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# Regulatory Impact Analysis of the Final Revisions to the National Ambient Air Quality Standards for Ground-Level Ozone

EO12866_O3-NAAQS-RIN2060-AP38-RIA-FINAL_20150825.docx

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## EPA-452/R-14-007 August 2015

Regulatory Impact Analysis of the Final Revisions to the National Ambient Air Quality Standards for Ground-Level Ozone

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Health and Environmental Impacts Division Research Triangle Park, NC 27711

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CONTACT INFORMATION This document has been prepared by staff from the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. Questions related to this document should be addressed to Robin Langdon, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, C439-02, Research Triangle Park, North Carolina 27711 (email: langdon.robin@epa.gov ). The docket number for this Regulatory Impact Analysis is EPA-HQOAR-2013-0169.

ACKNOWLEDGEMENTS
In addition to EPA staff from the Office of Ai r Quality Planning and Standards, personnel from the Office of Atmospheric Programs, the Office of Transportation and Air Quality, the Office of Policy Analysis and Review, and the Office of Policy’s National Center for Environmental Economics contributed data and analysis to this document.

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i TABLE OF CONTENTS

page
LIST OF TABLES ................................................................................................................ ..... viii

LIST OF FIGURES ............................................................................................................... ... xvii

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

Overview ...................................................................................................................... ... ES-1 ES.1 Overview of Analytical Approach .................................................................. ES-4 ES.1.1 Establishing the Baseline ............................................................................ ES-5 ES.1.2 Control Strategies and Emissions Reductions ............................................ ES-6 ES.1.2.1 Emissions Reductions from Identified Controls in 2025 ................... ES-7 ES.1.2.2 Emissions Reductions beyond Identified Controls in 2025 ............... ES-9 ES.1.2.3 Emissions Reductions beyond Identified Controls for Post-2025 ... ES-10 ES.1.3 Human Health Benefits ............................................................................ ES-11 ES.1.4 Welfare Co-Benefits of the Primary Standard .......................................... ES-13 ES.2 Results of Benefit-Cost Analysis .................................................................. ES-14 ES.3 Improvements between the Proposal and Final RIAs ................................... ES-19 ES.4 Uncertainty ................................................................................................... ES-20 ES.5 References .................................................................................................... ES-21

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

Introduction .................................................................................................................. ....... 1-1 1.1 Background .............................................................................................................. 1-2 1.1.1 National Ambient Air Quality Standards ....................................................... 1-2 1.1.2 Role of Executive Orders in the Regulatory Impact Analysis ....................... 1-3 1.1.3 Illustrative Nature of the Analysis ................................................................. 1-4 1.2 The Need for National Ambient Air Quality Standards ............................................ 1-5 1.3 Overview and Design of the RIA ............................................................................. 1-6 1.3.1 Establishing Attainment with the Current Ozone National Ambient Air

Quality Standard ....................................................................................................... 1-6 1.3.2 Establishing the Baseline for Evaluation of Revised and Alternative

Standards .................................................................................................................. 1- 8 1.3.3 Cost Analysis Approach .............................................................................. 1-11 1.3.4 Human Health Benefits ............................................................................... 1-11 1.3.5 Welfare Co-Benefits .................................................................................... 1-12 1.4 Updates between the Proposal and Final RIAs ....................................................... 1-12 1.5 Organization of the Regulatory Impact Analysis .................................................... 1-14 1.6 References .............................................................................................................. 1-15
CHAPTER 2: EMISSIONS, AIR QUALITY MODELING AND ANALYTIC METHODOLOGIES .......................................................................................................... 2-1

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ii Overview ...................................................................................................................... ...... 2-1 2.1 Emissions and Air Quality Modeling Platform ......................................................... 2-4 2.2 Projecting Ozone Levels into the Future................................................................... 2-6 2.2.1 Methods for Calculating Future Year Ozone Design Values ......................... 2-6 2.2.2 Emissions Sensitivity Simulations ................................................................. 2-8 2.2.3 Determining Ozone Response Factors from Emissions Sensitivity

Simulations ............................................................................................................. 2-13 2.2.4 Combining Response from Multiple Sensitivity Runs to Determine Tons

of Emissions Reductions to Meet Various NAAQS Levels .................................... 2-16 2.2.5 Monitoring Sites Excluded from Quantitative Analysis .............................. 2-18 2.3 Creating Spatial Surfaces for BenMap ................................................................... 2-20 2.4 Improvements in Emissions and Air Quality for the Final RIA .............................. 2-24 2.4.1 Improvement in Emissions .......................................................................... 2-24 2.4.2 Improvements in Air Quality Modeling ...................................................... 2-26 2.5 References .............................................................................................................. 2-27
APPENDIX 2A: ADDITIONAL AIR QUALITY ANALYSIS AND RESULTS ................. 2A-1

2A.1 2011 Emissions and Air Quality Modeling Platform ...................................... 2A-1 2A.1.1 Photochemical Model Description and Modeling Domain ......................... 2A-1 2A.1.2 Meteorological Inputs, Initial Conditions, and Boundary Conditions ......... 2A-2 2A.1.3 2025 Base Case Emissions Inputs .............................................................. 2A-4 2A.1.4 2011 Model Evaluation for Ozone ............................................................. 2A-8 2A.2 VOC Impact Regions ................................................................................... 2A-31 2A.3 Monitors Excluded from the Quantitative Analysis ...................................... 2A-31 2A.3.1 Sites without Projections Due to Insufficient Days .................................. 2A-32 2A.3.2 Winter Ozone ........................................................................................... 2A-33 2A.3.3 Monitoring Sites in Rural/Remote Areas of the West and Southwest ....... 2A-34 2A.4 Design Values for All Monitors Included in the Quantitative Analysis ........ 2A-37 2A.5 References .................................................................................................... 2A-66
Chapter 3 : CONTROL STRATEGIES AND EMISSIONS REDUCTIONS ............................ 3-1

Overview ...................................................................................................................... ...... 3-1 3.1 The 2025 Control Strategy Scenarios ....................................................................... 3-2 3.1.1 Approach for the Revised Standard of 70 ppb and Alternative Standard of 65 ppb ....................................................................................................... 3-2 3.1.2 Identified Control Measures .......................................................................... 3-7 3.1.3 Results ........................................................................................................... 3-9 3.2 The Post-2025 Scenario for California ................................................................... 3-16 3.2.1 Creation of the Post-2025 Baseline Scenario for California ........................ 3-16 3.2.2 Approach for Revised Standard of 70 ppb and Alternative Standard of 65

ppb for California ................................................................................................... 3-19 3.2.3 Results for California .................................................................................. 3-19 3.3 Improvements and Refinements since the Proposal RIA ........................................ 3-23 3.4 Limitations and Uncertainties ................................................................................. 3-25 3.5 References .............................................................................................................. 3-28

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

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

Baseline and Alternatives ............................................................................................. 3A-1 3A.2 Numeric Examples of Calculation Methodology for Changes in Design Values .............................................................................................................. 3 A-3 3A.3 Types of Control Measures ............................................................................ 3A-4 3A.4 Application of Control Measures in Geographic Areas .................................. 3A-5 3A.5 NO x 

### Control Measures for Non-EGU Point Sources ...................................... 3A-8 3A.6 VOC Control Measures for Non-EGU Point Sources ................................... 3A-14 3A.7 NO x 

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

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

Overview ...................................................................................................................... ...... 4-1 4.1 Estimating Engineering Costs .................................................................................. 4-2 4.1.1 Methods and Data ......................................................................................... 4-3 4.1.2 Engineering Cost Estimates for Identified Controls ...................................... 4-9 4.2 The Challenges of Estimating Costs for Unidentified Control Measures ................ 4-16 4.2.1 Impact of Technological Innovation and Diffusion ..................................... 4-18 4.2.2 Learning by Doing ...................................................................................... 4-22 4.2.3 Incomplete Characterization of Available NO x 

### Control Technologies ........ 4-24 4.2.4 Comparing Baseline Emissions and Controls across Ozone NAAQS RIAs

from 1997 to 2014 .................................................................................................. 4-31 4.2.5 Possible Alternative Approaches to Estimate Costs of Unidentified

Control Measures ................................................................................................... 4-32 4.2.6 Conclusion .................................................................................................. 4-36 4.3 Compliance Cost Estimates for Unidentified Emissions Controls .......................... 4-36 4.3.1 Methods ...................................................................................................... 4-37 4.3.2 Compliance Cost Estimates from Unidentified Controls ............................. 4-41 4.4 Total Compliance Cost Estimates ........................................................................... 4-42 4.5 Economic Impacts .................................................................................................. 4-43 4.5.1 Introduction ................................................................................................. 4-43 4.5.2 Summary of Market Impacts ....................................................................... 4-45 4.6 Differences between the Proposal and Final RIAs .................................................. 4-46 4.7 Uncertainties and Limitations ................................................................................. 4-47 4.8 References .............................................................................................................. 4-49

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

Overview ...................................................................................................................... ... 4A-1 4A.1 Cost of Identified Controls in Alternative Standards Analyses ....................... 4A-1 4A.2 Alternative Estimates of Costs Associated with Emissions Reductions from

Unidentified Controls ................................................................................................... 4A-6

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

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

4A.3.1 Regression Approach .................................................................................... 4A-7 4A.3.2 Simulation Approach ..................................................................................... 4A-9
CHAPTER 5: QUALITATIVE DISCUSSION OF EMPLOYMENT IMPACTS OF AIR QUALITY ....................................................................................................................... ... 5-1

Overview ...................................................................................................................... ...... 5-1 5.1 Economic Theory and Employment ......................................................................... 5-1 5.2 Current State of Knowledge Based on the Peer-Reviewed Literature ....................... 5-6 5.2.1 Regulated Sectors .......................................................................................... 5-7 5.2.2 Economy-Wide ............................................................................................. 5-8 5.2.3 Labor Supply Impacts ................................................................................... 5-8 5.3 Employment Related to Installation and Maintenance of NO x 

### Control Equipment .. 5-9 5.3.1 Employment Resulting from Addition of NO x 

### Controls at EGUs ............... 5-10 5.3.1.1 Existing EGUs Without SCR Systems (or SCR Systems Operating

less than Full Time) ......................................................................................... 5-11 5.3.1.2 Labor Estimates for Installing and Operating Individual SCR Systems 5-15 5.3.2 Assessment of Employment Impacts for Individual Industrial,

Commercial, and Institutional (ICI) Boilers and Cement Kilns .............................. 5-16

5.3.2.1 ICI Boilers ............................................................................................ 5-17 5.3.2.2 Cement Kilns ........................................................................................ 5-18 5.4 Conclusion ............................................................................................................. 5-19 5.5 References .............................................................................................................. 5-20

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

6.1 Summary .................................................................................................................. 6-1 6.2 Overview .................................................................................................................. 6-3 6.3 Updated Methodology Presented in the Proposal and Final RIAs ............................ 6-7 6.4 Human Health Benefits Analysis Methods ............................................................... 6-9 6.4.1 Health Impact Assessment .......................................................................... 6-10 6.4.2 Economic Valuation of Health Impacts ....................................................... 6-12 6.4.3 Estimating Benefits for 2025 and Post-2025 Analysis Years ...................... 6-14 6.4.4 Benefit-per-ton Estimates for PM 2.5 

### ............................................................ 6-15 6.5 Characterizing Uncertainty ..................................................................................... 6-17 6.5.1 Monte Carlo Assessment ............................................................................. 6-18 6.5.2 Sensitivity Analyses .................................................................................... 6-19 6.5.3 Supplemental Analyses ............................................................................... 6-20 6.5.4 Qualitative Assessment of Uncertainty and Other Analysis Limitations ..... 6-21 6.6 Benefits Analysis Data Inputs ................................................................................ 6-21 6.6.1 Demographic Data ...................................................................................... 6-21 6.6.2 Baseline Incidence and Prevalence Estimates ............................................. 6-22 6.6.3 Effect Coefficients ...................................................................................... 6-26 6.6.3.1 Ozone Exposure Metric ........................................................................ 6-33 6.6.3.2 Ozone Premature Mortality Effect Coefficients .................................... 6-34

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v 6.6.3.3 PM 2.5 

### Premature Mortality Coefficients ................................................ 6-39 6.6.3.4 Hospital Admissions and Emergency Department Visits ...................... 6-44 6.6.3.5 Acute Health Events ............................................................................. 6-47 6.6.3.6 Nonfatal Acute Myocardial Infarctions (AMI) (Heart Attacks) ............ 6-52 6.6.3.7 Worker Productivity ............................................................................. 6-53 6.6.3.8 Unquantified Human Health Effects ..................................................... 6-53 6.6.4 Economic Valuation Estimates .................................................................... 6-54 6.6.4.1 Mortality Valuation .............................................................................. 6-56 6.6.4.2 Hospital Admissions and Emergency Department Valuation ............... 6-66 6.6.4.3 Nonfatal Myocardial Infarctions Valuation .......................................... 6-68 6.6.4.4 Valuation of Acute Health Events ........................................................ 6-69 6.6.4.5 Growth in WTP Reflecting National Income Growth over Time ......... 6-71 6.6.5 Benefit per Ton Estimates Used in Modeling PM 2.5 

### -Related Co-benefits ... 6-75 6.7 Benefits Results ...................................................................................................... 6-77 6.7.1 Benefits of Attaining a Revised Ozone Standard in 2025 ............................ 6-77 6.7.2 Benefits of the Post-2025 Scenario .............................................................. 6-84 6.7.3 Uncertainty in Benefits Results (including Results of Sensitivity Analyses

and Supplemental Analyses) .................................................................................. 6-89

6.7.3.1 Sensitivity Analyses ............................................................................. 6-90 6.7.3.2 Supplemental Analyses......................................................................... 6-91 6.8 Discussion .............................................................................................................. 6-92 6.9 References .............................................................................................................. 6-95

APPENDIX 6A: COMPREHENSIVE CHAR ACTERIZATION OF UNCERTAINTY IN OZONE BENEFITS ANALYSIS .................................................................................... 6A-1

Overview ...................................................................................................................... ... 6A-1 6A.1 Description of Classifications Applied in the Uncertainty Characterization ... 6A-1 6A.1.1 Direction of Bias ........................................................................................ 6A-2 6A.1.2 Magnitude of Impact .................................................................................. 6A-2 6A.1.3 Confidence in Analytic Approach .............................................................. 6A-3 6A.1.4 Uncertainty Quantification ......................................................................... 6A-4 6A.2 Organization of the Qualitative Uncertainty Table ......................................... 6A-5 6A.3 References .................................................................................................... 6A-14
Appendix 6B: SENSITIVITY ANALYSES RELATED TO THE BENEFITS ANALYSIS . 6B-1

Overview ...................................................................................................................... ... 6B-1 6B.1 Sensitivity Analysis for Alternative C-R Functions for Short-term Exposure

to Ozone ...................................................................................................................... . 6B-2 6B.2 Sensitivity Analysis for Monetized Benefits for Premature Mortality from

Long-term Exposure to Ozone ...................................................................................... 6B-3 6B.3 Threshold Sensitivity Analysis for Premature Mortality Incidence and

Benefits from Long-term Exposure to Ozone ............................................................... 6B-4 6B.4 Sensitivity Analysis for Alternative C-R Functions for PM 2.5 

### –Related

Mortality ..................................................................................................................... .. 6B-6 6B.5 Sensitivity Analysis for Income Elasticity of Willingness-to-Pay ................ 6B-12

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vi 6B.6 Supplemental Analysis: Age Group-Differentiated Aspects of Short-Term Percent of Ozone-related Morta lity Reduced ................................................... 6B-13 6B.7 Supplemental Analysis: Evaluation of Mortality Impacts Relative to the

Baseline Pollutant Concentrations for both Short-Term Ozone Exposure-Related Mortality and Long-Term PM 2.5 

### Exposure-Related Mortality .................................... 6B-18 6B.8 Sensitivity Analysis of Ozone-related Impacts on Outdoor Worker

Productivity ................................................................................................................ 6 B-25 6B.9 References .................................................................................................... 6B-27

CHAPTER 7: IMPACTS ON PUBLIC WELFARE OF ATTAINMENT STRATEGIES TO MEET PRIMARY AND SECONDARY OZONE NAAQS ............................................... 7-1

Overview ...................................................................................................................... ...... 7-1 7.1 Welfare Benefits of Strategies to Attain Primary and Secondary Ozone Standards .. 7-2 7.2 Welfare Benefits of Reducing Ozone ....................................................................... 7-3 7.3 Additional Welfare Benefits of Strategies to Meet the Ozone NAAQS .................... 7-6 7.4 References ................................................................................................................ 7-8

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

Overview ...................................................................................................................... ...... 8-1 8.1 Results ...................................................................................................................... 8-1 8.2 Improvements between the Proposal and Final RIAs ............................................... 8-9 8.2.1 Relative Contribution of PM Benefits to Total Benefits .............................. 8-11 8.2.2 Developing Future Control Strategies with Limited Data ............................ 8-11 8.3 Net Present Value of a Stream of Costs and Benefits ............................................. 8-14 8.4 Framing Uncertainty ............................................................................................... 8-16 8.5 Key Observations from the Analysis ...................................................................... 8-18 8.6 References .............................................................................................................. 8-20

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

Overview ...................................................................................................................... ...... 9-1 9.1 Executive Order 12866: Regulatory Planning and Review ...................................... 9-1 9.2 Paperwork Reduction Act ......................................................................................... 9-1 9.3 Regulatory Flexibility Act ... ..................................................................................... 9-3 9.4 Unfunded Mandates Reform Act .............................................................................. 9-3 9.5 Executive Order 13132: Federalism ......................................................................... 9-3 9.6 Executive Order 13175: Consultation and Coordination with Indian Tribal

Governments ................................................................................................................... 9-4 9.7 Executive Order 13045: Protection of Children from Environmental Health &

Safety Risks .................................................................................................................. ... 9-4 9.8 Executive Order 13211: Actions that Significantly Affect Energy Supply,

Distribution, or Use ......................................................................................................... 9-5 9.9 National Technology Transfer and Advancement Act .............................................. 9-5 9.10 Executive Order 12898: Federal Actions to Address Environmental Justice in

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

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

APPENDIX 9A: SOCIO-DEMOGRAPHIC CHARACTERISTICS OF POPULATIONS IN CORE BASED STATISTICAL AREAS WITH OZONE MONITORS EXCEEDING REVISED AND ALTERNATIVE OZONE STANDARDS ............................................ 9A-1

Overview ...................................................................................................................... ... 9A-1 9A.1 Design of Analysis ......................................................................................... 9A-1 9A.1.1 Demographic Variables Included in Analysis ............................................ 9A-3 9A.2 Considerations in Evaluating and Interpreting Results ................................... 9A-4 9A.3 Presentation of Results ................................................................................... 9A-5

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viii LIST OF TABLES

Table ES-1. Summary of Emissions Reductions by Sector for the Identified Control Strategy for the Revised Stan dard Level of 70 ppb for 2025, except California (1,000 tons/year) .................................................................................................................... . ES-9

Table ES-2. Summary of Emissions Reductions by Sector for the Identified Control Strategy for Alternative Standard Level of 65 ppb for 2025, except California (1,000 tons/year) .................................................................................................................... . ES-9

Table ES-3. Summary of Emissions Reductions from the Unidentified Control Strategies for the Revised and Alternative Standard Levels for 2025, except California (1,000 tons/year) ................................................................................................................... ES-10

Table ES-4. Summary of Emissions Reductions from the Unidentified Control Strategies for the Revised and Alternative Standard Le vels for Post-2025 - California (1,000 tons/year) ................................................................................................................... ES-11

Table ES-5. Total Annual Costs and Benefits for U.S., except California in 2025 (billions of 2011$, 7% Discount Rate) .................................................................................... ES-15

Table ES-6. Summary of Total Number of Annual Ozone and PM-Related Premature Mortalities and Premature Morbidity: 2025 National Benefits .................................. ES-15

Table ES-7. Summary of Total Control Costs (Identified + Unidentified Control Strategies) by Revised and Alternative Standard Levels for 2025 - U.S., except California (billions of 2011$, 7% Discount Rate) ..................................................... ES-16

Table ES-8. Regional Breakdown of Monetized Ozone-Specific Benefits Results for 2025 (Nationwide Benefits of Attaining the Revised and Alternative Standard Levels Everywhere in the U.S., except California) ............................................................... ES-16

Table ES-9. Total Annual Costs and Benefits of the Identified + Unidentified Control Strategies Applied in California, Post-2025 (billions of 2011$, 7% Discount Rate) . ES-17

Table ES-10. Summary of Total Number of Annual Ozone and PM-Related Premature Mortalities and Premature Morbidity: Post-2025 ...................................................... ES-18

Table ES-11. Summary of Total Control Costs (Identified + Unidentified Control Strategies) by Revised and Alternative Sta ndards for Post-2025 - California (billions of 2011$, 7% Discount Rate) .................................................................................... ES-18

Table ES-12. Regional Breakdown of Monetized Ozone-Specific Benefits Results for Post-2025 (Nationwide Benefits of Attaining Revised and Alternative Standards just in California) ............................................................................................................. ES -18

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

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ix Table 2-2. List of Emissions Sensitivity Modeling Runs Modeled in CAMx to Determine Ozone Response Factors ............................................................................... 2-8

Table 2A-1. 2011 and 2025 Base Case NOx and VOC Emissions by Sector (thousand tons) ............................................................................................................ 2 A-8

Table 2A-2. MDA8 Ozone Performance Statistics Greater than or Equal to 60 Ppb for May through September by Climate Region, by Network ......................................... 2A-16

Table 2A-3. Key Monitoring Sites Used for the Ozone Time Series Analysis ................. 2A-24

Table 2A-4. Monitors without Projections due to Insufficient High Modeling Days to Meet EPA Guidance for Projecting Design Values ................................................... 2A-32

Table 2A-5. Monitors Determined to Have Design Values Affected by Winter Ozone Events .................................................................................... 2A-34

Table 2A-6. Monitors with Limited Response to Regional NOx and National VOC Emissions Reductions in the 2025 and Post-2025 Baselines ..................................... 2A-35

Table 2A-7. Design Values (ppb) for California Monitors ............................................... 2A-37

Table 2A-8. Design Values (ppb) for Continental U.S. Monitors outside of California ... 2A-41

Table 3-1. Identified Controls Applied for the Revised and Alternative Standard Analyses Strategies ........................................................................................................ 3- 9

Table 3-2. Number of Counties with Exceedances and Number of Additional Counties Where Reductions Were Applied for the 2025 Revised and Alternative Standards Analyses - U.S., except California................................................................................ 3-11

Table 3-3. 2011 and 2025 Base Case NOx and VOC Emissions by Sector (1000 tons) ... 3-13

Table 3-4. Summary of Emissions Reductions by Sector for the Identified Control Strategies Applied for the Revised 70 ppb Ozone Standard in 2025, except California (1,000 tons/year) .......................................................................................... 3-13

Table 3-5. Summary of Emissions Reductions by Sector for the Identified Control Strategies for the Alternative 65 ppb Ozone Standard in 2025 - except California (1,000 tons/year) .......................................................................................................... 3- 14

Table 3-6. Summary of Emissions Reductions for the Revised and Alternative Standards for the Unidentified Control Strategies for 2025 - except California (1,000 tons/year) .................................................................................................................... .. 3-15

Table 3-7. Summary of Emissions Reductions from the Identified + Unidentified Control Strategies by Alternative Standard Levels in 2025, Except California (1,000 tons/year) .................................................................................................................... .. 3-15

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x Table 3-8. Summary of Emissions Reductions (Identified + Unidentified Controls) Applied to Demonstrate Attainment in California for the Post-2025 Baseline (1,000 tons/year) .................................................................................................................... .. 3-21

Table 3-9. Summary of Emissions Reductions from Unidentified Control Strategy for the Revised and Alternative Standard Levels for Post-2025 - California (1,000 tons/year) .................................................................................................................... .. 3-23

Table 3-10. Summary of Emissions Reductions from the Identified + Unidentified Control Strategy by the Revised and Alternative Standard Levels for Post-2025 - California (1,000 tons/year) .......................................................................................... 3-23

Table 3A-1. Emissions Reductions Applied Beyond the Baseline Scenario to Create the 70 ppb Scenario ........................................................................................................... 3A- 2

Table 3A-2. Emissions Reductions Applied Beyond the Baseline Scenario to Create the 65 ppb Scenario ........................................................................................................... 3A- 2

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

Table 3A-4. Geographic Areas for Application of NOx Controls in the Baseline and Alternative Standard Analyses - U.S., except California ............................................. 3A-6

Table 3A-5. Geographic Areas for Application of VOC Controls in the Baseline and Alternative Standard Analyses - U.S., except California ............................................. 3A-7

Table 3A-6. Geographic Areas for Application of NO x 

### Controls in the Baseline and Alternative Standard Analyses – California ................................................................ 3A-8

Table 3A-7. Geographic Areas for Application of VOC Controls in the Baseline and Alternative Standard Analyses – California ................................................................ 3A-8

Table 3A-8. NO x 

### Control Measures Applied in the 70 ppb Analysis ................................. 3A-9

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

Table 3A-10. NOx Control Measures Applied in the 65 ppb Alternative Standard Analysis ...................................................................................................... 3A-12

Table 3A-11. VOC Control Measures Applied in the 65 ppb Alternative Standard Analysis ...................................................................................................... 3A-14

Table 4-1. Summary of Identified Annualized Control Costs by Sector for 70 ppb and 65 ppb for 2025 - U.S., except California (millions of 2011$) ..................................... 4-10

Table 4-2. NO x 

### and VOC Control Costs Applied for 70 ppb in 2025 – Average, Median, Minimum, Maximum, and Emissions Weighted Average Values ($/ton) ....... 4-12

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xi Table 4-3. By Sector, Discount Rates Used for Annualized Control Costs Estimates and Percent of Total Identified Control Costs ..................................................................... 4-16

Table 4-4. Control Measures in Dallas-Fort Worth SIP Not Reflected in the 1997 Ozone NAAQS RIA ..................................................................................................... 4-28

Table 4-5. Non-End-of-Pipe Control Measures from SIPs ............................................... 4-30

Table 4-6. Non-End-of-Pipe Measures in California ........................................................ 4-31

Table 4-7. Average NO x 

### Offset Prices for Four Areas (2011$, perpetual tpy) ................. 4-34

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

Table 4-9. Unidentified Control Costs in 2025 by Alternative Standard for 2025 – U.S., except California (millions of 2011$) ........................................................................... 4-41

Table 4-10. Unidentified Control Costs in 2025 by Alternative Standard for Post-2025 – California (millions of 2011$) ...................................................................................... 4-42

Table 4-11. Summary of Total Control Costs (Identified and Unidentified) by Alternative Level for 2025 - U.S., except California (millions of 2011$, 7% Discount Rate) .................................................................................... 4-42

Table 4-12. Summary of Total Control Costs (Identified and Unidentified) by Alternative Level for Post-2025 - California (millions of 2011$, 7% Discount Rate) .. 4-43

Table 4A-1. Costs for Identified NO x 

### Controls in the 70 ppb Analysis (2011$) ................. 4A-2

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

Table 4A-3. Costs for Identified NO x 

### Controls in the 65 ppb Analysis (2011$) ................. 4A-4

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

Table 4A-5. Summary of Total Control Costs (Identified and Unidentified) by Alternative Level for 2025 - U.S. using Alternative Cost Assumption for Unidentified Control Costs, except California (millions of 2011$) ............................. 4A-6

Table 4A-6. Summary of Total Control Costs (Identified and Unidentified) by Alternative Level for Post-2025 California - U.S. using Alternative Cost Assumption for Unidentified Control Costs (millions of 2011$) ..................................................... 4A-7

Table 4A-7. Costs and Number of Identified NO x 

### Controls by Sector in the CoST Database (2011$) ...................................................................................................... 4A-10

Table 4A-8. Simulation Percentage of NO x 

### Controls from Sectors Based on Application of Identified Controls ................................................................................................ 4A-11

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xii Table 4A-9. Simulation Percentage of NO x 

### Controls from Sectors Based on Remaining Emissions in Sectors .................................................................................................. 4A-11

Table 4A-10. Unidentified NO x 

### Control Costs by Alternative Standard using Alternative Methods for Estimation of Costs from Unidentified Controls (total costs in millions of 2011$, cost per ton in parentheses in $2011) ........................................................ 4A-12

Table 5-1. Summary of Direct Labor Impacts for SCR Installation at EGUs (FTEs) ....... 5-15

Table 5-2. Key Assumptions in Labor Analysis for EGUs ............................................... 5-16

Table 5-3. Summary of Direct Labor Impacts for Individual ICI Boilers ......................... 5-18

Table 5-4. Estimated Direct Labor Impacts for Individual SNCR Applied to a MidSized Cement Kiln (125-208 tons clinker/hr) ............................................................... 5-19

Table 6-1. Estimated Monetized Benefits of Attainment of the Revised and Alternative Ozone Standards for 2025 (nationwide benefits of attaining the standards everywhere in the U.S. except California) (billions of 2011$) ........................................................... 6-2

Table 6-2. Estimated Monetized Benefits of Attainment of the Revised and Alternative Ozone Standards for post-2025 (nationwide benefits of attaining the standards just in California) (billions of 2011$) ........................................................................................ 6-3

Table 6-3. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain the Primary Ozone Standards (endpoints included in the core analysis are identified with a red checkmark) .................................................................................... 6-5

Table 6-4. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact Functions, General Population ..................................................................................... 6-25

Table 6-5. Asthma Prevalence Rates ................................................................................ 6-26

Table 6-6. Criteria Used When Selecting C-R Functions ................................................. 6-28

Table 6-7. Health Endpoints and Epidemiological Studies Used to Quantify OzoneRelated Health Impacts ................................................................................................ 6-30

Table 6-8. Health Endpoints and Epidemiological Studies Used to Quantify PM 2.5 

### - Related Health Impacts ................................................................................................ 6-31

Table 6-9. Health Endpoints and Epidemiological Studies Used to Quantify OzoneRelated Health Impacts in Sensitivity Analyses ........................................................... 6-32

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

Table 6-11. Influence of Applied VSL Attributes on the Size of the Economic Benefits of Reductions in the Risk of Premature Death (U.S. EPA, 2006a) ............................... 6-62

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

Table 6-13. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart Attacks ........................................................................................... 6-69

Table 6-14. Estimated Costs Over a 5-Year Period of a Nonfatal Myocardial Infarction (in 2011$) .................................................................................................................... . 6-69

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

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

Table 6-17. Summary of PM 2.5 

### Benefit-per-ton Estimates ................................................. 6-76

Table 6-18. Population-Weighted Air Quality Change for the Revised and Alternative Annual Primary Ozone Standards Relative to the Analytical Baseline in 2025 ............ 6-78

Table 6-19. Sector-Specific NO x 

### Emissions Reductions for the Revised and Alternative Standard Levels ............................................................................................................ 6- 78

Table 6-20. Estimated Number of Avoided Ozone-Related Health Impacts for the Revised and Alternative Standard Levels (Incremental to the Baseline) for the 2025 Scenario (nationwide benefits of attaining the standards in the U.S. except California) .................................................................................................................. . 6-79

Table 6-21. Total Monetized Ozone-Related Benefits for the Revised and Alternative Annual Ozone Standards (Incremental to the Baseline) for the 2025 Scenario (nationwide benefits of attaining the standards everywhere in the U.S. except California) (millions of 2011) ...................................................................................... 6-80

Table 6-22. Estimated Number of Avoided PM 2.5 

### -Related Health Impacts for the Revised and Alternative Annual Ozone Standards (Incremental to the Baseline) for the 2025 Scenario (Nationwide Benefits of Attaining the Standards in the U.S. except California) ........................................................................................................ 6- 81

Table 6-23. Monetized PM 2.5 

### -Related Health Co-Benefits for the Revised and Alternative Annual Ozone Standards (Incremental to Baseline) for the 2025 Scenario (Nationwide Benefits of Attaining the Standards in the U.S. except California) (Millions of 2011) ....................................................................................................... 6-8 2

Table 6-24. Estimated Monetized Ozone and PM 2.5 

### Benefits for Revised and Alternative Annual Ozone Standards Incremental to the Baseline for the 2025 Scenario (Nationwide Benefits of Attaining the Standards in the U.S. Except California) – Identified + Unidentified Control Strategies (combined) and Identified Control Strategies Only (billions of 2011$) .............................................................................. 6-83

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xiv Table 6-25. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 2025 Scenario (Nationwide Benefits of Attaining the Standards in the U.S. except California) – Identified + Unidentified Control Strategies .......................................... 6-83

Table 6-26. Population-Weighted Air Quality Change for the Revised and Alternative Annual Primary Ozone Standards Relative to Baseline for Post-2025 ........................ 6-84

Table 6-27. Estimated Number of Avoided Ozone-Related Health Impacts for the Revised and Alternative Annual Ozone Standards (Incremental to the Baseline) for the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in California) .................................................................................................................. . 6-85

Table 6-28. Total Monetized Ozone-Only Benefits for the Revised and Alternative Annual Ozone Standards (Incremental to the Baseline) for the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in California) (millions of 2011) ........................................................................................................ 6 -86

Table 6-29. Estimated Number of Avoided PM 2.5 

### -Related Health Impacts for the Revised and Alternative Annual Ozone Standards (Incremental to the Baseline) for the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in California) .................................................................................................................. . 6-87

Table 6-30. Monetized PM 2.5 

### -Related Health Co-Benefits for the Revised and Alternative Annual Ozone Standards (Incremental to Baseline) for the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in California) (Millions of 2011$) ..................................................................................................... 6-88

Table 6-31. Estimate of Monetized Ozone and PM 2.5 

### Benefits for Revised and Alternative Annual Ozone Standards Incremental to the Baseline for the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in California) – Identified + Unidentified Control Strategies (billions of 2011$) ................................. 6-88

Table 6-32. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in California) – Identified + Unidentified Control Strategies .......................................... 6-89

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

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

Table 6B-2. Sensitivity Analysis for Monetized Benefits for Mortality from Long-term Exposure to Ozone (millions of 2011$) (2025 and post-2025 scenarios) .................... 6B-4

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

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xv Table 6B-4. Summary of Effect Estimates from Recent Cohort Studies in North America Associated with Change in Long-Term Exposure to PM 2.5 

### ......................................... 6B-8

Table 6B-5. PM 2.5 

### Co-benefit Estimates using Two Epidemiology Studies and Functions Supplied from the Expert Elicitation ......................................................................... 6B-11

Table 6B-6. Ranges of Elasticity Values Used to Account for Projected Real Income Growth ..................................................................................................................... 6 B-12

Table 6B-7. Ranges of Adjustment Factors Used to Account for Projected Real Income Growth to 2024 ........................................................................................................ 6B-12

Table 6B-8. Sensitivity of Monetized Ozone Benefits to Alternative Income Elasticities in 2025 (Millions of 2011$) ..................................................................................... 6B-13

Table 6B-9. Potential Reduction in Premature Mortality by Age Range from Attaining the Revised and Alternative Ozone Standards (2025 scenario) ................................ 6B-16

Table 6B-10. Potential Years of Life Gained by Age Range from Attaining the Revised and Alternative Ozone Standards (2025 Scenario) ................................................... 6B-16

Table 6B-11. Estimated Percent Reduction in All-Cause Mortality Attributed to the Proposed Primary Ozone Standards (2025 Scenario) ............................................... 6B-17

Table 6B-12. Population Exposure in the Baseline Sector Modeling (used to generate the benefit-per-ton estimates) Above and Below Various Concentration Benchmarks in the Underlying Epidemiology Studies ...................................................................... 6B-24

Table 6B-13. Definitions of Variables Used to Calculate Changes in Worker Productivity .................................................................................................. 6B-27

Table 6B-14. Population Estimated Economic Value of Increased Productivity among Outdoor Agricultural Workers from Attaining the Revised and Alternative Ozone Standards in 2025 (millions of 2011$) ...................................................................... 6B-27

Table 7-1. Welfare Effects of NO x 

### and VOC Emissions .................................................... 7-3

Table 8-1. Total Costs, Total Monetized Benefits, and Net Benefits of Control Strategies in 2025 for U.S., except California (billions of 2011$) .................................. 8-4

Table 8-2. Total Costs, Total Monetized Benefits, and Net Benefits of Control Strategies Applied in California, Post-2025 (billions of 2011$) ..................................... 8-4

Table 8-3. Summary of Identified Control Strategies Annualized Control Costs by Sector for 70 ppb and 65 ppb for 2025 - U.S., except California (millions of 2011$) .... 8-4

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xvi Table 8-4. Estimated Monetized Ozone and PM 2.5 

### Benefits for Revised and Alternative Annual Ozone Standards Incremental to the Baseline for the 2025 Scenario (Nationwide Benefits of Attaining the Standards in the U.S. except California) – Identified Control Strategies (billions of 2011$) ........................................................... 8-5

Table 8-5. Summary of Total Control Costs (Identified + Unidentified) by Revised and Alternative Standard Level for 2025 - U.S., except California (millions of 2011$, 7% Discount Rate) ................................................................................................................ 8-6

Table 8-6. Summary of Total Control Costs (Unidentified Control Strategies) by Revised and Alternative Level for Post-2025 - California (millions of 2011$, 7% Discount Rate) ................................................................................................................ 8-6

Table 8-7. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 2025 Scenario (nationwide benefits of attaining revised and alternative standard levels everywhere in the U.S. except Cali fornia) – Identified + Unidentified Control Strategies .................................................................................................................... .... 8-6

Table 8-8. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the Post-2025 Scenario (nationwide benefits of attaining revised and alternative standard levels just in California) – Identified + Unidentified Control Strategies ......................... 8-7

Table 8-9. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain the Primary Ozone Standards .............................................................................. 8-7

Table 8-10. Relevant Factors and Their Potential Implications for Attainment .................. 8-16

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

Table 9A-2 Summary of Population Totals and Demographic Categories for Areas of Interest and National Perspective ................................................................................ 9A-6

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xvii LIST OF FIGURES
Figure ES-1. Analytical Flowchart for Primary Standards Analyses ................................... ES-5
Figure ES-2. Projected Ozone Design Values in the 2025 Baseline Scenario ..................... ES-8
Figure ES-3. Projected Ozone Design Values in the Post-2025 Baseline .......................... ES-11
Figure 2-1. Process to Determine Emissions Reductions Needed to Meet Baseline and Alternative Standards Analyzed ..................................................................................... 2-3
Figure 2-2. Across-the-Board Emissions Reduction and Combination Sensitivity Regions ................................................................................. 2-12
Figure 2-3. Map of 200 km Buffer Regions in California, East Texas and the Northeast Created as Part of the Analysis for the November 2014 Proposal RIA ......................... 2-13
Figure 2-4. Map of VOC Impact Regions .......................................................................... 2-16
Figure 2-5. Process Used to Create Spatial Surfaces for BenMap ...................................... 2-24
Figure 2A-1. Map of the CAMx Modeling Domain Used for Ozone NAAQS RIA ............ 2A-2
Figure 2A-2a. AQS Ozone Monitoring Sites .................................................................. 2A-14
Figure 2A-2b. CASTNet Ozone Monitoring Sites .......................................................... 2A-14
Figure 2A-3. NOAA Nine Climate Regions ..................................................................... 2A-15
Figure 2A-4. Density Scatter Plots of Observed/Predicted MDA8 Ozone for the Northeast, Ohio River Valley, Upper Midwest, Southeast, South, Southwest, Northern Rockies, Northwest and West Regions .................................................................................... 2A-17
Figure 2A-5. Distribution of Observed and Predicted MDA8 Ozone by Month for the Period May through September for the Northeast Region, (a) AQS Network and (b) CASTNet Network. [symbol = median; top/bottom of box = 75th/25th percentiles; top/bottom line = max/min values] ............................................................................ 2A-18
Figure 2A-6. Distribution of Observed and Predicted MDA8 Ozone by Month for the Period May through September for the Ohio Valley Region, (a) AQS Network and (b) CASTNet Network .............................................................................................. 2A-18
Figure 2A-7. Distribution of Observed and Predicted MDA8 Ozone by Month for the Period May through September for the Upper Midwest Region, (a) AQS Network and (b) CASTNet Network ........................................................................................ 2A-19

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xviii Figure 2A-8. Distribution of Observed and Predicted MDA8 Ozone by Month for the Period May through September for the Southeast Region, (a) AQS Network and (b) CASTNet Network .................................................................................................... 2A-19
Figure 2A-9. Distribution of Observed and Predicted MDA8 Ozone by Month for the Period May through September for the South Region, (a) AQS Network and (b) CASTNet Network .................................................................................................... 2A-20
Figure 2A-10. Distribution of Observed and Predicted MDA8 Ozone by Month for the Period May through September for the Southwest Region, (a) AQS Network and (b) CASTNet Network .................................................................................................... 2A-20
Figure 2A-11. Distribution of Observed and Predicted MDA8 Ozone by Month for the Period May through September for the Northern Rockies Region, (a) AQS Network and (b) CASTNet Network ........................................................................................ 2A-21
Figure 2A-12. Distribution of Observed and Predicted MDA8 Ozone by Month for the Period May through September for the Northwest Region, (a) AQS Network and (b) CASTNet Network .................................................................................................... 2A-21
Figure 2A-13. Distribution of Observed and Predicted MDA8 Ozone by Month for the Period May through September for the West Region, (a) AQS Network and (b) CASTNet Network .................................................................................................... 2A-22
Figure 2A-14. Mean Bias (ppb) of MDA8 Ozone Greater than or Equal to 60 ppb over the Period May-September 2011 at AQS and CASTNet Monitoring ........................ 2A-22
Figure 2A-15. Normalized Mean Bias (%) of MDA8 Ozone Greater than or Equal to 60 ppb over the Period May-September 2011 at AQS and CASTNet Monitoring Sites . 2A-23
Figure 2A-16. Mean Error (ppb) of MDA8 Ozone Greater than or Equal to 60 ppb over the Period May-September 2011 at AQS and CASTNet Monitoring Sites ................ 2A-23
Figure 2A-17. Normalized Mean Error (%) of MDA8 Ozone Greater than or Equal to 60 ppb over the Period May-September 2011 at AQS and CASTNet Monitoring Sites . 2A-24
Figure 2A-18a. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 360810124 in Queens, New York ................... 2A-25
Figure 2A-18b. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 361030002 in Suffolk County, New York ...... 2A-25
Figure 2A-18c. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 240251001 in Harford Co., Maryland ............. 2A-26
Figure 2A-18d. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 420031005 in Allegheny Co., Pennsylvania ... 2A-26

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xix Figure 2A-18e. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 211110067 in Jefferson Co., Kentucky ........... 2A-27
Figure 2A-18f. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 261630019 in Wayne Co., Michigan .............. 2A-27
Figure 2A-18g. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 551170006 in Sheboygan Co., Wisconsin ...... 2A-28
Figure 2A-18h. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 484392003 in Tarrant Co., Texas ................... 2A-28
Figure 2A-18i. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 480391004 in Brazoria Co., Texas ................. 2A-29
Figure 2A-18j. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 80350004 in Douglas Co., Colorado ............... 2A-29
Figure 2A-18k. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 60195001 in Fresno Co., California ................ 2A-30
Figure 2A-18l. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 60710005 in San Bernardino Co., California .. 2A-30
Figure 2A-19. Change in July Average of 8-hr Daily Maximum Ozone Concentration (ppb) Due to 50% Cut in U.S. Anthropogenic VOC Emissions................................. 2A-31
Figure 2A-20. Location of Sites Identified in Table 2A-6 .............................................. 2A-36
Figure 3-1. Process to Find Needed Reductions to Reach the Revised and Alternative Standards ..................................................................................................................... ... 3-4
Figure 3-2. Buffers of 200 km for NOx Emissions Reductions around Projected Exceedance Areas .......................................................................................................... 3-5
Figure 3-3. Buffers of 100 km for VOC Emissions Reductions around Projected Exceedance Areas .......................................................................................................... 3-6
Figure 3-4. Process to Estimate the Control Strategies for the Revised and Alternative Standards ..................................................................................................................... ... 3-7
Figure 3-5. Projected Ozone Design Values in the 2025 Baseline Scenario ...................... 3-10
Figure 3-6. Counties Where NO X 

### Emissions Reductions Were Applied to Simulate Attainment with the Revised and Alternative Ozone Standards in the 2025 Analysis .. 3-11
Figure 3-7. Counties Where VOC Emissions Reductions Were Applied to Simulate Attainment with the Revised and Alternative Ozone Standards in the 2025 Analyses . 3-12

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xx Figure 3-8. Steps to Create the Post-2025 Baseline for California ..................................... 3-17
Figure 3-9. Counties Projected to Exceed 75 ppb in the Post-2025 Baseline Scenario ...... 3-20
Figure 3-10. Counties Where Emissions Reducti ons Were Applied to Demonstrate Attainment with the Current Standard .......................................................................... 3-21
Figure 3-11. Projected Ozone Design Values in the Post-2025 Baseline Scenario .............. 3-22
Figure 4-1. Identified Control Cost Curve for 2025 for All Identified NO x 

### Controls for All Source Sectors (EGU, non-EGU Point, Nonpoint, and Nonroad) ............................. 4-7
Figure 4-2. Identified Control Cost Curve for 2025 for All Identified VOC Controls for All Source Sectors (EGU, non-EGU Point, Nonpoint, and Nonroad) ............................. 4-9
Figure 4-3. Regions Used to Present Emissions Reductions and Cost Results ................... 4-13
Figure 4-4. Observed but Incomplete MACC (Solid Line) Based on Identified Controls in Current Tools and Complete MACC (dashed line) where Gaps Indicate Abatement Opportunities Not Identified by Current Tools ............................................................. 4-26
Figure 4A-1. Marginal Costs fo r Identified NO x 

### Controls for All Source Sectors with Regression Line for Unidentified Control Measures ................................................... 4A-8
Figure 4A-2. Marginal Costs for Identified NO x 

### Controls for All Source Sectors with Unidentified Control Measures from Regression Line Included .................................. 4A-9
Figure 5-1. Size Distribution of Identified 319 Existing Coal-Fired EGU Units Nationwide without SCR NOx Controls (or with SCRs Operated Less Than the Maximum Possible Amount of Time) .......................................................................... 5-14
Figure 5-2. Size Distribution of 37 Existing Coal-Fired EGU Units without SCR NOx Controls (or with SCRs Operated Less Than the Maximum Possible Amount of Time) in Areas Anticipated to Need Additional NOx Controls With the Alternative 65 ppb Ozone Standard Level ...................................................................................... 5-14
Figure 6-1. Illustration of BenMAP-CE Approach ............................................................ 6-12
Figure 6-2. Data Inputs and Outputs for the BenMAP-CE Program .................................. 6-14
Figure 6-3. Procedure for Generating Benefits Estimates for the 2025 and Post-2025 Scenarios ..................................................................................................................... . 6-15 Figure 6-4. Sensitivity Analysis for Short-Term Ozone-Related Mortality Benefits .......... 6-80
Figure 6-5. Sensitivity Analysis Long-Term PM 2.5 

### -Related Mortality Co-Benefits ........... 6-82

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xxi Figure 6B-1. Premature Ozone-related Deaths Avoided for the Revised and Alternative Standards (2025 scenario) According to the Baseline Ozone Concentrations............ 6B-19
Figure 6B-2. Cumulative Probability Plot of Premature Ozone-related Deaths Avoided for the Revised and Alternative Standards (2025 scenario) According to the Baseline Ozone Concentrations ............................................................................................... 6B-20
Figure 6B-3. Percentage of Adult Population (age 30+) by Annual Mean PM 2.5 

### Exposure in the Baseline Sector Modeling (used to generate the benefit-per-ton estimates)* ... 6B-24
Figure 6B-4. Cumulative Distribution of Adult Population (age 30+) by Annual Mean PM 2.5 

### Exposure in the Baseline Sector Modeling (used to generate the benefit-per-ton estimates) .................................................................................................................. 6 B-25

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

ES-1 EXECUTIVE SUMMARY

Overview In setting primary and secondary national ambient air quality standards (NAAQS), the EPA’s responsibility under the law is to establish standards that protect public health and welfare. The Clean Air Act (the Act) requires the EPA, for each criteria pollutant, to set a standard that protects public health with “an adequate margin of safety” and public welfare from “any known or anticipated adverse effects.” As interpreted by the Agency and the courts, the Act requires the EPA to base the decision for the primary standard on health considerations only; economic factors cannot be considered. The prohibition against considering cost in the setting of the primary air quality standards does not mean that costs, benefits or other economic considerations are unimportant. The Agency believes that consideration of costs and benefits is an essential decision-making tool for the efficient implementation of these standards. The impacts of costs, benefits, and efficiency are considered by the States when they make decisions regarding what timelines, strategies, and policies are appropriate for their circumstances.

The EPA has concluded that the current primary standard for ozone, set at a level of 75 ppb, does not provide requisite protection to public health with an adequate margin of safety, and is revising the standard to provide increased public health protection. Specifically, the EPA is retaining the indicator (ozone), averaging time (8-hour) and form (annual fourth-highest daily maximum, averaged over 3 years) of the existing primary standard and is revising the level of that standard to 70 ppb. The EPA has also concluded that the current secondary standard for ozone, set at a level of 75 ppb, is not requisite to protect public welfare from known or anticipated adverse effects, and is revising the standard to provide increased protection against vegetation-related effects on public welfare. Specifically, the EPA is retaining the indicator (ozone), averaging time (8-hour) and form (annual fourth-highest daily maximum, averaged over 3 years) of the existing secondary standard and is revising the level of that standard to 70 ppb. 1

1 The EPA has concluded that this revision will effectively curtail cumulative seasonal ozone exposures above 17 ppm-hrs in terms of a three-year average seasonal W126 index value, based on the three consecutive month period within the growing season with the maximum index value, with daily exposures cumulated for the 12-hour period from 8:00 am to 8:00 pm. Commented [A1]: Reviewer recommends ensuring this language matches the preamble.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

ES-2 The EPA performed an illustrative analysis of the potential costs, human health benefits, and welfare co-benefits of nationally attaining a revised primary ozone standard of 70 ppb and a primary alternative ozone standard level of 65 ppb. Because there are not additional costs and benefits of attaining the secondary standard, the EPA did not need to estimate any incremental costs and benefits associated with attaining a revised secondary standard. Per Executive Orders 12866 and 13563 and the guidelines of OMB Circular A-4, this Regulatory Impact Analysis (RIA) presents the analyses of the revised standard level of 70 ppb and an alternative standard level of 65 ppb. The cost and benefit estimates below are calculated incremental to a 2025 baseline that incorporates air quality improvements achieved through the projected implementation of existing regulations and full attainment of the existing ozone NAAQS (75 ppb). The 2025 baseline reflects, among other existing regulations, the 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards, Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles, the Tier 3 Motor Vehicle Emission and Fuel Standards, the Clean Power Plan, the Mercury and Air Toxics Standards, 2 

### and the Cross-State Air Pollution Rule, all of which will help many areas move toward attainment of the existing ozone standard (see Appendix 2, Section 2A.1.3 for additional information).

In this RIA we present the primary costs and benefits estimates for 2025. We assume that potential nonattainment areas everywhere in the U.S., excluding California, will be designated such that they are required to reach attainment by 2025, and we developed our projected baselines for emissions, air quality, pop ulations, and mortality baseline rates for 2025. We recognize that there are areas that are not required to meet the existing ozone standard by 2025 -- the Clean Air Act allows areas with more significant air quality problems to take additional time to reach the existing standard. Several areas in California are not required to meet the existing standard by 2025 and may not be required to meet a revised standard until sometime between 2032 and 2037. Because of data and resource constraints, we were not able to project emissions and air quality beyond 2025 for California; however, we adjusted baseline air

2 On June 29, 2015, the United States Supreme Court reversed the D.C. Circuit opinion affirming the Mercury and Air Toxics Standards (MATS). The EPA is reviewing th e decision and will determine any appropriate next steps once the review is complete. MATS is included in the baseline for this analysis, and the EPA does not believe including MATS substantially alters the results of this analysis. Commented [A2]: Please add a sentence why EPA doesn’t believe reults are substantively altered. Is it because the capital expenditures are largely incurred?

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

ES-3 quality to reflect mobile source emissions redu ctions for California that would occur between
2025 and 2030. 3 

### These emissions reductions were the result of mobile source regulations expected to be fully implemented by 2030.

The EPA will likely finalize designat ions for a revised ozone NAAQS in late 2017. Depending on the precise timing of the effective date of those designations, nonattainment areas classified as Marginal will likely have to attain in either late 2020 or early 2021. Nonattainment areas classified as Moderate will likely have to attain in either late 2023 or early 2024. If a Moderate nonattainment area qualifies for two 1-year extensions, the area may have as late as early 2026 to attain. Further, Serious nonattainment areas will likely have to attain in late 2026 or early 2027. As such, we selected 2025 as the primary year of analysis because it provided a good representation of the remaining air quality concerns that Moderate
nonattainment areas would face and because most areas of the U.S. will likely be required to meet a revised ozone standard by 2025. States with areas classified as Moderate and higher are required to develop attainment demonstration plans for those nonattainment areas.

While there is uncertainty about the precise timing of emissions reductions and related costs for California, we assume costs associated with the installation of controls occur through the end of 2037 and beginning of 2038. In addition, we estimate benefits for California using projected population demographics and baseline mortality rates for 2038. Because of the different timing for incurring costs and accruing benefits and for ease of discussion throughout the analyses, we refer to the different time periods for potential attainment as 2025 and post-2025 to reflect that: (1) we did not project emissions and air quality for any year other than 2025; (2) for California, emissions controls and associated costs are assumed to occur through the end of 2037 and beginning of 2038; and (3) for California benefits are estimated using population demographics and baseline mortality rates for 2038. It is not straightforward to discount the post-2025 results for California to compare with or add to the 2025 results for the rest of the U.S. While we estimate benefits using 2038 information, we do not have good information on precisely when the costs of controls will be incurred. Because of these differences in timing

3 At the time of this analysis, there were no future year emissions for California beyond 2030, and projecting emissions beyond 2030 could introduce additional uncertainty.

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ES-4 related to California attaining a revised standard, the separate costs and benefits estimates for post-2025 should not be added to the primary estimates for 2025.

ES.1 Overview of Analytical Approach This RIA consists of multiple analyses, including estimates of current and future emissions of relevant precursors (i.e., NOx and VOC) that contribute to the air quality problem and estimates of current and future ozone concentrations (Chapter 2 – Emissions, Air Quality Modeling and Analytic Methodologies); development of illustrative control strategies to attain the revised standard of 70 ppb and an alternative primary standard level of 65 ppb (Chapter 3 – Control Strategies and Emissions Reductions); estimates of the incremental costs of attaining the revised and alternative standard levels (Chapter 4 – Engineering Cost Analysis and Economic Impacts); a discussion of the theoretical framework used to analyze regulation-induced employment impacts, as well as information on employment related to installation of NOx controls on coal and gas-fired electric generating units, industrial boilers, and cement kilns (Chapter 5 – Qualitative Discussion of Employment Impacts of Air Quality); estimates of the incremental benefits of attaining the revised and alternative standard levels (Chapter 6 – HumanHealth Benefits Analysis Approach and Results); a qualitative discussion of the welfare cobenefits of attaining the revised standards (Chapter 7 – Impacts on Public Welfare of Attainment Strategies to Meeting Primary and Secondary Ozone NAAQS); a comparison and discussion of the benefits and costs (Chapter 8 – Comparison of Costs and Benefits); and an analysis of the impacts in the context of the relevant statutory and executive order requirements (Chapter 9 – Statutory and Executive Order Impact Analyses).

Because States are ultimately responsible for implementing strategies to meet revised standards, this RIA provides insights and analysis of a limited number of illustrative control strategies that states might adopt to meet a revised standard. The goal of this RIA is to provide estimates of the potential costs and benefits of the illustrative attainment strategies to meet the revised and alternative standard levels. The flowchart below (Figure ES-1) outlines the analytical steps taken to illustrate attainment with the revised and alternative standard levels, and the following discussion describes each of the major steps in the process.

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ES-5 Figure ES-1. Analytical Flowchart for Primary Standards Analyses
ES.1.1 Establishing the Baseline
The future year base case reflects emissions projected from 2011 to 2025 and incorporates current state and federal programs, including the Tier 3 Motor Vehicle Emission and Fuel Standards (U.S. EPA, 2014a) (see Appendix 2, Section 2A.1.3 for a discussion of the rules included in the base case). The base case does not include control programs specifically for the purpose of attaining the existing ozone standard (75 ppb). The baseline builds on the future year base case and reflects the additional emissions reductions needed to reach attainment of the current ozone standard (75 ppb), as well as adjustments for the Clean Power Plan (U.S. EPA, 2014b).

We performed a national scale air quality modeling analysis to estimate ozone concentrations for the future base case year of 2025. In addition, we modeled fifteen 2025 Commented [A3]: Please clarify that CPP pr oposal is used in the baseline. Please insert a sentence that gives information on how using the proposal rather than the final rule will alter the results of this RIA.

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ES-6 emissions sensitivity simulations. 4 

### The emissions sensitivity simulations were used to develop ozone response factors (ppb/ton) from the modeled response of ozone to changes in NOx and
VOC emissions from various sources and locations. These ozone response factors were then used
to determine the amount of emissions reductions needed to reach the 2025 baseline and to evaluate the revised and alternative standard levels of 70 and 65 ppb incremental to the
baseline. We used the estimated emissions reductions needed to reach the revised and alternative standard levels to analyze the costs and benefits.

ES.1.2 Control Strategies and Emissions Reductions
The EPA used the Control Strategy Tool (CoST) to estimate engineering control costs. We estimated costs for non-electric generating unit point (non-EGU point), nonpoint, and mobile nonroad sources. CoST calculates engineering costs using one of two different methods: (1) an equation that incorporates key operating unit information, such as unit design capacity or stack flow rate, or (2) an average annualized cost-per -ton factor multiplied by the total tons of reduction of a pollutant. The engineering cost analysis uses the equivalent uniform annual costs (EUAC) method, in which annualized costs are calculated based on the equipment life for the control measure and the interest rate incorporated into a capital recovery factor. Annualized costs represent an equal stream of yearly costs over the period the control technology is expected to operate.

The EPA analyzed illustrative control strategies that areas across the U.S. might employ to attain the revised primary ozone standard level of 70 ppb and an alternative standard level of 65 ppb. The EPA analyzed the impact that additional emissions control technologies and measures, across numerous sectors, would have on predicted ambient ozone concentrations incremental to the baseline. These control measures, also referred to as identified controls, are based on information available at the time of this analysis and include primarily end-of-pipe control technologies. In addition, to attain the revised and alternative primary standard levels analyzed, some areas needed additional emissions reductions beyond the identified controls, and

4 The approach of using emissions sensitivity simulations to determine the response of ozone at monitor locations to emissions changes in specific regions is similar to the approach used in the November 2014 proposal RIA. However, in the final RIA we conducted sensitivity simulations using ten regions compared to five much larger regions in the proposal RIA. Commented [A4]: Please insert a sentence on how EPA estimated the cost for the EGU sector. Table ES-1 includes EGU sector but the preceeding discussion doesn’t mention EGU.

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ES-7 we refer to these as unidentified controls or measures (see Chapter 3, Sections 3.1 and 3.2 for additional information). 5



### Using the ozone response factors mentioned above, we estimated the emissions reductions over and above the baseline that were needed to meet the revised standard of 70 ppb and an alternative standard level of 65 ppb. Costs of controls incremental to baseline emissions reductions are attributed to the costs of meeting the revised and alternative standard levels. These emissions reductions can come from specific identified controls, as well as unidentified controls in some areas. The baseline shows that by 2025, ozone concentrations would be significantly better than today under current requirements, and depending on the standard level analyzed, some areas in the Eastern, Central, and Western U.S. would need to develop and adopt additional controls to attain the revised and alternative standard levels (see Chapter 3, Section 3.1.3 and Figure 3-5 for additional details on the areas that would need to develop and adopt controls).

ES.1.2.1 Emissions Reductions from Identified Controls in 2025
Figure ES-2 shows the counties projected to exceed the revised and alternative standard levels analyzed for 2025 for areas other than California. For the revised standard of 70 ppb, emissions reductions were required for monitors in the Colorado, Great Lakes, North East, Ohio River Valley and East Texas regions (see Chapter 2, Figure 2-2 for a map of the regions). For the 65 ppb alternative standard level, in addition to the regions listed above, NOx emissionsreductions were required in the Arizona-New Mexico, Nevada, and Oklahoma-ArkansasLouisiana regions. VOC emissions reductions were required in Denver, Houston, Louisville, Chicago and New York City. Tables ES-1 and ES-2 show the emissions reductions from identified controls for the revised and alternative standard levels analyzed. We aggregate results by region – East and West, except California – to present cost and benefits estimates. See Chapter 4, Figure 4.3 for a representation of the East and West regions.

5 In the proposal RIA we discuss emissions reductions resulting from the application of known controls, as well as emissions reductions beyond known controls, using the terminology of “known controls” and “unknown controls.” In the final RIA, we have used slightly different terminology, consistent with past NAAQS RIAs. Here we refer to emissions reductions and controls as either “identified” controls or measures or “unidentified” controls or measures reflecting that unidentified controls or measures can include existing controls or measures for which the EPA does not have sufficient data to accurately estimate their costs.

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ES-8 Figure ES-2. Projected Ozone Design Values in the 2025 Baseline Scenario

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ES-9 Table ES-1. Summary of Emissions Reductions by Sector for the Identified Control Strategy for the Revised Standard Leve l of 70 ppb for 2025, except California (1,000 tons/year) a Geographic Area Emissions Sector NO x VOC

East EGU 45 - Non-EGU Point 85 1 Nonpoint 100 19 Nonroad 3 - Onroad - - Total 230 20

West EGU - - Non-EGU Point 6 - Nonpoint 1 - Nonroad - - Onroad - - Total 7 -

a Emissions reduction estimates are rounded to two significant figures. 

### Table ES-2. Summary of Emissions Reductions by Sector for the Identified Control Strategy for Alternative Standard Leve l of 65 ppb for 2025, except California (1,000 tons/year) a Geographic Area Emissions Sector NO x VOC

East EGU 110 - Non-EGU Point 220 5 Nonpoint 160 100 Nonroad 8 - Total 500 100

West EGU 0 - Non-EGU Point 33 - Nonpoint 22 5 Nonroad 1 - Total 56 5

a Emissions reduction estimates are rounded to two significant figures. 

### ES.1.2.2 Emissions Reductions beyond Identified Controls in 2025
There were several areas where identified controls did not achieve enough emissions reductions to attain the revised standard level of 70 ppb or alternative standard level of 65 ppb. The EPA then estimated the additional emissions reductions beyond identified controls needed to reach attainment (i.e., unidentified controls). Table ES-3 shows the emissions reductions needed from unidentified controls in 2025 for the U.S., ex cept California, for the revised and alternative standard levels analyzed. Commented [A5]: Going forward, EPA should offer more detailed discussion about unidentified controls—whether they are unknown sources or unknown technologies. Each may have different implications for costs.

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ES-10 Table ES-3. Summary of Emissions Reductions from the Unidentified Control Strategies for the Revised and Alternative Standard Levels for 2025, except California (1,000 tons/year) a Revised and Alternative Standard Levels Region NO x VOC

70 ppb East West 47

- -

- 65 ppb East West 820 40 -

-

a Estimates are rounded to two significant figures.



### ES.1.2.3 Emissions Reductions beyond Identified Controls for Post-2025 Figure ES-3 shows the counties projected to exceed the revised and alternative standard levels analyzed for the post-2025 analysis for California. For the California post-2025 revised and alternative standard level analyses, all identified controls were applied in the baseline, so
incremental emissions reductions to demonstrate attainment of the revised and alternative
standards were from unidentified controls. Table ES-4 shows the emissions reductions needed from unidentified controls for post-2025 for California for the revised and alternative standard levels analyzed.

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ES-11 Figure ES-3. Projected Ozone Design Values in the Post-2025 Baseline
Table ES-4. Summary of Emissions Reductions from the Unidentified Control Strategies for the Revised and Alternative Standard Levels for Post-2025 - California (1,000 tons/year) a Revised and Alternative Standard Levels Region NO x VOC

70 ppb CA 51 - 65 ppb CA 100 -

a Estimates are rounded to two significant figures.


### ES.1.3 Human Health Benefits
To estimate benefits, we follow a “damage-function” approach in calculating total benefits of the modeled changes in environmental quality. This approach estimates changes in individual health endpoints (specific effects that can be associated with changes in air quality)

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ES-12 and assigns values to those changes assuming independence of the values for those individual endpoints. Total benefits are calculated as the sum of the values for all non-overlapping health endpoints. The “damage-function” approach is the standard method for assessing costs and benefits of environmental quality programs and has been used in several recent published analyses (Levy et al., 2009; Fann et al., 2012a; Tagaris et al., 2009).

To assess economic values in a damage-function framework, the changes in environmental quality must be translated into effects on people or on the things that people value. In some cases, the changes in environmental quality can be directly valued, as is the case for changes in visibility. In other cases, such as for changes in ozone and PM, an impact analysis must first be conducted to convert air quality changes into effects that can be later assigned dollar values. For this RIA, the health impacts anal ysis is limited to those health effects that are directly linked to changes in ambient levels of ozone and PM 2.5 

### due to reductions in ozone precursor emissions. Emissions reductions of NOx or VOC to attain the ozone standards would simultaneously reduce ambient PM 2.5 

### concentrations.

Benefits estimates for ozone were generated using the damage-function approach outlined above wherein changes in ambient ozone concentrations were translated into reductions in the incidence of specific health endpoints (e.g., premature mortality or hospital admissions) usingthe environmental Benefits Mapping and Analysis Program – Community Edition (BenMAPCE).

In contrast to ozone, we used a benefit-per-ton approach to estimate PM 2.5 

### co-benefits. With this approach, we use the results of previous air quality modeling to derive benefit-per-tonestimates for NOx. These benefit-per-ton estimates provide the monetized human health cobenefits (the sum of premature mortality and premature morbidity) of reducing one ton of a
PM 2.5 

### precursor (such as NO X 

### ) from a specified source. We then combine these benefit-per-ton estimates with reductions in NOx emissions associated with meeting the revised and alternative standard levels. We acknowledge increased uncertainty associated with the benefit-per-ton approach

, 

### relative to using scenario-specific air quality modeling to estimate the PM 2.5 

### cobenefits. Commented [A6]: EPA may want to put in another heading to articulate the PM co-benefits.

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ES-13 In addition to ozone and PM 2.5 

### benefits, implementing emissions controls to attain the revised and alternative ozone standard levels would reduce exposure to other ambient pollutants (e.g., NO 2 

### ). However, we were not able to quantify the co-benefits of reduced exposure to these pollutants, nor were we able to estimate some anticipated health benefits associated with exposure to ozone and PM 2.5 

### due to data and methodology limitations.

ES.1.4 Welfare Effects Co-Benefits of Meeting Primary andthe SecondaryPrimary Standard
Section 302(h) of the Clean Air Act states that effects on welfare include, but are not limited to, “effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, weather, visibility, and climate, damage to and deterioration of property, and hazards to transportation, as well as effects on economic values and on personal comfort and well-being.” Ozone can affect ecological systems, leading to changes in the ecological community and influencing the diversity, health, and vigor of individual species (U.S. EPA, 2013). Ozone causes discernible injury to a wide array of vegetation (U.S. EPA, 2013). In terms of forest productivity and ecosystem diversity, ozone may be the pollutant with the greatest potential for region-scale forest impacts (U.S. EPA, 2013). Studies have demonstrated repeatedly that ozone concentrations observed in polluted areas can have substantial impacts on plant function (De Steiguer et al,. 1990; Pye, 1988).

In the RIA for the proposal, we quantified a small portion of the welfare impacts associated
with reductions in ozone concentrations to meet the alternative ozone standard levels analyzed. Using a model of commercial agriculture and forest markets, we analyzed the effects on consumers and producers of forest and agricultural products of changes in the W126 index resulting from meeting alternative standards levels. We also assessed the effects of those changes in commercial agricultural and forest yields on carbon sequestration and storage. The analysis provided limited quantitative information on the welfare co- benefits of meeting
alternative secondary standard levels, focusing only on one subset of ecosystem services. Commercial and non-commercial forests provide a number of additional services, including medicinal uses, non-commercial food and fiber production, arts and crafts uses, habitat, recreational uses, and cultural uses for Native American tribes. In this final RIA, we did not update this analysis and refer to the analysis conducted in the proposal RIA (U.S. EPA, 2014c).

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ES-14 We did not update the analysis from the proposal RIA because the welfare co- benefits estimates (i) in the proposal analysis were small, and we anticipated that the estimates in the final analysis would be even smaller, and (ii) are not added to the human health benefits estimates.

ES.2 Results of Benefit-Cost Analysis Below in Table ES-5, we present the primary costs and benefits estimates for 2025 for all areas except California. We anticipate that benefits and costs will likely begin occurring earlier than 2025, as states begin implementing control measures to show progress towards attainment. In these tables, ranges within the total benefits rows reflect multiple studies upon which the estimates associated with premature mortality were derived. PM 2.5 

### co-benefits account for approximately half to three-quarters of the estimated benefits, depending on the standard analyzed and on the choice of ozone and PM mortality functions used. In addition for 2025, Table ES-6 presents the numbers of premature deaths avoided for the revised and alternative standard levels analyzed, as well as the other health effects avoided. Table ES-7 provides information on the costs by geographic region for the U.S., except California in 2025, and Table ES-8 provides a regional breakdown of benefits for 2025. See the tables in Chapter 6 for
additional characterizations of the monetized benefits.
In the RIA we provide estimates of the costs of emissions reductions to attain the revised and alternative standard levels in three regions -- California, the rest of the western U.S., and the eastern U.S. In addition, we provide estimates of the benefits that accrue to each of these three regions resulting from both control strategies applied within the region and reductions in transport of ozone associated with emissions reductions in other regions.
The net benefits of emissions reductions strategies in a specific region reflect the benefits of the emissions reductions occurring both within and outside of the region minus the costs of the emissions reductions. Because the air quality modeling was conducted at the national level, we do not estimate separately the nationwide benefits associated with the emissions reductions occurring in any specific region. 6 

### As a result, we are only able to provide net benefits estimates at the national level. The difference between the costs for a specific region and the benefits

6 For California, we provide separate estimates of the costs and nationwide estimates of benefits, so it is appropriate to calculate net benefits. As such, we provide net benefits for the post-2025 analysis for California.

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ES-15 accruing to that region is not an estimate of net benefits of the emissions reductions in that region.

Table ES-5. Total Annual Costs and Benefits a 

### for U.S., except California in 2025 (billions of 2011$, 7% Discount Rate) b
Revised and Alternative Standard Levels 70 ppb 65 ppb Total Costs c $1.4 $15.5 Total Health Benefits $2.9 to $5.9 $15 to $30 Net Benefits $1.5 to $4.5 -$1.0 to $14

a Benefits are nationwide benefits of attainment everywhere except California. b The tables in Chapter 6 provide additional characterizations of the monetized benefits, including benefits estimated at a 3 percent discount rate. Estimating multiple years of costs and benefits is not possible for this RIA due to data and resource limitations. As a result, we provide a snapshot of costs and benefits in 2025, using the best available information to approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.

c The engineering costs in this table are annualized at a 7 percent discount rate to the extent possible. See Chapter 4 for more discussions.



### The guidelines of OMB Circular A-4 require providing comparisons of social costs and social benefits at discount rates of 3 and 7 percent. Ideally, streams of social costs and social benefits over time would be estimated and the net present values of each would be compared to determine net benefits of the illustrative attainment strategies. The three different uses of discounting in the RIA – (i) construction of annualized engineering costs, (ii) adjusting the valueof mortality risk for lags in mortality risk decreases, and (iii) adjusting the cost of illness for nonfatal heart attacks to adjust for lags in follow up costs -- are all appropriate. Our estimates of net benefits are the approximations of the net value (in 2025) of benefits attributable to emissions reductions needed to attain just for the year 2025.

Table ES-6. Summary of Total Number of Annual Ozone and PM-Related Premature Mortalities and Premature Morbidity: 2025 National Benefits a Revised and Alternative Standard Levels 70 ppb 65 ppb Ozone-related premature deaths avoided (all ages) 96 to 160 490 to 820 PM 2.5 -related premature deaths avoided (age 30+) 220 to 500 1,100 to 2,500 Other health effects avoided
Non-fatal heart attacks (age 18-99) (5 studies) PM 28 to 260 140 to 1,300
Respiratory hospital admissions (age 0-99) O3, PM 250 1,200 Cardiovascular hospital admissions (age 18-99) PM 80 Commented [A7]: Please break out ozone and PM co-benefits.

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ES-16 Revised and Alternative Standard Levels 70 ppb 65 ppb 400
Asthma emergency department visits (age 0-99) O3, PM 630 3,300 Acute bronchitis (age 8-12) PM 340 1,700 Asthma exacerbation (age 6-18) O3, PM 230,000 1,100,000 Lost work days (age 18-65) PM 28,000 140,000 Minor restricted activity days (age 18-65) O3, PM 620,000 3,100,000 Upper & lower respiratory symptoms (children 7-14)
PM 11,000 53,000 School loss days (age 5-17) O3 160,000 790,000

a Nationwide benefits of attainment everywhere except California. All values are rounded to two significant figures. Additional information on confidence intervals are available in the tables in Chapter 6.



### Table ES-7. Summary of Total Control Co sts (Identified + Unidentified Control Strategies) by Revised and Alternative Standard Levels for 2025 - U.S., except California (billions of 2011$, 7% Discount Rate) a

Revised and Alternative Standards Levels Geographic Area Total Control Costs (Identified and Unidentified) 70 ppb East 1.4 West <0.05 Total $1.4
65 ppb East 15 West <0.75 Total $15.5

a All values are rounded to two significant figures with a minor exception. Costs are annualized at a 7 percent discount rate to the extent possible. Costs associated with unidentified controls are based on an average cost-per-ton methodology (see Chapter 4, Section 4.3 for more discussion on the average-cost methodology).



### Table ES-8. Regional Breakdown of Monetized Ozone-Specific Benefits Results for 2025 (Nationwide Benefits of Attaining the Revised and Alternative Standard Levels Everywhere in the U.S., except California) a Revised and Alternative Standard Levels Region 70 ppb 65 ppb East b 98% 96% California 0% 0% Rest of West 2% 4%

a Because we use benefit-per-ton estimates to calculate the PM 2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits. b Includes Texas and states to the north and east. 

### To understand possible additional costs and benefits of fully attaining in California in a post-2025 timeframe, we provide separate results for California in Table ES-9. Because of the differences in the timing of achieving needed emissions reductions, incurring costs, and accruing

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ES-17 benefits for California, the separate costs and benefits estimates for post-2025 should not be added to the primary estimates for 2025. For the post-2025 timeframe, Table ES-10 presents the numbers of premature deaths avoided for the revise d and alternative standard levels analyzed, as well as the other health effects avoided. Table ES-11 provides information on the costs forCalifornia for post-2025, and Table ES-12 provides a regional breakdown of benefits for post2025.

The EPA presents separate costs and benefits results for California because assuming attainment in an earlier year than would be required under the Clean Air Act would likely lead to an overstatement of costs and benefits because California might benefit from some existing federal or state programs that would be implemented between 2025 and the ultimate attainment years; because additional new technologies may become available between 2025 and the attainment years; and because the cost of existing technologies might fall over time. As such, we use the best available data to estimate costs and benefits for California in a post-2025 timeframe, but because of data limitations and additional uncertainty associated with not projecting emissions and air quality beyond 2025, we recognize that the estimates of costs and benefits for California in a post-2025 timeframe are likely to be relatively more uncertain than the national
attainment estimates for 2025.

Table ES-9. Total Annual Costs and Benefits a 

### of the Identified + Unidentified Control Strategies Applied in California, Post-2025 (billions of 2011$, 7% Discount Rate) b
Revised and Alternative Standard Levels 70 ppb 65 ppb Total Costs c $0.80 $1.5 Total Health Benefits $1.2 to $2.1 $2.3 to $4.2 Net Benefits $0.4 to $1.3 $0.8 to $2.7

a Benefits are nationwide benefits of attainment in California. b The guidelines of OMB Circular A-4 require providing comparisons of social costs and social benefits at discount rates of 3 and 7 percent. The tables in Chapter 6 provide additional characterizations of the monetized benefits, including benefits estimated at a 3 percent discount rate. Estimating multiple years of costs and benefits is not possible for this RIA due to data and resource limitations. As a result, we provide a snapshot of costs and benefits in 2025, using the best available information to approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.

c The engineering costs in this table are annualized at a 7 percent discount rate to the extent possible. See Chapter 4 for more discussions.

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ES-18 Table ES-10. Summary of Total Number of Annual Ozone and PM-Related Premature Mortalities and Premature Morbidity: Post-2025 a Revised and Alternative Standard Levels 70 ppb 65 ppb Ozone-related premature deaths avoided (all ages) 72 to 120 150 to 240 PM 2.5 -related premature deaths avoided (age 30+) 43 to 98 84 to 190 Other health effects avoided Non-fatal heart attacks (age 18-99) (5 studies) PM 6 to 51 11 to 100 Respiratory hospital admissions (age 0-99) O3, PM 150 300 Cardiovascular hospital admissions (age 18-99) PM 16 31 Asthma emergency department visits (age 0-99) O3, PM 380 760 Acute bronchitis (age 8-12) PM 64 130 Asthma exacerbation (age 6-18) O3, PM 160,000 330,000 Lost work days (age 18-65) PM 5,300 10,000
Minor restricted activity days (age 18-65) O3, PM 360,000 720,000 Upper & lower respiratory symptoms (children 7-14)
PM 2,000 3,900 School loss days (age 5-17) O3 120,000 240,000

a Nationwide benefits of attainment in California. All values are rounded to two significant figures. Additional information on confidence intervals are available in the tables in Chapter 6.



