$2) $270

($67--$560) $3,200

($800--$6,600)

Threshold at 10 ug
7% $2,500 ($620-- $5,100) $0.8

($0.2--$1.7) $230

($57--$470) $2,700

($670--$5,600)

3% $590

($150-- $1,200) $0.2

($0.04--$0.3) $240

($61--$500) $830

($210--$1,700) Threshold at 12 ug 7% $490 ($120-- $1,000) $0.1

($0.03--$0.3) $200

($51--$420) $700

($180--$1,500)

3% -- -- $210 ($53--$440) $210

($53--$440)

More Certain that Benefits Are at Least as Large Threshold at

14 ug 7% -- -- $180 ($44--$370) $180

($44--$370)

a All estimates are rounded to 2 significant digit s. All rounding occurs after final summing of unro unded estimates. As such, totals will
not sum across columns.6c-8

6c.3 Income Elasticity of Willingness to Pay
As discussed in Chapter 6, our estimates of monetized benef its account for growth in real GDP per capita by adjusting the WTP for individual endpoints based on the central estimate of the adjustment factor for each of the categories (minor health effects, severe and chronic health effects, premature mortality, and visibility). We ex amined how sensitive the estimate of total benefits is to alternative estimates of th e income elasticities. Table 6c-3 lists the ranges of elasticity values used to calculate the income a djustment 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 c ategories, are presented in Table 6c-5.
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 Visibilityb
— —

a Derivation of these ranges can be found in Kleckne r and Neumann (1999) and Chestnut (1997). COI esti mates 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 Visibilityb
— —

a Based on elasticity values reported in Table C-4, U.S. Census population projections, and projections of real GDP per capita. b No range was applied for visibility because no ran ges were available in the current published literat ure.6c-9

Table 6c-6. Sensitivity of Monetized Benefits to Alte rnative Income Elasticities a Benefits Incremental to 080 ppm Partial Attainment Strategy

(Millions of 1999$)

Ozone Analysis PM Analysis Benefit Category

Lower Sensitivity Bound Upper

Sensitivity Bound Lower

Sensitivity Bound Upper

Sensitivity Bound
Minor Health Effect $64 $72 $9.7 $11
Severe and Chronic Health Effects -- -- $160 $190 Premature Mortalityb $2,000 $3,000 $2,800 $3,600
Total Benefits b $2,000 $3,100 $2,900 $3,800

aAll estimates rounded to two significant digits. b Using mortality effect estimate from Pope et al (20 02) to estimate PM 2.5 mortality and a 3 percent discount rate and mortality effect estimate from Bell (2004)

. c No range was applied for visibility because no ran ges were available in the current published literat ure.

Consistent with the impact of mortality on total bene fits, the adjustment factor for mortality has the largest impact on total benefits. The value of mortali ty 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 val ue 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 effe cts.

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 Standards, U.S. Environmental Protection Agency, Rese arch Triangle Park, NC and Air Quality Management Division, National Park Service, Denve r, CO.
Chestnut, L.G., and R.D. Rowe. 1990b. “A New National Park Visibility Value Estimates.” In Visibility and Fine Particles , Transactions of an AWMA/EPA International Specialty Conference, C.V. Mathai, ed. Air and Waste Management Asso ciation, Pittsburgh.6c-10

Clancy, L., P. Goodman, H. Sinclair, and D.W. Dockery. 2002. “Effect of Air-pollution Control on Death Rates in Dublin, Ireland: An Intervention Study.” Lancet Oct 19;360(9341):1210-4. Desvousges, W.H., F.R. Johnson, and H.S. Banzhaf. 1998. Environmental Policy Analysis With Limited Information: Principles and Applications o f the Transfer Method (New Horizons in Environmental Economics.) Edward Elgar Pub: London.

EPA-SAB-COUNCIL-ADV-00-001. October 1999. The Clean Air Act Amendments (CAAA) Section 812 Prospective Study of Costs and Benef its (1999): Advisory by the Health and Ecological Effects Subcommittee on Initial Assessment s of Health and Ecological Effects . Part 2.
EPA-SAB-COUNCIL-ADV-99-012. July 1999. The Clean Air Act Amendments (CAAA) Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Health and Ecological Effects Subcommittee on Initial Assessme nts of Health and Ecological Effects

. Part 1.
EPA-SAB-COUNCIL-ADV-01-004. September 2001. Review of the Draft Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Cost s of the Clean Air Act 1990-2020: An Advisory by a Special Panel of the Advisory Coun cil on Clean Air Compliance Analysis

.
EPA-SAB-COUNCIL-ADV-04-002. March 2004. Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of the Clean Air Act, 1990-2020: Advisory by the Health E ffects Subcommittee of the Advisory Council on Clean Air Compliance Analysis

. Kleckner, N., and J. Neumann. June 3, 1999. “Reco mmended Approach to Adjusting WTP Estimates to Reflect Changes in Real Income.” Memorandum to Ji m Democker, US EPA/OPAR.
Roosli M, Kunzli N, Braun-Fahrlander C, Egger M. 2005. “Ye ars of life lost attributable to air pollution in Switzerland: dynamic exposure-response model.” International Journal of Epidemiology34(5):1029-35.
U.S. Environmental Protection Agency (EPA). 2004. Air Qua lity Criteria for Particulate Matter, Volume II. Office of Research and Development. EPA/600/P-99/002bF, October 2004.7-1