### Table ES-11. Summary of Total Control Co sts (Identified + Unidentified Control Strategies) by Revised and Alternative Standards for Post-2025 - California (billions of 2011$, 7% Discount Rate) a

Revised and Alternative Standard Level Geographic Area Total Control Costs (Identified and Unidentified) 70 ppb California $0.80 65 ppb California $1.5

a All values are round ed to two significant figures. Costs are annualized at a 7 percent discount rate to the extent possible. Costs associated with unidentified controls are based on an average cost-per-ton methodology.



### Table ES-12. Regional Breakdown of Monetize d Ozone-Specific Benefits Results for Post2025 (Nationwide Benefits of Attaining Revised and Alternative Standards just in California) a Revised and Alternative Standard Levels Region 70 ppb 65 ppb East b 3% 2% California 90% 91% Rest of West 7% 7%

a Because we use benefit-per-ton estimates to calculate the PM 2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits.

b Includes Texas and states to the north and east.

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ES-19 ES.3 Improvements between the Proposal and Final RIAs In the regulatory impact anal yses for both the proposed and final ozone NAAQS, there were two geographic areas outside of California where the majority of emissions reductions were needed to meet the revised standard level of 70 ppb – Texas and the Northeast. In analyzing 70 ppb in the final RIA, there were approximately 50 percent fewer emissions reductions needed in these two geographic areas. For an alternative standard of 65 ppb in the final RIA, emissions reductions needed nationwide were approximately 20 percent lower than at proposal. The primary reason for the difference in emissions reductions estimated for attainment is that in the final RIA we conducted more geographically-refined air quality sensitivity modeling to develop improved ozone response factors (see Chapter 2, Section 2.2.2 for a more detailed discussion of the air quality modeling) and focused the emissions reduction strategies on geographic areas closer to the monitors with the highest design values (see Chapter 3, Section 3.1.1 for a more
detailed discussion of the emissi ons reduction strategies). Th e improvements in air quality modeling and emissions reduction strategies account for about 80 percent of the difference in needed emissions reductions between the proposal and final RIAs.

In Texas and the Northeast, the updated response factors and more focused emissions reduction strategies resulted in larger changes in ozone concentrations in response to more geographically focused emissions reductions. In east Texas, the ppb/ton ozone response factors used in the final RIA were 2 to 3 times more responsive than the factors used in the proposal RIA at controlling monitors in Houston and Dallas. In the Northeast, the ppb/ton ozone response factors used in the final RIA were 2.5 times more responsive than the factors used in the proposal RIA at the controlling monitor on Long Island, NY.

A secondary reason for the difference is that between the proposal and final RIAs we updated emissions inventories, models and model inputs for the base year of 2011. See Appendix 2, Section 2A.1.3 for additional discussion of the updated emissions inventories, models and model inputs. When projected to 2025, these changes in inventories, models and inputs had compounding effects for year 2025, and in some areas resulted in lower projected base case design values for 2025. The updated emissions inventories, models, and model inputs

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ES-20 account for about 20 percent of the difference in needed emissions reductions between the proposal and final RIAs.

These differences in the estimates of emissions reductions needed to attain the revised
and alternative standard levels affect the estimates for the costs and benefits in this RIA. For a revised standard of 70 ppb, the costs were 60 percent lower than at proposal and the benefits were 55 percent lower than at proposal. The percent decrease in costs is slightly more than the percent decrease in emissions reductions because a larger number of lower cost identified controls were available to bring areas into attainment with 70 ppb. 7 

### The percent decrease in benefits is similar to the percent decrease in emissions reductions. For an alternative standard level of 65 ppb, the costs were less than three percent more than those estimated at proposal and the benefits were 22 percent lower than at proposal. The percent change in costs was less than the percent decrease in emissions reductions because in the final analysis we applied identified controls in smaller geographic areas, resulting in fewer identified controls available within those areas and an increase in higher cost unidentified controls being applied to bring areas into attainment with 65 ppb. The percent decrease in benefits is similar to the percent decrease in
emissions reductions.

ES.4 Uncertainty Despite uncertainties inherent in any complex, quantitative analysis, the overall underlying analytical methods used in this RIA have been peer-reviewed. For a detailed discussion on uncertainty associated with developing illustrative control strategies to attain the alternative standard levels, see Chapter 3, Section 3.4. For a description of the key assumptions and uncertainties related to ozone benefits, see Chapter 6, Section 6.5, and for an additional qualitative discussion of sources of uncertainty associated with both ozone-related benefits and
PM 2.5 

### -related co-benefits, see Appendix 6A. For a discussion of the limitations and uncertainties

7 In the final RIA, outside of California all areas were projected to meet the current standard of 75 ppb. As such, no identified controls were used to bring areas into attainment with 75 ppb. In the proposal RIA, some of these lower cost controls were used to bring areas into attainment with 75 ppb, making them unavailable for application in the analysis of 70 ppb. Commented [A8]: Please clarify that this is CASAC review and CoST review.

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ES-21 in the engineering cost analyses, see Chapter 4, Section 4.7. For a general discussion about key factors that could impact how air quality changes over time, see Chapter 8, Section 8.3.

ES.5 References
De Steiguer, J., Pye, J., Love, C. 1990. Air Pollution Dama ge to U.S. Forests. Journal of Forestry, 88(8), 17-22.
Fann N, Lamson A, Wesson K, Risley D, Anenberg SC, Hubbell BJ. 2012a. “Estimating the National Public Health Burden Associated with Exposure to Ambient PM 2.5 and ozone. Risk Analysis,” Risk Analysis 32(1): 81-95.
Levy JI, Baxter LK, Schwartz J. 2009. “Uncertainty and variability in health-related damages from coal-fired power plants in the United States.” Risk Analysis 29(7) 1000-1014.
Pye, J.M. 1988. Impact of ozone on the growth and yield of trees: A review. Journal of Environmental Quality, 17, 347-360.
Tagaris E, Liao KJ, Delucia AJ, et al. 2009. “Potential impact of climate change on air-pollution related human health effects.” Environmental Science & Technolology 43: 4979–4988.
U.S. Environmental Protection Agency (U.S. EPA). 2013. Integrated Science Assessment of Ozone and Related Photochemical Oxidants (Final Report). EPA-600/R-10/076F. February. Available at <http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492>.
U.S. Environmental Protection Agency (U.S. EPA). 2014a. Control of Air Pollution from Motor Vehicles: Tier 3 Motor Vehicle Emission and Fuel Standards. Office of Transportation and Air Quality. Available at <http://www.epa.gov/otaq/tier3.htm>.
U.S. Environmental Protection Agency (U.S. EPA). 2014b. Proposed Carbon Pollution Guidelines for Existing Power Plants and Emission Standards for Modified and Reconstructed Power Plants. Available at <http://www2.epa.gov/sites/production/files/2014-06/documents/20140602ria-clean-power-plan.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2014c. Regulatory Impact Analysis of the Proposed Revisions to the National Ambient Air Quality Standards for Gr ound-Level Ozone. Office of Air Quality Planning and Standards, Research Triangle Park, NC. November. Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/20141125ria.pdf >.

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1-1 CHAPTER 1: INTRODUCTION AND BACKGROUND

Introduction The Environmental Protection Agency (EPA) initiated the current ozone National Ambient Air Quality Standards (NAAQS) review in September 2008. Between 2008 and 2014, the EPA prepared draft and final versions of the Integrated Science Assessment, the Health and Welfare Risk and Exposure Assessments, and th e Policy Assessment. Multiple drafts of these documents were available for public review and comment. In addition, as required by the Clean Air Act (CAA), the documents were peer-reviewed by the Clean Air Scientific Advisory Committee (CASAC), the Administrator’s independent advisory committee established by the
CAA. The final documents for this review reflect the EPA staff’s consideration of the comments and recommendations made by the CASAC and the public on draft versions of these documents.

The EPA has concluded that the current primary standard for ozone

, 

### set at a level of 75 ppb, is not requisite to protect public health with an adequate margin of safety, and is revising the standard to provide increased public health protection. Specifically, the EPA is retaining the indicator (ozone), averaging time (8-hour) and form (annual fourth-highest daily maximum, averaged over 3 years) of the existing primary standard and is revising the level of that standard to 70 ppb. The EPA is making this revision to increase public health protection, including for “at-risk” populations such as children, older adults, and people with asthma or other lung diseases, against an array of ozone-related adverse health effects. For short-term ozone exposures, these effects include decreased lung function, increased respiratory symptoms and pulmonary inflammation, effects that result in serious indicators of respiratory morbidity such as emergency department visits and hospital admissions, and all-cause (total non-accidental) mortality. For long-term ozone exposures, these health effects include a variety of respiratory morbidity effects and respiratory mortality. The EPA has also concluded that the current secondary standard for ozone, set at a level of 75 ppb, is not requisite to protect public welfare from known or anticipated adverse effects, and is revising the standard to provide increased protection against vegetation-related effects on public welfare. Specifically, the EPA is retaining the indicator (ozone), averaging time (8-hour) and form (annual fourth-highest daily maximum, averaged over 3 years) of the existing secondary

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1-2 standard and is revising the level of that standard to 70 ppb. The EPA has concluded that this revision will effectively curtail cumulative seasonal ozone exposures above 17 ppm-hrs, in terms of a three-year average seasonal W126 index value, based on the three consecutive month period within the growing season with the maximum index value, with daily exposures cumulated for the 12-hour period from 8:00 am to 8:00 pm. Thus, the EPA has concluded that this revision will provide the requisite protection against known or anticipated adverse effects to the public
welfare. This Regulatory Impact Analysis (RIA) analyzes the human health benefits and costs and welfare cobenefits of the revised standard of 70 ppb as well as a more stringent alternative level of 65 ppb. In setting primary ambient air quality standards, the EPA’s responsibility under the law is to establish standards that protect public health, regardless of the costs of implementing those standards. As interpreted by the Agency and the courts, the CAA requires the EPA to create standards based on health considerations only.

The prohibition against the consideration of cost in the setting of the primary air quality standards, however, does not mean that costs or other economic considerations are unimportant or should be ignored. The Agency believes that consideration of costs and benefits is essential to making efficient, cost-effective decisions for implementing these standards. The impact of cost and efficiency is considered by states during the implementation process, as they decide what timelines, strategies, and policies are appropriate for their circumstances. This RIA is intended to inform the public about the potential costs and benefits that may result when new standards are implemented, but it is not part of setting the standards.

This chapter summarizes provides a brief background on NAAQS, the need for NAAQS, and an overview of this RIA, including a discussion of its design. The EPA prepared this RIA both to provide the public with information on the benefits and costs of meeting a revised ozone NAAQS and to meet the requirements of Executive Orders 12866 and 13563.

1.1 Background
1.1.1 National Ambient Air Quality Standards
Sections 108 and 109 of the CAA govern the establishment and revision of the NAAQS. Section 108 (42 U.S.C. 7408) directs the Administrator to identify pollutants that “may

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1-3 reasonably be anticipated to endanger public health or welfare” and to issue air quality criteria for them. These air quality criteria are intended to “accurately reflect the latest scientific knowledge useful in indicating the kind and extent of all identifiable effects on public health or welfare which may be expected from the presence of [a] pollutant in the ambient air.” Ozone is one of six pollutants for which the EPA has developed air quality criteria.

Section 109 (42 U.S.C. 7409) directs the Administrator to propose and promulgate “primary” and “secondary” NAAQS for pollutants identified under section 108. Section 109(b)(1) defines a primary standard as an ambient air quality standard “the attainment and maintenance of which in the judgment of the Administrator, based on [the] criteria and allowing an adequate margin of safety, [is] requisite to protect the public health.” A secondary standard, as defined in section 109(b)(2), must “specify a level of air quality the attainment and maintenance of which in the judgment of the Administrator, based on [the] criteria, is requisite to protect the public welfare from any known or anticipated adverse effects associated with the presence of [the] pollutant in the ambient air.” Welfare effects as defined in section 302(h) [42 U.S.C. 7602(h)] include but are not limited to “effects on soils, water, crops, vegetation, manmade materials, animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and hazards to transportation, as well as effects on economic values and on personal comfort and well-being.”

Section 109(d) of the CAA directs the Administrator to review existing criteria and standards at 5-year intervals. When warranted by such review, the Administrator is to retain or revise the NAAQS. After promulgation or revision of the NAAQS, the standards are implemented by the states.

1.1.2 Role of Executive Orders in the Regulatory Impact Analysis
While this RIA is separate from the NAAQS decision-making process, several statutes and executive orders still apply to any public documentation. The analyses required by these statutes and executive orders are presented in detail in Chapter 9, and below we briefly discuss requirements of Orders 12866 and 13563 and the guidelines of the Office of Management and Budget (OMB) Circular A-4 (U.S. OMB, 2003).

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1-4 In accordance with Executive Orders 12866 and 13563 and the guidelines of OMB Circular A-4, the RIA analyzes the benefits and costs associated with emissions controls to attain the revised 8-hour ozone standard of 70 ppb in ambient air, incremental to a baseline of attaining the existing standard (8-hour ozone standard of 75 ppb). 8 

### OMB Circular A-4 requires analysis of one potential alternative standard level more stringent than the revised standard and one less stringent than the revised standard. In this RIA, we analyze a more stringent alternative standard level of 65 ppb. The existing standard of 75 ppb represents the less stringent alternative standard and the costs and benefits of this standard were presented in the 2008 ozone NAAQS RIA (U.S. EPA, 2008a). Further, as discussed in the notice of final rulemaking, the available scientific evidence and quantitative risk and exposure information on the health effects of ozone exposure provide strong support for a revised standard of 70 ppb, but do not identify a bright line for identifying any specific standard level between 70 and 75 ppb for analysis in the RIA. As such, we did not analyze a standard between 70 and 75 ppb in this RIA.

1.1.3 Illustrative Nature of the Analysis
The control strategies presented in this RIA are an illustration of one possible set of control strategies states might choose to implement to meet the revised standards. States—not the EPA—will implement the revised NAAQS and will ultimately determine appropriate emissions control strategies and measures. State Implementation Plans (SIPs) will likely vary from the EPA’s estimates provided in this analysis due to differences in the data and assumptions that states use to develop these plans. Because states are ultimately responsible for implementing strategies to meet the revised standards, the control strategies in this RIA are considered hypothetical. The hypothetical strategies were constructed with the understanding that there are inherent uncertainties in projecting emissions and control applications. Additional important uncertainties and limitations are documented in the relevant portions of the RIA.

8 On April 30, 2012 the EPA issued final designations for the 2008 ozone NAAQS. After final designations, areas have up to three years to submit attainment SIPs. Beca use of the timing of these SIP submittals, the EPA does not have the most current information on control measures and emissions reductions needed to meet the current standard of 75 ppb. To account for potential emissions reductions associated with meeting the current standard, we estimate these emissions reductions in defining the baseline.

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1-5 The EPA’s national program rules require technology application or emissions limits for a specific set of sources or source groups. In contrast, a NAAQS establishes a standard level and requires states to identify and secure emissions reductions to meet the standard level from any set of sources or source groups. To avoid double counting the impacts of NAAQS and other national program rules, the EPA includes federal regulations and enforcement actions in its baseline for this analysis (See Section 1.3.1 for additional discussion of the baseline). The benefits and costs of the revised standards will not be realized until specific control measures are mandated by SIPs or other federal regulations.

1.2 The Need for National Ambi ent Air Quality Standards

OMB Circular A-4 indicates that one of the reasons a regulation such as the NAAQS may be issued is to address a market failure. The major types of market failure include: externality, market power, and inadequate or asymmetric information. Correcting market failures is one reason for regulation, but it is not the only reason. Other possible justifications include improving the function of government, removing distributional unfairness, or promoting privacy and personal freedom.

Environmental problems are classic examples of externalities -- uncompensated benefits or costs imposed on another party as a results of one’s actions. For example, the smoke from a factory may adversely affect the health of local residents and soil the property in nearby neighborhoods. If bargaining was costless and all property rights were well defined, people would eliminate externalities through bargaining without the need for government regulation.

From an economics perspective, setting an air quality standard is a straightforward remedy to address an externality in which firms emit pollutants, resulting in health and environmental problems without compensation for those incurring the problems. Setting a standard with a reasonable margin of safety attempts to place the cost of control on those who emit the pollutants and lessens the impact on those who suffer the health and environmental problems from higher levels of pollution. For additional discussion on the ozone air quality problem, see Chapter 2 of the Policy Assessment for the Review of the Ozone National Ambient Air Quality Standards (US EPA, 2014).

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1-6 1.3 Overview and Design of the RIA The RIA evaluates the costs and benefits of hypothetical national control strategies to attain the revised ozone standard of 70 ppb and an alternative ozone standard level of 65 ppb.

1.3.1 Establishing Attainment with the Current Ozone National Ambient Air Quality Standard
The RIA is intended to evaluate the overall potential costs and benefits of reaching attainment with the revised and alternative ozone standard levels. To develop and evaluate control strategies for attaining a more stringent primary standard, it is important to estimate ozone levels in the future after attaining the current NAAQS of 75 ppb, and taking into account projections of future air quality reflecting on-the-books Federal regulations, substantial federal regulatory proposals, enforcement actions, state regulations, population and where possible, economic growth. Establishing this baseline for the analysis then allows us to estimate the incremental costs and benefits of attaining the revised and alternative standard levels.

Attaining 75 ppb reflects emissions reductions (i) already achieved as a result of national regulations, (ii) expected prior to 2025 from recently promulgated national regulations (i.e., reductions that were not realized before promulgation of the previous standard, but are expected prior to attainment of the current ozone standard), and (iii) from additional controls that the EPA
estimates need to be included to attain the current standard. Additional emissions reductions achieved as a result of state and local agency regulations and voluntary programs are reflected to the extent that they are represented in emissions inventory information submitted to the EPA by state and local agencies. We took two steps to develop the baseline for this analysis, a baseline that reflects attainment of 75 ppb. First, national ozone concentrations were projected to the analysis year (2025) based on forecasts of population and where possible, economic growth and the application of emissions controls resulting from national rules promulgated prior to this analysis, as well as state programs and enforcement actions. Second, we estimated additional emissions reductions needed to meet the current standard of 75 ppb and make adjustments for the proposed Clean Power Plan. 9

9 We adjusted the 2025 base case to reflect the proposed Clean Power Plan. Because of the timing associated with completing the analyses in this final RIA and finalizing final Clean Power Plan, we were not able to reflect the final Commented [A9]: Please see highlighted text. Please add a sentence on the effect of using proposal vs final in regards to benefits and costs.

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1-7 Below is a list of some of the national rules reflected in the baseline. For a more complete list, please see the Technical Support Document: Preparation of Emissions Inventories for the Version 6.2, 2011 Emissions Modeling Platform (US EPA, 2015).

 Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units (Proposed Rule) (U.S. EPA, 2014a)

 Tier 3 Motor Vehicle Emission and Fuel Standards (U.S. EPA, 2014c)

 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards (U.S. EPA, 2012)

 Cross State Air Pollution Rule (CSAPR) (U.S. EPA, 2011)

 Mercury and Air Toxics Standards (U.S. EPA, 2011a) 10



###  Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles (U.S. EPA, 2011d) 11



###  C3 Oceangoing Vessels (U.S. EPA, 2010)

 Reciprocating Internal Combustion Engines (RICE) NESHAPs (U.S. EPA, 2010a)

 Regulation of Fuels and Fuel Additives: Modifications to Renewable Fuel Standard Program (RFS2) (U.S. EPA, 2010b)

 Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; Final Rule for Model-Year 2012-2016 (U.S. EPA, 2010c)

 Hospital/Medical/Infectious Waste Incinerators: New Source Performance Standards and Emission Guidelines: Final Rule Amendments (U.S. EPA, 2009)

 Emissions Standards for Locomotives and Marine Compression-Ignition Engines (U.S. EPA, 2008b)

Clean Power Plan in the baseline. We will add a qualitative discussion on differences between the Clean Power Plan proposal and final and how those differences may affect the baseline.
10 On June 29, 2015, the United States Supreme Court reversed the D.C. Circuit opinion affirming the Mercury and Air Toxics Standards (MATS). The EPA is reviewing the decision and will determine any appropriate next steps once the review is complete. MATS is included in the baseline for this analysis, and the EPA does not believe including MATS substantially alters the results of this analysis.
11 This rule is Phase 1 of the Heavy Duty Greenhouse Gas Standards for New Vehicles and Engines (76 FR 57106, September 15, 2011) and is included in the 2025 base case. Phase 2 of the Heavy Duty Greenhouse Gas Standards for New Vehicles and Engines (80 FR 40138, July 13, 2015) is not included in the 2025 base case because the rulemaking was not finalized in time to include in this analysis. Commented [A10]: Please include a sentence on the implications for the benefits and costs for not including this rule. Some stakeholders pinpointed mobile source as an important contributor to ozone.

Formatted: Highlight

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1-8  Control of Emissions for Nonroad Spark Ignition Engines and Equipment (U.S. EPA, 2008c)

 Regional Haze Regulations and Guidelines for Best Available Retrofit Technology Determinations (U.S. EPA, 2005a)

 NO x 

### Emission Standard for New Commercia l Aircraft Engines (U.S. EPA, 2005)

The baseline for this analysis does not assume emissions controls that might be implemented to meet the current PM 2.5 

### , NO 2 

### , or SO 2 

### NAAQS. For the current PM 2.5 

### and SO 2 

### NAAQS, the Agency has not issued final designations and does not have information on what areas would need emissions controls; for the current NO 2 

### NAAQS there are no nonattainment areas. We did not conduct this analysis incremental to controls applied as part of previous NAAQS analyses because the data and modeling on which these previous analyses were based are now considered outdated and are not compatible with the current ozone NAAQS analysis. 12 

### More importantly, all control strategies analyzed in NAAQS RIAs are hypothetical.

1.3.2 Establishing the Baseline for Evaluation of Revised and Alternative Standards
The RIA evaluates, to the extent possible, the costs and benefits of attaining the revised and alternative ozone standards incremental to attaining the current ozone standard and implementing existing and expected regulations. We assume that potential nonattainment areas everywhere in the U.S., excluding California, will be designated such that they are required to attain the revised standard by 2025. As such, we developed our projected baselines for emissions, air quality, and populations and present the primary costs and benefits estimates for
2025.

The selection of 2025 as the analysis year in the RIA does not predict or prejudge attainment dates that will ultimately be assigned to individual areas under the CAA. The CAA

12 There were no additional NOx controls applied in the 2012 PM 2.5 NAAQS RIA, and therefore there would be little

to no impact on the controls selected in this analysis. In addition, the only geographic areas that exceed the alternative ozone standard levels analyzed in this RIA and in the 2012 PM 2.5 NAAQS RIA are in California. The attainment dates for a new PM 2.5 NAAQS would likely precede attainment dates for a revised ozone NAAQS. While the 2012 PM 2.5 NAAQS RIA concluded that controls on directly emitted PM 2.5 were the most costeffective controls on a $/ug basis, states may choose to adopt different control options. These options could include NOx controls, and it is difficult to determine the impact on costs and benefits for this RIA because it depends highly on the control measures that would be chosen and the costs of these measures. Commented [A11]: Please insert a footnote on the implications for benefits and costs for not including PM NAAQS.

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1-9 contains a variety of potential attainment dates and flexibility to move to later dates (up to 20 years), provided that the date is as expeditious as practicable. The EPA will likely finalize designations for a revised ozone NAAQS in late 2017. Depending on the precise timing of the effective date of those designations, nonattainment areas classified as Marginal will likely have to attain in either late 2020 or early 2021. Nonattainment areas classified as Moderate will likely have to attain in either late 2023 or early 2024. If a Moderate nonattainment area qualifies for two 1-year extensions, the area may have as late as early 2026 to attain. Lastly, Serious nonattainment areas will likely have to attain in late 2026 or early 2027. We selected 2025 as the primary year of analysis because it provides a good representation of the remaining air quality
concerns that Moderate nonattainment areas would face and because most areas of the U.S. will likely be required to meet a revised ozone standard by 2025. States with areas classified as Moderate and higher are required to develop attainment demonstration plans for those
nonattainment areas.

The EPA recognizes that areas designated nonattainment for the revised ozone NAAQS and classified as Marginal or Moderate will likely incur some costs prior to the 2025 analysis year. The Agency, however, anticipates that on-the-books federal emissions control measures 13 

### will be sufficient to bring the majority of these areas into attainment by 2025. Areas designated nonattainment and classified as Marginal are required to develop emission inventories, emission statements, and produce a CAA section 110 infrastructure SIP. These areas are not required to develop any control measures aside from the federal emissions control measures reflected in the baseline. As a result, the Agency anticipates that costs in these Marginal areas will be minimal. In addition to the federal control measures and the requirements for Marginal nonattainment areas, states with nonattainment areas designated as Moderate are required by the CAA to develop state implementation plans (SIPs) demonstrating attainment by no later than the assigned attainment date. The CAA also requires these states to address Reasonably Available Control Technologies (RACT) for sources in the Moderate nonattainment area, which could lead to additional point source controls in an area beyond the federal emissions control measures. Additionally, the CAA requires some Moderate areas with larger populations to implement basic

13 These federal control measures are listed above in section 1.3.1.

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1-10 vehicle inspection and maintenance (I/M) in the area. Should these federal programs and CAA required programs prove inadequate for the area to attain the revised standard by the attainment date, the state would need to identify additional emissions control measures in its SIP to meet attainment requirements.

In addition, in estimating the incremental costs and benefits of the revised and alternative standards, we recognize that there are areas that are not required to meet the existing ozone standard by 2025 -- the CAA allows areas with more significant air quality problems to take additional time to reach the existing standard. Several areas in California are not required to meet the existing standard by 2025 and may not be required to meet a revised standard until
sometime between 2032 and 2037. 14,15 

### Because of data and resource constraints, we were not able to project emissions and air quality beyond 2025 for California, however, we adjusted baseline air quality to reflect mobile source emi ssions reductions for California that would occur between 2025 and 2030; these emissions reductions were the result of mobile source regulations expected to be fully implemented by 2030. 16 

### While there is uncertainty about the precise timing of emissions reductions and related costs for California, we assume costs occur through the end of 2037 and beginning of 2038. In addition, we model benefits for California using projected population demographics for 2038.

Because of the difference in timing for incurring costs and accruing benefits and for ease of discussion throughout the analyses, we refer to the different time periods for potential attainment as 2025 and post-2025 to reflect that (1) we did not project emissions and air quality for any year other than 2025; (2) for California, emissions controls and associated costs are assumed to occur through the end of 2037 and beginning of 2038; and (3) for California benefits are modeled using population demographics in 2038. It is not straightforward to discount the

14 The EPA will likely finalize designations for a revised ozone NAAQS in late 2017. Depending on the precise

timing of the effective date of those designations, nonattainment areas classified as Severe 15 will likely have to attain by December 31, 2032 and nonattainment areas classified as Extreme will likely have to attain by December 31, 2037.
15 In this RIA before deciding to continue to analyze California beyond the future analysis year of 2025, we

reviewed California’s NOx and VOC emissions within existing nonattainment areas. The vast majority of these emissions come from emissions sources located in existing nonattainment areas that would likely have to attain the final standard sometime between 2032 and 2037. As a result, we concluded that analyzing California separately and after 2025 continued to be an appropriate analytical decision.
16 At the time of this analysis, there were no future year emissions for California beyond 2030, and projecting

emissions beyond 2030 could introduce additional uncertainty.

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1-11 post-2025 results for California to compare with or add to the 2025 results for the rest of the U.S. While we estimate benefits using 2038 information, we do not have good information on precisely when the costs of controls will be incurred. Because of these differences in timing related to California attaining a revised standard, the separate costs and benefits estimates for post-2025 should not be added to the primary estimates for 2025.

1.3.3 Cost Analysis Approach
The EPA estimated total costs under partial and full attainment of the revised and alternative ozone standard levels analyzed. These cost estimates reflect only engineering costs, which generally includes the costs of purchasing, installing, and operating the referenced control technologies. The technologies and control strategies selected for analysis illustrate one way in which nonattainment areas could meet a revised standard. There are numerous ways to construct and evaluate potential control programs that would bring areas into attainment with alternative standards, and the EPA anticipates that state and local governments will consider programs that
are best suited for local conditions.

The partial-attainment cost analysis reflects the engineering costs associated with applying end-of-pipe controls, or identified controls. Cost s for full attainment include estimates for the costs associated with the additional emissions reductions that are needed beyond identified controls. The EPA recognizes that the portion of the cost estimates from emissions reductions beyond identified controls reflects substantial uncertainty about which sectors and which technologies might become available for cost-effective application in the future.

1.3.4 Human Health Benefits
The EPA estimated human health (i.e., mortality and morbidity effects) under both partial and full attainment of the three alternative ozone standard levels analyzed. We considered an array of health impacts attributable to changes in ozone and PM 2.5 exposure and estimated these benefits using the BenMAP tool, which has been used in many recent RIAs (e.g., U.S. EPA, 2011a, 2011c) and The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011b). The EPA has incorporated an array of policy and technical updates to the benefits analysis approach applied in this RIA, including incorporation of the most recent epidemiology studies evaluating mortality and morbidity associated with ozone and PM 2.5 

### exposure, and an expanded Commented [A12]: Two?

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1-12 uncertainty assessment. Each of these updates is fully described in the health benefits chapter (Chapter 6). In addition, unquantified health benefits are also discussed in Chapter 6.

1.3.5 Welfare Benefits of Meeting Primary and Secondary StandardCo-Benefits
Even though the primary standards are designed to protect against adverse effects to human health, the emissions reductions would have welfare co-benefits in addition to the direct
human health benefits. The term welfare co-benefits covers both environmental and societal benefits of reducing pollution. Welfare co-benefits of the primary ozone standard include reduced vegetation effects resulting from ozone exposure, reduced ecological effects from particulate matter deposition and from nitrogen emissions, reduced climate effects, and changes in visibility. Welfare co-benefits are discussed further in Chapter 7.

1.4 Updates between the Proposal and Final RIAs For NAAQS RIAs, the Agency always reviews the underlying data used and makes methodological and model improvements both between proposal and final analyses and between different NAAQS analyses. For this final RIA, we made updates to the emissions inventory based on public comments and input from the states, updated the oil and gas sector emissions projections based on input from the states, and used updated versions of IPM and the onroad mobile source model. For a detailed discussion of these emissions inventory, model, and model input updates, see Appendix 2, Section 2A.1.3. In addition, based on the analyses in the proposal RIA, in this final NAAQS RIA the EPA decided to conduct more refined air quality modeling to assess emissions changes closer to monitors in certain areas, specifically Texas and the
Northeast.

The net effects of the emissions inventory, model, and model input updates are changes in projected 2025 ozone air quality design values (DVs) 17 

### in many areas. These new projected DVs were higher than previously modeled for the proposal RIA in some locations and lower in others.

17 The DV is calculated as the 3-year average of the annual 4 th highest daily maximum 8-hour ozone concentration in parts per billion, with decimal digits truncated. The DV is a metric that is compared to the standard level to determine whether a monitor is violating the NAAQS. The ozone DV is described in more detail in Chapter 2, Section 2.2.

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1-13 The new projections show lower 2025 DVs in Central Texas from Houston to Dallas, the El Paso area (NM and TX) and Big Bend, Texas, and several states in the central U.S., including Oklahoma, Kansas, Missouri, Arkansas, Mississippi, Tennessee, and southern Kentucky. The new projections also show higher 2025 DVs in Denver, Las Vegas, Phoenix, Charlotte, the upper Midwest, and parts of the New York/New Jersey areas. See Appendix 2A, Section 2A.4 for detailed information on the updated DVs.

We also conducted additional air quality modeling runs to provide more spatially resolved air quality response factors, allowing us to more appropriately represent the effectiveness of emissions reductions from sources closer to receptor monitors compared to the regional response factors used for the November 2014 proposal RIA (see Chapter 2, Section 2.2.2 for a discussion of the additional air quality modeling). In the final RIA, there were approximately 50 percent
fewer emissions reductions needed in Texas and the Northeast to reach a revised standard of 70 ppb. For an alternative standard of 65 ppb in the final RIA, emissions reductions needed nationwide were approximately 20 percent lower than at proposal.

The primary reasons for the difference in emissions reductions estimated in the final RIA are the more spatially resolved air quality modeling and resulting improved ozone response factors, as well as the focus of the emissions reduction strategies on geographic areas closer to the monitors with the highest design values (see Chapter 3, Section 3.1.1 for a more detailed discussion of the emissions reduction strategies). The improvements in air quality modeling and emissions reduction strategies account for about 80 percent of the difference in estimated needed emissions reductions between the proposal and final RIAs.

For example, in analyzing the revised standard of 70 ppb, in Texas and the Northeast the updated response factors and more focused emissions reduction strategies resulted in larger changes in ozone concentrations in response to more geographically focused emissions reductions. In east Texas, the air quality response factors used in the final RIA were 2 to 3 times more responsive than the factors used in the proposal RIA at controlling monitors in Houston and Dallas. In the Northeast, the air quality response factors used in the final RIA were 2.5 times more responsive than the factors used in the proposal RIA at the controlling monitor on Long Island, NY.

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1-14 The updates made to the emissions inventories, models, and model inputs for the base year of 2011 account for the remaining 20 percent of the difference in estimated emissions reductions needed between the proposal and final RIAs. When projected to 2025, these changes in inventories, models and inputs had compounding effects for year 2025, and in some areas resulted in lower projected base case DVs for 2025.

For additional information on how the revised emissions reduction estimates affect the cost estimates, see Chapter 4, Section 4.6. For additional information on how the revised emissions reduction estimates affect the benefits estimates, see Chapter 6, Section 6.1.

1.5 Organization of the Regulatory Impact Analysis
This RIA is organized into the following remaining chapters:

 

### Chapter 2 : Emissions, Air Quality Modeling and Analytic Methodologies . The data, tools, and methodology used for the air quality modeling are described in this chapter, as well as the post-processing techniques used to produce a number of air quality metrics for input into the analysis of benefits and costs.

 

### Chapter 3: Control Strategies and Emissions Reductions . The chapter presents the hypothetical control strategies, the geographic areas where controls were applied, and the results of the modeling that predicted ozone concentrations in 2025 after applying the control strategies.

 

### Chapter 4: Engineering Cost Analysis and Economic Impacts. The chapter summarizes the data sources and methodology used to estimate the engineering costs of partial and full attainment of the three alternative standard levels analyzed.

 

### Chapter 5: Qualitative Discussion of Employment Impacts of Air Quality. The chapter provides a discussion of some possible types of employment impacts of reducing emissions of ozone precursors.

 

### Chapter 6: Human Health Benefits Analysis Approach and Results . The chapter quantifies the health-related benefits of the ozone-related air quality improvements associated with the three alternative standard levels analyzed.

 

### Chapter 7 : Impacts on Public Welfare of Attainment Strategies to Meet the Primary and Secondary Ozone NAAQS. The chapter includes a discussion of the welfare-related benefits of meeting alternative primary and secondary ozone standards and a limited quantitative analysis for effects associated with changes in yields of commercial forests and agriculture, and associated changes in carbon sequestration and storage.

 

### Chapter 8: Comparison of Benefits and Costs . The chapter compares estimates of the total benefits with total costs and summarizes the net benefits of the three alternative standards analyzed.

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1-15  

### Chapter 9 : Statutory and Executive Order Impact Analyses . The chapter summarizes the Statutory and Executive Order impact analyses.
1.6 References
U.S. Environmental Protection Agency (U.S. EPA). 2004. Control of Emissions of Air Pollution from Nonroad

Diesel Engines and Fuel. Office of Transportation and Air Quality. Available at http://www.regulations.gov/search/R egs/contentStreamer?objectId=09 000064800be203&disposition=attachment &contentType=pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2005. New Emission Standards for New Commercial Aircraft

Engines. Office of Transportation and Air Quality. Available at http://www.epa.gov/oms/regs/nonroad/aviation/420f05015.htm.
U.S. Environmental Protection Agency (U.S. EPA). 2005a. Regional Haze Regulations and Guidelines for Best

Available Retrofit Technology Determinations. Office of Air Quality Planning and Standards. Available at http://www.epa.gov/fedrgstr/EPA-AIR/2005/July/Day-06/a12526.pdf and http://www.epa.gov/visibility/fs_2005_6_15.html.
U.S. Environmental Protection Agency (U.S. EPA). 2006

. Air Quality Criteria for Ozone and Related

Photochemical Oxidants (Final). EPA/600/R-05/004aF-cF. Office of Research and Development, Research Triangle Park, NC. Available at http://cfpub.epa.gov/ncea/CFM/recordisplay.cfm?deid=149923



### . U.S. Environmental Protection Agency (U.S. EPA). 2008a. Final Ozone NAAQS Regulatory Impact Analysis, US

EPA, Office of Air Quality Planning and Standards, Research Triangle Park, NC, EPA-452/R-08-003. Available at http://www.epa.gov/ttnecas1/regdata/RIAs/452_R_08_003.pdf.
U.S. Environmental Protection Agency (U.S. EPA).2008b. Emissions Standards for Locomotives and Marine

Compression-Ignition Engines. Office of Transportation and Air Quality. Available at http://www.epa.gov/otaq/regs /nonroad/420f08004.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2008c. Control of Emissions for Nonroad Spark Ignition

Engines and Equipment. Office of Transportation and Air Quality. Available at http://www.epa.gov/otaq/re gs/nonroad/marinesi- equipld/bondfrm.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2009. Standards of Performance for New Stationary Sources

and Emission Guidelines for Existing Sources: Hospital/Medical/Infectious Waste Incinerators. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at http://www.epa.gov/ttn/atw/129/hmiwi/fr06oc09.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2010. Control of Emissions from New Marine CompressionIgnition Engines at or Above 30 Liters per Cylinder

. Office of Transportation and Air Quality. Available at http://www.regulations.gov/search/R egs/contentStreamer?objectId=090000 6480ae43a6&disposition=attachment &contentType=pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2010a. National Emission Standards for Hazardous Air

Pollutants for Reciprocating Internal Combustion Engines. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at http://www.epa.gov/ttn/atw/icengines/fr/fr20au10.pdf

. U.S. Environmental Protection Agency (U.S. EPA). 2010b. Regulation of Fuels and Fuel Additives: Modifications

to Renewable Fuel Standard Program (RFS2). Available at http://www.gpo.gov/fdsys/pkg/FR-2010-12- 21/pdf/2010-31910.pdf.

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1-16 U.S. Environmental Protection Agency (U.S. EPA). 2010c. Light-Duty Vehicle Greenhouse Gas Emission

Standards and Corporate Average Fuel Economy Standards; Final Rule for Model-Year 2012-2016. Available at http://www.gpo.gov/fdsys/pkg/FR-2010 -05-07/pdf/2010-8159.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2011. Cross-State Air Pollution Rule. Office of Atmospheric

Programs, Washington, D.C. Available at http://www.gpo.gov/fdsys/pkg/FR-2011-08-08/pdf/2011-17600.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2011a. Regulatory Impact Analysis for the Final Mercury and

Air Toxics Standards. Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-11- 011. Available at http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2011b. The Benefits and Costs of the Clean Air Act from 1990

to 2020. Office of Air and Radiation, Washington, D.C. Available at: http://www.epa.gov/cleanairactbenefits/feb11/fullreport_rev_a.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2011c. Regulatory Impact Analysis for the Federal

Implementation Plans to Reduce Interstate Transport of Fine Particulate Matter and Ozone in 27 States; Correction of SIP Approvals for 22 States. Available at http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2011d. Greenhouse Gas Emissions Standards and Fuel

Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles. Available at http://www.gpo.gov/fdsys/pkg/FR- 2011-09-15/pdf/2011-20740.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2012. 2017 and Later Model Year Light-Duty Vehicle

Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards. Available at http://www.gpo.gov/fdsys/pkg/FR- 2012-10-15/pdf/2012-21972.pdf.
U.S. Environmental Protection Agency, 2014. Policy Assessment for the Review of the Ozone National Ambient

Air Quality Standards, US EPA, OAQPS, 2014, RTP, NC, EPA-452/R-14-006.
U.S. Environmental Protection Agency (U.S. EPA). 2014a. Carbon Pollution Emission Guidelines for Existing

Stationary Sources: Electric Utility Ge nerating Units (Proposed Rule). US EPA, OAQPS, 2014, RTP, NC, 79 FR 34829, EPA-HQ-OAR-2013-0602.
U.S. Environmental Protection Agency, 2014b. Regulatory Impact Analysis for the Proposed Carbon Pollution

Guidelines for Existing Power Plants and Emission Standards for Modified and Reconstructed Power Plants, US EPA, OAQPS, 2014, RTP, NC, EPA-452/R-14-002.
U.S. Environmental Protection Agency, 2014c. Control of Air Pollution from Motor Vehicles: Tier 3 Motor Vehicle

Emissions and Fuel Standards Rule, US EPA, OTAQ, 2014, Washington, DC, EPA-420-R-14-005.
U.S. Environmental Protection Agency, 2015. Preparation of Emissions Inventories for the Version 6.2, 2011

Emissions Modeling Platform. Available at: http://www.epa.gov/ttn/chief/emch/index.html#2011

. U.S. Office of Management and Budget. Circular A-4, September 17, 2003, available at

http://www.whitehouse.gov/omb/circulars/a004/-.pdf



### .

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2-1 CHAPTER 2: EMISSIONS, AIR QUALITY MODELING AND ANALYTIC METHODOLOGIES

Overview This regulatory impacts analysis (RIA) evaluates the costs as well as the health and environmental benefits associated with complying with the revised (70 ppb) and alternative (65 ppb) National Ambient Air Quality Standards (NAAQS) for ozone. For this purpose, we use air quality modeling to project ozone concentrations into the future. This chapter describes the data, tools and methodology used for the analysis, as well as the post-processing techniques used to produce a number of ozone metrics necessary for this analysis.

Throughout this chapter, the base year mode ling refers to model simulations conducted for 2011, while the 2025 base case simulation refers to modeling conducted with emissions projected to the year 2025 including all current on-the-books federal regulations. 18 

### As described in section 2.1, the emissions inputs for the 2011 base year and 2025 base case simulations were updated between the November 2014 proposal RIA (EPA, 2014a) and this final analysis. These updates were made in response to comments provided by states and newly available emissions models and projection information. In the following sections, the 2025 base case from the November 2014 proposal RIA will be referred to as the “proposal 2025 base case” while the updated 2025 base case will be referred to as the “f inal 2025 base case”. In addition, a series of emissions sensitivity 19 

### modeling runs were conducted to determine the response of ozone to changes in 2025 emissions. These sensitivity runs were used to develop ozone response factors (ppb/ton) that represent the modeled response of ozone to changes in NO x 

### and VOC emissions
from various sources and locations. 20



### The following scenarios were developed based on applying the ozone response factors to the final 2025 base case ozone concentrations: (1) the baseline scenario (a scenario that includes

18 Emissions reductions to attain the 2012 PM 2.5 NAAQS are not included in the proposal or final 2025 base case

because the scenarios modeled in the 2012 PM 2.5 NAAQS RIA did not reflect any NOx emissions reductions (US EPA, 2012).
19 Sensitivity refers to modeling simulations designed to capture the response of ozone concentrations to changes in

emissions.
20 All emissions sensitivity model runs were created with reductions incremental to the proposal 2025 base case scenario.

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2-2 attainment of the current standard of 75 ppb) 21 

### and (2) the revised standard level scenario and an alternative standard level scenario that both represent incremental emissions reductions beyond the baseline to meet levels of 70 and 65 ppb respectively. 22 

### For each scenario we calculated emissions reductions necessary to meet the target standard level and resulting ozone concentrations at ozone monitoring locations. We used the emissions reductions as inputs in the estimation of control strategies (Chapter 3) and costs (Chapter 4) associated with attaining the
revised and alternative ozone standard levels. The emissions reductions were also used to estimate changes in health-related ozone concentration metrics under each scenario allowing us
to calculate the health-related benefits that would result from the reductions in emission and ozone concentrations associated with meeting various standard levels (Chapter 6). Figure 2-1 below outlines these general steps and Table 2-1 lists all of the scenarios discussed above with their respective definitions.

21 As described in chapter 1, section 1.3.2, we use a “2025 baseline scenario” for areas of the contiguous U.S. outside of California and a “post-2025 baseline scenario” for California due to the later attainment dates for some areas in that state.
22 For the revised standard and the alternative standard we present both a scenario which represents only the portion of emissions reductions that come from identified controls (identified control strategies) and one that represents total emissions reductions necessary to attain the respective standard levels (identified + unidentified control strategies).

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2-3 Figure 2-1. Process to Determine Emissions Reductions Needed to Meet Baseline and Alternative Standards Analyzed

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2-4 Table 2-1. Terms Describing Different Scenarios Discussed in This Analysis Scenario name Definition Base year Photochemical model simulations for 2011 using best estimates or actual meteorology, emissions and resulting ozone concentrations
2025 base case Modeling conducted with emissions projected to the year 2025 including all current on-the-books federal regulations and using 2011 meteorology Proposal 2025 base case The 2025 base case from the November 2014 proposal RIA
Final 2025 base case The updated 2025 base case that includes improvements to 2011 emissions and 2025 emissions projections described in section 2.1

2025 baseline 2025 ozone concentrations from the final 2025 base case that have been adjusted to account for potential impacts from the proposed Clean Power Plan. 23 Costs and benefits of revised and alternative standard levels for all areas of the contiguous U.S. outside of California are calculated incremental to this scenario.

Post-2025 baseline 2025 ozone concentrations from the final 2025 base case that have been adjusted to account for potential impacts from the Clean Power Plan plus additional emissions reductions in California to attain of the current (75 ppb) ozone standard sometime after 2025. Costs and benefits of revised and alternative standard levels for California are calculated incremental to this scenario.
Revised standard Emissions reductions and resulting ozone concentrations incremental to the baseline scenario needed to reach attainment of the 70 ppb ozone standard.
Alternative standard Emissions reductions and resulting ozone concentrations incremental to the baseline scenario that would be needed to reach attainment of a 65 ppb ozone standard. Emissions reductions from identified control strategies The portion of emissions reductions and resulting ozone concentrations that come from identified emissions controls described in chapter 3
Emissions reductions from identified + unidentified control strategies Total emissions reductions and resulting ozone concentrations that are applied to reach either the revised or alternative ozone standard



### The remainder of the chapter is organized as follows: Section 2.1 describes the 2025 base case emissions and air quality modeling simulation; Section 2.2 describes how we project ozone levels into the future including the methodology for constructing the baseline, revised standard, and alternative standard scenarios (this methodology is applied in chapter 3 sections 3.1 and 3.2); and Section 2.3 describes the creation of spatial surfaces that serve as inputs to health benefits calculations discussed in Chapter 6.

2.1 Emissions and Air Quality Modeling Platform The 2011-based modeling platform was used to provide emissions, meteorology and other inputs to the 2011 and 2025 air quality model simulations. This platform was chosen

23 No additional reductions to meet the current (75 ppb) standard are applied because no areas outside of California are projected to violate the current standard once ozone adjustments for the Clean Power Plan are made.

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2-5 because it represents the most recent, complete set of base year emissions information currently available for national-scale modeling.

We use the Comprehensive Air Quality Model with Extensions (CAMx version 6.11, Environ, 2014) for photochemical model simulations performed for the RIA. CAMx requires a variety of input files that contain information pertaining to the modeling domain and simulation period. These files include gridded, hourly emissions estimates and meteorological data, and initial and boundary conditions. Separate emissions inventories were prepared for the final 2011 base year, the final 2025 base case, the proposal 2025 base case and the 2025 emissions sensitivity simulations. An operational model performance evaluation for ozone was performed to estimate the ability of the CAMx modeling sy stem to replicate 2011 measured concentrations. This evaluation focused on statistical assessments of model predictions versus observations paired in time and space depending on the sampling period of measured data. Consistent with EPA’s guidance for attainment demonstration modeling, we have applied the model predictions performed as part of the ozone NAAQS in a relative manner for projecting future concentrations of ozone. The National Research Council (NRC, 2002) states that using air quality modeling in a relative manner “may help reduce the bias introduced by modeling errors and, therefore, may be more accurate than using model results directly (absolute values) to estimate future pollutant levels”. Thus, the results of this evaluation together with the manner in which we are applying model predictions gives us confidence that our air quality model applications using the CAMx 2011 modeling platform provides a scientifically credible approach for assessing ozone for the ozone NAAQS rule.

Information on the components of the 2011-based modeling platform, including information on the 2011 base year and 2025 base case emission inventories, and the model evaluation methodology and results are provided in Appendix 2A. Additional details on the final 2011 base year and 2025 base case emissions inventories can also be found in the Technical Support Document (TSD): Preparation of Emissions Inventories for the Version 6.2, 2011 Emissions Modeling Platform (US EPA, 2015). Section 4 of the TSD summarizes the control and growth assumptions by source type that were used to create the U.S. final 2025 base case emissions inventory and includes a table of those assumptions for each major source sector.

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2-6 Section 2.4 of this document summarizes the changes to the emissions inventories used in the final modeling as compared to the November 2014 proposal modeling.

2.2 Projecting Ozone Levels into the Future
In this section we present the methods used to create the future baseline and the two scenarios that demonstrate attainment of the revised and alternative NAAQS levels analyzed in this RIA. First, in section 2.2.1, we describe the procedures for projecting ozone “design values” into the future. In section 2.2.2, we present the development of 15 emissions sensitivity simulations and in section 2.2.3 we show how to calculate ppb/ton ozone response factors from these sensitivity simulations. Next, in section 2.2.4, we describe the approach for using this information to construct the baseline, revised standard and alternative standard scenarios. The implementation of these methods using the 2025 base case ozone levels together with the ozone response factors and the resulting emissions scenarios and associated ozone levels is presented in Chapter 3. Finally, in section 2.2.5 we discuss a small subset of monitoring sites that were not included in the quantitative analysis.

2.2.1 Methods for Calculating Future Year Ozone Design Values
Hourly ozone concentrations are used to calculate a statistic referred to as a “design value” (DV), which is then compared to the standard level to determine whether a monitor is above or below the NAAQS level being analyzed. For ozone, the DV is calculated as the 3-year average
of the annual 4 th 

### highest daily maximum 8-hour ozone concentration in parts per billion (ppb), with decimal digits truncated. For the purpose of this analysis, the data handling and data completeness criteria used are those being finalized for the new NAAQS in Appendix U to 40 CFR Part 50 – Interpretation of the Primary and Secondary National Ambient Air Quality Standards for Ozone. For the purpose of this analysis, ozone DVs were derived from data reported in EPA’s air quality system (AQS) for the years 2009-2013. The base period DVs were calculated as the average of 3 consecutive DVs (2009-2011, 2010-2012, and 2011-2013) which creates a 5-year weighted average DV. The 5-year weighted average DV is used as the base from which to project a future year DV as is recommended by the EPA in its SIP modelingguidance (US EPA, 2014c) because it tends to minimize the year-to-year meteorologicallydriven variability in ozone concentrations given that the future year meteorology is unknown.

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2-7 For sites with fewer than five years of valid monitoring data available, the current year DV was calculated using a minimum of three years of consecutive valid data (i.e., at least one complete DV). If a monitor had less than three consecutive years of data, then no current year DV was calculated for that site and the monitor was not used in this analysis.

Future year ozone design values were calculated at monitor locations using the Model Attainment Test Software program (Abt Associates, 2014 ) . This program calculates the 5-year weighted average DV based on observed data and projects future year values using the relative response predicted by the model as described below. Equation 2-1 describes the recommended model attainment test in its simplest form, as applied for monitoring site i :
ሺDVFሻ ୧ 

### ൌ ሺ ܴܴܨ ሻ ௜ 

### ൈ ሺܦܸܤሻ ௜ 

### Equation 2-1

DVF ୧ 

### is the estimated design value for the future year in which attainment is required at monitoring site i ; RRF ୧ 

### is the relative response factor at monitoring site i ; and DVB ୧ 

### is the base design value monitored at site i . The relative response factor for each monitoring site ሺ ܴܴܨ ሻ ௜ 

### is the fractional change of ozone in the vicinity of the monitor that is simulated on high ozone days. The recently released draft version of EPA’s ozone and PM 2.5 

### photochemical modeling guidance (US EPA, 2014c) includes updates to the recommended ozone attainment test used to calculate future year design values for attainment demonstrations. The guidance recommends calculating RRFs based on the highest 10 modeled ozone days in the ozone season near each monitor location. Given the similar goal of this analysis relative to an attainment demonstration, we are using the recommended modeling guidance attainment test approach for the analyses. Specifically, the RRF is calculated based on the 10 highest days in the base year modeling in the vicinity of the monitor location when the base 8-hr daily maximum ozone values were greater than or equal to 60 ppb for that day. 24 

### In cases for which the base model simulation did not have

24 In determining the ozone RRF we considered model response in grid cells immediately surrounding the monitoring site along with the grid cell in which the monitor is located, as is currently recommended by the EPA in its SIP modeling guidance (US EPA, 2014c). The RRF was based on a 3 x 3 array of 12 km grid cells centered on the location of the grid cell containing the monitor. The grid cell with the highest base ozone value in the 3 x 3 array was used for both the base and future components of the RRF calculation.

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2-8 10 days with ozone values greater than or equal to 60 ppb at a site, we used all days where ozone was greater than or equal to 60 ppb, as long as there were at least 5 days that met that criteria. At monitor locations with fewer than 5 days with ozone greater than or equal to 60 ppb, no RRF or DVF was calculated for the site and the monitor in question was not included in this analysis.

2.2.2 Emissions Sensitivity Simulations
A total of fifteen emissions sensitivity modeling runs were conducted to determine ozone response to reductions of NO x 

### and VOC emissions in different areas. (See Table 2-2 for a list of the sensitivity runs). The sensitivity modeling provides an efficient and flexible approach that allowed us to evaluate ozone responses from multiple source regions and several levels of
emissions reductions simultaneous ly. All emissions sensitivity simulations incl uded emissions reductions incremental to the proposal 2025 base case. 25 

### Ozone response factors (ppb/ton) were created by comparing changes in projected ozone levels between the proposal 2025 base case and the individual emission sensitivity simulations. These response factors were then applied to the final 2025 base case design values. There were three types of sensitivity runs, each of which is described in more detail below: (1) explicit emissions control cases; (2) across-the-board reductions in anthropogenic emissions in different areas; and (3) combination cases that included both explicit emissions controls and across-the-board reductions.

Table 2-2. List of Emissions Sensitivity Mo deling Runs Modeled in CAMx to Determine Ozone Response Factors

25 Modeling incremental changes from the proposal 2025 base case provided consistency with sensitivity simulations performed for the proposal and allowed us to leverage a subset of sensitivity simulations created as part of that proposal. This was necessary due to timing and resource constraints. Since the sensitivity simulations are used to create relative ppb/ton response factors, it is appropriate to apply changes derived from these sensitivities to the final 2025 base case modeling since atmospheric chemistry regimes are not likely to have changed substantially between the proposal and final 2025 base case simulations. Emissions Sensitivity Simulation Region Pollutant Emissions Change Types

1 National All Clean Power Plan Explicit control 2 National VOC 50% VOC cut Across-the-board 3 California NOx CA explicit emissions control Explicit control 4 N. California NOx Sensitivity 3 + 50% NOx cut in N. CA Combination 5 N. California NOx Sensitivity3 + 90% NOx cut in N. CA Combination

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2-9 Explicit Emissions Controls: Two explicit emissions control sensitivity modeling runs were conducted. These emissions control sensitivity runs are referred to as “explicit emissions control” runs because they represent the impact of sets of specific controls rather than sensitivities to all anthropogenic emissions. First, we modeled one possible representation of implementing the EPA’s proposed carbon pollution guidelines under section 111(d) of the Clean Air Act (CAA) (i.e., option 1 state; hereafter referred to as the Clean Power Plan sensitivity). Emissions for this simulation are described in the regulatory impact analysis for that proposed rule (EPA, 2014d). Second, we conducted an additional emissions control sensitivity run that included NOx emissions reductions from controls applied to specific sources in California. Based on analysis conducted for the November 2014 proposal RIA (EPA, 2014a) and projected design values (DVs) 26 

### from the final 2025 base case, it was determined that California was the only region for which all identified controls would be exhausted before reaching the baseline. Therefore, we created a sensitivity run in which all identified NO x 

### emissions controls below $15,000/ton were applied in California. The explicit controls were only applied in a 200 km buffer area around counties in California projected to violate 70 ppb in the proposal 2025 base case. The EPA’s Control Strategy Tool (CoST) (EPA, 2014e) was used to determine the potential reductions in this area. NO x 

### controls were identified for all nonpoint, non-EGU point, and nonroad sources. This emissions sensitivity was created as part of the analysis for the November 2014 proposal RIA (EPA, 2014a). The assumptions about which sources were

26 The design value is the metric that is compared to the standard level to determine whether a monitor is violating

the NAAQS. The ozone design value is described in more detail in section 2.2.

6 S. California NOx Sensitivity 3 + 50% NOx cut in S. CA Combination 7 S. California NOx Sensitivity 3 + 90% NOx cut in S. CA Combination 8 Nevada NOx 50% NOx cut Across-the-board 9 Arizona/New Mexico NOx 50% NOx cut Across-the-board 10 Colorado NOx 50% NOx cut Across-the-board 11 E. Texas NOx 50% NOx cut Across-the-board 12 Oklahoma/Arkansas/Louisiana NOx 50% NOx cut Across-the-board 13 Great Lakes NOx 50% NOx cut Across-the-board 14 Ohio River Valley NOx 50% NOx cut Across-the-board 15 Northeast Corridor NOx 50% NOx cut Across-the-board

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2-10 available for controls in California are the same as those described in Chapter 3, with the exception that for proposal, only controls with a cost under $15,000 per ton were considered. In the final rule we have identified additional controls in California (i.e. controls with a cost between $15,000 per ton and $19,000 per ton: the thresholds applied for proposal and final RIA respectively) which were not included in the California explicit emissions control case that we modeled but which were accounted for using ppb/ton response factors from combination
emissions sensitivities described below.

Across-the-board Emissions Reductions: We performed across-the-board sensitivity modeling for areas of the U.S. projected to contain monitors with ozone design values greater than 65 ppb in the proposal 2025 base case. We created 8 regions that contain these monitoring sites, as shown in Figure 2-2. The boundaries of these regions were generally defined in terms of the borders of a single state or a small group of ad jacent states. In addition, we also used the two “buffer regions” (one in East Texas and the other in the Northeastern U.S.) that were created in the analysis for the November 2014 proposal RIA for areas with 2025 baseline DVs above 70 ppb and were not updated using the final 2025 base case modeling. These buffers around counties projected to violate 70 ppb allowed us to target reductions in locations close to the highest ozone monitors, an approach that is likely to be most effective at reducing ozone concentrations for these relatively isolated violations. 27 

### The two buffer regions were determined based on 200 km buffers around all monitors projected to be above 70 ppb in the proposal 2025 base case. In Texas, the buffer region was restricted to counties within state boundaries. In the Northeast, the buffer was restricted to a subset of the states/counties that are currently under the jurisdiction of the Ozone Transport Commission (OTC), a multistate region that already has interstate cooperation for air quality planning. The Texas and Northeast buffer areas are shown in Figure 2-3. 28 

### Unlike in California, it was not clear that all identified controls would be required in any one region to meet the 65 and/or 70 ppb standard levels. Therefore, in these two

27 Note that counties projected to violate the alternative 65 ppb standard are more broadly distributed throughout the U.S. and less isolated in nature. Therefore it may be less important to differentiate between impacts from very local emissions within 200 km of a violating county compared to impacts from emissions across a statewide or multistate region in designing control strategies for those areas.
28 The 200 km buffers are shaded in orange and counties that contained one or more monitors projected to be above 70 ppb in the proposal 2025 base case modeling are shaded in blue. Commented [A13]: Please insert discussion on why unknown control costs should be less than known control costs.

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2-11 regions we generated more general emissions response factors using an across-the-board 50% reduction in U.S. anthropogenic NOx emissions. We also performed a VOC sensitivity run with a 50% cut in anthropogenic VOC emissions across the 48 contiguous states.

Combination Emissions Sensitivities: We conducted four additional emissions sensitivity modeling runs that combined the explicit emissions controls with across-the-board reductions in California. Based on a previous EPA analysis (EPA, 2014a; EPA, 2014f) we identified California as the region most likely to need NOx reductions beyond 50% to reach the revised and alternative standard levels. Therefore, we modeled both a 50% and a 90% NOx emissions reduction in California to capture nonlinearities in ozone response to large NOx emissions changes. The 50% and 90% NOx reductions were applied in Northern and Southern California separately recognizing that the topography in California effectively isolates the air shed in the San Joaquin Valley from the southernmost portion of the state which has the effect of limiting the impact of emissions from Southern California on ozone in Northern California and vice versa. The geographic delineation of Northern and Southern California for these emissions sensitivity simulations is shown in Figure 2-2. In all four California emissions sensitivities, the 50% and 90% NOx reductions were applied on top of the California explicit controls sensitivity run (sensitivity simulation #3). Formatted: Highlight

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2-12 Figure 2-2. Across-the-Board Emissions Reduction and Combination Sensitivity Regions 29

29 Combination Sensitivities were used for the two California regions whereas, Across-the-Board Sensitivities were used in all other regions.

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2-13 Figure 2-3. Map of 200 km Buffer Regions in California, East Texas and the Northeast Created as Part of the Analysis for the November 2014 Proposal RIA 30



### 2.2.3 Determining Ozone Response Factors from Emissions Sensitivity Simulations Section 2.2.1 describes, in general terms, how the 2025 projections for ozone DVs were computed. This procedure was followed for the proposal and final 2025 base case modeling and for each of the fifteen emissions sensitivity modeling simulations. Using the projected DVs and corresponding emissions changes, a unique ozone response factor (ppb/ton) was calculated for each emissions sensitivity at each ozone monitor using equation 2-2:

30 The California buffer was used to determine the area over which explicit controls were applied in the California explicit control sensitivity simulation (sensitivity simulation #3). The Texas and Northeast buffers were used to delineate the areas over which across-the-board anthropogenic NOx emissions reductions were applied in sensitivity simulations #11 and #15 respectively.

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2-14 ܴ ௜,௝ 

### ൌ ஽௏ ೔,ೕష ஽௏ మబమఱ್ೌೞ೐,ೕ ∆ா ೔ 

### Equation 2-2

In equation 2-2, R i,j 

### represents the ozone response at monitor j to emissions changes between the 2025 proposal base case and the sensitivity simulation i; DV i,j 

### represents the DV at monitor j for emissions sensitivity i; DV 2025base,j 

### represents the DV at monitor j in the proposal 2025 base case;
and Δ E i 

### represents the difference in NO x 

### or VOC emissions (tons) between the proposal 2025 base case and emissions sensitivity run i.

In California where emissions reductions in four sensitivity runs (i) were incremental to emissions reductions in another run (k), the following equation was used:

ܴ ௜,௝ 

### ൌ ஽௏ ೔,ೕష ஽௏ ೖ,ೕ ∆ா ೔ೖ 

### Equation 2-3

in which Δ E ik 

### represents the difference in NO x 

### emissions (tons) between the emissions run k and emissions run i. For emissions sensitivity simulations #4 and #6 (50% NOx reductions), k represented emissions sensitivity #3 (California explicit control). For emissions sensitivity simulations #5 and #7 (90% NOx reductions), k represented emissions sensitivities #4 and #6 respectively.

For the VOC emissions sensitivity run, we determined it was appropriate to compute response factors for smaller geographic areas than were modeled in the emissions sensitivity simulations shown in Figure 2-2. Past work has shown that impacts of anthropogenic VOC emissions on ozone DVs in the U.S. tend to be much more localized than reductions in NOx (Jin et al., 2008). Consistent with past analyses (US EPA, 2008) we made the simplifying assumption that VOC reductions do not affect ozone at distances more than 100 km from the emissions source. Consequently, we created a se ries of VOC impact regions in 7 areas (Figure 2-4) for which our modeling showed that ozone is responsive to VOC emissions reductions and which had the highest ozone DVs in the NOx sensitivity regions: New York City, Chicago, Louisville, Houston, Denver, and Northern and Southern California. 31 

### VOC impact regions were

31 The following additional local VOC areas were also explored but were found not to be helpful in reaching the revised or alternative NAAQS levels in th is analysis: Dallas, Detro it, Pittsburgh, and Baltimore. This may be due to Commented [A14]: EPA talks about nonlinearity in ozone response to large NOx drop (2-11) . But Eq 2-2 and 2-3 seem to implicitly assume linearity. Would you add discussion to clarify what appears to be contradiction?

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2-15 delineated by creating a 100km buffer around counties containing monitors violating 60 ppb in the proposal 2025 base case modeling. In addi tion, VOC impact regions were constrained by state boundaries except in cases where a current nonattainment area straddled multiple states (e.g., New Jersey and Connecticut counties that are included in the New York City nonattainment area were also included in the New York City VOC impact region). The in-state constraint was also waived for the Chicago area since it is well established that emissions from Chicago and Milwaukee are often advected over Lake Michigan where they photochemically react and then affect locations in Wisconsin, Illinois, Indiana, and Michigan that border Lake Michigan (Dye et al., 1995). For California, the VOC impact regions were delineated identically to the Northern and Southern California regions used in the NOx emissions sensitivity runs except that the Northern California region did not extend beyond the 200 km buffer shown in Figure 2-3. To create the ozone response factors to VOC for each monitoring site within a VOC impact region, an ozone DV response factor (R i,j 

### ) was calculated using the VOC emissions reductions that occurred within that area based on the U.S. 50% VOC emissions sensitivity modeling run.

the construct of the attainment scenarios analyzed and does not mean that VOC controls would not be effective in these areas under alternative assumptions about regional NOx controls.

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

2.2.4 Combining Response from Multiple Sensitivity Runs to Determine Tons of Emissions Reductions to Meet Various NAAQS Levels

Ozone DVs were calculated for the baseline scenario as well as for the revised and alternative standards using Equation 2-4 in which ܦܸ ଶ଴ଶହ,௝ 

### is the ozone DV at monitor j in the final 2025 base case, ܴ ௡,௝ 

### is the ozone response factor for sensitivity n at monitor j, and ∆ܧ ௡ 

### is the tons of emissions reductions from region n being applied to reach the desired standard level:

ܦܸ

௝ 

### ൌ ܦܸ ଶ଴ଶହ,௝ 

### ൅ ൫ܴ ଵ,௝ 

### ൈ ∆ܧ ଵ 

### ൯ ൅ ൫ܴ ଶ,௝ 

### ൈ ∆ܧ ଶ 

### ൯ ൅ ൫ܴ ଷ,௝ 

### ൈ ∆ܧ ଷ 

### ൯൅⋯ Equation 2-4

For the baseline as well as the two alternative standards analyzed, we determine the least amount of emissions reductions (tons) needed in each region ( ∆ܧ ௡ 

### (to bring the ozone DVs at all

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2-17 monitors down to the particular standard level being analyzed. Note that California was analyzed independent of the rest of the country due to the later attainment dates in many California counties. Therefore, in determining the necessary emissions reductions, we did not account for any impacts of California reductions on other areas of the U.S. and vice versa. The application of equation 2-4 to determine emissions reductions necessary to meet the various standard levels at U.S. locations outside of California is presented in chapter 3, section 3.2.

Because California included multiple incremental sensitivity simulations, Equation 2-4 had to be slightly modified for calculating DV changes to emissions reductions in that state. The modeled impacts from multiple California sensitivity simulations were combined in a linear manner to estimate the overall impacts. For example, at any monitor in California we could use the following equation to determine the DVs that would result from a 75% reduction in Northern California emissions beyond the explicit emissions control sensitivity simulation:

ܦܸ ଻ହ%஼஺,௝ 

### ൌ ܦܸ ଶ଴ଶହ,௝ 

### ൅ ቀ ܴ ஼஺ ೐ೣ೛೗೔೎೔೟೎೚೙೟ೝ೚೗ ,௝ 

### ൈ ∆ܧ ஺஼ ೐ೣ೗೚ೝ೟೙೚೎೟೔೎೔೗೛ 

### ቁ ൅ ൫ܴ ே஼஺ହ଴ேை௫,௝ 

### ൈ ∆ܧ ହ଴ேை௫ 

### ൯ ൅ ൫ܴ ே஼஺ଽ଴ேை௑,௝ 

### ൈ ∆ܧ ଻ହேை௑ 

### ൯ Equation 2-5

In equation 2-5, ܦܸ ଶ଴ଶହ,௝ 

### represents the projected DV from the final 2025 base case at monitor j, ∆ܧ ோ_௘௫௣௟௜௖௜௧௖௢௡௧௥௢௟ 

### represents the difference in NO x 

### emissions between the proposal
2025 base case and the 2025 California explicit emissions control sensitivity; ∆ܧ ହ଴ேை௫ 

### represents the difference in NO x 

### emissions between the 2025 California explicit emissions control sensitivity and the combined California explicit emissions control with 50% Northern California NOx cuts sensitivity; and ∆ܧ ଻ହேை௑ 

### represents the additional emissions reductions needed to reach a 75% NOx cut in Northern California above and beyond the emissions reductions in the combined California explicit emissions control run with 50% Northern California NOx cuts run. Note that in this equation, emission reductions in Northern California impact monitors (j) in both the Northern and Southern California regions. Similar to the methods applied in other regions,
we determine the smallest amount of emission s reductions (tons) in northern and southern California regions necessary to decrease all ozone DVs in each region to the standard level being analyzed. The application of equation 2-5 to determine emissions reductions necessary to meet the various standard levels in California is presented in chapter 3, section 3.3.

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2-18 We make several assumptions as part of this approach. First, we assume that every ton of
NO x 

### or VOC reduced within a region results in the same ozone response regardless of where the emissions reductions come from within the region because we do not have any information on the differential ozone response from emissions changes at different locations within the region. However, the somewhat smaller emissions sensitivity regions used in this analysis compared to the November 2014 proposal RIA provide a more spatially resolved representation of the ozone response to emissions changes and thus reduces but does not eliminate this uncertainty. Second,
we assume that NO x 

### and VOC responses are additive. Third, we assume that the responses from multiple regions are additive. Fourth, we assume that ozone response within each of these sensitivity simulations is linear (i.e., the first ton of NO x 

### reduced results in the same ozone response as the last ton of NO x 

### reduced). In California where we have multiple levels of emissions reductions, we assume linearity within each simulation, but we are able to capture discrete shifts in ozone response based on the multiple sensitivity simulations (i.e., one response for explicit emissions control run reductions, another response level up to 50% NO x 

### emissions reductions beyond the explicit emissions control run, and a third level of response between 50%
and 90% NO x 

### emissions reductions beyond the explicit emissions control run). Finally, outside of California, the ozone response to NO x 

### reductions greater than 50% is based on an extrapolation beyond the modeled emissions reductions. However, only East Texas and the Northeast require NOx reductions greater than 50% in the 65 ppb scenario and in both cases the NOx reductions are not substantially greater than 50% (52% and 56% respectively), so we expect that the ozone response from the 50% sensitivity is appropriate for extrapolation to 52% and 56% with only a small amount of additional uncertainty.

2.2.5 Monitoring Sites Excluded from Quantitative Analysis
There were 1,225 ozone monitors with complete ozone data for at least one DV period covering the years 2009-2013. We included 1,165, or 95% of these sites in the analysis to
determine the tons of emissions reductions necessa ry for each of the three scenarios (i.e., the baseline and two alternative standard level scenarios). However, there were three types of sites that were excluded from this analysis. First, we did not analyze the baseline or attainment levels at each of the 41 sites that did not have a valid projected final 2025 base case DV because there were fewer than 5 modeled days above 60 ppb in the 2011 CAMx simulation, as required in the

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2-19 EPA SIP modeling guidance (US EPA, 2014c). It is unlikely that these sites would have any substantial impact on costs and benefits because the reason that projections could not be made is that they have no more than 4 modeled days above 60 ppb. Only one of these 41 sites (site 311079991 in Knox County, NE) has a base year DV greater than 65 ppb. These sites are listed in Appendix 2A.

Second, seven sites for which the DVs were influenced by wintertime ozone episodes were not included because the modeling tools are not currently sufficient to properly characterizeozone formation during wintertime ozone episodes. It is not appropriate to apply the modelbased response (RRF) developed here based on summertime conditions to a wintertime ozone event, which is driven by different characterizations of chemistry and meteorology. Since there was no technically feasible method for projecting DVs at these sites, these sites were not included in determining required reductions in NO x 

### and VOCs to meet current or alternative standard levels. Wintertime ozone events tend to be very localized phenomena driven by local emissions from oil and gas operations (Schnell et al., 2009; Rappengluck et al., 2014; Helmig et al., 2014). Consequently, the emissions reductions needed to lower wintertime ozone levels would likely be different from those targeted for summertime ozone events. It follows that there could be additional emissions reductions required to lower ozone at these locations and thus potential additional costs and benefits that are not quantified in this analysis. Appendix 2A includes a list of sites influenced by wintertime ozone and the methodology used to identify
those sites.

Finally, while the majority of the sites had projected ozone exceedances primarily caused by local and regional emissions, there were a set of 12 relatively remote, rural sites in the Western U.S. with projected baseline DVs between 66 and 69 ppb that showed limited response
to the NO x 

### and VOC emissions sensitivities we modeled. Air agencies responsible for attainment at these locations may choose to pursue one or more of the Clean Air Act provisions that offer varying degrees of regulatory relief. Regulatory relief may include:

 Relief from designation as a nonattainment area (through exclusion of data affected by exceptional events)

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2-20  Relief from the more stringent requirements of higher nonattainment area classifications (through treatment as a rural transport area; through exclusion of data affected by exceptional events; or through international transport provisions)

 Relief from adopting more than reasonable controls to demonstrate attainment (through international transport provisions) In addition, some of these sites could potentially benefit from the CAA’s interstate transport provisions found in sections 110(a)(2)(D) and 126. These sites were initially identified in the November 2014 RIA proposal (EPA, 2014a) and more detailed discussion of their characteristics were provided in Appendix 3A of that document. Only the subset of those sites with DVs greater than 65 ppb in the 2025 baseline scenario are excluded in this analysis since sites projected to have DVs at or below 65 ppb would not incur any additional costs or benefits.

2.3 Creating Spatial Surfaces for BenMap
The emissions reductions for attainment of the current, revised, and an alternative NAAQS level determined in chapter 3 were used to create spatial fields of ozone concentrations (i.e., spatial surfaces) for input into the calculation of health benefits associated with attainment of each NAAQS level, incremental to the baseline. The spatial surfaces used to calculate ozonerelated health benefits with the BenMap tool (Chapter 6) are described below.

Health benefits associated with meeting different ozone standard levels were calculated based on the following three ozone metrics, as described in more detail in Chapter 6: May-Sep seasonal mean of 8-hr daily maximum ozone, Apr-Sep seasonal mean of 1-hr daily maximum ozone, and May-Sep seasonal mean of 9-hr daily average ozone (6am-3pm). For each metric, spatial fields (i.e., gridded surfaces) were created for a total of 8 scenarios, including:

 2025 baseline

 post-2025 baseline

 2025 70 ppb identified control strategies

 2025 70 ppb identified + unidentified control strategies

 post-2025 70 ppb identified + unidentified control strategies

 2025 65 ppb identified control strategies

 2025 65 ppb identified + unidentified control strategies

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2-21  post-2025 65 ppb identified + unidentified control strategies The surfaces created for the 2025 scenarios represent attainment at all contiguous U.S. monitors outside of California, while the surfaces for the post-2025 scenarios represent all contiguous U.S. monitors including those in California meeting the standard being evaluated. The effects due only to California meeting the standard are isolated in Chapter 6 through a series of BenMap simulations using these surfaces and varying assumptions about population demographics. In addition, for the 2025 scenarios we include “identified control” and “identified + unidentified control” strategies in which the identified control strategies only include ozone changes resulting from emissions reductions from identified control measures, while the identified + unidentified controls strategies include ozone changes resulting from all emissions reductions necessary to attain the standard from both identified controls and unidentified

measures.
The ozone surfaces were created using the following steps, which are described in more detail below and depicted in Figure 2-5.

 Step 1: Create spatial fields of gridded ozone concentrations for each of the three seasonal metrics using the model-predicted hourly ozone concentrations.

 Step 2: Create spatial fields of gridded ozone response factors for each seasonal metric.

 Step 3: Create spatial field of gridded ozone concentrations for baseline, revised standard, and alternative standard scenarios and each seasonal ozone metric

 Step 4: Create 2011 enhanced Voronoi Neighbor Averaging (eVNA) fused surface of 2011 modeled and 2010-2012 observed values for each seasonal ozone metric

 Step 5: Create eVNA fused modeled/monitored surface for each attainment scenario and each seasonal ozone metric
Step 1: Create spatial fields of seasonal ozone metrics for each model simulation

 Inputs: Hourly gridded model concentrations for final 2011 base year, proposal and final 2025 base cases, and the fifteen 2025 emissions sensitivity simulations detailed in Section 2.2.2

 Outputs: Seasonal ozone metrics for 2011, proposal and final 2025 base cases, and fifteen 2025 emissions sensitivity simulations (18 total spatial fields for each metric)

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2-22 Step 2: Create spatial fields of ppb/ton ozone response factors

 Inputs: Seasonal ozone metrics for proposal 2025 base case and fifteen 2025 emissions sensitivity simulations ( from Step 1) ; Amount of emissions reductions (tons) modeled in each emissions sensitivity

 Outputs: Gridded ozone response factor (ppb/ton) for each seasonal ozone metric from each emissions sensitivity simulation

 Methods:

 Calculate the change in the seasonal ozone metrics between each emissions sensitivity simulation (i) and the proposal 2025 base case. This step results in 15 spatial fields of gridded ozone changes ( ∆ܱ ଷ 

### ) for each seasonal ozone metric.

 Divide each of the spatial fields of ozone changes by the tons of emissions reductions applied in that emissions sensitivity simulation compared to the proposal 2025 base case: ( ∆ை య ∆ா 

### ). This step results in 15 spatial fields of gridded ozone response factors (ppb/ton) for each seasonal ozone metric.

Step 3: Create spatial field seasonal ozone metrics for baseline, revised standard, and alternative
standard scenarios

 Inputs: Gridded ozone response factor for each seasonal ozone metric from each emissions sensitivity simulation ( from Step 2) ; Amount of emissions reductions from each region (from Appendix 3A); Gridded ozone surface for each seasonal metric from the final 2025 base case ( from Step 1)

.

 Outputs: Gridded seasonal ozone metrics for each attainment scenario

 Methods:

 The gridded ozone response factors from Step 2 were multiplied by the relevant tons of emissions reductions for each sensitivity and then summed to create a gridded field representing the scenario using question (Equation 2-6)

ܱ3 ௫௬,௦,௠ 

### ൌ ܱ3 ௫௬,ଶ଴ଶହ,௠ 

### ൅ ൫ܴ ௫௬,ଵ,௠ 

### ൈ ∆ܧ ଵ,௦ 

### ൯ ൅ ൫ܴ ௫௬,ଶ,௠ 

### ൈ ∆ܧ ଶ,௦ 

### ൯ ൅ ൫ܴ ௫௬,ଶ,௠ 

### ൈ ∆ܧ ଷ,௦ 

### ൯൅⋯ Equation 2-6

In equation 2-6, ozone xy,s,m 

### represents the ozone concentrations at grid cell

x,y , for scenario s , and using metric, m . Similarly ozone xy,2025,m 

### represents the modeled ozone from the final 2025 base case simulation at grid cell x,y

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2-23 aggregated to metric m . R xy,1,m 

### represents the ozone response factor (ppb/ton) in grid cell x,y using metric m, for the sensitivity simulation #1. Finally Δ E 1,s 

### represents the amount of emissions reductions from sources modeled in sensitivity #1 that were found to be necessary for scenario s

. Identified control strategy ozone surfaces at each standard level were created by only including Δ E values for emissions coming from identified controls as described in Chapter 3. Post-2025 surfaces include all emissions reductions outside of California that we estimate in 2025 plus
additional reductions in California which would occur after 2025. 32



### Step 4: Create 2011 enhanced Voronoi Neighbor Averaging (eVNA) fused surface of 2011
modeled and observed values for each seasonal ozone metric

 Inputs: 2010-2012 observed ozone values (seasonal ozone metrics at each monitor location); 2011 modeled ozone (seasonal ozone metrics at each grid cell) ( from Step 1)

 Outputs: 2011 fused modeled/monitored surfaces for each seasonal ozone metric

 Methods: The MATS tool was used to create a fused gridded 2011 field using both ambient and modeled data using the eVNA technique (Abt, 2014). This method essentially takes an interpolated field of observed data and adjusts it up or down based on the modeled spatial gradients. For this purpose, the 2010-2012 ambient data was interpolated and fused with the 2011 model data. One “fused” eVNA surface was created for each of the two seasonal ozone metrics.

Step 5: Create eVNA fused modeled/monitored surfaces for baseline, revised standard, and
alternative standard scenarios

32 A small error was discovered in the post-2025 surfaces in that the baseline surface included 202,000 tons of NOx emission reductions in California rather than the actual 206,000 tons of NOx emissions reductions applied to reach 75 ppb in California. This error was carried through to the 70 ppb and 65 ppb surfaces for the post-2025 scenarios so the incremental changes in ozone between the baseline and alternative NAAQS level surfaces should not be significantly impacted.

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2-24  Inputs: 2011 fused model/observed surfaces for each seasonal ozone metric ( from Step 4) ; modeled seasonal ozone metrics (gridded fields) for 2011 ( from Step 1) and each attainment scenario ( from Step 3 ).

 Outputs: Fused modeled/monitored surface for each attainment scenario and each seasonal ozone metric

 Methods: The 24 model-based surfaces (i.e., 8 scenarios and 3 metrics) were used as inputs in the MATS tool along with the gridded 2011 base year and eVNA surfaces. For each metric and each scenario a gridded RRF field was created by dividing the gridded ozone field for scenario s by the gridded base year 2011 model field. This RRF field was then multiplied by the 2011 eVNA field to create a gridded eVNA field for each scenario.

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

2.4 Improvements in Emissions and Air Quality for the Final RIA
2.4.1 Improvement in Emissions
Between proposal and the final rule, improvements were implemented in both the base (2011) and future year (2025) emissions scenarios. The proposal emissions are documented in

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2-25 the Version 6.1, 2011 Emissions Modeling Platform (US EPA, 2014b) TSD. Many improvements to the inventories resulted from the Federal Register notices for the 2011 and 2018 Emissions Modeling platforms released in November 2013 and June 2014. Comments on these notices were received from states, industry , and other organizations. Although the 2025 emissions were not specifically released for comment, improved methodologies and data were also applied to the updated 2025 emissions wherever possible. For example, many improvements were made on the National Electric Energy Data System database that is a key input in the preparation of future year EGU inventories; state agencies and regional planning organizations provided specific growth and control factors for stationary sources; and improvements were made to the modeling of onroad mobile sources in the base and future years.

Most updates to the 2011 emissions are reflected in the 2011 National Emissions Inventory (NEI) version 2. These updates included 1) the use of the 2014 version of the Motor Vehicle Emissions Simulator (MOVES2014) for onroad mobile source emissions, along with many upgrades to the input databases used by MOVES; 2) updated oil and gas emissions based on the Oil and Gas Emissions Estimate Tool version 2.0; 3) version 3.6.1 of the Biogenic Emission Inventory System along with improved land use data; 4) many updates to point and nonpoint source emissions submitted directly into the Emission Inventory System (EIS) by states; 5) improved temporal allocation of electric generating unit (EGU) and onroad mobile source emissions; 6) upgraded VOC speciation to be consistent with the most recent chemical mechanism available in CAMx (i.e., CB6); and 7) improved spatial surrogates for heavy-duty trucks, buses, and other types of vehicles. In addition, Canadian emissions were upgraded to the latest available data from Environment Canada for the year 2010 and Mexican emissions were upgraded to use the 2008 Inventario Nacional de Emisiones de Mexico, whereas for the proposal modeling the Mexican emissions had been based on those developed for 1999. The cumulative national impact of the changes to 2011 emissions between the proposal and final RIA resulted in a 1% increase in NOx emissions and no change in VOC emissions, although local changes were larger.

Improvements to the 2025 emissions included 1) using the Integrated Planning Model (IPM) version 5.14 with associated input databases and a representation of the Cross-State Air Pollution Rule (CSAPR); 2) using MOVES2014 to represent emission reductions from the Tier 3

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2-26 Final rulemaking and recent light and heavy duty greenhouse gas mobile source rules; 3) use of Annual Energy Outlook (AEO) 2014 for projections of vehicle miles traveled, oil and gas growth, and growth in other categories; and 4) improved representation of growth and controls for non-EGU stationary source emissions. The cumulative national impact of the changes to the 2025 emissions between the proposal and final RIAs resulted in a 2% reduction in NOx emissions and a 1% increase in VOC emissions, although localized changes were larger. For more information on the improvements to the 2025 emissions, see Appendix 2A and the Emissions Modeling TSD.

The net effects of the emissions inventory, model, and model input updates are changes in projected 2025 ozone air quality design values (DVs) in many areas. These new projected DVs were higher than previously modeled for the proposal RIA in some locations and lower in others. The new projections show lower 2025 DVs in Central Texas from Houston to Dallas, the El Paso area (NM and TX) and Big Bend, Texas, and several states in the central U.S., including Oklahoma, Kansas, Missouri, Arkansas, Mississippi, Tennessee, and southern Kentucky. The new projections also show higher 2025 DVs in Denver, Las Vegas, Phoenix, Charlotte, the upper Midwest, and some parts of the New York/New Jersey areas. See Appendix 2A, Section 2A.4 for detailed information on the updated DVs.

2.4.2 Improvements in Air Quality Modeling
In this final RIA, we used emissions sensitivity simulations to determine the response of ozone at monitor locations to emissions changes in specific regions, similar to the approach used in the November 2014 proposal RIA (EPA, 2014a). However, when we reviewed the analysis for the November 2014 proposal RIA we determined that in certain locations (e.g., Texas and the Northeast) where violations of the 70 ppb scenario were limited to fairly localized areas, the analysis could be improved by using more geographically refined ozone response factors. In addition, we determined that smaller regions would also provide more refined ozone responses across the rest of the U.S. As a result, in this final RIA we designed 10 smaller regions to determine ozone response factors (see Figure 2-2), compared to the 5 larger regions used in the proposal RIA (see Figure 3-3 in EPA, 2014a). This more geographically refined resolution allows us to more accurately represent the increased effectiveness of emissions reductions closer

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2-27 to monitor locations compared to emissions reductions from sources that are further away. For example, in the proposal RIA, we analyzed one large Southwest region and made no differentiation between the impacts of emissions from Nevada, Utah, New Mexico, Arizona, or Colorado on the monitors in Denver. In the final RIA, the smaller regions allow us to differentiate the impact of NOx emissions reductions in Colorado on ozone concentrations in Denver compared to NOx emissions reductions in Arizona and New Mexico on ozone in Denver. Similarly, in the final RIA we differentiate the impacts of east Texas emissions on ozone at Dallas and Houston monitors from impacts of emis sions in west Texas, Louisiana, Oklahoma, Mississippi, Arkansas, Kansas and Missouri on those same monitors (in the proposal RIA, we used one large central U.S. region that did not differential these impacts).

In Texas and the Northeast, the improved response factors resulted in larger changes in ozone concentrations in response to the more geographically focused emissions reductions. For example, in east Texas, the ppb/ton ozone response factors used in the final RIA were 2 to 3 times more responsive than the factors used in the proposal RIA at controlling monitors in Houston and Dallas. In the Northeast, the ppb/ton ozone response factors used in the final RIA were 2.5 times more responsive than the factors used in the proposal RIA at the controlling monitor on Long Island, NY. The more geographically refined modeling and improved ozone response factors resulted in fewer emissions reductions needed to meet a revised standard of 70 ppb and an alternative standard level of 65 ppb. For additional discussion on how these improved response factors affect emissions reductions needed to reach a revised standard of 70 ppb and an alternative standard level of 65 ppb, see Chapter 3, Section 3.3. For additional discussion on how the improved response factors and reduced emissions reductions impact cost estimates, see Chapter 4, Section 4.6, and for additional discussion on how this impacts benefits estimates, see Chapter 6, Section 6.1.

2.5 References

Abt Associates, 2014. User’s Guide: Modeled Attainment Test Software.

http://www.epa.gov/scram001/modelingapps_mats.htm
Dye T.S., Roberts P.T., Korc, M.E. (1995) Observations of transport processes for ozone and ozone precursors

during the 1991 Lake Michigan ozone study, J. Appl. Met., 34 (8), 1877–1889
ENVIRON, 2014. User's Guide Comprehensive Air Quality Model with Extensions version 6.1, www.camx.com.

ENVIRON International Corporation, Novato, CA. Commented [A15]: Not sure what this means. Does this mean that because of increased sensitivity, less NOx reduction leads to greater ozone reduction compared to the proposal?

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2-28 Helmig, D., Thompson, C.R., Evans, J., Boylan, P., Hueber, J., Park, J.H. (2014). Highly elevated atmospheric

levels of volatile organic compounds in the Uintah Basin, Utah, Environmental Science & Technology, 48, 4707- 4715.
Jin, L., Tonse, S., Cohan, D. S., Mao, X. L., Harley, R. A., Brown, N. J. (2008) Sensitivity analysis of ozone

formation and transport for a central California air pollution episode. Environ. Sci. Technol ., 42, 3683–3689.
National Research Council (NRC), 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations, Washington, DC: National Academies Press.
Rappengluck, B., Ackermann, L., Alvarez, S., Golovko, J., Buhr, M., Field, R.A., Soltis, J., Montague, D.C., Hauze,

B., Adamson, S., Risch, D., Wilkerson, G., Bush, D., Stoeckenius, T., Keslar, C. (2014). Strong wintertime ozone events in the Upper Green River basin, Wyoming, Atmospheric Chemistry and Physics, 14, 4909-4934.
Schnell, R.C., Oltmans, S.J., Neely, R.R., Endres, M.S., Molenar, J.V., White, A.B. (2009) Rapid photochemical

production of ozone at high concentrations in a rural site during winter, Nature Geoscience, 2, 120-122.
U.S. Environmental Protection Agency (2008) Final ozone NAAQS regulatory impact analysis, US EPA, OAQPS,

RTP, NC, EPA-452/R-08-003
U.S. Environmental Protection Agency (2012) Regulatory Impact Analysis for the Final Revision to the National

Ambient Air Quality Standards for Particulate Matter, US EPA, OAQPS, 2012, RTP, NC, EPA-452/R-12-005. http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf
U.S. Environmental Protection Agency (2014a) Regulatory Impact Analysis of the Proposed Revisions to the

National Ambient Air Quality Standards for Ground-Level Ozone, US EPA, OAQPS, 2014, RTP, EPA-452/P-14- 006, http://www.epa.gov/ttn/ecas/regdata/RIAs/20141125ria.pdf
U.S. Environmental Protection Agency (2014b) Preparat ion of Emissions Inventories for the Version 6.1, 2011

Emissions Modeling Platform (http://www.epa.gov/ttn/chief/emch)
U.S. Environmental Protection Agency (2014c) Draft Modeling Guidance for demonstrating attainment of air

quality goals for ozone, PM2.5, and regional haze. September 2014, U.S. Environmental Protection Agency , Research Triangle Park, NC, 27711
U.S. Environmental Protection Agency (2014d) Regulatory Impact Analysis for the Proposed Carbon Pollution

Guidelines for Existing Power Plants and Emission Standards for Modified and Reconstructed Power Plants, US EPA, OAQPS, 2014, RTP, NC, EPA-542/R-14-002
U.S. Environmental Protection Agency (2014e) Control Strategy Tool (CoST) Documentation Report. Office of Air

Quality Planning and Standards, Research Triangle Park, NC. Available at ht tp://www.epa.gov/ttnecas1/cost.htm. U.S. Environmental Protection Agency (2014f) Health Risk and Exposure Assessment for Ozone, final report. US

EPA, OAQPS, 2014, RTP, NC, EPA-452/R-14-004a
U.S. Environmental Protection Agency (2015) Preparation of Emissions Inventories for the Version 6.2, 2011

Emissions Modeling Platform (http://www.epa.gov/ttn/chief/emch)

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2A-1 APPENDIX 2A: ADDITIONAL AIR QUALITY ANALYSIS AND RESULTS
2A.1 2011 Emissions and Air Quality Modeling Platform
2A.1.1 Photochemical Model Description and Modeling Domain
CAMx is a three-dimensional grid-based Eulerian air quality model designed to estimate the formation and fate of oxidant precursors, primary and secondary particulate matter concentrations, and deposition over regional and urban spatial scales (e.g., over the contiguous U.S.) (Environ, 2014). Consideration of the different processes (e.g., transport and deposition) that affect primary (directly emitted) and secondary (formed by atmospheric processes) pollutants at the regional scale in different locations is fundamental to understanding and assessing the effects of emissions control measures that affect air quality concentrations. Because it accounts for spatial and temporal variations as well as differences in the reactivity of emissions, CAMx is useful for evaluating the impacts of the control strategies on ozone concentrations. CAMx is applied with the carbon-bond 6 revision 2 (CB6r2) gas-phase chemistry mechanism (Ruiz and Yarwood, 2013).

Figure 2A-1 shows the geographic extent of the modeling domain that was used for air quality modeling in this analysis. The domain covers the 48 contiguous states along with the southern portions of Canada and the northern portions of Mexico. This modeling domain contains 25 vertical layers with a top at about 17,600 meters, or 50 millibars (mb), and horizontal resolution of 12 km x 12 km. The model simulations produce hourly air quality concentrations for each 12 km grid cell across the modeling domain.

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2A-2 Figure 2A-1. Map of the CAMx Modeling Domain Used for Ozone NAAQS RIA
2A.1.2 Meteorological Inputs, Initial Conditions, and Boundary Conditions
Meteorological fields, initial conditions, and boundary conditions were specified for the 2011 base year model application and remain ed unchanged for each future-year modeling simulation. The assumption of constant meteorology and boundary conditions was applied for two reasons: 1) this allows us to isolate the impacts of U.S. emissions changes, and 2) there is considerable uncertainty in the direction and magnitude in any changes in these parameters. EPA recognizes that changes in climate and international emissions may impact these model inputs. Specifically, climate change may lead to temperature increases, higher stagnation frequency, and increased wildfire activity, all of which could lead to higher ozone concentrations. In the western U.S. over the last 15 years, increasing wildfires have already been observed (Dennison et al., 2014). Potential future elevated ozone concentrations could, in turn,

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2A-3 necessitate more stringent emissions reductions. However, there are significant uncertainties regarding the precise location and timing of climate change impacts on ambient air quality. Generally, climate projections are most robust for periods at least several decades in the future because the forcing mechanisms that drive near-term natural variability in climate patterns (e.g., El Nino, North American Oscillation) have substantially larger signals over short time spans than the driving forces related to long-term climate change. Boundary conditions, which are impacted by international emissions and may also influence future ozone concentrations, are held constant in this analysis based on a similar rationale regarding the significant uncertainty in estimating
future levels.

Meteorological inputs reflecting 2011 conditions across the contiguous U.S. were derived from Version 3.4 of the Weather Research Forecasting Model (WRF) (Skamarock, 2008). These inputs included hourly-varying horizontal wind components (i.e., speed and direction), temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell in each vertical layer. Details of the annual 2011 meteorological model simulation and evaluation are provided in a separate technical support document (US EPA, 2014a).

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, GEOS-Chem standard version 8-03-02 (Yantosca and Carouge, 2010) with 8-02-01 chemistry. The global GEOS-Chem model simulates atmospheric chemical and physical processes driven by assimilated meteorological observations from the NASA’s Goddard Earth Observing System (GEOS-5; additional information available at: http://gmao.gsfc.nasa.gov/GEOS/ and http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-5). This model was run for 2011 with a grid resolution of 2.0 degrees x 2.5 degrees (latitude-longitude). The predictions were used to provide one-way dynamic boundary conditions at one-hour intervals and an initial concentration
field for the CAMx simulations. A model evaluation was conducted to validate the appropriateness of this version and model configuration of GEOS-Chem for predicting selected measurements relevant to their use as boundary conditions for CAMx. This evaluation included using satellite retrievals paired with GEOS-Chem grid cell concentrations (Henderson, 2014). More information is available about the GEOS-Chem model and other applications using this
tool at: http://www-as.harvard.edu/chemistry/trop/geos

.

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2A-4 2A.1.3 2025 Base Case Emissions Inputs CAMx requires detailed emissions inventories containing temporally allocated (i.e., hourly) emissions for each grid-cell in the modeling domain for a large number of chemical species that act as primary pollutants and prec ursors to secondary pollutants. The annual emission inventories were preprocessed into CAMx-ready inputs using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system (Houyoux et al., 2000).

The 2025 EGU projected inventory represents demand growth, fuel resource availability, generating technology cost and performance, and other economic factors affecting power sector behavior. The EGU emissions were developed using the IPM 33 

### version 5.14 ( http://epa.gov/powersectormodeling/psmodel514.html ). IPM reflects the expected 2025 emissions accounting for the effects of environmental rules and regulations, consent decrees and settlements, plant closures, units built, control devices installed, and forecast unit construction through the calendar year 2025. Improvements to the National Electric Energy Data System database, a key input in the preparation of future year EGU inventories, were implemented as a result of updated information becoming available and based on comments submitted in response the January 2014 Federal Register notice. In this analysis, the projected EGU emissions include impacts from the Final Mercury and Air Toxics Standard (MATS) announced on December 21, 2011 and the CSAPR issued July 6, 2011. 34



### Projections for most stationary emissions sources other than EGUs (i.e., non-EGUs) were developed by using the EPA Control Strategy Tool (CoST) to create post-controls future year inventories. CoST is described in chapter 4 (section 4.1.1) and at http://www.epa.gov/ttnecas1/cost.htm. The 2025 ba se case non-EGU stationary source emissions inventory includes all enforceable national rules and programs including the Reciprocating Internal Combustion Engines (RICE) and cement manufacturing National Emissions Standards for Hazardous Air Pollutants (NESHAPs) and Boiler Maximum Achievable Control Technology (MACT) reconsideration reductions. Projection factors and percent reductions for non-EGU point sources reflect comments received by EPA in response to the January 2014 Federal

33 IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power sector.
34 An emissions modeling sensitivity run described in Section 2.2.2 also includes a representation of EPA’s proposed carbon pollution guidelines under section 111(d) of the Clean Air Act (CAA).

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2A-5 Register Notice, along with emissions reductions due to national and local rules, control programs, plant closures, consent decrees and settlements. Some improvements made based on comments included the use of growth and control factors provided by states and by regional organizations on behalf of states. Reductions to criteria air pollutant (CAP) emissions from stationary engines resulting as cobenefits to the Reciprocating Internal Combustion Engines (RICE) National Emission Standard for Hazar dous Air Pollutants (NESHAP) are included.
Reductions due to the New Source Performance Standards (NSPS) VOC controls for oil and gas sources, and the NSPS for process heaters, internal combustion engines, and natural gas turbines
are also included.

Regional projection factors for point and nonpoint oil and gas emissions were developed using Annual Energy Outlook (AEO) 2014 projections from year 2011 to year 2025 ( http://www.eia.gov/forecasts/aeo/ ) . Projected emissions for corn ethanol, cellulosic ethanol and biodiesel plants, refineries and upstream impacts represent the Energy Independence and Security Act (EISA) renewable fuel standards mandate in the Renewable Fuel Standards Program (RFS2). Airport-specific terminal area forecast (TAF) data were used for aircraft to account for projected changes in landing/takeoff activity.

Projection factors for livestock are based on expected changes in animal population from 2005 Department of Agriculture data, updated according to EPA experts in July 2012; fertilizer application NH3 emissions projections includ e upstream impacts representing EISA. Area fugitive dust projection factors for categories related to livestock estimates are based on expected changes in animal population and upstream impacts from EISA. Fugitive dust for paved and unpaved roads take growth in VMT and population into account. Residential Wood Combustion (RWC) projection factors reflect assumed growth of wood burning appliances based on salesdata, equipment replacement rates and change outs. These changes include growth in loweremitting stoves and a reduction in higher emitting stoves. Impacts from the New Source Performance Standards (NSPS) for wood burning devices are also included.

Projection factors for the remaining nonpoint sources such as stationary source fuel combustion, industrial processes, solvent utilization, and waste disposal, reflect emissions reductions due to control programs along with comments on the growth and control of these

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2A-6 sources as a result of the January 2014 Federal Register notice and information gathered from prior rulemakings and outreach to states on emission inventories. Future year portable fuel container (PFC) inventories reflect the impact of the final Mobile Source Air Toxics (MSAT2).

The MOVES2014-based 2025 onroad mobile source emissions account for changes in activity data and the impact of on-the-books national rules including: the Tier 3 Vehicle Emission and Fuel Standards Program, the 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Aver age Fuel Economy Standards (LD GHG), the Renewable Fuel Standard (RFS2), the Mobile Source Air Toxics Rule, the Light Duty Green House Gas/Corporate Average Fuel Efficiency (CAFE) standards for 2012-2016, the Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles, the Light-Duty Vehicle Tier 2 Rule, and the Heavy-Duty Diesel Rule. The MOVES-based 2025 emissions also include state rules related to the adoption of LEV standards, inspection and maintenance programs, Stage II refueling controls, and local fuel restrictions. For California, the base case emission s included most of this state’s on-the-books regulations, such as those for idling of heavy-duty vehicles, chip reflash, public fleets, track trucks, drayage trucks, and heavy duty trucks and buses. The California emissions do not reflect the impacts of the GHG/Smartway regulation, nor do they reflect state GHG regulations for the projection of other emissions sectors because that information was not included in the provided inventories. The input databases for MOVES, the methods for projecting activity data, and the emissions estimation methods implemented with MOVES were improved from those used in the proposal modeling with some improvements based on comments received via the January 2014 Federal Register notice.

The nonroad mobile 2025 emissions, including railroads and commercial marine vessel emissions also include all national control programs. These control programs include the Clean Air Nonroad Diesel Rule – Tier 4, the Nonroad Spark Ignition rules, and the Locomotive-Marine Engine rule. For ocean-going vessels (Class 3 marine), the emissions data reflect the 2005 voluntary Vessel Speed Reduction (VSR) within 20 nautical miles, the 2007 and 2008 auxiliary engine rules, the 40 nautical mile VSR program, the 2009 Low Sulfur Fuel regulation, the 2009- 2018 cold ironing regulation, the use of 1% sulfur fuel in the Emissions Control Area (ECA) zone, the 2012-2015 Tier 2 NO x 

### controls, the 2016 0.1% sulfur fuel regulation in ECA zone, and

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2A-7 the 2016 International Marine Organization (IMO) Tier 3 NO x 

### controls. Control and growthrelated assumptions for 2025 came from the Emissions Modeling Platform and are described in more detail in EPA (2014b). Non-U.S. and U.S. category 3 commercial marine emissions were projected to 2025 using consistent methods that incorporated controls based on ECA and IMO global NO x 

### and SO 2 

### controls. For California, the 2025 emissions for these categories reflect the state’s Off-Road Construction Rule for “In-Use Diesel”, cargo handling equipment rules in place as of 2011 (see http://www.arb.ca.gov/ports/cargo/cargo.htm), and state rules through 2011 related to Transportation Refrigeration Units, the Spark-Ignition Marine Engine and Boat Regulations adopted on July 24, 2008 for pleasure craft, and the 2007 and 2010 regulations to
reduce emissions from commercial harbor craft.

All modeled 2011 and 2025 emissions cases use the 2010 Canada emissions data. Note that 2010 is the latest year for which Environment Canada had provided data at the time the modeling was performed, and no accompanying future-year projected base case inventories were provided in a form suitable for this analys is. For Mexico, emissions compiled from the Inventario Nacional de Emisiones de Mexico, 2008 were used for 2011, as that was the latest complete inventory available. For 2025, projected emissions for the year 2025 based on the 2008 inventory were used (ERG, 2014). Offshore oil platform emissions for the United States represent the year 2011 and are consistent with those in the 2011 National Emissions Inventory, version 2. Biogenic and fire emissions were held constant for all emissions cases and were based on 2011-specific data. Table 2A-1 shows the modeled 2011 and 2025 NO X 

### and VOC emissions by sector. Additional details on the emissions by state are given in the emissions modeling TSD.

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2A-8 Table 2A-1. 2011 and 2025 Base Case NOx and VOC Emissions by Sector (thousand tons) Sector 2011 NO x 2025 NO x 2011 VOC 2025 VOC EGU-point 2,000 1,400 36 42 NonEGU-point 1,200 1,200 800 830 Point oil and gas 500 460 160 190 Wild and Prescribed Fires 330 330 4,700 4,700

Nonpoint oil and gas 650 720 2,600 3,500

Residential wood

combustion 34 35 440 410 Other nonpoint 760 790 3,700 3,500

Nonroad 1,600 800 2,000 1,200 Onroad 5,700 1,700 2,700 910 C3 Commercial marine vessel (CMV) 130 100 5 9
Locomotive and C1/C2 CMV 1,100 680 48 24 Biogenics 1,000 1,000 41,000 41,000 TOTAL 15,000 9,300 58,000 56,000



### 2A.1.4 2011 Model Evaluation for Ozone
An operational model evaluation was conducted for the 2011 base year CAMx annual model simulation performed for the 12-km U.S. modeling domain. The purpose of this evaluation is to examine the ability of the 2011 air quality modeling platform to represent the magnitude and spatial and temporal variability of measured (i.e., observed) ozone concentrations within the modeling domain. The evaluation presented here is based on model simulations using the v2 version of the 2011 emissions platform (i.e., case name 2011eh_cb6v2_v6_11g, also called the “final RIA 2011 base year” in chapter 2) 35 

### . The model evaluation for ozone focuses on comparisons of model predicted 8-hour daily maximum concentrations to the corresponding observed data at monitoring sites in the EPA Air Quality System (AQS) and the Clean Air Status and Trends Network (CASTNet) (Figures 2A-2a and 2A-2b).

Included in the evaluation are statistical measures of model performance based upon model-predicted versus observed concentrations that were paired in space and time. Model performance statistics were calculated for several spatial scales and temporal periods. Statistics were calculated for individual monitoring sites and for each of nine climate regions of the 12-km

35 For an evaluation of the proposal RIA 2011 base year modeling, please see appendix 3-A of EPA, 2014d.

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2A-9 U.S. modeling domain. The regions include the Northeast, Ohio Valley, Upper Midwest, Southeast, South, Southwest, Nort hern Rockies, Northwest and West 36,37 

### , which are defined based upon the states contained within the National Oceanic and Atmospheric Administration (NOAA) climate regions (Figure 2A-3) 38 

### as were originally identified in Karl and Koss (1984).

For maximum daily average 8-hour (MDA8) ozone, model performance statistics were created for each climate region for the May through September ozone season. 39 

### In addition to the performance statistics, we prepared several graphical presentations of model performance for MDA8 ozone. These graphical presentations include:

(1) density scatter plots of observed AQS data and predicted MDA8 ozone concentrations for May through September;

(2) regional maps that show the mean bias and error as well as normalized mean bias and
error calculated for MDA8 ≥ 60 ppb for May through September at individual AQS and CASTNet monitoring sites;

(3) bar and whisker plots that show the distribution of the predicted and observed MDA8 ozone concentrations by month (May through September) and by region and by network; and

(4) time series plots (May through September) of observed and predicted MDA8 ozone concentrations for 12 representative high ozone sites in the urban areas with the highest projected ozone levels in each region from the 2025 base case CAMx simulation.

The Atmospheric Model Evaluation Tool (AMET) was used to calculate the model performance statistics used in this document (Gilliam et al., 2005). For this evaluation of the

36 The nine climate regions are defined by States where: Northeast includes CT, DE, ME, MA, MD, NH, NJ, NY, PA, RI, and VT; Ohio Valley includes IL, IN, KY, MO, OH, TN, and WV; Upper Midwest includes IA, MI, MN, and WI; Southeast includes AL, FL, GA, NC, SC, and VA; South includes AR, KS, LA, MS, OK, and TX; Southwest includes AZ, CO, NM, and UT; Northern Rockies includes MT, NE, ND, SD, WY; Northwest includes ID, OR, and WA; and West includes CA and NV.
37 Note most monitoring sites in the West region are located in California (see Figures 2A-2a and 2A-2b), therefore statistics for the West will be mostly representative of California ozone air quality.
38 NOAA, National Centers for Environmenta l Information scientists have identi fied nine climatically consistent regions within the contiguous U.S., http://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php.
39 In calculating the ozone season statistics we limited the data to those observed and predicted pairs with observations that are greater than or equal 60 ppb in order to focus on concentrations at the upper portion of the distribution of values.

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2A-10 ozone predictions in the 2011 CAMx modeling platform, we have selected the mean bias, mean error, normalized mean bias, and normalized mean error to characterize model performance, statistics which are consistent with the recommendations in Simon et al. (2012) and the draft photochemical modeling guidance (US EPA, 2014c). As noted above, we calculated the performance statistics by climate region for the period of May through September ozone season.

Mean bias (MB) is used as average of the difference (predicted – observed) divided by the total number of replicates ( n ). Mean bias is given in units of ppb and is defined as:

MB = ଵ

௡ 

### ∑ ሺ ܲെܱ ሻ ௡ ଵ 

### , where P = predicted and O = observed concentrations.

Mean error (ME) calculates the absolute value of the difference (predicted - observed) divided by the total number of replicates ( n ). Mean error is given in units of ppb and is defined

as:

ME = ଵ

௡ 

### ∑ |ܲ െ ܱ| ௡ ଵ


### Normalized mean bias (NMB) is used as a normalization to facilitate a range of concentration magnitudes. This statistic averages the difference (predicted - observed) over the sum of observed values. NMB is a useful model performance indicator because it avoids over inflating the observed range of values, especially at low concentrations. Normalized mean bias is given in percentage units and is defined as:

NMB = ∑ ሺ௉ିைሻ ೙ భ ∑ ሺைሻ

೙ భ 

### ∗ 100

Normalized mean error (NME) is also similar to NMB, where the performance statistic is
used as a normalization of the mean error. NME calculates the absolute value of the difference (predicted - observed) over the sum of observed values. Normalized mean error is given in percentage units and is defined as:

NME = ∑ |௉ିை| ೙ భ ∑ ሺைሻ

೙ భ 

### ∗ 100

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2A-11 As described in more detail below, the model performance statistics indicate that the 8- hour daily maximum ozone concentrations predicted by the 2011 CAMx modeling platform closely reflect the corresponding 8-hour observed ozone concentrations in space and time in each region of the 12-km U.S. modeling domain. The acceptability of model performance was judged by considering the 2011 CAMx performance results in light of the range of performance found in recent regional ozone model applications (NRC, 2002; Phillips et al., 2007; Simon et al., 2012; US EPA, 2005; US EPA, 2009; US EPA, 2011). These other modeling studies represent a wide range of modeling analyses that cover various models, model configurations, domains, years and/or episodes, chemical mechanisms, and aerosol modules. Overall, the ozone model performance results for the 2011 CAMx simulations are within the range found in other recent peer-reviewed and regulatory applications. The model performance results, as described in this document, demonstrate that the predictions from the 2011 modeling platform closely replicate the corresponding observed concentrations in terms of the magnitude, temporal fluctuations, and spatial differences for 8-hour daily maximum ozone.

The density scatter plots of MDA8 ozone are provided Figure 2A-4. The 8-hour ozone model performance bias and error statistics by network for the ozone season (May-September average) for each region are provided in Table 2A-2. The statistics shown were calculated using data pairs on days with observed 8-hour ozone of ≥ 60 ppb. The distributions of observed and predicted 8-hour ozone by month in the 5-month ozone season for each region are shown in Figures 2A-5 through 2A-13. Spatial plots of the mean bias and error as well as the normalized mean bias and error for individual monitors are shown in Figures 2A-14 through 2A-17. The statistics shown in these two sets of figures were calculated over the ozone season using data pairs on days with observed 8-hour ozone of ≥ 60 ppb. Time series plots of observed and predicted 8-hour ozone during the ozone season at the 12 representative high ozone monitoring sites are provided in Figure 2A-18, (a) through (l). These sites are listed in Table 2A-3.

The density scatter plots in Figure 2A-4 provide a qualitative comparison of modelpredicted and observed MDA8 ozone concentrations. In these plots the intensity of the colors indicates the density of individual observed/predicted paired values. The greatest number of individual paired values is denoted by the core area in white. The plots indicate that the predictions correspond closely to the observations in that a large number of observed/predicted

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2A-12 paired values lie along or close to the 1:1 line shown on each plot. Overall, performance is best
for observed values ≥ 60. The model tends to over-predict the observed values to some extent particularly at low and mid-range concentrations generally < 60 ppb in each of the regions. This feature is most evident in the South and Southeast states. In the West, high concentrations are under-predicted and low and mid-range concentrations are over-predicted. Observed and predicted values are in close agreement in the Southwest and Northwest regions.

As indicated by the statistics in Table 2A-2, bias and error for 8-hour daily maximum ozone are relatively low in each region. Generally, MB for 8-hour ozone ≥ 60 ppb during the ozone season is less than 5 ppb except at AQS sites in the Western region and at rural CASTNet sites in the South, Southwest and Western regions for which ozone is somewhat under-predicted. The monthly distribution of 8-hour daily maximum ozone during the ozone season generally corresponds well with that of the observed concentrations, as indicated by the graphics in Figures 2A-5 through 2A-13. The distribution of predicted concentrations tends to be close to that of the
observed data at the 25 th 

### percentile, median and 75th percentile values for each region, although there is a small persistent overestimation bias for these metrics in the Northeast, Southeast, and Ohio Valley regions, and under-prediction at CASTNet sites in the West and Southwest 40 

### . The CAMx model, as applied here, also has a tendency to under-predict the highest observational concentrations at both the AQS and CASTNet network sites.

Figures 2A-14 through 2A-17 show the spatial variability in bias and error at monitor locations. Mean bias, as seen from Figure 2A-14, is less than 5 ppb at many sites across the East with over-prediction of 5 to 10 ppb at some sites from the Southeast into the Northeast. Elsewhere, mean bias is generally in the range of -5 to -10 ppb. Figure 2A-15 indicates that the normalized mean bias for days with observed 8-hour daily maximum ozone greater than or equal to 60 ppb is within ± 10 percent at the vast majority of monitoring sites across the modelingdomain. There are regional differences in model performance, where the model tends to overpredict from the Southeast into the Northeast and generally under predict in the Southwest,

40 The over-prediction at CASTNet sites in the Northwest may not be representative of performance in rural areas of this region because there are so few observed and predicted data pairs in this region.

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2A-13 Northern Rockies, Northwest and West. Model performance in the Ohio Valley and Upper Midwest states shows both under and over predictions.

Model error, as seen from Figure 2A-16, is 10 ppb or less at most of the sites across the modeling domain. Figure 2A-17 indicates that the normalized mean error for days with observed 8-hour daily maximum ozone greater than or equal to 60 ppb is within 10 percent at the vast majority of monitoring sites across the modeling domain. Somewhat greater error (i.e., greater than 15 percent) is evident at sites in several areas most notably along portions of the Northeast and in portions of Florida, North Dakota, Illinois, Ohio, North Carolina, and the western most part of the modeling domain.

In addition to the above analysis of overall model performance, we also examine how well the modeling platform replicates day to day fluctuations in observed 8-hour daily maximum concentrations at 12 high ozone monitoring sites. For this site-specific analysis we present the time series of observed and predicted 8-hour daily maximum concentrations by site over the ozone season, May through September. These monito rs were chosen because they are high ozone sites in those urban areas with the highest projected ozone levels in the 2025 base case simulation. The results, as shown in Figures 2A-1 8 (a) through (l), indicate that the modeling platform replicates the day-to-day variability in ozone during this time period. For example, several of the sites not only have minimal bias but also accurately capture the day-to-day variability in the observations: Alleghany County, PA; Wayne County, MI; Jefferson County, KY. Many additional sites generally track well and capture day-to-day variability but underestimate some of the peak ozone days: Tarrant County, TX; Brazoria County, TX; Harford County, MD; Queens County, NY; Suffolk County, NY; Sheboygan County, WI; DouglasCounty, CO. Note that at the site in Brazoria County, TX there is an extended period from midJuly to mid-August with very low observed ozone concentrations, mostly in the range of 30 to 40 ppb. The model predicted values during this period in the range of 40 to 60 ppb which is not quite as low as the observed values. Finally, the daily modeled ozone at the two California sites evaluated correlates well with observations but has a persistent low bias. Looking across all 12 sites indicates that the modeling platform is able to capture the site to site differences in the short-term variability of ozone concentrations.

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

Figure 2A-2b. CASTNet Ozone Monitoring Sites

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2A-15 Figure 2A-3. NOAA Nine Climate Regions (source: http://www.ncdc.noaa.gov/monitoringreferences/maps/us-climate-regions.php#references )

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2A-16 Table 2A-2. MDA8 Ozone Performance Statistics Greater than or Equal to 60 Ppb for May through September by Climate Region, by Network

Network Climate region No. of Obs MB ME NMB (%) NME (%)

AQS Northeast 3,998 2.2 7.4 3.2 10.8 Ohio Valley 6,325 0.3 7.6 0.4 11.3 Upper Midwest 1,162 -3.0 7.5 -4.4 11.0 Southeast 37,280 -2.5 8.1 -3.6 11.9 South 5,694 -3.7 8.1 -5.4 11.7 Southwest 6,033 -5.2 7.9 -7.8 12.0 Northern Rockies 380 -5.9 7..4 -9.4 11.7 Northwest 79 -5.4 8.1 -8.5 12.6 West 8,665 -7.3 9.5 -10.3 13.5

CASTNet Northeast 264 2.3 6.1 3.4 9.1 Ohio Valley 107 -2.3 6.2 -3.4 9.4 Upper Midwest 38 -3.9 5.9 -5.8 8.8 Southeast 2,068 -5.0 8.2 -7.5 12.1 South 215 -7.1 8.0 -10.7 12.0 Southwest 382 -7.7 8.6 -11.7 13.1 Northern Rockies 110 -7.8 8..1 -12.2 12.8 Northwest - - - - - West 425 -12.1 12.5 -16.6 17.1

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2A-17 Figure 2A-4. Density Scatter Plots of Observed/Predicted MDA8 Ozone for the Northeast, Ohio River Valley, Upper Midwest, Southeast, South, Southwest, Northern Rockies, Northwest and West Regions

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2A-18 Figure 2A-5. Distribution of Observed a nd Predicted MDA8 Ozone by Month for the Period May through September for the Northeast Region, (a) AQS Network and (b) CASTNet Network. [symbol = me dian; top/bottom of box = 75th/25th percentiles; top/bottom line = max/min values]

Figure 2A-6. Distribution of Observed a nd Predicted MDA8 Ozone by Month for the Period May through September for the Ohio Valley Region, (a) AQS Network and (b) CASTNet Network b a

b a

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2A-19 Figure 2A-7. Distribution of Observed a nd Predicted MDA8 Ozone by Month for the Period May through September for the Upper Midwest Region, (a) AQS Network and (b) CASTNet Network

Figure 2A-8. Distribution of Observed a nd Predicted MDA8 Ozone by Month for the Period May through September for the Southeast Region, (a) AQS Network and (b) CASTNet Network b a

b a

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2A-20 Figure 2A-9. Distribution of Observed a nd Predicted MDA8 Ozone by Month for the Period May through September for the South Region, (a) AQS Network and (b) CASTNet Network

Figure 2A-10. Distribution of Observed and Predicted MDA8 Ozone by Month for the Period May through September for the Southwest Region, (a) AQS Network and (b) CASTNet Network b a

b a

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2A-21 Figure 2A-11. Distribution of Observed a nd Predicted MDA8 Ozone by Month for the Period May through September for the Northern Rockies Region, (a) AQS Network and (b) CASTNet Network

Figure 2A-12. Distribution of Observed a nd Predicted MDA8 Ozone by Month for the Period May through September for the Northwest Region, (a) AQS Network and (b) CASTNet Network b a b a

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2A-22 Figure 2A-13. Distribution of Observed a nd Predicted MDA8 Ozone by Month for the Period May through September for the West Region, (a) AQS Network and (b) CASTNet Network

Figure 2A-14. Mean Bias (ppb) of MDA8 Ozone Greater than or Equal to 60 ppb over the Period May-September 2011 at AQS and CASTNet Monitoring b a

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2A-23 Figure 2A-15. Normalized Mean Bias (%) of MDA8 Ozone Greater than or Equal to 60 ppb over the Period May-September 2011 at AQS and CASTNet Monitoring Sites

Figure 2A-16. Mean Error (ppb) of MDA8 Ozon e Greater than or Equal to 60 ppb over the Period May-September 2011 at AQS and CASTNet Monitoring Sites

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-24 Figure 2A-17. Normalized Mean Error (%) of MDA8 Ozone Greater than or Equal to 60 ppb over the Period May-September 20 11 at AQS and CASTNet Monitoring Sites

Table 2A-3. Key Monitoring Sites Used for the Ozone Time Series Analysis
County State Monitoring Site ID Queens N ew Yor k 360810124
Suffolk N ew Yor k 361030002 Harford Maryland 240251001 Allegheny Penns ylvania 420031005
Jefferson Kentucky 211110067 Wayne Michigan 261630019 Sheboygan Wisconsin 551170006
Tarrant Texas 484392003
Brazoria Texas 480391004
Douglas Co lorado 80350004 Fresno California 60195001
San Bernardino California 60710005

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-25 Figure 2A-18a. Time Series of Observed (b lack) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 360810124 in Queens, New York

Figure 2A-18b. Time Series of Observed (b lack) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 361030002 in Suffolk County, New York

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-26 Figure 2A-18c. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 240251001 in Harford Co., Maryland

Figure 2A-18d. Time Series of Observed (b lack) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 420031005 in Allegheny Co., Pennsylvania

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-27 Figure 2A-18e. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 211110067 in Jefferson Co., Kentucky

Figure 2A-18f. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 261630019 in Wayne Co., Michigan

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-28 Figure 2A-18g. Time Series of Observed (b lack) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 551170006 in Sheboygan Co., Wisconsin

Figure 2A-18h. Time Series of Observed (b lack) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 484392003 in Tarrant Co., Texas

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-29 Figure 2A-18i. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 480391004 in Brazoria Co., Texas

Figure 2A-18j. Time Series of Observed (b lack) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 80350004 in Douglas Co., Colorado

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2A-30 Figure 2A-18k. Time Series of Observed (b lack) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 60 195001 in Fresno Co., California

Figure 2A-18l. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May through September 2011 at Site 60710005 in San Bernardino Co., California

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2A-31 2A.2 VOC Impact Regions As described in Chapter 2, we defined VOC impact regions for the following urban areas: New York City, Chicago, Louisville, Houston, Denver , Northern California and Southern California. 41 

### Not only did these areas have the highest design values in each region, but ozone in these areas was also sensitive to VOC emissions reductions in our modeling. Figure 2A-19 shows the impact of 50% U.S. anthropogenic VOC cuts on July monthly average 8-hour daily maximum
ozone concentrations across the US. Ozone in each of the areas listed above is shown to have at least 0.2 ppb response to VOC emissions cuts.

Figure 2A-19. Change in July Average of 8-hr Daily Maximum Ozone Concentration (ppb) Due to 50% Cut in U.S. Anthropogenic VOC Emissions
2A.3 Monitors Excluded from the Quantitative Analysis There were 1,225 ozone monitors with complete ozone data for at least one DV period covering the years 2009-2013. Of those sites, we quantitatively analyzed 1,165 in this analysis. As discussed in Chapter 2, 60 sites were excluded from the quantitative analysis of emissions

41 Other local VOC areas that had similar levels of ozone response to the 50% VOC reduction were also explored but were found not to be helpful in reaching alternative NAAQS levels in this analysis: Dallas, Detroit, Pittsburgh, and Baltimore. This may be due to the construct of the attainment scenarios explored here and does not mean that VOC controls might not be effective in these areas under alternate assumptions about regional NOx controls.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-32 reductions needed to reach alternative standard levels. These sites fall into one of three categories, as discussed in more detail in the following three subsections.

2A.3.1 Sites without Projections Due to Insufficient Days
Some monitors were excluded from the analysis because no future design value could be projected at the site. This occurred when there were not enough modeled high ozone days (4 or fewer) at the site to compute a design value according to EPA SIP modeling guidance (US EPA, 2014c). A list of the 41 sites in this category is given in Table 2A-4.

Table 2A-4. Monitors without Projections due to Insufficient High Modeling Days to Meet EPA Guidance for Projecting Design Values Site ID Lat Long State County 60010009 37.74307 -122.17 California Alameda 60010011 37.81478 -122.282 California Alameda 60131004 37.9604 -122.357 California Contra Costa 60231004 40.77694 -124.178 California Humboldt 60450008 39.14566 -123.203 California Mendocino 60750005 37.76595 -122.399 California San Francisco 60811001 37.48293 -122.203 California San Mateo 60932001 41.72689 -122.634 California Siskiyou 160230101 43.46056 -113.562 Idaho Butte 230031100 46.69643 -68.033 Maine Aroostook 260330901 46.49361 -84.3642 Michigan Chippewa 270052013 46.85181 -95.8463 Minnesota Becker 270177416 46.70527 -92.5238 Minnesota Carlton 270750005 47.94862 -91.4956 Minnesota Lake 270834210 44.4438 -95.8179 Minnesota Lyon 271370034 48.41333 -92.8306 Minnesota Saint Louis 300298001 48.51017 -113.997 Montana Flathead 300490004 46.8505 -111.987 Montana Lewis and Clark 311079991 42.8292 -97.854 Nebraska Knox 380070002 46.8943 -103.379 North Dakota Billings 380130004 48.64193 -102.402 North Dakota Burke 380150003 46.82543 -100.768 North Dakota Burleigh 380171004 46.93375 -96.8554 North Dakota Cass 380250003 47.3132 -102.527 North Dakota Dunn 380530002 47.5812 -103.3 North Dakota McKenzie 380570004 47.29861 -101.767 North Dakota Mercer 380650002 47.18583 -101.428 North Dakota Oliver 410170122 44.0219 -121.26 Oregon Deschutes

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2A-33 Site ID Lat Long State County 410290201 42.22989 -122.788 Oregon Jackson 410591003 45.82897 -119.263 Oregon Umatilla 460110003 44.3486 -96.8073 South Dakota Brookings 530090013 48.29786 -124.625 Washington Clallam 530330080 47.56824 -122.309 Washington King 530530012 46.7841 -121.74 Washington Pierce 530531010 46.75833 -122.124 Washington Pierce 530570020 48.39779 -122.505 Washington Skagit 530730005 48.95074 -122.554 Washington Whatcom 550030010 46.602 -90.656 Wisconsin Ashland 551250001 46.052 -89.653 Wisconsin Vilas 560390008 43.67083 -110.599 Wyoming Teton 560391011 44.55972 -110.401 Wyoming Teton



### 2A.3.2 Winter Ozone High winter ozone concentrations that have been observed in mountain valleys in the Western U.S. are believed to result from the combination of strong wintertime inversions, large NOx and VOC emissions from nearby oil and gas operations, increased UV intensity due to reflection off of snow surfaces, and potentially still uncharacterized sources of free radicals. Current modeling tools are not sufficient to properly characterize ozone formation for these winter ozone episodes due to (1) the challenging task of capturing complex local “cold pool” meteorology using a model resolution that is optimized to capture regional and synoptic scale process, (2) uncertainties in quantifying the loca l emissions from oil and gas operations, and (3) uncertainties in the chemistry that occurs both in the atmosphere and on snow surfaces during these episodes. Therefore, it was not appropriate to project ozone design values at monitors impacted by winter events. To identify sites impacted by winter events, we examined the ambient data that went into creating the 2009-2013 5-year weighted design value in locations known to have conditions conducive to winter ozone formation (i.e., all sites in Wyoming, Utah, and Colorado). At these sites, we evaluated the four highest 8-hr daily maximum ozone values in each year from 2009-2013 to identify wintertime ozone episodes. A site was categorized as having a design value impacted by wintertime ozone if at least 20% of the days examined (4 out 20) had ozone values greater than or equal to 75 ppb and occurred during a “winter” month

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-34 (November-March). The seven sites identified as being affected by wintertime ozone events are
listed in Table 2A-5.

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

Site ID lat long State County # of

summer DV days* >= 75 # of winter DV days* >= 75 highest winter 8- hr daily

max 2009- 2013 DV

081030006 40.09 -108.76 Colorado Rio Blanco 0 7 106 71 560130099 42.53 -108.72 Wyoming Fremont 1 4 93 67 560350097 42.98 -110.35 Wyoming Sublette 0 3 83 64 560350099 42.72 -109.75 Wyoming Sublette 0 4 123 77 560350100 42.79 -110.06 Wyoming Sublette 0 4 84 67 560350101 42.87 -109.87 Wyoming Sublette 0 4 89 66 560351002 42.37 -109.56 Wyoming Sublette 0 4 94 68 *DV days defined here are the days with the 4 highest 8-hr daily maximum ozone values in each year from 2009- 2013 (20 days).


### 2A.3.3 Monitoring Sites in Rural/Remote Areas of the West and Southwest
As mentioned in Chapter 2, model-predicted ozone concentrations at 12 sites in rural/remote areas in the West and Southwest were excluded from the quantitative analysis. These 12 sites are a subset of 26 sites identified in the November 2014 RIA proposal (US EPA, 2014d). The original 26 sites had two common characteristics. First, they had small modeled responses to large regional NOx and VOC reductions in 2025 compared to other sites in the region. Second, these monitors would have DVs that remain above the standard after applying reductions needed to bring large urban areas in the region into attainment. All of these 12 monitoring sites have 2025 baseline concentrations below 70 ppb. Therefore, no emissions reductions would be required for these sites to meet a primary standard of 70 ppb in 2025. Of the 26 sites identified in the RIA proposal, only the sites with 2025 baseline DVs (or post-2025 baseline DVs for CA) above 65 ppb are excluded from this analysis. More details on these 12 sites are provided in Table 2A-6. We have qualitatively characterized the predominant ozone influence for each site in Table 2A-6. These qualitative characterizations are based on the modeled response to large regional NOx reductions in 2025, proximity to the Mexican border (i.e., potential influence from trans-border pollution) and altitude (e.g., potential influence of

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-35 ozone transported from the free troposphere: stratospheric intrusions or long range transport of international anthropogenic ozone). Figure 2A-20 shows the location of all sites listed in Table 2A-7 and for demonstrative purposes assigns each site to a category based on the predominantsource of ozone in that location. The table and figure indicate that all 12 sites have 2025 or post2025 baseline design values below 70 ppb as mentioned above. Of the 12 sites, 5 sites are characterized as border sites, 5 sites are characterized as being strongly influenced by California emissions, and 2 sites are influenced by other ozone sources.

Table 2A-6. Monitors with Limited Response to Regional NOx and National VOC Emissions Reductions in the 2025 and Post-2025 Baselines

Name Site ID State County Altitude (m) Monitor Type Predominant O3 Sources 2009- 2013 DV Baseline DV

Chiricahua NM 40038001 Arizona Cochise 1570 CASTNET Mexican border 72 67

Grand Canyon NP 40058001 Arizona Coconino 2152 CASTNET California + Other sources 71 66

Yuma Supersite 40278011 Arizona Yuma 51 SLAMS Mexican border + California 75 66

El Centro-9 th st 60251003 California Imperial - SLAMS California + Mexican Border 81 68

Yosemite NP 60430003 California Mariposa 5265 CASTNET California + Other sources 77 67

Sequoia and Kings Canyon NP 61070006 California Tulare 1890 Non-EPA Federal (NPS) California + Other sources 81 69

Weminuche Wilderness Area 80671004 Colorado La Plata 2367 Non-EPA Federal (USFS) Southwest region + Other sources 72 68

Great Basin NP 320330101 Nevada White Pine 2060 CASTNET California + Other sources 72 66

BLM land near Carlsbad 350151005 New Mexico Eddy 780 SLAMS Central region + Southwest region + Mexican border 70 67

Big Bend NP 480430101 Texas Brewster 1052 CASTNET Mexican border 70 68

BLM Land/Carlsbad 483819991 Texas Randall 780 SLAMS Central region + Mexican border + Other sources 73 66

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2A-36 Name Site ID State County Altitude (m) Monitor Type Predominant O3 Sources 2009- 2013 DV Baseline DV

Zion NP 490530130 Utah Washington 1213 Non-EPA Federal (NPS) California + Other sources 71 66



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

In Figure 2A-20, the colored dots categorize sites by the predominant source of ozone. Many sites may be influenced by more than one source but are placed in a single category for illustrative purposes in the Figure. All ozone monitoring sites categorized as not substantially affected by natural or transported influences in Table 2A-6 are shown as small diamonds. Gray diamonds represent sites that had DVs less than or equal to 65 ppb in the 2025 baseline (or post-

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-37 2025 baseline for California sites). Black diamonds represent sites that had DVs greater than 65 ppb in the 2025 baseline (or post-2025 baseline for California sites).

2A.4 Design Values for All Monitors Included in the Quantitative Analysis
Table 2A-7. Design Values ( ppb) for California Monitors

Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65

60010007 37.68753 -121.784 California Alameda 67 62 58 54 60012001 37.65446 -122.032 California Alameda 54 50 48 45 60050002 38.33991 -120.764 California Amador 60 55 51 47 60070007 39.71404 -121.619 California Butte 61 56 52 48 60070008 39.76154 -121.842 California Butte 53 49 46 43 60090001 38.20185 -120.682 California Calaveras 63 57 54 50 60111002 39.20294 -122.018 California Colusa 53 49 46 43 60130002 37.93601 -122.026 California Contra Costa 66 61 57 53 60131002 38.00631 -121.642 California Contra Costa 64 58 55 51 60170010 38.72528 -120.822 California El Dorado 66 60 55 50 60170012 38.81161 -120.033 California El Dorado 62 59 57 56 60170020 38.89094 -121.003 California El Dorado 66 60 55 50 60190007 36.70551 -119.742 California Fresno 82 74 70 65 60190011 36.78532 -119.774 California Fresno 81 73 69 64 60190242 36.84139 -119.874 California Fresno 81 75 70 66 60192009 36.63423 -120.382 California Fresno 64 59 55 52 60194001 36.5975 -119.504 California Fresno 77 69 65 60 60195001 36.81911 -119.717 California Fresno 83 75 70 65 60210003 39.53376 -122.192 California Glenn 56 52 49 46 60250005 32.67619 -115.484 California Imperial 71 63 62 61 60254003 33.0325 -115.624 California Imperial 66 56 54 53 60254004 33.21361 -115.545 California Imperial 64 53 52 50 60270101 36.50861 -116.848 California Inyo 67 64 63 63 60290007 35.34609 -118.852 California Kern 80 74 69 64 60290008 35.05444 -119.404 California Kern 75 69 65 61 60290011 35.05055 -118.147 California Kern 71 62 60 57 60290014 35.35609 -119.041 California Kern 77 70 66 61 60290232 35.43887 -119.017 California Kern 76 70 66 61 60295002 35.23668 -118.789 California Kern 74 67 63 59 60296001 35.50359 -119.273 California Kern 74 68 65 61 60311004 36.3144 -119.645 California Kings 74 68 63 59 60333001 39.0327 -122.922 California Lake 50 47 44 42 60370002 34.1365 -117.924 California Los Angeles 75 56 52 48

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-38 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 60370016 34.14435 -117.85 California Los Angeles 88 65 61 56 60370113 34.05111 -118.456 California Los Angeles 62 49 46 42 60371002 34.17605 -118.317 California Los Angeles 72 53 49 45 60371103 34.06659 -118.227 California Los Angeles 61 46 42 39 60371201 34.19925 -118.533 California Los Angeles 83 64 60 56 60371302 33.90139 -118.205 California Los Angeles 58 54 52 50 60371602 34.01194 -118.07 California Los Angeles 63 52 48 44 60371701 34.06703 -117.751 California Los Angeles 79 61 57 52 60372005 34.1326 -118.127 California Los Angeles 73 54 50 46 60374002 33.82376 -118.189 California Los Angeles 57 52 50 49 60376012 34.38344 -118.528 California Los Angeles 89 66 61 57 60379033 34.67139 -118.131 California Los Angeles 80 62 59 55 60390004 36.86667 -120.01 California Madera 70 64 61 57 60392010 36.95326 -120.034 California Madera 74 68 64 60 60410001 37.97231 -122.52 California Marin 47 44 42 39 60430006 37.54993 -119.845 California Mariposa 65 61 58 55 60470003 37.2816 -120.435 California Merced 72 66 62 58 60530002 36.49577 -121.732 California Monterey 50 44 42 40 60530008 36.20929 -121.126 California Monterey 50 44 42 40 60531003 36.69676 -121.637 California Monterey 46 40 38 36 60550003 38.31094 -122.296 California Napa 53 49 46 43 60570005 39.23433 -121.057 California Nevada 62 57 53 49 60570007 39.31656 -120.845 California Nevada 60 55 51 47 60590007 33.83062 -117.938 California Orange 62 51 48 46 60591003 33.67464 -117.926 California Orange 60 50 47 45 60592022 33.63003 -117.676 California Orange 62 46 43 41 60595001 33.92513 -117.953 California Orange 68 55 52 49 60610003 38.93568 -121.1 California Placer 67 60 55 50 60610004 39.10028 -120.953 California Placer 60 54 50 47 60610006 38.74573 -121.266 California Placer 70 64 58 53 60650004 34.007 -117.521 California Riverside 78 61 57 54 60650008 33.7411 -115.821 California Riverside 56 47 46 45 60650009 33.44787 -117.089 California Riverside 60 45 43 41 60650012 33.92086 -116.858 California Riverside 87 65 61 57 60650016 33.58333 -117.083 California Riverside 64 48 45 42 60651016 33.945 -116.83 California Riverside 88 66 62 58 60652002 33.70853 -116.215 California Riverside 74 60 58 55 60655001 33.85275 -116.541 California Riverside 81 63 60 57 60656001 33.78942 -117.228 California Riverside 80 59 55 52 60658001 33.99958 -117.416 California Riverside 88 67 63 58

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-39 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65
60658005 33.99564 -117.493 California Riverside 84 64 60 56 60659001 33.67649 -117.331 California Riverside 75 55 52 49 60659003 33.61241 -114.603 California Riverside 60 54 53 52 60670002 38.71209 -121.381 California Sacramento 66 60 55 50 60670006 38.61378 -121.368 California Sacramento 67 61 56 51 60670010 38.55823 -121.493 California Sacramento 62 57 52 48 60670011 38.30259 -121.421 California Sacramento 63 57 53 49 60670012 38.6833 -121.164 California Sacramento 76 69 63 58 60670014 38.65078 -121.507 California Sacramento 60 55 51 47 60675003 38.49448 -121.211 California Sacramento 72 66 60 55 60690002 36.8441 -121.362 California San Benito 54 47 45 43 60690003 36.48522 -121.157 California San Benito 62 55 52 50 60710001 34.89501 -117.024 California San Bernardino 70 58 56 53 60710005 34.2431 -117.272 California San Bernardino 100 75 70 65 60710012 34.42613 -117.564 California San Bernardino 86 66 63 59 60710306 34.51001 -117.331 California San Bernardino 77 61 57 54 60711004 34.10374 -117.629 California San Bernardino 91 69 65 60 60711234 35.76387 -117.397 California San Bernardino 64 61 60 59 60712002 34.10002 -117.492 California San Bernardino 97 74 69 64 60714001 34.41807 -117.286 California San Bernardino 89 68 64 59 60714003 34.05977 -117.147 California San Bernardino 96 72 67 62 60719002 34.07139 -116.391 California San Bernardino 82 66 63 61 60719004 34.10688 -117.274 California San Bernardino 91 67 63 58 60730001 32.63123 -117.059 California San Diego 59 52 51 50 60730003 32.79119 -116.942 California San Diego 63 50 48 46 60730006 32.83646 -117.129 California San Diego 63 51 49 47 60731001 32.95212 -117.264 California San Diego 58 49 47 46 60731002 33.12771 -117.075 California San Diego 58 45 43 41 60731006 32.84224 -116.768 California San Diego 71 55 53 51 60731008 33.21703 -117.396 California San Diego 57 44 42 41 60731010 32.70149 -117.15 California San Diego 54 48 47 46 60731016 32.84547 -117.124 California San Diego 59 48 46 44 60731201 33.36259 -117.09 California San Diego 59 45 43 41 60732007 32.55216 -116.938 California San Diego 54 48 47 46 60771002 37.95074 -121.269 California San Joaquin 59 54 50 46 60773005 37.6825 -121.441 California San Joaquin 71 65 61 57 60790005 35.63163 -120.691 California San Luis Obispo 56 51 49 47 60792006 35.25658 -120.67 California San Luis Obispo 47 42 41 39 60793001 35.36631 -120.843 California San Luis Obispo 47 42 41 40 60794002 35.03146 -120.501 California San Luis Obispo 51 45 43 41

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-40 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 60798001 35.49158 -120.668 California San Luis Obispo 54 49 47 45 60798005 35.64368 -120.231 California San Luis Obispo 68 62 59 56 60798006 35.35472 -120.04 California San Luis Obispo 65 60 57 54 60830008 34.46245 -120.026 California Santa Barbara 52 46 45 44 60830011 34.42778 -119.691 California Santa Barbara 50 43 42 41 60831008 34.94915 -120.438 California Santa Barbara 43 38 36 35 60831013 34.72556 -120.428 California Santa Barbara 55 48 46 45 60831014 34.54166 -119.791 California Santa Barbara 59 51 50 48 60831018 34.52744 -120.197 California Santa Barbara 49 45 44 43 60831021 34.40278 -119.458 California Santa Barbara 59 52 50 49 60831025 34.48974 -120.047 California Santa Barbara 61 54 53 52 60832004 34.63782 -120.458 California Santa Barbara 47 42 41 40 60832011 34.44551 -119.828 California Santa Barbara 50 44 43 42 60833001 34.60582 -120.075 California Santa Barbara 53 46 45 43 60834003 34.59611 -120.63 California Santa Barbara 54 49 48 46 60850002 36.99957 -121.575 California Santa Clara 60 54 50 47 60850005 37.3485 -121.895 California Santa Clara 58 54 51 48 60851001 37.22686 -121.98 California Santa Clara 61 56 53 49 60852006 37.07938 -121.6 California Santa Clara 63 58 54 51 60852009 37.31844 -122.07 California Santa Clara 58 54 51 47 60870007 36.98392 -121.989 California Santa Cruz 48 44 41 39 60890004 40.54958 -122.38 California Shasta 51 46 43 40 60890007 40.45291 -122.299 California Shasta 57 52 48 45 60890009 40.68925 -122.402 California Shasta 59 54 50 47 60893003 40.53681 -121.574 California Shasta 58 55 53 51 60950004 38.10251 -122.238 California Solano 53 49 47 43 60950005 38.22707 -122.076 California Solano 57 52 49 45 60953003 38.35837 -121.95 California Solano 58 54 50 47 60970003 38.4435 -122.71 California Sonoma 39 37 35 33 60990005 37.64158 -120.995 California Stanislaus 67 61 57 53 60990006 37.48798 -120.837 California Stanislaus 77 70 65 60 61010003 39.13877 -121.619 California Sutter 54 50 46 43 61010004 39.20557 -121.82 California Sutter 63 58 54 51 61030004 40.26208 -122.094 California Tehama 64 59 55 52 61030005 40.17583 -122.237 California Tehama 62 57 54 51 61070009 36.48944 -118.829 California Tulare 79 73 69 65 61072002 36.33218 -119.291 California Tulare 71 65 61 57 61072010 36.03183 -119.055 California Tulare 76 70 66 62 61090005 37.98158 -120.38 California Tuolumne 61 57 53 50 61110007 34.20824 -118.869 California Ventura 65 51 48 46

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-41 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65
61110009 34.40285 -118.81 California Ventura 65 51 49 46 61111004 34.44657 -119.23 California Ventura 67 58 56 54 61112002 34.27574 -118.685 California Ventura 73 57 54 51 61113001 34.25324 -119.143 California Ventura 55 46 44 42 61130004 38.53445 -121.773 California Yolo 58 53 50 47 61131003 38.66121 -121.733 California Yolo 60 55 51 48


### Table 2A-8. Design Values (ppb) for Contin ental U.S. Monitors outside of California

Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65

10030010 30.498 -87.8814 Alabama Baldwin 53 52 52 51 10331002 34.75878 -87.6506 Alabama Colbert 47 45 45 43 10499991 34.2888 -85.9698 Alabama DeKalb 51 50 50 47 10510001 32.49857 -86.1366 Alabama Elmore 50 48 48 47 10550011 33.90404 -86.0539 Alabama Etowah 47 46 46 45 10690004 31.19066 -85.4231 Alabama Houston 50 49 49 48 10730023 33.55306 -86.815 Alabama Jefferson 55 54 54 53 10731003 33.48556 -86.915 Alabama Jefferson 56 55 54 54 10731005 33.33111 -87.0036 Alabama Jefferson 57 55 55 54 10731009 33.45972 -87.3056 Alabama Jefferson 56 55 55 54 10731010 33.54528 -86.5492 Alabama Jefferson 56 55 54 54 10732006 33.38639 -86.8167 Alabama Jefferson 56 55 55 54 10735002 33.70472 -86.6692 Alabama Jefferson 54 53 53 52 10735003 33.80167 -86.9425 Alabama Jefferson 55 54 53 53 10736002 33.57833 -86.7739 Alabama Jefferson 59 57 57 56 10890014 34.68767 -86.5864 Alabama Madison 54 53 53 51 10890022 34.77273 -86.7562 Alabama Madison 51 50 50 48 10970003 30.76994 -88.0875 Alabama Mobile 53 51 51 51 10972005 30.47467 -88.1411 Alabama Mobile 53 52 52 51 11011002 32.40712 -86.2564 Alabama Montgomery 50 49 49 48 11030011 34.51874 -86.9769 Alabama Morgan 54 54 54 52 11130002 32.46797 -85.0838 Alabama Russell 50 49 49 48 11170004 33.31732 -86.8251 Alabama Shelby 55 54 54 53 11190002 32.36401 -88.2019 Alabama Sumter 50 47 47 46 11250010 33.0896 -87.4597 Alabama Tuscaloosa 46 45 45 44 40051008 35.20611 -111.653 Arizona Coconino 63 63 63 63 40070010 33.6547 -111.107 Arizona Gila 63 63 63 61 40128000 34.2319 -113.58 Arizona La Paz 65 65 65 65 40130019 33.48385 -112.143 Arizona Maricopa 67 67 67 65

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-42 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 40131004 33.56033 -112.066 Arizona Maricopa 69 68 68 65 40131010 33.45223 -111.733 Arizona Maricopa 59 59 59 56 40132001 33.57454 -112.192 Arizona Maricopa 65 65 65 62 40132005 33.70633 -111.856 Arizona Maricopa 65 65 65 62 40133002 33.45793 -112.046 Arizona Maricopa 65 64 64 62 40133003 33.47968 -111.917 Arizona Maricopa 66 65 65 63 40134003 33.40316 -112.075 Arizona Maricopa 67 67 67 64 40134004 33.29898 -111.884 Arizona Maricopa 63 62 62 60 40134005 33.4124 -111.935 Arizona Maricopa 60 60 60 58 40134008 33.82169 -112.017 Arizona Maricopa 64 64 64 61 40134010 33.63713 -112.342 Arizona Maricopa 60 60 60 57 40134011 33.37005 -112.621 Arizona Maricopa 57 57 57 55 40137003 33.29023 -112.161 Arizona Maricopa 61 61 61 59 40137020 33.48824 -111.856 Arizona Maricopa 64 63 63 61 40137021 33.50799 -111.755 Arizona Maricopa 66 65 65 63 40137022 33.47461 -111.806 Arizona Maricopa 63 62 62 60 40137024 33.50813 -111.839 Arizona Maricopa 63 63 63 61 40139508 33.9828 -111.799 Arizona Maricopa 61 61 61 59 40139702 33.54549 -111.609 Arizona Maricopa 64 63 63 61 40139704 33.61103 -111.725 Arizona Maricopa 64 63 63 61 40139706 33.71881 -111.672 Arizona Maricopa 63 63 63 60 40139997 33.50383 -112.096 Arizona Maricopa 67 67 67 64 40170119 34.8225 -109.892 Arizona Navajo 61 59 59 58 40190021 32.17454 -110.737 Arizona Pima 61 58 58 57 40191011 32.20441 -110.878 Arizona Pima 57 55 55 53 40191018 32.42526 -111.064 Arizona Pima 59 58 58 56 40191020 32.04767 -110.774 Arizona Pima 60 56 56 54 40191028 32.29515 -110.982 Arizona Pima 57 55 55 54 40191030 31.87952 -110.996 Arizona Pima 59 56 56 55 40191032 32.173 -110.98 Arizona Pima 57 54 54 53 40191034 32.38082 -111.127 Arizona Pima 56 55 54 53 40213001 33.4214 -111.544 Arizona Pinal 62 62 62 59 40213003 32.95436 -111.762 Arizona Pinal 59 59 59 57 40213007 32.50831 -111.308 Arizona Pinal 61 60 60 59 40217001 33.08009 -111.74 Arizona Pinal 61 61 61 59 40218001 33.29347 -111.286 Arizona Pinal 65 64 64 62 40258033 34.5467 -112.476 Arizona Yavapai 63 63 63 63 50199991 34.1795 -93.0988 Arkansas Clark 55 53 52 49 50350005 35.19729 -90.1931 Arkansas Crittenden 61 60 60 54 51010002 35.83273 -93.2083 Arkansas Newton 55 53 53 52

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-43 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65
51130003 34.45441 -94.1433 Arkansas Polk 64 62 60 57 51190007 34.75619 -92.2813 Arkansas Pulaski 53 50 49 48 51191002 34.83572 -92.2606 Arkansas Pulaski 56 52 52 50 51191008 34.68134 -92.3287 Arkansas Pulaski 56 53 52 51 51430005 36.1797 -94.1168 Arkansas Washington 60 58 57 56 80013001 39.83812 -104.95 Colorado Adams 66 66 65 60 80050002 39.56789 -104.957 Colorado Arapahoe 70 70 69 64 80050006 39.63852 -104.569 Colorado Arapahoe 64 64 63 59 80130011 39.95721 -105.238 Colorado Boulder 65 65 64 60 80310014 39.75176 -105.031 Colorado Denver 63 63 62 58 80310025 39.70401 -104.998 Colorado Denver 62 62 61 57 80350004 39.53449 -105.07 Colorado Douglas 70 70 69 64 80410013 38.95834 -104.817 Colorado El Paso 64 64 63 61 80410016 38.8531 -104.901 Colorado El Paso 65 65 65 63 80450012 39.54182 -107.784 Colorado Garfield 63 63 63 60 80519991 38.9564 -106.986 Colorado Gunnison 64 64 64 63 80590002 39.80033 -105.1 Colorado Jefferson 62 62 62 57 80590005 39.63878 -105.139 Colorado Jefferson 66 67 66 61 80590006 39.9128 -105.189 Colorado Jefferson 71 71 70 65 80590011 39.74372 -105.178 Colorado Jefferson 71 71 70 65 80590013 39.54152 -105.298 Colorado Jefferson 63 63 62 58 80677001 37.13678 -107.629 Colorado La Plata 64 63 63 63 80677003 37.10258 -107.87 Colorado La Plata 62 62 62 61 80690007 40.2772 -105.546 Colorado Larimer 66 66 66 61 80690011 40.59254 -105.141 Colorado Larimer 71 71 70 65 80690012 40.6421 -105.275 Colorado Larimer 64 64 63 59 80691004 40.57747 -105.079 Colorado Larimer 64 64 63 58 80770020 39.13058 -108.314 Colorado Mesa 64 64 64 62 80810002 40.50695 -107.891 Colorado Moffat 60 59 59 58 80830006 37.35005 -108.592 Colorado Montezuma 61 61 61 60 80830101 37.19833 -108.49 Colorado Montezuma 60 60 60 59 81030005 40.03889 -107.848 Colorado Rio Blanco 60 60 59 58 81230009 40.38637 -104.737 Colorado Weld 70 70 69 64 90010017 41.00361 -73.585 Connecticut Fairfield 70 70 67 59 90011123 41.39917 -73.4431 Connecticut Fairfield 65 65 62 54 90013007 41.1525 -73.1031 Connecticut Fairfield 71 70 68 60 90019003 41.11833 -73.3367 Connecticut Fairfield 73 72 70 62 90031003 41.78472 -72.6317 Connecticut Hartford 61 61 58 51 90050005 41.82134 -73.2973 Connecticut Litchfield 57 56 54 47 90070007 41.55222 -72.63 Connecticut Middlesex 65 64 61 53

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-44 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65
90090027 41.3014 -72.9029 Connecticut New Haven 63 63 61 54 90099002 41.26083 -72.55 Connecticut New Haven 71 71 68 60 90110124 41.35362 -72.0788 Connecticut New London 66 65 63 56 90131001 41.97639 -72.3881 Connecticut Tolland 62 62 59 51 90159991 41.8402 -72.01 Connecticut Windham 57 56 54 47 100010002 38.98475 -75.5552 Delaware Kent 59 58 56 49 100031007 39.55111 -75.7308 Delaware New Castle 60 59 57 49 100031010 39.81722 -75.5639 Delaware New Castle 61 59 57 49 100031013 39.77389 -75.4964 Delaware New Castle 62 61 58 50 100032004 39.73944 -75.5581 Delaware New Castle 60 59 56 49 100051002 38.64448 -75.6127 Delaware Sussex 61 60 58 51 100051003 38.7792 -75.1627 Delaware Sussex 64 63 61 55

110010041 38.89722 -76.9528 District Of Columbia District of Columbia 58 57 55 46

110010043 38.92185 -77.0132 District Of Columbia District of Columbia 62 61 58 49
120013011 29.54472 -82.2961 Florida Alachua 50 50 50 49 120030002 30.20111 -82.4411 Florida Baker 52 51 50 50 120050006 30.13043 -85.7315 Florida Bay 52 51 50 50 120090007 28.05361 -80.6286 Florida Brevard 53 52 52 52 120094001 28.31056 -80.6156 Florida Brevard 54 53 53 53 120110033 26.07354 -80.3385 Florida Broward 52 51 51 51 120112003 26.29203 -80.0965 Florida Broward 50 50 50 50 120118002 26.087 -80.111 Florida Broward 53 53 53 53 120210004 26.27 -81.711 Florida Collier 49 48 48 48 120230002 30.17806 -82.6192 Florida Columbia 52 51 51 50 120310077 30.47773 -81.5873 Florida Duval 52 50 50 49 120310100 30.261 -81.454 Florida Duval 53 51 51 50 120310106 30.37822 -81.8409 Florida Duval 52 50 50 50 120330004 30.52537 -87.2036 Florida Escambia 56 53 52 52 120330018 30.36805 -87.271 Florida Escambia 58 55 55 54 120550003 27.18889 -81.3406 Florida Highlands 53 52 51 51 120570081 27.74003 -82.4651 Florida Hillsborough 60 57 57 57 120571035 27.92806 -82.4547 Florida Hillsborough 56 54 54 54 120571065 27.89222 -82.5386 Florida Hillsborough 60 58 58 58 120573002 27.96565 -82.2304 Florida Hillsborough 56 55 55 54 120590004 30.84861 -85.6039 Florida Holmes 49 48 48 46 120619991 27.8492 -80.4554 Florida Indian River 54 53 53 53 120690002 28.525 -81.7233 Florida Lake 54 52 52 52 120712002 26.54786 -81.98 Florida Lee 52 51 51 51 120713002 26.44889 -81.9394 Florida Lee 50 49 48 48