Chapter 7: Discussion of Ozone Secondary Standard
Exposures to ozone have been associated with a wide array of vegetation and ecosystem effects in the published literature. These effects include those that damage or impair the intended use of the plant or ecosystem. Such effects are considered adverse to the publ ic welfare and can include: reduced plant growth, visi ble foliar (leaf) injury, reduced plant vigor (e.g., increased susceptibility to harsh weather, disease, insect pest infestation, and competition), reduced crop yields, and changes in ecos ystems and associated ecosystem services.
Vegetation effects research has shown that seasonal air quality indices that cumulate peak-weighted hourly ozone concentrations are the best candidates for relating exposure to plant growth effects. On the basis of this research, as well as other information considered in this review (e.g., policy-relevant backgro und (PRB) levels), the Staff Paper concluded that the cumulative, seasonal index referr ed to as “W126” is the most appropriate index for relating vegetation response to ambient ozone exposures. Based on additional conclusions regarding appropriate diurnal and seasonal exposure windows, the Staff Paper concluded that it was appropriate for the Administrator to consider a cumulative seasonal secondary standard, expressed as an ind ex of the annual sum of weighted hourly concentrations (using the W126 form), se t at a level in the range of 7 to 21 ppm-hours. The index would be cumulated over the 12-hour daylight window (8:00 a.m. to 8:00 p.m.) during the consecutive 3 month period durin g the ozone season with the maximum index value (hereafter referred to as the 12-hour, maximum 3-month W126).
The Staff Paper also considered the extent to whichthere is overlap between county-level air quality measured in terms of the 8-hour average form of the current secondary standard and that measured in terms of the 12-hour W126, alte rnative cumulative, seasonal form. These comparisons were done using 3-year averages for both forms, as well as using the 3-year average current 8-hour form and the annual W126 county-level air quality values. This Staff Paper assessment used 2002-2004 county-level air quality data from the AQS sites and the subset of CASTNET sites having the highest ozone levels for the counties in which they are located. Sinc e the completion of the Staff Paper, this analysis has been updated using the more recent 3-year pe riod of 2003 to 2005. Results from the more recent (2003-2005) 3-year aver age comparisons (see Table 1 below) showed that after meeting the current 3-year avera ge form of the 0.08-ppm, 8- hour average standard, the number of counties not meeting a 3 -year average W126 form ranged from 11 at the upper level of the proposed W126 rang e (21 ppm-hours) to 76 counties (W126 of 15 ppm-hours- representing the upper boun d of the CASAC recommended range), to 221 counties at the lower end of th e proposed W126 range (7 ppm-hours). The degree of overlap is greater when levels within the proposed range (0.070-0.075 ppm) for a revised 8-hour average standard are met, specifically the number of counties still exceeding a W126 form of a standard range from 0 at a W126 level of 21 ppm-hours to 25 at a W126 level of 7 ppm-hours. The Staff Paper n otes that when individual years are compared (e.g., using the annua l W126 level) significant variability occurs between years in the degree of overlap between th e numbers of counties meeting7-2

various levels of the 8-hour and W126 forms and, th erefore, cautions that the degree of protection for vegetation provided by an 8-hour average fo rm in terms of cumulative, seasonal exposures would not be expected to be cons istent on a year to year basis.
The Staff Paper also identified additional aspects of this analysis that would sug gest caution should be used in interpreting these results. First, due to the lack of more complete monitor coverage in many rural areas, the S taff Paper concluded that this analysis may not be an accurate reflection of the situati on in non-monitored, rural counties. Because of the lack of monitoring in rur al areas where important vegetation and ecosystems are located, it remains uncertain as to the extent to which air quality improvements designed to reduce 8-hour ozone average concentrations would reduce ozone exposures measured by a seasonal, cumulative W 126 index. The Staff Paper indicated this to be an important consideration because: (1) the biological d atabase stresses the importance of cumulative, seasonal exp osures in determining plant response; (2) plants have not been specifically tested for the importance of daily maximum 8-hour ozone concentrations in relation to plant response; and (3) the effects of attainment of a 8-hour standard in upwind urban areas on rural air quality distributions cannot be characterized with confidence due to the lack of monitori ng data in rural and remote

areas. In addition, though within the range of 8-hour aver age levels being proposed the numbers of counties exceeding mid- to low levels of W126 are great ly reduced, many of these counties contain areas of national public interest. Fo r example, at the 8-hour level of 0.075 ppm, 12 counties would still exceed the W126 level of 15 ppm-hours. Most of these counties contain high elevation, rural or remo te sites where ozone air quality distributions tend to be flatter and the potential for disconnect between 8-hour average and cumulative, seasonal forms, greater. Therefore, the S taff Paper notes that additional rural high elevation areas important for vegetationthat are not currently monitored likely experience similar ozone exposure patterns. These factor s are important considerations in determining whether the current 8-hour form can approp riately provide requisite protection for vegetation.
Due to time and resource limitations, EPA did not c alculate the costs and monetized benefits of a separate secondary standard. Considerationto these costs and benefits will be provided in the final RIA.7-3

Table 7.1 Comparison of number of counties exceedin g various W126 levels when meeting various levels of the 8-hr standard for the 3-year p eriod 2003-2005 1

1 The top value in each box represents the number of counties meeting the 8-hour level based on 2003-2005 data but exceeding the W126 level based on a 3-year W126 average for the 2003-2005 period. The numbers in parentheses indicate the range in the number of counties that exceed the W126 level on an annual basis in one of the three years—2003, 2004, 2005– based on 1-year W126 values . The range indicates significant interannual variability. Levels of 12-hr W126 (ppm-hrs) 8-hr level met >21 >15 >7 0.084 ppm 11

(7-27) 76

(23-173) 221

(244-382) 0.075 ppm 0

(0-7) 11

(3-33) 114

(49-134) 0.070 ppm 0

(0-1) 2

(1-6) 25

(13-36)8-1

Chapter 8: Conclusions and Implications of the Illustrativ e Benefit-Cost Analysis
Synopsis
EPA has performed an illustrative analysis to estimat e the costs and human health benefits of nationally attaining alternative ozone standards. We h ave considered 4 alternative standards incremental to attaining the current ozone standard: 0.079 ppm, 0.075 ppm, 0.070 ppm, and 0.065 ppm. This chapter summarizes these results and disc usses the implications of the analysis. This analysis serves both to satisfy the requirements of E.O. 12866 and to provide the public with an estimate of the potential costs and benefits of attaining alternative ozone standards. The benefit and cost estimates below are calculated incr emental to a 2020 baseline that incorporates air quality improvements achieved through the projected implementation of existing regulations and full attainment of the current standards for oz one and PM NAAQS (including the hypothetical control strategy developed in the RIA f or full attainment of the PM NAAQS 15/35 promulgated in September, 2006). This RIA presents two set s of results: The first reflects full attainment in all locations except two areas of California, which are plann ing to meet the current standards after 2020, and so have estimated costs and benefits for the analyzed standards for partial attainment in 2020 (their “glidepath” targe ts). 1 The second estimate, for California only, presents the additional costs and benefits that might result from California fu lly attaining the standards in a year beyond 2020. Finally, this chap ter provides additional context for the RIA analysis and a discussion of limitations and uncertainti es. In addition, given the technological limitations associated with reducing ozone precurso rs, we provide estimated cost and benefit numbers based on both partial attainment (manageable with current technologies) and full attainment (manageable in some locations only with hyp othetical technologies).

8.1 Results
Presentation of Results
There are two sets of results presented below. The first set of results is for 2020. For analytical purposes explained previously, we assume that almos t all areas of the country will meet each alternative standard in 2020 through the development of technologies at least as effective as the hypothetical strategies used in this illustration. It is expected that benefits and costs will begin occurring earlier, as states begin implementing control me asures to attain earlier or to show progress towards attainment. Some areas with very h igh levels of ozone do not plan to meet even the current standard until after 2020; specifically, two California areas have adopted plans for post-2020 attainment as noted above. In these locations, we provide e stimates of the costs and benefits of attaining a “glidepath” target in our 2 020 analysis year. 2 The 2020 results thus do

1 Because these two areas adopted 8-hour ozone implementation plans calling for post-2020 attainment after we had completed much of our analys is, these areas are assumed to meet the current standard in 2020 and 2021 respectively, somewhat earlier than the date in their plans, which results in a steeper glide path, and higher c osts and benefits, for all the standards analyzed. See chapter 4.