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-45 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65
120730012 30.43972 -84.3464 Florida Leon 48 48 48 47 120730013 30.48444 -84.1994 Florida Leon 48 48 48 47 120813002 27.63278 -82.5461 Florida Manatee 53 51 51 51 120814012 27.48056 -82.6189 Florida Manatee 53 52 51 51 120814013 27.44944 -82.5222 Florida Manatee 51 49 49 49 120830003 29.17028 -82.1008 Florida Marion 52 51 51 51 120830004 29.1925 -82.1733 Florida Marion 50 49 49 49 120850007 27.17246 -80.2407 Florida Martin 51 50 50 50 120860027 25.73338 -80.1618 Florida Miami-Dade 58 58 58 58 120860029 25.58638 -80.3268 Florida Miami-Dade 56 56 56 56 120910002 30.42653 -86.6662 Florida Okaloosa 52 50 50 49 120950008 28.45417 -81.3814 Florida Orange 58 56 56 56 120952002 28.59639 -81.3625 Florida Orange 59 58 58 58 120972002 28.34722 -81.6367 Florida Osceola 52 51 51 51 120990009 26.73083 -80.2339 Florida Palm Beach 55 54 54 54 120990020 26.59123 -80.0609 Florida Palm Beach 54 53 53 53 121010005 28.33194 -82.3058 Florida Pasco 53 51 51 51 121012001 28.195 -82.7581 Florida Pasco 54 53 53 52 121030004 27.94639 -82.7319 Florida Pinellas 55 54 54 54 121030018 27.78587 -82.7399 Florida Pinellas 55 53 53 53 121035002 28.09 -82.7008 Florida Pinellas 53 52 52 52 121056005 27.93944 -82.0003 Florida Polk 54 52 52 51 121056006 28.02889 -81.9722 Florida Polk 55 53 52 52 121130015 30.39413 -87.008 Florida Santa Rosa 56 54 53 53 121151005 27.30694 -82.5706 Florida Sarasota 57 56 56 55 121151006 27.35028 -82.48 Florida Sarasota 55 53 53 53 121152002 27.08919 -82.3626 Florida Sarasota 54 52 52 52 121171002 28.74611 -81.3106 Florida Seminole 55 53 53 52 121272001 29.10889 -80.9939 Florida Volusia 47 45 45 45 121275002 29.20667 -81.0525 Florida Volusia 51 49 49 49 121290001 30.0925 -84.1611 Florida Wakulla 53 52 51 51 130210012 32.80541 -83.5435 Georgia Bibb 53 49 49 47 130510021 32.06923 -81.0488 Georgia Chatham 51 50 50 49 130550001 34.47429 -85.408 Georgia Chattooga 51 49 49 47 130590002 33.91807 -83.3445 Georgia Clarke 51 50 50 49 130670003 34.01548 -84.6074 Georgia Cobb 55 54 54 53 130730001 33.58214 -82.1312 Georgia Columbia 51 51 50 49 130770002 33.40404 -84.746 Georgia Coweta 49 48 48 47 130850001 34.37632 -84.0598 Georgia Dawson 49 48 48 47 130890002 33.68797 -84.2905 Georgia DeKalb 56 55 55 54

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-46 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 130970004 33.74366 -84.7792 Georgia Douglas 52 51 51 50 131210055 33.72019 -84.3571 Georgia Fulton 59 58 58 57 131270006 31.16974 -81.4959 Georgia Glynn 48 47 47 47 131350002 33.96127 -84.069 Georgia Gwinnett 55 54 54 53 131510002 33.43358 -84.1617 Georgia Henry 59 58 58 57 132130003 34.7852 -84.6264 Georgia Murray 52 51 51 48 132150008 32.5213 -84.9448 Georgia Muscogee 50 50 49 49 132230003 33.9285 -85.0453 Georgia Paulding 53 51 51 49 132319991 33.1787 -84.4052 Georgia Pike 52 51 51 50 132450091 33.43335 -82.0222 Georgia Richmond 53 52 51 50 132470001 33.59108 -84.0653 Georgia Rockdale 55 54 54 53 132611001 31.9543 -84.0811 Georgia Sumter 53 52 52 51 160010010 43.6007 -116.348 Idaho Ada 60 59 59 59 160010017 43.5776 -116.178 Idaho Ada 60 60 60 60 160010019 43.63459 -116.234 Idaho Ada 54 53 53 53 160550003 47.78891 -116.805 Idaho Kootenai 47 47 47 47 170010007 39.91541 -91.3359 Illinois Adams 57 56 55 54 170190007 40.24491 -88.1885 Illinois Champaign 60 59 59 56 170191001 40.05224 -88.3725 Illinois Champaign 61 60 60 57 170230001 39.21086 -87.6683 Illinois Clark 58 58 58 53 170310001 41.67099 -87.7325 Illinois Cook 64 63 63 58 170310032 41.75583 -87.5454 Illinois Cook 57 57 57 57 170310064 41.79079 -87.6016 Illinois Cook 53 52 52 52 170310076 41.7514 -87.7135 Illinois Cook 63 63 62 58 170311003 41.98433 -87.792 Illinois Cook 49 49 49 50 170311601 41.66812 -87.9906 Illinois Cook 62 62 61 57 170314002 41.85524 -87.7525 Illinois Cook 52 51 51 51 170314007 42.06029 -87.8632 Illinois Cook 48 48 48 49 170314201 42.14 -87.7992 Illinois Cook 56 55 55 57 170317002 42.06186 -87.6742 Illinois Cook 54 54 54 56 170436001 41.81305 -88.0728 Illinois DuPage 58 58 57 53 170491001 39.06716 -88.5489 Illinois Effingham 58 58 57 54 170650002 38.08216 -88.6249 Illinois Hamilton 64 65 65 61 170831001 39.11054 -90.3241 Illinois Jersey 61 60 60 59 170859991 42.2869 -89.9997 Illinois Jo Daviess 58 57 56 55 170890005 42.04915 -88.273 Illinois Kane 63 62 62 58 170971007 42.46757 -87.81 Illinois Lake 57 57 57 58 171110001 42.22144 -88.2422 Illinois McHenry 61 60 60 55 171132003 40.51874 -88.9969 Illinois McLean 59 57 56 53 171150013 39.86683 -88.9256 Illinois Macon 59 59 58 56

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-47 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 171170002 39.39608 -89.8097 Illinois Macoupin 57 56 55 54 171190008 38.89019 -90.148 Illinois Madison 62 61 61 59 171191009 38.72657 -89.96 Illinois Madison 63 62 62 60 171193007 38.86067 -90.1059 Illinois Madison 62 61 60 59 171199991 38.869 -89.6228 Illinois Madison 60 59 59 58 171430024 40.68742 -89.6069 Illinois Peoria 53 50 50 47 171431001 40.7455 -89.5859 Illinois Peoria 61 58 57 54 171570001 38.17628 -89.7885 Illinois Randolph 58 57 57 55 171613002 41.51473 -90.5174 Illinois Rock Island 50 49 49 47 171630010 38.61203 -90.1605 Illinois Saint Clair 62 61 61 59 171670014 39.83152 -89.6409 Illinois Sangamon 59 58 57 56 171971011 41.22154 -88.191 Illinois Will 55 55 54 50 172012001 42.33498 -89.0378 Illinois Winnebago 58 57 57 53 180030002 41.22142 -85.0168 Indiana Allen 57 56 56 53 180030004 41.09497 -85.1018 Indiana Allen 58 57 57 53 180110001 39.99748 -86.3952 Indiana Boone 60 60 60 55 180150002 40.54046 -86.553 Indiana Carroll 58 58 57 54 180190008 38.39383 -85.6642 Indiana Clark 65 65 64 58 180350010 40.30002 -85.2454 Indiana Delaware 56 56 55 52 180390007 41.71805 -85.8306 Indiana Elkhart 56 55 55 50 180431004 38.30806 -85.8342 Indiana Floyd 65 65 64 58 180550001 38.98558 -86.9901 Indiana Greene 68 68 67 62 180570006 40.0683 -85.9925 Indiana Hamilton 59 58 58 54 180590003 39.93504 -85.8405 Indiana Hancock 55 55 54 50 180630004 39.759 -86.3971 Indiana Hendricks 57 56 56 52 180690002 40.96071 -85.3798 Indiana Huntington 55 54 54 50 180710001 38.92084 -86.0805 Indiana Jackson 57 57 57 51 180810002 39.41724 -86.1524 Indiana Johnson 58 58 58 53 180839991 38.7408 -87.4853 Indiana Knox 65 65 64 59 180890022 41.60668 -87.3047 Indiana Lake 55 55 55 53 180890030 41.6814 -87.4947 Indiana Lake 57 56 56 54 180892008 41.63946 -87.4936 Indiana Lake 57 56 56 54 180910005 41.71702 -86.9077 Indiana LaPorte 66 65 65 61 180910010 41.6291 -86.6846 Indiana LaPorte 59 59 59 55 180950010 40.00255 -85.6569 Indiana Madison 55 55 54 50 180970050 39.85892 -86.0213 Indiana Marion 60 59 59 54 180970057 39.74902 -86.1863 Indiana Marion 59 58 58 53 180970073 39.78949 -86.0609 Indiana Marion 60 60 59 55 180970078 39.8111 -86.1145 Indiana Marion 59 59 58 54 181090005 39.57563 -86.4779 Indiana Morgan 56 56 56 51

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-48 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 181230009 38.11316 -86.6036 Indiana Perry 65 65 65 59 181270024 41.61756 -87.1992 Indiana Porter 57 57 57 55 181270026 41.51029 -87.0385 Indiana Porter 55 54 54 51 181290003 38.00529 -87.7184 Indiana Posey 61 61 61 56 181410010 41.5517 -86.3706 Indiana St. Joseph 52 52 51 48 181410015 41.69669 -86.2147 Indiana St. Joseph 58 57 57 52 181411007 41.7426 -86.1105 Indiana St. Joseph 53 53 52 48 181450001 39.61342 -85.8706 Indiana Shelby 62 61 61 56 181630013 38.11395 -87.537 Indiana Vanderburgh 63 63 62 58 181630021 38.01325 -87.5779 Indiana Vanderburgh 63 63 62 58 181670018 39.48615 -87.4014 Indiana Vigo 55 54 54 50 181670024 39.56056 -87.3131 Indiana Vigo 55 55 54 50 181699991 40.816 -85.6611 Indiana Wabash 61 61 61 57 181730008 38.052 -87.2783 Indiana Warrick 63 63 63 58 181730009 38.1945 -87.3414 Indiana Warrick 61 61 60 55 181730011 37.95451 -87.3219 Indiana Warrick 64 64 63 58 190170011 42.74306 -92.5131 Iowa Bremer 53 52 52 51 190450021 41.875 -90.1776 Iowa Clinton 57 56 55 53 190850007 41.83226 -95.9282 Iowa Harrison 55 54 54 53 190851101 41.78026 -95.9484 Iowa Harrison 56 55 55 54 191130028 41.91056 -91.6519 Iowa Linn 55 54 54 53 191130033 42.28101 -91.5269 Iowa Linn 53 53 53 52 191130040 41.97677 -91.6877 Iowa Linn 53 53 53 52 191370002 40.96911 -95.045 Iowa Montgomery 56 55 55 54 191471002 43.1237 -94.6935 Iowa Palo Alto 57 56 55 55 191530030 41.60316 -93.6431 Iowa Polk 49 48 48 47 191630014 41.69917 -90.5219 Iowa Scott 55 54 53 52 191630015 41.53001 -90.5876 Iowa Scott 56 56 55 53 191690011 41.88287 -93.6878 Iowa Story 50 49 49 48 191770006 40.69508 -92.0063 Iowa Van Buren 55 54 53 51 191810022 41.28553 -93.584 Iowa Warren 53 52 51 50 200910010 38.83858 -94.7464 Kansas Johnson 61 60 60 59 201030003 39.32739 -94.951 Kansas Leavenworth 58 57 57 56 201070002 38.13588 -94.732 Kansas Linn 59 58 58 57 201619991 39.1021 -96.6096 Kansas Riley 62 61 61 60 201730001 37.78139 -97.3372 Kansas Sedgwick 55 54 54 53 201730010 37.70207 -97.3148 Kansas Sedgwick 64 63 62 61 201730018 37.89751 -97.4921 Kansas Sedgwick 62 61 61 60 201770013 39.02427 -95.7113 Kansas Shawnee 62 61 61 61 201910002 37.47689 -97.3664 Kansas Sumner 65 64 64 63

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-49 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 201950001 38.77008 -99.7634 Kansas Trego 65 65 65 64 202090021 39.11722 -94.6356 Kansas Wyandotte 55 54 54 53 210130002 36.60843 -83.7369 Kentucky Bell 50 49 49 45 210150003 38.91833 -84.8526 Kentucky Boone 59 58 57 51 210190017 38.45934 -82.6404 Kentucky Boyd 58 58 57 50 210290006 37.98629 -85.7119 Kentucky Bullitt 62 62 61 56 210373002 39.02188 -84.4745 Kentucky Campbell 66 66 65 58 210430500 38.23887 -82.9881 Kentucky Carter 56 56 55 49 210470006 36.91171 -87.3233 Kentucky Christian 53 53 52 49 210590005 37.78078 -87.0753 Kentucky Daviess 67 67 67 61 210610501 37.13194 -86.1478 Kentucky Edmonson 57 57 57 53 210670012 38.06503 -84.4976 Kentucky Fayette 58 58 58 52 210890007 38.54814 -82.7312 Kentucky Greenup 59 59 58 51 210910012 37.93829 -86.8972 Kentucky Hancock 66 66 66 60 210930006 37.70561 -85.8526 Kentucky Hardin 59 59 58 53 211010014 37.8712 -87.4638 Kentucky Henderson 68 68 68 63 211110027 38.13784 -85.5765 Kentucky Jefferson 66 65 65 59 211110051 38.06091 -85.898 Kentucky Jefferson 68 68 67 61 211110067 38.22876 -85.6545 Kentucky Jefferson 71 71 70 63 211130001 37.89147 -84.5883 Kentucky Jessamine 56 57 57 52 211390003 37.15539 -88.394 Kentucky Livingston 61 65 65 60 211451024 37.05822 -88.5725 Kentucky McCracken 64 69 68 64 211759991 37.9214 -83.0662 Kentucky Morgan 57 56 56 49 211850004 38.4002 -85.4443 Kentucky Oldham 68 68 67 60 211930003 37.28329 -83.2093 Kentucky Perry 56 56 55 49 211950002 37.4826 -82.5353 Kentucky Pike 56 56 55 48 211990003 37.09798 -84.6115 Kentucky Pulaski 51 51 50 46 212130004 36.70861 -86.5663 Kentucky Simpson 53 53 53 48 212218001 36.78389 -87.8519 Kentucky Trigg 56 57 56 51 212219991 36.7841 -87.8499 Kentucky Trigg 57 58 57 52 212270008 37.03544 -86.2506 Kentucky Warren 51 50 50 46 212299991 37.7046 -85.0485 Kentucky Washington 57 57 57 52 220050004 30.23389 -90.9683 Louisiana Ascension 63 62 62 61 220150008 32.53626 -93.7489 Louisiana Bossier 66 64 62 59 220170001 32.67639 -93.8597 Louisiana Caddo 64 62 60 56 220190002 30.14333 -93.3719 Louisiana Calcasieu 66 66 65 64 220190008 30.26167 -93.2842 Louisiana Calcasieu 60 60 59 58 220190009 30.22778 -93.5783 Louisiana Calcasieu 63 62 60 57
220330003 30.41976 -91.182 Louisiana East Baton Rouge 67 67 67 65

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-50 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65

220330009 30.46198 -91.1792 Louisiana East Baton Rouge 64 63 63 62

220330013 30.70092 -91.0561 Louisiana East Baton Rouge 60 59 59 58
220470009 30.22056 -91.3161 Louisiana Iberville 62 62 61 60 220470012 30.20699 -91.1299 Louisiana Iberville 65 65 64 63 220511001 30.04357 -90.2751 Louisiana Jefferson 64 64 63 63 220550007 30.2175 -92.0514 Louisiana Lafayette 60 60 59 58 220570004 29.76389 -90.7652 Louisiana Lafourche 62 61 61 60 220630002 30.3125 -90.8125 Louisiana Livingston 62 62 62 61 220710012 29.99444 -90.1028 Louisiana Orleans 60 59 58 58 220730004 32.50971 -92.0461 Louisiana Ouachita 55 55 55 54 220770001 30.68174 -91.3662 Louisiana Pointe Coupee 63 62 62 61 220870004 29.93961 -89.9239 Louisiana St. Bernard 59 58 58 57 220890003 29.98417 -90.4106 Louisiana St. Charles 61 60 60 59 220930002 29.99444 -90.82 Louisiana St. James 58 58 58 57
220950002 30.05833 -90.6083 Louisiana St. John the Baptist 63 62 62 61 221030002 30.4293 -90.1997 Louisiana St. Tammany 63 62 62 61
221210001 30.50064 -91.2136 Louisiana West Baton Rouge 59 59 59 58 230010014 43.97462 -70.1246 Maine Androscoggin 50 49 48 44 230052003 43.56104 -70.2073 Maine Cumberland 57 57 55 50 230090102 44.3517 -68.227 Maine Hancock 58 57 56 52 230090103 44.37705 -68.2609 Maine Hancock 55 54 53 49 230112005 44.23062 -69.785 Maine Kennebec 50 50 49 45 230130004 43.91796 -69.2606 Maine Knox 55 55 53 49 230173001 44.25092 -70.8606 Maine Oxford 46 45 45 42 230194008 44.73598 -68.6708 Maine Penobscot 47 46 45 42 230230006 44.005 -69.8278 Maine Sagadahoc 49 49 47 43 230290019 44.53191 -67.5959 Maine Washington 49 49 48 44 230290032 44.96363 -67.0607 Maine Washington 46 46 45 42 230310038 43.65676 -70.6291 Maine York 49 48 47 43 230310040 43.58889 -70.8773 Maine York 52 51 50 46 230312002 43.34317 -70.471 Maine York 60 59 57 52 240030014 38.9025 -76.6531 Maryland Anne Arundel 64 63 61 51 240051007 39.46202 -76.6313 Maryland Baltimore 65 63 61 53 240053001 39.31083 -76.4744 Maryland Baltimore 67 66 63 53 240090011 38.53672 -76.6172 Maryland Calvert 63 63 60 51 240130001 39.44417 -77.0417 Maryland Carroll 61 60 59 51 240150003 39.70111 -75.86 Maryland Cecil 66 65 62 54 240170010 38.50417 -76.8119 Maryland Charles 61 60 58 50

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-51 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 240199991 38.445 -76.1114 Maryland Dorchester 61 60 58 52 240210037 39.42276 -77.3752 Maryland Frederick 62 62 60 52 240230002 39.70595 -79.012 Maryland Garrett 59 59 58 52 240251001 39.41 -76.2967 Maryland Harford 74 73 70 59 240259001 39.56333 -76.2039 Maryland Harford 63 61 59 49 240290002 39.3052 -75.7972 Maryland Kent 62 61 58 50 240313001 39.11444 -77.1069 Maryland Montgomery 60 59 57 49 240330030 39.05528 -76.8783 Maryland Prince George's 61 60 58 49 240338003 38.81194 -76.7442 Maryland Prince George's 63 62 60 50 240339991 39.0284 -76.8171 Maryland Prince George's 62 61 58 50 240430009 39.56558 -77.7216 Maryland Washington 60 59 58 52 245100054 39.32889 -76.5525 Maryland Baltimore (City) 62 62 59 50 250010002 41.9758 -70.0236 Massachusetts Barnstable 59 59 57 51 250034002 42.63668 -73.1674 Massachusetts Berkshire 57 57 55 50 250051002 41.63328 -70.8792 Massachusetts Bristol 59 59 57 50 250070001 41.33047 -70.7852 Massachusetts Dukes 64 64 62 54 250092006 42.47464 -70.9708 Massachusetts Essex 58 57 56 52 250094005 42.81441 -70.8178 Massachusetts Essex 57 56 55 50 250095005 42.77084 -71.1023 Massachusetts Essex 56 56 54 49 250130008 42.19438 -72.5551 Massachusetts Hampden 59 59 56 49 250150103 42.40058 -72.5231 Massachusetts Hampshire 52 52 50 44 250154002 42.29849 -72.3341 Massachusetts Hampshire 57 56 54 47 250170009 42.62668 -71.3621 Massachusetts Middlesex 55 54 52 46 250171102 42.41357 -71.4828 Massachusetts Middlesex 54 53 51 45 250213003 42.21177 -71.114 Massachusetts Norfolk 59 59 57 52 250250041 42.31737 -70.9684 Massachusetts Suffolk 56 55 54 50 250250042 42.3295 -71.0826 Massachusetts Suffolk 49 49 48 44 250270015 42.27432 -71.8755 Massachusetts Worcester 55 55 53 47 250270024 42.0997 -71.6194 Massachusetts Worcester 55 54 53 47 260050003 42.76779 -86.1486 Michigan Allegan 70 69 69 63 260190003 44.61694 -86.1094 Michigan Benzie 62 61 61 56 260210014 42.19779 -86.3097 Michigan Berrien 68 68 67 62 260270003 41.89557 -86.0016 Michigan Cass 63 62 62 57 260370001 42.79834 -84.3938 Michigan Clinton 57 56 55 51 260490021 43.04722 -83.6702 Michigan Genesee 61 60 60 56 260492001 43.16834 -83.4615 Michigan Genesee 60 60 59 55 260630007 43.83639 -82.6429 Michigan Huron 61 61 60 57 260650012 42.73862 -84.5346 Michigan Ingham 57 56 56 52 260770008 42.27807 -85.5419 Michigan Kalamazoo 61 60 59 55 260810020 42.98417 -85.6713 Michigan Kent 60 59 59 54

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-52 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 260810022 43.17667 -85.4166 Michigan Kent 59 58 58 53 260910007 41.99557 -83.9466 Michigan Lenawee 60 60 59 55 260990009 42.73139 -82.7935 Michigan Macomb 67 67 66 62 260991003 42.51334 -83.006 Michigan Macomb 69 68 68 63 261010922 44.307 -86.2426 Michigan Manistee 61 60 60 55 261050007 43.95333 -86.2944 Michigan Mason 62 61 60 56 261130001 44.31056 -84.8919 Michigan Missaukee 58 57 57 53 261210039 43.27806 -86.3111 Michigan Muskegon 66 66 65 60 261250001 42.46306 -83.1832 Michigan Oakland 66 65 65 60 261390005 42.89445 -85.8527 Michigan Ottawa 63 62 62 57 261470005 42.95334 -82.4562 Michigan St. Clair 65 65 65 60 261530001 46.28888 -85.9502 Michigan Schoolcraft 60 60 59 55 261579991 43.6138 -83.3591 Michigan Tuscola 58 57 57 52 261610008 42.24057 -83.5996 Michigan Washtenaw 62 62 62 58 261619991 42.4165 -83.902 Michigan Washtenaw 60 60 59 55 261630001 42.22862 -83.2082 Michigan Wayne 61 61 61 57 261630019 42.43084 -83.0001 Michigan Wayne 70 70 69 65 261659991 44.1809 -85.739 Michigan Wexford 56 55 55 51 270031001 45.40184 -93.2031 Minnesota Anoka 53 53 53 52 270031002 45.13768 -93.2076 Minnesota Anoka 57 56 56 56 270353204 46.39674 -94.1303 Minnesota Crow Wing 51 49 49 49 270495302 44.47375 -93.0126 Minnesota Goodhue 53 53 53 52 270953051 46.2053 -93.7595 Minnesota Mille Lacs 48 47 47 47 271095008 43.99691 -92.4504 Minnesota Olmsted 53 53 53 52 271377550 46.81826 -92.0894 Minnesota Saint Louis 42 41 41 40 271390505 44.79144 -93.5125 Minnesota Scott 54 53 53 53 271453052 45.54984 -94.1335 Minnesota Stearns 53 50 50 50 271636015 45.11728 -92.8553 Minnesota Washington 52 52 51 51 271713201 45.20916 -93.6692 Minnesota Wright 55 52 52 52 280010004 31.56075 -91.3904 Mississippi Adams 55 54 54 53 280110001 33.74606 -90.723 Mississippi Bolivar 61 60 60 59 280330002 34.82166 -89.9878 Mississippi DeSoto 57 55 55 51 280450003 30.30083 -89.3959 Mississippi Hancock 53 50 50 49 280470008 30.39037 -89.0498 Mississippi Harrison 56 51 51 50 280490010 32.38573 -90.1412 Mississippi Hinds 49 48 48 47 280590006 30.37829 -88.5339 Mississippi Jackson 59 58 58 57 280750003 32.36457 -88.7315 Mississippi Lauderdale 50 49 49 47 280810005 34.26492 -88.7662 Mississippi Lee 51 50 50 48 281619991 34.0026 -89.799 Mississippi Yalobusha 52 51 51 49 290030001 39.9544 -94.849 Missouri Andrew 60 59 58 57

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-53 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65
290190011 39.0786 -92.3152 Missouri Boone 56 56 56 55 290270002 38.70608 -92.0931 Missouri Callaway 56 55 55 54 290370003 38.75976 -94.58 Missouri Cass 57 57 57 56 290390001 37.69 -94.035 Missouri Cedar 61 60 59 57 290470003 39.40745 -94.2654 Missouri Clay 63 62 62 61 290470005 39.30309 -94.3766 Missouri Clay 62 61 61 60 290470006 39.33191 -94.5808 Missouri Clay 64 63 63 62 290490001 39.5306 -94.556 Missouri Clinton 64 63 63 62 290770036 37.25614 -93.2999 Missouri Greene 56 55 55 54 290770042 37.31951 -93.2046 Missouri Greene 58 57 57 56 290970004 37.2385 -94.4247 Missouri Jasper 65 62 61 58 290990019 38.44863 -90.3985 Missouri Jefferson 64 63 63 62 291130003 39.0447 -90.8647 Missouri Lincoln 63 62 62 61 291370001 39.47514 -91.7891 Missouri Monroe 58 57 57 55 291570001 37.70264 -89.6986 Missouri Perry 62 62 62 59 291831002 38.87255 -90.2265 Missouri Saint Charles 66 65 65 64 291831004 38.8994 -90.4492 Missouri Saint Charles 65 64 64 62
291860005 37.90084 -90.4239 Missouri Sainte Genevieve 61 60 60 59
291890005 38.4902 -90.7052 Missouri Saint Louis 59 59 58 57 291890014 38.7109 -90.4759 Missouri Saint Louis 65 64 64 63 292130004 36.70773 -93.222 Missouri Taney 59 57 57 55 295100085 38.6565 -90.1986 Missouri St. Louis City 63 63 62 61 300870001 45.36615 -106.49 Montana Rosebud 53 52 52 52 310550019 41.24749 -95.9731 Nebraska Douglas 58 57 57 57 310550028 41.20796 -95.9459 Nebraska Douglas 51 50 50 50 310550035 41.30676 -95.961 Nebraska Douglas 54 53 53 53 311090016 40.98472 -96.6772 Nebraska Lancaster 47 47 46 46 320010002 39.47247 -118.784 Nevada Churchill 52 52 52 52 320030022 36.39101 -114.907 Nevada Clark 63 62 62 60 320030023 36.80791 -114.061 Nevada Clark 57 57 57 56 320030043 36.10637 -115.253 Nevada Clark 69 69 69 65 320030071 36.16975 -115.263 Nevada Clark 69 69 69 65 320030073 36.17342 -115.333 Nevada Clark 68 68 68 65 320030075 36.27058 -115.238 Nevada Clark 68 67 67 64 320030538 36.14296 -115.056 Nevada Clark 63 63 63 60 320030540 36.1419 -115.079 Nevada Clark 63 63 63 60 320030601 35.97813 -114.846 Nevada Clark 65 65 65 63 320031019 35.78567 -115.357 Nevada Clark 67 67 67 65 320032002 36.19126 -115.123 Nevada Clark 63 63 63 60

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-54 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 320190006 39.60279 -119.248 Nevada Lyon 60 60 60 60 320310016 39.52508 -119.808 Nevada Washoe 59 58 58 58 320310020 39.46922 -119.775 Nevada Washoe 59 59 59 59 320310025 39.39984 -119.74 Nevada Washoe 59 59 59 58 320311005 39.54092 -119.747 Nevada Washoe 59 59 59 59 320312002 39.25041 -119.957 Nevada Washoe 53 53 53 53 320312009 39.64526 -119.84 Nevada Washoe 59 59 59 58 325100002 39.16725 -119.732 Nevada Carson City 59 59 59 59 330012004 43.56611 -71.4964 New Hampshire Belknap 51 51 50 46 330050007 42.93047 -72.2724 New Hampshire Cheshire 50 50 48 44 330074001 44.27017 -71.3038 New Hampshire Coos 58 57 57 54 330074002 44.30817 -71.2177 New Hampshire Coos 51 51 50 48 330090010 43.62961 -72.3096 New Hampshire Grafton 50 49 48 45 330111011 42.71866 -71.5224 New Hampshire Hillsborough 54 53 51 46 330115001 42.86175 -71.8784 New Hampshire Hillsborough 57 56 55 49 330131007 43.2185 -71.5145 New Hampshire Merrimack 52 52 50 46 330150014 43.07533 -70.748 New Hampshire Rockingham 54 53 52 47 330150016 43.04528 -70.7138 New Hampshire Rockingham 54 54 52 48 330150018 42.86254 -71.3802 New Hampshire Rockingham 55 55 53 47 340010006 39.46487 -74.4487 New Jersey Atlantic 60 59 57 50 340030006 40.87044 -73.992 New Jersey Bergen 64 63 61 53 340071001 39.68425 -74.8615 New Jersey Camden 68 67 64 55 340110007 39.42227 -75.0252 New Jersey Cumberland 59 57 55 47 340130003 40.72099 -74.1929 New Jersey Essex 65 64 61 53 340150002 39.80034 -75.2121 New Jersey Gloucester 69 68 65 56 340170006 40.67025 -74.1261 New Jersey Hudson 64 63 61 53 340190001 40.51526 -74.8067 New Jersey Hunterdon 63 62 60 52 340210005 40.28309 -74.7426 New Jersey Mercer 64 63 61 53 340219991 40.3125 -74.8729 New Jersey Mercer 62 61 59 51 340230011 40.46218 -74.4294 New Jersey Middlesex 66 65 62 53 340250005 40.27765 -74.0051 New Jersey Monmouth 67 65 63 54 340273001 40.78763 -74.6763 New Jersey Morris 63 62 60 52 340290006 40.06483 -74.4441 New Jersey Ocean 67 66 63 54 340315001 41.05862 -74.2555 New Jersey Passaic 62 61 59 52 340410007 40.92458 -75.0678 New Jersey Warren 52 52 50 44 350010023 35.1343 -106.585 New Mexico Bernalillo 58 58 58 57 350010024 35.0631 -106.579 New Mexico Bernalillo 59 59 59 58 350010027 35.1539 -106.697 New Mexico Bernalillo 62 62 62 61 350010029 35.01708 -106.657 New Mexico Bernalillo 59 59 59 58 350010032 35.06407 -106.762 New Mexico Bernalillo 58 57 57 56

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-55 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65
350011012 35.1852 -106.508 New Mexico Bernalillo 63 62 62 61 350011013 35.19324 -106.614 New Mexico Bernalillo 60 60 60 58 350130008 31.93056 -106.631 New Mexico Dona Ana 57 56 56 56 350130017 31.79583 -106.558 New Mexico Dona Ana 58 58 58 57 350130020 32.04111 -106.409 New Mexico Dona Ana 59 58 58 58 350130021 31.79611 -106.584 New Mexico Dona Ana 62 61 61 61 350130022 31.78778 -106.683 New Mexico Dona Ana 62 61 61 61 350130023 32.3175 -106.768 New Mexico Dona Ana 57 57 56 56 350171003 32.69194 -108.124 New Mexico Grant 61 61 61 60 350250008 32.72666 -103.123 New Mexico Lea 61 61 61 60 350290003 32.2558 -107.723 New Mexico Luna 58 57 57 56 350431001 35.29944 -106.548 New Mexico Sandoval 55 55 55 54 350439004 35.61528 -106.724 New Mexico Sandoval 58 58 58 58 350450009 36.74222 -107.977 New Mexico San Juan 57 57 57 56 350450018 36.80973 -107.652 New Mexico San Juan 62 62 62 61 350451005 36.79667 -108.473 New Mexico San Juan 56 55 55 54 350451233 36.8071 -108.695 New Mexico San Juan 56 55 55 54 350490021 35.61975 -106.08 New Mexico Santa Fe 60 59 59 59 350610008 34.8147 -106.74 New Mexico Valencia 58 58 58 56 360010012 42.68075 -73.7573 New York Albany 57 56 54 49 360050133 40.8679 -73.8781 New York Bronx 67 66 64 57 360130006 42.49963 -79.3188 New York Chautauqua 61 61 60 55 360130011 42.29071 -79.5896 New York Chautauqua 61 61 60 55 360150003 42.11096 -76.8022 New York Chemung 57 57 56 53 360270007 41.78555 -73.7414 New York Dutchess 58 57 55 48 360290002 42.99328 -78.7715 New York Erie 61 61 60 56 360310002 44.36608 -73.9031 New York Essex 57 57 56 54 360310003 44.39308 -73.8589 New York Essex 57 57 56 53 360410005 43.44957 -74.5163 New York Hamilton 57 56 55 52 360430005 43.68578 -74.9854 New York Herkimer 56 55 54 52 360450002 44.08747 -75.9732 New York Jefferson 62 62 61 60 360530006 42.73046 -75.7844 New York Madison 55 54 53 50 360551007 43.14618 -77.5482 New York Monroe 60 59 59 57 360610135 40.81976 -73.9483 New York New York 65 64 63 58 360631006 43.22386 -78.4789 New York Niagara 64 64 64 61 360650004 43.30268 -75.7198 New York Oneida 53 52 52 49 360671015 43.05235 -76.0592 New York Onondaga 59 59 58 56 360715001 41.52375 -74.2153 New York Orange 56 55 53 46 360750003 43.28428 -76.4632 New York Oswego 58 58 58 56 360790005 41.45589 -73.7098 New York Putnam 57 56 54 47

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-56 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 360810124 40.73614 -73.8215 New York Queens 72 71 70 65 360830004 42.78189 -73.4636 New York Rensselaer 57 56 55 49 360850067 40.59664 -74.1253 New York Richmond 73 72 70 63 360870005 41.18208 -74.0282 New York Rockland 62 61 59 51 360910004 43.01209 -73.6489 New York Saratoga 56 55 54 49 360930003 42.79901 -73.9389 New York Schenectady 54 53 52 47 361010003 42.09142 -77.2098 New York Steuben 57 56 55 52 361030002 40.74529 -73.4192 New York Suffolk 74 73 70 62 361030004 40.96078 -72.7124 New York Suffolk 67 66 63 54 361030009 40.82799 -73.0575 New York Suffolk 71 70 68 59 361099991 42.4006 -76.6538 New York Tompkins 58 58 57 54 361111005 42.14403 -74.4943 New York Ulster 58 58 56 51 361173001 43.23086 -77.1714 New York Wayne 56 56 56 54 361192004 41.05192 -73.7637 New York Westchester 65 64 62 54 370030004 35.929 -81.1898 North Carolina Alexander 52 51 50 49 370110002 35.97222 -81.9331 North Carolina Avery 50 49 49 46 370119991 36.1058 -82.0454 North Carolina Avery 50 49 49 44 370210030 35.5001 -82.5999 North Carolina Buncombe 52 51 51 49 370270003 35.93583 -81.5303 North Carolina Caldwell 51 50 50 49 370319991 34.8848 -76.6203 North Carolina Carteret 51 50 50 48 370330001 36.30703 -79.4674 North Carolina Caswell 55 54 54 53 370370004 35.75722 -79.1597 North Carolina Chatham 49 48 47 46 370510008 35.15869 -78.728 North Carolina Cumberland 54 53 53 51 370511003 34.96889 -78.9625 North Carolina Cumberland 55 54 54 52 370590003 35.89707 -80.5573 North Carolina Davie 54 53 53 51 370630015 36.03294 -78.9054 North Carolina Durham 52 51 51 50 370650099 35.98833 -77.5828 North Carolina Edgecombe 56 54 54 52 370670022 36.11056 -80.2267 North Carolina Forsyth 58 57 57 55 370670028 36.20306 -80.2158 North Carolina Forsyth 54 53 53 51 370670030 36.026 -80.342 North Carolina Forsyth 56 55 55 53 370671008 36.05083 -80.1439 North Carolina Forsyth 55 55 55 53 370690001 36.09619 -78.4637 North Carolina Franklin 52 52 51 50 370750001 35.25793 -83.7956 North Carolina Graham 54 54 54 51 370770001 36.14111 -78.7681 North Carolina Granville 56 54 54 53 370810013 36.10071 -79.8105 North Carolina Guilford 57 56 56 55 370870008 35.50716 -82.9634 North Carolina Haywood 50 49 49 47 370870035 35.37917 -82.7925 North Carolina Haywood 55 54 54 52 370870036 35.59 -83.0775 North Carolina Haywood 55 54 54 52 370990005 35.52444 -83.2361 North Carolina Jackson 55 54 54 51 371010002 35.59083 -78.4619 North Carolina Johnston 55 54 54 52

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-57 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65
371070004 35.23146 -77.5688 North Carolina Lenoir 54 52 52 50 371090004 35.43856 -81.2768 North Carolina Lincoln 57 55 55 53 371139991 35.0608 -83.4306 North Carolina Macon 50 49 49 48 371170001 35.81069 -76.8978 North Carolina Martin 51 51 50 48 371190041 35.2401 -80.7857 North Carolina Mecklenburg 65 64 64 63 371191005 35.11316 -80.9195 North Carolina Mecklenburg 60 60 60 59 371191009 35.34722 -80.695 North Carolina Mecklenburg 62 61 61 60 371239991 35.2632 -79.8365 North Carolina Montgomery 50 49 49 48 371290002 34.36417 -77.8386 North Carolina New Hanover 50 49 49 48 371450003 36.30697 -79.092 North Carolina Person 63 56 56 54 371470006 35.63861 -77.3581 North Carolina Pitt 55 54 54 52 371570099 36.30889 -79.8592 North Carolina Rockingham 57 56 56 55 371590021 35.55187 -80.395 North Carolina Rowan 57 56 55 54 371590022 35.53448 -80.6676 North Carolina Rowan 57 57 56 55 371730002 35.43551 -83.4437 North Carolina Swain 49 49 48 46 371790003 34.97389 -80.5408 North Carolina Union 54 53 53 52 371830014 35.85611 -78.5742 North Carolina Wake 54 53 53 52 371830016 35.59694 -78.7925 North Carolina Wake 57 56 56 54 371990004 35.76541 -82.2649 North Carolina Yancey 54 53 53 51 390030009 40.77094 -84.0539 Ohio Allen 61 61 60 56 390071001 41.9597 -80.5728 Ohio Ashtabula 62 62 61 55 390090004 39.30798 -82.1182 Ohio Athens 58 58 57 52 390170004 39.38338 -84.5444 Ohio Butler 67 66 66 59 390170018 39.52948 -84.3934 Ohio Butler 67 67 66 59 390179991 39.5327 -84.7286 Ohio Butler 65 64 64 58 390230001 40.00103 -83.8046 Ohio Clark 61 61 60 55 390230003 39.85567 -83.9977 Ohio Clark 61 60 60 54 390250022 39.0828 -84.1441 Ohio Clermont 65 65 64 57 390271002 39.43004 -83.7885 Ohio Clinton 63 63 62 56 390350034 41.55523 -81.5753 Ohio Cuyahoga 58 57 57 56 390350060 41.49212 -81.6784 Ohio Cuyahoga 52 52 52 52 390350064 41.36189 -81.8646 Ohio Cuyahoga 56 56 56 55 390355002 41.53734 -81.4588 Ohio Cuyahoga 57 57 57 56 390410002 40.35669 -83.064 Ohio Delaware 60 59 59 54 390479991 39.6359 -83.2605 Ohio Fayette 58 57 57 52 390490029 40.0845 -82.8155 Ohio Franklin 67 66 65 59 390490037 39.96523 -82.9555 Ohio Franklin 61 61 60 54 390490081 40.0877 -82.9598 Ohio Franklin 58 58 57 52 390550004 41.51505 -81.2499 Ohio Geauga 60 60 59 54 390570006 39.66575 -83.9429 Ohio Greene 59 59 58 52

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-58 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65
390610006 39.2787 -84.3661 Ohio Hamilton 70 70 69 62 390610010 39.21494 -84.6909 Ohio Hamilton 66 65 65 58 390610040 39.12886 -84.504 Ohio Hamilton 68 67 67 60 390810017 40.36644 -80.6156 Ohio Jefferson 61 60 59 54 390830002 40.31003 -82.6917 Ohio Knox 60 59 59 53 390850003 41.67301 -81.4225 Ohio Lake 58 58 58 57 390850007 41.72681 -81.2422 Ohio Lake 53 53 53 52 390870011 38.62901 -82.4589 Ohio Lawrence 54 54 53 47 390870012 38.50811 -82.6593 Ohio Lawrence 59 59 58 51 390890005 40.02604 -82.433 Ohio Licking 59 58 58 52 390930018 41.42088 -82.0957 Ohio Lorain 54 54 54 54 390950024 41.64407 -83.5463 Ohio Lucas 55 55 55 53 390950027 41.49417 -83.7189 Ohio Lucas 59 58 58 54 390950034 41.67521 -83.3069 Ohio Lucas 61 61 60 58 390970007 39.78819 -83.4761 Ohio Madison 60 59 59 53 390990013 41.09614 -80.6589 Ohio Mahoning 58 58 57 51 391030004 41.0604 -81.9239 Ohio Medina 57 57 57 52 391090005 40.08455 -84.1141 Ohio Miami 59 59 58 53 391130037 39.78563 -84.1344 Ohio Montgomery 63 62 61 55 391219991 39.9428 -81.3373 Ohio Noble 52 52 51 47 391331001 41.18247 -81.3305 Ohio Portage 56 56 55 50 391351001 39.83562 -84.7205 Ohio Preble 59 59 58 54 391510016 40.82805 -81.3783 Ohio Stark 62 61 61 55 391510022 40.71278 -81.5983 Ohio Stark 58 57 57 52 391514005 40.9314 -81.1235 Ohio Stark 59 58 58 53 391530020 41.10649 -81.5035 Ohio Summit 60 59 59 53 391550009 41.45424 -80.591 Ohio Trumbull 57 56 56 51 391550011 41.24046 -80.6626 Ohio Trumbull 62 62 61 55 391650007 39.42689 -84.2008 Ohio Warren 64 64 63 57 391670004 39.43212 -81.4604 Ohio Washington 60 60 59 53 391730003 41.37769 -83.6111 Ohio Wood 61 60 60 56 400019009 35.75074 -94.6697 Oklahoma Adair 64 61 60 58 400159008 35.11194 -98.2528 Oklahoma Caddo 63 61 60 59 400170101 35.47922 -97.7515 Oklahoma Canadian 62 61 61 59 400219002 35.85408 -94.986 Oklahoma Cherokee 65 61 60 59 400270049 35.32011 -97.4841 Oklahoma Cleveland 63 62 61 59 400310651 34.63298 -98.4288 Oklahoma Comanche 64 64 63 60 400370144 36.10548 -96.3612 Oklahoma Creek 62 59 59 58 400430860 36.15841 -98.932 Oklahoma Dewey 65 65 64 63 400719010 36.95622 -97.0314 Oklahoma Kay 63 62 61 60

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-59 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65
400871073 35.15965 -97.4738 Oklahoma McClain 62 61 60 59 400892001 34.477 -94.656 Oklahoma McCurtain 61 59 58 56 400979014 36.22841 -95.2499 Oklahoma Mayes 67 63 62 60 401090033 35.47704 -97.4943 Oklahoma Oklahoma 65 64 64 62 401090096 35.4778 -97.303 Oklahoma Oklahoma 63 63 62 61 401091037 35.61413 -97.4751 Oklahoma Oklahoma 66 65 64 63 401159004 36.92222 -94.8389 Oklahoma Ottawa 63 61 60 58 401210415 34.90227 -95.7844 Oklahoma Pittsburg 66 64 63 60 401359021 35.40814 -94.5244 Oklahoma Sequoyah 62 60 59 57 401430137 36.35744 -95.9992 Oklahoma Tulsa 65 63 62 61 401430174 35.95371 -96.005 Oklahoma Tulsa 64 60 60 59 401430178 36.1338 -95.7645 Oklahoma Tulsa 65 62 62 60 401431127 36.2049 -95.9765 Oklahoma Tulsa 66 63 62 61 410050004 45.25928 -122.588 Oregon Clackamas 54 54 54 54 410090004 45.76853 -122.772 Oregon Columbia 45 45 45 45 410390060 44.02631 -123.084 Oregon Lane 48 48 48 48 410391007 43.8345 -123.035 Oregon Lane 49 49 49 49 410470004 44.81029 -122.915 Oregon Marion 49 49 49 49 410510080 45.49664 -122.603 Oregon Multnomah 51 51 51 51 410671004 45.40245 -122.854 Oregon Washington 50 50 50 50 420010002 39.93 -77.25 Pennsylvania Adams 58 56 55 49 420019991 39.9231 -77.3078 Pennsylvania Adams 59 58 56 50 420030008 40.46542 -79.9608 Pennsylvania Allegheny 67 67 65 59 420030010 40.44558 -80.0162 Pennsylvania Allegheny 65 64 63 57 420030067 40.37564 -80.1699 Pennsylvania Allegheny 65 64 63 57 420031005 40.61395 -79.7294 Pennsylvania Allegheny 71 71 69 62 420050001 40.81418 -79.5648 Pennsylvania Armstrong 65 64 63 56 420070002 40.56252 -80.5039 Pennsylvania Beaver 62 62 61 58 420070005 40.68472 -80.3597 Pennsylvania Beaver 66 66 65 59 420070014 40.7478 -80.3164 Pennsylvania Beaver 65 64 63 58 420110006 40.51408 -75.7897 Pennsylvania Berks 59 57 55 48 420110011 40.38335 -75.9686 Pennsylvania Berks 63 61 59 52 420130801 40.53528 -78.3708 Pennsylvania Blair 66 64 62 55 420170012 40.10722 -74.8822 Pennsylvania Bucks 66 65 62 54 420210011 40.30972 -78.915 Pennsylvania Cambria 61 60 58 52 420270100 40.81139 -77.877 Pennsylvania Centre 63 62 61 54 420279991 40.7208 -77.9319 Pennsylvania Centre 65 64 62 55 420290100 39.83446 -75.7682 Pennsylvania Chester 62 59 57 49 420334000 41.1175 -78.5262 Pennsylvania Clearfield 63 63 61 54 420430401 40.24699 -76.847 Pennsylvania Dauphin 59 57 55 49

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-60 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 420431100 40.27222 -76.6814 Pennsylvania Dauphin 63 60 58 50 420450002 39.83556 -75.3725 Pennsylvania Delaware 61 60 58 50 420479991 41.598 -78.7674 Pennsylvania Elk 55 55 54 48 420490003 42.14175 -80.0386 Pennsylvania Erie 60 60 59 54 420550001 39.96111 -77.4756 Pennsylvania Franklin 56 55 54 48 420590002 39.80933 -80.2657 Pennsylvania Greene 58 57 56 50 420630004 40.56333 -78.92 Pennsylvania Indiana 66 65 63 57 420690101 41.47912 -75.5782 Pennsylvania Lackawanna 61 60 58 52 420692006 41.44278 -75.6231 Pennsylvania Lackawanna 59 58 56 50 420710007 40.04667 -76.2833 Pennsylvania Lancaster 66 61 58 51 420710012 40.04383 -76.1124 Pennsylvania Lancaster 65 61 59 51 420730015 40.99585 -80.3464 Pennsylvania Lawrence 61 60 59 53 420750100 40.33733 -76.3834 Pennsylvania Lebanon 63 61 59 52 420770004 40.61194 -75.4325 Pennsylvania Lehigh 62 61 59 52 420791100 41.20917 -76.0033 Pennsylvania Luzerne 55 54 52 46 420791101 41.26556 -75.8464 Pennsylvania Luzerne 55 54 52 46 420810100 41.2508 -76.9238 Pennsylvania Lycoming 58 56 55 50 420850100 41.21501 -80.4848 Pennsylvania Mercer 62 61 60 54 420859991 41.4271 -80.1451 Pennsylvania Mercer 55 54 54 49 420890002 41.08306 -75.3233 Pennsylvania Monroe 54 53 51 45 420910013 40.11222 -75.3092 Pennsylvania Montgomery 63 61 59 51 420950025 40.62806 -75.3411 Pennsylvania Northampton 61 59 57 50 420958000 40.69222 -75.2372 Pennsylvania Northampton 56 55 53 47 420990301 40.45694 -77.1656 Pennsylvania Perry 59 58 57 51 421010004 40.00889 -75.0978 Pennsylvania Philadelphia 55 54 52 45 421010024 40.0764 -75.0115 Pennsylvania Philadelphia 69 68 65 56 421011002 40.03599 -75.0024 Pennsylvania Philadelphia 66 65 63 54 421119991 39.9878 -79.2515 Pennsylvania Somerset 54 53 52 46 421174000 41.64472 -76.9392 Pennsylvania Tioga 59 58 57 52 421250005 40.14667 -79.9022 Pennsylvania Washington 60 60 58 52 421250200 40.17056 -80.2614 Pennsylvania Washington 60 59 58 52 421255001 40.44528 -80.4208 Pennsylvania Washington 62 61 60 55 421290006 40.42808 -79.6928 Pennsylvania Westmoreland 62 62 60 54 421290008 40.30469 -79.5057 Pennsylvania Westmoreland 60 60 58 51 421330008 39.96528 -76.6994 Pennsylvania York 62 57 55 48 421330011 39.86097 -76.4621 Pennsylvania York 62 58 56 48 440030002 41.61524 -71.72 Rhode Island Kent 60 59 57 50 440071010 41.84157 -71.3608 Rhode Island Providence 59 59 57 50 440090007 41.49511 -71.4237 Rhode Island Washington 63 63 60 53 450010001 34.32532 -82.3864 South Carolina Abbeville 47 46 46 44

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-61 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65
450030003 33.34223 -81.7887 South Carolina Aiken 49 48 48 47 450070005 34.62324 -82.5321 South Carolina Anderson 53 52 52 51 450150002 32.98725 -79.9367 South Carolina Berkeley 49 49 49 48 450190046 32.94102 -79.6572 South Carolina Charleston 51 50 50 49 450250001 34.61537 -80.1988 South Carolina Chesterfield 50 50 49 48 450290002 33.00787 -80.965 South Carolina Colleton 48 47 47 45 450310003 34.2857 -79.7449 South Carolina Darlington 53 52 52 50 450370001 33.73996 -81.8536 South Carolina Edgefield 46 45 45 44 450450016 34.75185 -82.2567 South Carolina Greenville 52 51 51 50 450451003 35.0574 -82.3729 South Carolina Greenville 50 49 49 48 450770002 34.65361 -82.8387 South Carolina Pickens 54 53 53 51 450770003 34.85154 -82.7446 South Carolina Pickens 50 50 49 48 450790007 34.09396 -80.9623 South Carolina Richland 51 50 50 49 450790021 33.81468 -80.7811 South Carolina Richland 46 45 44 43 450791001 34.13126 -80.8683 South Carolina Richland 54 53 53 52 450830009 34.98871 -82.0758 South Carolina Spartanburg 56 55 55 54 450910006 34.93582 -81.2284 South Carolina York 50 49 49 48 460330132 43.5578 -103.484 South Dakota Custer 58 58 57 57 460710001 43.74561 -101.941 South Dakota Jackson 52 52 52 51 460930001 44.15564 -103.316 South Dakota Meade 53 53 52 52 460990008 43.54792 -96.7008 South Dakota Minnehaha 56 56 55 55 461270003 42.88021 -96.7853 South Dakota Union 54 53 53 52 470010101 35.96522 -84.2232 Tennessee Anderson 55 55 54 48 470090101 35.63149 -83.9435 Tennessee Blount 59 59 58 52 470090102 35.60306 -83.7836 Tennessee Blount 51 50 50 45 470259991 36.47 -83.8268 Tennessee Claiborne 48 47 47 43 470370011 36.205 -86.7447 Tennessee Davidson 52 52 51 46 470370026 36.15074 -86.6233 Tennessee Davidson 56 55 55 49 470419991 36.0388 -85.7331 Tennessee DeKalb 54 54 53 49 470651011 35.23348 -85.1816 Tennessee Hamilton 55 55 54 50 470654003 35.10264 -85.1622 Tennessee Hamilton 56 55 54 50 470890002 36.10563 -83.6021 Tennessee Jefferson 58 57 56 51 470930021 36.08551 -83.7648 Tennessee Knox 53 53 52 47 470931020 36.01919 -83.8738 Tennessee Knox 55 54 54 48 471050109 35.72089 -84.3422 Tennessee Loudon 57 56 55 49 471210104 35.28938 -84.9461 Tennessee Meigs 55 54 54 50 471490101 35.73288 -86.5989 Tennessee Rutherford 53 53 52 47 471550101 35.69667 -83.6097 Tennessee Sevier 57 57 56 52 471550102 35.56278 -83.4981 Tennessee Sevier 57 56 56 52 471570021 35.2175 -90.0197 Tennessee Shelby 60 59 58 52

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-62 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 471570075 35.1517 -89.8502 Tennessee Shelby 61 60 59 53 471571004 35.37815 -89.8345 Tennessee Shelby 58 57 56 51 471632002 36.54144 -82.4248 Tennessee Sullivan 60 60 59 53 471632003 36.58211 -82.4857 Tennessee Sullivan 59 59 59 53 471650007 36.29756 -86.6531 Tennessee Sumner 60 60 59 54 471650101 36.45398 -86.5641 Tennessee Sumner 57 56 56 51 471870106 35.95153 -87.137 Tennessee Williamson 55 55 54 48 471890103 36.06083 -86.2863 Tennessee Wilson 57 57 56 52 480271047 31.088 -97.6797 Texas Bell 63 62 60 56 480290032 29.51509 -98.6202 Texas Bexar 67 66 63 59 480290052 29.63206 -98.5649 Texas Bexar 68 67 64 60 480290059 29.27538 -98.3117 Texas Bexar 60 59 57 53 480391004 29.52044 -95.3925 Texas Brazoria 76 75 70 64 480391016 29.04376 -95.4729 Texas Brazoria 64 63 61 57 480610006 25.8925 -97.4938 Texas Cameron 57 57 56 54 480850005 33.13242 -96.7864 Texas Collin 69 68 64 58 481130069 32.81995 -96.8601 Texas Dallas 69 68 64 58 481130075 32.91921 -96.8085 Texas Dallas 70 69 65 59 481130087 32.67645 -96.8721 Texas Dallas 69 68 64 58 481210034 33.21906 -97.1963 Texas Denton 71 70 66 60 481211032 33.41064 -96.9446 Texas Denton 70 69 65 59 481390016 32.48208 -97.0269 Texas Ellis 66 65 62 57 481391044 32.17543 -96.8702 Texas Ellis 61 60 57 53 481410029 31.78577 -106.324 Texas El Paso 54 54 54 54 481410037 31.76829 -106.501 Texas El Paso 62 62 62 61 481410044 31.7657 -106.455 Texas El Paso 60 60 60 60 481410055 31.74674 -106.403 Texas El Paso 58 58 58 58 481410057 31.6675 -106.288 Texas El Paso 57 57 57 57 481410058 31.89391 -106.426 Texas El Paso 61 60 60 60 481671034 29.25447 -94.8613 Texas Galveston 70 69 67 64 481830001 32.37868 -94.7118 Texas Gregg 70 66 62 55 482010024 29.90104 -95.3261 Texas Harris 71 70 66 60 482010026 29.80271 -95.1255 Texas Harris 70 69 66 61 482010029 30.03953 -95.6739 Texas Harris 69 68 64 59 482010046 29.82809 -95.2841 Texas Harris 67 66 62 57 482010047 29.83472 -95.4892 Texas Harris 67 66 62 55 482010051 29.62361 -95.4736 Texas Harris 69 68 64 58 482010055 29.69574 -95.4993 Texas Harris 70 69 65 58 482010062 29.62583 -95.2675 Texas Harris 68 67 63 57 482010066 29.72472 -95.5036 Texas Harris 66 66 62 55

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-63 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65
482010070 29.73513 -95.3156 Texas Harris 67 66 62 56 482010075 29.75278 -95.3503 Texas Harris 68 67 63 57 482010416 29.68639 -95.2947 Texas Harris 69 68 64 58 482011015 29.76165 -95.0814 Texas Harris 67 66 63 59 482011034 29.76797 -95.2206 Texas Harris 73 72 67 61 482011035 29.73373 -95.2576 Texas Harris 70 69 65 59 482011039 29.67003 -95.1285 Texas Harris 75 74 70 65 482011050 29.58305 -95.0155 Texas Harris 72 71 68 64 482030002 32.66899 -94.1675 Texas Harrison 63 61 59 55 482150043 26.22623 -98.2911 Texas Hidalgo 56 55 55 53 482151048 26.13108 -97.9373 Texas Hidalgo 55 54 53 52 482210001 32.44231 -97.8035 Texas Hood 65 64 61 56 482311006 33.15308 -96.1156 Texas Hunt 61 60 57 52 482450009 30.03644 -94.0711 Texas Jefferson 64 63 60 56 482450011 29.8975 -93.9911 Texas Jefferson 65 64 62 58 482450022 29.86395 -94.3178 Texas Jefferson 62 61 58 54 482450101 29.728 -93.894 Texas Jefferson 69 69 67 64 482450102 29.9425 -94.0006 Texas Jefferson 62 61 58 55 482450628 29.865 -93.955 Texas Jefferson 63 62 60 57 482451035 29.97892 -94.0109 Texas Jefferson 63 62 60 56 482510003 32.35359 -97.4367 Texas Johnson 68 67 64 59 482570005 32.56495 -96.3177 Texas Kaufman 62 60 57 53 483091037 31.65307 -97.0707 Texas McLennan 64 63 60 56 483390078 30.3503 -95.4251 Texas Montgomery 66 66 62 57 483491051 32.03194 -96.3991 Texas Navarro 63 61 58 54 483550025 27.76534 -97.4342 Texas Nueces 64 63 62 59 483550026 27.83241 -97.5554 Texas Nueces 64 63 61 59 483611001 30.08526 -93.7613 Texas Orange 64 63 61 57 483611100 30.19417 -93.8669 Texas Orange 61 59 57 53 483670081 32.86878 -97.9059 Texas Parker 68 67 64 59 483739991 30.7017 -94.6742 Texas Polk 60 60 58 55 483970001 32.93652 -96.4592 Texas Rockwall 66 65 62 56 484230007 32.34401 -95.4158 Texas Smith 65 62 60 55 484390075 32.98789 -97.4772 Texas Tarrant 70 69 66 60 484391002 32.80582 -97.3566 Texas Tarrant 69 68 65 59 484392003 32.9225 -97.2821 Texas Tarrant 74 73 69 62 484393009 32.98426 -97.0637 Texas Tarrant 73 72 68 61 484393011 32.65637 -97.0886 Texas Tarrant 69 68 65 59 484530014 30.35442 -97.7603 Texas Travis 64 63 60 56 484530020 30.48317 -97.8723 Texas Travis 61 60 58 54

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-64 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65
484690003 28.83617 -97.0055 Texas Victoria 62 60 58 54 484790016 27.51127 -99.5203 Texas Webb 59 59 58 56 490030003 41.49271 -112.019 Utah Box Elder 61 60 60 60 490037001 41.94595 -112.233 Utah Box Elder 61 60 60 60 490050004 41.73111 -111.838 Utah Cache 59 59 59 58 490071003 39.60996 -110.801 Utah Carbon 65 62 62 62 490110004 40.90297 -111.884 Utah Davis 62 61 61 61 490131001 40.20865 -110.841 Utah Duchesne 63 63 63 62 490352004 40.73639 -112.21 Utah Salt Lake 66 65 65 65 490353006 40.73639 -111.872 Utah Salt Lake 66 65 65 64 490370101 38.45861 -109.821 Utah San Juan 64 64 64 64 490450003 40.54331 -112.3 Utah Tooele 65 65 65 64 490490002 40.25361 -111.663 Utah Utah 63 63 63 62 490495008 40.43028 -111.804 Utah Utah 60 59 59 59 490495010 40.13634 -111.661 Utah Utah 63 63 63 62 490530006 37.129 -113.637 Utah Washington 62 62 62 62 490570002 41.20632 -111.976 Utah Weber 65 64 64 64 490571003 41.30361 -111.988 Utah Weber 65 65 65 64 500030004 42.88759 -73.2498 Vermont Bennington 53 53 52 47 500070007 44.52839 -72.8688 Vermont Chittenden 53 52 52 50 510030001 38.07657 -78.504 Virginia Albemarle 54 54 53 49 510130020 38.8577 -77.0592 Virginia Arlington 63 63 60 51 510330001 38.20087 -77.3774 Virginia Caroline 56 55 53 47 510360002 37.34438 -77.2593 Virginia Charles 61 59 58 56 510410004 37.35748 -77.5936 Virginia Chesterfield 58 56 55 51 510590030 38.77335 -77.1047 Virginia Fairfax 63 62 59 50 510610002 38.47367 -77.7677 Virginia Fauquier 50 49 48 43 510690010 39.28102 -78.0816 Virginia Frederick 55 54 53 48 510719991 37.3297 -80.5578 Virginia Giles 48 48 47 44 510850003 37.60613 -77.2188 Virginia Hanover 59 57 56 54 510870014 37.55652 -77.4003 Virginia Henrico 61 58 58 55 511071005 39.02473 -77.4893 Virginia Loudoun 60 59 57 49 511130003 38.52199 -78.4358 Virginia Madison 60 59 58 54 511390004 38.66373 -78.5044 Virginia Page 56 55 55 50 511479991 37.1655 -78.3069 Virginia Prince Edward 54 50 50 48 511530009 38.85287 -77.6346 Virginia Prince William 58 58 56 49 511611004 37.28342 -79.8845 Virginia Roanoke 54 53 53 51 511630003 37.62668 -79.5126 Virginia Rockbridge 52 51 51 48 511650003 38.47753 -78.8195 Virginia Rockingham 55 55 54 50 511790001 38.48123 -77.3704 Virginia Stafford 54 53 51 43

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-65 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 511970002 36.89117 -81.2542 Virginia Wythe 54 53 53 49 515100009 38.8104 -77.0444 Virginia Alexandria City 62 61 59 50 516500008 37.10373 -76.387 Virginia Hampton City 60 59 59 57 518000004 36.90118 -76.4381 Virginia Suffolk City 60 59 59 57 518000005 36.66525 -76.7308 Virginia Suffolk City 56 55 54 52 530110011 45.61667 -122.517 Washington Clark 50 50 50 50 530330010 47.5525 -122.065 Washington King 50 50 50 50 530330017 47.49022 -121.773 Washington King 49 49 49 49 530330023 47.1411 -121.938 Washington King 55 55 55 55 530630001 47.41645 -117.53 Washington Spokane 51 51 51 51 530630021 47.67248 -117.365 Washington Spokane 51 51 51 51 530630046 47.82728 -117.274 Washington Spokane 50 50 50 50 530670005 46.95256 -122.595 Washington Thurston 48 47 47 47 540030003 39.44801 -77.9641 West Virginia Berkeley 56 56 54 49 540110006 38.42413 -82.4259 West Virginia Cabell 58 57 57 50 540219991 38.8795 -80.8477 West Virginia Gilmer 52 52 51 45 540250003 37.90853 -80.6326 West Virginia Greenbrier 54 53 53 48 540291004 40.42154 -80.5807 West Virginia Hancock 63 63 62 57 540390010 38.3456 -81.6283 West Virginia Kanawha 64 64 63 55 540610003 39.64937 -79.9209 West Virginia Monongalia 63 62 61 54 540690010 40.11488 -80.701 West Virginia Ohio 61 60 59 53 540939991 39.0905 -79.6617 West Virginia Tucker 56 55 55 49 541071002 39.32353 -81.5524 West Virginia Wood 57 57 56 50 550090026 44.53098 -87.908 Wisconsin Brown 57 56 55 52 550210015 43.3156 -89.1089 Wisconsin Columbia 57 56 55 52 550250041 43.10084 -89.3573 Wisconsin Dane 56 56 55 52 550270001 43.46611 -88.6211 Wisconsin Dodge 62 61 61 57 550290004 45.237 -86.993 Wisconsin Door 64 63 63 58 550350014 44.761 -91.143 Wisconsin Eau Claire 51 51 50 49 550390006 43.6874 -88.422 Wisconsin Fond du Lac 61 60 60 56 550410007 45.563 -88.8088 Wisconsin Forest 53 53 52 50 550550002 43.002 -88.8186 Wisconsin Jefferson 58 58 57 54 550590019 42.50472 -87.8093 Wisconsin Kenosha 59 58 59 59 550610002 44.44312 -87.5052 Wisconsin Kewaunee 63 62 62 57 550630012 43.7775 -91.2269 Wisconsin La Crosse 53 52 52 51 550710007 44.13862 -87.6161 Wisconsin Manitowoc 66 66 65 60 550730012 44.70735 -89.7718 Wisconsin Marathon 53 52 52 49 550790010 43.01667 -87.9333 Wisconsin Milwaukee 56 56 55 53 550790026 43.06098 -87.9135 Wisconsin Milwaukee 60 60 60 57 550790085 43.181 -87.9 Wisconsin Milwaukee 64 64 63 59

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-66 Site ID Lat Long State County O3 DV for Scenario: Base Case Baseline 70 65 550870009 44.30738 -88.3951 Wisconsin Outagamie 60 59 59 56 550890008 43.343 -87.92 Wisconsin Ozaukee 66 65 65 61 550890009 43.49806 -87.81 Wisconsin Ozaukee 62 61 61 57 551010017 42.7139 -87.7986 Wisconsin Racine 57 57 57 56 551050024 42.50908 -89.0628 Wisconsin Rock 60 60 59 55 551110007 43.4351 -89.6797 Wisconsin Sauk 55 53 53 50 551170006 43.679 -87.716 Wisconsin Sheboygan 71 71 70 65 551199991 45.2066 -90.5969 Wisconsin Taylor 53 53 52 51 551270005 42.58001 -88.499 Wisconsin Walworth 60 60 59 56 551330027 43.02008 -88.2151 Wisconsin Waukesha 58 57 57 53 560019991 41.3642 -106.24 Wyoming Albany 65 65 65 64 560050123 44.6522 -105.29 Wyoming Campbell 60 59 59 59 560050456 44.14696 -105.53 Wyoming Campbell 59 59 59 59 560070100 41.38694 -107.617 Wyoming Carbon 60 59 59 58 560130232 43.08167 -107.549 Wyoming Fremont 60 59 59 59 560210100 41.18223 -104.778 Wyoming Laramie 63 62 62 60 560350700 42.48636 -110.099 Wyoming Sublette 60 60 60 60 560359991 42.9288 -109.788 Wyoming Sublette 62 62 62 62 560370077 41.158 -108.619 Wyoming Sweetwater 59 58 58 58 560370200 41.67745 -108.025 Wyoming Sweetwater 57 56 56 56 560370300 41.75056 -109.788 Wyoming Sweetwater 60 60 60 60 560410101 41.3731 -111.042 Wyoming Uinta 58 58 58 58



### 2A.5 References
Dennison, P.E., Brewer, S.C., Arnold, J.D., Moritz, M.A. (2014). Large wildfire trends in the western United States,

1984-2011. Geophysical Research Letters, 41, 2928-2933.
ERG, 2009. Development of Mexico National Emissions In ventory Projections for 2008, 2012, and 2030. Available

at: http://www.azdeq.gov/environ/air/plan/download/dmneip.pdf.
Gilliam, R. C., W. Appel, and S. Phillips. The Atmospheric Model Evaluation Tool (AMET): Meteorology Module. Presented at 4th Annual CMAS Models-3 Users Conference, Chapel Hill, NC, September 26 - 28, 2005. (http://www.cmascenter.or
Henderson, B.H., Akhtar, F., Pye, H.O.T., Napelenok, S.L., Hutzell, W.T. (2014). A database and tool for boundary

conditions for regional air quality modeling: description and evaluations, Geoscientific Model Development , 7, 339-360.
Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla, P.S. (2000), Emissions inventory development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project, Journal of Geophysical Research – Atmospheres , 105(D7), 9079-9090.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2A-67 Karl, T. R. and Koss, W. J., 1984: "Regional and National Monthly, Seasonal, and Annual Temperature Weighted by Area, 1895-1983." Historical Climatology Series 4-3, National Climatic Data Center, Asheville, NC, 38 pp. National Research Council (NRC), 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations, Washington, DC: National Academies Press.
Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform: Air Toxics, Ozone, and Particulate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.
Ruiz, L.H., Yarwood, G., 2013. Interactions between Organic Aerosol and NOy: Influence on Oxidant Production.

http://aqrp.ceer.utexas.edu/projectinfoFY12_13/12-012/12-012%20Final%20Report.pdf

. Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X., Wang, W., Powers,

J.G., 2008, A description of the advanced research WRF version 3, National Center for Atmospheric Research, Boulder, CO, USA, NCAR/TN-475+STR
Simon, H., Baker, K., Phillips, S., 2012: Compilation and interpretation of photochemical model performance statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139.
U.S. Environmental Protection Agency (US EPA), 2005; Technical Support Document for the Final Clean Air Interstate Rule: Air Quality Modeling; Office of Air Quality Planning and Standards; RTP, NC; (CAIR Docket OAR-2005-0053-2149).
U.S. Environmental Protection Agency (US EPA) 2009; Technical Support Document for the Proposal to Designate an Emissions Control Area for Nitrogen Oxides, Sulfur Oxides, and Particulate Matter: EPA-420-R-007, 329pp. ( http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09007.pdf )
U.S. Environmental Protection Agency (2014a) Meteorological Model Performance for Annual 2011 Simulation

WRF v3.4, US EPA, OAQPS, 2014, RTP, NC http://www.epa.gov/scram001/
U.S. Environmental Protection Agency (2014b) Preparation of Emissions Inventories for the Version 6.1, 2011 Emissions Modeling Platform (http://www.epa.gov/ttn/chief/emch)
U.S. Environmental Protection Agency (US EPA) 2014c; Draft Modeling Guidance for demonstrating attainment of air quality goals for ozone, PM2.5, and regional haze. September 2014, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711.
U.S. Environmental Protection Agency (US EPA), 2014d; Regulatory Impact Analysis of the Proposed Revisions to the National Ambient Air Quality Standards for Ground- Level Ozone, Office of Air Quality Planning and Standards; RTP, NC, EPA-452/P-14-006
Yantosca, B. and Carouge, C., 2010, GEOS-Chem v8-03-01 User’s Guide, Atmospheric Chemistry Modeling Group, Harvard University, Cambridge, MA, http://acmg.seas.harvard.edu/geos/doc/archive/man.v8-03- 02/index.html

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

3-1 CHAPTER 3 : CONTROL STRATEGIES AND EMISSIONS REDUCTIONS

Overview
To estimate the costs and benefits of alternative ozone standard levels, the EPA has analyzed hypothetical control strategies that areas across the country might employ to attain the revised ozone standard level of 70 ppb and a more stringent alternative standard of 65 ppb. The future year for analyzing the incremental costs and benefits of meeting a revised ozone standard
is 2025. 42 

### This analysis year was chosen because most areas of the U.S. will be required to meet a revised ozone standard by 2025. California was analyzed independently from the rest of the U.S. because of the potential for longer compliance timelines in many areas. Consequently, we created two baseline scenarios, a 2025 baseline for all areas outside of California and a post-2025
baseline for California.

This chapter documents the (i) emissions control measures EPA applied to illustrate attainment with the revised ozone standard of 70 ppb and the alternative standard of 65 ppb and (ii) projected emissions reductions associated with the measures. The chapter is organized into five sections. Section 3.1 provides a summary of the steps that we took to determine necessary emissions reductions to create the 2025 baseline and the control strategies to reach the revised standard level of 70 ppb and an alternative standard of 65 ppb in the continental U.S. outside of California. Section 3.2 describes the steps we took to determine necessary emissions reductions to create the post 2025-baseline and the control strategy to reach 70 ppb and 65 ppb for California. In Section 3.3 we discuss key differences between the results from the analysis conducted for the proposal RIA and this analysis. In Section 3.4 we list the key limitations and uncertainties associated with the control strategy analysis. And finally, Section 3.5 includes the references for the chapter.

To conduct the control strategy analyses, we first require information on total emissions reductions needed to simulate attainment. For that purpose we need (i) projected future design

42 Please see Chapter 1, Section 1.3.2 for a detailed discussion of the potential nonattainment designations and their timing.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

3-2 value and design value (DV) targets for each area, (ii) the sensitivity of ozone DVs to the NOx and VOC emissions reductions, and (iii) available NOx and VOC reductions from identified controls (as will be described in section 3.1.1). Second, to find an illustrative control strategy to achieve the emissions reductions needed, we need information about available identified
controls 43 

### for specific sources and associated emissions reductions. More details on air quality modeling and information about projected future DVs, DV targets, and ppb/ton ozone response factors are provided in Chapter 2. In this chapter we calculate the necessary emissions reductions and describe the creation of hypothetical control strategies for the post-2025 baseline and for the revised and alternative standard levels analyzed.

3.1 The 2025 Control Strategy Scenarios To create the baseline, we projected 2025 ozone DVs for the base case scenario as described in Section 2.2 of Chapter 2. We adjusted the 2025 base case for all areas of the U.S. to account for emissions reductions from the Clean Power Plan in creating the 2025 baseline. In addition, because in the final 2025 base case projections no monitors outside of California were projected to violate the current standard of 75 ppb, no additional controls were applied to create
the 2025 baseline.

3.1.1 Approach for the Revised Standard of 70 ppb and Alternative Standard of 65 ppb
The control strategies applied to illustrate attainment of the revised and alternative standards analyzed involved several steps. We applied regional and local ppb/ton ozone response factors to estimate resulting ozone DVs at air quality monitor locations to find the target emissions levels. Then we applied controls to reach those targets levels. These steps are
described in this section.

43 In the proposal RIA we discuss emissions reductions resulting from the application of known controls, as well as emissions reductions beyond known controls, using the terminology of “known controls” and “unknown controls.” In the final RIA, we have used slightly different terminology, consistent with past NAAQS RIAs. Here we refer to emissions reductions and controls as either “identified” controls or measures or “unidentified” controls or measures reflecting that unidentified controls or measures can include existing controls or measures for which the EPA does not have sufficient data to accurately estimate their costs.

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3-3 As described in Chapter 2, we performed a series of photochemical modeling simulations to determine the response of ozone DVs at monitor locations to emissions reductions in specific regions (NOx emissions reductions) and urban areas (VOC emissions reductions). We estimated the necessary emissions reductions sequentially, one region at a time. For each air quality sensitivity region (see Chapter 2, Figure 2-2 for a map of the sensitivity regions), we determined the amount of emissions reductions necessary for all monitors within the region to meet the standard level analyzed.

To implement this approach, we ranked the monitors in descending order by baseline DV, and the region that included the monitor with the highest projected baseline DV (East Texas) was analyzed first. We estimated the emissions reductions to decrease ozone concentrations to the level needed for that region. We then estimated the impact that those emissions reductions would have on all other remaining regions. After emissions reductions were estimated for each region, the remaining m onitors were re-ordered based on the resulting DVs and the next region with the highest baseline DV was targeted for emissions reductions, if needed, and the impact of its reductions were estimated for the remaining regions. We repeated this process until all regions had been analyzed. For each region analyzed, we determined (i) the quantity of emissions reductions from available identified NOx & VOC controls, and (ii) the impact of these controls on ozone concentrations. If additional decreases in ozone concentrations were needed in the region being analyzed, additional emissions reductions would have to come from unidentified controls. Figure 3-1 shows a summary of this process. A numeric example of the calculation methodology is provided in Appendix 3A. In addition, ozone DVs at all evaluated monitors are provided for each scenario in Appendix 2A, Section
2A.4.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

3-4 Figure 3-1. Process to Find Needed Reductions to Reach the Revised and Alternative Standards Because emissions reductions in NOx and VOC have different resulting air quality impacts on ozone and because different combinations of reductions from these pollutants could potentially render the same reduction in ozone, it is important to know for each region, a-priori, the total potential available reductions of these two pollutants from identified controls. To find these potentially available tons of NOx and VOC emissions reductions, we ran a maximum emissions reductions run using CoST (Control Strategy Tool) (a description of CoST, its algorithms, and the control strategy applied to obtain the necessary reductions follows in section 3.1.2), and applied the reductions from these controls as part of the process described above to obtain the total needed emissions reductions. First, we estimated the available NOx and VOC
emissions reductions from identified controls to determine the reductions in ozone concentrations. Then we estimated any additional NO X 

### emissions reductions needed from unidentified controls to achieve the target reduction. We did not apply any unidentified VOC
controls. 44



### To define the geographic areas within which we would obtain NO X 

### emissions reductions, we created a 200 kilometer buffer around each county with a DV projected to exceed the standard level being analyzed, but we limited the buffer to within the borders of the state

44 Past air quality modeling experience has indicated that in most areas NOx emissions reductions are more effective at reducing ozone concentrations at the monitor with the highest DV.

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3-5 containing the exceeding county. The area outside the buffer but within the air quality modeling sensitivity region was also identified. To define the geographic areas within which we would obtain VOC emissions reductions, we created a 100 kilometer buffer around the county with the projected monitor exceedance in areas where the modeling showed that ozone concentrations are responsive to VOC emissions reductions. Figures 3-2 and 3-3 are maps displaying the NO X 

### and VOC buffers respectively. We used these buffers in estimating available emissions reductions to target the application of identified controls as close to the projected exceeding monitors as possible, within each region.