2 See footnote 1.8-2

not represent a complete “full attainment” scenario for the entire nation, particularly for more stringent alternative new standards examined. In order t o gain an understanding of the possible additional costs and benefits of fully attaining in California, we provide an additional set of results focusing on California.
By the year 2030, various mobile source rules, such as the onroad and nonroad diesel rules, among others, would be expected to be fully implement ed. Because California will likely not have to attain until closer to 2030, it is important to reflect the impact those rules might have on the emissions that affect ozone nonattainment. To reflect the emission reductions that are expected from these rules, we subtract those tons from our estimates of the emis sions reductions that might be needed for California to fully attain in 20 20, thus making our analysis more consistent with full attainment later than 2020. EP A did the analysis this way because to force full attainment in California in an earlier year wou ld not be consistent with the CAA, and would likely lead to an overstatement of costs because those area s might benefit from these existing federal or state programs that would be implemented between 2020 and the attainment year (see detail in Chapter 4); because additional new technolog ies may become available between 2020 and the attainment year; and/or the cost of existing technologies might fall over time due to economic factors such as economies of scale or improvements in the effi ciency of installing and operating controls (‘learning by doing’). On the other hand, it is also possible that n ew technologies might not meet the specifications, dev elopment time lines, or cost estimates provided in this analysis.
It is not appropriate to add together the 2020 natio nal attainment, California glidepath estimate and the estimate of California full attainment as an estimate of national full attainment in 2020 It is not appropriate to do this because each estimate is based on different baseline conditions for emissions and air quality. In addition, both estim ates include estimates of California glidepath results, leading to the potential for double counting if added together.
The following set of tables summarizes the costs an d benefits of the scenarios analyzed, and shows the net benefits for each of the scenarios ac ross a range of modeling assumptions concerning the calculation of costs and benefits. Tables 8.1a-c present benefits and costs of national attainment in 2020, including the “glidepath” targets for California. Companion Table 8.2 provides the estimated reductions in premature mortality and morbidity for national attainment in 2020, including the “glidepath” targe ts for California. Tables 8.3a-c present the additional costs and benefits of full attainment fo r California (“glidepath” plus future year attainment added together into one total); Table 8. 4 is the companion table showing estimated reductions in premature mortality and morbidity.

The individual row estimates for benefits reflect the variability in the functions available for estimating the largest source of benefits – avoided ozone premature mort ality. Ranges within the total benefits column reflect variability in the estimate s of PM premature mortality co-benefits across the available effect estimates. Ranges in the total costs column reflect different assumptions about the extrapolation of costs. The low end o f the range of net benefits is constructed by subtracting the highest cost from th e lowest benefit, while the high end of the range is constructed by subtracting the lowest cost from the highest benefit. Following these8-3

tables is a discussion of the implications of these estimates, as well as the uncertainties and limitations that should be considered in interpreting the estimates.8-4

Table 8.1a National Annual Costs and Benefits: 0.079 ppm Stan dard in 2020

(including California glidepath ) Mean Total Benefits, in Billions of 1999$ Premature Mortality Function or Assumption Reference Total Benefits* Total Costs** Net Benefits

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

Bell et al. 2005 $1.6 to $12 $3 to $3.3 -$1.7 to $8 .9 Ito et al. 2005 $1.7 to $12 $3 to $3.3 -$1.7 to $8.9 Meta-analysis Levy et al. 2005 $1.6 to $12 $3 to $3.3 -$1.7 to $8 .9 Assumption that association is not causal*** $1.1 to $11 $3 to $3.3 -$2.2 to $8.4

Table 8.1b National Annual Costs and Benefits: 0.075 ppm Sta ndard in 2020

(including California glidepath ) Mean Total Benefits, in Billions of 1999$ Premature Mortality Function or Assumption Reference Total Benefits* Total Costs** Net Benefits

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

Bell et al. 2005 $7.3 to $20 $5.5 to $8.8 -$1.5 to $15 Ito et al. 2005 $7.8 to $21 $5.5 to $8.8 -$1. to $15 Meta-analysis Levy et al. 2005 $8.7 to $22 $5.5 to $8.8 -$0.1 to $16 Assumption that association is not causal*** $1.5 to $15 $5.5 to $8.8 -$7.3 to $9

Table 8.1c National Annual Costs and Benefits: 0.070 ppm Sta ndard in 2020

(including California glidepath) Mean Total Benefits, in Billions of 1999$ Premature Mortality Function or Assumption Reference Total Benefits* Total Costs** Net Benefits

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

Bell et al. 2005 $9.7 to $31 $10 to $22 -$12 to $21 Ito et al. 2005 $10 to $32 $10 to $22 -$11 to $22 Meta-analysis Levy et al. 2005 $11 to $33 $10 to $22 -$10 to $23 Assumption that association is not causal*** $2.5 to $24 $10 to $22 -$20 to $148-5

Table 8.1d National Annual Costs and Benefits : 0.065 ppm Stan dard in 2020

(including California glidepath) Mean Total Benefits, in Billions of 1999$ Premature Mortality Function or Assumption Reference Total Benefits* Total Costs** Net Benefits

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

Bell et al. 2005 $18 to $55 $17 to $46 -$28 to $38 Ito et al. 2005 $19 to $56 $17 to $46 -$27 to $39 Meta-analysis Levy et al. 2005 $20 to $57 $17 to $46 -$27 to $40 Assumption that association is not causal*** $4.3 to $42 $17 to $46 -$42 to $25
*Includes ozone benefits, and PM 2.5 co-benefits. Range w as developed by adding the estimate from the ozone premature mortality function to both the lower and upper ends of the range of the PM2.5 premature mortality functions characterized in the expert elicitation **Range reflects lower and upper bound cost estimates ***Total includes ozone morbidity benefits only8-6 Table 8.2: Summary of Total Number of Annual Ozone and PM 2.5 -Related Premature Mortalities and Premature Morbidity Avoided: 2020 N ational 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) 200 to 1,900 430 to 2,600 670 t o 4,300 1,200 to 7,400

Bell (2005) 260 to 2,000 1,100 to 3,300 1,500 to 5, 100 2,800 to 9,000 Ito (2005) 270 to 2,000 1,200 to 3,300 1,600 to 5,2 00 3,000 to 9,200 Meta-Analysis Levy (2005) 260 to 2,000 1,300 to 3,500 1,800 to 5, 400 3,000 to 9,200 No Causality 180 to 1,900 230 to 2,400 390 to 4,000 660 to 6,900

Combined Estimate of Morbidity

Acute Myocardial Infarction 1,100 1,400 2,300 4,000 Hospital and ER Visits 1,300 5,600 7,600 13,000 Chronic Bronchitis 370 470 780 1,300 Acute Bronchitis 950 1,200 2,000 3,500 Asthma Exacerbation 7,300 9,400 16,000 27,000 Lower Respiratory Symptoms 8,100 10,000 17,000 29,00 0 Upper Respiratory Symptoms 5,900 7,500 13,000 22,000 School Loss Days 50,000 610,000 780,000 1,300,000 Work Loss Days 51,000 65,000 110,000 190,000 Minor Restricted Activity Days 430,000 2,000,000 2,7 00,000 4,700,0008-7

Table 8.3a California: Annual Costs and Benefits of Attaining 0.079 ppm Standard (beyond 2020)* Mean Total Benefits, in Billions of 1999$ Premature Mortality Function or Assumption Reference Total Benefits** Total Costs*** Net Benefits

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

Bell et al. 2005 $0.2 to $0.7 $0.3 to $1.7 -$1.5 to $0.4 Ito et al. 2005 $0.3 to $0.7 $0.3 to $1.7 -$1.4 to $0.4 Meta-analysis Levy et al. 2005 $0.2 to $0.7 $0.3 to $1.7 -$1.5 to $0.4 Assumption that association is not causal**** $0.05 to $0.5 $0.3 to $1.7 -$1.6 to $0.2

Table 8.3b California: Annual Costs and Benefits of Attaining 0.070 ppm Standard (beyond 2020)* Mean Total Benefits, in Billions of 1999$ Premature Mortality Function or Assumption Reference Total Benefits** Total Costs*** Net Benefits

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

Bell et al. 2005 $1.9 to $4.7 $2 to $13 -$11 to $2. 7 Ito et al. 2005 $2.1 to $4.8 $2 to $13 -$11 to $2.9 Meta-analysis Levy et al. 2005 $2.1 to $4.8 $2 to $13 -$11 to $2. 9 Assumption that association is not causal**** $0.4 to $3.1 $2 to $13 -$13 to $1.2

Table 8.3c California: Annual Costs and Benefits of Attaini ng 0.075 ppm Standard (beyond 2020)* Mean Total Benefits, in Billions of 1999$ Premature Mortality Function or Assumption Reference Total Benefits** Total Costs*** Net Benefits

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

Bell et al. 2005 $1.1 to $2.6 $1.1 to $6.2 -$5.1 to $1.5 Ito et al. 2005 $1.2 to $2.7 $1.1 to $6.2 -$5.1 to $1.6 Meta-analysis Levy et al. 2005 $1.2 to $2.7 $1.1 to $6.2 -$5 to $1.6 Assumption that association is not causal**** $0.2 to $1.7 $1.1 to $6.2 -$6 to $0.68-8

Table 8.3d California: Annual Costs and Benefits of Attainin g 0.065 ppm Standard (beyond 2020)* Mean Total Benefits, in Billions of 1999$ Premature Mortality Function or Assumption Reference Total Benefits** Total Costs*** Net Benefits

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

Bell et al. 2005 $3.1 to $7.2 $2.9 to $21 -$17 to $4 .3 Ito et al. 2005 $3.4 to $7.4 $2.9 to $21 -$17 to $4.5 Meta-analysis Levy et al. 2005 $3.3 to $7.4 $2.9 to $21 -$17 to $ 4.5 Assumption that association is not causal**** $0.5 to $4.6 $2.9 to $21 -$20 to $1.7

* Tables present the total of CA glidepath in 2020, plus the additional increment needed to reach full attainment in a year beyond 2020 ** Includes ozone benefits and PM 2.5 co-benefits. Ran ge was developed by adding the estimate from the ozone premature mortality function to both the lower and upper ends of the range of the PM2.5 premature mortality functions characterized in the expert elicitation ***Range reflects lower and upper bound cost estimat es ****Total includes ozone morbidity benefits only8-9 Table 8.4: Summary of Total Number of Annual Ozone and PM 2.5 -Related Premature Mortalities and Premature Morbidity Avoided: California Post 2020 Attainment Combined Estimate of Mortality Standard Alternative and Model or Assumption Combined Range of Ozone Benefits and PM2.5 Co-Benefits 0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm NMMAPS Bell (2004) 17 to 93 61 to 310 110 to 570 1 80 to 840

Bell (2005) 42 to 120 170 to 410 300 to 760 490 to 1,200 Ito (2005) 45 to 120 180 to 430 320 to 780 530 to 1 ,200 Meta-Analysis Levy (2005) 46 to 120 180 to 430 320 to 780 520 to 1 ,200 No Causality 8.2 to 84 26 to 270 49 to 500 72 to 740

Combined Estimate of Morbidity

Acute Myocardial Infarction 49 160 290 430 Hospital and ER Visits 200 790 1,400 2,200 Chronic Bronchitis 17 53 99 150 Acute Bronchitis 43 140 260 380 Asthma Exacerbation 330 1,100 2,000 2,900 Lower Respiratory Symptoms 360 1,200 2,200 3,200 Upper Respiratory Symptoms 270 850 1,600 2,300 School Loss Days 30,000 120,000 210,000 340,000 Work Loss Days 2,300 7,400 14,000 20,000 Minor Restricted Activity Days 87,000 340,000 600,00 0 960,0008-10

8.2 Discussion of Results
Relative Contribution of PM benefits to total benefits
Because of the relatively strong relationship betwe en PM2.5 concentrations and premature mortality, PM co-benefits resulting from reductions in NOxemissions can make up a large fraction of total montetized benefits, depending on the specific PM mortality impact function used, and on the relative magnitude of ozone benefits, which is dependent on the specific ozone mortality function assumed. PM co-benefits based on d aily average concentrations are calculated over the entire year, while ozone related ben efits are calculated only during the summer ozone season. Because the control strategies evaluated in this RIA are assumed to operate year round rather than only during the ozon e season, this means that PM benefits will accumulate during both the ozone season and the rest o f the year.
PM co-benefits account for between 13 and 99 percent of co-benefits, depending on the standard analyzed and on the choice of ozone and PM mortality functions used. The estimate with the lowest fraction from PM co-benefits occurs when ozone m ortality is based on the Levy et al (2005) study and when PM2.5 mortality is based on the function provided by “Expert K” 3 from the expert elicitation. The estimate with the highe st fraction from PM co-benefits occurs when no ozone mortality reductions are included (followin g the assumption of no causal relationship between ozone and mortality) and when PM2.5 mortalityis based on the function provided by “Expert E” 4 from the expert elicitation.
Impact of Uncertainty in the Magnitude of ozone bene fits