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

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3-6 Figure 3-3. Buffers of 100 km for VO C Emissions Reductions around Projected Exceedance Areas

Once we completed the process of estimating the necessary emissions reductions to meet the revised standard of 70 ppb and alternative standard of 65 ppb for each region, we applied control strategies to simulate attainment with them. For each air quality sensitivity region containing a monitor projected to exceed either the revised or the alternative standard, we applied NO X 

### controls to simulate attainment with the respective standard. If these controls did not bring the area into attainment and VOC reductions were needed, then we applied a control strategy within the 100 km buffer to reach the VOC target reductions. If the quantity of emissions reductions needed were greater than the available emissions reductions from NO X 

### and VOC controls within the buffer, additional identified controls were applied within the remaining air quality sensitivity region outside the buffer. If further emission reductions were needed

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3-7 within the region then we assumed that unidentified controls would be used for that region to meet the standard analyzed. Figu re 3-4 illustrates this process.

Figure 3-4. Process to Estimate the Control Strategies for the Revised and Alternative Standards
3.1.2 Identified Control Measures
Control measures applied to meet the revised and alternative standards were identified for four emissions sectors: Electric Generating Units (EGUs), Non-Electric Generating Unit Point Sources (Non-EGUs), Nonpoint (Area) Sources, and Nonroad Mobile Sources. Onroad mobile source controls were not applied because they are largely addressed in existing rules such as the Tier 3 rule (U.S. EPA, 2014a). Controls applied for the revised and alternative standard analyses
are listed in Table 3-1.

The control measures we applied were identified using the EPA’s Control Strategy Tool (CoST) (U.S. EPA, 2014b), the NONROAD Model (U.S. EPA, 2005) and the Integrated Planning Model (IPM) (U.S. EPA, 2015). 45 

### CoST models emissions reductions and engineering

45 For the final RIA, an updated version of IPM was used. As a result of the updated version of IPM, after accounting for emissions reductions from the proposed Clean Power Plan we applied fewer controls to EGU sources than we applied in the proposal RIA.

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3-8 costs associated with control strategies applied to non-EGUs, area, and mobile sources of air pollutant emissions by matching control measures to emissions sources using algorithms such as "maximum emissions reduction", "least cost", and "apply measures in series". For this control strategy analysis, we applied both the maximum emissions reduction (when all available reductions were needed) and least cost algorithms 46 

### (when not all available reductions were needed). These controls are described further in Appendix 3A.

Nonpoint and nonroad mobile source emissions data are generated at the county level, and therefore controls for these emissions sectors were applied at the county level. EGU and non-EGU point source controls are applied to individual point sources. Control measures were applied to non-EGU point and nonpoint sources of NO x 

### , including: industrial boilers, commercial and institutional boilers, reciprocating internal combustion engines in the oil and gas industry and other industries, glass manufacturing furnaces, and cement kilns. The analysis for nonroad mobile sources applied NO x 

### controls to diesel engines. VOC controls applied included surface coating, solvents, and fuel storage tanks.

To more accurately depict available controls, the EPA employed a decision rule in which controls were not applied to any non-EGU point or nonpoint sources with less than 25 tons/year of emissions per pollutant for NO X 

### and 10 tons/year for VOC. This decision rule is more inclusive of sources than the decision rule employed in the previous Ozone and PM 2.5 

### NAAQS RIAs where we applied a minimum of 50 tons/year for each pollutant. The reason for not applying controls to sources below these levels is that many of these sources likely already have controls in place that may not be reflected in the emissions inventory inputs, and we don’t believe it is cost effective to apply an additional control device (see Chapter 4, Section 4.1.1 for a brief discussion of the emissions inventory inputs). Furthermore, controls were not applied if their cost per ton exceeded $19,000/ton for NOx of $33,000/ton for VOC (see Chapter 4, Section 4.1.1 for a discussion about these cutoff values). In addition, we only apply controls that replace existing controls if replacement controls are at le ast 10% more effective than the existing control. This is because we assume that replacement below that level would not be cost effective.

46 A maximum emissions reductions run in CoST will yield the same result as a least cost control strategy run with 100% control. Commented [A16]: Please see unknown vs known control costs comment.

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3-9 Table 3-1. Identified Controls Applied for the Revised and Alternative Standard Analyses Strategies Sector NO x VOC Non-EGU Point LEC (Low Emission Combustion) Solvent Recovery System SCR (Selective Catalytic Reduction) Work Practices, and Material Reformulation/Substitution SNCR (Selective Non-Catalytic Reduction) Low-VOC materials Coatings and AddOn Controls NSCR (Non-Selective Catalytic Reduction) Low VOC Adhesives and Improved Application Methods LNB (Low NO x Burner Technology) Permanent Total Enclosure (PTE) LNB + SCR Solvent Substitution, Non-Atomized Resin Application Methods LNB + SNCR Petroleum Wastewater Treatment Controls OXY-Firing Biosolid Injection Technology LNB + Flue Gas Recirculation LNB + Over Fire Air Ignition Retard Natural Gas Reburn Ultra LNB Nonpoint NSCR (Non-Selective Catalytic Reduction) Process Modification to Reduce Fugitive VOC Emissions LEC (Low Emission Combustion) Reformulation to Reduce VOC Content LNB (Low NO x Burner Technology) Incineration (Thermal, Catalytic, etc) to Reduce VOC Emissions LNB Water Heaters Low Pressure/Vacuum (LPV) Relief Valves in Gasoline Storage Tanks Biosolid Injection Technology Reduced Solvent Utilization Episodic Burn Ban Gas Recovery in Landfills EGU SCR and SNCR Nonroad Diesel Retrofits & Engine Rebuilds



### 3.1.3 Results
Figure 3-5 shows the counties projected to exceed the revised standard and alternative standard analyzed for the 2025 baseline for areas other than California. For the 70 ppb control strategy, NO X 

### emissions reductions were required for monitors in the following regions: Colorado, Great Lakes, North East, Ohio River Valley and East Texas (see Chapter 2, Figure 2-2 for a depiction of the sensitivity regions). VOC reductions were required in Houston (see Chapter 2, Figure 2-4 for a depiction of the VOC impact regions). For the 65 ppb alternative standard, in addition to the regions listed above, NOx reductions were also applied in the Arizona-New Mexico, Nevada, and Oklahoma-Arkansas-Louisiana regions. VOC reductions

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3-10 were required in Denver, Houston, Louisville, Chicago and New York City. 47 

### It is important to note that for both the 70 ppb revised standard as well as the 65 ppb alternative standard when VOC reductions were needed all available reductions from identified controls were applied using the maximum emissions reductions algorithm. In all of these areas, we used all of the available identified VOC controls, and for remaining reductions in ozone concentrations we applied unidentified NOx controls. Summaries of the emissions reductions are presented by region and source category in Appendix 3A.

Figure 3-5. Projected Ozone Design Values in the 2025 Baseline Scenario Table 3-2 shows the number of exceeding counties and the number of neighboring counties to which controls were applied for the revised and alternative standards analyzed. Figure 3-6 shows counties where NOx controls were applied for the revised and alternative

47 These five urban areas were determined to have ozone that was sensitive to reductions of VOC emissions in some locations and were the areas with the highest ozone DVs in their respective regions. See Chapter 2, section 2.3 and Appendix 2A.

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3-11 standards, and Figure 3-7 depicts counties where VOC controls were applied for the revised and alternative standards analyzed. For a complete list of geographic areas for the revised and alternative standards analyzed see Appendix 3A.

Table 3-2. Number of Counties with Exceedances and Number of Additional Counties Where Reductions Were Applied for the 2025 Revised and Alternative Standards Analyses - U.S., except California Revised and Alternative Standards Number of Counties with Exceedances Number of Additional Counties Where Reductions Were Applied
70 ppb 14 663 65 ppb 50 1,170



### Figure 3-6. Counties Where NO X 

### Emissions Reductions Were Applied to Simulate Attainment with the Revised and Alt ernative Ozone Standards in the 2025 Analysis

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3-12 Figure 3-7. Counties Where VOC Emissions Reductions Were Applied to Simulate Attainment with the Revised and Alt ernative Ozone Standards in the 2025 Analyses Table 3-3 shows the modeled 2011 and 2025 base case NOx and VOC emissions by sector (this table is also Table 2A-1 in Appendix 2A). Additional details on the emissions by state are given in the emissions modeling TSD. Tables 3-4 and 3-5 show the emissions reductions from identified controls for the revised and alternative standard analyzed. The largest emission reductions were in the non-EGU point source and nonpoint source sectors. For details regarding emissions reductions by control measure see Appendix 3.A.

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3-13 Table 3-3. 2011 and 2025 Base Case NOx and VOC Emissions by Sector (1000 tons) Sector 2011 NO x 2025 NO x 2011 VOC 2025 VOC EGU-point 2,000 1,400 36 42 NonEGU-point 1,200 1,200 800 830 Point oil and gas 500 460 160 190 Wild and Prescribed Fires 330 330 4,700 4,700

Nonpoint oil and gas 650 720 2,600 3,500

Residential wood

combustion 34 35 440 410 Other nonpoint 760 790 3,700 3,500

Nonroad 1,600 800 2,000 1,200 Onroad 5,700 1,700 2,700 910 C3 Commercial marine vessel (CMV) 130 100 5 9
Locomotive and C1/C2 CMV 1,100 680 48 24 Biogenics 1,000 1,000 41,000 41,000 TOTAL 15,000 9,300 58,000 56,000



### Table 3-4. Summary of Emissions Reductions by Sector for the Identified Control Strategies Applied for the Revised 70 ppb Ozone Standard in 2025, except California (1,000 tons/year) a Geographic Area Emissions Sector NO x VOC

East EGU 45 - Non-EGU Point 85 1 Nonpoint 100 19 Nonroad 3 - Onroad - - Total 230 20

West EGU - - Non-EGU Point 6 - Nonpoint 1 - Nonroad - - Onroad - - Total 7 -

a Emissions reduction estimates are rounded to two significant figures. Commented [A17]: Did EPA include subpart J regulations that would increase the VOC capture from oil and gas industry in the baseline?

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3-14 Table 3-5. Summary of Emissions Reductions by Sector for the Identified Control Strategies for the Alternative 65 ppb Ozone Standard in 2025 - except California (1,000 tons/year) a Geographic Area Emissions Sector NO x VOC

East EGU 110 - Non-EGU Point 220 5 Nonpoint 160 100 Nonroad 8 - Total 500 100

West EGU 0 - Non-EGU Point 33 - Nonpoint 22 5 Nonroad 1 - Total 56 5

a Emissions reduction estimates are rounded to two significant figures. 

### As mentioned previously, there were several areas where identified controls did not achieve enough emissions reductions to meet the revised and alternative standards of 70 and 65 ppb. Texas East was the only area where identified controls were not enough to get the needed emissions reductions for 70 ppb. Great Lakes, Colorado, Texas East, Ohio River Valley, Northeast and Nevada were the areas where identified controls were not enough to get the needed emissions reductions for 65 ppb. See Chapter 2, Figure 2-2 for a map showing these areas. To complete the analysis, the EPA then assumed that the remaining reductions needed to
meet the standard would be obtained from unidentified controls. Table 3-6 shows the emissions reductions needed from unidentified controls in 2025 for the U.S., except California, for the revised and alternative standards analyzed.

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3-15 Table 3-6. Summary of Emissions Reductions for the Revised and Alternative Standards for the Unidentified Control Strategies for 2025 - except California (1,000 tons/year) a Revised and Alternative Standards Region NO x VOC
70 ppb b East West 47

- -

- 65 ppb c East West 820 40 -

-

a Estimates are rounded to two significant figures. b Unidentified controls for the revised standard of 70 ppb are needed in the Texas East (see Chapter 2, Figure 2-2 for

a description of these regions).

c Unidentified controls for the 65 ppb alternative standard are needed in Nevada, Colorado, Texas East, Great Lakes,

Ohio River Valley and North East (see Chapter 2, Figure 2-2 for a description of these regions). 

### Table 3-7 summarizes the total (identified and unidentified) emissions reductions needed to meet the revised and alternative standard levels in 2025 for the East and West, except California (see Chapter 4, Figure 4-3 for a map depicting the East and West regions). In the East for 2025, the unidentified NOx emissions reductions needed as percentage of the total reductions increases from 17 percent to 62 percent as the standard level analyzed decreases from 70 ppb to 65 ppb. In the West, unidentified NO X 

### emissions reductions are only needed for the 65 ppb alternative standard and account for 42 percent of the total reductions needed. No unidentified VOC reductions are needed in the East or West for the 70 ppb and 65 ppb standard levels.

Table 3-7. Summary of Emissions Reductions from the Identified + Unidentified Control Strategies by Alternative Standard Levels in 2025, Except California (1,000 tons/year) a
Alternative Standard Geographic Area Emissions Reductions 70 ppb 65 ppb

East NOx Identified 230 500 NOx Unidentified 50 820 % NOx Unidentified 17% 62% VOC Identified 20 100 VOC Unidentified 0 0 % VOC Unidentified 0% 0%

West NOx Identified 7 56 NOx Unidentified 0 40 % NOx Unidentified 0% 42% VOC Identified 0 5 VOC Unidentified 0 0 % VOC Unidentified 0% 0%

a Estimates are rounded to two significant figures.

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3-16 3.2 The Post-2025 Scenario for California The post-2025 baseline and alternative standard level scenarios for California were created using similar methods to those described above in Section 3.1. However, in contrast to the rest of the U.S., substantial emissions reductions were needed in California to meet the current standard of 75 ppb. All identified controls were used to meet the current standard in this process, so the revised and alternative standards analyzed in California relied entirely on unidentified

measures.

3.2.1 Creation of the Post-2025 Baseline Scenario for California
The final 2025 base case projections predict several areas of California would have ozone DVs above the current standard level of 75 ppb. Therefore, we estimated emissions reductions in the following order to construct the post-2025 baseline scenario for California: (1) emissions changes from the Clean Power Plan, (2) mobile source emissions changes between 2025 and 2030, (3) identified controls of NO x 

### emissions from nonpoint, non-EGU point, and nonroad sources, (4) identified controls of VOC emissions, and (5) additional NO x 

### reductions beyond identified controls (i.e., unidentified controls). All controls applied to these sources were above and beyond reductions from on-the-books regulations that were included in the final 2025 base case modeling. The following paragraphs and Figure 3-8 outline these steps.

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3-17 Figure 3-8. Steps to Create the Post-2025 Baseline for California
To create the post-2025 baseline, in Step 1 we accounted for emissions reductions from
the Clean Power Plan. 48 

### In Step 2 we applied the 2025 to 2030 mobile source emissions reductions because many locations in California will likely have attainment dates farther into the future than 2025. Although emissions projections years beyond 2025 were not available, California provided emissions projections in the year 2030 of both VOC and NO x 

### for onroad, nonroad, locomotive, and C1/C2 commercial marine vessel sectors by county. There were both increases and decreases between 2025 and 2030 depending on the county and sector, but overall

48 We adjusted the 2025 base case to reflect emissions reductions from the Clean Power Plan to create the post-2025 baseline.

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3-18 these mobile source changes re sulted in: (1) VOC emissions th at were 1% less than those modeled in the California base case in both the Northern and Southern California sub-regions (see Chapter 2, Figure 2-2 for a depiction of the California sub-regions), and (2) NO x 

### emissions
that were 4% less than those modeled in the California base case in the Northern California subregion and 3% less than those modeled in the Southern California sub-region. The NO x 

### and VOC mobile source emissions changes were applied to create the post-2025 baseline scenario in California using the response ratios developed from the air quality sensitivity simulations as described in Chapter 2. In Step 3 available NOx reductions from identified control measures were applied from three sectors: 49 

### Non-Electric Generating Unit Point Sources (Non-EGUs), Nonpoint (Area) Sources, and Nonroad Mobile Sources. No controls for EGUs within the parameters of size (25 tpy) and dollar per ton control costs less than $19,000 per ton were available for California. Table 3-1 above also includes identified controls that we applied in California. In Step 4, the ppb/ton from the sensitivities were applied to determine ozone reductions. Then, in Step 5, VOC controls were applied in the California counties indicated in Figure 3-10.

In Step 6, we used additional reductions (assumed to come from unidentified NO X 

### controls) in Southern California and associated regional ppb/ton response factors from the Southern California combined sensitivity simulations to reduce DVs at Southern California monitors to reach the current standard of 75 ppb. As described in Chapter 2 and shown in the example calculation in Appendix 3-A, we applied emissions responses derived from multiple emissions sensitivity simulations to capture the nonlinear response of large emissions reductions in Southern California. Similarly, we used unidentified reductions and associated ppb/ton response factors from Northern California to redu ce DVs at Northern California monitors. Since the highest projected DVs occurred in Southern California, we first quantified necessary
emissions reductions from unidentified NOx reduction measures to reduce all Southern California monitors to 75 ppb or lower. We then recalculated the resulting Northern California DVs before determining how many additional emissions reductions from unidentified NOx

49 In establishing the baseline, the U.S. EPA selected a set of cost-effective controls to simulate attainment of the

current ozone standard. These control sets are hypothetical because states w ill ultimately determine controls as part of the SIP process.

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3-19 control measures in Northern California would be necessary to bring all Northern California monitors into attainment with the current standard of 75 ppb. Summaries of the emissions reductions are presented for the post-2025 baseline in Appendix 3A. The resulting ozone DVs at all evaluated monitors are also provided in Appendix 2A, Section 2A.4.

The post-2025 baseline for this analysis presents one scenario of future year air quality based upon specific control measures, addition al emissions reductions beyond identified controls, promulgated federal rules such as Tier 3, and specific years of initial values for air quality monitoring and emissions data. This analysis presents one illustrative approach relying on the identified federal measures and other strategies that states may employ. California may ultimately employ other strategies and/or other federal rules may be adopted that would also help in achieving attainment with the current standard.

3.2.2 Approach for Revised Standard of 70 ppb and Alternative Standard of 65 ppb for California
We created the post-2025 70 ppb and 65 ppb scenarios by applying emissions reductions incrementally to the post-2025 baseline. As mentioned above, all identified measures in California were exhausted in reaching the post-2025 baseline. We started with the post-2025 baseline and then applied NOx from unidentified controls to meet the revised and alternative standard levels. As with the baseline, we first identified the NOx reductions in Southern California that would be required to bring Southern California monitors down to the revised and
alternative standard levels. We then recalculated the Northern California DVs that would result from the Southern California emissions reductions and applied additional Northern California unidentified NOx emissions reductions to bring all Northern California monitors down to the revised and alternative standard levels. Also, as was done for the baseline, we applied ppb/ton response levels that were derived from multiple emissions sensitivities to capture nonlinear responses of ozone to large emissions reductions in California (see example calculation in Appendix 3-A).

3.2.3 Results for California Nine counties in California were projected to exceed the current ozone standard of 75 ppb in the post-2025 baseline scenario (see Figure 3-9). Figure 3-10 shows areas where identified

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3-20 control measures were applied to bring ozone DVs in those counties into attainment with the current standard and establish the baseline. Table 3-8 includes a summary of NOx and VOC emissions reductions needed to demonstrate attainment of the current ozone standard of 75 ppb.

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

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3-21 Figure 3-10. Counties Where Emissions Reductions Were Applied to Demonstrate Attainment with the Current Standard

Table 3-8. Summary of Emissions Reductions (Identified + Unidentified Controls) Applied to Demonstrate Attainment in California for the Post-2025 Baseline (1,000 tons/year) a

Emissions Sector NO x VOC

Identified Controls EGU - - Non-EGU Point 14 1 Nonpoint 14 54 Nonroad 4 - Onroad - - Total 32 55 Unidentified Controls All 160 - Total 190 55 Percent Unidentified 84% 0%

a Emission reduction estimates are rounded to two significant figures.

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3-22 Figure 3-11 shows the California counties projected to exceed the revised and alternative standards analyzed for the post-2025 baseline analysis. Table 3-9 shows the emissions reductions needed from unidentified controls to meet the revised standard level of 70 ppb and alternative standard level of 65 ppb in those counties for the post-2025 analysis. Table 3-10 highlights that there were no identified NOx emissions reductions available for meeting the revised and alternative standard levels for post-2025 California and that 100 percent of the NOx emissions
reductions needed were unidentified controls.

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

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3-23 Table 3-9. Summary of Emissions Reductions from Unidentified Control Strategy for the Revised and Alternative Standard Levels for Post-2025 - California (1,000 tons/year) a Alternative Standard Region NO x VOC 70 ppb CA 51 - 65 ppb CA 100 -

a Estimates are rounded to two significant figures.



### Table 3-10. Summary of Emissions Reductions from the Identified + Unidentified Control Strategy by the Revised and Alternative Standard Levels for Post-2025 - California (1,000 tons/year) a Alternative Standard Geographic Area Emissions Reductions 70 ppb 65 ppb

California NOx Identifie d 00 NOx Unidentified 51 100 % NOx Unidentified 100% 100% VOC Identified 0 0 VOC Unidentified 0 0 % VOC Unidentified 0% 0%

a Estimates are rounded to two significant figures.


### 3.3 Improvements and Refinements since the Proposal RIA In the regulatory impact analyses for both the ozone NAAQS proposal and final, there were two geographic areas outside of California where the majority of emissions reductions were needed to meet an alternative standard level of 70 ppb – Texas and the Northeast. In analyzing the revised standard of 70 ppb for the final RIA, there were approximately 50 percent fewer emissions reductions needed in these two areas. For an alternative standard of 65 ppb, emissions reductions needed nationwide were approximately 20 percent lower than at proposal.

The primary reason for the difference in emissions reductions needed for both 70 and 65 ppb is that in the final RIA we conducted more geographically-refined air quality sensitivity modeling to develop improved response factors (i.e., changes in ozone concentrations in response to emissions reductions). More detailed air quality modeling and improved response factors account for 80 percent of the difference in needed emissions reductions between proposal and final. See Chapter 2, Section 2.4.2 for a discussion of the air quality modeling.

For the analysis of the revised standard of 70 ppb, in Texas and the Northeast, the improved and refined response factors and more geographically focused emissions reductions

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3-24 strategies resulted in larger changes in ozone concentrations. In east Texas, the ppb/ton response factors used in the final RIA were 2 to 3 times more responsive than the factors used in the proposal RIA at controlling monitors in Houston and Dallas. In the Northeast, the ppb/ton response factors used in the final RIA were 2.5 times more responsive than the factors used in the proposal RIA at the controlling monitor on Long Island, NY.

A secondary reason for the difference is that between the proposal and final RIAs we updated models and model inputs for the base year of 2011. See Appendix 2, Section 2A.1.3 for additional discussion of the updated models and model inputs. When projected to 2025, these changes in models and inputs had compounding effects for year 2025, and in some areas resulted in lower projected base case design values for 2025. In these areas, the difference between the base case design values and a standard of 70 ppb was smaller, thus requiring fewer emissions reductions to attain the 70 ppb revised standard.

Note that the more spatially refined emissions sensitivity modeling had more impact on the results at 70 ppb than it did on the results at 65 ppb due to the more localized nature of projected exceedances at 70 ppb. For example, as described above, the new sensitivity regions showed that emissions reductions in eastern Texas would have a larger impact on ozone in Houston and Dallas than the same emissions reductions would have if they were spread over the central U.S. states used in the proposal RIA. Conversely, these same east Texas emissions reductions would have less impact on violating monitors in Louisiana or Oklahoma. Therefore, for the 65 ppb scenario, additional local controls were necessary in Louisiana and Oklahoma.

As a consequence of the use of more geographically refined sensitivity regions, emissions reductions control strategies were also applied in geographic areas closer to the monitors of projected exceedances. For example, in the proposal RIA, the Central region included Texas, Oklahoma, Kansas, Missouri, Arkansas, Louisiana and Mississippi, meaning that controls could be applied anywhere in those states after identified controls had been exhausted within the 200 km buffer. But in the final RIA, the only geographic area where we applied controls was East Texas. Thus, once identified control measures were exhausted there, we had to obtain remaining
reductions from unidentified control measures. While the total amount of emissions needed to

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3-25 meet the 65 ppb alternative standard is lower than it was in the proposal RIA, the fraction of
emissions reductions from identified controls was smaller.

3.4 Limitations and Uncertainties EPA’s analysis is based on its best judgment for various input assumptions that are uncertain. As a general matter, the Agency selects the best available information from engineering studies of air pollution controls and has set up what it believes is the most reasonable modeling framework for analyzing the cost, emissions changes, and other impacts of regulatory controls. However, the control strategies above are subject to important limitations and uncertainties. In the following, we discuss the limitations and uncertainties that are most significant.

 Illustrative control strategy: A control strategy is the set of actions that States may take to meet a standard, such as which industries should be required to install end-of-pipe controls or certain types of equipment and technology. The illustrative control strategy analysis in this RIA presents only one potential pathway to attainment. The control strategies are not recommendations for how a revised ozone standard should be implemented, and States will make all final decisions regarding implementation strategies for the revised NAAQS. We do not presume that the control strategies presented in this RIA are an exhaustive list of possibilities for
emissions reductions.

 Emissions inventories and air quality modeling: These serve as a foundation for the projected ozone values, control strategies and costs in this analysis and thus limitations and uncertainties for these inputs impact the results, especially for issues such as future year emissions projections and information on controls currently in place at sources. Limitations and uncertainties for these inputs are discussed in previous chapters devoted to these subject areas. In addition, there are factors that affect emissions, such as economic growth and the makeup of the economy (e.g., growth in the oil and natural gas sector), that introduce additional uncertainty.

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3-26  Projecting level and geographic scope of exceedances: Estimates of the geographic areas that would exceed revised alternative levels of the standard in a future year, and the level to which those areas would exceed, are approximations
based on a number of factors. The actual nonattainment determinations that would result from a revised standard will likely depend on the consideration of local issues, changes in source operations between the time of this analysis and implementation of a new standard, and changes in control technology over time.

 Assumptions about the baseline: There is significant uncertainty about the illustration of the impact of rules, especially the Clean Power Plan because there is significant flexibility for states to determine which measures to apply to comply
with the standard.

 Sequential processing of re gional emission reductions: Because this method prioritizes emissions reductions in the re gions with the highest ozone values first but then does not go back and re-evaluate the amount of reduction in the higher priority region after emissions reductions have been applied in lower-priority regions, there is the potential to reduce a greater quantity of emissions at monitors in the higher priority regions. For instance, in the 65 ppb scenario, in the Northeast, the monitor which required the largest emissions reductions to reach 65 ppb was located in Queens, NY. After identifying necessary emissions reductions in the Northeast region, that monitor had a projected DV of 65.996 ppb (which truncates to 65 ppb). Additional reductions from lower priority regions such as the Ohio River Valley and the Great Lakes, brought the DV at that site down to 65.002 ppb. In theory, fewer tons of emissions reductions could then have been applied in the Northeast to reach a DV less than 66 ppb. However, if emissions reductions in the Northeast were rolled back, then necessary reductions in all lower priority regions would need to be recalculated and consequently the degree to which the
Northeast emissions reductions were rolled back would also need to be recalculated. This could be quantified either in an iterative process or through a linear programming model that found a least cost solution based on all response factors and associated costs. Neither of these options were available for this analysis, but it should be noted that this likely leads to some overestimate in our

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

3-27 calculation of tons of emissions reductions necessary to meet the 70 and 65 ppb standard levels and in the resulting costs and benefits.

 Applicability of control measures: The applicability of a control measure to a specific source varies depending on a number of process equipment factors such as age, design, capacity, fuel, and operating parameters. These can vary considerably from source to source and over time. This analysis makes assumptions across broad categories of sources nationwide.

 Control measure advances over time: The control measures applied do not reflect potential effects of technological change that may be available in future years . and
tT he effects of “learning by doing” or “learning by researching” are not accounted for in the emissions reduction estimates. Thus, all estimates of impacts associated with control measures applied reflect our current knowledge, and not projections,
of the measures’ effectiveness or costs . In our analysis, we do not have the necessary data for cumulative output, fuel sales, or emissions reductions for allsectors included in order to properly generate control costs that reflect learningcurve impacts or the impacts of technological change. We believe the effect of including these impacts may changewould be to lower our estimates of costs for our projected year control strategies.

 Pollutants to be targeted: Local knowledge of atmospheric chemistry in each geographic area may result in a different prioritization of pollutants (VOC and
NO x 

### ) for control. For the baseline in this analysis, we included only promulgated or proposed rules, but that there may be additional regulations promulgated in the future that reduce NOx or VOC emissions (see Chapter 4, Section 4.2 for additional discussion of the Phase 2 Heavy Duty Greenhouse Gas Standards for New Vehicles and Engines). These regulations could reduce the current baseline levels of
emissions. Commented [A18]: This discussion is little out of balance. Depending on the nature and the extent of technology change, the costs can either increase or decrease. Chapter 4 discussion on technology should also be modified to be more balanced.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

3-28 3.5 References
U.S. Environmental Protection Agency (U.S. EPA). User’s Guide for the Final NONROAD 2005 Model. Available

at http://www.epa.gov/otaq/models/nonrdmdl/nonrdmdl2005/420r05013.pdf. U.S. Environmental Protection Agency (U.S. EPA). 2014a. Control of Air Pollution from Motor Vehicles: Tier 3

Motor Vehicle Emission and Fuel Standards. Office of Transportation and Air Quality. Available at http://www.epa.gov/otaq/tier3.htm. U.S. Environmental Protection Agency (U.S. EPA). 2014b. Control Strategy Tool (CoST) Documentation Report. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at http://www.epa.gov/ttnecas1/cost.htm .U.S. Environmental Protection Agency (U.S. EPA). 2015. EPA Base Case v.5.14 Using the Integrated Planning Model, Incremental Documentation. Available at http://www.epa.gov/airmarkets/documents/ipm/EPA_Base_Case_v514_Incremental_Documentation.pdf

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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

Overview Chapter 3 describes the approach that EPA used in applying control measures to
demonstrate attainment of alternative ozone standard levels of 70. This Appendix contains more detailed information about the control strategy analyses, including numerical examples of the calculation methods for changes in ozone DVs, the control measures that were applied and the geographic areas in which they were applied.

3A.1 Target Emissions Reductions Needed to Create the Baseline, Post-2025 Baseline and Alternatives Tables 3A-1 to 3A-3 depict emissions reductions required in each region to reach the alternative standard level scenarios for the U.S. except California, and the post-2025 Baseline and alternative standard levels for California. These emissions reductions were determined using the methodology described in Chapter 3, Sections 3.1 and 3.2 and illustrated in the numerical example in section 3A.2 of this Appendix. Sector-specific controls used for these reductions are discussed in more detail in Chapter 3. These emissions reductions were used to create the ozone surfaces described in Chapter 2, Section 2.4.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

3A-2 Table 3A-1. Emissions Reductions Applied Beyond the Baseline Scenario to Create the 70 ppb Scenario Emissions reductions (thousand tons) applied from
NOx reductions from

identified controls VOC reductions from identified controls Additional NOx reductions from unidentified measures Northeast 111 - - Ohio River Valley 27 - - Great Lakes 18 - - East Texas 123 20 (Houston) - Colorado 7 - -
N. California Exhausted in baseline scenario Exhausted in baseline scenario 35

S. California Exhausted in baseline scenario Exhausted in baseline scenario 16



### Table 3A-2. Emissions Reductions Applied Beyo nd the Baseline Scenario to Create the 65 ppb Scenario Emissions reductions (thousand tons) applied from
NOx reductions from

identified controls VOC reductions from identified controls Additional NOx reductions from unidentified measures Northeast 163 41 (NY area) 285 Ohio River Valley 169 7 (Louisville area) 112 Great Lakes 197 39 (Chicago area) 56 OK/AR/LA 24 - -

E. Texas 123 20 (Houston area) 188 AZ/NM 29 - - Colorado 36 5 (Denver area) 20 Nevada 10 - -
N. California Exhausted in baseline scenario Exhausted in baseline scenario 65.5

S. California Exhausted in baseline scenario Exhausted in baseline scenario 32



### Table 3A-3. Emissions Reductions Applied to Create the Post-2025 Baseline Scenario* Emissions reductions (thousand tons) applied from 2025-2030 California mobile source changes NOx reductions from identified controls VOC reductions from identified controls Additional NOx reductions from

unidentified

measures N. California 8 (NOx) 3 (VOC) 16 27 24

S. California 6 (NOx) 3 (VOC) 16 29 136 

### *These emission are in addition to changes modeled in the simulation representing option 1(state) of the proposed carbon pollution gu idelines under section 111(d) of the CAA.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

3A-3 3A.2 Numeric Examples of Calculation Methodology for Changes in Design Values In this section we use the data for two monitoring sites to demonstrate how changes in design values were calculated, as described in Sections 3.1 and 3.2. For each monitor, numerical examples are given for calculating the emissions reductions necessary to attain the current standard of 75 ppb (i.e., the baseline scenario) as well as the 70 ppb scenario, which is incremental to the baseline. Note that design values are truncated when they are compared to a standard level, so a calculated design value of 75.9 is truncated to 75 ppb and, therefore, meets the current 75 ppb standard. Similarly, a design value of 70.9 would meet an alternative standard level of 70. For each monitor, we start with the base case design value, then account for ozone changes simulated in the 111(d) sensitivity simulation and then apply equation 2-5 from Chapter
2.

ܦܸ

௝ 

### ൌ ܦܸ ଶ଴ଶହ,௝ 

### ൅ ൫ܴ ଵ,௝ 

### ൈ ∆ܧ ଵ 

### ൯ ൅ ൫ܴ ଶ,௝ 

### ൈ ∆ܧ ଶ 

### ൯ ൅ ൫ܴ ଷ,௝ 

### ൈ ∆ܧ ଷ 

### ൯൅⋯ Eq 2-5

Example 1. Fresno California monitor 60195001 (baseline):

ܦܸ ଴଺ଵଽହ଴଴ଵ,௕௔௦௘௟௜௡௘



### ൌ 83.4
ถ ஽௏

లబభవఱబబభ,మబమఱ 

### ൅ െ0.7
ถ ∆஽௏ లబభవఱబబభ,భభభ೏ 

### ᇩ

ᇭ

ᇭ

ᇭ

ᇪ

ᇭ

ᇭ

ᇭ

ᇫ ேை௫ା௏ை஼ 

### ൅ ൭ െ5.0 ൈ 10 ିହ



### ᇣ

ᇧ ᇧ ᇧ

ᇤ

ᇧ ᇧ ᇧ

ᇥ ோ లబభవఱబబభ,಴ಲ೎೚೙೟ೝ೗ 

### ൈ 32,000

ᇣ

ᇧ

ᇤ

ᇧ

ᇥ ∆ா ಴ಲ೎೚೙೟ೝ೗ 

### ൱ ᇩ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇪ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇫ ேை௫ 

### ൅ ൭ െ1.4 ൈ 10 ିସ



### ᇣ

ᇧ ᇧ ᇧ

ᇤ

ᇧ ᇧ ᇧ

ᇥ ோ లబభవఱబబభ,಴ಲ೎೚೙೟ೝ೚೗శఱబಿೀೣ,ಿ಴ಲ 

### ൈ 8000 ൅ 24,000

ᇣ

ᇧ

ᇧ

ᇧ

ᇧ

ᇤ

ᇧ

ᇧ

ᇧ

ᇧ

ᇥ ∆ா ೘೚್೔೗೐,మబయబ ା∆ா 

### ൱ ᇩ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇪ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇫ ேை௫ 

### ൅ ൭ െ9.9 ൈ 10 ି଺



### ᇣ

ᇧ ᇧ ᇧ

ᇤ

ᇧ ᇧ ᇧ

ᇥ ோ లబభవఱబబభ,ೇೀ಴_ఱబ,ಿ಴ಲ 

### ൈ 3000 ൅ 27,000

ᇣ

ᇧ

ᇧ

ᇧ

ᇧ

ᇤ

ᇧ

ᇧ

ᇧ

ᇧ

ᇥ ∆ா ೘೚್೔೗೐,మబయబ ା∆ா 

### ൱ ᇩ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇪ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇫ ௏ை஼ 

### ൅ ൭ െ2.3 ൈ 10 ି଺



### ᇣ

ᇧ ᇧ ᇧ

ᇤ

ᇧ ᇧ ᇧ

ᇥ ோ లబభవఱబబభ,಴ಲ೎೚೙೟ೝ೚೗శఱబಿೀೣ,ೄ಴ಲ 

### ൈ 6000 ൅ 100,000

ᇣ

ᇧ

ᇧ

ᇧ

ᇧ

ᇤ

ᇧ

ᇧ

ᇧ

ᇧ

ᇥ ∆ா ೘೚್೔೗೐,మబయబ ା∆ா 

### ൱ ൅ ൭ െ2.6 ൈ 10 ି଺



### ᇣ

ᇧ ᇧ ᇧ

ᇤ

ᇧ ᇧ ᇧ

ᇥ ோ లబభవఱబబభ,಴ಲ೎೚೙೟ೝ೚೗శవబಿೀೣ,ೄ಴ಲ 

### ൈ 36,000

ᇣ

ᇧ

ᇤ

ᇧ

ᇥ ∆ா 

### ൱ ൌ 75.9	݌݌ܾ

Example 2. Fresno California monitor 60195001 (65 ppb scenario):

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

3A-4 ܦܸ

௝,଺ହ 

### ൌ 75.9
ถ ஽௏ ೕ,್ೌೞ೐೗೔೙೐ 

### ൅ ൭ െ1.4 ൈ 10 ିସ



### ᇣ

ᇧ ᇧ ᇧ

ᇤ

ᇧ ᇧ ᇧ

ᇥ ோ లబభవఱబబభ,಴ಲ೎೚೙೟ೝ೚೗శఱబಿೀೣ,ಿ಴ಲ 

### ൈ 35,000

ᇣ

ᇧ

ᇤ

ᇧ

ᇥ ∆ா 

### ൱ ᇩ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇪ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇫ ேை௫	௨௣	௧௢	ହ଴%	௢௙	஼஺	௠௢ௗ௘௟௘ௗ	௖௢௡௧௥௢௟	௦௘௡௦௜௧௜௩௜௧௬ 

### ൅ ൭ െ2.6 ൈ 10 ି଺



### ᇣ

ᇧ ᇧ ᇧ

ᇤ

ᇧ ᇧ ᇧ

ᇥ ோ లబభవఱబబభ,಴ಲ೎೚೙೟ೝ೚೗శవబಿೀೣ,ೄ಴ಲ 

### ൈ 16,000

ᇣ

ᇧ

ᇤ

ᇧ

ᇥ ∆ா 

### ൱ ൌ 70.9	݌݌ܾ

Example 3. Dallas monitor 484392003 (baseline):

ܦܸ ସ଼ସଷଽଶ଴଴ଷ,௕௔௦௘௟௜௡௘ 

### ൌ 74.3
ถ ஽௏

రఴరయవమబబయ,మబమఱ 

### ൅ െ1.0
ถ ∆஽௏ రఴరయవమబబయ,భభభ೏ 

### ᇩ

ᇭ

ᇭ

ᇭ

ᇪ

ᇭ

ᇭ

ᇭ

ᇫ ேை௫ା௏ை஼ 

### ܾ݌݌	73.3 ൌ

Example 4. Dallas monitor 484392003 (65 ppb scenario):

ܦܸ ସ଼ସଷଽଶ଴଴ଷ,଺ହ 

### ൌ 73.3
ถ ஽௏ రఴరయవమబబయ,್ೌೞ೐೗೔೙೐ 

### ൅ ൭ െ3.3 ൈ 10 ିହ



### ᇣ

ᇧ ᇧ ᇧ

ᇤ

ᇧ ᇧ ᇧ

ᇥ ோ రఴరయవమబబయ,ಶ.೅೐ೣೌೞ 

### ൈ 123,000

ᇣ

ᇧ

ᇤ

ᇧ

ᇥ ∆ா 

### ൱ ᇩ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇪ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇫ ேை௫ 

### ൅ ൭ െ4.1 ൈ 10 ି଼



### ᇣ

ᇧ ᇧ ᇧ

ᇤ

ᇧ ᇧ ᇧ

ᇥ ோ రఴరయవమబబయ,ಿ೚ೝ೟೓೐ೌೞ೟ 

### ൈ 111,000

ᇣ

ᇧ

ᇤ

ᇧ

ᇥ ∆ா 

### ൱ ᇩ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇪ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇫ ேை௫ 

### ൅ ൭ െ5.1 ൈ 10 ି଻



### ᇣ

ᇧ ᇧ ᇧ

ᇤ

ᇧ ᇧ ᇧ

ᇥ ோ రఴరయవమబబయ,಴೚೗೚ೝೌ೏೚ 

### ൈ 7000

ᇣ

ᇤ

ᇥ ∆ா 

### ൱ ᇩ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇪ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇫ ேை௫ 

### ൅ ൭ െ2.6 ൈ 10 ି଻



### ᇣ

ᇧ ᇧ ᇧ

ᇤ

ᇧ ᇧ ᇧ

ᇥ ோ రఴరయవమబబయ,ೀ೓೔೚ೃ೔ೡ೐ೝೇೌ೗೗೐೤ 

### ൈ 27,000

ᇣ

ᇧ

ᇤ

ᇧ

ᇥ ∆ா 

### ൱ ᇩ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇪ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇫ ேை௫ 

### ൅ ൭ െ1.9 ൈ 10 ି଻



### ᇣ

ᇧ ᇧ ᇧ

ᇤ

ᇧ ᇧ ᇧ

ᇥ ோ రఴరయవమబబయ,ಸೝ೐ೌ೟ಽೌೖ೐ೞ 

### ൈ 18,000

ᇣ

ᇧ

ᇤ

ᇧ

ᇥ ∆ா 

### ൱ ᇩ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇪ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇭ

ᇫ ேை௫



### ܾ݌݌	69.3 ൌ

3A.3 Types of Control Measures Several types of control measures were applied in the analyses for the baseline and alternative standard levels. These can be grouped into the following classes:

NO x 

### Reductions – NO x 

### control measures for nonEGU point, nonpoint, and nonroad sources. For each of these sources, we identified the most effective control (i.e., control with the highest percent reduction) that could be applied to the source, given the following constraints:

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

3A-5  the source must emit at least 25 tons/yr of NO x 

### (see description of controls on smaller sources below);

 any control for nonEGU sources must result in a reduction of NO x 

### emissions of at least 5 tons/yr; and

 any replacement control (i.e., a more effective control replacing an existing control) must achieve at least 10% more reduction than the existing control (e.g., we would not replace a 60% control with a 65% control).

 NO x 

### Reductions from EGU SCRs and SNCRs – applied to coal-fired EGUs where they are in place but are idle.
VOC Reductions – VOC control measures for nonEGU and nonpoint sources that:

 emit at least 10 tons/yr of VOC;

 any control must result in a reduction of VOC of at least 1 ton/yr; and

 any replacement control must achieve 10% more reduction than the existing control.

3A.4 Application of Control Measures in Geographic Areas Control measures were applied, to obtain the emissions reductions described in Section 3A.1 of this Appendix, to geographic areas including or adjacent to areas that were projected to
exceed the baseline and alternative standards. If all non-EGU NO x 

### reductions were needed, then the maximum emissions reductions algorithm in CoST was used. Where less non-EGU NO x 

### reductions were needed than were available, these were obtained using the least cost algorithm. Where VOC reductions were needed, all potentially available VOC reductions were needed so these were identified using the maximum emissions reduction algorithm. No unidentified
controls were needed for VOC emissions reductions. Tables 3A-4 and 3A-5 show where controls were applied and where unidentified controls were needed in the U.S. except California. Tables 3A-6 and 3A-7 show where controls were applied and where unidentified controls were
needed in California.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

3A-6 Table 3A-4. Geographic Areas for Application of NOx Controls in the Baseline and Alternative Standard Analyses - U.S., except California a 

### Geographic Areas and Controls Baseline 70 ppb 65 ppb EAST North East

Inside buffer

Non-EGU x x EGU x x Outside buffer x x Unidentified U OK + AR + LA

Inside buffer

Non-EGU x EGU Outside buffer Unidentified Ohio River Valley

Inside buffer

Non-EGU x x EGU x x Outside buffer x x Unidentified U TX East

Inside buffer

Non-EGU x x EGU Outside buffer x x Unidentified U U

WEST AZ + NM

Inside buffer

Non-EGU x EGU Outside buffer x Unidentified Colorado

Inside buffer

Non-EGU x 

### x EGU Outside buffer x Unidentified U

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

3A-7 Geographic Areas and Controls Baseline 70 ppb 65 ppb Great Lakes

Inside buffer

Non-EGU x x EGU x Outside buffer x x Unidentified U Nevada

Inside buffer

Non-EGU x EGU Outside buffer x 

### Unidentified U

a “x” indicates known controls were applied; “U” indicates unknown control reductions.


### Table 3A-5. Geographic Areas for Application of VOC a 

### Controls in the Baseline and Alternative Standard Analyses - U.S., except California b 

### Geographic Area Baseline 70 ppb 65 ppb EAST x North East New York, New Jersey, Long Island, NY-NJ-CT

Ohio River Valley Louisville, KY x

TX East Houston-Galveston-Brazoria, TX x x

WEST Colorado Denver-Boulder-Greeley-Ft.Collins-Loveland, CO x

Great Lakes Chicago-Lake Michigan, WI-IL-IN-MI x

a No unidentified VOC controls were needed to attain any of the standards; b “x” indicates known controls were applied

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

3A-8 Table 3A-6. Geographic Areas for Application of NO x a 

### Controls in the Baseline and Alternative Standard Analyses – California b Geographic Areas and Control Groups Baseline 70 ppb 65 ppb California California North Identified x California North Unidentified U U U California South Identified x California South Unidentified U U U

a All reductions were calculated using the maximum reductions algorithm b “x” indicates known controls were applied; “U” indicates unknown control reductions.


### Table 3A-7. Geographic Areas for Application of VOC a 

### Controls in the Baseline and Alternative Standard Analyses – California b Geographic Areas and Control Groups Baseline 70 ppb 65 ppb California California North – San Joaquin x Identified U U U Unidentified California South – Los Angeles Identified x Unidentified U U U

a All reductions were calculated using the maximum reductions algorithm b “x” indicates known controls were applied; “U” indicates unknown control reductions.



### 3A.5 NO x 

### Control Measures for Non-EGU Point Sources Several types of NO x 

### control technologies exist for non-EGU point sources: selective catalytic reduction (SCR), selective noncatalytic reduction (SNCR), natural gas reburn (NGR), coal reburn, and low-NO x 

### burners (LNB). In some cases, LNB accompanied by flue gas recirculation (FGR) is applicable, such as when fuel-borne NO x 

### emissions are expected to be of greater importance than thermal NO x 

### 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
NO x 

### emissions. All of these control measures are available for application on industrial boilers.

Besides industrial boilers, other non-EGU point source categories covered in this RIA include petroleum refineries, kraft pulp mills, cement kilns, stationary internal combustion engines, glass manufacturing, combustion turbines, and incinerators. NO x 

### control measures

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

3A-9 available for petroleum refineries, particularly process heaters at these plants, include LNB, SNCR, FGR, and SCR along with combinations of these technologies. NO x 

### control measures available for kraft pulp mills include those available to industrial boilers, namely LNB, SCR, SNCR, along with water injection. NO x 

### control measures available for cement kilns include those available to industrial boilers, namely LNB, SCR, and SNCR. Non-selective catalytic reduction (NSCR) can be used on stationary internal combustion engines. OXY-firing, a technique to modify combustion at glass manufacturing plants, can be used to reduce NO x 

### at such plants. LNB, SCR, and SCR plus steam injection (SI) are available measures for combustion turbines. Finally, SNCR is an available control technology at incinerators.

Tables 3A-8 through 3A-11 contain lists of the NO x 

### and VOC control measures applied in these analyses for non-EGU point sources, EG Us, nonpoint sources, and nonroad sources. The table also presents the associated emission reductions for the baseline and alternative standard analyses. The number of geographic areas in which they were applied expanded as the level of the alternative standard analyzed became more stringent.

Table 3A-8. NO x 

### Control Measures Applied in the 70 ppb Analysis
NOx Control Measure Reductions (tons/year) Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 8,723 Biosolid Injection Technology - Cement Kilns 5,383 EGU SCR & SNCR 44,951 Episodic Burn Ban 2,797 Excess O3 Control 229 Ignition Retard - IC Engines 618 Low Emission Combustion - Gas Fired Lean Burn IC Engines 17,676 Low NOx Burner - Coal Cleaning 270 Low NOx Burner - Commercial/Institutional Boilers & IC Engines 21,417 Low NOx Burner - Gas-Fired Combustion 9,237 Low NOx Burner - Glass Manufacturing 247 Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 5,580 Low NOx Burner - Industrial Combustion 25 Low NOx Burner - Lime Kilns 2,433 Low NOx Burner - Natural Gas-Fired Turbines 6,276 Low NOx Burner - Residential Water Heaters & Space Heaters 19,900 Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 359 Low NOx Burner and Flue Gas Recirculation - Fluid Catalytic Cracking Units 84 Low NOx Burner and Flue Gas Recirculation - Iron & Steel 399 Low NOx Burner and SCR - Industr/Commercial/Institutional Boilers 8,408

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3A-10 NOx Control Measure Reductions (tons/year) Mid-Kiln Firing - Cement Manufacturing 1,241 Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 39,258 Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 2,832 OXY-Firing - Glass Manufacturing 11,984 Replacement of Residential & Commercial/Institutional Water Heaters 8,641 Selective Catalytic Reduction (SCR) - Cement Kilns 10,176 Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 1,709 Selective Catalytic Reduction (SCR) - Glass Manufacturing 3,481 Selective Catalytic Reduction (SCR) - IC Engines, Diesel 863 Selective Catalytic Reduction (SCR) - ICI Boilers 4,618 Selective Catalytic Reduction (SCR) - Industrial Incinerators 1,384 Selective Catalytic Reduction (SCR) - Iron & Steel 155 Selective Catalytic Reduction (SCR) - Process Heaters 784 Selective Catalytic Reduction (SCR) - Sludge Incinerators 100 Selective Catalytic Reduction (SCR) - Space Heaters 24 Selective Catalytic Reduction (SCR) - Utility Boilers 1,391 Selective Non-Catalytic Reduction (SNCR) - Cement Manufacturing 2,405 Selective Non-Catalytic Reduction (SNCR) - Coke Manufacturing 1,589 Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 58 Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 365 Selective Non-Catalytic Reduction (SNCR) - Sludge Incinerators 33 Ultra-Low NOx Burner - Process Heaters 329

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3A-11 Table 3A-9. VOC Control Measures Applied in the 70 ppb Analysis
VOC Control Measure Reductions (tons/year) Control Technology Guidelines - Wood Furniture Surface Coating 272 Control of Fugitive Releases - Oil & Natural Gas Production 9 Flare - Petroleum Flare 94 Incineration - Other 10,717 LPV Relief Valve - Underground Tanks 1,299 MACT - Motor Vehicle Coating 10 Permanent Total Enclosure (PTE) - Surface Coating 369 RACT - Graphic Arts 260 Reduced Solvent Utilization - Surface Coating 27 Reformulation - Architectural Coatings 5,246 Reformulation - Pesticides Application 171 Reformulation-Process Modification - Automobile Refinishing 220 Reformulation-Process Modification - Cutback Asphalt 655 Reformulation-Process Modification - Other 113 Reformulation-Process Modification - Surface Coating 178 Solvent Recovery System - Printing/Publishing 13 Wastewater Treatment Controls- POTWs 207

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3A-12 Table 3A-10. NOx Control Measures Applied in the 65 ppb Alternative Standard Analysis NOx Control Measure Reductions (tons/year) Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 16,423 Biosolid Injection Technology - Cement Kilns 5,907 EGU SCR & SNCR 109,503 Episodic Burn Ban 3,283 Ignition Retard - IC Engines 575 Low Emission Combustion - Gas Fired Lean Burn IC Engines 75,724 Low NOx Burner - Coal Cleaning 475 Low NOx Burner - Commercial/Institutional Boilers & IC Engines 36,210 Low NOx Burner - Fiberglass Manufacturing 65 Low NOx Burner - Gas-Fired Combustion 11,889 Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 21,918 Low NOx Burner - Industrial Combustion 25 Low NOx Burner - Lime Kilns 4,616 Low NOx Burner - Natural Gas-Fired Turbines 12,109 Low NOx Burner - Residential Water Heaters & Space Heaters 51,703 Low NOx Burner - Steel Foundry Furnaces 294 Low NOx Burner - Surface Coating Ovens 26 Low NOx Burner and Flue Gas Recirculation - (ICI) Boilers 477 Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 429 Low NOx Burner and Flue Gas Recirculation - Fluid Catalytic Cracking Units 59 Low NOx Burner and Flue Gas Recirculation - Iron & Steel 781 Low NOx Burner and Flue Gas Recirculation - Process Heaters 548 Low NOx Burner and Flue Gas Recirculation - Starch Manufacturing 67 Low NOx Burner and SCR - Industr/Commercial/Institutional Boilers 24,281 Low NOx Burner and SNCR - Industr/Commercial/Institutional Boilers 482 Natural Gas Reburn - Natural Gas-Fired E GU Boilers 590 Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 70,008 Non-Selective Catalytic Reduction - Nitric Acid Manufacturing 491 Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 8,791 OXY-Firing - Glass Manufacturing 27,100 Replacement of Residential Water Heaters 133 Selective Catalytic Reduction (SCR) - Ammonia Mfg 2,336 Selective Catalytic Reduction (SCR) - Cement Kilns 26,144 Selective Catalytic Reduction (SCR) - Coke Ovens 1,243 Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 4,078 Selective Catalytic Reduction (SCR) - IC Engines, Diesel 3,574 Selective Catalytic Reduction (SCR) - ICI Boilers 9,963 Selective Catalytic Reduction (SCR) - Industrial Incinerators 1,723 Selective Catalytic Reduction (SCR) - Iron & Steel 1,777 Selective Catalytic Reduction (SCR) - Process Heaters 2,744

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3A-13 NOx Control Measure Reductions (tons/year) Selective Catalytic Reduction (SCR) - Sludge Incinerators 1,771 Selective Catalytic Reduction (SCR) - Space Heaters 286 Selective Catalytic Reduction (SCR) - Taconite 4,248 Selective Catalytic Reduction (SCR) - Utility Boilers 1,391 Selective Non-Catalytic Reduction (SNCR) - Coke Mfg 2,880 Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 159 Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 1,057 Selective Non-Catalytic Reduction (SNCR) - Municipal Waste Combustors 67 Selective Non-Catalytic Reduction (SNCR) - Sludge Incinerators 113 Selective Non-Catalytic Reduction (SNCR) - Utility Boilers 235 Ultra-Low NOx Burner - Process Heaters 854

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3A-14 Table 3A-11. VOC Control Measures Applied in the 65 ppb Alternative Standard Analysis VOC Control Measure Reductions (tons/year) Control Technology Guidelines - Wood Furniture Surface Coating 2,988 Control of Fugitive Releases - Oil & Natural Gas Production 30 Flare - Petroleum Flare 108 Gas Recovery - Municipal Solid Waste Landfill 290 Improved Work Practices, Material Substitution, Add-On Controls - Printing 8 Improved Work Practices, Material Substitution, Add-On Controls -Industrial Cleaning Solvents 248 Incineration - Other 16,710 LPV Relief Valve - Underground Tanks 4,871 Low VOC Adhesives and Improved Application Methods - Industrial Adhesives 237 Low-VOC Coatings and Add-On Controls - Surface Coating 274 MACT - Motor Vehicle Coating 1,934 Permanent Total Enclosure (PTE) - Surface Coating 3,286 Petroleum and Solvent Evaporation - Surface Coating Operations 250 RACT - Graphic Arts 5,586 Reduced Solvent Utilization - Surface Coating 3,047 Reformulation - Architectural Coatings 52,378 Reformulation - Industrial Adhesives 1,110 Reformulation - Pesticides Application 3,957 Reformulation-Process Modification - Automobile Refinishing 4,879 Reformulation-Process Modification - Cutback Asphalt 2,555 Reformulation-Process Modification - Oil & Natural Gas Production 291 Reformulation-Process Modification - Other 546 Reformulation-Process Modification - Surface Coating 5,622 Solvent Recovery System - Printing/Publishing 854 Solvent Substitution and Improved Application Methods - Fiberglass Boat Mfg 14 Wastewater Treatment Controls- POTWs 234



### 3A.6 VOC Control Measures for Non-EGU Point Sources VOC controls were applied to a number of non-EGU point sources. Some examples are permanent total enclosures (PTE) applied to paper and web coating operations and fabric operations, and incinerators or thermal oxidizers applied to wood products and marine surface coating operations. A PTE confines VOC emissions to a particular area where they can be destroyed or used in a way that limits emissions to the outside atmosphere, and an incinerator or thermal oxidizer destroys VOC emissions through exposure to high temperatures (2,000 degrees Fahrenheit or higher). Another control is petroleum and solvent evaporation applied to printing and publishing sources as well as to surface coating operations.

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3A-15 3A.7 NO x 

### Control Measures for Nonpoin t (Area) and Nonroad Sources The nonpoint source sector of the emissions inventory is composed of sources that are generally too small and/or numerous to estimate emissions on an individual source basis (e.g., dry cleaners, residential furnaces, woodstoves, fireplaces, backyard waste burning, etc). Instead, we estimate their emissions for each county as a whole, often using an emissions factor that is applied to a surrogate of activity such as population or number of houses.

Control measures for nonpoint sources are also applied at the county level, i.e., to the county level emissions as a whole. Seve ral control measures were applied to NO x 

### emissions from nonpoint sources. One is low NO x 

### burner technology to reduce NO x 

### emissions. This control is applied to industrial oil, natural gas, and coal combustion sources. Other nonpoint source
controls include the installation of low-NO x 

### space heaters and water heaters in commercial and institutional sources, and episodic bans on open burning. The open burning control measure applied to yard waste and land clearing debris. It consists of periodic daily bans on burning such waste, as the predicted ozone levels indicate that such burning activities should be postponed. This control measure is not applied to any prescribed burning activities.

Retrofitting diesel nonroad equipment can provide NO x 

### and HC benefits. The retrofit strategies included in the RIA nonroad retrofit measure are:

• Installation of emissions after-treatment devices called selective catalytic reduction (“SCRs”)

• Rebuilding engines (“rebuild/upgrade kit”)

We chose to focus on these strategies due to their high NO x 

### emissions reduction potential and widespread application.

3A.8 VOC Control Measures for Nonpoint (Area) Sources Some VOC controls for nonpoint sources are for the use of low or no VOC materials for graphic art sources. Other controls involve the application of limits for adhesive and sealant VOC content in wood furniture and solvent source categories. The OTC solvent cleaning rule establishes hardware and operating requirements for specified vapor cleaning machines, as well

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3A-16 as solvent volatility limits and operating practices for cold cleaners. The Low Pressure/Vacuum Relief Valve control measure is the addition of low pressure/vacuum (LP/V) relief valves to gasoline storage tanks at service stations with Stage II control systems. LP/V relief valves prevent breathing emissions from gasoline storage tank vent pipes. Another control based on a California South Coast Air Quality Management District (SQAQMD) establishes VOC content limits for metal coatings along with application procedures and equipment requirements. Switching to Emulsified Asphalts is a generic control measure replacing VOC-containing cutback asphalt with VOC-free emulsified asphalt. The Reformulation control measures include switching to and/or encouraging the use of low-VOC materials.

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

Overview This chapter provides estimates of the engineering costs of the control strategies presented in Chapter 3 for the revised primary standard of 70 ppb and an alternative standard level of 65 ppb and summarizes the data sources and methodologies used to estimate the engineering costs presented in this regulatory impact analysis (RIA). As discussed in Chapter 3, identified control measures were applied to EGU, non-EGU point, nonpoint (area), and nonroad mobile sources to demonstrate attainment with the revised and alternative standards analyzed. 50


### In several areas identified controls did not achieve the emissions reductions needed to attain the revised and alternative standards analyzed. In these areas, the EPA assumed that further controls would be applied to reach attainment. These additional controls are referred to as unidentified
controls.

The total cost estimates include the costs of both identified and unidentified control technologies and measures. The estimated total costs of attaining the revised and alternative standards are partly a function of (1) assumptions used in the analysis, including assumptions about which areas will require emissions controls and the sources and controls available in those areas; (2) the level of sufficient, detailed information on identified control measures needed to estimate engineering costs; and (3) the future year baseline emissions from which the emissions
reductions needed to attain are measured.

The remainder of the chapter is organized as follows. Section 4.1 presents the engineering costs associated with the application of identified controls. Section 4.2 discusses the

50 In Chapter 3, Table 3-7 lists the specific control technologies applied in the identified control measures analysis. In addition, in the proposal RIA we discuss emissions reductions resulting from the application of known controls, as well as emissions reductions beyond known controls, or in short, known controls and unknown controls . In the final RIA we refer to those sets of emissions reductions and controls as identified controls or measures and unidentified controls or measures. This terminology has been used in prior NAAQS RIAs and reflects that we haveillustrated control strategies primarily using end-of-pipe controls and many additional controls that are not end-ofpipe (e.g., energy efficiency) that we have not identified here could also be part of a states’ strategies to reduce emissions.

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4-2 challenges associated with estimating costs for unidentified controls, including a brief discussion of some of the limitations of EPA’s control strategy tools and available data on NOx control technologies, and a brief discussion of the challenges in estimating baseline emissions over time. Section 4.3 presents the estimated costs associated with unidentified controls. Section 4.4 provides the total compliance cost estimates. Section 4.5 includes a discussion of potential economic impacts. Section 4.6 concludes with a discussion of the uncertainties and limitations associated with these components of the RIA.

4.1 Estimating Engineering Costs The engineering costs described in this chapter generally include the costs of purchasing, installing, operating, and maintaining the technologies applied. The costs associated with monitoring, testing, reporting, and recordkeeping for affected sources are not included in the annualized cost estimates as this data is not generally available and can vary substantially from one facility to another. For a variety of reasons, actual control costs may vary from the estimates the EPA presents. As discussed throughout this analysis, the technologies and control strategies selected for analysis illustrate one way in which nonattainment areas could meet a revised standard. There are numerous ways to construct and evaluate potential control programs that would bring areas into attainment with a revised standard, and the EPA anticipates that state and local governments will consider programs best su ited for local conditions. In addition, the EPA recognizes that there is substantial uncertainty in the portion of the engineering cost estimates associated with unidentified controls. The estimates presented herein are based on assumptions about the sectors and technologies that might become available for cost-effective control application in the future.

The engineering cost estimates are limited in their scope. This analysis focuses on the
emissions reductions needed for attainment of the revised standard and an alternative standard analyzed. The EPA understands that some states will incur costs both designing State Implementation Plans (SIPs) and implementing new control strategies to meet final revised standards. However, the EPA does not know what specific actions states will take to design their SIPs to meet final revised standards. Therefore, we do not present estimated costs that government agencies may incur for managing the requirement, implementing these (or other)

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4-3 control strategies, or for offering incentives that may be necessary to encourage the implementation of specific technologies, especi ally for technologies that are not necessarily
market driven.

4.1.1 Methods and Data
The EPA uses the Control Strategy Tool (CoST) (U.S. EPA, 2014a) to estimate engineering control costs. CoST was used in two parts of the analysis. First, CoST was applied to help determine potential NOx and VOC emissions reductions for each of the emissions sensitivity regions (see Chapter 2 Figure 2.2 for a map of these regions). Secondly, CoST was used to estimate the identified controls costs for the measures identified in Chapter 3. We estimated costs for non-electric generating unit point (non-EGU point), nonpoint, and mobile nonroad sources. CoST calculates engineering costs using one of two different methods: (1) an equation that incorporates key operating unit information, such as unit design capacity or stack flow rate, or (2) an average annualized cost-per -ton factor multiplied by the total tons of reduction of a pollutant. Most control cost information within CoST was developed based on the cost-per-ton approach because estimating engineering costs using an equation requires much more detailed data, and parameters used in these equations are not readily available or broadly representative across sources within the emissions inventory. The cost equations used in CoST estimate annual, capital and/or operating and maintenance (O&M) costs and are used primarily for some larger sources such as industrial/commercial/institutional (ICI) boilers and petroleum refinery process heaters. Information on CoST control measures, including cost-per-ton factors and cost equations, can be found in the tool documentation. 51 

### Costs for selective reduction catalysts (SCR) applied as part of the analysis for reducing NO x 

### emissions at coal-fired electric generating units (EGUs) were estimated using documentation for the Integrated Planning Model (IPM) (Sargent & Lundy, 2013).

When sufficient information is available to estimate a control cost using equations, the capital costs of the control equipment must be annualized. Capital costs are converted to annual

51 CoST documentation is available at: http://www.epa.gov/ttnecas1/cost.htm

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4-4 costs using the capital recovery factor (CRF). 52 

### The engineering cost analysis uses the equivalent uniform annual costs (EUAC) method, in which annualized costs are calculated based on the equipment life for the control measure and the interest rate incorporated into the CRF. Annualized costs represent an equal stream of yearly costs over the period the control technology is expected to operate. Where possible, calculations are used to calculate total annual control cost (TACC), which is a function of capital costs (CC) and O&M costs. Operating costs are calculated as a function of annual O&M and other variable costs. The resulting TACC equation is TACC = (CRF * CC) + O&M. For more information on the EUAC method and the TACC, refer to the EPA Air Pollution Control Cost Manual (U.S. EPA, 2003) and EPA’s Guidelines for Preparing Economic Analyses, Chapter 6 (US. EPA, 2014b).

Engineering costs will differ depending on the quantity of emissions reduced, emissions unit capacity, and stack flow, which can vary over time. Engineering costs will also differ in nominal terms by the year for which the costs are calculated (e.g., 2011$ versus 2008$). 53 

### For capital investment, in order to attain standards in 2025 we assume capital investment occurs at the beginning of 2025. We make this simplifying assumption because (i) we do not know what all firms making capital investments for control measures will do and when they will do it and (ii) we do not have nor know of a better data source with possible capital investment schedules. The estimates of annualized costs include annualized capital and annual O&M costs for those controls included in the identified control strategy analysis. We make no assumptions about capital investments prior to 2025 or additional capital investment in years beyond 2025. The controls applied and their respective engineering costs are described in the Chapter 4 Appendix.

CoST relies on detailed data from the National Emissions Inventory (NEI), including detailed information by source on emissions, installed control devices, and control device efficiency. Much of this underlying NEI data serves as key inputs into the control strategy analysis. The EPA receives NEI submissions from state, local, and tribal (SLT) air agencies.

52 The capital recovery factor incorporates the interest rate and equipment life (in years) of the control equipment. The capital recovery factor formula is expressed as r*(1+r)^n/[(1+r)^n -1]. Where r is the real rate of interest and n is the number of time periods. EPA generally uses 7% for all non-EGU sectors. For the EGU sector, 5% is used.
53 The engineering costs will not be any different in real (inflation-adjusted) terms if calculated in 2011 versus other

year dollars, if the other-year dollars are properly adjusted. For this analysis, all costs are reported in real 2011 dollars. Commented [A19]: Please see edit. Not wedded to any particular language. Just want to flag for the readers than 7% is used.

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4-5 Information on whether a source is currently controlled, by what control device, and control device efficiency, is required under the Air Emissions Reporting Rule (AERR) used to collect the NEI data. This information is only required to be provided when controls are present for the sources. Since controls are not present on every source, it is not possible for the EPA to enforce systematically (i.e., through electronic reporting) the requirement to report control devices. As a result, control information may not be fully reported by SLT agencies and would therefore not be available for purposes of the control strategy analysis.

As indicated earlier, EPA needed to determine the universe of potential NOx and VOC controls and emissions reductions for each of the emissions sensitivity regions. To accomplish this, the EPA reviewed the emissions inventory and universe of potential control information from CoST to identify and employ (i) size thre sholds for minimum emissions reductions (e.g., applying a control device should result in a minimum of 5 tons of NOx emissions reductions), (ii) size thresholds for application of control devi ces (e.g., apply a control device to sources of 25 tons of NOx emissions or more), and (iii) cost-p er-ton thresholds for appl ying controls from the CoST database (e.g., do not apply controls that cost more than $19,000/ton to reduce NOx emissions). The above steps are taken to mitigate potential double counting of controls due to possible missing control measure information in the NEI and to reduce the number of cases where additional control measures are applied in impractical circumstances.

The highest cost-per-ton estimates are often associated with controls that reduce very small increments of NOx emissions or are unique applications of a particular control. For example, in some cases, controls that were developed primarily to address other pollutant emissions, such as SO 2 

### , also achieve NO X 

### reductions and could be applied for this purpose. These controls are well characterized in the CoST database because they have been used for SO 2 

### control, but the degree to which sources would adopt these controls specifically to obtain NO X 

### reductions is uncertain. To reduce the number of cases where additional control measures are applied in impractical circumstances, we selected cost-per-ton thresholds for applying both NOx and VOC controls from the CoST database. We aggregated the raw data on all identified controls for NOx in the control measures database by cost per ton and plotted an identified control cost curve. It is important to note that this identified control cost curve is not a complete representation of the marginal abatement cost curve. A marginal abatement cost curve presents

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4-6 the least-cost approach to achieving any specific level of emissions reduction. In contrast, the identified control cost curve is a series of cost-per-ton estimates based on a specific emissions inventory combined with details from CoST about possible control measures that could be applied. The identified control cost curve defines how many tons of emissions reductions can be achieved at various cost levels from identified control technologies. While emissions reductions and their associated costs may be available for many different control measures, not all of these measures will be the most cost-effective way of achieving a given level of abatement, and therefore should not be used to construct the marginal abatement cost curve. In addition, we lack information on the control measures and costs for the remaining uncontrolled NOx emissions (see more detailed discussion on incomplete representation of marginal abatement cost curve in section 4.2).

Because the identified control cost curve reflects incomplete information, it is necessary to take steps to identify likely impractical control applications and to remove them from the analysis. 54 

### We determined that applying an exponential trend line would produce a reasonable cost threshold for identified controls, and we used the assumption in this analysis. To determine a cost threshold for identified NOx controls, we used the full dataset on NOx control measures and plotted an exponential trend line through the identified control cost curve. 55 

### Figure 4-1
shows the identified control cost curve for all the NOx control measures contained in the CoST database, aggregated by cost per ton, and the exponential trend line. As the figure indicates, the curves intersect at $19,000 per ton, meaning control costs above $19,000 per ton begin increasing at more than an exponential rate. We selected $19,000 per ton as the control cost value above which we would not apply additional identified NOx controls because controls above this value are not likely to be cost-effective. In the control strategy analysis for 70 ppb, there are a total of only eight control applications in three geographic areas where identified NOx controls are applied at a cost of $19,000/ton. In addition, for a standard of 70 ppb, in east Texas, the Northeast, the Great Lakes, and the Ohio River Valley there are a total of 25 control

54 Examples of control applications that could be removed from the analysis include: (i) applying SCR to small lean burn natural gas-fired reciprocating internal combustion engines to reduce NOx emissions -- these units are often in very remote locations and the requirements for ammonia or urea storage and replenishment are not practical, and ii) retrofit controls on small ICI boilers with space limitations that make the retrofit too difficult,
55 The full dataset on NOx control measures includes approximately 120,000 individual observations, and when aggregated by cost per ton, the dataset includes 1,500 observations.

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4-7 applications between $15,000/ton and $19,000/ton, representing approximately 5 percent of the total cost of identified NOx controls and approximately 1 percent of the total NOx emissions
reductions from identified controls.

Figure 4-1. Identified Control Cost Curve for 2025 for All Identified NO x 

### Controls for All Source Sectors (EGU, non

- EGU Point, Nonpoint, and Nonroad)

In Section 4.3 we present an average cost-per-ton approach to estimate the costs of achieving any additional NOx emission reductions that may be needed after the application of
the identified controls discussed above. 56 

### That is, we apply a constant, average cost per ton of $15,000/ton to capture total costs associated with the NOx emissions reductions achieved through unidentified controls. A portion of these total costs is likely at a value below $15,000/ton and a portion is likely at a value above $15,000/ton. The control cost data used in Figure 4-1

56 We do not apply unidentified VOC control measures in the control strategy analyses. Commented [A20]: Please explain why this is reasonable when known costs are higher than unknown costs.

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4-8 reflects the universe of potential NOx controls from CoST prior to any thresholds being applied. This raw data has a median control cost of $10,400/ton and 97 percent of the emissions
reductions from these controls are availa ble at a cost less than $15,000/ton. 57 

### Because we apply very few identified controls above $15,000/ton and because the $15,000/ton average cost value associated with unidentified measures is intended to capture costs above and below that value, we believe it is reasonable to estimate costs from unidentified measures using a $15,000/ton average cost, even though that value is slightly lower than the $19,000/ton value above which we do not apply additional identified NOx controls.

In the control strategy analyses, identified VOC controls are applied in the non-EGU point and nonpoint emissions sectors and in (i) fewer locations than identified NOx controls, and (ii) specific locations where the relative effectiveness of VOC controls will have a greater effect on ozone concentrations. For example, in analyzing emissions reductions needed for a standard of 70 ppb, we applied identified VOC controls only in a portion of the Houston buffer region, while we applied identified NOx controls in five larger geographic locations. Because identified VOC controls are generally more expensive than identified NOx controls and are only effective in a limited number of locations, it is reasonable to define a separate and higher cost threshold for applying VOC controls (for a detailed discussion of the contribution of VOC emissions to ozone formation, see Chapter 2, Section 2.1 of the November 2014 proposal RIA). We aggregated the raw data on all available identified measures for VOC in the control measures database by cost per ton and plotted an identified control cost curve for VOC controls. The dataset on VOC controls is significantly less robust with approximately 14,000 individual observations and 100 observations when aggregated by cost per ton, and the identified control cost curve revealed a clear point -- $33,000 per ton -- above which costs began increasing at more than an exponential rate. Therefore, we selected $33,000 per ton as the control cost value above which we would not apply additional identified VOC controls. In the control strategy analysis for 70 ppb, there are a total of only six applications in one geographic area (Houston) where identified VOC controls are applied at a cost of $33,000/ton. Figure 4-2 represents the

57 In the raw data, the average control cost is $17,800/ton and the emissions-weighted average cost is $3,000/ton. The average control cost is influenced by a few very high cost control applications that we do not apply in the identified control strategy analyses.

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4-9 identified control cost curve for all VOC control measures contained in the CoST control measures database, aggregated by cost per ton. As with the NOx identified control cost curve, it is important to note that this curve provides an incomplete representation of the marginal
abatement cost curve for all VOC abatement because we do not have information on the control measures and costs for the remaining uncontrolled VOC emissions (see more detailed discussion on incomplete representation of marginal abatement cost curve in section 4.2).

Figure 4-2. Identified Control Cost Curve for 2025 for All Identified VOC Controls for All Source Sectors (EGU, non

- EGU Point, Nonpoint, and Nonroad)
4.1.2 Engineering Cost Estimates for Identified Controls
In this section, we provide engineering cost estimates for the identified controls detailed in Chapter 3 that include control technologies for EGUs, non-EGU point, nonpoint and mobile nonroad sources. Onroad mobile source controls were not applied because they are largely addressed in existing rules such as the recent Tier 3 rule. Engineering costs generally refer to the equipment installation expense, the site preparation costs for the application, and annual

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4-10 operating and maintenance costs. Note that in many cases the application of these control strategies does not result in areas reaching attainment for the revised ozone standard of 70 ppb and alternative standard of 65 ppb and additional emission reductions beyond identified controls are needed (unidentified controls).

See Table 4-1 for summaries of control costs from the application of identified controls for the final standard of 70 ppb and an alternative standard of 65 ppb. Costs are listed by sector for both the eastern and western U.S., except California. Note that any incremental costs for identified controls for California (post-2025) for the revised standard of 70 ppb and an alternative standard of 65 ppb are zero because all identified controls for California were applied in the demonstration of attainment for the current standard of 75 ppb (baseline). We aggregate results by region – East and West, except California – to present cost and benefits estimates. See Figure 4.3 for a representation of these regions.

Table 4-1. Summary of Identified Annualized Control Costs by Sector for 70 ppb and 65 ppb for 2025 - U.S., except Cali fornia (millions of 2011$) a Geographic Area Emissions Sector Identified Control Costs for 70 ppb Identified Control Costs for 65 ppb 7 Percent Discount Rate b 7 Percent Discount Rate b

East EGU 52 c 130 c Non-EGU Point 260 d 750 d Nonpoint 360 1,500 Nonroad 13 e 36 e Total 690 2,400

West EGU - - Non-EGU Point 4 d 49 d Nonpoint <1 88 Nonroad - 4 e Total 4 140 Total Identified Control Costs 690 2,600

a All values are rounded to two significant figures. b The numbers presented in this table reflect the engineering costs annualized at a 7 percent discount rate, to the extent possible.

c EGU sector control cost data is calculated using a capital charge rate between 7 and 12 percent for retrofit controls depending on the type of equipment. d A share of the non-EGU point source sector costs can be calculated using both 3 and 7 percent discount rates. When applying a 3 percent discount rate where possible, the total non-EGU point source sector costs are $250 million for 70 ppb and $740 million for 65 ppb.

e Nonroad sector control cost data is calculated using a 3 percent discount rate.

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4-11 The total annualized engineering costs associated with the application of identified controls, using a 7 percent discount rate, are approximately $690 million for the final annual standard of 70 ppb and $2.6 billion for a 65 ppb alternative standard. Table 4-2 below provides summary statistics by emissions source category of the NOx and VOC control cost data from the identified control strategy for the revised standard of 70 ppb. 58 

### The costs of NO x 

### controls, in terms of dollars per ton of NO x 

### reduction for the standards analyzed were approximately $1,200/ton on average for the EGU sector. 59 

### The costs of NOx controls were $2,600/ton for the non-EGU point sector on average, with a range of $0/ton to $19,000/ton, a median of $960/ton, and an emissions weighted average of $2,800/ton; $760/ton for the nonpoint sector on average, with a range of $0 to $2,000/ton, a median of $970/ton, and an emissions weighted average of $1,000/ton; and $4,600/ton for the nonroad sector on average, with a range of $3,300/ton to $5,300/ton, a median of $4,600/ton, and an emissions weighted average of $4,500/ton. The costs of VOC controls in terms of dollars per ton of VOC reduction for the standards analyzed were approximately $11,000/ton for the non-EGU point sector on average, with a range of $1,200/ton to $25,000/ton, a median of $9,800/ton, and an emissions weighted average of $8,100/ton; and $11,000/ton for the nonpoint sector on average, with a range of $24 to $33,000/ton, a median of $15,000/ton, and an emissions weighted average of $14,000/ton.