. The degree to which net benefits are positive depen ds largely on the size of the effect estimate used for the relationship between premature mortality and ozone and to a les ser extent on the cost extrapolation methodology. In the cases where n et benefits are negative, the magnitude of the economic loss depends largely on the extrapolation metho d used to calculate the costs of full attainment. Because of the high degree of uncertai nty in these calculations, overall conclusions about the magnitude of net benefits and the likelih ood they will be positive or negative for any of our evaluated scenarios cannot be drawn with any de gree of confidence. As such, we cannot conclude that strategies for attainment of a tighter ozone NAAQS would either pass or fail a cost-benefit test. In other words, we cannot make an estimate of whether costs will outweigh benefits (or vice versa). As we improve our databa ses of control technologies and refine our understanding of the magnitude of the relationships between air pollution and premature mortality, our confidence in estimates of costs and benefits will likely improve.

3 As discussed in Chapter 6, one way in which we char acterize the model uncertainty associated with the relationship between particulate matter an d premature mortality was to conduct an expert elicitation. The elicitation yielded twelve d ifferent functions, generated by asking 12 experts a structured set of questions, leading each to articulate a functional form for the relationship, in a probabilistic estimate of uncertai nty. Among the twelve experts, Expert K's function characterizes the weakest relationship between PM and premature mortality, whereas Expert E's function characterizes the strongest relatio nship.

4 See above.8-11

Challenges to Modeling Full Attainment in All Areas
Because of relatively higher ozone levels in several large urban areas (Southern California, Chicago, Houston, and the Northeastern urban corrid or, including New York and Philadelphia) and because of limitations on the available databas e of currently known emissions control technologies, EPA recognized from the outset that known and re asonably anticipated emissions controls would likely be insufficient to bring many areas into attainment with either the current or alternative, more stringent ozone standards. Therefore, we designed this analysis in two stages: the first stage focused on analyzing the air qual ity improvements that could be achieved through application of documented, well-characterized emissions controls, and the costs and benefits associated with those controls. The second sta ge utilized extrapolation methods to estimate the costs and benefits of additional emiss ions reductions needed to bring all areas into full attainment with the standards. Clearly, the se cond stage analysis is a highly speculative exercise, as it is based on estimating emission red uctions and air quality improvements without any information about the specific controls that wo uld be available to do so.
The structure of the RIA reflects this 2-stage analyti cal approach. Separate chapters are provided for the cost, emissions and air quality impacts of modeled controls and for extrapolated costs and air quality impacts. We have used the information currently available to develop reasonable approximations of the costs and benefits of the extrapo lated portion of the emissions reductions necessary to reach attainment. However, due to the high level of uncertainty in all aspects of the extrapolation, we judged it appropriate to provide sep arate estimates of the costs and benefits for the modeled stage and the extrapolated stage, as well as an overall estimate for reaching full attainment. There is a single chapter on benefits, because the methodology for estimating benefits does not change between stages. However, in that chapter, we again provide separate estimates of the benefits associated with the modeled and extrapolated portions of the analysis.
In both stages of the analysis, it should be recogn ized that all 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 responsible for developing and implementing emissions control programs to reach attainment with the ozone NAAQS, with the timing of attainment being determined by future deci sions by states and EPA. Our estimates are intended to provide information on the general magni tude 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 areasonable picture of the types of emissions controls that are currently available, the direct costs of those controls, the levels of emissions reductions that may be achieved with these controls, the ai r quality impact that can be expected to result from reducing emissions, and the public h ealth benefits of reductions in ambinent ozone levels. This analysis identifies those areas of th e U.S. where our existing knowledge of control strategies is not sufficient to allow us to model attainment, and where additional data or research may be needed to develop strategies for attainment. E PA plans to address some of these areas in the RIA analysis for the final rule through additional r esearch on control technologies, sensitivity analyses using air quality models, and refinement of me thods for extrapolating the costs and benefits of reaching full attainment.8-12

In many ways, regulatory impact analyses for proposed ac tions are a learning process that can yield valuable information about the technical and pol icy issues that are associated with a particular regulatory action. This is especially true for RIAs for proposed NAAQS, where we are required to stretch our understanding of both sc ience and technology to develop scenarios that illustrate how certain we are about how economi cally feasible the attainment of these standards might be regionally. The proposed ozone NAAQS RIA provided great challenges when compared to previous RIAs. Why was this so? Primarily because as we tighten standards across multiple pollutants with overlapping precursors ( e.g. the recent tightening of the PM 2.5 standards), we move further down the list of cost-ef fective known and available controls. As we deplete our database of available choices of known controls, we are left with background emissions and remaining anthropogenic emissions for which we do not have enough knowledge to determine how and at what cost reductions can be achieved in the future when attainment would be required. With the more stringent NAAQS, mor e areas will need to find ways of reducing emissions, and as existing technologies ar e either inadequate to achieve desired reductions, or as the stock of low-cost existing te chnologies is depleted (causing the cost per ton of pollution reduced to increase), there will be pr essure 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 evalu ated by states and localities, the actual technological path is highly uncertain.
Because of the lack of knowledge regarding the devel opment of future emissions control technologies, a significant portion of our analysis is based on extrapolating from available data to generate the emissions reductions necessary to reach full attainment of an alternative ozone NAAQS and the resulting costs and benefits. Studies indicate that it is not uncommon for preregulatory cost estimates to be higher than later e stimates, in part because of inability to predict technological advances. Over longer time horizons, such as the time allowed for areas with high levels of ozone pollution to meet the ozone NAAQS, the opportunity for technical advances is greater (See Chapter 5 for detail). Also, due to th e nature of the extrapolation method for benefits (which focuses on reductions in ozone only a t monitors that exceed the NAAQS), we generally understate the total benefits that would result from implementing additional emissions controls to fully attain the ozone NAAQS (i.e., assuming that the application of control strategies would result in ozone reductions both at nonattaining and attaining monitors). On the other hand, the possibility also exists that benefits are overestimated, both because it is possible that new technologies might not meet the specifications, d evelopment time lines, or cost estimates provided in this analysis and because the analysis assumes there are quantifiable benefits to reducing ambient ozone below each of the alternativ e standards.
Estimated benefits and costs may reflect both bias and uncertainty. While we strive to avoid bias and characterize uncertainty to the extent possible , we note that in some cases, biased estimates were used due to data and/or methodological limitation s. 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 an d cost extrapolations do not have a clear directional bias, however, they are much more uncerta in relative to the modeled emissions and cost estimates, because of the lack of refined info rmation about the relationship between8-13