58 Across all of the data in the control strategy analysis for a standard of 70 ppb, the average control cost is $5,000/ton and the emissions-weighted average cost is $2,000/ton.
59 After accounting for the Clean Power Plan in the Baseline (see Chapters 2 and 3), remaining EGUs not affected by the Clean Power Plan were plants where NO X controls existed, but had not been dispatched. This dollar per ton value represents the average operation and maintenance cost of running such controls.

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4-12 Table 4-2. NO x 

### and VOC Control Costs Applied for 70 ppb in 2025 – Average, Median, Minimum, Maximum, and Emissions Weighted Average Values ($/ton) a

Emissions Sector Average Cost/Ton Median Cost/Ton Minimum Cost/Ton Maximum Cost/Ton Emissions Weighted Average Cost/Ton
NO x Controls

EGU 1,200 1,200 1,200 1,200 1,200 Non-EGU Point 2,600 960 0 19,000 2,800 Nonpoint 760 970 0 2,000 1,000 Nonroad 4,600 4,600 3,300 5,300 4,500 VOC Controls Non-EGU Point 11,000 9,800 1,200 25,000 8,100 Nonpoint 11,000 15,000 24 33,000 14,000

a The numbers presented in this table reflect the engineering costs annualized at a 7 percent discount rate to the extent possible. EGU control cost data is calculated using a capital charge rate between 7 and 12 percent for retrofit controls depending on the type of equipment. Nonroad control cost data is calculated using a 3 percent discount rate.

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4-13 Figure 4-3. Regions Used to Present Emissions Reductions and Cost Results The numbers presented in Tables 4-1 and 4-2 reflect the engineering costs annualized at the different discount rates discussed below and include rates of 3 percent and 7 percent, which is to the extent possible consistent with the guidance provided in the Office of Management and Budget’s (OMB) (2003) Circular A-4. Discount rates refer to the rate at which capital costs are
annualized. 60 

### A higher discount, or interest, rate results in a larger annualized cost of capital

60 In the cost analysis, the discount rate refers to the interest rate used in the discounted cash flow analysis to determine the present value of future cash flows. A social discount rate is a discount rate used in computing the value of monies spent on social projects or investments, such as environmental protection. The social discount rate is directly analogous to the discount rate we use in the engineering cost analysis, as well as certain rates used in corporate finance (e.g., hurdle rate or a project appropriate discount rate), so the mathematics are identical. In benefits analyses, the discount rate is used to discount benefits that occur in time periods after the year in which emissions reductions take place. As a result, the way the discount rate is used in the cost analysis is different from the way it is used in the benefits analysis. For an explanation of the benefits calculations, see Chapter 6. In both cases, the values at different discount rates do not indicate that the value is the present value of a stream of annualized benefits or costs.

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4-14 estimate. It is important to note that it is not possible to estimate both 3 percent and 7 percent discount rates for a number of the controls included in this analysis. Because we obtain control cost data from many sources, we are not always able to obtain consistent data across original
data sources. 61 

### If disaggregated control cost data is not available (i.e., where capital, equipment life value, and O&M costs are not separated out and where we only have a $/ton value), EPA assumes that the estimated control costs are annualized using a 7 percent discount rate. When disaggregated control cost data is available (i.e ., where capital, equipment life value, and O&M costs are separated out) we can and do recalculate costs using a 3 percent discount rate. For the engineering costs provided in this analysis, we estimate costs for the sectors as follows:

 For EGU controls , the annualized EGU control costs were not estimated for either 3 or 7 percent. This is due to the complexity of investment decisions in the EGU sector. Decisions about investments in control equipment are not uniform across the sector, are made in different time frames, with different loan rates and thus, ultimately different capital recovery factors. Equipment pay off times, depreciation rates and capacities that factor into the capital charge rate vary. According to the IPM v5.13 documentation (U.S. EPA, 2013 Chapters 5 and 8), capital charge rates can vary from 7 percent to 12 percent depending on the type of equipment. See the IPM v5.13 documentation cited for a more in depth discussion. EGU control costs represent 8 percent and 5 percent of the compliance cost estimates for identified controls for the final standard of 70 ppb and an alternative standard level of 65 ppb, respectively.

 For non-EGU point source controls , some disaggregated data are available, and we were able to calculate costs at both 3 and 7 percent discount rates for those controls. For the final and alternative standards analyzed in this RIA,approximately 29 and 24 percent, respectively, of identified control costs for nonEGU point sources are disaggregated at a level that could be recalculated at a 3 percent discount rate. Non-EGU point source control costs represent 38 percent

61 Data sources can include state and technical studies, which frequently do not include the original data source.

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4-15 and 31 percent, respectively, of the compliance cost estimates for identified controls for the final standard of 70 ppb and alternative standard level of 65 ppb.

 For nonpoint source controls , because we do not have disaggregated control
cost data total annualized costs for these sectors are assumed to be calculated using a 7 percent discount rate. Nonpoint source control costs represent 53 percent and 62 percent, respectively, of the compliance cost estimates for
identified controls for the final standard of 70 ppb and an alternative standard level of 65 ppb.

 For nonroad mobile source controls , the cost estimates for control of emissions from nonroad diesel engines are prepared using a net present value (NPV) approach, which is different from the approach applied for other sources whose emissions are controlled in the illustrative control strategies applied in the RIA (U.S. EPA, 2007). To be consistent with the engineering cost estimates for other emissions sources, we would need to use the EUAC method to calculate control costs for nonroad diesel engines. To use the EUAC method we need information on the portion of annual costs that is from the annualization of the original capital expense for these nonroad controls and the portion that is from annual operation and maintenance. The cost estimates for the nonroad diesel engine retrofit controls did not include estimates for operating costs, and we do not have sufficient information to determine if the annual cost estimates reflect only capital costs. As a result, we are unable to estimate annual costs at interest rates of 3 percent and 7 percent for these controls. The nonroad diesel engine retrofit costs are estimated using a 3 percent interest rate. 62 

### Nonroad mobile source control

62 The capital recovery factor, used to convert capital costs to annual costs, requires both an interest rate and an equipment life. While we do have the expected lifetime for these controls, we are not able to estimate these costs at a different interest rate using the EUAC based on the lack of annualized capital cost data.

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4-16 costs represent 2 percent of the compliance cost estimates for identified controls for the final standard of 70 ppb and an alternative standard level of 65 ppb.

Table 4-3 summarizes the discount rates discussed above and the percent of total identified control costs for each emissions sector for the final standard of 70 ppb and an alternative standard of 65 ppb. Because we do not have a full set of costs at the 3 percent discount rate or the 7 percent discount rate and because we believe the majority of the identified control costs is calculated at a 7 percent discount rate, Table 4-1 presents engineering cost estimates based on a 7 percent discount rate.

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

Emissions Sector Discount Rate Percent of Total Identified Control Costs for 70 pp b Percent of Total Identified Control Costs for 65 pp b EGU 7 – 12% 8 5 Non-EGU Point 3 and 7% 38 31 Nonpoint 7% 53 62 Nonroad 3% 2 2 Total 100% 100%


### 4.2 The Challenges of Estimating Co sts for Unidentified Control Measures Some areas are unable to attain the revised and alternative levels of the standard using only identified controls. In these areas, it is necessary to assume the application of currently unidentified control measures to estimate the full cost of attaining the standards analyzed. The EPA’s application of unidentified control measures does not mean the Agency has concluded that all unidentified control measures are currently not commercially available or do not exist. Unidentified control technologies or measures can include existing controls or measures for which the EPA does not have sufficient data to accurately estimate engineering costs. Likewise, the control measures in the CoST database do not include abatement possibilities from energy efficiency measures, fuel switching, input or process changes, or other abatement strategies that are non-traditional in the sense that they are not the application of an end-of-pipe control. In addition, there will likely be some emissions reductions from currently unidentified control technologies as a result of state-specific rules that are not in the future year baseline emissions projections or are not yet finalized. See the discussion in Section 4.2.3 for examples of existing

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4-17 control measures for which the EPA does not have sufficient data to estimate engineering costs, as well as state-specific rules that are not in the future year baseline emissions projections.

The EPA’s application of unidentified control measures does reflect the Agency’s experience that some portion of controls to be applied in the future may not be currently available but will be deployed or developed over time. The EPA believes that a portion of the estimated emissions reductions needed to comply with a revised standard can be secured through future technologies, national regulatory programs, and/or state regulatory programs or measures for which information is either not currently complete or not currently available. As an example, in the 1997 ozone NAAQS RIA, NOx emissions reductions that were estimated from the mobile source Tier 2 standards were not considered as part of the “known” controls, even though theRIA acknowledged the potential for these mobile source standards to provide substantial costeffective controls and emissions reductions. While in 1997 these emissions reductions were considered to come from “unknown”, or unidentified, controls, in retrospect, they were achieved through mobile source controls. Looking forward, the EPA estimates that the Phase 2 of the Heavy Duty Greenhouse Gas Standards for New Vehicles and Engines 63 

### will provide additional
NOx emissions reductions. 64



### The remainder of this section presents and discusses various factors that should be considered when estimating the costs of applying costs to emission reductions from unidentified control measures for the future using only information on a limited set of today’s available control technologies or measures. We start with discussions about the role of technological innovation and change from the economics literature: Section 4.2.1 discusses the impact of technological innovation and diffusion on available control technologies; and Section 4.2.2

63 Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles (Phase 1 of the Heavy Duty Greenhouse Gas Standards for New Vehicles and Engines) was included in the 2025 base case (see Chapter 1, Section 1.3.1 for a list of rules in the base case).
64 The focus of the Phase 1 (76 FR 57106, September 15, 2011) and Phase 2 (80 FR 40138, July 13, 2015) Heavy Duty GHG rules is to reduce GHG emissions and fuel consum ption, but there can also be NOx reductions that stem largely from a switch in using the on-road engine to using an auxiliary power unit (APU) during extended idling. Because the Heavy Duty GHG standards are performance-based and manufacturers can choose their own mix of technologies to meet the standards, the standards provide an incentive for APU use but do not require it. Thus, the impact on NOx emissions depends on the assumptions and projections for APU use. The EPA expects increased APU usage would result from the Phase 2 rule. After considering the revised APU projections, the EPA estimates that the two Heavy Duty GHG rules combined would reduce NOx emissions by up to 120,000 tons in 2025 and 450,000 tons in 2050.

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4-18 presents information on improvements in control technologies over time through learning by doing.

Following the sections on the role of technological innovation and change, we include the following discussions related to limitations in the currently available information on traditional end-of-pipe technologies or measures and on projecting the future air quality problem being analyzed: Section 4.2.3 discusses the incomplete characterization of the supply of available control technologies and why the abatement supply curve from identified controls presented in the previous section provides an incomplete picture of all currently available pollution abatement opportunities; Section 4.2.4 discusses how over time as EPA reviews NAAQS standards, relevant information about future year baseline emissions and possible control technologies is revealed in the current RIA development process that was not available to analysts for previous RIAs; and Section 4.2.5 includes information on how NOx offset prices and Section 185 fees could serve as reasonable proxies for the costs associated with emissions reductions from unidentified controls. Finally, we describe how we use this information to help inform the unidentified control cost methodology applied in section 4.3.

4.2.1 Impact of Technological Innovation and Diffusion
In general, the marginal abatement cost curve (MACC) at any particular point in time for a defined set of emitting sectors will be an increasing function of the level of abatement. 65 

### That is, marginal costs are increasing as the amount of emissions are reduced. However, it is important to note that the MACC is not just the relationship between marginal cost and abatement, but also should be constructed as the envelope of least cost approaches for any given level of abatement. As previously noted, the identified control cost curve derived from data in CoST may include measures that may not be the most cost-effective way of achieving the emissions reduction, and as a result the cost curve derived using that data may not represent the complete MACC. The aggregated MACC is the horizontal summation of individual firms/sectors marginal abatement curves, and is generally thought to reflect the overall marginal and total abatement costs when a least cost approach is implemented. This aggregate MACC gives the efficient MAC level for

65 The marginal abatement cost curve (MACC) is a representation of how the marginal cost of additional emissions abatement changes with increasing levels of abatement. Commented [A21]: This discussion appears to be unbalanced currently. Suggest focusing on NOx reducing technologies specifically which will allow EPA to focus on more later stages of technology development (e.g., learning by doing) rather than earlier stages of technology development (e .g., technology cliff). Suggest deleting general discussion on pp 4-17 through 4-23. Given that SCR and other NOx reducing technolgoies have been around over 20 years, please offer learning-by-doing discussion specific to SCR, NOx adsorbers and other NOx or VOC reducing technologies.

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4-19 each firm/sector for any aggregate emissions target for a given time period. However, the MACC represents the efficient MAC level only under some fairly restrictive conditions, including 1) all abatement opportunities across all sectors and locations have been identified and included in the cost curve, and 2) information about applicability of controls is available with no uncertainty. In addition, the MACC for a current time period will only hold for a future time period if no technical change (either introduction of new technologies or reduction in cost of existing technologies) or learning by doing occurs between the present and future time periods.

In regulatory analyses of NAAQS, we typically assess costs of abatement in a future year or years selected to represent implementation of the standards. As such, a MACC constructed based on currently available information on abatement opportunities will not be the best representation of a future MACC. A future MACC will likely reflect technological innovation and diffusion, such as the introduction of new technologies or improvements in effectiveness or applicability of existing technologies. Additionally, environmental policy can create incentives and constraints that influence the rate and direction of technical change (Jaffe et al. 2002) as well as the rate of diffusion and adoption of the innovations (Sterner and Turnheim 2009).

Technological innovation and diffusion can affect the MACC in several ways. Some examples of the potential effects of technical change are:

1. New control technologies may be developed that cost less than existing technologies.

2. A new control technology may be developed to address an uncontrolled emissions

source.

3. The efficiency of an existing control measure may increase. In some cases, the control efficiency of a measure can be improved through technological advances.

4. The cost of an existing control measure may decrease.

5. The applicability of an existing control measure to other emissions sources may increase.

Overall, these five examples describe ways that technological change can reduce both the amount of unidentified abatement needed, shift the MACC, decrease the MAC, decrease average

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4-20 costs, and decrease total costs relative to the case where it is assumed that the current MACC reflects all possible abatement opportunities both in the present and future. It is also possible in cases where there is a strictly binding emissions reduction target that new control technologiescan be introduced and adopted with much higher marginal costs. However, if there are cost offramps, such as those provided by Section 185 of the CAA, those higher cost technologies may not be adopted (see Section 4.2.5 for a brief discussion of Section 185 fees).

Regulatory policies can also help induce technological change when a standard cannot be met either (1) with existing technology or (2) with existing technology at an acceptable cost, but over time market demand will provide incentives for industry to invest in research and development of appropriate technologies. These incentives are discussed in Gerard and Lave (2005), who demonstrate that the 1970 Clean Air Act induced significant technical change that reduced emissions for 1975 and 1976 automobiles. Those mandated improvements went beyond the capabilities of existing technologies by using regulatory pressure to incentivize the development of catalytic converting technology in 1975. Induced technological change can correspond to examples 1 through 3 above.

There are many other examples of low-emission technologies developed and/or commercialized over the past 15 or 20 years, such as:

1. Selective catalytic reduction (SCR) and ultra-low NOx burners for NOx emissions;

2. Scrubbers that achieve 95 percent or greater SO 2 

### control on boilers;

3. Sophisticated new valve seals and leak detection equipment for refineries and chemical plants to reduce VOC and HAP emissions;

4. Low or zero VOC paints, consumer products and cleaning processes;

5. Chlorofluorocarbon (CFC) free air conditioners, refrigerators, and solvents;

6. Water and powder-based coatings to replace petroleum-based formulations to reduce VOC and HAP emissions;

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4-21 7. Vehicles with lower NOx emissions than believed possible in the late 1980s due to improvements in evaporative controls, catalyst design and fuel control systemsfor light-duty vehicles; and treatment devices and retrofit technologies for heavyduty engines;

8. Idle-reduction technologies for engines to reduce NOx and PM 2.5 

### emissions, including truck stop electrification efforts; and

9. Improvements in gas-electric hybrid vehicles and cleaner fuels to reduce NOx
emissions.

These technologies were not commercially available two decades ago, and some were not even in existence. Yet today, all of these technologies are on the market, and many are widely employed. Several are key components of major pollution regulatory programs.

As Brunnermeier and Cohen (2003) demonstrate, there is a positive correlation, other things held constant, between environmental innovations (measured as the number of relevant environmental patent applications) and specific regulations imposed on an industry (measured in terms of the frequency of government compliance inspections). Lanjouw and Mody (1996) show empirically a positive relationship between responses to environmental regulations (i.e., increases in pollution abatement expenditure) and new technology (i.e., relevant patent applications) in the United States, Japan, and Germany. They show that in each of these countries, even though on different timelines, the share of environmental patents increased considerably in response to stricter environmental regulations. Similarly, Popp (2004) studied the relationship between environmental regulation and new technology focusing on SO 2 

### and NOx. The study was performed using patent data from the United States, Japan, and Germany. Popp found that more stringent regulation enhanced domestic patenting by domestic inventors.

While regulation may influence the direction and intensity of emissions-related research and development activities, “crowding out” of investment resources may occur as resources are directed away from other opportunities, potentially leading to opportunity costs that offset savings resulting from research and development successes (Popp and Newell 2012). In a study that links energy-related patent activity and firm financial data, Popp and Newell (2012) find that

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4-22 while increases in alternative energy patents result in fewer patents for other energy technologies, this result is due to firm-level pr ofit-maximizing behavior rather than constraints on the magnitude of research and development resources. Alternatively, Kneller and Manderson (2012) find evidence in the United Kingdom that environment-related research and development resulting from more stringent regulation may crowd out other research and development activities but that environment-related capital does not crowd out non-environmental capital. Another factor to consider is the degree to which a particular sector is likely to be close to fully controlled, e.g., in comparing existing emissions with uncontrolled emissions levels, is the percent of control close to 100 percent? In those cases, achieving additional reductions through technological change is likely to be more difficult and costly, because the benefits of investment in those technologies is smaller, due to smaller remaining potential for abatement.

4.2.2 Learning by Doing
As experience is gained in the application of control technologies or pollution control practices, firms learn how to operate the controls more efficiently and learn how to apply controls to additional sources. What is known as “learning by doing” or “learning curve impacts” has also made it possible to achieve greater emissions reductions than had been feasible earlier, or reduce the costs of emissions control relative to original estimates. Learning curve impacts can be defined generally as the extent to which variable costs (of production and/or pollution control) decline as firms gain experience with a specific technology. Such impacts have been identified to occur in a number of studies conducted for various production processes. These impacts manifest themselves as a lowering of expected costs for operation of technologies in the future below what they may otherwise have been. For example, Rubin et al. (2004) show that capital costs of flue gas desulphurization (FGD) and selective catalytic reduction (SCR) systems have decreased over time as a result of research and development activities and learning by doing, among other factors, and that failing to account for these technological dynamics can lead to incorrect estimates of future regulatory costs.

Rubin et al. (2012) discuss how the cost of control technologies can decline over time using the example of post-combustion SO 2 

### and NOx combustion systems. After an increase in costs during an initial commercialization period, costs decreased by at least 50 percent over the

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4-23 course of two decades. The 1997 Ozone NAAQS RIA includes information on historical and projected “progress ratios” for existing technologies. These ratios show declining costs over time, due to learning by doing, economies of scale, reductions in O&M costs, and technological improvements in manufacturing processes. Other discrete examples include the dramatic 85 percent decline in prices of the catalyst used in operating SCR between 1980 and 2005 (Cichanowicz, 2010). In addition, analyses performed for the 2008 Ozone NAAQS RIA found controls originally developed for one source type were being applied to new source categories. For example, SCR, originally developed for use by EGUs, is now used in the cement manufacturing sector, and SNCR is now pertinent to a large number of additional boiler source categories. In some cases, these newly found controls proved to be more effective than what had been applied in the past. For example, for industrial and manufacturing incinerators, where previously SNCR was the NOx control technology, SCR was applied in 2008, increasing the control efficiency from 45 percent to 90 percent. These examples serve as evidence of a learning effect – production and implementation costs decrease as learning and repetitive use occurs.

A typical learning curve adjustment is to reduce either capital or operation and maintenance costs by a certain percentage given a doubling of output from that sector or for that technology. In other words, capital or operation and maintenance costs will be reduced by some percentage for every doubling of output for the given sector or technology. The magnitude of learning curve impacts on pollution control costs was estimated for a variety of sectors as part of the cost analyses done for the Direct Cost Estimates Report for the Second EPA Section 812 Prospective Analysis of the Clean Air Act Amendments of 1990. 66 

### In the Report, learning curve adjustments were included for those sectors and technologies for which learning curve data were available. For all technologies and industries, a default learning rate of 10 percent was adopted based on SAB advice. No adjustments were used for on-road and non-road controls. The 10 percent adjustment is a 10 percent cost reduc tion per doubling of emission reductions. The literature supports a rate of up to 20 percent for many technologies (Dutton and Thomas, 1984).

66 Industrial Economics, Incorporated and E.H. Pechan and Associates, Direct Cost Estimates for the Clean Air Act Second Section 812 Prospective Analysis: Final Report, prepared for U.S. EPA, Office of Air and Radiation, February 2011. Available at http://www.epa.gov/cleanairactbenefits/feb11/costfullreport.pdf.

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4-24 The impact of this on costs in the Report was to reduce costs of local controls in nonattainment areas by 9.9 percent in 2020.

Learning by doing can reduce costs in a number of ways: through the reduction of operating and maintenance costs, finding new ways to use existing technologies, etc. Due to learning, potential abatement has increased at a cost less than the cost threshold. For this RIA, however, we do not have the necessary data and resources to properly generate control costs that reflect learning curve impacts.

4.2.3 Incomplete Characterization of Available NO x 

### Control Technologies
Our experience with Clean Air Act implementation shows that numerous factors, such as technical change and development of innovative strategies, can lead to emissions reductions, or abatement, that may not seem possible today, while potentially reducing costs over time. For example, facility-level data collected through the U.S. Census Bureau’s Pollution Abatement Costs and Expenditures (PACE) survey suggests that this may have happened in the manufacturing sector in recent decades. Based on surveys of approximately 20,000 plants classified in manufacturing industries, the PACE data show during the 1994-2005 time period, a period of increasing regulatory stringency, spending on air pollution abatement as a percentage of revenues decreased for the manufacturing sector. 67 

### Although exogenous factors, such as changes in economic conditions, may have contributed to the relative share in costs of pollution abatement, it is also possible that technological change and innovation may have contributed to
this relative decline.

67 The Pollution Abatement Costs and Expenditures (PACE) survey collects facility-level data on pollution abatement capital expenditures and operating costs for compliance with local, state, and federal regulations and voluntary or market-driven pollution abatement activities. In 2005, the most recent year PACE data were collected, the U.S. manufacturing sector spent $3.9 billion dollars on air capital expenditures and incurred $8.6 billion dollars in operating costs for air pollution prevention and treatment. These figures represent less than 3% of total new capital expenditures and less than 0.18% of total revenue for the manufacturing sector, respectively. These percentages have declined since 1994, when air capital expenditures were less than 4% of total new capital expenditures and air pollution abatement operating costs were less than 0.2% of total revenue. Levinson (2009) finds that most of the pollution reductions in the U.S. come from changes in technology as opposed to changes in imports or changes in the types of domestically produced goods. He finds that even though manufacturing output increased by 24% from 1987 to 2001, emissions of four common air pollutants from the sector declined 25% over that time period and the most important factor contributing to the decrease in pollution is technical change or innovation.

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4-25 Underlying the selection of controls described in Appendix 3A is the concept of the MACC. Adding newly developed control technologies, or changing either the abatement amount or cost of the technology, will change the shape of the overall MACC. The engineering cost estimates in section 4.1 are estimated primarily from end-of-pipe controls and only included limited process-oriented control measures, such as switching to lower-emitting fuel or energy sources and installing energy efficiency measures. As a result, the MACC derived in the previous section from identified controls represents an incomplete supply curve that only partially captures the abatement supply. An illustrative depiction of an “observed but incomplete” MACC and the complete underlying MACC is presented below in Figure 4-4. In the figure, the solid line traces out a hypothetical observed MACC, while the dashed line characterizes the combination of observed and unobserved abatement possibilities. The inclusion of the unobserved abatement increases the supply of abatement.

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4-26 Figure 4-4. Observed but Incomplete MACC (Solid Line) Based on Identified Controls in Current Tools and Complete MACC (dashed line) where Gaps Indicate Abatement Opportunities Not Identified by Current Tools

Because of the incomplete characterization of the full range of NOx abatement possibilities, it is important to understand the composition of the cost information EPA has available and uses to construct the partial MACC. The nature of available information on the cost of NOx abatement measures is somewhat complex. EPA’s control strategy tools undergo continuous improvement, and as the need for additional abatement opportunities increases, additional evaluation of uncontrolled emissions takes place. During these evaluations, additional abatement opportunities from applying identified controls typically are found. These abatement opportunities or additional controls are added to the CoST database and will be available for future analyses. In addition, in some cases we may have specific knowledge of potential additional control measures due to an impending regulation (e.g., Tier 3), but until a regulation is finalized those identified controls are not included in any concurrent analyses.

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4-27It is also important to understand that EPA’s control strategy tools largely focus on end-ofpipe controls and a limited set of emissions inventory sectors, whereas opportunities for emissions reductions through non-end-of-pipe controls or measures exist. For example, we reviewed the existing control strategies indicated in the SIP for the Dallas-Fort Worth area for the 1997 ozone NAAQS, and we compared the strategies and measures in that SIP to the measures the EPA analyzed in the 1997 ozone NAAQS regulatory impact analysis. The EPA analyzed several industrial source categories and measures that were reflected in the 1997 Dallas-Fort Worth SIP, including existing control measures for stationary sources such as cement kilns, industrial boilers, iron and steel mills, as well as enhanced inspection and maintenance programs for mobile sources. 68 

### The Dallas-Fort Worth SIP recognized the need for additional control strategies and measures to achieve further emissions reductions – strategies and measures that were not reflected in EPA’s 1997 control strategy analysis. These additional control measures included transportation control measures, additional voluntary mobile emission reduction programs, and energy efficiency/renewable energy measures. Table 4-4 below includes examples of each of these types of programs or measures.

68 The Dallas-Fort Worth SIP also reflected the following existing voluntary mobile emission reduction programs: alternative fuel vehicle program; employee trip reduction program; and vehicle retirement program. Information on the Dallas-Fort Worth SIP is available at http://www.nctcog.org/trans/air/sip/future/lists.asp.

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4-28 Table 4-4. Control Measures in Dallas-Fort Worth SIP Not Reflected in the 1997 Ozone NAAQS RIA

Transportation Control Measures Bicycle/Pedestrian Projects Grade Separation Projects HOV/Managed Lane Projects Intersection Improvement Projects Park and Ride Projects Rail Transit Projects Vanpool Projects Voluntary Mobile Source Emission Reduction Pro g rams Clean Vehicle Program Employee Trip Reduction Locally Enforced Idling Restriction Diesel Freight Idling Reduction Progra m
Other State and Local Programs: Energy Efficiency/Renewable Energy Residential Building Code Commercial Building Code Federal Facilities Projects Political Subdivision Projects 69 Electric Utility-Sponsored Programs 70 Wind Power Projects Additional Measures Clean School Bus Program Texas Low Emission Diesel Stationary Diesel and Dual-Fired Engine Control Measures



### Further, Table 4-5 includes specific non-end-of-pipe control measures from approved SIPs in Texas and Louisiana, including measures from the Dallas-Fort Worth SIP (i.e., energy efficiency measures). The approved, non-end-of-pipe control measures include local transportation measures, local building energy efficiency requirements, and mobile source sector measures. In addition, Table 4-6 includes examples of approved non-end-of-pipe control measures in California. California is also currently developing the following additional

measures:

69 These projects are typically building system retrofits, non-building lighting projects, and other mechanical and electrical systems retrofits, such as municipal water and waste water treatment systems.
70 These programs include air conditioner replacements, ventilation duct tightening, and commercial and industrial equipment replacement.

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4-29  Encouraging Use of Warm Mix Asphalt over Hot Mix Asphalt - European and American companies have developed several techniques, collectively known as warm-mix asphalt (WMA), to increase the workability of asphalt by lowering the viscosity at temperatures as much as 100°F below that of hot-mix asphalt (HMA). WMA was introduced in Europe in 1997 and in the United States in 2002. WMA has shown potential for reducing emissions associated with the production of asphalt for paving projects when compared to HMA. Lower temperatures required for production, storage, transport, and application translates to lower fuel consumption, which in turn reduces the criteria air pollutant emissions associated
with combustion. 71



###  Replacement of gas-powered leaf blowers and mowers – The South Coast Air Quality Management District has a program that subsidizes the replacement of existing two-stroke backpack blowers currently used by commercial landscapers/gardeners with new four-stroke backpack blowers that have significantly reduced emissions and noise levels. 72



### All of these additional and non-end-of-pipe measures and associated emissions reductions are not reflected in EPA’s control strategy tools and represent additional abatement opportunities not accurately captured in this analysis.

71 For additional information see http://www.fhwa.dot.gov/everydaycounts/technology/asphalt/intro.cfm ; slide 31 aci-na.org/static/entransit/sunday_warmmix_logan.pdf. FHWA estimates that warm mix asphalt uses 20 percent less energy than hot mix asphalt. Also see http://www.fhwa.dot.gov/everydaycounts/technology/asphalt/intro.cfm. Airports Council International reports 10-55 percent lower energy consumption and 20-55 percent reduction in emissions. (See http://aci-na.org/static/entransit/sunday_warmmix_logan.pdf, Slide 31)).
72 Typically, ten exchange events are set up across the District, and for the convenience of the participants, the exchange events take place during consecutive weekdays. At the event site, the old leaf blowers will be tested for operation and then drained of all flui ds in a responsible manner and collected for scrapping. The vendor will haul the traded-in blowers to a scrappi ng yard where they are crushe d and recycled. The vendor w ill also provide training for the proper use of the equipment at each of the exchange sites. ( http://www.aqmd.gov/docs/default-source/LawnEquipment/leafblower-brochure.pdf?sfvrsn=9 )

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4-30 Table 4-5. Non-End-of-Pipe Control Measures from SIPs
Measure Projected Emissions Reductions (tons per day) Area Citation

Energy Efficiency 73 0.04 (NOx) Shreveport/Bossier City Area, Louisiana 70 FR 48880 August 22, 2005 Energy Efficiency 74 0.72 (NOx) Dallas/Fort Worth (DFW), Texas 73 FR 47835 August 15, 2008 Transportation Emission Reduction Measures 75 0.72 (NOx) 0.83 (VOC) Austin - Early Action Compact Area, Texas 70 FR 48640 August 19, 2005 Voluntary Mobile Emission Reduction Program (VMEP) 2.63 (NOx) 0.61 (VOC) DFW area 74 FR 1906 January 14, 2009

Texas Emission Reduction Plan (TERP) 76, 77 14.2 (NOx) DFW area 74 FR 1906 January 14, 2009 Texas Low Emission Diesel (TxLED) 78 Up to 6 (NOx); on-going and varies annually East and Central Texas (includes DFW area) 79 FR 67068 November 12, 2014

73 The measures involved installing energy conservation equipment in 33 city buildings. Measures included upgrades to the lighting, mechanical and control systems, water conservation upgrades, and other miscellaneous activities. This was an Early Action Compact (EAC) area SIP.
74 The NOx emissions reductions in the DFW area were due to energy efficiency measures in new construction for single and multi-family residences. This measure was initially submitted within a SIP to provide emissions reductions of 5 percent in the DFW area; actual reductions fell short of the 5 percent goal, but this measure was eventually approved with other measures that, by providing additional emissions reductions, improved air quality in the area.
75 The transportation projects were to reduce vehicle use, improve traffic flow, and/or reduce congested conditions throughout the Austin EAC area (this was an EAC SIP). The Austin EAC area included Bastrop, Caldwell, Hays, Travis, and Williamson counties and the cities of Austin, Bastrop, Elgin, Lockhart, Luling, Round Rock, and San Marcos. 76 TERP is a discretionary economic incentive program: economic incentives to reduce emissions. The approved TERP is a grant program, unique to Texas, that provides funds through the Texas Commission on Environmental Quality in a variety of categories, including emissions reduction incentive grants, rebate grants (including grants for small businesses), and heavy and light duty motor vehicle purchase or lease programs, all with the goal of improving air quality in Texas. Examples of TERP programs include assisting small businesses in purchasing lower-emission diesel vehicles, helping school districts to reduce emissions from school buses, and providing funds to support research and development of pollution-reducing technology. TERP is available to all public and private fleet operators that operate qualifying equipment in any of the ozone nonattainment counties within Texas. TERP was also approved into the Texas EAC SIPs, providing at least 2 tons per day in NOx reductions in each of three areas (Austin, Tyler/Longview, and San Antonio).
77 The State of Texas reports on the TERP every other year and the emissions reductions cited here were based on an estimated $6,000 per ton.
78 The TxLED fuel program was initially approved by EPA on November 14, 2001 (66 FR 57196) and hasundergone subsequent revisions, the latest on May 5, 2013 (78 FR 26255). TxLED fuel is required for use by onhighway vehicles and non-road equipment (including marine vessels) in 110 counties in eastern and central Texas. Use of this boutique fuel reduces NOx emissions.

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4-31 Table 4-6. Non-End-of-Pipe Measures in California
Program or Standard Area Citation Estimated Emissions Reductions Carl Moyer Memorial Air Quality Standards Attainment Program ( selected project types )* San Joaquin Valley, California 79 FR 29327 (May 22, 2014) 3.78 tpd NOx credited in San Joaquin Valley Proposition 1B: Goods Movement Emission Reduction Program ( selected project types )* San Joaquin Valley, California 79 FR 29327 (May 22, 2014) 1.23 tpd NOx credited in San Joaquin Valley California Reformulated Gasoline (RFG) Program California (statewide) 60 FR 43379 (August 21, 1995), revised 75 FR 26653 (May 12, 2010) California Diesel Fuel Program California (statewide) 60 FR 43379 (August 21, 1995), revised 75 FR 26653 (May 12, 2010) Regional Clean Air Incentives Market (RECLAIM) South Coast, California 63 FR 32621 (June 15, 1998), revised 71 FR 51120 (August 29, 2006) and 76 FR 50128 (August 12, 2011) * Program not approved into SIP but relied upon for emission reduction credit through state commitment.



### Lack of information about the MAC for emissions reductions not characterized in CoST is not an indication that controlling those tons is necessarily more difficult than controlling NOx from other sources that are in the database, or that the MAC for those tons is necessarily higher than all of the costs of controls already in the database. Some sectors are controlled at a higher rate than others, and in those cases, getting additional NOx reductions may indeed require higher cost controls. However, other sectors may not be as well-controlled, and lower cost controls may
be available.

4.2.4 Comparing Baseline Emissions and Contro ls across Ozone NAAQS RIAs from 1997 to 2014 Many factors affect the future year baseline emissions used in a NAAQS analysis, including the future year being analyzed, the projected air quality in that year, the emissions inventories used, emissions projections methodologies, and any federal and/or state regulatory programs or measures that are reflected in the emissions projections. The EPA believes that while these factors and changes are difficult to track individually, additional federal and/or state

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4-32 regulatory programs promulgated and the new data on available control technologies or measures can, over time, result in emissions reductions and control measures being reclassified from unidentified to identified measures. Each ozone NAAQS analysis since 1997 has required at
least some emissions reductions from controls that were considered unidentified at the time of analysis, but evidence indicates that over time new information becomes available that changes
the characterization of these emissions reductions from unidentified measures to identified measures. For example, in the 1997 ozone N AAQS RIA, NOx emissions reductions that were expected to result from the at that time upcoming mobile source Tier 2 standards were not characterized as resulting from identified controls, even though the RIA acknowledged the potential for these standards to provide substantial cost-effective controls and emissions reductions. As a result, in 1997 these cost-effecti ve emissions reductions were considered to be from unidentified controls, while in retrospect they were actually from identified controls. Likewise, the 2008 ozone NAAQS RIA did not include controls on EGUs that would later be predicted to result from the Mercury and Air Toxics Standards or the Clean Power Plan. As a result, in 2008 emissions reductions needed from unidentified controls were estimated to be higher in some regions of the U.S. than those estimated in this RIA. In general, during the time between the promulgation of a NAAQS and the required date of attainment, additional rules may be developed and additional analyses performed that shed light on how emissions reductions that were once thought to be unavailable from identified control measures are obtained through tangible means. Improvements over time, both in information and engineering, lead to an increase in identified controls and as a result emission reductions obtained only through unidentified controls in one analysis may be realized through identified controls in subsequent analyses.

4.2.5 Possible Alternative Approaches to Estimate Costs of Unidentified Control Measures
In determining how to estimate the costs of achieving the emission reductions needed from unidentified control measures (see Section 4.3), we examined what information could be gleaned from existing regional NOx offset prices. In ozone nonattainment areas, new sources interested in locating in that area and existing sources interested in expanding are required to offset any emissions increases. If those emissions increases are NO x 

### emissions, the source typically purchases NO x 

### emission reduction credits (ERCs), or offsets, from within that

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4-33 particular nonattainment area. Within nonattai nment areas, offset prices fluctuate because of changes in the available supply of offsets and changes in demand for offsets. Offset supply increases when facilities shut down or when they make process or other changes that reduce emissions permanently. Offset demand depends on the industrial base in a given area and fluctuates with changes in economic growth. For example, in the San Joaquin Valley, in recent years offset prices have increased because of increased oil and gas industry development.

We identified historical NO x 

### offset prices in several nonattainment areas, including the San Joaquin Valley and the South Coast in California, Houston, TX, and New York region. For the San Joaquin Valley Air Pollution Control District, we collected information on NO x 

### offset prices using the California Air Resources Board’s Emission Reduction Offset Transaction Cost Summary Reports for 2002 through 2013. 79 

### For the South Coast Air Quality Management District, we collected information on prices for perpetual NO x 

### RECLAIM Trading Credit (RTC) for 2003 through 2012 from the Listing of Trade Registrations

. 80 

### Lastly, we collected information on NOx offset prices in the Houston-Galveston nonattainment area for 2010 through
2013 from the Trade Report 81 

### and the New York-New Jersey-Connecticut nonattainment area from 2000 through 2013 from industry representatives.

Table 4-7 presents the price data we were able to collect for these four regions, adjusted to 2011 dollars using the Gross Domestic Product Implicit Price Deflator. The offset prices in this table are denominated in units of perpetual tons, or tons per year, and represent a one-time payment or cost, not an annual payment or cost. The prices constitute average of the trades in the regions for the year given. The data series fo r the California regions are more complete than those for Houston and the New York region.

79 http://www.arb.ca.gov/nsr/erco/erco.htm
80 http://www.aqmd.gov/home/programs/business/about-reclaim/reclaim-trading-credits
81 http://www.tceq.state.tx.us/airquality/banking/mass_ect_prog.html

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4-34 Table 4-7. Average NO x 

### Offset Prices for Four Areas (2011$, perpetual tpy) a
NOx Offset Prices ($/ perpetual tpy) San Joaquin Valley South Coast Houston TX New York Region 2000 N/A N/A N/A 25,000 2001 N/A N/A N/A 12,000 2002 36,000 N/A N/A 12,000 2003 28,000 N/A N/A 12,000 2004 25,000 12,000 N/A 12,000 2005 25,000 31,000 N/A 11,000 2006 21,000 163,000 N/A 11,000 2007 21,000 206,000 N/A N/A 2008 48,000 210,000 N/A N/A 2009 58,000 128,000 N/A N/A 2010 62,000 98,000 36,000 N/A 2011 64,000 56,000 N/A N/A 2012 47,000 47,000 N/A N/A 2013 42,000 N/A 97,000 4,000 Average 40,000 106,000 66,000 12,000 Maximum 64,000 210,000 97,000 25,000

a All values are rounded to two significant figures.



### To more directly compare offset prices to potential annual costs for unidentified emissions controls, we annualized the perpetual, tons per year prices using the same engineering cost equations as used in the main analysis to estimate annualized control cost. We converted the offset cost to an annual cost by using the capital recovery factor (CRF) discussed in Section 4.1.1. In a capital cost context, the CRF incorporates the interest rate and lifetime of the purchased capital. In this instance, although the offsets are perpetual in nature, we assumed a lifetime of 20 years in order to make the cost basis more comparable to the control cost estimates. Also, we used 7 percent for the interest rate. Table 4-8 presents the average and
maximum annualized NO x 

### offset prices in 2011 dollars.

Table 4-8. Annualized NOx Offset Pr ices for Four Areas (2011$, tons) a Annualized NOx Offset Prices ($/ton) San Joaquin

Valley California South Coast Houston TX New York Region Average $ 4,000 $ 10,000 $ 6,000 $ 1,000 Maximum $ 6,000 $ 20,000 $ 9,000 $ 2,000

a All values are rounded to two significant figures.

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4-35 From an economic perspective, these offset prices may represent the shadow value of a ton of emissions since the purchaser of the offset chose to purchase the offset rather than curtail their business activities or purchase some other pollution control technology. It is possible that these offset prices could serve as reasonable proxies for the costs associated with emissions reductions from unidentified measures or controls. The cost information informing the identified control strategy traces out an incomplete marginal abatement cost curve in that, as discussed in Chapter 3, the controls used in the identified control analysis are primarily end-of-pipe technologies. The identified control estimates for NO x 

### do not account for other forms of abatement, e.g., switching to lower emitting fuels or increasing energy efficiency. The estimates also do not account for institutional or market arrangements that allow firms to buy or sell emissions offsets in nonattainment regions wi th emissions constraints. These voluntary exchanges may enable abatement at lower costs than may otherwise be available. The benefit of these market transaction data is that the prices ar e revealed by the interaction of offset supply and demand in regions with differentiated characteristics and emissions and air quality profiles. In each region, offset supply is a function of the emissions inventory and offset demand is a function of economic growth, and neither offset supply nor demand is infinite.

An alternative proxy for estimating the costs of unidentified control measures is using the current annualized section 185 fee rate. The section 185 fee program requirement applies to any ozone nonattainment area that is classified as Severe or Extreme under the NAAQS. If a Severe or Extreme nonattainment area fails to attain the ozone NAAQS by the required date, section 185 of the Clean Air Act requires each major stationary source of VOC and NOx located in such area to pay a fee to the state for each calendar year following the attainment year for emissions above
a baseline amount and until the area reaches attainment. 82,83 

### The fee was set in the 1990 Clean Air Act at $5,000 per ton of VOC and NOx emissions above the baseline amount and is adjusted annually for inflation based on the Consumer Price Index. The 2013 annualized section 185 fee rate was $9,398.67 per ton. Examples of states or areas that have adopted section 185 fee

82 For additional information on developing fee programs required by Clean Air Act Section 185, see the January 5, 2010 memorandum from the EPA’s Office of Air Quality Planning and Standards, available at: http://www.epa.gov/ttn/naaqs/aqmguide/collection/cp2/bakup/20100105_page_section_185_fee_programs.pdf

. 83 In 1990, the Clean Air Act set the fee at $5,000/ton of VOC and NOx emitted by the source during the calendar year in excess of 80 percent of the baseline amount. A source’s baseline amount is the lower of the amount of actual or allowable emissions under the permit for the source during the attainment year.

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4-36 programs include: (a) Texas for the Houston-Galveston nonattainment area, which adopted its fee program in May 2013, 84 

### and (b) the South Coast Air Quality Management District, which amended Rule 317 that governs its fee program in February 2011.

4.2.6 Conclusion
The preceding sections have discussed the ways in which various factors might affect the observed marginal abatement costs and the resulting total abatement costs estimated in this RIA. Based on past experience with Clean Air Act implementation, the EPA believes that it is reasonable to anticipate that the marginal cost of emissions reductions will decline over time due to technological improvements and more widespread adoption of previously considered niche control technologies, as well as the development of innovative strategies. 85 

### In addition, while we examined other approaches to estimate the costs of unidentified measures, we concluded that these approaches could potentially undervalue the costs of unidentified controls. As the EPA continuously improves its data and tools, we expect to better characterize the currently unobserved pieces of the MACC.

4.3 Compliance Cost Estimates for Unidentified Emissions Controls This section presents the methodology and results for the costs of emissions reductions
from unidentified control measures needed to demonstrate full attainment of the revised and alternative standards analyzed. We refer to the costs of emissions reductions from unidentified
controls as unidentified control costs. 86 

### As discussed in Chapter 3, the application of the identified control strategies was not sufficient in reaching full, nationwide attainment of the revised standard of 70 ppb and the alternative standard of 65 ppb analyzed. Therefore, the engineering costs detailed in Section 4.1 represent only the costs of partial attainment.

84 Additional information on Texas’s actions is avai lable at http://www.tceq.texas .gov/airquality/point-sourceei/sipsection185.html
85 See Chapter 3, Section 3.4 for additional discussion of uncertainties associated with predicting technological

advancements that may occur between now and 2025.
86 In previous analyses, these costs were referred to as extrapolated costs.

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4-37 4.3.1 Methods
On the issue of estimating the costs of unidentified control measures, in 2007 the EPA’s Science Advisory Board offered the following advice:

The Project Team has been unable to identify measures that yield sufficient emission reductions to comply with the National Ambient Air Quality Standards (NAAQS) and relies on unidentified pollution control measur es to make up the difference. Emission reductions attributed to unidentified measures appear to account for a large share of emission reductions required for a few large metropolitan areas but a relatively small share of emission reductions in other locations and nationwide.
The Council agrees with the Project Team that there is little credibility and hence limited value to assigning costs to these unidentified measures. It suggests taking great care in reporting cost estimates in cases where unidentified measures account for a significant share of emission reductions. At a minimum, the components of the total cost associated with identified and unidentified measures should be clearly distinguished. In some cases, it may be preferable to not quantify the costs of unidentified measures and to simply report the quantity and share of emissions reductions attributed to these measures.
When assigning costs to unidentified measures, the Council suggests that a simple, transparent method that is sensitive to the degree of uncertainty about these costs is best. Of the three approaches outlined, assuming a fixed cost/ton appears to be the simplest and most straightforward. Uncertainty might be represented using alternative fixed costs per ton of emissions avoided. 87



### While we have considered alternative methodologies to predict future abatement supply curves, we are currently unable to quantitatively predict future shifts in the abatement supply curve with sufficient confidence to use in this RIA. For most NAAQS RIAs prepared during the past five years, EPA estimated the costs for unidentified controls using a pair of methodologies: a “fixed cost” approach, following the SAB advice, and a “hybrid” approach that has not yet been reviewed by the SAB. 88 

### We refer to the fixed cost approach as the “average cost” approach here. The average cost approach uses an assumed national average cost per ton for unidentified

87 U.S. Environmental Protection Agency. June 2007. Advisory Council on Clean Air Compliance Analysis (COUNCIL), Council Advisory on OAR’s Direct Cost Report and Uncertainty Analysis Plan. Washington, DC.
88 The three approaches mentioned above, outlined for SAB review, for assigning costs to unidentified measures included: (1) the fixed cost approach that assigns all unidentified measures a fixed cost per ton; (2) an approachbased on an upward sloping cost curve that uses information from the identified control measure analysis on an areaspecific basis; and (3) an approach that adjusts the upward sloping cost curve projections using information about cost changes over time to reflect factors such as learning by doing and induced innovation.

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4-38 controls needed for attainment, as well as two alternative assumed values employed for sensitivity analysis (Appendix 4A). The range of estimates reflects different assumptions about the cost of additional emissions reductions beyond those in the identified control strategies. While we use a constant, average cost per ton to estimate the costs of the emissions reductions beyond identified controls, this does not imply that the MACC is not upward sloping. The constant, average cost per ton is designed to capture total costs associated with the abatement of the emissions reductions from unidentified controls – because of the incomplete information available to inform the characterization of the MACC, a portion of those total costs is likely at a value below the average cost per ton and a portion is likely at a value above the average cost per

ton.

The alternative values used in the sensitivity analysis implicitly reflect different assumptions about the amount of technological progress and innovation in emissions reduction strategies that may be expected in the future. The average cost approach reflects a view that because we have incomplete data on existing control technologies, and because no cost data exists for unidentified future technologies, measures, or strategies, it is unclear whether approaches using hypothetical cost curves will be more accurate or less accurate in forecasting total national costs of unidentified controls than an average cost approach that uses a range of national cost-per-ton values.

The hybrid approach assumed increasing marginal costs of control along an upwardsloping marginal cost curve. The hybrid approach assumed the rate of increase in the marginal costs of abatement is proportional to the weighted ratio of the amount of abatement using identified controls to the remaining needed abatement using unidentified controls. 89 

### Under this approach, the relative costs of unidentified controls in different geographic areas reflected the expectation that average per-ton control costs are likely to be higher in areas needing a higher ratio of emissions reductions from unidentified and identified controls. However, the weight, which reflected the anticipated degree of difficulty of achieving needed emissions reductions,

89 See, for example, Section 7.2 and Appendix 7.A.2 in the December 2012 RIA for the final PM 2.5 NAAQS, available at http://www.epa.gov/ttn/ecas/regdata/RIAs/finalria.pdf.

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4-39 and the ratios that informed the slope of the ma rginal abatement cost curve in previous NAAQS analyses were strong assumptions that have not been empirically tested.

When used to estimate costs for end-of-pipe technologies, the hybrid approach assumed all emissions reductions come from the highest cost margin of the abatement supply curve which, as explained in the previous section, is unlikely for much of the unobserved abatement capacity in the present and future. For example, EPA’s control strategy tools largely focus on a limited set of emissions inventory sectors, whereas abatement opportunities exist in other sectors.

For areas needing significant additional emission reductions, much pollution abatement is likely needed from sectors that historically have not been intensively regulated and thus have relatively more available potential emissions reductions. If national standards become more stringent, new regions or firms will be added to the regulated domain. These new entrants, with their relatively untapped abatement supply, will contribute to an outward shift in abatement supply. The newly regulated regions and firms will also face new incentives for technical change and innovation that may lower costs over the long run by developing new, more efficient compliance strategies. Because the point of departure for the hybrid approach cost curve is based on our current database, which includes only existing controls, it will systematically overstate future costs if any cost-reducing technological change occurs.

As noted in previous NAAQS analyses, the EPA continues to explore other sources of
information to inform the estimates of costs associated with unidentified controls. For this RIA we examined the full set of identified controls, examined evidence that suggests that over time new information and data emerges that shifts emissions reductions from the unidentified to the identified category, as well as explored whether NOx offset prices can serve as reasonable proxies for the costs of emissions reductions not identified by current tools.

Based upon deliberations informing this discussion, the EPA Science Advisory Board Council Advisory’s advice, and the requirements of E.O. 12866 and OMB circular A-4, which provide guidance on the estimation of benefits and costs of regulations, in this RIA, we follow the Council recommendations by using an average cost per ton as a central estimate and conduct sensitivity analysis using alternative average costs to explore how sensitive total costs are to these assumptions. While the average cost approach has limitations, we agree with the Council

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4-40 that the approach is both transparent and strikes a balance between the likelihood that some unidentified abatement would arise at lower segments of the identified cost curve while other sources of abatement may come at the higher cost margin.

As discussed in Section 4.1.1, we apply a constant, average cost per ton of $15,000/ton to capture total costs associated with the NOx emissions reductions achieved through unidentified controls. To explore how sensitive total costs are to this assumption, we also use alternative assumptions of the average cost. Specifically, we conduct a first sensitivity analysis using an assumed cost of $10,000 per ton and a second sensitivity analysis using an assumed $20,000 per ton. This range is inclusive of the annualized NOx offset prices observed in recent years in the areas likely to need unidentified controls to achieve the standard (Table 4-8), and if anything, suggests the central estimate of $15,000/ton is conservative. In the RIA for the ozone NAAQS proposal, EPA requested comments on the methods presented to estimate emissions reductions needed beyond identified controls, including the parameter estimate of $15,000/ton. We received comments that the parameter estimate of $15,000/ton (2011$) was low and should be adjusted to reflect inflation. The EPA has elected to retain the parameter estimate of $15,000/ton (2011$) because inflation was low between 2006 and 2011. While the Agency received comments on its methods, alternative approaches have not been subjected to peer review and therefore could not be applied here.

Because cost changes due to technological change will be available on a national-level, it makes sense to use national-level average cost per ton in the primary analysis. However, as indicated by the variation in NOx offset prices across regions shown in Table 4-7, regional factors may play a significant role in the estimati on of control costs. In the RIA for proposal, EPA stated it may review alternative methodologies and sources of regional information that could result in the average cost methodology being applied more regionally. The EPA reviewed data on end-of-pipe controls more closely and worked on identifying information from measures used in SIPs. The data in CoST on end-of-pipe technologies was not sufficiently robust to generate regional data, and obtaining detailed cost information from state or local SIPs requires a longer-term effort. Despite the regional variation in offset prices, we believe the $15,000/ton estimate represents a conservative value because it is higher than the majority of the annualized

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4-41 offset values in Table 4-8, and because the values we use in the sensitivity analyses include the highest annualized offset value.

4.3.2 Compliance Cost Estimates from Unidentified Controls
Table 4-9 presents the control cost estimates for unidentified controls for the East and West in 2025, except for California for the final standard of 70 ppb and an alternative standard of 65 ppb using an assumed average cost of $15,000/ton, as well as values of $10,000/ton and $20,000/ton. Appendix 4A includes potential alternative methods for either assigning an average cost value or for determining values used in sensitivity analyses.

Table 4-9. Unidentified Control Costs in 2025 by Alternative Standard for 2025 – U.S., except California (millions of 2011$)

Alternative Level Geographic Area Unidentified Control Cost $10,000/ton $15,000/ton $20,000/ton
70 ppb East 470 700 930 West - - - Total 470 700 930
65 ppb East 8,200 12,000 16,500 West 400 610 810 Total 8,600 13,000 17,000

a All values are rounded to two significant figures. 

### Table 4-10 presents the unidentified control cost estimates for post -2025 for California for the final standard of 70 ppb and an alternative standard level of 65 ppb using an assumed average cost of $15,000/ton, as well as values of $10,000/ton and $20,000/ton.

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4-42 Table 4-10. Unidentified Control Costs in 2025 by Alternative Standard for Post-2025 – California (millions of 2011$)

Alternative Level Geographic Area Unidentified Control Cost $10,000/ton $15,000/ton $20,000/ton 70 ppb California 510 800 1,020 65 ppb California 1,000 1,500 2,000

a All values are rounded to two significant figures. 

### 4.4 Total Compliance Cost Estimates As discussed throughout this RIA, we present the primary costs and benefits estimates for 2025. We assume that potential nonattainment areas everywhere in the U.S., excluding California, will be designated such that they are required to reach attainment by 2025, and we developed our projected baselines for emissions, air quality, and populations for 2025. Please see Chapter 1, Section 1.3.2 for additional detailed discussion on potential nonattainment designations and timing.

Tables 4-11 and 4-12 present summaries of the total national annual costs (identified and unidentified) of attaining the revised standard of 70 ppb and alternative standard of 65 ppb – Table 4-11 presents the total national annual costs by alternative standard for 2025 for all of the U.S., except California and Table 4-12 presents the total national annual costs by alternative standard for post-2025 for California. As discussed in Section 4.1.2, because we do not have a full set of costs at the 3 percent discount rate or the 7 percent discount rate and because we believe the majority of the identified control costs is calculated at a 7 percent discount rate, Tables 4-11 and 4-12 present engineering cost estimates based on a 7 percent discount rate.

Table 4-11. Summary of Total Control Costs (I dentified and Unidentified) by Alternative Level for 2025 - U.S., except California (millions of 2011$, 7% Discount Rate) a

Alternative Level Geographic Area Total Control Costs (Identified and Unidentified) 70 ppb East 1,400 West <5 Total $1,400
65 ppb East 15,000 West 750 Total $15,500

a All values are rounded to two significant figures. Unidentified control costs are based on the average cost approach.

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4-43 Table 4-12. Summary of Total Control Costs (I dentified and Unidentified) by Alternative Level for Post-2025 - California (millions of 2011$, 7% Discount Rate) a

Alternative Level Geographic Area Total Control Costs (Identified and Unidentified) 70 ppb California 800 65 ppb California 1,500

a All values are rounded to two significant figures. Unidentified control costs are based on the average cost approach. 

### 4.5 Economic Impacts

4.5.1 Introduction
This section addresses the potential economic impacts of the illustrative control strategies
for the alternative ozone standards. The control costs are uncertain for several reasons. The controls that the states ultimately choose to implement will likely differ from the illustrative control strategies for which costs are estimated in this chapter. The flexibility afforded to states by the Clean Air Act also allows them to adopt programs that include design elements that may mitigate or promote particular economic impacts based on their individual priorities. The cost estimates become more uncertain because of the length of time before they will be implemented. By the 2025 and post-2025 time frames, changes in technology, changes in implemented regulations, and changes in relative prices will all add to the uncertainty in the cost analysis. Finally, the portion of costs that is from unidentified controls is not allocated to particular

sectors.

Economic impacts focus on the behavioral response to the costs imposed by a policy being analyzed. The responses typically analyzed are market changes in prices, quantities produced and purchased, changes in international trade, changes in profitability, facility closures, and employment. Often, these behavioral changes are used to estimate social costs if there is
indication that the social costs differ from the estimate of control costs because behavioral change results in other ways of meeting the requirements (e.g., facilities choosing to reduce emissions by producing less rather than adding pollution control devices).

The alternative ozone standards are anticipated to impact multiple markets in many times and places. Computable General Equilibrium (CGE) models are one possible tool for evaluating

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4-44 the impacts of a regulation on the broader economy because this class of models explicitly captures interactions between markets across the entire economy. To support the Final Ozone NAAQS of March 2008 (Final Ozone NAAQS Regulatory Impact Analysis), among other rulemakings, the EPA used the Economic Model for Policy Analysis (EMPAX) to estimate the market impacts of the portion of the cost that was associated with the application of identified controls (excluding the unidentified control costs). EMPAX is a dynamic computable general equilibrium (CGE) model that forecasts a new equilibrium for the entire economy after a policy intervention. While a CGE model captures the effects of behavioral responses on the part of consumers or other producers to changes in price that are missed by an engineering estimate of compliance costs, most CGE models do not model the environmental externality - or the benefits that accrue to society from mitigating it. When benefits from a regulation are expected to be substantial, social cost cannot be interpreted as a complete characterization of economic welfare. To the extent that the benefits affect behavioral responses in markets, the social cost measure may also be potentially biased.

EPA included specific types of health benefits in a CGE model for the prospective analysis, The Benefits and Costs of the Clean Air Act from 1990 to 2020 (EPA 2011), and demonstrated the importance of their inclusion when evaluating the economic welfare effects of policy. However, while the external Council on Clean Air Compliance Analysis (Council) peer review of this EPA report (Hammitt 2010) stated that inclusion of benefits in an economy-wide model, specifically adapted for use in that stud y, “represent[ed] a significant step forward in benefit-cost analysis”, serious technical challenges remain when attempting to evaluate the benefits and costs of potential regulatory actions using economy-wide models.

To begin to address these technical challenges, the EPA has established a new Science Advisory Board (SAB) panel on economy-wide modeling to consider the technical merits and challenges of using CGE and other economy-wide modeling tools to evaluate costs, benefits, and economic impacts of air regulations. The EPA will use the recommendations and advice of this SAB panel as an input into its process for improving benefit-cost and economic impact analyses that are used to inform decision-making at the Agency. The panel will also be asked to identify potential paths forward for improvements that could address the challenges posed when economy-wide models are used to evaluate the effects of regulations.

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4-45The advice from the SAB panel formed specifically to address the subject of economywide modeling was not available in time for this analysis. Given the ongoing SAB panel on economy-wide modeling, the uncertain nature of costs, the Council’s advice regarding the importance of including benefit-side effects, and the lack of available multi-year air quality projections needed to include these benefit-side effects, EPA has not conducted CGE modeling for this analysis. Instead, this section proceeds with a qualitative discussion of market impacts.

4.5.2 Summary of Market Impacts
Consider an added cost to produce a good associated with the pollution control required to reach the alternative ozone standards. Such a good is either one developed for the consumer (called a consumption good), or one used in the production of other goods for consumption (called an intermediate good). Some goods are both consumption and intermediate goods. First, consider the direct impact on the market facing the increased cost. In this case for the market facing the increased cost, the price will go up and the amount sold will go down. The magnitude of these shifts depends on a number of factors. The greater the unit cost increase relative to the price of the good the greater will be the changes. The more responsive a consumer is to a change in the price of a consumption good or the more responsive a purchase of an intermediate good is to a change in price, the greater will be the changes. For the alternative ozone standards, many goods will have direct changes in costs of production. This makes the assumption of isolated markets too simple. With multiple intermediate goods affected, then the intermediate goods and consumption goods they are used to produce are affected. As fewer intermediate goods and consumption goods are purchased at a higher pric e, other intermediate goods and consumption goods that serve as substitutes become more attractive and more are sold at a higher price. All of these market changes lead to changes in income, which can lead to changes in purchases of consumption goods. Quantities of intermediate goods used to reduce emissions would also change. Considering all of these changes, it is not possible to qualitatively conclude the direction of price and quantity changes for any single market. Any conclusions about changes in international trade, profits, closures, or social cost is impossible in a qualitative analysis.

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4-46 4.6 Differences between the Proposal and Final RIAs Several changes in the analysis for this final RIA have resulted in lower control costs compared to the proposal RIA. As discussed in Chapter 3, Section 3.3, improved emissions inventory and model inputs as well as more refined air quality modeling resulted in approximately 50 percent fewer emissions reductions needed in Texas and the Northeast to reach the revised standard of 70 ppb compared to the proposal RIA. For an alternative standard of 65 ppb, we needed approximately 20 percent fewer emissions reductions nationwide than at proposal. In addition, because of the more refined air quality modeling, control strategies were applied in smaller geographic areas closer to monitors projected to exceed 70 and 65 ppb. Also, in the proposal RIA we applied controls to reach the current standard of 75 ppb in Texas and the Northeast, and in this final RIA these controls were not needed in these areas to reach the current standard. Not applying controls to reach the current standard, as well as applying controls in smaller geographic areas had impacts on the cost estimates that are discussed below.

In the final RIA, to reach a revised standard of 70 ppb in 2025 we applied a larger number of lower cost identified controls because (i) fewer emissions reductions were needed overall, and (ii) we did not apply any identified controls to reach 75 ppb. This meant that the cost of
additional reductions could be estimated from lower cost identified controls in the final RIA. As a result, total estimated costs to reach 70 ppb were lower than costs estimates in the proposal RIA by 55 percent.

In the final RIA, to reach an alternative standard of 65 ppb, while fewer reductions were needed, the area where we applied identified controls was smaller than in the proposal RIA, resulting in exhausting the supply of identified controls available in these areas. We needed additional, higher-cost unidentified controls to being these areas into attainment. For example, in the proposal RIA, in analyzing 65 ppb we applied controls across the state of Texas as well as in surrounding states inside the “Central” region (Oklahoma, Kansas, Missouri, Arkansas, Louisiana and Mississippi). Because of the size of the area in which controls were applied for proposal, there were more identifi ed controls from which to choose. In the final RIA, we applied controls only in east Texas, which meant there were fewer identified controls available and we relied more heavily on unidentified controls. Overall, because we applied all available identified

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4-47 controls in the final RIA, we relied on uniden tified controls for 57 percent of the emissions reductions needed, whereas in the proposal RIA we relied on unidentified controls for 40 percent
of the emissions reductions needed to reach 65 ppb. Because unidentified controls are more expensive than identified controls and we relied on more unidentified controls in the final RIA, the estimated costs did not decrease in proportion to the decrease in needed emissions reductions and are about the same as in the proposal RIA.

4.7 Uncertainties and Limitations The EPA acknowledges several important limitations of this analysis, which include the following:

Boundary of the cost analysis : In this engineering cost analysis we include only the impacts to the regulated industry, such as the costs for purchase, installation, operation, and maintenance of control equipment over the lifetime of the equipment. As mentioned above, recordkeeping, reporting, testing and monitoring costs are not included. In some cases, costs are estimated for changes to a process such as switching from one fuel to another less polluting fuel. Additional profit or income may be generated by industries supplying the regulated industry, especially for control equipment manufacturers, distributors, or service providers. These types of secondary impacts are not included in this engineering cost analysis.

Cost and effectiveness of control measures : Our application of control measures reflect average retrofit factors and equipment lives that are applied on a national scale. We do not account for regional or local variation in capital and annual cost items such as energy, labor, materials, and others. Our estimates of control measure costs may over- or under-estimate the costs depending on how the difficulty of actual retrofitting and equipment life compares with our control assumptions. In addition, our estimates of control efficiencies for the identified controls assume that the control devices are properly installed and maintained. There is also variability in scale of application that is difficult to reflect for small area sources of emissions.

Discount rate : Because we obtain control cost data from many sources, we are not always able to obtain consistent data across original data sources. If disaggregated control cost data are not available (i.e., where capital, equipment life value, and operation and maintenance [O&M] costs

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4-48 are not separated out), the EPA assumes that the estimated control costs are annualized using a 7 percent discount rate. When disaggregated control cost data are available (i.e., where capital, equipment life value, and O&M costs are separated out), we can and do recalculate costs using a 3 percent discount rate. In general, we have some disaggregated data available for non-EGU point source controls, but we do not have any disaggregated control cost data for nonpoint (area) source controls. In addition, while these discount rates are consistent with OMB guidance, the actual real discount rates may vary regionally or locally.

Identified control costs : We estimate that there is an accuracy range of +/- 30 percent for nonEGU point source control costs. This level of accuracy is described in the EPA Air Pollution Control Cost Manual, which is a basis for the estimation of non-EGU control cost estimatesincluded in this RIA. This level of accuracy is consistent with either the budget or bid/tenderlevel of cost estimation as defined by the AACE International. 90 

### The accuracy for nonpoint control costs estimates has not been determined, but it is likely no more accurate than those for non-EGU point source control costs.

Differences between ex ante and ex post compliance cost estimates: In comparing regulatory cost estimates before and after regulation, ex ante cost estimate predictions may differ from actual costs. Harrington et al. (2000) surveyed the predicted and actual costs of 28 federal and state rules, including 21 issued by the U.S. Environmental Protection Agency and the Occupational Safety and Health Administration (OSHA). In 14 of the 28 rules, predicted total costs were overestimated, while analysts underestimated costs in three of the remaining rules. In EPA rules where per-unit costs were specifically evaluated, costs of regulations were overestimated in five cases, underestimated in four cases, and accurately estimated in four cases (Harrington et al. 2000). The collection of lite rature regarding the accuracy of cost estimates seems to reflect these splits. A recent EPA report, the “Retrospective Study of the Costs of EPA Regulations” that examined the compliance costs of five EPA regulations in four case studies, 91

90 AACE International. Recommended Practice No. 18R-97. Cost Estimate Classification System – As Applied in

Engineering, Procurement, and Construction for the Process Industries. Revised on November 29, 2011. Available at http://www.aacei.org/non/rps/18R-97.pdf.
91 The four case studies in the 2014 Retrospective Study of the Costs of EPA Regulations examine five EPA

regulations: the 2001/2004 National Emission Standards for Hazardous Air Pollutants and Effluent Limitations Guidelines, Pretreatment Standards, and New Source Performance Standards on the Pulp and Paper Industry; Critical Use Exemptions for Use of Methyl Bromide for Growing Open Field Fresh Strawberries in California for

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4-49 found that several of the case studies suggested that cost estimates were over-estimated ex ante , but did not find the evidence to be conclusive. The EPA stated in the report that the small number of regulatory actions covered, as well as significant data and analytical challenges associated with the case studies limited the certainty of this conclusion.

Costs of unidentified controls: In addition to the application of identified controls, the EPA assumes the application of unidentified controls for attainment in the projection year for this analysis.

4.8 References
Brunnermeier, S.B., Cohen, M.A., 2003. Determinants of environmental innovation in US manufacturing industries.

Journal of Environmental Economics and Management 45, 278-293.
Dutton, J. M., Thomas, A., 1984. Treating Progress Functions as a Managerial Opportunity. Academy of

Management Review 9(2), 235-247.
Gerard, D., Lave, L.B., 2005. Implem enting t echnology-forcing policies: The 1970 Cl ean Air Act Amendments and

the introduction of advanced automotive emissions controls in the United States. Technological Forecasting and Social Change 72, 761-778.
Hammitt, J.K., 2010. Review of the final integrated report for the second section 812 prospective study of the

benefits and costs of the clean air act. Available at http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/1E6218DE3BFF682E852577F B005D46F1/$File/EPA-COUNCIL-11-001-unsigned.pdf.
Harrington, W., Morgenstern, R.D., Nelson, P., 2000. On the accuracy of regulatory cost estimates. Journal of

Policy Analysis and Management 19, 297-322.

Jaffe, A., Newell, R., Stavins, R., 2002. Environmental Policy and Technological Change . Environmental and Resource

Economics 22, 41-70.
Kneller, R., Manderson, E., 2012. Environmental regulations and innovation activity in UK manufacturing

industries. Resource and Energy Economics 34, 211-235.
Levinson, A. Technology, 2009. International Trade, and Pollution from US Manufacturing. American Economic

Review 99, 2177-2192.
Popp, D., 2006. International innovation and diffusion of air pollution control technologies: the effects of NO x and

SO 2 regulation in the US, Japan, and Germany. Journal of Environmental Economics and Management 51, 46-71.
Popp, D., Newell, R., 2012. Where does energy R&D come from? Examining crowding out from energy R&D.

Energy Economics 34, 980-991.
Rubin, E.S., Yeh, S., Hounshell, D.A., Taylor, M.R., 2004. Experience curves for power plant emission control

the 2004-2008 Seasons; the 2001 National Primary Drinking Water Regulations for Arsenic; and the 1998 Locomotive Emission Standards.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-50 technologies. International Journal of Energy Technology and Policy 2, 52-69.
Sargent & Lundy, L.L.C. 2013. IPM Model – Updates to Cost and Performance for APC Technologies, SCR Cost

Development Methodology. Chicago, IL. Available at http://www.epa.gov/airmarkets/progsregs/epaipm/docs/v513/attachment5_3.pdf.U.S. Environmental Protection Agency (U.S. EPA). 1997. Regulatory Impact Analyses for the Particulate Matter and Ozone National Ambient Air Quality Standards and Proposed Regional Haze Rule. Office of Air Quality Planning and Stan dards, Research Triangle Park, NC. Available at http://www.epa.gov/ttn/oarpg/naaqsfin/ria.html.
Sterner, T., Turnheim, B., 2009. Innovation and diffusion of environmental technology: Industrial NOx abatement in

Sweden under refunded emission payments. Ecological Economics 68, 2996-3006.
U.S. Environmental Protection Agency (U.S. EPA). 2003. EPA Air Pollution Control Cost Manual. Office of Air

Quality Planning and Standards, Research Triangle Park, NC. Available at http://epa.gov/ttn/catc/products.html#cccinfo

. U.S. Environmental Protection Agency (U.S. EPA). 2008. Final Ozone NAAQS Regulatory Impact Analysis. Office

of Air Quality Planning and Standards, Research Triangle Park, NC. Available at http://www.epa.gov/ttn/ecas/regdata/RIAs/452_R_08_003.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2010. Regulatory Impact Analysis (RIA) for the Proposed

Reconsideration of the ozone National Ambient Air Quality Standards (NAAQS). Office of Air Quality Planning and Standards, Research Triangle Park, NC. January. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/s1- supplemental_analysis_full.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011. The Benefits and Costs of the Clean Air Act from 1990 to

2020. Final Report. Office of Air and Radiation, Washington, DC. Available at: http://www.epa.gov/air/sect812/feb11/fullreport_rev_a.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2013. Documentation for EPA Base Case v.5.13 Using the

Integrated Planning Model. Office of Air Atmospheric Programs, Washington, DC. Available at http://www.epa.gov/powersectormodeling/BaseCasev513.html.
U.S. Environmental Protection Agency (U.S. EPA). 2014a. Control Strategy Tool (CoST) Documentation Report.

Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at http://www.epa.gov/ttnecas1/cost.htm

. U.S. Environmental Protection Agency (U.S. EPA). 2014b. Guidelines for Preparing Economic Analyses, Chapter 6.

Available at http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html#download

. U.S. Environmental Protection Agency (U.S. EPA). 2014c. Retrospective Study of the Costs of EPA Regulations: A

Report of Four Case Studies. Available at http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0575.pdf/$file/EE0575.pdf.

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4A-1 APPENDIX 4A: ENGINEERING COST ANALYSIS

Overview Chapter 4 describes the engineering cost analysis approach that EPA used to demonstrate attainment of the revised standard of 70 ppb and an alternative ozone standard level of 65 ppb. This Appendix contains more detailed information about the control costs of the identified control strategy analyses by control measure as well as sensitivity analyses for the average cost approach used to estimate costs for the unidentified emissions controls. Specifically, results using two alternative cost assumptions for unidentified controls are presented, and in addition the findings from six alternative approaches for estimating these costs are described and presented. These include a regression-based approach and five simulation-based variations. Table 4A-10 at the end of this Appendix provides a summary of these alternative approaches.

4A.1 Cost of Identified Controls in Alternative Standards Analyses This section presents costs of identified controls for the alternative standards analyses. Costs are in terms of 2011 dollars and include values for all portions of the U.S. that were part of the analyses. However, because all available identi fied controls for Califo rnia were applied as part of the baseline analysis, no identified controls were available for the alternative standards analyses in California. The costs for the standards analyzed do not include any identified control costs for California. Tables 4A-1 and 4A-2 present the costs for identified controls by measure for the 70 ppb alternative standard analysis for NOx and VOC respectively. Tables 4A-3 and 4A-4 present the costs for identified controls by measure for the 65 ppb alternative standard analysis.

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4A-2 Table 4A-1. Costs for Identified NO x 

### Controls in the 70 ppb Analysis (2011$)

NO x Control Measure Cost a (million) Average $/ton
Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 3.49 400 Biosolid Injection Technology - Cement Kilns 2.22 413 EGU SCR & SNCR 51.69 1,150 Episodic Burn Ban - - 92 Excess O3 Control 0.01 24 Ignition Retard - IC Engines 0.73 1,185 Low Emission Combustion - Gas Fired Lean Burn IC Engines 14.66 829 Low NOx Burner - Coal Cleaning 0.30 1,125 Low NOx Burner - Commercial/Institutional Boilers & IC Engines 23.78 1,110 Low NOx Burner - Gas-Fired Combustion 8.96 970 Low NOx Burner - Glass Manufacturing 0.28 1,141 Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 6.33 1,135 Low NOx Burner - Industrial Combustion 0.03 1,255 Low NOx Burner - Lime Kilns 2.22 913 Low NOx Burner - Natural Gas-Fired Turbines 12.27 1,955
Low NOx Burner - Residential Water Heaters & Space Heaters 22.57 1,134 Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 1.87 5,199 Low NOx Burner and Flue Gas Recirculation - Fluid Catalytic Cracking Units 0.17 2,091 Low NOx Burner and Flue Gas Recirculation - Iron & Steel 0.25 619 Low NOx Burner and SCR - Industr/Commercial/Institutional Boilers 62.83 7,473 Mid-Kiln Firing - Cement Manufacturing 0.09 73 Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 41.27 1,051 Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 12.87 4,545 OXY-Firing - Glass Manufacturing 44.23 3,691 Replacement of Residential & Commercial/Institutional Water Heaters -- -- 93 Selective Catalytic Reduction (SCR) - Cement Kilns 54.22 5,328 Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 7.54 4,414 Selective Catalytic Reduction (SCR) - Glass Manufacturing 4.03 1,157 Selective Catalytic Reduction (SCR) - IC Engines, Diesel 3.29 3,814 Selective Catalytic Reduction (SCR) - ICI Boilers 17.35 3,756 Selective Catalytic Reduction (SCR) - Industrial Incinerators 5.27 3,805 Selective Catalytic Reduction (SCR) - Iron & Steel 0.80 5,180 Selective Catalytic Reduction (SCR) - Process Heaters 6.65 8,483

92 An ozone season episodic burn ban is a daily ban of open burning of yard/agricultural waste on an ozone season day where ozone exceedances are predicted. There are minimal administrative costs associated with this measure, and we have not quantified those costs in this or previous analyses. For additional information on these measures go to http://www.epa.gov/ttn/ecas/cost.htm

. The EPA will continue to conduct res earch on possible costs for this measure, and if applicable update costs for this measure in future analyses.
93 We have not quantified specific costs for this measure in this or previous analyses. For additional information on these measures go to http://www.epa.gov/ttn/ecas/cost.htm

. The EPA will continue to conduct research on possible costs for this measure, and if applicable update costs for this measure in future analyses.

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4A-3 NO x Control Measure Cost a (million) Average $/ton Selective Catalytic Reduction (SCR) - Sludge Incinerators 0.38 3,805 Selective Catalytic Reduction (SCR) - Space Heaters 0.11 4,661 Selective Catalytic Reduction (SCR) - Utility Boilers 0.18 128 Selective Non-Catalytic Reduction (SNCR) - Cement Manufacturing 3.13 1,303 Selective Non-Catalytic Reduction (SNCR) - Coke Manufacturing 4.25 2,673 Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 0.11 1,842 Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 0.60 1,645 Selective Non-Catalytic Reduction (SNCR) - Sludge Incinerators 0.06 1,842 Ultra-Low NOx Burner - Process Heaters 0.14 420

a All values are rounded to two significant figures.



### Table 4A-2. Costs for Identified VOC Co ntrols in the 70 ppb Analysis (2011$)
VOC Control Measure Cost a (million) Average $/ton Control Technology Guidelines - Wood Furniture Surface Coating 8.87 32,595 Control of Fugitive Releases - Oil & Natural Gas Production 0.02 2,689 Flare - Petroleum Flare 0.31 3,305 Incineration - Other 155.85 14,543 LPV Relief Valve - Underground Tanks 2.29 1,763 MACT - Motor Vehicle Coating 0.00 192 Permanent Total Enclosure (PTE) - Surface Coating 4.53 12,289 RACT - Graphic Arts 1.66 6,386 Reduced Solvent Utilization - Surface Coating 0.03 1,232 Reformulation - Architectural Coatings 86.00 16,394 Reformulation - Pesticides Application 2.59 15,157 Reformulation-Process Modification - Automobile Refinishing 2.58 11,734 Reformulation-Process Modification - Cutback Asphalt 0.02 24 Reformulation-Process Modification - Other 0.35 3,102 Reformulation-Process Modification - Surface Coating 0.59 3,304 Solvent Recovery System - Printing/Publishing 0.02 1,232 Wastewater Treatment Controls- POTWs 0.70 3,366

a All values are rounded to two significant figures.

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4A-4 Table 4A-3. Costs for Identified NO x 

### Controls in the 65 ppb Analysis (2011$)
NO x Control Measure Cost a (million) Average $/ton Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 7.25 441 Biosolid Injection Technology - Cement Kilns 2.44 413 EGU SCR & SNCR 125.93 1,150 Episodic Burn Ban - - 94 Ignition Retard - IC Engines 0.73 1,262 Low Emission Combustion - Gas Fired Lean Burn IC Engines 57.85 764 Low NOx Burner - Coal Cleaning 0.69 1,451 Low NOx Burner - Commercial/Institutional Boilers & IC Engines 38.59 1,066 Low NOx Burner - Fiberglass Manufacturing 0.10 1,522 Low NOx Burner - Gas-Fired Combustion 11.53 970 Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 56.57 2,581 Low NOx Burner - Industrial Combustion 0.03 1,255 Low NOx Burner - Lime Kilns 4.21 913 Low NOx Burner - Natural Gas-Fired Turbines 25.63 2,117 Low NOx Burner - Residential Water Heaters & Space Heaters 103.38 1,999 Low NOx Burner - Steel Foundry Furnaces 0.27 929 Low NOx Burner - Surface Coating Ovens 0.09 3,585 Low NOx Burner and Flue Gas Recirculation - (ICI) Boilers 2.00 4,197 Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 2.23 5,199 Low NOx Burner and Flue Gas Recirculation - Fluid Catalytic Cracking Units 0.26 4,347 Low NOx Burner and Flue Gas Recirculation - Iron & Steel 0.48 619 Low NOx Burner and Flue Gas Recirculation - Process Heaters 2.85 5,199 Low NOx Burner and Flue Gas Recirculation - Starch Manufacturing 0.35 5,199 Low NOx Burner and SCR - Industr/Co mmercial/Institutional Boilers 151.27 6,230 Low NOx Burner and SNCR - Industr/Commercial/Institutional Boilers 1.93 3,997 Natural Gas Reburn - Natural Gas-Fired EGU Boilers 1.41 2,388 Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 54.24 775 Non-Selective Catalytic Reduction - Nitric Acid Manufacturing 0.94 1,905 Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 39.78 4,525 OXY-Firing - Glass Manufacturing 110.92 4,093 Replacement of Residential Water Heaters -- -- 95 Selective Catalytic Reduction (SCR) - Ammonia Mfg 6.77 2,896 Selective Catalytic Reduction (SCR) - Cement Kilns 161.93 6,194 Selective Catalytic Reduction (SCR) - Coke Ovens 9.70 7,798 Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 18.56 4,551

94 An ozone season episodic burn ban is a daily ban of open burning of yard/agricultural waste on an ozone season day where ozone exceedances are predicted. There are minimal administrative costs associated with this measure, and we have not quantified those costs in this or previous analyses. For additional information on these measures go to http://www.epa.gov/ttn/ecas/cost.htm

. The EPA will continue to conduct research on possible costs for this measure, and if applicable update costs for this measure in future analyses.
95 We have not quantified specific costs for this measure in this or previous analyses. For additional information on these measures go to http://www.epa.gov/ttn/ecas/cost.htm

. The EPA will continue to conduct research on possible costs for this measure, and if applicable update costs for this measure in future analyses.

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4A-5 NO x Control Measure Cost a (million) Average $/ton Selective Catalytic Reduction (SCR) - IC Engines, Diesel 13.10 3,664 Selective Catalytic Reduction (SCR) - ICI Boilers 36.23 3,636 Selective Catalytic Reduction (SCR) - Industrial Incinerators 6.56 3,805 Selective Catalytic Reduction (SCR) - Iron & Steel 6.81 3,834 Selective Catalytic Reduction (SCR) - Process Heaters 20.24 7,376 Selective Catalytic Reduction (SCR) - Sludge Incinerators 10.26 5,796 Selective Catalytic Reduction (SCR) - Space Heaters 1.33 4,631 Selective Catalytic Reduction (SCR) - Taconite 27.40 6,449 Selective Catalytic Reduction (SCR) - Utility Boilers 0.18 128 Selective Non-Catalytic Reduction (SNCR) - Coke Mfg 7.70 2,673 Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 0.29 1,842 Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 1.97 1,861 Selective Non-Catalytic Reduction (SNCR) - Municipal Waste Combustors 0.12 1,842 Selective Non-Catalytic Reduction (SNCR) - Sludge Incinerators 0.21 1,863 Selective Non-Catalytic Reduction (SNCR) - Utility Boilers 0.33 1,390 Ultra-Low NOx Burner - Process Heaters 1.24 1,447

a All values are rounded to two significant figures.



### Table 4A-4. Costs for Identified VOC Co ntrols in the 65 ppb Analysis (2011$)

VOC Control Measure Cost a (million) Average ($/ton) Control Technology Guidelines - Wood Furniture Surface Coating 97.41 32,595 Control of Fugitive Releases - Oil & Natural Gas Production 0.08 2,689 Flare - Petroleum Flare 0.36 3,305 Gas Recovery - Municipal Solid Waste Landfill 0.32 1,106 Improved Work Practices, Material Substitution, Add-On Controls - Printing 0.00 159 Improved Work Practices, Material Substitution, Add-On Controls -Industrial Cleaning Solvents (0.34) (1,360) Incineration - Other 240.54 14,395 LPV Relief Valve - Underground Tanks 8.59 1,763 Low VOC Adhesives and Improved Application Methods - Industrial Adhesives 0.06 270 Low-VOC Coatings and Add-On Controls - Surface Coating 0.73 2,668 MACT - Motor Vehicle Coating 0.37 192 Permanent Total Enclosure (PTE) - Surface Coating 45.92 13,973 Petroleum and Solvent Evaporation - Surface Coating Operations 0.09 376 RACT - Graphic Arts 35.67 6,386 Reduced Solvent Utilization - Surface Coating 5.36 1,758 Reformulation - Architectural Coatings 858.67 16,394 Reformulation - Industrial Adhesives 13.34 12,017 Reformulation - Pesticides Application 59.97 15,157 Reformulation-Process Modification - Automobile Refinishing 57.25 11,734 Reformulation-Process Modification - Cutback Asphalt 0.06 24 Reformulation-Process Modification - Oil & Natural Gas Production 0.19 641 Reformulation-Process Modification - Other 1.94 3,548

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4A-6 VOC Control Measure Cost a (million) Average ($/ton) Reformulation-Process Modification - Surface Coating 21.00 3,736 Solvent Recovery System - Printing/Publishing 1.05 1,232 Solvent Substitution and Improved Application Methods - Fiberglass Boat Mfg 0.06 4,310 Wastewater Treatment Controls- POTWs 0.79 3,366

a All values are rounded to two significant figures.



### 4A.2 Alternative Estimates of Costs Associated with Emissions Reductions from Unidentified Controls This section presents alternative estimates of the unidentified control costs using alternative average cost per ton of emissions reductions from unidentified controls of $10,000 per ton and $20,000/ton. 96 

### Table 4A-5 presents the estimates of the total control costs for 2025 East and West, without California, when using the alternative per ton cost assumptions for emissions reductions from unidentified controls. Table 4A-6 presents the estimates of the total control costs for post-2025 California when using the alternative per ton cost assumptions for
emissions reductions from unidentified controls.

Table 4A-5. Summary of Total Control Costs (I dentified and Unidentified) by Alternative Level for 2025 - U.S. using Alternative Cost Assumption for Unidentified Control Costs, except California (millions of 2011$) a

Alternative Level Geographic Area Total Control Costs (Identified and Unidentified) Unidentified Control Cost =

$10,000/ton Unidentified Control Cost =

$15,000/ton Unidentified Control Cost =

$20,000/ton

70 ppb East 1,100 1,400 1,600 West <5 <5 <5 Total 1,200 1,400 1,600

65 ppb East 11,000 15,000 19,000 West 550 750 950 Total 11,000 15,500 20,000

a All values are rounded to two significant figures.

96 The EPA decided to use the alternative values of $10,000 per ton and $20,000 per ton because these values were used in the following recent RIAs, and we did not identify other more appropriate alternative values: the November 2014 proposal RIA, the December 2012 Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter and the June 2012 Regulatory Impact Analysis for the Proposed Revisions to the National Ambient Air Quality Standards for Particulate Matter

.

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4A-7 Table 4A-6. Summary of Total Control Costs (I dentified and Unidentified) by Alternative Level for Post-2025 California - U.S. using Alternative Cost Assumption for Unidentified Control Costs (millions of 2011$) a

Alternative Level Geographic Area Total Control Cost Unidentified Control Cost =

$10,000/ton Unidentified Control Cost =

$15,000/ton Unidentified Control Cost =

$20,000/ton 70 ppb California 510 800 1,020 65 ppb California 1,000 1,500 2,000

a All values are rounded to two significant figures.



### 4A.3 Alternative Approaches to Estimating the Costs Associated with Emissions Reductions from Unidentified Controls
4A.3.1 Regression Approach Using all observations under the cost per ton threshold for identified controls ($19,000/ton for NOx), a linear regression is estimated and used to predict the price of the additional unidentified controls required to attain a particular level of the standard. That is, to meet a particular level of the standard, it is assumed that all reductions that can be achieved at a cost less than the cost threshold will first be exhausted and any additional tons required can be achieved at a cost determined by the value of the regression line at those tons. Hence, the total cost of the tons of reductions for which controls are unidentified is the area under the regression line for tons between the total identified tons and the total tons of reduction required to meet the level of the standard being analyzed (see Figure 4A-1 below). Using this methodology, the light gray shaded area under the regression line represents the cost of unidentified controls needed to attain the 70 ppb level of the standard ($960 million), while the darker gray shaded area represents the additional cost of unidentified controls needed to attain the 65 ppb level of the standard (an additional $13 billion, for a total of $14 billion in unidentified control costs).

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4A-8 Figure 4A-1. Marginal Costs for Identified NO x 

### Controls for All Source Sectors with Regression Line for Unidentified Control Measures An alternative interpretation of this approach that is in line with the discussion of the incomplete characterization of the MACC (Chapter 4, Section 4.2.3) is that the points on the regression line represent potential controls that could be applied to sectors or processes that are
not characterized in the CoST tool. 97 

### The MACC can then be redrawn including these points, as appears below in Figure 4A-2. In this curve, the dark red line represents the expanded MACC curve including controls needed to attain the 70 ppb level of the standard. The blue portion of the line includes additional controls needed to attain the 65 ppb level of the standard. As this is simply a way of visualizing how controls estimated from the regression line could be incorporated into the MACC, the resulting unidentified control cost estimates ($960 million for

97 Examples of controls or measures that are not in the CoST tool include local transportation measures, energy efficiency measures, or fuel switching applications.

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4A-9 the 70 ppb level of the standard and an additional $13 billion to attain the 65 ppb level of the standard) are unchanged.

Figure 4A-2. Marginal Costs for Identified NO x 

### Controls for All Source Sectors with Unidentified Control Measures from Regression Line Included
4A.3.2 Simulation Approach
Another approach to estimate the cost of unidentified controls is to randomly sample from the complete MACC (without a cost threshold) to “fill in” the tons of reduction on the MACC needed for attainment. Sampling is done with replacement, and since the sampling is random, it is repeated 1000 times to limit the influence of any particular simulation. The mean of the total cost estimates is then the estimate of the cost of the unidentified controls. Alternatively, the mean cost per ton of the estimates can be interpreted as the cost per ton estimate for the unidentified
controls.

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4A-10 The implicit assumption when applying this simulation approach is that the controls in the incomplete MACC curve are representative of the types of controls (both in cost and effectiveness) that could be applied to alternative sources not yet controlled or adequately characterized in the CoST database, and also that controls that may be developed in the future (prior to the attainment date) will be similar in cost and effectiveness to controls currently available. While we do not believe that controls with costs greater than the cost threshold are likely to be applied, they are not removed from the simulation dataset in order to provide a more complete set of identified abatement possibilities.

The CoST database used for this analysis contains approximately 120,000 individual controls applicable to five broad sectors. While there is geographic specificity in the applicability of the controls, the simulation is currently being performed on a national scale. The cost per ton and number of available controls differs considerably between sectors, as shown below in Table 4A-7, and for this reason it is important to determine how broadly to sample when selecting controls to attain a particular level of the standard.

Table 4A-7. Costs and Number of Identified NO x 

### Controls by Sector in the CoST Database (2011$)

Sector Number of Controls Cost per Ton
Minimum Median Mean Maximum nonpt 2,069 $413 $1,008 $1,455 $2,005 nonroad 111,943 $3,330 $4,618 $4,619 $5,300 np_oilgas 772 $78 $649 $747 $2,019 pt_oilgas 3,892 $12 $649 $1,120 $44,860 ptnonipm 2,756 $18 $3,814 $12,147 $354,974



### In this simulation approach, we investigate three methods for selecting available controls to expand the MACC to simulate attainment. First, the sectors are aggregated into three broad sectors (nonroad, nonpoint, and non-EGU point sources) and controls are selected from these sectors in the same proportion as the control strategy discussed in Chapter 3 for these three sectors for each level of the standard. These percentages appear below in Table 4A-8.

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4A-11 Table 4A-8. Simulation Percentage of NO x 

### Controls from Sectors Based on Application of Identified Controls Sector 70 ppb 65 ppb non-EGU point 46% 56% nonpoint 52% 42% nonroad 2% 2%



### Using this method, since only 2 percent of the controls to meet any of the standards were applied to nonroad sources, only 2 percent of the tons from random draws are allowed to comefrom this sector. The majority of simulated controls are then drawn from the nonpoint and nonEGU point sectors.

A second method of assigning the proportions is based upon the number of available tons of reduction remaining in each sector after the application of the controls discussed in Chapter 3. To calculate this, the tons of reduction from the control scenarios are subtracted from the projected inventories, and then the percentages used for the simulation exercise are based upon the proportion of remaining emissions in the three sectors. These percentages appear below in
Table 4A-9.

Table 4A-9. Simulation Percentage of NO x 

### Controls from Sectors Based on Remaining Emissions in Sectors Sector 70 ppb 65 ppb non-EGU point 33% 30% nonpoint 36% 36% nonroad 31% 34%



### This method leads to a larger proportion of the simulated controls being selected from the nonroad sector, because this sector has a relatively large quantity of remaining emissions in 2025. Accordingly, the proportion of simulated controls drawn from the non-EGU point and nonpoint sectors is lower using this method. A third method imposes no restrictions on the selection of controls, so controls are randomly selected from the complete set regardless of the

sector.

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4A-12 Instead of randomly selecting from the MACC, it is also possible to simulate attainment by selecting controls from along the regression line shown in Figure 4A-1. For the purposes of this exercise, controls are first selected from the point where identified controls exceed the cost threshold up to the total tons of reduction necessary to meet a 65 ppb ozone standard. Each control is assumed to provide one ton of reduction at a cost calculated using the regression equation. This is different from using the area under the regression line to calculate the cost of unidentified controls, because in this case controls can be selected from any point along the regression line beyond the point where identified controls cross the cost threshold. As a result, the cost of the first additional ton of control as identified by the regression line is approximately $9,300 and this value rises to slightly more than $19,000 for the last ton required to attain the 65 ppb standard. A second variation on this approach selects tons of control from any point along the regression line. The results of the simulations appear in Table 4A-10.

Table 4A-10. Un identified NO x 

### Control Costs by Alternativ e Standard using Alternative Methods for Estimation of Costs from Unidentified Controls (total costs in millions of 2011$, cost per ton in parentheses in $2011)

Level of Standard Tons of Unidentified NO x reductions Regression Approach Simulation Approach

Sector Percentages from Applied Controls Sector Percentages from Remaining Emissions Random Draws from all Identified

Controls Random Draws from Regression Line Beyond

Identified Controls Random Draws from

Entire Regression Line 70 ppb 97,000 b $960 $250 $310 $290 $1,400 $940 ($9,800) ($2,500) ($3,100) ($3,000) ($14,000) ($9,600)
65 ppb 960,000 c $14,000 $2,700 $3,000 $2,900 $14,000 $9,300 ($14,000) ($2,800) ($3,100) ($3,000) ($14,000) ($9,600)

a All values are rounded to two significant figures. b Total tons of NO x reductions required includes 51,000 tons for Post-2025 California and 46,000 tons for the rest of

the United States in 2025. Because these simulations are designed to be proof of concept, we combined the emissions reductions needed and controls applied in these analyses.

c Total tons of NO x reductions required includes 100,000 tons for Post-2025 California and 860,000 tons for the rest of

the United States in 2025. Because these simulations are designed to be proof of concept, we combined the emissions reductions needed and controls applied in these analyses. 

### Cost estimates based on the regression approach or sampling from the regression line are consistently higher than those based on sampling from the identified controls, as should be expected. While the simulations that sampled from identified controls were allowed to select controls beyond the cost per ton threshold applied in the identified control strategy described in Chapter 3, there are a limited number of controls above the cost per ton threshold in the database.

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4A-13 As a result, the simulation is far more likely to select cheaper controls simply because of their prevalence in the data. Another observation that can be made is that requiring a certain percentage of controls from particular sectors does affect the results, but not to the extent that might be expected. While the cost of controls vary between sectors, the simulations produced similar results regardless of restrictions on the distribution of controls across sectors. Finally, while the cost per ton estimates varied considerably across the approaches, all cost per ton estimates were below the $15,000/ton estimate used as the primary estimate of the cost of
unidentified controls.

The EPA continues to investigate methods to better estimate the cost of currently unidentified controls. While we have reason to believe that technological advances over the coming years will both lead to new types of controls as well as reduce the cost of currently available controls, we do not presently possess the capability to accurately predict the rate at which technological progress will occur or the potential impacts such progress will have on the cost of controls. As a result, the simulations presented herein draw upon currently identified controls and their current costs, or a linear regression of these data. The results of the simulations are sensitive to the percentage of unidentified controls required from each sector, and for this reason we plan to continue investigating methods for assigning these percentages based upon the degree to which sectors have already been controlled. Furthermore, while the simulations in this appendix were performed at the national level, we recognize that areas differ greatly in their industrial base, and for this reason the simulations should be performed at a more disaggregated level using data about the emissions sources in each area, available controls, and the degree to which sources in the area have already been c ontrolled. Because these simulations are designed to be proof of concept, we combined the emissions reductions needed and controls applied in these analyses.

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5-1 CHAPTER 5: QUALITATIVE DISCUSSION OF EMPLOYMENT IMPACTS OF AIR QUALITY

Overview Executive Order 13563 directs federal agencies to consider regulatory impacts on job creation and employment. According to the Executive Order, “our regulatory system must protect public health, welfare, safety, and our environment while promoting economic growth, innovation, competitiveness, and job creation. It must be based on the best available science” (Executive Order 13563, 2011). Although standard benefit-cost analyses have not typically included a separate analysis of regulation-induced employment impacts, 98 

### we typically conduct employment analyses for economically significant rules. While the economy continues moving toward full employment, employment impacts are of particular concern and questions may arise about their existence and magnitude. This chapter discusses some, but not all, possible types of labor impacts that may result from measures to decrease NOx emissions. 99



### Section 5.1 describes the theoretical framework used to analyze regulation-induced employment impacts, discussing how economic theory alone cannot predict whether such impacts are positive or negative. Section 5.2 presents an overview of the peer-reviewed literature relevant to evaluating the effect of environmental regulation on employment. Section 5.3 discusses employment related to installation of NOx controls on coal and gas-fired electric generating units, industrial boilers, and cement kilns.

5.1 Economic Theory and Employment Regulatory employment impacts are difficult to disentangle from other economic changes affecting employment decisions over time and across regions and industries. Labor market responses to regulation are complex. They depend on labor demand and supply elasticities and possible labor market imperfections (e.g., wage stickiness, long-term unemployment, etc). The unit of measurement (e.g., number of jobs, types of job hours worked, and earnings) may affect

98 Labor expenses do, however, contribute toward total costs in the EPA’s standard benefit-cost analyses.
99 The employment analysis in this RIA is part of EPA’s ongoing effort to “conduct continuing evaluations of potential loss or shifts of employment which may result from the administration or enforcement of [the Act]” pursuant to CAA section 321(a).

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5-2 observability of that response. Net employment impacts are composed of a mix of potential declines and gains in different areas of the economy (the directly regulated sector, upstream and downstream sectors, etc.) over time. In light of these difficulties, economic theory provides a constructive framework for analysis.

Microeconomic theory describes how firms adjust input use in response to changes in
economic conditions. 100 

### Labor is one of many inputs to production, along with capital, energy, and materials. In competitive markets, firms choose inputs and outputs to maximize profit as a function of market prices and technological constraints. 101,102



### Berman and Bui (2001) adapt this model to analyze how environmental regulations affect
labor demand. 103 

### They model environmental regulation as effectively requiring certain factors of production, such as pollution abatement capital, at levels that firms would not otherwise choose.

Berman and Bui (2001) model two components that drive changes in firm-level labor demand: output effects and substitution effects. 104 

### Regulation affects the profit-maximizing quantity of output by changing the marginal cost of production. If regulation causes marginal cost to increase, it will place upward pressure on output prices, leading to a decrease in demand, and resulting in a decrease in production. The output effect describes how, holding labor intensity constant, a decrease in production causes a decrease in labor demand. As noted by Berman and Bui, although many assume that regulation increases marginal cost, it need not be the case. A regulation could induce a firm to upgrade to less polluting and more efficient equipment that lowers marginal production costs. In such a case, output could increase for facilities that do not exit the industry. For example, improving the heat rate of a utility boiler increases fuel efficiency, lowering marginal production costs, and thereby potentially increasing

100 See Layard and Walters (1978), a standard microeconomic theory textbook, for a discussion, in Chapter 9.
101 See Hamermesh (1993), Ch. 2, for a derivation of the firm’s labor demand function from cost-minimization.
102 In this framework, labor demand is a function of quantity of output and prices (of both outputs and inputs).
103 Morgenstern, Pizer, and Shih (2002) develop a similar model.
104 The authors also discuss a third component, the impact of regulation on factor prices, but conclude that this effect is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer and Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) a demand effect; 2) a cost effect; and 3) a factor-shift effect.

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5-3 the boiler’s generation. An unregulated profit-maximizing firm may not have chosen to install such an efficiency-improving technology if the investment cost were too high.

The substitution effect describes how, holding output constant, regulation affects laborintensity of production. Although stricter environmental regulation may increase use of pollution control equipment and energy to operate that equipment, the impact on labor demand is ambiguous. Equipment inspection requirements, specialized waste handling, or pollution technologies that alter the production process may affect the number of workers necessary to produce a unit of output. Berman and Bui (2001) model the substitution effect as the effect of regulation on pollution control equipment and expenditures required by the regulation and the corresponding change in labor-intensity of production.

In summary, as output and substitution effects may be positive or negative, theory cannot predict the direction of the net effect of regulation on labor demand at the level of the regulated firm. Operating within the bounds of standard economic theory, however, empirical estimation of net employment effects on regulated firms is possible when data and methods of sufficient detail and quality are available. The literature, however, illustrates difficulties with empirical estimation. For example, studies sometimes rely on confidential plant-level employment data from the U.S. Census Bureau, possibly combined with pollution abatement expenditure data that are too dated to be reliably informative. In addition, the most commonly used empirical methods, for example Greenstone (2002), likely overstat e employment impacts because they rely on relative comparisons between more regulated and less regulated counties, which can lead to “double counting” of impacts when production and employment shift from more regulated toward less regulated areas. Thus the empirical methods cannot be used to estimate net employment effects. 105



### The conceptual framework described thus far focused on regulatory effects on plant-level decisions within a regulated industry. Employment impacts at an individual plant do not necessarily represent impacts for the sector as a whole. The approach must be modified when applied at the industry level.

105 See Greenstone (2002) p. 1212.

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5-4 At the industry-level, labor demand is more responsive if: (1) the price elasticity of demand for the product is high, (2) other factors of production can be easily substituted for labor, (3) the supply of other factors is highly elastic, or (4) labor costs are a large share of total production costs. 106 

### For example, if all firms in an industry are faced with the same regulatory compliance costs and product demand is inelastic, then industry output may not change much, and output of individual firms may change slightly. 107 

### In this case the output effect may be small,
while the subs titution effect depends on in put substitutability. Suppose, for example, that new equipment for heat rate improvements requires labor to install and operate. In this case the substitution effect may be positive, and with a small output effect, the total effect may be positive. As with potential effects for an individual firm, theory cannot determine the sign or magnitude of industry-level regulatory effects on labor demand. Determining these signs and magnitudes requires additional sector-specific empirical study. For environmental rules, much of the data needed for these empirical studies are not publicly available, would require significant time and resources in order to access confidential U.S. Census data for research, and also would not be necessary for other components of a typical regulatory impact analysis (RIA).

In addition to changes to labor demand in the regulated industry, net employment impacts encompass changes in other related sectors. For example, the proposed guidelines may increase demand for pollution control equipment and services. This increased demand may increase revenue and employment in the firms supporting this technology. At the same time, the regulated industry is purchasing the equipment and these costs may impact labor demand at regulated firms. Therefore, it is important to consider the net effect of compliance actions on employment across multiple sectors or industries.

If the U.S. economy is at full employment, even a large-scale environmental regulation is unlikely to have a noticeable impact on aggregate net national employment. 108 

### Instead, labor would primarily be reallocated from one productive use to another (e.g., from producing electricity or steel to producing high efficiency equipment), and net national employment effects

106 See Ehrenberg & Smith, p. 108.
107 This discussion draws from Berman and Bui (2001), pp. 293.
108 Full employment is a conceptual target for the economy where everyone who wants to work and is available to do so at prevailing wages is actively employed. The unemployment rate at full employment is not zero.

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5-5 from environmental regulation would be small and transitory (e.g., as workers move from one job to another). 109



### Affected sectors may experience transitory effects as workers change jobs. Some workers may retrain or relocate in anticipation of new requirements or require time to search for new jobs, while shortages in some sectors or regions could bid up wages to attract workers. These adjustment costs can lead to local labor disruptions. Although the net change in the national workforce is expected to be small, localized reductions in employment may adversely impact individuals and communities just as localized increases may have positive impacts.

If the economy is operating at less than full employment, economic theory does not clearly indicate the direction or magnitude of the net impact of environmental regulation on employment; it could cause either a short-run net increase or short-run net decrease (Schmalansee and Stavins, 2011). For example, the Congressional Budget Office considered EPA’s Mercury and Air Toxics Standards and regulations for industrial boilers and process heaters as potentially leading to short-run net increases in economic growth and employment, driven by capital investments for compliance with the regulations (Congressional Budget Office, 2011). An important research question is how to accommodate unemployment as a structural feature in economic models. This feature may be important in assessing large-scale regulatory impacts on employment (Smith 2012).

Environmental regulation may also affect labor supply. In particular, pollution and other environmental risks may impact labor productivity or employees’ ability to work. 110 

### While the theoretical framework for analyzing labor supply effects is analogous to that for labor demand, it is more difficult to study empirically. There is a small emerging literature, described in the next section that uses detailed labor and environmental data to assess these impacts.

To summarize, economic theory provides a framework for analyzing the impacts of environmental regulation on employment. The net employment effect incorporates expected employment changes (both positive and negative) in the regulated sector and elsewhere. Labor

109 Arrow et. al. 1996; see discussion on bottom of p. 8. In practice, distributional impacts on individual workers can be important, as discussed in later paragraphs of this section.
110 E.g. Graff Zivin and Neidell (2012).

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5-6 demand impacts for regulated firms, and also for the regulated industry, can be decomposed into output and substitution effects which may be either negative or positive. Estimation of net employment effects for regulated sectors is possible when data of sufficient detail and quality are available. Finally, economic theory suggests that labor supply effects are also possible. In the next section, we discuss the empirical literature.

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

The labor economics literature contains an extensive body of peer-reviewed empirical work analyzing various aspects of labor demand, relying on the theoretical framework discussed in the preceding section. 111 

### This work focuses primarily on effects of employment policies such as labor taxes and minimum wages. 112 

### In contrast, the peer-reviewed empirical literature specifically estimating employment effects of environmental regulations is more limited.

Empirical studies, such as Berman and Bui (2001) and Ferris, Shadbegian, and Wolverton (2014), suggest that regulation-induced net employme nt impacts may be zero or slightly positive, but small in the regulated sector. Other research on regulated sectors suggests that employment growth may be lower in more regulated areas (Greenstone 2002, Walker 2011, 2013). However since these latter studies compare more regulated to less regulated counties, this methodological approach likely overstates employment impacts to the extent that regulation causes plants to locate in one area of the country rather than a nother, which would lead to “double counting” of the employment impacts. List et al. (2003) find some evidence that this type of geographic relocation may be occurring. Environmental regulations may affect sectors that support pollution reduction earlier than the regulated industry. Rules are usually announced well in advance of their effective dates and then typically provide a period of time for firms to invest in technologies and process changes to meet the new requirements. When a regulation is promulgated, the initial response of firms is often to order pollution control equipment and services to enable compliance when the regulation becomes effective. Estimates of short-term increases in demand for specialized labor within the environmental protection sector have been prepared for several EPA regulations in the past, including the Mercury and Air Toxics

111 Again, see Hamermesh (1993) for a detailed treatment.
112 See Ehrenberg & Smith (2000), Chapter 4: “Employment Effects: Empirical Estimates” for a concise overview.

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5-7 Standards (MATS). 113 

### Overall, the peer-reviewed literatur e does not contain evidence that environmental regulation has a large impact on net employment (either negative or positive) in the long run across the whole economy.

5.2.1 Regulated Sectors
Berman and Bui (2001) examine how an increase in local air quality regulation affects manufacturing employment in the South Coast Air Quality Management District (SCAQMD), which includes Los Angeles and its suburbs. From 1979 to 1992 the SCAQMD enacted some of the country’s most stringent air quality regulations. Using SCAQMD’s local air quality regulations, Berman and Bui identify the effect of environmental regulations on net employment in regulated manufacturing industries relative to other plants in the same 4-digit SIC industries but in regions not subject to local regulations. 114 

### The authors find that “while regulations do impose large costs, they have a limited effect on employment” (Berman and Bui, 2001, p. 269). Their conclusion is that local air quality regulation “probably increased labor demand slightly” but that “the employment effects of both compliance and increased stringency are fairly precisely estimated zeros, even when exit and dissuaded entry effects are included” (Berman and Bui, 2001, p. 269). 115



### A small literature examines impacts of environmental regulations on manufacturing employment. Greenstone (2002) and Walker (2011, 2013) study the impact of air quality regulations on manufacturing employment, estima ting the net effects in nonattainment areas relative to attainment areas. Kahn and Mansur (2013) study environmental regulatory impacts on geographic distribution of manufacturing employment, controlling for electricity prices and labor regulation (right to work laws). Their methodology identifies employment impacts by focusing on neighboring counties with different ozone regulations. They find limited evidence that environmental regulations may cause employment to be lower within “county-border-pairs.” This result suggests that regulation may cause an effective relocation of labor across a county border, but since one county’s loss may be another’s gain , such shifts cannot be transformed into

113 U.S. EPA (2011b).
114 Berman and Bui include over 40 4-digit SIC industries in their sample. They do not estimate the number of jobs created in the environmental protection sector.
115 Including the employment effect of existing plants and plants dissuaded from opening will increase the estimated impact of regulation on employment.

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5-8 an estimate of a national net effect on employment. Moreover this result is sensitive to model specification choices.

5.2.2 Economy-Wide
As noted above it is very difficult to estimate the net national employment impacts of environmental regulation. Given the difficulty with estimating national impacts of regulations, EPA has not generally estimated economy-wide employment impacts of its regulations in its benefit-cost analyses. However, in its continuing effort to advance the evaluation of costs, benefits, and economic impacts associated with environmental regulation, EPA has formed a panel of experts as part of EPA’s Science Advisory Board (SAB) to advise EPA on the technical merits and challenges of using economy-wide econ omic models to evaluate the impacts of its regulations, including the impact on net national employment. 116 

### Once EPA receives guidance from this panel it will carefully consider this input and then decide if and how to proceed on economy-wide modeling of employment impacts of its regulations.

5.2.3 Labor Supply Impacts
The empirical literature on environmental regulatory employment impacts focuses primarily on labor demand. However, there is a nascent literature focusing on regulation-induced effects on labor supply. 117 

### Although this literature is limited by empirical challenges, researchers have found that air quality improvements lead to reductions in lost work days (e.g., Ostro 1987). Limited evidence suggests worker productivity may also improve when pollution is reduced. Graff Zivin and Neidell (2012) used detailed worker-level productivity data from 2009 and 2010, paired with local ozone air quality monitoring data for one large California farm growing multiple crops, with a piece-rate payment structure. Their quasi-experimental structure identifies an effect of daily variation in monitored ozone levels on productivity. They find “ozone levels well below federal air quality standards have a significant impact on productivity: a 10 parts per

116 For further information see: http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67CF77B54734285257BB0004F87ED?OpenDocument
117 For a recent review see Graff-Zivin and Neidell (2013).

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5-9 billion (ppb) decreases in ozone concentrations increases worker productivity by 5.5 percent.” (Graff Zivin and Neidell, 2012, p. 3654). 118



### This section has outlined the challenges associated with estimating regulatory effects on both labor demand and supply for specific sectors. Th ese challenges make it difficult to estimate net national employment estimates that would appropriately capture the way in which costs, compliance spending, and environmental benefits propagate through the macro-economy.

5.3 Employment Related to Inst allation and Maintenance of NO x 

### Control Equipment This section discusses employment related to installation of NOx controls on coal and gas-fired electric generating units (EGUs), industrial boilers, and cement kilns, which are among the highest NOx-emitting source categories in EPA’s emissions inventory (see Chapter 2 and Appendix 2A for more detail on emissions). The employment analysis in this section is an illustrative analysis, estimating the amount of labor involved with installing advanced NOx emission control systems at each of these three different types of NOx emission sources. The analysis also estimates the labor needed to operate existing advanced NOx systems more frequently (e.g., year round). Sections 5.3.1 and 5.3.2 below contain estimates of the number of direct short-term and long-term jobs that would be created by addition of NOx controls at a single facility (or unit) in each of these three categories of emissions sources, for various size units. Because the apportionment of emissions control across emissions sources in this RIA analysis is an illustrative model plant analysis, and is not necessarily representative of the controls that will be required in individual state SIPs, the EPA did not estimate short-term or long-term employment that would result from addition of NOx controls at these three source categories either everywhere throughout the country, or at facilities located in areas anticipated to need additional NOx reductions for the 65 ppb and 70 ppb standard alternatives.

118 The EPA is not quantifying productivity impacts of reduced pollution in this rulemaking using this study. In light of this recent research, however, the EPA is considering how best to incorporate possible productivity effects in the future.

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5-10 5.3.1 Employment Resulting from Addition of NO x 

### Controls at EGUs
This section presents an illustrative analysis of the direct labor needs to install and operate SCRs at three common sizes of coal-fired EGUs: 300 MW, 500 MW and 1000 MW. As discussed below, the illustrative analysis is for a “model plant” of each size, using consistent assumptions about the plant’s operation that impact the material and labor needs of a representative plant, such as the capacity factor, heat rate, and type of coal. The analysis does not include an estimate of the aggregate total of the labor needed for installing and running SCRs at any particular level of the revised ozone standard; nor does it reflect plant-specific variations in labor needs due to regional differences in prices and labor availability, existing control technology at the plant, etc.

Coal-fired EGUs are likely to apply additional NOx controls in response to State Implementation Plans (SIPs) approved pursuant to a revised ozone standard. While many EGUs have already installed and operate various NOx control devices, there are additional existing coal-fired EGUs that could further decrease NOx emissions by either (a) upgrading or replacing their existing NOx emissions reducing systems, or (b) operating their existing NOx systems for more hours in the year than the IPM model predicts they will in the IPM v. 5.14 base case 119 

### for 2025. While all existing coal-fired EGUs already have low NOx burners, there are EGUs that currently have a selective non-catalytic reduction (SNCR) post-combustion NOx control system that could be replaced with a selective catalytic reduction (SCR) system installed that would reduce their NOx emissions rate (and hence quantity of NOx) at those units.

The EPA used a bottom up engineering analysis using data on labor productivity, engineering estimates of the types of labor needed to manufacture, construct and operate SCRs
on EGUs. The EPA’s labor estimates include not only labor directly involved with installing SCRs on EGUs and on-site labor used to operat e the SCRs once they become operational, but also include the labor requirements in selected major upstream sectors directly involved in manufacturing the materials used in SCR systems (steel), as well as the chemicals used to

119 Note that the IPM v. 5.14 base case is not the base case used in the final Clean Power Plan analysis (using IPM v. 5.15), which is used for other analyses in this RIA. Furthermore neither the v. 5.14 nor the v 5.15 base cases used in this RIA include the illustrative estimated EGU responses to the Clean Power Plan. The base case, however, is only used in the labor analysis to select the three illustrative sizes of EGUs in the model plant estimates.

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5-11 operate an SCR system (ammonia and the catalyst used to in the construction and operation of SCR systems, such as steel, concrete, or chemicals used to manufacture NOx controls).

The analysis draws on information from seven primary sources:

 Documentation for EPA Base Case v.5.13 Using the Integrated Planning Model. November, 2013

 IPM updates included in V. 5.14 EPA Base Case v.5.14 Using IPM: Incremental Documentation. March, 2015.

 “Engineering and Economic Factors Affecting the Installation of Control Technologies: An Update”. By James E. Staudt, Andover Technology Partners. December, 2011.

 “Regulatory Impact Analysis (RIA) for the final Transport Rule”. June 2011

 “Regulatory Impact Analysis for the Clean Power Plan Final Rule”. August, 2015.

 The National Electric Energy Data System (NEEDS) Version 5.14. March, 2015.

 EPA Base Case for IPM v. 5.14 estimates for 2025. March, 2015

5.3.1.1 Existing EGUs Without SCR Systems (or SCR Systems Operating less than Full Time)
Using EPA’s National Electric Energy Data System (NEEDS) v. 5.14 and the IPM 2025 estimates from the v 5.14 base case, the EPA identified all existing coal-fired EGUs in the contiguous United States that:

(1) The IPM 5.14 base case estimates will be operating in 2025. (2) Either do not already have an SCR NOx emission control system installed, or have an SCR NOx system that could be utilized more to further reduce NOx emissions.

Coal-fired units that currently have an SNCR system but not an SCR system were identified directly from NEEDS 5.14, which includes detailed information on the type of emissions control systems that are installed. NEEDS 5.14 also includes the NOx emission rate (lbs/MMBtu) that the unit could achieve if the required state of the art (SOA) NOx emission controls were operated. 120 

### The 2025 estimates from IPM v. 5.14 base case, in combination with the SOA NOx rates from NEEDS, were used to determine which units with an SCR installed were capable of lowering their annual NOx emissions by operating the SCR as often as possible. If IPM estimated that the predicted NOx annual emissions in 2025 exceeded the amount of NOx

120 Mode 4 NOx emission rate. For more details see the NEEDS 5.14 documentation page 6, and IPM v. 5.13 User Guide Section 3.9.2.

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5-12 that would be emitted if the 2025 quantity of coal was consumed and the unit achieved the SOA NOx emission rate from NEEDS, then the unit is assumed to be operating in 2025 running the SCR to its maximum potential all the time.

Furthermore, the EPA identified the subset of these EGUs that are in areas anticipated to need additional NOx reductions under an alternative ozone standard level of 65 ppb, as well as the smaller subset of the EGUs that are in areas anticipated to need additional NOx reductions under a 70 ppb ozone standard.

The EPA identified 319 existing coal-fired EGUs nationwide (total capacity 122.4 GW) that are estimated to continue to be in operation in the 2025 base case that either do not already have an SCR system (30 units, 5.4 GW) or have an SCR system that is not working full time (289 units, 117.1 GW) . These 319 EGUs include units both in areas anticipated to need to reduce NOx emissions (37 units, 8.5 GW) with a 65 ppb ozone NAAQS, as well as the EGUs in all other areas (282 units, 114.0 MW). All of the 30 EGUs that do not have an SCR are in areas expected to need to reduce NOx emissions with a 65 ppb NAAQS (30 units, 5.4 GW). The areas that are expected to need to reduce NOx emissions with a 65 ppb NAAQS also have 7 units (3.1 GW) have an SCR system not working full time.

Upgrading the 30 EGUs to an SCR emission control system will reduce NOx emissions in areas expected to need additional NOx controls with a 65 ppb NAAQS by a total of 41,400 tons. Operating the 7 EGUs with SCRs system estimated to not be operated as much as possible in the 65 ppb ozone NAAQS areas an additional 18,300 tons, for a combined total of 59,700 tons of NOx reduced annually.

Of the 37 EGUs estimated to be able to reduce NOx emissions in the 65 ppb NAAQS areas, 5 EGUs (1.7 GW) are also in the areas needing additional NOx reductions under a revised 70 ppb ozone NAAQS standard. Installing SCRs on 2 of these units, and running the existing SCRs as much as possible on the other 3 units, a total of 11,200 tons of NOx in the 70 ppb ozone NAAQS areas.

Given the nationwide size distribution of the existing EGUs that do not already have an SCR, or do not operate the existing SCR system full time, we present the illustrative labor

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5-13 analysis for three different sized “model plants”: 300 MW capacity, 500 MW, and 800 MW. These three capacity sizes of model plants were selected by examining the distribution of existing coal-fired EGUs that can either be upgraded to an SCR, or have the existing SCR operated to as much as possible. Because of th e relatively small number (37) of the identified EGUs in the 65 ppb areas, the distribution of 319 EGUs nationwide better reflects the distribution of EGUs identified. Figure 5-1 shows the nationwide capacity size distribution of the 319 units. For comparison Figure 5-2 shows the size distribution of the 37 units in the 65 ppb ozone NAAQS areas. As can be seen in Figure 5- 1, the 300 MW capacity “model plant” is the most common candidate for installing an SCR system in the 65 ppb areas, but 500 MW and 800 MW units (which are common in the national set of 319 identified units) also exist in the 65 ppb

areas.

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5-14 Figure 5-1. Size Distribution of Identi fied 319 Existing Coal-Fired EGU Units Nationwide without SCR NOx Controls (or with SCRs Operated Less Than the Maximum Possible Amount of Time)

Figure 5-2. Size Distribution of 37 Existing Coal-Fired EGU Units without SCR NOx Controls (or with SCRs Operated Less Than the Maximum Possible Amount of Time) in Areas Anticipated to Need Additional NOx Controls With the Alternative 65 ppb Ozone Standard Level 0 10 20 30 40 50 60 70 80 90#ofEGUs

0 5 10 15 20 25 30 35

0 ‐ 400 MW 450 ‐ 650 MW 650 ‐ 1350 MW#ofEGUs

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5-15 5.3.1.2 Labor Estimates for Installing and Operating Individual SCR Systems
All labor estimates in this illustrative analysis are in terms of person-years (i.e., full time equivalents, or FTEs).

The labor involved with manufacturing and installing the SCRs is a one-time labor need, and occurs over a 2 to 3 year construction period; the estimated FTEs during the construction phase are presented as the cumulative amount of labor over the multi-year period. The construction phase labor includes both labor directly involved with installing the SCR on site (including boiler makers, general labor and engineering) and labor to manufacture the SCR .

There are three types of annual labor estimated to operate an SCR, and will be needed each year the EGU is in operation. The largest category is on-site labor at the EGU. The estimated amounts of direct labor involved with installing SCR systems is shown in Table 5-1.

Table 5-1. Summary of Direct Labor Impa cts for SCR Installation at EGUs (FTEs) Plant Size

300 MW 500 MW 1000 MW Construction Phase (One time, Total Labor over 2-3 Year Period)

Direct Construction-related Employment 158.7 264.4 528.8 Operation Phase (Annual Operations) Operation and Maintenance 1.9 2.8 4.6

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5-16 The key assumptions used in the labor analysis are presented in Table 5-2.

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

Assumptions Key Factor Source 300 MW 500 MW 1000 MW

Capital Investment to Install SCR Utility-owned Capital Recovery Rate for Environmental Retrofits (12.1%) IPM 5.13 Base Case Documentation $86.1 million $133 million $244 million

Result: FTEs to Install an SCR 1,100 labor hours/MW Staudt, 2011 158.7 FTEs 264.4 FTEs 528.9 FTEs

Labor Cost (fixed O&M) per Year IPM analysis of CPP baseline $218,000 $310,500 $513,000

Result: FTEs per Year 8.9 FTEs per $1 million of Fixed O&M CSAPR RIA 1.95 2.76 4.57

Result: Total FTEs to Operate an SCR Annually 1.95 FTEs 2.76 FTEs 4.57 FTEs



### 5.3.2 Assessment of Employment Impacts for Individual Industrial, Commercial, and Institutional (ICI) Boilers and Cement Kilns
Facilities other than electric power generators are likely to apply NOx controls in response to State Implementation Plans (SIPs) approved pursuant to a revised ozone standard. In addition to EGUs, and also in an illustrative analysis, the EPA estimated the amount and types of direct labor that might be used to apply and operate NOX controls for representative categories of ICI boilers and cement kilns. As with EGUs, the EPA used a bottom up engineering analysis using data on labor productivity, engineering estimates of the types of labor needed to manufacture, construct and operate NOx controls on ICI boilers and cement kilns. In addition, the numbers presented in this section are only indicative of the relative number and types of labor that might be used at these two categories of plants, without calculating an estimate of the labor that would be required by them in the aggregate (SC&A, 2014).

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5-17 5.3.2.1 ICI Boilers
There are a number of control technologies available to reduce NOx emissions from ICI boilers. The EPA anticipates that the most commonly applied control technology for ICI boilers that could require NOx reductions as part of an ozone SIP will be selective catalytic reduction (SCR). The analysis calculates s labor requirements to fabricate, install, and operate different sizes of SCR for coal, oil and natural gas ICI boilers. Estimated total labor costs are a function of total capital costs and boiler size in EPA’s Coal Utility Environmental Cost (CUECost) model. Total SCR capital costs of ICI boilers was estimated using the EPA’s Control Strategy tool (CoST) model.

Just over 24% of total capital costs are for labor used in SCR fabrication. This percentage was multiplied by the total capital cost, and the resulting dollar amount was converted into full time equivalents (FTE) based on the average annual salary of workers (as outlined in IEC, 2011). The annual compensation came from the Bureau of Labor Statistics (BLS). This salary number was adjusted to ac count for benefits also based on BLS data. The total fabrication expenditures were divided by the average fabrication labor compensation to estimate the number of full time equivalent workers in SCR fabrication (SC&A, 2014).

The calculation of construction or installation labor is based on previous research on labor required for SCR installation at utility boilers. (Staudt 2011). Based on that, we estimate that 27% of SCR capital costs are spent on installation labor. We applied that percentage to the estimates of the capital costs of SCR for ICI boilers to give us the total labor expenditures, which we then converted to FTE based on average annual compensation provided by BLS.

Operation and Maintenance labor was estimated using the CUECost model ( http://nepis.epa.gov/Adobe/PDF/P1005ODM.pdf ). Maintenance and administrative labor for SCR is estimated to be small in relation to fabrication and construction, with the caveat that available information on which to base an estimate is sparse. According to the approach used in the CUECost model, most utility boilers require a full time worker to operate and maintain the equipment. ICI boilers are much smaller, however.. Table 5-3 below provides summary labor estimates for SCR at varying sized ICI boilers.

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5-18 Table 5-3. Summary of Direct Labo r Impacts for Individual ICI Boilers

Plant Type Boiler Size (MMBtu/hr) One-Time Employment Impacts 1 (Annual FTEs) Recurring Annual Employment Impacts 2 (FTEs per year) Coal-fired 750 19.5 1.2 500 15.2 1.1 400 13.6 1.0 250 10.7 0.9 Oil-fired 250 9.8 0.9 150 7.3 0.9 100 5.5 0.8 50 3.2 0.8 Natural Gas-fired 250 10.5 0.9 150 11.0 0.9 100 8.4 0.9 50 6.5 0.8
1. 

### Includes Fabrication and Installation Labor
2. 

### Includes Operations, Maintenance, and Administrative Support

Similar to the calculations for SCR applied to EGUs, an illustrative analysis based on engineering costs estimates labor requirements in upstream sectors for SCR applied to representative ICI boilers. This includes labor requirements in selected major upstream sectors directly involved in manufacturing the materials used in SCR systems (steel), as well as the chemicals used to operate an SCR system (ammonia and the catalyst used in the construction and operation of SCR systems, such as steel, concrete, or chemicals used to manufacture NOxcontrols). For an SCR installed at a representative 750 MMBtu/hr coal boiler, we estimate onetime employment impacts in these related sectors as:of 8.6 FTE, annual recurring impacts of 2.17 FTE for an SCR operated year-round, and annual recurring impacts of 0.90 FTE for an SCR operated during the five-month ozone season (SC&A, 2014).

5.3.2.2 Cement Kilns
There are a number of technologies that can be used to control NOx emissions at cement kilns. The analysis focused on synthetic non-catalytic reduction (SNCR) as the most likely choice for future NOx controls at cement kilns affected by requirements in ozone SIPs. Although SNCR is not considered an appropriate technology for wet and long dry kilns, most new or recently constructed kilns will likely be preheater and precalciner kilns, and these kilns will likely operate using SNCR as a control technology.

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5-19 The capital costs for equipment supply fabrication were estimated for an SNCR system for a mid-sized preheater and precalciner kiln (125 to 208 tons of clinker per hour). The percent of equipment supply fabrication costs of these systems attributable to labor is 44%. (Wojichowski, 2014). This labor cost was converted to FTE using BLS data. A similar methodology was used to estimate installation labor. Labor costs for SNCR installation were estimated by the vendor to be 17% of the total cost. That was converted to FTE using BLS data.
This information is summarized in Table 5-4.

Table 5-4. Estimated Direct Labor Impact s for Individual SNCR Applied to a MidSized Cement Kiln (125-208 tons clinker/hr)
Kiln Type Preheater / Precalciner Equipment Supply Fabrication FTE 1.5 Installation FTE 0.9 O&M Annual Recurring FTE 0.1



### In addition, an illustrative analysis of labor requirements in upstream sectors for SNCR applied to cement kilns include the labor requirements in selected major upstream sectors directly involved in manufacturing the materials used in SNCR systems (steel), as well as the reagent used to operate an SNCR system. For an SNCR installed at a 3,000 tons clinker per day capacity cement kiln, we estimate employment impacts in these related sectors as one-time employment impacts of 0.22-0.34 FTE, annual recurring impacts of 0.41 FTE for an SNCR operated year-round, and annual recurring impacts of 0.28 FTE for an SNCR operated during the five-month ozone season (SC&A, 2014).

5.4 Conclusion This chapter presents qualitative and quantitative discussions of potential employment impacts of the Ozone NAAQS. The qualitative discussion identifies challenges associated with estimating net employment effects and discusses anticipated impacts related to the rule. It includes an in-depth discussion of economic theory underlying analysis of employment impacts. The employment impacts for regulated firms can be decomposed into output and substitution effects, both of which may be positive or negative. Consequently, economic theory alone cannot predict the direction or magnitude of a regulation’s employment impact. It is possible to combine theory with empirical studies specific to the regulated firms and other relevant sectors if data and

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5-20 methods of sufficient detail and quality are available. Finally, economic theory suggests that environmental regulations may have positive impacts on labor supply and productivity as well.

We examine the peer-reviewed economics literature analyzing various aspects of labor demand, relying on the above theoretical framework. Determining the direction of employment effects in regulated industries is challenging because of the complexity of the output and substitution effects. Complying with a new or more stringent regulation may require additional inputs, including labor, and may alter the relative proportions of labor and capital used by regulated firms (and firms in other relevant industries) in their production processes. The available literature illustrates some of the difficulties for empirical estimation. Econometric studies of environmental rules converge on the finding that employment effects, whether positive or negative, have been small in regulated sectors.

The illustrative quantitative analysis in this chapter projects a subset of potential employment impacts in the electricity generation sector and for ICI boilers and cement kilns. States have the responsibility and flexibility to implement plans to meet the Ozone NAAQS. As such, given the wide range of approaches that may be used, quantifying the associated employment impacts is difficult. This analysis presents employment impact estimates based on used a bottom up engineering analysis using data on labor productivity, engineering estimates of the types of labor needed to manufacture, construct and operate NOx controls for EGUs, ICI
boilers and cement kilns.

5.5 References
Arrow, K. J.; M. L. Cropper; G. C. Eads; R. W. Hahn; L. B. Lave; R. G. Noll; Paul R. Portney; M. Russell; R. Schmalensee; V. K. Smith and R. N. Stavins. 1996. “Benefit-Cost Analysis in Environmental, Health, and Safety Regulation: A Statement of Principles.” American Enterprise Institute, the Annapolis Center, and Resources for the Future; AEI Press. Available at <http://www.hks.harvard.edu/fs/rstavins/Papers/Benefit Cost Analysis in Environmental.AEI.1996.pdf>. Accessed June 5, 2015.
Berman, E. and L. T. M. Bui. 2001. “Environmental Regulation and Labor Demand: Evidence from the South Coast Air Basin.” Journal of Public Economics. 79(2): 265-295.
Bureau of Labor Statistics, 2011. “National Compensation Survey: Occupational Earnings in the United States, 2010.” Located online at: http://www.bls.gov/ncs/ncswage2010.htm#Wage_Tables
Bureau of Labor Statistics, 2014. “Employer Costs for Employee Compensation”. Released September 10, 2014. Located online at: http://www.bls.gov/news.release/ecec.toc.htm

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5-21 Congressional Budget Office (2011), Statement of Douglas W. Elmendorf, Director, before the Senate Budget Committee, “Policies for Increasing Economic Growth and Employment in 2012 and 2013” (November 15)<http://www.cbo.gov/sites/default/files/11-15-Outlook_Stimulus_Testimony.pdf>
Ehrenberg, R. G. and R. S. Smith. 2000. Modern Labor Economics: Theory and Public Policy. Addison Wesley Longman, Inc., Chapter 4.
Executive Order 13563 (January 21, 2011). “Improving Regulation and Regulatory Review. Section 1. General Principles of Regulation.” Federal Register 76 (14): 3821-3823.
Ferris, A. E., R. J. Shadbegian, A. Wolverton. 2014. “The Effect of Environmental Regulation on Power Sector Employment: Phase I of the Title IV SO 2 Trading Program.” Journal of the Association of Environmental and Resource Economists. 1(4): 521-553.
Greenstone, M. 2002. “The Impacts of Environmental Regulations on Industria l Activity: Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufactures.” Journal of Political Economy. 110(6): 1175-1219.
Hamermesh, D. S. 1993. Labor Demand. Princeton, NJ: Princeton University Press. Chapter 2.
Kahn, M.E. and E. T. Mansur. 2013. “Do Local Energy Prices and Regulation Affect the Geographic Concentration of Employment?” Journal of Public Economics . 101:105–114.
Layard, P.R.G. and A. A. Walters. 1978. Microeconomic Theory . McGraw-Hill, Inc. Chapter 9.
List, J. A.; D. L. Millimet; P. G. Fredriksson and W. W. McHone. 2003. “Effects of Environmental Regulations on Manufacturing Plant Births: Evidence from a Propensity Score Matching Estimator.” The Review of Economics and Statistics. 85 (4): 944-952.
Marshall, A. 1920. Principles of Economics . Library of Economics and Liberty. Available at: <http://www.econlib.org/library/Marshall/marP.html >

. SC&A,2014:, Assessment of Employment Impacts of NOx Controls. Memorandum prepared to Jason Price, IEc and Ellen Kurlansky, US EPA., prepared by J. Wilson; M Mullen; and j Schreiber, SC&A, Inc. September 2014
Schmalansee, R. and R. Stavins (2011). “A Guide to Economic and Policy Analysis for the Transport Rule.” White Paper. Boston, MA. Exelon Corp.
Smith, K. 2012. “Reflections : In Search of Crosswalks between Macr oeconomics and Environmental Economics.” Review of Environmental Economics and Policy . 6(2):298-317.
Staudt, 2011: Staudt, James E., Engineering and Economic Factors Affecting the Installation of Control Technologies—An Update, Andover Technology Partners, North Andover, MA, December 15, 2011.
U.S. Environmental Protection Agency (U.S. EPA), 2002: USEPA Office of Air Quality Planning and Standards, EPA Air Pollution Control Cost Manual, Sixth Edition, EPA/452/B-02-001, Research Triangle Park, NC, January 2002.
U.S. Environmental Protection Agency (U.S. EPA), 2007: Alternative Control Techniques Document Update – NOx Emissions from New Cement Kilns, EPA-453/R-07-006, prepared by EPA and Research Triangle Institute for EPA Office of Air Quality Planning and Standards, Sector Policies and Program s Division, November 2007.
U.S. Environmental Protection Agency (U.S. EPA). 2011. Regulatory Impact Assessment for the Cross State Air Pollution Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf.

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5-22 U.S. Environmental Protection Agency (U.S. EPA). 2015. Regulatory Impact Analysis for the Clean Power Plan Final Rule. Office of Air and Radiation, Washington, D.C. Available at: http://www.epa.gov/airquality/cpp/cppfinal-rule-ria.pdf

. Wojichowski, 2014: David Wojichowski, De-Nox Technologies, LLC, SNCR cost estimates provided to Maureen Mullen, SC&A via email, September 25, 2014.

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6-1 CHAPTER 6: HUMAN HEALTH BENEFITS ANALYSIS APPROACH AND RESULTS
6.1 Summary This chapter of the Regulatory Impact Analysis (RIA) presents the estimated human health benefits for the revised National Ambient Air Quality Standards (NAAQS) for ozone. In this chapter, we quantify the health-related benefits of the ozone air quality improvements resulting from the illustrative emissions control strategies that reduce emissions of the ozone precursor pollutants (i.e., nitrogen oxides (NO x 

### ) and volatile organic compounds (VOCs)) toreach the revised and alternative ozone NAAQS standard levels. We also estimate the health cobenefits of the fine particulate matter (PM 2.5 

### )-related air quality improvements that would occur as a result of reducing NO x 

### emissions. 121



### We selected 2025 as the primary year of analysis because the Clean Air Act requires most areas of the U.S. to meet a revised ozone standard by 2025. The benefits of each standard alternative are estimated as being incremental to attaining the existing standard of 75 ppb. 122 

### These estimated benefits are incremental to the benefits estimated for several recent rules (e.g., U.S. EPA, 2011c and U.S. EPA, 2014a). We estimated the benefits of California attaining a revised standard in 2038 to account for the fact that many locations in this state must attain a revised standard later than the rest of the U.S. In this chapter, we refer to estimates of nationwide benefits of attaining an alternative standard everywhere in the U.S. except California as the 2025
scenario . The post-2025 scenario refers to estimates of nationwide benefits of attaining an alternative standard just in California. Because we estimate incremental costs and benefits for these two distinct scenarios reflecting attainment in different years it is not appropriate to either sum, or directly compare, the estimates.

Table 6-1 summarizes the estimated monetized benefits (total and ozone only) of attaining the revised and alternative ozone standards of 70 ppb and 65 ppb, respectively, in 2025. Table 6-2 presents the same types of benefit estimates for the scenario. These estimates reflect the sum of the economic value of estimated morbidity and mortality effects related to changes in

121 VOC reductions associated with simulated attainment of the revised and alternative ozone standards also have the

potential to impact PM 2.5 concentrations, but we were not able to estimate those effects.
122 The current standard is the 4 th highest daily maximum 8-hour ozone concentration of 75 ppb.

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6-2 exposure to ozone and PM 2.5 

### . Although these tables present ozone and PM 2.5 

### -related benefits separately, it is not appropriate to compare the ozone-only benefits to total costs.

Compared with benefit estimates generated in the proposal RIA, the total benefit estimates generated for the 2025 scenario are ~55% lower for the revised standard (70 ppb) and ~22% lower for the alternative standard (65 ppb). Benefit estimates for the post-2025 scenarios are slightly higher than those generated at proposal (~6% for the revised standard and ~2% for the alternative standard). The proposal and final RIA estimates differ principally because we projected smaller reduction in ozone concentrations needed to simulate attainment. We have also slightly modified our approach to estimating morbidity benefits, which had a negligible (~1%) influence on the total monetized benefits in this RIA (see sections 6.3 and 6.6.3).

Table 6-1. Estimated Monetized Benefits of Attainment of the Revised and Alternative Ozone Standards for 2025 (nationwide benefits of attaining the standards everywhere in the U.S. except California) (billions of 2011$) a Discount

Rate 70 ppb 65 ppb Ozone-only Benefits c b $1.0 to $1.7 $5.3 to $8.7 PM 2.5 Co-benefits d 3% $2.1 to $4.7 $10 to $23 7% $1.9 to $4.2 $9.3 to $21

Total Benefits 3% $3.1 to $6.4 +B $16 to $32 +B 7% $2.9 to $5.9 +B $15 to $30 +B

a Rounded to two significant figures. It was not possible to quantify all benefits in this analysis due to data limitations. “B” is the sum of all unquantified health and welfare benefits. These estimates reflect the economic value of avoided morbidities and premature deaths using risk coefficients from the studies noted. b Ozone-only benefits reflect short-term exposure impacts and as such are assumed to occur in the same year as ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category.

c Range reflects application of effect estimates from Smith et al. (2009) and Zanobetti and Schwartz (2008). d Range reflects application of effect estimates from Krewski et al. (2009) and Lepeule et al. (2012).

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6-3 Table 6-2. Estimated Monetized Benefits of Attainment of the Revised and Alternative Ozone Standards for post-2025 (nationwide benefits of attaining the standards just in California) (billions of 2011$) a Discount

Rate 70 ppb 65 ppb Ozone-only Benefits c b $0.79 to $1.3 $1.6 to $2.6 PM 2.5 Co-benefits d 3% $0.40 to $0.91 $0.79 to $1.8 7% $0.37 to $0.82 $0.71 to $1.6

Total Benefits 3% $1.2 to $2.2 +B $2.4 to $4.4 +B
7% $1.2 to $2.1 +B $2.3 to $4.2 +B

a Rounded to two significant figures. It was not possible to quantify all benefits in this analysis due to data limitations. “B” is the sum of all unquantified health and welfare benefits. These estimates reflect the economic value of avoided morbidities and premature deaths using risk coefficients from the studies noted. b Ozone-only benefits reflect short-term exposure impacts and as such are assumed to occur in the same year as ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category.

c Range reflects application of effect estimates from Smith et al. (2009) and Zanobetti and Schwartz (2008). d Range reflects application of effect estimates from Krewski et al. (2009) and Lepeule et al. (2012).



### The control measures (identified and unidentified) applied to reach the revised and alternative ozone standards would reduce other ambient pollutants, including VOCs and NO 2


### . However, because the method used in this analysis to simulate attainment does not account for changes in ambient concentrations of other pollutants, we were unable to quantify the co-benefits of reduced exposure to these pollutants. Due to limited data and methods, we were unable to estimate some anticipated health benefits associated with exposure to ozone and PM 2.5


### .

6.2 Overview
This chapter presents estimated health benefits for the revised and alternative ozone standards (70 ppb and 65 ppb, respectively) that the EPA could quantify, given the available resources, data and methods. This chapter characterizes the benefits of the application of the identified and unidentified control strategies for the revised and alternative ozone standards by answering three key questions:

1. What health effects are avoided by reducing ambient ozone levels to attain the revised and alternative ozone standards?

2. What is the economic value of these effects?

3. What are the co-benefits of reductions in ambient PM 2.5 

### associated with reductions in emissions of ozone precursors (specifically NOx)?

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6-4 In this analysis, we quantify an array of adverse health impacts attributable to ozone and
PM 2.5 

### that would be avoided by attaining a revised ozone standard. The Integrated Science Assessment for Ozone and Related Photochemical Oxidants (“ozone ISA”) (U.S. EPA, 2013a) identifies the human health effects associated with ozone exposure, which include prematuredeath and a variety of illnesses associated with acute (days-long) and chronic (months to yearslong) exposures. Similarly, the Integrated Science Assessment for Particulate Matter (“PM ISA”) (U.S. EPA, 2009b) identifies the human health effects associated with ambient particles, which include premature death and a variety of illnesses associated with acute and chronic exposures. Air pollution can affect human health in a variety of ways. In Table 6-3 we summarize the “categories” of effects and describe those that we quantified for this analysis and those we were unable to quantify due to lack of resources, data, or methods.

This list of benefit categories is not exhaustive, and we are not always able to quantify each effect completely. In this RIA, we only quantify endpoints that are classified in the ozone and PM ISAs as being causally related, or likely to be causally related, to each pollutant. We excluded endpoints not identified as having at least a causal or likely causal relationship with the affected pollutants. Selecting endpoints in this way should not imply that these pollutants are unrelated to other human health and environmental effects. Following this criterion, we excluded some effects that were identified in previous lists of unquantified benefits in other RIAs (e.g., UVb exposure), but are not identified in the most recent ISA as having a causal or likely causal relationship with ozone.

This benefits analysis relies on an array of data inputs—including emissions estimates, modeled ozone concentrations, health impact functions and valuation estimates among others— which are themselves subject to uncertainty and may in turn contribute to the overall uncertainty in this analysis. We employ several techniques to characterize this uncertainty, which are
described in detail in sections 6.5 and 6.7.3.

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6-5 Table 6-3. Human Health Effects of Pollutants Po tentially Affected by Strategies to Attain the Primary Ozone Standards (endpoints included in the core analysis are identified with a red checkmark)

Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized Source of More Information Improved Human Health Reduced incidence of premature mortality from exposure to ozone Premature mortality based on short

‐ term exposure (all ages)  

Section 6.6 Premature respiratory mortality based on long

‐ term exposure (age 30–99) b b

Reduced incidence of morbidity from exposure to ozone Hospital admissions—respiratory (age > 65)  

Emergency department visits for asthma (all ages)  

Asthma exacerbation (age 6

‐ 18)   Minor restricted

‐ activity days (age 18–65)   School absence days (age 5–17)   Decreased outdoor worker productivity (age 18–65) b b

Other respiratory effects (e.g., medication use, pulmonary inflammation, decrements in lung functioning) — —

ozone ISA d Cardiovascular (e.g., hospital admissions, emergency department visits) — —
Reproductive and developmental effects (e.g., reduced birthweight, restricted fetal growth) — —

Reduced incidence of premature mortality from exposure to PM 2.5 Adult premature mortality based on cohort study estimates and expert elicitation estimates (age >25 or age >30)  

Section 6.6 Infant mortality (age <1)   Reduced incidence of morbidity from exposure to PM 2.5 Non

‐ fatal heart attacks (age > 18)   Hospital admissions—respiratory (all ages)  

Hospital admissions—cardiovascular (age >20)  

Emergency department visits for asthma (all ages)  

Acute bronchitis (age 8–12)   Lower respiratory symptoms (age 7–14)   Upper respiratory symptoms (asthmatics age 9–11)  

Asthma exacerbation (asthmatics age 6– 18)  

Lost work days (age 18–65)   Minor restricted

‐ activity days (age 18–65)   Chronic Bronchitis (age >26)

— — Emergency department visits for cardiovascular effects (all ages) — —

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6-6 Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized Source of More Information Strokes and cerebrovascular disease (age 50–79) — —

Other cardiovascular effects (e.g., other ages) — —

PM ISA c Other respiratory effects (e.g., pulmonary function, non
‐ asthma ER visits, non
‐ bronchitis chronic diseases, other ages and populations) — —

Reproductive and developmental effects (e.g., low birth weight, pre
‐ term births, etc.) — —

PM ISA c,d Cancer, mutagenicity, and genotoxicity effects — —

Reduced incidence of morbidity from exposure to NO 2 Asthma hospital admissions (all ages)

— —

NO 2 ISA e Chronic lung disease hospital admissions (age > 65) — —
Respiratory emergency department visits (all ages) — —

Asthma exacerbation (asthmatics age 4– 18) — —

Acute respiratory symptoms (age 7–14)

— — Premature mortality

— —

NO 2 ISA c,d Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — —

b We quantified these benefits, but they are not part of the core monetized benefits.

c We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods. d We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over the strength of the association.

e We assess these benefits qualitatively due to time and resource limitations for this analysis. 

### The remainder of this chapter is organized as follows: Section 6.3 includes a discussion of the methodological updates represented in this analysis; Section 6.4 includes a discussion of the methodologies used in the human health benefits analyses; Section 6.5 includes a characterization of uncertainty; Section 6.6 details the data inputs used in the analysis, including demographic data, baseline incidence and prevalence estimates, effect coefficients, and economic valuation estimates; Section 6.7 presents the results; and Section 6.8 includes a brief discussion of the results. In addition, the chapter has several appendices that provide more details on the following: Appendix 6A includes a detailed characterization of uncertainty in the analysis; Appendix 6B includes additional sensitivity analyses and supplemental analysis.

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6-7 6.3 Updated Methodology Presented in the Proposal and Final RIAs This RIA incorporates an array of policy and technical updates to the benefits analysis methods that since the previous review the ozone standards in 2008 and the proposed reconsideration in 2010. These updates to the methods are summarized here in chronological
order.

Following the publication of the 2008 ozone NAAQS RIA (U.S. EPA, 2008a), EPA changed its methods for estimating the benefits of improved air quality, which were reflected in the 2010 ozone reconsideration RIA (U.S. EPA, 2010d). Two significant changes included:

1. When estimating PM 2.5 

### -related health effects, EPA no longer assumed a minimum concentration at which no effects occurred, while still reporting a range of sensitivity estimates based on the EPA’s PM 2.5 

### mortality expert elicitation (see the Portland Cement NESHAP proposal RIA (U.S. EPA, 2009a) for further discussion).

2. To be consistent with Agency guidance, EPA revised the Value of Statistical Life (VSL) it used to quantify the value of reduced mortality risk (see the NO 2 

### NAAQS final RIA (U.S. EPA, 2010a) for further discussion).
Following completion of the 2010 reconsideration RIA, EPA made a number of additional updates to the methods, which are reflected in the 2014 RIA for the proposed ozone NAAQS (U.S. EPA, 2014c), including:

1. The population demographic data in BenMAP-CE (U.S. EPA, 2015a) reflects the 2010 Census and future projections based on economic forecasting models developed by Woods and Poole, Inc. (Woods and Poole, 2012). These data replace older demographic projection data from Woods and Poole (2007). This update was introduced in the final PM NAAQS RIA (U.S. EPA, 2012a).

2. The baseline incidence rates used to quantify air pollution-related hospital admissions and emergency department visits and the asthma prevalence rates were updated to replace the earlier rates. This update was introduced in the final CSAPR (U.S. EPA, 2011c).

3. The cost-of-illness estimates for hospital admissions, including median wages, have been updated to reflect 2007 data. This update was introduced in the proposal PM NAAQS RIA (U.S. EPA, 2012a).

4. Updates affecting estimated ozone-related effects:

a. New studies used to quantify ozone-related premature death . The ozone ISA (U.S. EPA, 2013a) identifies several new epidemiological studies examining the association

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6-8 between ozone exposure and premature mortality. We include two new multi-city studies to estimate deaths attributable to short-term exposure (Smith et al., 2009 and Zanobetti and Schwartz 2008). We also estimate long-term respiratory deaths using Jerrett et al. (2009). 123 

### We introduced this update in the proposal RIA (U.S. EPA, 2014c).

b. New studies used to quantify ozone-related morbidity effects . The ozone ISA (U.S. EPA, 2013a) identifies several new epidemiological studies examining the association between short-term ozone exposure and respiratory hospitalizations, respiratory emergency department visits, and exacerbated asthma. We introduced this update in the proposal RIA (U.S. EPA, 2014c).

c. Expanded uncertainty assessment . We added a comprehensive, qualitative assessment of the various uncertain parameters and assumptions within the benefits analysis and expanded the evaluation of air quality benchmarks for ozone-related mortality. We introduced this expanded assessment in the proposal RIA (U.S. EPA, 2014c).

5. Updates affecting estimated PM 2.5 

### -related effects:

a. New studies used to quantify PM 2.5 

### -related premature death . We updated the American Cancer Society cohort study to Krewski et al. (2009) and updated the Harvard Six Cities cohort study to Lepeule et al. (2012). The update for the American Cancer Society cohort was introduced in the proposal RIA for the PM NAAQS review (U.S. EPA, 2012a) and the update for the Harvard Six Cities cohort was introduced in the final RIA for the PM NAAQS review (U.S. EPA, 2012c).

b. New studies used to quantify PM 2.5 

### morbidity . Based on the PM ISA and PM Provisional Assessment, we added several new studies and morbidity endpoints to our health impact assessment, including hospital admissions and emergency department visits. These updates were introduced in the proposal (U.S. EPA, 2012a) and final RIAs for the PM NAAQS review (U.S. EPA, 2012c).

c. More recent survival rates for non-fatal acute myocardial infarctions . Based on recent data from Agency for Healthca re Research and Quality’s Healthcare Utilization Project National Inpatient Sample database (AHRQ, 2009), we identified death rates for adults hospitalized with acute myocardial infarction stratified by age. These rates replaced the survival rates from Rosamond et al. (1999). This update was introduced in the final RIA for the PM NAAQS review (U.S. EPA, 2012c).

d. Expanded uncertainty assessment . We expanded the comprehensive assessment of the various uncertain parameters and assumptions within the benefits analysis including the evaluation of air quality benchmarks. This update was introduced in the proposed

123 Because we do not have information on the cessation lag for premature mortality from long-term ozone exposure, we do not include the monetized benefits in the core analysis. Instead, monetized benefits associated with long-term ozone-related respiratory mortality are included as a sensitivity analysis (see Appendix 5B, section 5B.2).

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6-9 CSAPR RIA (U.S. EPA, 2010f) and refined in the final PM NAAQS RIA (U.S. EPA, 2012c).
Following completion of the proposal RIA (U.S. EPA, 2014c), we slightly modified the methods applied in this RIA. As described in sections 6.6.3, we modified the set of epidemiology studies and associated effect coefficients used in estimating changes in asthma exacerbation and respiratory hospital admissions associated with ozone exposure. Because these changes do not involve our approach for estimating air pollution-related premature deaths, which largely drives overall monetized benefits, they have a negligible (~1%) impact on total monetized benefits (see section 6.6.3 for further discussion).

6.4 Human Health Benefits Analysis Methods We follow a “damage-function” approach in calculating total benefits of the modeled changes in environmental quality. This approach estimates changes in individual health endpoints (i.e., specific effects that can be associated with changes in air quality) and assigns values to those changes assuming independence of the values for those individual endpoints. Total benefits are calculated simply as the sum of the values for all non-overlapping health endpoints. The “damage-function” approach is the standard method for assessing costs and benefits of environmental quality programs and has been used in several recent published analyses (Levy et al., 2009; Fann et al., 2012a; Tagaris et al., 2009).

To assess economic values in a damage-function framework, the changes in environmental quality must be translated into effects on people or on the things that people value. In some cases, the changes in environmental quality can be valued directly, as is the case for changes in visibility. In other cases, such as for changes in health outcomes associated with reductions in ozone and PM concentrations, an impact analysis must first be conducted to convert air quality changes into effects that can be assigned dollar values. For the purposes of this RIA, the health impacts analysis (HIA) is limited to those health effects that are directly linked to ambient levels of air pollution and specifically to those linked to ozone and PM 2.5


### . We note at the outset that the EPA rarely has the time or resources to perform extensive new research to measure directly either the health outcomes or their values for regulatory analyses. Thus, similar to Kunzli et al. (2000) and other, more recent health impact analyses, our

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6-10 estimates are based on the best available methods of benefits transfer. Benefits transfer is the science and art of adapting primary research from similar contexts to obtain the most accurate measure of benefits for the environmental quality change being analyzed. Adjustments are made for the level of environmental quality change, the socio-demographic and economic characteristics of the affected population, a nd other factors to improve the accuracy and
robustness of benefits estimates.

Benefits estimates for ozone were generated using the damage function approach outlined above wherein potential changes in ambient ozone levels (associated with future attainment of the revised and alternative standard levels) were explicitly modeled and then translated into reductions in the incidence of specific health endpoints (see Chapter 2 for more information). In contrast, in estimating PM 2.5 

### co-benefits we utilized a reduced form approach. The details of both approaches are described in additional detail below.

6.4.1 Health Impact Assessment
The health impact assessment (HIA) quantifies the changes in the incidence of adverse health impacts resulting from changes in human exposure to PM 2.5 

### and ozone air quality. HIAs are a well-established approach for estimating the retrospective or prospective change in adverse health impacts expected to result from population-level changes in exposure to pollutants (Levy et al., 2009). PC-based tools such as the environmental Benefits Mapping and Analysis Program – Community Edition (BenMAP-CE) can systematize health impact analyses by applying a database of key input parameters, including health impact functions and population projections— provided that key input data are available, including air quality estimates and risk coefficients (U.S. EPA, 2015a). Analysts have applied the HIA approach to estimate human health impacts resulting from hypothetical changes in pollutant levels (Hubbell et al., 2005; Tagaris et al., 2009; Fann et al., 2012a). The EPA and others have relied upon this method to predict future changes in health impacts expected to result from the implementation of regulations affecting air quality (e.g., U.S. EPA, 2014a). For this assessment, the HIA is limited to those health effects that are directly linked to ambient ozone and PM 2.5 

### concentrations. There may be other indirect health impacts associated with applying emissions controls, such as occupational health exposures.

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6-11 The HIA approach used in this analysis involves three basic steps: (1) using projections of ozone air quality 124 

### and estimating the change in the spatial distribution of the ambient air quality; (2) determining the subsequent change in population-level exposure; and (3) calculating health impacts by applying concentration-response (C-R) relationships drawn from the epidemiological literature to this change in population exposure (Hubbell et al., 2009).