emissions reductions and ozone changes in specific locations, and because of the difficulties in extrapolating costs along a marginal cost curve well be yond the observed data without accounting for shifts in the cost-curve due to impr ovements in technology or use of technologies over time. Of course, these benefits and costs wil l only be realized if the emission reductions projected in this extrapolated approach actually occur in the future.
8.3 What did we learn through this analysis?
1) As in our analysis for the PM NAAQS RIA, in selecting controls, we focused more on the ozone cost-effectiveness (measured as $/ppb) than on the NOx or VOC cost-effectiveness (measured as $/ton). When compared on a $/ton basis, many VOC controls (average $/ton of $4,100) appear cost-e ffective relative to NOx reductions (average $/ton of $3,600). However, whe n compared on a $/ppb basis, NOx reductions (average $/ppb of $36 million) are almost always more costeffective than VOC controls (average $/ppb of $164 million) because of the much lower conversion of VOC to ozone. The one exceptio n to this is in urban areas which are VOC limited. In those locations, NOx red uctions can actually result in increases in ozone, and as such, VOC reductions can be cost-effective relative to NOx on a $/ppb basis.
2) Our knowledge of technologies that might achieve NOx an d VOC reductions to attain alternative ozone NAAQS is insufficient. In some areas of the U.S., our existing controls database was insufficient to meet even the current ozone standard. After applying existing rules and the hypothetical controls applied in the PM NAAQS RIA across the nation (excluding California), we we re able to identify controls for 35 states and DC that reduced overall NOx emissions by 17 percent and VOC by 4 percent. For California, the percentages were 8 percent for NOx and 10 percent for VOC. After these reductions, remaining emissio ns were still substantial, with over 7 million tons of NOx and 9 million tons of VOCs remai ning. The large remaining inventories of NOx and VOC emissions suggests that additional control measures need to be developed, with appropriate consid eration of the relative effectiveness of NOx and VOC in achieving ozone reductions.
3) Most of the overall reductions in NOx achieved in o ur illustrative control strategy were from non-EGU point sources. This was due to the fact that: 1) EGUs have been heavily controlled under the recent NOx SIP call and Clean Air Interstate Rules. The EGU program we included in our strategy for meeting the alternative ozone standards was not intended to achieve overall r eductions in NOx beyond the CAIR caps, but instead to obtain NOx emission r eductions in areas where they would more effectively reduce ozone concentrations in d ownwind nonattainment areas; and 2) mobile sources are already subject to ong oing emission reduction programs through the Tier 2 highway, onroad diesel and nonroad diesel rules. Thus, the opportunities for controlling NOx emissions wer e much greater in the non-EGU sector than in the mobile or EGU sectors. Howeve r, the remaining NOx emissions from EGU and mobile sectors are still great er than non-EGU sources, and additional reductions from these sectors mayneed to be considered8-14 in developing strategies to achieve full attainment. We are evaluating technologies and programs that might be applied in the se sectors in the future. Exploratory analyses indicate that there are opport unities to achieve emission reductions from EGU peaking units on High Energy Deman d Days (HEDD) with targeted strategies. Another area under analysis is th e energy efficiency/clean distributed generation based emission reductions. Pote ntial changes in the generation mix as a result of increase in the use o f renewables and Renewable Portfolio Standards (RPS) are also likely to create changes in emission beh avior. However, overall regional or national emissions level s stay constant under a given cap.
4) Some EPA existing mobile source programs will help areas reach attainment. These programs promise to continue to help areas re duce ozone concentrations between 2020 and 2030. In California, continued impl ementation of mobile source rules including the onroad and nonroad diesel rules and the locomotive and marine engines rule are projected to reduce NOx emis sions by an additional 25 percent and VOC emissions by an additional 11 percent d uring this time period. These additional reductions will significantly reduc e the overall cost of attainment relative to what California might have needed to re duce from other sectors if attainment were to be required in 2020. However, de laying attainment by 10 years will result in delayed health benefits as well. Ba sed on a simple scaling exercise, we estimate that between $0.3 and $1 billion in benefits could have been realized from full attainment with the 0.070 ppm alternat ive each year between 2020 and 2030. However, the potential for extra costs of up to between $0.3 and $4 billion per year suggests that allowing for delayed attainment until 2030 for these severe nonattainment areas may make economic sense. We are unable at this time to identify controls that would achieve the full attainment in California by 2020.
5) Tightening the ozone standards can provide significa nt, but not uniform, health benefits . The magnitude of the benefits is highly uncertain , and is not expected to be uniform throughout the nation . While our illustrative analyses showed that the benefits of implementing a tighter standard will lik ely result in reduced health impacts for the nation as a whole, the particular sc enarios 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 compl exities involved in the atmospheric processes which govern the transformatio n of emissions into ozone result in some locations and times when reducing NOxemissions can actually increase ozone levels on some days (see Chapter 2 fo r 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 ef fect of implementing NOx controls is to increase overall ozone levels and increa se the health effects associated with ozone. This same phenomenon results i n some areas also seeing increases in PM 2.5 formation. The second reason is that the particular control strategy that we modeled for EGU sources is a modif ication to controls on sources within the overall cap and trade program in the East ern U.S, established under the8-15 CAIR. As with any cap and trade program, changes i n requirements at particular sources will result in shifts in power generation and emissions at other sources. Because under our chosen EGU control scenario the ov erall emissions cap for the CAIR region remains the same, some areas of the country will see a decrease in emissions, while others will see an increase. This is not unexpected, and is an essential element of the cap and trade program. Our goal in selecting the EGU control strategy was to focus the emissions reductions in areas likely to be nefit the most from EGU NOx emissions reductions, with emissi ons increases largely occurring in areas in attainment with the ozone NAA QS. However, this necessarily means that in those areas where emissio ns increases occurred, ozone levels would also be expected to increase, with commensurate increases in health impacts. On a national level, however, we expected overall he alth benefits of the modeled EGU strategy to be positive. In addition, our air quality modeling analysis showed that while ozone levels did increas e in some areas, none of these increases resulted in an attaining area moving into n onattainment. Adjustments to our control scenario might achieve a pattern of redu ctions that achieves further air quality improvement.
6) There is uncertainty in Estimating the Benefits of 0. 079 ppm and 0.075 ppm EPA employed a monitor rollback approach to estimate the bene fits of attaining an alternative standard of 0.079 ppm nationwide. This approach likely understates the benefits that would occur due to implementationof actual controls because controls implemented to reduce ozone concentrations at the highest monitor would likely result in some reductions in ozone concentr ations at attaining monitors down-wind (i.e. the controls would lead to co ncentrations below the standard in down-wind locations). Therefore, air qua lity improvements and resulting health benefits from full attainment would be more widespread than we have estimated in our rollback analysis.
EPA calculated 0.075 ppm benefits by interpolating the 0.07 0 ppm benefits estimates. 5 This interpolation approach may overestimate benefi ts relative to a modeled control scenario developed specifically to attain the 0.075 ppm alternative. The interpolation method scales down be nefits only at the monitors we project to exceed 0.075 ppm—but it still captures the b enefits achieved by the 0.070 ppm regional control strategy that occur outsi de of these projected nonattainment areas. To the extent that a modeled emis sion control strategy to attain 0.075 ppm does not include these broader regional e mission reductions, total benefits would be lower than those we have estimated in this RIA.
Interpolation and monitor rollback methods of benefi ts estimation are inherently different. As described above, for the purposes of reviewing this analysis, the reader should understand that the benefits describe d for attaining a standard of 0.079 ppm are likely understated, whereas the estimated benefits of attaining a standard of 0.075 ppm are likely overstated. EPA wi ll develop and present consistent approaches for the alternative standards for the final RIA.

5 This procedure is detailed in Appendix 6A.8-16

7) Tightening the ozone standards can incur significant, but uncertain, costs An engineering cost comparison demonstrates that the cost of the 0.070 ppm Ozone NAAQS control strategy ($3.9 billion per year) is o nly slightly higher than the Clean Air Interstate Rule ($3.6 billion per year) and roughly one and half to just over four times higher than the PM NAAQS 15/35 control strategy with annual engineering costs of $850 million. It should be n oted that for the Ozone NAAQS $3.9 billion represent the cost of partial att ainment. Full attainment using extrapolation methods are expected to increase to tal costs significantly. For example, total costs for the 0.070 ppm standard are s ignificant at $13 to $26 billion. Yet, the magnitude and distribution of costs across sectors and areas is highly uncertain. Our estimates of costs for a set of modeled NOx and VOC controls comprise only a small part of the estimated costs of full attainment. These estimated costs for the modeled set of control s are still uncertain, but they are based on the best available information on control technologies, and have their basis in real, tested technologies. Estimating cost s of full attainment required significant extrapolation of the cost curve for known technologies, and was based on generalized relationships between emissions and ozone levels. Based on air quality modeling sensitivity analyses, there is cle arly significant spatial variability in the relationship between local and regional NOx emission reductions and ozone levels across urban areas. However, because we wer e unable to analyze all of the urban areas that are expected to need reductions, w e used the same ratio of ozone to emissions throughout the U.S. This introduces signi ficant uncertainty into the calculation of the emissions reductions that might be needed to reach full attainment. In addition, because VOCs are generall y much less effective than NOx in achieving ozone reductions at key monitors (with the exception of California), we did not use any VOC control data in the extrapolation to full attainment. This meant that in some areas, we assumed the need for more expensive NOx controls than might be required if a sp ecific area chose to use a combination of NOx and VOC controls. However, VOC controls would have to be very inexpensive relative to NOx controls on a per ton basis in order for VOC controls to be a cost-effective substitute for NOx reductions. Extrapolating costs by applying a cost-curve based on known technologies al so introduces uncertainties. For some locations, the extrapolation requires only a modest reduction beyond known controls. In these cases, the extrapolation is likely reasonable and not as prone to uncertainties. However , for areas where the bulk of air quality improvements were derived from extrapolated emissions reductions that go well beyond the area of the known controls, the increasing marginal costs can suggest a cost per ton which stretches credibil ity. For example, in California, extrapolation to full attainment results in a margi nal cost for the last ton of NOx of $89,645 in Los Angeles and $74,495 in Kern County, which are five to six times larger than the marginal cost at the last known cos t effective control. Economic theory would suggest that as marginal costs rise, research and development to produce new, more cost effective tec hnologies will also increase, leading to a downward shift in the overall cost curve . We did not assume any8-17 shift in the cost curve to reflect technological inn ovation, instead we provide a sensitivity analysis by showing estimates assuming a high and low fixed cost per ton. We are likely overstating costs in the future when using the marginal cost and high fixed estimates.
8) Non-EGU point source controls dominate the estimated cost s. These costs account for about 70 percent of modeled costs. The average cost per ton for these reductions is approximately $3,400, and the highest m arginal cost for the last cost effective control applied is $15,267. Mobile source controls were also significant contributors to overall costs, accounting for over 25 percent of total modeled costs.
9) The economic impacts (i.e. social costs) of the cost of these modeled controls were not included in this analysis. Incorporating the economic impact of the extrapolated portion of the costs was too uncertain to be included as part of these estimates, and it was determined best to keep the modeled an d extrapolated costs on the same basis. However, incorporating any econ omic impacts would increase the total cost of attainment in 2020 for a revised ozone standard.
10) California costs and benefits are highly uncertain. . California faces large challenges in meeting any alternative standard, but their largest challenges may be in attaining the existing standard. Because our an alysis suggested that all available controls would be exhausted in attempting (unsuccessfully) to meet the current 0.08 ppm standard (effectively 0.084 ppm) all of the benefits and costs in California are based on extrapolation. Both the benefi ts and the costs associated with the assumed NOx and VOC reductions in Californ ia are particularly uncertain. The costs are uncertain to the point wh ere we have little confidence that they represent a meaningful characterization of possi ble future costs of implementation in California. As such, we recommend comp arison of costs and benefits for the rest of the U.S. as a basis for judgin g the relative merits of implementation. Costs for full attainment in Calif ornia will clearly be substantial, but the level of uncertainty about those costs is s imply too great to provide any useful conclusions. This is also true for many other areas of the U.S., but the uncertainties are magnified in the case of California

. 11) Costs and benefits will depend on implementation ti meframes. States will ultimately select the specific timelines for implem entation as part of their State Implementation Plans. To the extent that states seek classi fication as extreme nonattainment areas, the timeline for implementatio n may be extended beyond 2020, meaning that the amount of emissions reduction s that will be required in 2020 will be less, and costs and benefits in 2020 w ill also be lowered.9-1



### Chapter 9: 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 analyzes 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.

9.1 Executive Order 12866: Regulatory Planning and Review
Under section 3(f)(1) of Executive Order (EO) 12866 (58 FR 517 35, 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 0.075 ppm, 0.070 ppm, and 0.065 ppm. The RIA contains illustrative analyses that consider a limited number of emissions control scenarios that States and Regional Plann ing Organizations might implement to achieve these alternative ozone NAAQS. However, the Cle an 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 considere d in the development of State plans to implement the standards. Accordingly, although an RIA has been prepared, the results of the RIA have not been considered in issuing this rule.

9.2 Paperwork Reduction Act
This RIA does not impose an information collection burden under the provisions of the Paperwork Reduction Act, 44 U.S.C. 3501 et seq. Th ere are no information collection requirements directly associated with revisions to a NAAQS under section 109 of the CAA.
Burden is defined as the total time, effort, or financial resources expended by persons to generate, maintain, retain, or disclose or provide information to or for a Fede ral 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.9-2

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 acurrently valid OMB control number. The OMB control numbers for EPA’s regulations in 40 CFR are listed in 40 CFR part 9.

9.3 Regulatory Flexibility Act
The EPA has determined that it is not necessary to p repare a regulatory flexibility analysis in connection with this RIA. For purposes of assessin g the impacts of today’s rule on small entities, small entity is defined as: (1) a small b usiness that is a small industrial entity as define d by the Small Business Administration’s (SBA) regulations at 13 CFR 121.201; (2) a small governmental jurisdiction that is a government of acity, county, town, school district or special district with a population of less than 50,000; and(3) a small organization that is any not-forprofit enterprise which is independently owned and operated and is not dominant inits field.
After considering the economic impacts of today’s rule on small entities, EPA has concluded that this action will not have a significant economic impact on a substantial number of small entities. This rule will not impose any requirements on small 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).

9.4 Unfunded Mandates Reform Act
Title II of the Unfunded Mandates Reform Act of 1995 (UMRA), Public Law 104-4, establishes requirements for Federal agencies to assess the eff ects of their regulatory actions on State, local, and Tribal governments and the private sector. Under sect ion 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 no t apply when they are inconsistent with applicable law. Moreover, section 205 allows EPA to adopt an alternative other than the least costly, most cost-effective or least burdenso me alternative if the Administrator publishes with the final rule an explanation why that alternative was n ot adopted. Before EPA establishes any regulatory requirements that may significantly or uniquely affect small governments, including Tribal governments, it must have developed unde r section 203 of the UMRA a small government agency plan. The plan must provide for notifyin g potentially affected small governments, enabling officials of affected small governm ents to have meaningful and timely input in the development of EPA regulatory proposals with significant Federal i ntergovernmental mandates, and informing, educating, and advising small gov ernments on compliance with the regulatory requirements.
This proposal contains no Federal mandates (under th e regulatory provisions of Title II of the UMRA) for State, local, or Tribal governments or th e private sector. The rule imposes no new9-3

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. See 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 pursuant to the Unfunded Mandates Reform Act would not furnish any information which the court could consider in revie wing the NAAQS). Accordingly, EPA has determined that the provisions of secti ons 202, 203, and 205 of the UMRA do not apply to this final decision. The EPA ack nowledges, 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. Accordingl y, EPA has addressed unfunded mandates in the notice that announces the revisions to 40 CFR part 58, and will, as appropriate, address unfunded mandates whe n it proposes any revisions to 40 CFR part 51.

9.5 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 the relationship between the national government and the States, or on the distribution of power and responsibilities among the various levels of government.”
At the time of this proposal, EPA concludes that the propos ed rule would not have substantial direct effects on the States, on the relationship between the national government and the States, or on the distribution of power and responsibilities among the various levels of government, as specified in Executive Order 13132. However, EPA re cognized that States would have a substantial interest in this rule and any correspon ding revisions to associated SIP requirements and air quality surveillance requirements, 40 CFR part 51 and 40 CFR part 58, respectively. Therefore, in the spirit of Executive Order 13132, and consistent with EPA policy to promote communications between EPA and State and local governments , EPA specifically solicits comment on the rule from State and local officials at this ti me.

9.6 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 tribal officials in the development of regulatory policies that have tribal implications.” This rule concerns the establishment of ozone NAAQS. The Tribal Authority Rule gives Tribes the oppo rtunity to develop and implement9-4

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.
This proposed rule does not have Tribal implication s, as specified in Executive Order 13175. It does not have a substantial direct effect on one or more Indian Tribes, s ince Tribes are not obligated to adopt or implement any NAAQS. Thus, E xecutive Order 13175 does not apply to this rule. However, in the spirit of efficaciousness, EPA staff participated in the regularly scheduled Tribal Air call sponsored by the National Tribal A ir Association during the spring of 2007 as this proposal was under development. EPA sp ecifically solicits additional comment on the proposed NAAQS rule from Tribal officials.

9.7 Executive Order 13045: Protection of Children from Environmental Health & Safety Risks
Executive Order 13045, “Protection of Children from Environmental Health Risks and Safety Risks” (62 FR 19885, April 23, 1997) applies to any rule that: (1) is determined to be “economically significant” as defined under Executive 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 cr iteria, 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 13 045 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 popu lation subgroups such as the elderly and people with existing heart or lung disease, are potentially susce ptible to health effects resulting from ozone exposure. Because children are considered a potentially susceptible population, we have carefully evaluated the environmental health effects of exposure to ozone pollution to this sub-population. These effects and the size of the population affected are summarized in section 8.7 of the Criteria Document and section 3.6 of the Staff Paper, and th e results of our evaluation of the effects of ozone p ollution on children are discussed in sections II.A-C of the NAAQS proposal preamble.

9.8 Executive Order 13211: Actions that Significantly Affect Energy Supply, Distribution or Use
This proposed rule is not a “significant energy act ion” as defined in Executive Order 13211, “Actions Concerning Regulations That Significantly Affe ct Energy Supply, Distribution, or Use” (66 FR 28355 (May 22, 2001)) because in the Agency’ s judgment it is not likely to have a significant adverse effect on the supply, distribution, or use of energy. The purpose of this rule is9-5

to establish revised NAAQS for ozone. The rule does not presc ribe specific pollution control strategies by which these ambient standards will be met. Such strategies will be developed by States on a case-by-case basis, and EPA cannot predict whether the control options selected by States will include regulations on energy suppliers , distributors, or users. Thus, EPA concludes that this rule is not likely to have any adverse en ergy effects and does not constitute a significant energy action as defined in Executive Order 13211.

9.9 National Technology Transfer Advancement Act
Section 12(d) of the National Technology Transfer 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 standar ds 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 deci des not to use available and applicable voluntary consensus standards. Since EPA is not changing any of the monitoring requirements as part of this proposal, there are no impacts associated with the NTTAA.

9.10 Executive Order 12898: Federal Actions to Address EnvironmentalJustice in Minority Populations and Low-Income Populations
Executive Order 12898, “Federal Actions to Address E nvironmental 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 standards 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 withpreexisting conditions. Thus it remains EPA’s conclusion that this rule is not expected to have dispr oportionate negative impacts on minority or low income populations.United States Office of Air Quality Planning and Standards Publication No. EPA-452/R-07-008Environmental Protection Air Quality Strategies andStandards Division July 2007Agency Research Triangle Park, NC