A typical health impact function might look as follows: ∆ݕ ൌ 1 െ ൫݁ ఉ∙∆௫ 

### ൯ݕ ௢ 

### ∙ ܲ݌݋ ) 6.1 (

where y 0 

### is the baseline incidence rate for the health endpoint being quantified (for example, a health impact function quantifying changes in mortality would use the baseline, or background, mortality rate for the given population of interest); Pop is the population affected by the change in air quality;  x is the change in air quality; and β is the effect coefficient drawn from the epidemiological study. Figure 6-1 provides a simplified overview of this approach.

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6-12 Figure 6-1. Illustration of BenMAP-CE Approach

6.4.2 Economic Valuation of Health Impacts
After quantifying the change in adverse health impacts, the final step is to estimate the economic value of these avoided impacts. The appropriate economic value for a change in a health effect depends on whether the health effect is viewed ex ante (before the effect has occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population. The appropriate economic measure is therefore ex ante willingness to pay (WTP) for changes in risk. Epidemiological studies generally provide estimates of the changes in risk for a particular health effect for a given increment of air pollution (often per 10 ppb ozone). These relative risks can be used to develop risk coefficients that relate a unit reduction in ozone or
PM 2.5 

### to changes in the incidence of a health effect. In order to value these changes in incidence, WTP for changes in risk need to be converted into WTP per statistical incidence. This measure is Baseline Air Quality Post

‐ Policy Scenario Air Quality

Incremental Air Quality Improvement

Ozone Reduction

Population Ages 30 ‐ 99 Background Incidence

Rate Effect Estimate Mortality Reduction

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6-13 calculated by dividing individual WTP for a risk reduction by the related observed change in risk. For some health effects, such as hospital admissions, WTP estimates are generally not available. In these cases, we use the costs of treating or mitigating the effect, which generally understate the true value of reductions in risk of a health effect because they exclude the value of avoided pain and suffering from the health effect.

We use the BenMAP-CE version 1.1 (U.S. EPA, 2015a, 2015b) to estimate the health impacts and monetized health benefits for the standards evaluated here. Figure 6-2 shows the data inputs and outputs for the BenMAP-CE program. The environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE) is an open-source PC-based tool that quantifies the number and economic value of air pollution-related deaths and illnesses. 125

125 As compared to the version that it replaces (BenMAP v4), BenMAP-CE uses the same computational algorithms

and input data to calculate benefits for a given air quality change, both versions report the same estimates, within rounding. BenMAP-CE differs from the legacy version of BenMAP in two important ways: (1) it is open-source and the uncompiled code is available to the public; and (2) it is written in C#, which is both more broadly used and modern than the code it replaces (Delphi). BenMAP-CE was last used to support the Health Risk and Exposure Assessment for Ozone (ozone HREA) (U.S. EPA, 2014b).

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6-14 Figure 6-2. Data Inputs and Outputs for the BenMAP-CE Program
6.4.3 Estimating Benefits for 2025 and Post-2025 Analysis Years
Portions of the country with more significant air quality problems (particularly several areas in California) may not be required to meet the revised standard until as late as December 31, 2038. Consequently, for the revised and alternative standards we evaluate a 2025 scenario reflecting the nationwide benefits of attaining the standards everywhere in the U.S. except California. We then evaluate a post-2025 scenario, which represents nationwide benefits from attaining the standards in California. We report the 2025 and 2038 estimates separately because deriving a summed estimate would require us to calculate the Present Value (PV) of the stream of benefits occurring between those two years, which is not possible with the available data.

Our approach for estimating the benefits of attaining the revised and alternative ozone standards post-2025 is illustrated in Figure 6-3. In this figure, Simulation A represents our approach for estimating the benefits of attaining the revised and alternative ozone standards in every state except California in 2025. To estimate benefits for the post-2025 scenario, we first estimated the benefits occurring in 2038 from all areas (including California) attaining the Modeled Baseline and Post

‐ Control Ambient Ozone

Ozone Health

Functions

Economic Valuation Functions 2025 Population Projections

Ozone Incremental Air Quality Change

Ozone

‐ Related Health Impacts Woods & Poole Population Projections

Background Incidence and Prevalence Rates

Monetized Ozone

‐ related Benefits

Blue identifies a user
‐ selected input within the BenMAP

‐ CE program Green identifies a data input generated outside of the BenMAP

‐ CE program

Census Population Data

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6-15 revised and alternative standards (Simulation C). Next, we simulated the nationwide benefits of attaining the revised and alternative ozone standards in 2038 for every state except California (Simulation B). We then subtracted Simulation B from Simulation C to calculate the benefits of attaining the revised and alternative ozone stan dards after 2025— that is, to calculate the nationwide benefits from California alone attaining the standards in 2038. Important caveats are associated with this approach mentioned in Section 6.1 and discussed further in section 6.7.3.

Figure 6-3. Procedure for Generating Bene fits Estimates for the 2025 and Post-2025 Scenarios

6.4.4 Benefit-per-ton Estimates for PM 2.5 

### We used a reduced form approach to estimate the PM 2.5 

### co-benefits in this RIA due to data and resource constraints. Specifically, we used “benefit-per-ton” estimates. EPA has applied this approach in several previous RIAs (e.g., U.S. EPA, 2014a). These benefit-per-ton estimates provide the total monetized human health co-benefits (the sum of premature mortality) of reducing one ton of NOx (as a PM 2.5 

### precursor) from a specified source. In general, these estimates apply the same benefits methods (e.g., health impact assessment then economic 75 ppb baseline (CA does not attain 75)

70 ppb full attainment scenario everywhere but CA

70 ppb full attainment scenario everywhere

including CA 75 ppb baseline reductions in

(all locations attain 75) 

### A

C 2025 scenario : Nationwide benefits in 2025 resulting from all areas in the U.S. attaining the alternative standard under consideration excluding California (here 70ppb). This is Simulation A

70 ppb full attainment scenario everywhere but CA 75 ppb baseline (CA does not attain 75) 

### B BenMAP Benefits Simulations

Benefits year = 2025

Cost year = 2025

Benefits year = 2038

Cost year = 2037/2038

Benefits year = 2038

Cost year = 2037/2038 Post

‐ 2025 scenario : Nationwide benefits in 2038 having California attain the alternative standard (here 70ppb). This is the difference between Simulation C and Simulation B

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6-16 valuation) for all PM 2.5 

### impacts attributable to a sector, and these benefits are then divided by the
tons of a PM 2.5 

### precursor (e.g., NOx) from that sector.

We used national benefit-per-ton estimates described in the TSD: Estimating the Benefit per Ton of Reducing PM 2.5 

### Precursors from 17 Sectors (U.S. EPA, 2013b). The national estimates used in this RIA were derived using th e approach published in Fann et al. (2012b), but have since been updated to reflect the epidemiology studies and Census population data first applied in the final PM 2.5 

### NAAQS RIA (U.S. EPA, 2012c). The approach in Fann et al. (2012b) is similar to the work previously published by Fann et al. (2009), but the newer study includes improvements that provide more refined estimates of PM 2.5 

### -related health benefits for emissions reductions in the various sectors. Specifically, the air quality modeling data reflect industrial sectors that are more narrowly defined. In addition, the updated air quality modeling data reflects more recent emissions data -- a 2005 baseline projected to 2016 rather than 2001 baseline projected to 2015 -- and has higher spatial resolution (12 km rather than 36 km grid cells).

In Section 6.6, we describe all of the data inputs used in deriving the benefit-per-ton values for each sector, including the demographic data, baseline incidence, and valuation functions. The benefit-per-ton estimates (by sector) that resulted from this modeling are presented in Section 6.6.5. We then multiply these benefit-per-ton estimates for each sector with the NOx emission
reductions from that sector to estimate the PM 2.5 

### co-benefits. 126 

### Additional information on the source apportionment modeling for each of the se ctors can be found in Fann et al. (2012b) and the TSD (U.S. EPA, 2013b). Specifically for this analysis, we applied the benefit-per-ton estimates for 2025 and 2030 in generating PM 2.5 

### co-benefit estimates for the 2025 and post-2025 scenarios, respectively. 127



### Applying benefit-per-ton estimates introduces uncertainty, which we discuss in section 6.7.3 and Appendix 6A. The benefit-per-ton estimates used here reflect specific geographic patterns of emissions reductions and specific air quality and benefits modeling assumptions associated with the derivation of those estimates. Consequently, these estimates may not reflect

126 For unidentified controls, we use a weighted average of the benefit-per-ton estimates from all of the sectors.
127 We do not have benefit-per-ton estimates for 2038. The last year available is 2030, which is an underestimate of

the 2038 benefits because the population grows and ages over time.

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6-17 local variability in factors associated with PM 2.5 

### -related health impacts (e.g., population density, baseline health incidence rates). Therefore, using benefit-per-ton values to estimate co-benefits may lead to higher or lower benefit estimates than if co-benefits were calculated based on direct air quality modeling. In addition, the use of national benefit-per-ton estimates results in a known underestimation bias in California in the post -2025 scenario due to population density.

6.5 Characterizing Uncertainty In any complex analysis using estimated parameters and inputs from numerous models, there are likely to be many sources of uncertainty, and this analysis is no exception. This analysis includes many data sources as inputs, includi ng emissions inventories, air quality data from models (with their associated parameters and inputs), population data, population estimates, health effect estimates from epidemiology studies, economic data for monetizing benefits, and assumptions regarding the future state of the world (e.g., regulations, technology, and human behavior). Each of these inputs may be uncertain and would affect the benefits estimates. When the uncertainties from each stage of the analysis are compounded, even small uncertainties can have large effects on the total quantified benefits.

After reviewing the EPA’s approach, the National Research Council (NRC) (2002, 2008), which is part of the National Academies of Science, concluded that the EPA’s general methodology for calculating the benefits of reducing air pollution is reasonable and informative in spite of inherent uncertainties. The NRC also highlighted the need to conduct rigorous quantitative analyses of uncertainty and to present benefits estimates to decision makers in ways that foster an appropriate appreciation of their inherent uncertainty. Since the publication of these reports, the EPA continues to improve how it characterizes uncertainty in health incidence and benefits estimates. In response to these recommendations, we incorporated additional quantitative and qualitative characterizations of uncertainty. Although we are not yet able to perform the probabilistic uncertainty assessment the NAS envisioned, we added several quantitative and qualitative analyses. These additional analyses characterize uncertainty related to estimated premature mortality, since this endpoint is assigned the largest dollar value. For other inputs into the benefits analysis, such as the air quality data, it is difficult to address uncertainty probabilistically due to the complexity of the underlying air quality models and

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6-18 emissions inputs. Therefore, we decline to construct alternative assumptions simply for the purpose of probabilistic uncertainty characterization when there is no scientific literature to support those alternate assumptions.

To characterize uncertainty and variability, we follow an approach that combines elements from two recent analyses by the EPA (U.S. EPA, 2010b; 2014b), and use a tiered approach developed by the World Health Organization (WHO) (WHO, 2008). We present this assessment in Appendix 6A (results of these assessments are summarized in section 6.7.3). Data limitations prevent us from treating each source of uncertainty quantitatively and from reaching a full-probabilistic simulation of our results, but we were able to consider the influence of uncertainty in the risk coefficients and economic valuation functions by incorporating several quantitative analyses: Monte Carlo assessments, sensitivity analyses, and supplemental analyses.

6.5.1 Monte Carlo Assessment
Similar to other recent RIAs, we used Monte Carlo methods for characterizing random sampling error associated with the C-R functions from epidemiological studies and random effects modeling to characterize both sampling error and variability across the economic valuation functions. The Monte Carlo simulation in the BenMAP-CE software randomly samples from a distribution of incidence and valuation estimates to characterize the effects of uncertainty on output variables. Specifically, we used Monte Carlo methods to generate confidence intervals around the estimated health impact and monetized benefits. The reported standard errors in the epidemiological studies determined the distributions for individual effect estimates for endpoints estimated using a single study. For endpoints estimated using a pooled estimate of multiple studies, the confidence intervals reflect both the standard errors and the variance across studies. The confidence intervals around the monetized benefits incorporate the epidemiology standard errors, as well as the distribution of the valuation function. These confidence intervals do not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates. We report confidence intervals for ozone-related

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6-19 benefits, but were unable to provide confidence intervals for PM 2.5 

### -related co-benefits because we used the benefit-per-ton estimates.

6.5.2 Sensitivity Analyses
Because over 90% of the monetized benefits are from avoided premature mortality, it is particularly important to characterize the uncertainties associated with reductions in premature mortality. Each of the sensitivity analyses completed for this RIA are briefly described below and section 6.7.3.1 provides a discussion of the results and observations stemming from these sensitivity analyses.

 Alternative C-R functions for short-term ozone exposure-related mortality: Alternative C-R functions are useful for asse ssing uncertainty beyond random statistical error, including uncertainty in the functional form of the model or alternative study designs. We used two multi-city studies (Smith et al., 2009; Zanobetti and Schwartz 2008) to estimate short-term ozone-related mo rtality in our core estimate. We performed a sensitivity analysis using effect coefficients from additional multi-city studies and meta-analyses utilized in prior RIAs (Bell et al., 2004; 2005, Huang, 2005; Ito et al., 2005; Levy et al., 2005), as well as alternative model specifications from the Smith et al. (2009) study (see Figure 6-4 and Appendix 6B, section 6B.1). When selecting studies for the core and sensitivity analyses we considered NAS (p. 80, NRC, 2008) and CASAC recommendations (U.S. EPA-SAB, 2012, 2014).

 Potential thresholds in the long-term ozone exposure-related respiratory mortality C-R function: Consistent with the ozone HREA, we estimate premature respiratory deaths from long-term exposure to ozone. The Jerrett et al. (2009) study explored potential thresholds in the C-R function. We use the results of the threshold analyses conducted by Jerrett et al. (2009) to conduct a sensitivity analysis evaluating models with a range of potential thresholds in addition to a non-threshold (see Appendix 6B, section 6B.3)

.

 Alternative C-R functions in estimating long-term PM 2.5 

### exposure-related mortality: In estimating PM 2.5 

### co-benefits, we use two studies (Krewski et al., 2009; Lepeule et al., 2012). To better understand the C-R relationship between PM 2.5 

### exposure and premature mortality, the EPA conducted an expert elicitation in 2006 (Roman et al., 2008; IEc, 2006). 128 

### We apply the functions from the experts as a characterization of uncertainty (see Figure 6-5 and Appendix 6B, section 6B.2).

 Cessation lag for long-term O 3 

### exposure-related respiratory mortality: We do not know how long-term O 3 

### exposure-related respiratory deaths are distributed over time and

128 Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, usually

of multiple experts, are obtained (Ayyub, 2002).

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6-20 so we use two lag structures originally developed for PM 2.5 

### (the 20-year segmented lag used for PM 2.5 

### and an assumption of zero lag) in this sensitivity analysis (see Appendix 6B, section 6B.2).

 Income elasticity in the specification of willingness-to-pay (WTP) functions used for mortality and morbidity endpoints: The degree to which the WTP function used in valuing mortality and some morbidity endpoints changes in proportion to future changes in income is uncertain. We evaluated the potential impact of this factor on the monetized benefits in a sensitivity analysis (see Appendix 6B, section 6B.5).
Even these multiple estimates (including confidence intervals, where available) cannot account for the role of other input variables in contributing to overall uncertainty, including emissions and air quality modeling, baseline incidence rates, and population exposure estimates. Furthermore, the approach presented here does not yet include methods for addressing correlation between input parameters and identifying a reasonable upper and lower bounds for input distributions. As a result, the reported confidence intervals and range of estimates give an incomplete picture of the overall uncertainty in the estimates. Thus, confidence intervals reported for individual endpoints and for total benefits should be interpreted within the context of the larger uncertainty surrounding the entire analysis.

6.5.3 Supplemental Analyses
A number of supplemental analyses provide additional perspectives on the benefits results (the results of which are described in section 6.7.3.2), including:

 Age group-differentiated aspects of short-term ozone exposure-related mortality: We examined several risk metrics intended to characterize how mortality risk reductions are distributed across different age ranges. These include (a) estimated reduction in life years lost, (b) distribution of mortality incidence reductions across age ranges, and (c) estimated reductions in baseline mortality incidence rates by age group.

 Analysis of baseline ozone concentrations used in estimating ozone mortality: We assess the relationship between short-term exposure-related mortality for ozone and the distribution of baseline ozone concentrations.

 Analysis of baseline PM 2.5 

### concentrations used in modeling short-term ozone exposure-related mortality: We also include a similar plot of the baseline annual PM 2.5 

### levels used in estimating PM 2.5 

### mortality from the earlier analysis that generated the benefit-per-ton values.

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6-21  Outdoor worker productivity : In this supplemental analysis, we quantify the economic value of improved productivity among outdoor agricultural workers using Graff Zivin and Neidell (2012).
6.5.4 Qualitative Assessment of Uncertainty and Other Analysis Limitations
To more fully address uncertainties, including those we cannot quantify, we apply a fourtiered approach using the WHO uncertainty framework (WHO, 2008), which provides a means for systematically linking the characterization of uncertainty to the sophistication of the underlying risk assessment. The EPA has applied similar approaches in previous analyses (U.S. EPA, 2010b, 2011a, 2012a, 2012b). Using this framework, we summarize the key uncertainties in the health benefits analysis, including our assessment of the direction of potential bias, magnitude of impact on the monetized benefits, degree of confidence in our analytical approach, and our ability to assess the source of uncertainty. More information on this approach and the qualitative uncertainty characterization are available in Appendix 6A.

6.6 Benefits Analysis Data Inputs In Figure 6-2 above, we summarized the key data inputs to the health impact and economic valuation estimate. Below we summarize the data sources for each of these inputs, including demographic projections, incidence and prevalence rates, effect coefficients, and economic valuation. We indicate where we have updated key data inputs since the benefits analysis conducted for the 2008 ozone NAAQS RIA (U.S. EPA, 2008a) and the 2010 ozone NAAQS Reconsideration RIA (U.S. EPA, 2010d). As noted above, only slight modifications have been made to the epidemiology studies used to estimate two morbidity endpoints since proposal, and those modifications are described in section 6.6.3.

6.6.1 Demographic Data
Quantifying the incidence and dollar value of pollution impacts requires information regarding the demographic characteristics of the exposed population, including age, location, and income. We use population projections based on economic forecasting models developed by Woods and Poole, Inc. (Woods and Poole, 2012). The Woods and Poole (WP) database contains county-level projected population by age, sex, and race to 2040, relative to a baseline using the 2010 Census data; the 2008 ozone NAAQS RIA incorporated WP projections relative to a

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6-22 baseline using 2000 Census data. Projections in each county are determined simultaneously with every other county in the United States to take into account patterns of economic growth and migration. The sum of growth in county-level populations is constrained to equal a previously determined national population growth, based on Bureau of Census estimates (Hollman et al., 2000). According to WP, linking county-level growth projections together and constraining to a national-level total growth avoids potential errors introduced by forecasting each county independently. County projections are developed in a four-stage process:

 First, national-level variables such as income, employment, and populations are forecasted.

 Second, employment projections are made for 179 economic areas defined by the Bureau of Economic Analysis (U.S. BEA, 2004), using an “export-based” approach, which relies on linking industrial-sector production of non-locally consumed production items, such as outputs from mining, agriculture, and manufacturing with the national economy. The export-based approach requires estimation of demand equations or calculation of historical growth rates for output and employment by sector.

 Third, population is projected for each economic area based on net migration rates derived from employment opportunities and following a cohort-component method based on fertility and mortality in each area.

 Fourth, employment and population projections are repeated for counties, using the economic region totals as bounds. The age, sex, and race distributions for each region or county are determined by aging the population by single year of age, by sex, and by race for each year through 2040 based on historical rates of mortality, fertility, and migration.

6.6.2 Baseline Incidence and Prevalence Estimates
Epidemiological studies of the association between pollution levels and adverse health effects generally provide an estimate of the relationship of air quality changes to the relative risk of a health effect, rather than estimating the absolute number of avoided cases. For example, a 5 ppb decrease in 8-hour maximum daily ozone concentration might be associated with a decrease in hospital admissions of three percent. The baseline incidence of the health effect is necessary to convert this relative change into a number of cases. A baseline incidence rate is the estimate of the number of cases of the health effect per year in the assessment location, as it corresponds to baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate must be multiplied by the corresponding population number. For example, if the baseline

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6-23 incidence rate is the number of cases per year per million people, that number must be multiplied by the millions of people in the total population in that location.

Table 6-4 summarizes the sources of baseline incidence rates and provides national average (where used) incidence rates for the endpoints included in the analysis. For both baseline incidence and prevalence data, we used age-specific rates where available. We applied C-R functions to individual age groups and then summed over the relevant age range to estimate total population benefits. In many cases we used a single national incidence rate, due to a lack of more spatially disaggregated data; in these cases, whenever possible we used national average rates, because these data are most applicable to a national assessment of benefits. For some studies, however, the only available incidence information comes from the studies themselves; in these cases, incidence in the study population is assumed to represent typical incidence at the national level. County, state and regional incidence rates are available for hospital admissions, and county-level data are available for premature mortality.

We projected mortality rates such that future mortality rates are consistent with our projections of population growth (U.S. EPA, 2015b). To perform this calculation, we began first with an average of 2004–2006 cause-specific mortality rates. Using Census Bureau projected national-level annual mortality rates stratified by age range, we projected these mortality rates to 2050 in 5-year increments (U.S. EPA, 2015b; U.S. Bureau of the Census, 2002).

The baseline incidence rates for hospital admissions and emergency department visits reflect the updated rates first applied in the CSAPR RIA (U.S. EPA, 2011c). In addition, we previously updated the baseline incidence rates for acute myocardial infarction. These updated rates (AHRQ, 2007) provide a better representation of the rates at which populations of differentages, and in different locations, visit the hospital and emergency department for air pollutionrelated illnesses. Also, the updated baseline incidence rates are more spatially refined. For many locations within the U.S., these data are resolved at the county- or state-level, providing a better characterization of the geographic distribution of hospital and emergency department visits than the previous national rates. Lastly, these rates reflect unscheduled hospital admissions only, which represents a conservative assumption that most air pollution-related visits are likely to be

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6-24 unscheduled. If a portion of scheduled hospital admissions are air pollution-related, this assumption would underestimate these benefits.

For the set of endpoints affecting the asthmatic population, in addition to baseline incidence rates, prevalence rates of asthma in the population are needed to define the applicable population. Table 6-5 lists the prevalence rates used to determine the applicable population for asthma symptoms. Note that these reflect recent asthma prevalence and assume no change in prevalence rates in future years. We last updated these rates in the CSAPR RIA (U.S. EPA, 2011c).

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6-25 Table 6-4. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact Functions, General Population

Endpoint Parameter Rates Value Source Mortality Daily or annual mortality rate projected to 2025 a Age

‐ , cause
‐ , and county

‐ specific rate CDC WONDER (2004–2006) U.S. Census bureau, 2000 Hospitalizations Daily hospitalization rate Age

‐ , region

‐ , state

‐ , county ‐ and cause
‐ specific rate 2007 HCUP data files b

ER Visits Daily ER visit rate for asthma and cardiovascular events Age

‐ , region

‐ , state

‐ , county ‐ and cause
‐ specific rate 2007 HCUP data files b

Nonfatal Myocardial Infarction (heart attacks) Daily nonfatal myocardial infarction incidence rate per person, 18+ Age

‐ , region

‐ , state

‐ , and county

‐ specific rate 2007 HCUP data files b adjusted by 0.93 for probability of surviving after 28 days (Rosamond et al., 1999) Asthma Exacerbations Incidence among asthmatic African

‐ American children

daily wheeze daily cough daily shortness of breath 0.173 0.145 0.074 Ostro et al. (2001)

Acute Bronchitis Annual bronchitis incidence rate, children 0.043 American Lung Association (2002, Table 11) Lower Respiratory Symptoms Daily lower respiratory symptom incidence among children c 0.0012 Schwartz et al. (1994, Table 2)

Upper Respiratory Symptoms Daily upper respiratory symptom incidence among asthmatic children 0.3419 Pope et al. (1991, Table 2)

Work Loss Days Daily WLD incidence rate per person (18–65) Aged 18–24 Aged 25–44 Aged 45–64 0.00540 0.00678 0.00492 1996 HIS (Adams, Hendershot, and Marano, 1999, Table 41); U.S. Census Bureau (2000)

School Loss Days Rate per person per year, assuming 180 school days per year 9.9 National Center for Education Statistics (1996) and 1996 HIS (Adams et al., 1999, Table 47); Minor Restricted

‐ Activity Days Daily MRAD incidence rate per person 0.02137 Ostro and Rothschild (1989, p. 243)

a Mortality rates are only available at 5-year increments. b Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional-level hospital and emergency department discharges for a variety of International Classification of Diseases (ICD) codes (AHRQ, 2007).

c Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze.

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6-26 Table 6-5. Asthma Prevalence Rates

Population Group Asthma Prevalence Rates Value Source All Ages 0.0780 American Lung Association (2010, Table 7)

< 18 0.0941 5–17 0.1070 18–44 0.0719 45–64 0.0745 65+ 0.0716 African American, 5–17 0.1776 American Lung Association (2010, Table 9) African American, <18 0.1553 American Lung Association a

a Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008). 

### 6.6.3 Effect Coefficients
In this section, we describe our general pr ocess for selecting effe ct coefficients from epidemiology studies. The first step in selecting effect coefficients is to identify the health endpoints to be quantified. We based our selection of health endpoints on consistency with the EPA’s ISAs, with input and advice from the SAB-HES. 129 

### In addition, we included more recent epidemiology studies from the ozone ISA (U.S. EPA, 2013a), PM ISA (U.S. EPA, 2009b), and the PM Provisional Assessment (U.S. EPA, 2012d). 130 

### In selecting health endpoints for ozone, we also considered the suite of endpoints included in core modeling for the ozone HREA, whichwas supported by CASAC (U.S. EPA-SAB, 2012, 2014). In general, we follow a weight-ofevidence approach, based on the biological plausibility of effects, availability of C-R functions from well conducted peer-reviewed epidemiological studies, cohesiveness of results across studies, and a focus on endpoints reflecting public health impacts (like hospital admissions) rather than physiological responses (such as changes in clinical measures like Forced Expiratory Volume [FEV1]).

There are several types of data that can support the determination of types and magnitude of health effects associated with air pollution exposures. These sources of data include toxicological studies (including animal and cellular studies), human clinical trials, and observational epidemiology studies. All of these data sources provide important contributions to the weight of evidence surrounding a particular health impact. However, only epidemiology

129 The SAB-HES is a scientific review panel specifically established to provide advice on the use of the scientific

literature in developing benefits analyses for the EPA’s Report to Congress on The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a).
130 The peer-reviewed studies in the Provisional Assessment have not yet undergone external review by the SAB.

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6-27 studies provide direct C-R relationships that can be used to evaluate population-level impacts of reductions in ambient pollution levels in a health impact assessment.

For the data-derived estimates, we relied on the published scientific literature to ascertain the relationship between ozone and PM 2.5 

### and adverse human health effects. We evaluated epidemiological studies using the selection criteria summarized in Table 6-6. These criteria include consideration of whether the study was peer-reviewed, the match between the pollutant studied and the pollutant of interest, the study design and location, and characteristics of the study population, among other considerations. In general, using C-R functions from more than a single study can provide a more representative distribution of the effect estimate. However, there are often differences between studies examining the same endpoint, making it difficult to pool the results in a consistent manner. For example, studies may examine different pollutants or different age groups. For this reason, we consider very carefully the set of studies available examining each endpoint and select a consistent subset that provides a good balance of population coverage and match with the pollutant of interest. In many cases, either because of a lack of multiple studies, consistency problems, or clear superiority in the quality or comprehensiveness of one study over others, a single published study is selected as the basis of
the effect estimate.

When several effect estimates for a pollutant and a given health endpoint (with the exception of mortality) 131 

### have been selected, they are quantitatively combined or pooled to derive a more robust estimate of the relationship. The BenMAP Manual Technical Appendices for an earlier version of the program provides details of the procedures used to combine multiple impact functions (U.S. EPA, 2015b). In general, we used fixed or random effects models to pool estimates from different single city studies of the same endpoint. Fixed effect pooling simply weights each study’s estimate by the inverse variance, giving more weight to studies with greater statistical power (lower variance). Random effects pooling accounts for both within-study variance and between-study variability, due, for example, to differences in population susceptibility. We used the fixed effect model as our null hypothesis and then determined whether the data suggest that we should reject this null hypothesis, in which case we would use

131 In the case of mortality, we do not pool results. Instead, we provide the results from each study separately.

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6-28 the random effects model. 132 

### Pooled impact functions are used to estimate hospital admissions and asthma exacerbations. When combining evidence across multi-city studies (e.g., cardiovascular hospital admission studies), we use equal weights pooling. The effect estimates drawn from each multi-city study are themselves pooled across a large number of urban areas. For this reason, we elected to give each study an equal weight rather than weighting by the inverse of the variance reported in each study.

Effect estimates selected for a given health endpoint were applied consistently across all locations nationwide. This applies to both impact functions defined by a single effect estimate and those defined by a pooling of multiple effect estimates. Although the effect estimate may vary from one location to another (e.g., because of differences in population susceptibilities or differences in the composition of PM), location-specific effect estimates are generally not
available.

Table 6-6. Criteria Used When Selecting C-R Functions Consideration Comments Peer-Reviewed Research Peer-reviewed research is exclusively used to select C-R functions.
Study Type Prospective cohort vs. ecological : Among studies that consider chronic exposure (e.g., over a year or longer), prospective cohort studies are preferred over ecological studies because they control for important individual-level confounding variables that cannot be controlled for in ecological studies. Multi-city vs. pooled/meta-analysis: Multi-city time series studies have advantages to meta-analyses. Multi-city studies use a consistent model structure and can include factorsthat explain differences between effect estimates among the cities. By contrast, metaanalyses can become imprecise and the results difficult to interpret due to the aggregation of large sets of studies. In addition, meta-analyses can suffer from publication bias, which can result in high-biased effect estimates. Although we generally prefer multi-city studies, we may consider meta-analyses if multi-city studies are not available. Study Period Studies examining a relatively longer period of time (and therefore having more data) are preferred, because they have greater statistical power to detect effects. Studies that are more recent are also preferred because of possible changes in pollution mixes, medical care, and lifestyle over time. However, when there are only a few studies available, studies from all years would be included.
Seasonality While the measurement of PM is typically collected across the full year, ozone monitoring seasons can vary substantially across different regions of the country. Consequently, studies matching the ozone seasons in the air quality modeling are preferred.

132 EPA recently changed the algorithm BenMAP used to calculate study variance, which is used in the pooling

process. Prior versions of the model calculated population variance, while the version used here calculates sample variance.

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6-29 Consideration Comments Population Attributes The most technically appropriate measures of benefits would be based on impact functions that cover the entire sensitive population but allow for heterogeneity across age or other relevant demographic factors. In the absence of effect estimates specific to age, sex, preexisting condition status, or other relevant factors, it may be appropriate to select effect estimates that cover the broadest population to match with the desired outcome of the analysis, which is total national-level health impacts.
Study Size Studies examining a relatively large sample are preferred because they generally have more power to detect small magnitude effects. A large sample can be obtained in several ways, including through selection of a large population or through repeated observations on a smaller population (e.g., through a symptom diary recorded for a panel of asthmatic children).
Study Location U.S. studies are more desirable than non-U.S. studies because of potential differences in pollution characteristics, exposure patterns, medical care system, population behavior, and lifestyle. Depending on the endpoint and the study, we may consider using Canadian studies. National estimates are most appropriate when benefits are nationally distributed; the impact of regional differences may be important when benefits only accrue to a single

area.

Pollutants Included in Model An important factor affecting the specification of co-pollutant models for ozone and PM is sampling frequency. While ozone is typically measured every hour of each day during the ozone season for a specific location, PM is typically measured every 3 rd or 6 th day. For this reason, co-pollutant models are preferred for estimating PM effects because this approach controls for the potential ozone effect while not diminishing the effective sample size available for specifying the PM effect. However, when estimating the ozone effect, the use of co-pollutant models (with PM) can substantially reduce sample size since only days with both ozone and PM can be used. While these co-pollutant models may control for potential PM effects, they also result in a substantially less robust characterization of the ozone effect due to the reduced number of ozone measurements. For this reason, while we generally favor co-pollutant models in modeling PM benefits, for ozone we generally favor single pollutant models.
Measure of PM In general, impact functions based on PM 2.5 are preferred to PM 10 because of the focus on reducing emissions of PM 2.5 precursors and because air quality modeling was conducted for this size fraction of PM. Where PM 2.5 functions are not available, PM 10 functions are used as surrogates, recognizing that there will be potential downward (upward) biases if the fine fraction of PM 10 is more (less) toxic than the coarse fraction.
Economically Valuable Health Effects Some health effects, such as forced expiratory volume and other technical measurements of lung function, are difficult to value in monetary terms. Therefore, we generally do not include these effects in benefits analyses. Non-overlapping Endpoints Although the benefits associated with each individual health endpoint may be analyzed separately, care must be exercised in selecting health endpoints to include in the overall benefits analysis because of the possibility of double-counting of benefits.



### The specific studies from which effect estimates are drawn are shown in Tables 6-7 and 6-8. We highlight in red those studies that have been added since the benefits analysis conducted for the ozone reconsideration (U.S. EPA, 2010d) or the 2008 ozone NAAQS RIA (U.S. EPA, 2008a). In all cases where effect estimates are drawn directly from epidemiological studies,

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6-30 standard errors are used as a partial representation of the uncertainty in the size of the effect estimate. Table 6-9 summarizes those health endpoints and studies we have included in sensitivity analyses.

Table 6-7. Health Endpoints and Epidemiologi cal Studies Used to Quantify Ozone-Related Health Impacts a

Endpoint Study Study Population Relative Risk or Effect Estimate ( β ) (with 95 th Percentile Confidence Interval or SE) Premature Mortality

Premature mortality—short

‐ term Smith et al. (2009) All ages β = 0.00032 (0.00008) Zanobetti and Schwartz (2008) β = 0.00051 (0.00012)

Premature respiratory mortality

‐ long

‐ term Jerrett et al. (2009) >29 years β = 0.003971 (0.00133) Hospital Admissions
Respiratory Pooled estimate: Katsouyanni et al. (2009) > 65 years β = 0.00064 (0.00040) penalized splines

Asthma

‐ related

emergency department visits Pooled estimate: Glad et al. (2012)

0

‐ 99 years β = 0.00306 (0.00117) Ito et al. (2007) β = 0.00521 (0.00091) Mar and Koenig (2010) β = 0.01044 (0.00436) (0

‐ 17 yr olds) β = 0.00770 (0.00284) (18

‐ 99 yr olds) Peel et al. (2005) β = 0.00087 (0.00053) Sarnat et al. (2013) β = 0.00111 (0.00028) Wilson et al. (2005) RR = 1.022 (0.996

– 1.049) per 25 Other Health Endpoints

Asthma exacerbation Pooled estimate: b Mortimer et al. (2002) Schildcrout et al. (2006) 6–18 years β = 0.00929 (0.00387) β = 0.00222 (0.00282)

School loss days Pooled estimate: Chen et al. (2000) 5

‐ 17 years β = 0.015763 (0.004985)
Gilliland et al. (2001) β = 0.007824 (0.004445) Acute respiratory symptoms (MRAD) Ostro and Rothschild (1989) 18–65 years β = 0.002596 (0.000776)

a Studies highlighted in red represent updates incorporated since the 2008 ozone NAAQS RIA (U.S. EPA, 2008a). b The original study populations were 5 to 12 years for the O’Conner et al. (2008) and Schildcrout et al. (2006) and 5-9 years for the Mortimer et al. (2002) study. Based on advice from the SAB-HES, we extended the applied population to 6-18 years for all three studies, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. EPA-SAB (2004a) and NRC (2002)

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6-31 Table 6-8. Health Endpoints and Epidem iological Studies Used to Quantify PM 2.5 

### -Related Health Impacts a

Endpoint Study Study Population Relative Risk or Effect Estimate ( β ) (with 95 th Percentile Confidence Interval or SE) Premature Mortality Premature mortality— cohort study, all

‐ cause Krewski et al. (2009) > 29 years
> 24 years RR = 1.06 (1.04–1.06) per 10 μg/m 3 Lepeule et al. (2012) RR = 1.14 (1.07–1.22) per 10 μg/m 3 Premature mortality— all

‐ cause Woodruff et al. (1997) Infant (< 1 year) OR = 1.04 (1.02–1.07) per 10 μg/m 3

Chronic Illness Nonfatal heart attacks Peters et al. (2001) Adults (> 18 years) OR = 1.62 (1.13–2.34) per 20 μg/m 3 Pooled estimate: Pope et al. (2006) β = 0.00481 (0.00199) Sullivan et al. (2005) β = 0.00198 (0.00224) Zanobetti et al. (2009) β = 0.00225 (0.000591) Zanobetti and Schwartz (2006) β = 0.0053 (0.00221) Hospital Admissions Respiratory Zanobetti et al. (2009) —ICD 460

‐ 519 (All respiratory) > 64 years β =0.00207 (0.00446)

Kloog et al. (2012) —ICD 460

‐ 519 (All Respiratory β =0.0007 (0.000961)

Moolgavkar (2000)—ICD 490– 496 (Chronic lung disease) 18–64 years 1.02 (1.01–1.03) per 36 μg/m 3

Babin et al. (2007) —ICD 493 (asthma) < 19 years β =0.002 (0.004337)

Sheppard (2003)—ICD 493 (asthma) < 18 RR = 1.04 (1.01–1.06) per 11.8 μg/m 3
Cardiovascular Pooled estimate: Zanobetti et al. (2009) —ICD 390

‐ 459 (all cardiovascular) > 64 years β =0.00189 (0.000283)

Peng et al. (2009)— ICD 426

‐ 427; 428; 430

‐ 438; 410

‐ 414; 429; 440

‐ 449 (Cardio

‐ , cerebro ‐ and peripheral vascular disease) β =0.00068 (0.000214)

Peng et al. (2008)— ICD 426

‐ 427; 428; 430

‐ 438; 410

‐ 414; 429; 440

‐ 449 (Cardio

‐ , cerebro ‐ and peripheral vascular disease) β =0.00071 (0.00013)

Bell et al. (2008)— ICD 426

‐ 427; 428; 430

‐ 438; 410

‐ 414; 429; 440

‐ 449 (Cardio

‐ , cerebro ‐ and peripheral vascular disease) β =0.0008 (0.000107)

Moolgavkar (2000)— ICD 390– 429 (all cardiovascular) 20–64 years RR=1.04 (t statistic: 4.1) per 10 μg/m 3

Asthma

‐ related emergency department visits Pooled estimate: Mar et al. (2010) All ages RR = 1.04 (1.01–1.07) per 7 μg/m 3 Slaughter et al. (2005) RR = 1.03 (0.98–1.09) per 10 μg/m 3 Glad et al. (2012) β =0.00392 (0.002843) Other Health Endpoints Acute bronchitis Dockery et al. (1996) 8–12 years OR = 1.50 (0.91–2.47) per 14.9 μg/m 3

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6-32 Endpoint Study Study Population Relative Risk or Effect Estimate ( β ) (with 95 th Percentile Confidence Interval or SE) Asthma exacerbations Pooled estimate: Ostro et al. (2001) (cough, wheeze, shortness of breath) b 6–18 years b OR = 1.03 (0.98–1.07) OR = 1.06 (1.01–1.11) OR = 1.08 (1.00–1.17) per 30 μg/m 3 Mar et al. (2004) (cough, shortness of breath) RR = 1.21 (1–1.47) per RR = 1.13 (0.86–1.48) per 10 μg/m 3 Work loss days Ostro (1987) 18–65 years β =0.0046 (0.00036) Acute respiratory symptoms (MRAD) Ostro and Rothschild (1989) (Minor restricted activity days) 18–65 years β =0.00220 (0.000658) Upper respiratory symptoms Pope et al. (1991) Asthmatics, 9–11 years 1.003 (1–1.006) per 10 μg/m 3

Lower respiratory symptoms Schwartz and Neas (2000) 7–14 years OR = 1.33 (1.11–1.58) per 15 μg/m 3

a Studies highlighted in red represent upd ates incorporated sinc e the ozone NAAQS RIA (U.S. EPA, 2008a). These updates were introduced in the PM NAAQS RIA (U.S. EPA, 2012c). b The original study populations were 8 to 13 years for the Ostro et al. (2001) study and 7 to 12 years for the Mar et al. (2004) study. Based on advice from the SAB-HES, we extended the applied population to 6-18 years, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. EPA-SAB (2004a,b) and NRC (2002). 

### Table 6-9. Health Endpoints and Epidemiologi cal Studies Used to Quantify Ozone-Related Health Impacts in Sensitivity Analyses a

Endpoint Study Study Population Effect Estimate ( β ) (with 95 th Percentile Confidence Interval) Premature Mortality

Premature respiratory mortality

‐ long

‐ term Jerrett et al. (2009)

‐ based models:

> 29 years ‐ non
‐ threshold ozone only (86 cities) β =0.00266 (0.000969)

‐ non
‐ threshold ozone only (96 cities) β =0.00286 (0.000942)

‐ threshold 40 ppb b β =0.00312 (0.00096)

‐ threshold 45 ppb β =0.00336 (0.001)

‐ threshold 50 ppb β =0.00356 0.00106)

‐ threshold 55 ppb β =0.00417 (0.00118)

‐ threshold 56 ppb β =0.00432 (0.00121)

‐ threshold 60 ppb β =0.00402 (0.00137) Premature mortality

‐ short

‐ term Smith et al. (2009) (co

‐ pollutant model with PM 10 )

All ages β =0.00026 (0.00017) Bell et al. (2005) β =0.00080 (0.00021) Levy et al. (2005) β =0.00112 (0.00018) Bell et al. (2004) β =0.00026 (0.00009) Ito et al. (2005) β =0.00117 (0.00024) Schwartz et al. (2005) β =0.00043 (0.00015) Huang et al. (2005) β =0.00026 (0.00009)

a Studies highlighted in red represent updates incorporated since the 2008 ozone NAAQS RIA (U.S. EPA, 2008a). b All threshold models are ozone-only and based on the full 96 city dataset.

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6-33 6.6.3.1 Ozone Exposure Metric
Both the NMMAPS analysis and the individual time series studies upon which the metaanalyses were based use the 24-hour average or 1-hour maximum ozone concentrations as exposure metrics. The 24-hour average is not the most relevant ozone exposure metric to characterize population-level exposure. Given that the majority of the people tend to be outdoors during the daylight hours and concentrations are highest during the daylight hours, the 24-hour average metric is not appropriate. Moreover, the 1-hour maximum metric uses an exposure window different than that used for the current ozone NAAQS. Together, this means that the most biologically relevant metric, and the one used in the ozone NAAQS since 1997, is the maximum daily 8-hour average ozone. Thus, we converted ozone mortality health impact functions that use a 24-hour average or 1-hour maximum ozone metric to maximum 8-hour

average ozone concentration using standard conversion functions.

This practice is consistent with the form of the current ozone standard. This conversion also does not affect the relative magnitude of the health impact function from a mathematical standpoint. An equivalent change in the 24-hour average, 1-hour maximum and 8-hour maximum will provide the same overall change in incidence of a health effect. 133 

### The conversion ratios are based on observed relationships between the 24-hour average and 8-hour maximum ozone values. For example, in the Bell et al., 2004 analysis of ozone-related premature mortality, the authors found that the relationship between the 24-hour average, the 8-hour maximum, and the 1-hour maximum was 2:1.5:1, so that the derived health impact effect estimate based on the 1-hour maximum should be half that of the effect estimate based on the 24-hour values (and the 8-hour maximum three-quarters of the 24-hour effect estimate).

In the sensitivity analyses for this benefits analysis, we apply national effect estimates based on the pooled multi-city results reported in Bell et al. (2004) and the three meta-analysis studies. Bell et al. (2004), Bell et al. (2005), Levy et al. (2005), and Ito et al. (2005) all provide

133 However, different ozone metrics may not be well correlated (from either a spatial or temporal standpoint) within

a given geographic area, which means that application of ratio-converted effect estimates for the same endpoint can result in different incidence estimates for the same location under certain conditions. This introduces uncertainty into the use of these ratio-adjusted effect estimates (see Appendix 6A).

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6-34 national conversion ratios between daily average and 8-hour and 1-hour maxima, based on
national data.

6.6.3.2 Ozone Premature Mortality Effect Coefficients
Mortality Effect Coefficients for Short-term Ozone Exposure. The overall body of evidence indicates that there is likely to be a causal relationship between short-term ozone exposure and premature death. The 2013 ozone ISA concludes that the evidence suggests that ozone effects are independent of the relationship between PM and mortality. (U.S. EPA, 2013a). However, the ISA notes that the interpretation of the potential confounding effects of PM on ozone-mortality risk estimates requires caution due to the PM sampling schedule (in most cities) which limits the overall sample size available for evaluating potential confounding of the ozone effect by PM (U.S. EPA 2013a). 134



### In 2006, the EPA requested an NAS study to answer the following four key questions regarding ozone-related mortality: (1) How did the epidemiological literature to that point improve our understanding of the size of the ozone-related mortality effect?; (2) How best can EPA quantify the level of ozone-related mortality impacts from short-term exposure?; (3) How might EPA estimate the change in life expectancy?; and (4) What methods should EPA use to estimate the monetary value of changes in ozone-related mortality risk and life expectancy?

In 2008, the NAS (NRC, 2008) issued a series of recommendations to the EPA regarding the quantification and valuation of ozone-related short-term mortality. Chief among these was that “...short-term exposure to ambient ozone is likely to contribute to premature deaths” and the committee recommended that “ozone-related mortality be included in future estimates of the health benefits of reducing ozone exposures...” The NAS also recommended that “...the greatestemphasis be placed on the multi-city and NMMAPS studies without exclusion of the metaanalyses” (NRC, 2008). In addition, NAS recommended that EPA “should give little or no weight to the assumption that there is no causal association between estimated reductions in premature mortality and reduced ozone exposure” (NRC, 2008). In 2010, the Health Effects

134 Consequently, as noted later in this section, while we have used a single-pollutant model in generating short-term ozone-related mortality benefits for this RIA, we have included a co-pollutants model as a sensitivity analysis to address potential uncertainty associated with this assumption of an independent ozone mortality effect.

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6-35 Subcommittee of the Advisory Council on Clean Air Compliance Analysis, while reviewing
EPA’s The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a), also confirmed the NAS recommendation to include ozone mortality benefits (U.S. EPA-SAB, 2010a).

In view of the findings of the ozone ISA, the NAS panel, the SAB-HES panel, and the CASAC panel, we estimate ozone-related premature mortality for short-term exposure in the core health effects analysis using effect coefficients from the Smith et al. (2009) NMMAPS analysis and the Zanobetti and Schwartz (2008) multi-city study with several additional studies as sensitivity analyses. This emphasis on ne wer multi-city studies is consistent with recommendations provided by the NAS in their ozone mortality report (NRC, 2008). CASAC supported using the Smith et al. (2009) and Zanobetti and Schwartz (2008) studies for the ozone HREA (U.S. EPA-SAB, 2012, 2014), and these are multi-city studies published more recently (as compared with other multi-city studies or meta-analyses included in the sensitivity analyses – see discussion below).

Smith et al. (2009) reanalyzed the NMMAPS dataset, evaluating the relationship between short-term ozone exposure and mortality. While th is study reproduces the core national-scale estimates presented in Bell et al. (2004), it also explored the sensitivity of the mortality effect to different model specifications including (a) re gional versus national Bayes-based adjustment, 135 

### (b) co-pollutant models considering PM 10 

### , (c) all-year versus ozone- season based estimates, and (d) consideration of a range of ozone metrics, including the daily 8-hour max. In addition, the Smith et al. (2009) study did not use the trimmed mean approach employed in the Bell et al. (2004) study in preparing ozone monitor data. 136 

### In selecting effect estimates from Smith et al.

135 In Bayesian modeling, effect estimates are “updated” from an assumed prior value using observational data. In

the Smith et al. (2009) approach, the prior values are either a regional or national mean of the individual effect estimates obtained for each individual city. The Bayesian adjusted city-specific effect estimates are then calculated by updating the selected prior value based on the relative precision of each city-specific estimate and the variation observed across all city-specific individual effect estimates. City-specific estimates are pulledtowards the prior value if they have low precision and/or if there is low overall variation across estimates. Cityspecific estimates are given less adjustment if they are precisely estimated and/or there is greater overall variation across estimates.
136 There are a number of concerns regarding the trimmed mean approach including (1) the potential loss of temporal

variation in the data when the approach is used (this could impact the size of the effect estimate), and (2) a lack of complete documentation for the approach, which prevents a full reviewing or replication of the technique.

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6-36 (2009), we focused on an ozone-only estimate for non-accidental mortality using the 8-hour max metric for the warmer ozone season. For the sensitivity analysis, we included a co-pollutant model (ozone and PM 10 

### ) from Smith et al. (2009) for all-cause mortality, using the 8-hour max ozone metric for the ozone season. Using a single pollutant model for the core analysis and the co-pollutant model in the sensitivity analysis reflects our concern that the reduced sampling frequency for days with co-pollutant measurements (1/3 and 1/6) could affect the ability of the study to characterize the ozone effect. This choice is consistent with the ozone ISA, which concludes that ozone effects are likely to be independent of the relationship between PM and mortality (U.S. EPA, 2013a).

The Zanobetti and Smith (2008) study evaluated the relationship between ozone exposure (using an 8-hour mean metric for the warm season June-August) and all-cause mortality in 48U.S. cities using data collected between 1989 and 2000. The study presented single pollutant CR functions based on shorter (0-3 day) and longer (0-20 day) lag structures, with the comparison of effects based on these different lag structures being a central focus of the study. We used the shorter day lag based C-R function since this had the strongest effect and tighter confidence interval. We converted the effect estimate from an 8-hour mean metric to an equivalent effect estimate based on an 8-hour max. To do this, we used the ozone metric approach wherein the original effect estimate (and standard error) is multiplied by the appropriate ozone metric adjustment ratio. 137



### Mortality Effect Coefficient for Long-term Ozone Exposure. We also estimated longterm ozone exposure-related respiratory mortality incidence. Support for modeling long-term exposure-related mortality incidence comes from the ozone ISA as well as recommendations provided by CASAC in their review of the ozone HREA (U.S. EPA-SAB, 2014, p. 3 and 9).

137 These adjustment ratios are created by (a) obtaining summary air quality (composite monitor values) for each

urban study area/ozone season combination reflected in the original epidemiology study, (b) calculating the ratio of the 8-hour max to the study-specific air metric (for each of the urban study areas), and (c) taking the average of these urban-study area ratios. Ratio adjustment of the effect estimate does introduce uncertainty into the benefits estimates generated using these adjusted effect estimates; however, adjustments of relatively similar metrics (e.g., 8-hour max and 8-hour mean), as is the case with the Zanobetti and Schwartz (2008) study, are likely to introduce less uncertainty than adjustments for more disparate ratios (e.g., 24-hour or 1-hour max ratios to 8-hour max equivalents).

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6-37 While the ozone ISA concludes that evidence is suggestive of a causal association between total mortality and long-term ozone exposure, specifically with regard to respiratory health effects (including mortality), the ISA concludes that there is likely to be a causal association (U.S. EPA, 2013a). Consistent with the ozone HREA, we use Jerrett et al. (2009) to estimate premature respiratory mortality from long-term ozone exposure. In review of the ozone HREA, CASAC concluded that “Jerrett et al. (2009) is an appropriate study to use at this time as the basis for the long-term mortality risk estimates given its adequacy and the lack of alternative data.” (U.S. EPA-SAB, 2014). This advice supersedes previous advice provided by the SAB-HES to include long-term ozone exposure-related mortality only as a sensitivity analysis.

The Jerrett et al. (2009) study was the first to explore the relationship between long-term ozone exposure and respiratory mortality (rather than other causes of mortality). Jerrett et al. (2009) exhibits a number of strengths including (a) the study was based on the 1.2 million participant American Cancer Society cohort drawn from all 50 states, DC, and Puerto Rico (included ozone data from 1977, 5 years before enrollment in the cohort began, to 2000); (b) it included co-pollutant models that controlled for PM 2.5 

### ; and (c) it explored the potential for a threshold concentration associated with the long-term mortality endpoint. However, attributes of this study affect how we interpret the long-term exposure-related respiratory mortality estimates. First, while CASAC notes that Jerrett et al. (2009) was well designed, it is a single study and provides the only quantitative basis for estimating this endpoint. By comparison, we estimate short-term exposure-related mortality risk using several studies.

There is also the potential existence and location of a threshold in the C-R function relating mortality and long-term ozone concentrations, which can greatly affect the results. CASAC concluded, “it is not clear whether the 56 ppb threshold model is a better predictor of respiratory mortality than when using a linear model for the Jerrett et al. data” (U.S. EPA-SAB, 2014, p. 13-14)

Reflecting this CASAC advice, we estimate long-term exposure-related respiratory mortality using a non-threshold co-pollutant model (with PM 2.5 

### ) from Jerrett et al. (2009). Because the Jerrett et al. (2009) study uses seasonal average metrics (rather than shorter single day or multi-day lagged models as with time series studies studies), co-pollutants models

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6-38 obtained from Jerrett et al., (2009) are not affected by the lower PM 2.5 

### sampling rates. Using a co-pollutant model is consistent with this study applying seasonal average metrics that are insensitive to co-pollutant monitoring for PM 2.5 

### . The effect estimates used to model long-term ozone-attributable mortality are calculated using a seasonal average of peak (1-hour maximum) measurements. These long-term exposure metrics can be viewed as long-term exposures to daily peak ozone over the warmer months, as compared with annual average levels such as are used in PM benefits. Therefore, combining long-term and short-term ozone-attributable mortalityestimates could lead to double counting. Estimates of short-term ozone mortality are for allcauses, while estimates of long-term ozone mortality are for respiratory-related mortality only.

Sensitivity Analysis: Alternate Mortality Effect Coefficients for Short-term Ozone Exposure . Although we believe the evidence supports an ozone-only effect on short-term exposure-related mortality (as supported by the ozone ISA), we recognize limitations in the ability of studies to explore copollutants effects due to lower sampling frequency for PM relative to ozone. For that reason, we conduct a sensitivity analysis using the co-pollutants model (with
PM 10 

### ) from Smith et al. (2009).

Sensitivity Analysis: Threshold-Based Ef fect Coefficients for Long-term Ozone Exposure. Consistent with the ozone HREA (U.S. EP A, 2014b), we explore the sensitivity of estimated ozone-related deaths to a concentration threshold using the Jerrett et al. (2009) study. 138 

### In their memo clarifying the results of their study (see Sasser, 2014), the authors note that in terms of goodness of fit, long-term health risk models including ozone clearly performed better than models without ozone, indicating the improved predictions of respiratory mortality when ozone is included. In exploring different functional forms, they report that the model including a threshold at 56 ppb had the lowest log-likelihood value of all models evaluated (i.e., linear models and models including thresholds ranging from 40-60 ppb), and thus provided the best overall statistical fit to the data. However, they also note that it is not clear whether the 56ppb threshold model is a better predictor of respiratory mortality than when using a linear (nothreshold) model for this dataset. Using one statistical test, the model with a threshold at 56 ppb

138 The approach in the ozone HREA to explore the potential for thresholds related to long-term exposure-related

mortality is described in Sasser (2014). That memorandum also describes additional data obtained from the authors of Jerrett et al. (2009) to support modeling potential thresholds.

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6-39 was determined to be statistically superior to the linear model. Using another, more stringent test, none of the threshold models considered were statistically superior to the linear model. Under the less stringent test, although the threshold model produces a statistically superior prediction than the linear model, there is uncertainty about the specific location of the threshold, if one exists. This is because the confidence intervals on the model predictions indicate that a threshold could exist anywhere from 0 to 60 ppb. The authors conclude that considerable caution should be exercised in using any specific threshold, particularly when the more stringent statistical test indicates there is no significantly improved prediction.

Based on this additional information from the authors (Sasser, 2014), we have chosen to reflect the uncertainty about the existence and location of a potential threshold by estimating mortality attributable to long-term ozone exposures using a range of threshold-based effect estimates as sensitivity analyses. Specifically, we generate additional long-term risk results using unique risk models that include a range of thre sholds from 40 ppb to 60 ppb in 5 ppb increments, while also including a model with a threshold equal to 56 ppb, which had the lowest log
likelihood value for all models examined. 139 

### In addition to exploring the impact of potentialthresholds, as part of the sensitivity analysis we explore the impact of using ozone-only (nonthreshold) models in estimating long-term exposure-related respiratory mortality. 140



### 6.6.3.3 PM 2.5 

### Premature Mortality Coefficients

PM 2.5 

### Mortality Effect Coefficients for Adults. A substantial body of published
scientific literature documents the association between elevated PM 2.5 

### concentrations and increased premature mortality (U.S. EPA, 2009b). This body of literature reflects thousands of epidemiology, toxicology, and clinical studies. The PM ISA completed as part of the most recent review of the PM NAAQS, which was twice reviewed by the SAB-CASAC (U.S. EPA-SAB, 2009a, 2009b), concluded that there is a causal relationship between mortality and both long-

139 There is a separate effect estima te (and associated standard error) for each of the fitted threshold models

estimated in Jerrett et al. (2009). As a result, the sensitivity of estimated mortality attributable to long-term ozone concentrations is affected by both the assumed threshold level (below which there is no effect of ozone) and the effect estimate applied to ozone concentrations above the threshold.
140 The set of ozone-only non-threshold effect estimates include (a) a value based on the 86 cities for which there are

co-pollutant monitoring data for both ozone and PM 2.5 (this is best compared with the core estimate based on the co-pollutant non-threshold model), and (b) a value based on the 96 cities for which there is PM 2.5 data (these 96 cities were used in developing the threshold-based effect estimates used in the analysis).

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6-40 term and short-term exposure to PM 2.5 

### based on the entire body of scientific evidence (U.S. EPA, 2009b). The size of the mortality effect estimates from epidemiological studies, the serious nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality risk reduction the most significant health endpoint quantified in this analysis.

Researchers have found statistically significant associations between PM 2.5 

### and premature mortality using different types of study designs. Time-series methods have been used to relate short-term (often day-to-day) changes in PM 2.5 

### concentrations and changes in daily mortality rates up to several days after a period of exposure to elevated PM 2.5 

### concentrations. Cohort methods have been used to examine the potential relationship between community-level
PM 2.5 

### exposures over multiple years (i.e., long-term exposures) and community-level annual mortality rates that have been adjusted for individual level risk factors. When choosing between using short-term studies or cohort studies for estimating mortality benefits, cohort analyses are thought to capture more of the public health impact of exposure to air pollution over time because they account for the effects of long-term exposures, as well as some fraction of shortterm exposures (Kunzli et al., 2001; NRC, 2002). The National Research Council (NRC) stated that “it is essential to use the cohort studies in benefits analysis to capture all important effects from air pollution exposure” (NRC, 2002, p. 108). The NRC further noted that “the overall effect estimates may be a combination of effects from long-term exposure plus some fraction from short-term exposure. The amount of overlap is unknown” (NRC, 2002, p. 108-9). To avoid double counting, we focus on applying the risk coefficients from the long-term cohort studies in estimating the mortality impacts of reductions in PM 2.5


### . Over the last two decades, several studies using “prospective cohort” designs have been published that are consistent with the earlier body of literature. Two prospective cohort studies, often referred to as the Harvard “Six Cities Study” (Dockery et al., 1993; Laden et al., 2006; Lepeule et al., 2012) and the “American Cancer Society” or “ACS study” (Pope et al., 1995; Pope et al., 2002; Pope et al., 2004; Krewski et al., 2009), provide the most extensive analyses of
ambient PM 2.5 

### concentrations and mortality. These studies have found consistent relationships between fine particle indicators and premature mortality across multiple locations in the United States. The credibility of these two studies is further enhanced by the fact that the initial published studies (Pope et al., 1995; Docker y et al., 1993) were subject to extensive

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6-41 reexamination and reanalysis by an independent team of scientific experts commissioned by the Health Effects Institute (HEI) and by a Special Panel of the HEI Health Review Committee (Krewski et al., 2000). Publication of studies confirming and extending the findings of the 1993Six Cities Study and the 1995 ACS study using more recent air quality data and a longer followup period for the ACS cohort provides additional validation of the findings of these original studies (Pope et al., 2002, 2004; Laden et al., 2006; Krewski et al., 2009; Lepeule et al., 2012). The SAB-HES also supported using these two cohorts for analyses of the benefits of PM reductions, and concluded, “the selection of these cohort studies as the underlying basis for PM mortality benefit estimates [is] a good choice. These are widely cited, well studied and extensively reviewed data sets” (U.S. EPA-SAB, 2010a). As both the ACS and Six Cities studies have inherent strengths and weaknesses, we present benefits estimates using relative risk estimates from the most recent extended reanalysis of these cohorts (Krewski et al., 2009; Lepeule et al., 2012). Presenting results using both ACS and Six Cities is consistent with other recent RIAs (e.g., U.S. EPA, 2010c, 2011a, 2011c). The PM ISA concludes that the ACS and Six Cities cohorts provide the strongest evidence of the association between long-term PM 2.5 

### exposure and premature mortality with support from a number of additional cohort studies (described below).

The extended analyses of the ACS cohort data (Krewski et al., 2009) refined the earlier ACS studies by (a) extending the follow-up period by 2 years to the year 2000, for a total of 18 years; (b) incorporating almost double the number of urban areas; (c) addressing confounding by spatial autocorrelation by incorporating ecolog ical, or community-level, co-variates; and (d) performing an extensive spatial analysis using land use regression modeling in two large urban areas. These enhancements make this analysis well-suited for the assessment of mortality risk from long-term PM 2.5 

### exposures for the EPA’s benefits analyses.

In 2009, the SAB-HES again reviewed the choice of mortality risk coefficients for benefits analysis, concluding that “[t]he Krewski et al. (2009) findings, while informative, have not yet undergone the same degree of peer review as have the aforementioned studies. Thus, the SAB-HES recommends that EPA not use the Krewski et al. (2009) findings for generating the Primary Estimate” (U.S. EPA-SAB, 2010a). Since this time, the Krewski et al. (2009) has undergone additional peer review, which we believe strengthens the support for including this

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6-42 study in this RIA. For example, the PM ISA (U.S. EPA, 2009b) included this study among the key mortality studies. In addition, the risk assessment supporting the PM NAAQS (U.S. EPA, 2010b) used risk coefficients drawn from the Krewski et al. (2009) study, the most recent reanalysis of the ACS cohort data. The PM risk assessment cited a number of advantages that informed the selection of the Krewski et al. ( 2009) study as the source of the core effect estimates, including the extended period of observation, the rigorous examination of model forms and effect estimates, the coverage for ecological variables, and the large dataset with over 1.2 million individuals and 156 MSAs (U.S. EPA, 2010b). The CASAC also provided extensive peer review of the PM risk assessment and supported the use of effect estimates from this study (U.S. EPA-SAB, 2009a, b, 2010b).

Consistent with the PM risk assessment (U.S. EPA, 2010b) which was reviewed by the CASAC (U.S. EPA-SAB, 2009a, b), we use the all-cause mortality risk estimate based on the random-effects Cox proportional hazard model that incorporates 44 individual and 7 ecological covariates (RR=1.06, 95% confidence intervals 1.04–1.08 per 10 μg/m 3 

### increase in PM 2.5 

### ). The relative risk estimate (1.06 per 10 μg/m 3 

### increase in PM 2.5 

### ) is identical to the risk estimate drawn from the earlier Pope et al. (2002) study, though the confidence interval around the Krewski et al. (2009) risk estimate is tighter.

In the most recent Six Cities study, which was published after the last SAB-HES review, Lepeule et al. (2012) evaluated the sensitivity of previous Six Cities results to model specifications, lower exposures, and averaging time using eleven additional years of cohort follow-up that incorporated recent lower exposures. The authors found significant associations
between PM 2.5 

### exposure and increased risk of all-cause, cardiovascular and lung cancer mortality. The authors also concluded that the C-R relationship was linear down to PM 2.5 

### concentrations of 8 μ g/m 3 

### and that mortality rate ratios for PM 2.5 

### fluctuated over time, but without clear trends, despite a substantial drop in the sulfate fraction. We use the all-cause mortality risk estimate based on a Cox proportional hazard model that incorporates 3 individual covariates. (RR=1.14, 95% confidence intervals 1.07–1.22 per 10 μg/m 3 

### increase in PM 2.5 

### ). The relative risk estimate is slightly smaller than the risk estimate drawn from Laden et al. (2006), with relatively smaller confidence intervals.

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6-43 Given that monetized benefits associated with PM 2.5 

### are driven largely by reductions in premature mortality, it is important to characterize the uncertainty in this endpoint. In order to do so, we utilize the results of an expert elicitation sponsored by the EPA and completed in 2006 (Roman et al., 2008; IEc, 2006). The results of that expert elicitation can be used as a characterization of uncertainty in the C-R functions. The co-benefits results derived from expert elicitation is discussed in Appendix 6B (section 6B.4).

PM 2.5 

### Mortality Effect Coefficients for Infants. In addition to the adult mortality studies described above, several studies show an association between PM exposure and premature mortality in children under 5 years of age. 141 

### The PM ISA states that less evidence is available regarding the potential impact of PM 2.5 

### exposure on infant mortality than on adult mortality and the results of studies in several countries include a range of findings with some finding significant associations. Specifically, the PM ISA concluded that evidence exists for a stronger effect at the post-neonatal period and for respiratory-related mortality, although this trend is not consistent across all studies. In addition, compared to avoided premature deaths estimated for adult mortality, avoided premature deaths for infants are significantly smaller because the number of infants in the population is much smaller than the number of adults and the epidemiology studies on infant mortality provide smaller risk coefficients associated with exposure to PM 2.5


### . In 2004, the SAB-HES noted the release of the WHO Global Burden of Disease Study focusing on ambient air, which cites several recently published time-series studies relating daily PM exposure to mortality in children (U.S. EPA-SAB, 2004). With regard to the cohort study conducted by Woodruff et al. (1997), the SAB-HES noted several strengths of the study, including the use of a larger cohort drawn from a large number of metropolitan areas and efforts to control for a variety of individual risk factors in infants (e.g., maternal educational level, maternal ethnicity, parental marital status, and maternal smoking status). Based on these findings, the SAB-HES recommended that the EPA incorporate infant mortality into the primary

141 For the purposes of this analysis, we only calculate benefits for infants age 0–1, not all children under 5 years old.

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6-44 benefits estimate and that infant mortality be evaluated using an impact function developed from the Woodruff et al. (1997) study (U.S. EPA-SAB, 2004).

In 2010, the SAB-HES again noted the increasing body of literature relating infant mortality and PM exposure and supported the inclusion of infant mortality in the monetized benefits (U.S. EPA-SAB, 2010a). The SAB-HES generally supported the approach of estimating infant mortality based on Woodruff et al. (1997) but also noted that a more recent study by Woodruff et al. (2006) continued to find associations between PM 2.5 

### and infant mortality in California. The SAB-HES also noted, “when PM 10 

### results are scaled to estimate PM 2.5 

### impacts, the results yield similar risk estimates.” Consistent with The Benefits and Costs of the Clean Air
Act 1990 to 2020 (U.S. EPA, 2011a), we continue to rely on the earlier 1997 study in part due to the national–scale of the earlier study.

6.6.3.4 Hospital Admissions and Emergency Department Visits
We pool together the incidence estimates using several different studies for many of the hospital admission endpoints. Some studies have examined the relationship between air pollution and emergency department (ED) visits. Since most emergency department visits do not result in an admission to the hospital (i.e., most people going to the emergency department are treated and return home), we treat hospital admissions and emergency department visits separately, taking account of the fraction of emergency department visits that are admitted to the hospital. Specifically, within the baseline incidence rates, we parse out the scheduled hospital visits from unscheduled ones as well as the hospital visits that originated in the emergency department.

The ozone ISA states that studies consistently found positive associations between shortterm ozone exposures and asthma and COPD hospital admissions and ED visits, with more limited evidence for pneumonia (U.S. EPA, 2013a). The ISA found no evidence of a threshold between short term ozone exposure and respiratory hospital admissions and ED visits. The ISA also observes that effect estimates remained robust to copollutants (U.S. EPA 2013a).

Considering these observation from the ISA and a thoroughly reviewing available epidemiological studies, we estimate respiratory hospital admissions (for 65-99 year olds) using an effect estimate obtained from Katsouyanni et al. (2009) and asthma-related emergency room

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6-45 visits (for all ages) using several single-city studies. Although Katsouyanni et al. (2009) provides effect estimates specific to the summer season, we adjusted the 1-hour max metric to the equivalent 8-hour max effect estimates. 142 

### The study provides summer season single pollutant effect estimates based both on natural and penalized splines. We have re-evaluated the choice of these models in this final RIA. In contrast to the proposal RIA, where we averaged both the penalized and natural spline estimates, we decided to focus on the penalized spline model because it displays a higher degree of precision, thus less potential for random error. 143 

### While Katsouyanni et al. (2009) included a set of effect estimates based on co-pollutant modeling (with
PM 10 

### ), but we could not use them because they were based on the full year rather than the

summer season.
A number of studies are available to model respiratory ED visits. Because we do not yet have the information needed to value this endpoint, we focused on the narrower category of asthma-related ED visits We used a set of single city studies together with random-effects pooling to generate a single pooled estimate. The set of single city studies used in this calculation include: Peel et al. (2005) and Sarnat et al. (2013) both for Atlanta, Wilson et al. (2005) and Mar and Koenig (2009) for Seattle, Wilson et al. (2005) for Portland, Maine,, Ito et al. (2007) for New York City, and Glad et al. (2012) for Pittsburgh. We note that of these single city studies, only the Ito et al. (2007) study included a co-pollutant model (for PM 2.5 

### ). 144 

### In addition, two of the studies required adjustments to reflect the 8-hour max air metric. Specifically, Glad et al. (2012) uses the 1-hour max air metric, while Sarnat et al. (2013) used the 24-hour average metric. Each required the use of air metric ratios to adjust their betas. In generating a single pooled benefit estimate for this endpoint, we used random/fixed effects pooling to combine estimates across these single city studies.

142 Given that Katsouyanni et al. (2009) included a larger number of cities (14), rather than constructing an air metric

adjustment ratio based on this set of urban study areas, we used a national ratio to adjust effect estimates to represent the 8-hour metric.
143 We evaluated the impact from this change (i.e., use of the penalized spline model alone as contrasted with an average of estimates generated using both the natural and penalized splines), and this change made a negligible (<<1%) difference in monetized ozone benefits due to the similarity in effect coefficients between the two models.
144 While we have included co-pollutant models as sensitivity analyses for mortality, we did not include separate copollutant models for any of the morbidity endpoints as sensitivity analyses because morbidity endpoint represent a small fraction of the total monetized benefits.

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6-46 The two main groups of hospital admissions estimated in this analysis for PM 2.5 

### are: respiratory admissions and cardiovascular admissions. There is not sufficient evidence linking
PM 2.5 

### with other types of hospital admissions. Both asthma- and cardiovascular-related visits have been linked to exposure to PM 2.5 

### in the United States, though as we note below, we are able to assign an economic value to asthma-related events only. To estimate the effects of PM 2.5 

### air pollution reductions on asthma-related ER visits, we use the effect estimates from studies of children 18 and under by Mar et al. (2010), Slaughter et al. (2005), and Glad et al. (2012). The first two studies examined populations 0 to 99 in Washington, while Glad et al. examined populations 0-99 in Pittsburgh, Pennsylvania. Mar and colleagues perform their study in Tacoma, while Slaughter and colleagues base their study in Spokane. We apply random/fixed effects pooling to combine evidence across these two studies.

To estimate avoided incidences of cardiovascular hospital admissions associated with exposure to PM 2.5 

### , we used studies by Moolgavkar (2000), Zanobetti et al. (2009), Peng et al. (2008, 2009) and Bell et al. (2008). Only Moolgavkar (2000) provided a separate effect estimate for adults 20 to 64, while the remainder estimate risk among adults over 64. Total cardiovascular hospital admissions are the sum of the pooled estimate for adults over 64 and the single study estimate for adults 20 to 64. To avoid double-counting benefits from reductions in myocardial infarctions when applying the impact function for cardiovascular hospital admissions, we first adjusted the baseline cardiovascular hospital admissions to remove admissions for myocardial infarctions. We applied equal weights pooling to the multi-city studies assessing risk among adults over 64 because these studies already incorporated pooling across the city-level estimates. One potential limitation of our approach is that while the Zanobetti et al. (2009) study assesses all cardiovascular risk, the Bell et al. (2008), and Peng et al. (2008, 2009) studies estimate a subset of cardiovascular hospitalizations as well as certain cerebro- and peripheral-vascular diseases. To address the potential for the pooling of these four studies to produce a biasedestimate, we match the pooled risk estimate with a baseline incidence rate that excludes cerebroand peripheral-vascular disease. An alternative approach would be to use the Zanobetti et al. (2009) study alone, though this would prevent us from drawing upon the strengths of the three multi-city studies.

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6-47 To estimate avoided incidences of respiratory hospital admissions associated with PM 2.5 

### , we used a number of studies examining total respiratory hospital admissions as well as asthma and chronic lung disease. We estimated impacts among three age groups: adults 65 and over, adults 18 to 64 and children 0 to 17. For adults over 65, the multi-city studies by Zanobetti et al. (2009) and Kloog et al. (2012) provide effect coefficients for total respiratory hospital admissions. We pool these two studies using equal weights. Moolgavkar et al. (2003) examines
PM 2.5 

### and chronic lung disease hospital admissions (not including asthma) in Los Angeles, California among adults 18 to 64. For children 0 to 18, we pool two studies using random/fixed effects. The first is Babin et al. (2007), which assessed PM 2.5 

### and asthma hospital admissions in Washington, DC among children 1 to 18; we adjusted the age range for this study to apply to children 0 to 18. The second is Sheppard et al. (2003), which assessed PM 2.5 

### and asthma hospitalizations in Seattle, Washington, among children 0 to 18.

6.6.3.5 Acute Health Events
A number of acute health effects not requiring hospitalization are also associated with exposure to ozone and PM 2.5 

### . The sources for the effect estimates used to quantify these endpoints are described below.

Asthma exacerbations. For this RIA, we followed the SAB-HES recommendations regarding asthma exacerbations in developing the core estimate (U.S. EPA-SAB, 2004a). Although certain studies of acute respiratory events characterize these impacts among onlyasthmatic populations, others consider the full population, including both asthmatics and nonasthmatics. For this reason and to avoid double counting impacts, incidence estimates derived from studies focused only on asthmatics cannot be added to estimates from studies that consider the full population. To prevent such double-counting, we estimated the exacerbation of asthma among children and excluded adults from the calculation. Asthma exacerbations occurring in adults are assumed to be captured in the general population endpoints, such as work loss days and minor restricted activity days (MRADs). Finally, we note the important distinction between the exacerbation of asthma among asthmatic populations and the onset of asthma among populations not previously suffering from asthma. In this RIA, we quantify the exacerbation of asthma among asthmatic populations and not the onset of new cases of asthma.

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6-48 Based on advice from the SAB-HES (U.S. EPA-SAB 2004a), regardless of the age ranges included in the source epidemiology studies, we extend the applied population to ages 6 to 18, reflecting the common biological basis for the effect in children in the broader age group. This age range expansion is also supported by NRC (2002, pp. 8, 116).

To characterize asthma exacerbations in children from exposure to ozone, for the proposed RIA, we selected three multi-city studies (Mortimer et al., 2002; O’Connor et al., 2008; Schildcrout et al., 2006). All three of these studies required the application of air metric ratios to adjust effect estimates to represent the 8-hour max metric. 145 

### In this final RIA, following a final review of our technical approach, we removed O’Connor (2008) due to concerns about potential exposure measurement error and residual confounding from meteorological variables in its 19- day lag structure. Current evidence in the oz one ISA suggests a more immediate effect with exposure to ozone for respiratory-related effects, such as hospital admissions and emergency department visits, with additional supporting evidence from studies of respiratory symptoms (U.S. EPA, 2013a). Consequently, the 19-day lag structure reflected in the O’Connor (2008) study is not as strongly supported by the evidence as the shorter lag structures associated with the
other two asthma exacerbation studies. 146 

### In generating the asthma exacerbation estimate, we pool estimates from Mortimer et al. (2012) and Schildcrout et al. (2006) using a random/fixed effects approach applied within BenMAP-CE (U.S. EPA, 2015a). 147



### To characterize asthma exacerbations in children from exposure to PM 2.5 

### , we selected two studies (Ostro et al., 2001; Mar et al., 2004) that followed panels of asthmatic children. Ostro et al. (2001) followed a group of 138 African-American children in Los Angeles for 13 weeks, recording daily occurrences of respiratory symptoms associated with asthma exacerbations (e.g., shortness of breath, wheeze , and cough). This study found a statistically significant association between exposure to PM 2.5 

### , measured as a 12-hour average, and the daily

145 Mortimer et al. (2002) had effect estimates based on an 8-hour mean metric, O’Connor et al. (2008) used a 24-

hour metric, and Schildcrout et al. (2006) was based on a 1-hour max metric.
146 We evaluated the impact from excluding O’Connor (2008) in estimating asthma exacerbations, and the change made a negligible (<1%) difference in monetized ozone benefits. Furthermore, the combined changes in estimating asthma exacerbations and respiratory hospital admissions resulted in only a 1% change in monetized ozone benefits compared to the approaches used in the proposal RIA (U.S. EPA, 2014c).
147 BenMAP-CE applies a chi-squared test to determine whether a fixed or random effect pooling approach should be used (for additional detail see U.S. EPA (2015b), p. 206).

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6-49 prevalence of shortness of breath and wheeze endpoints. Although the association was not statistically significant for cough, the results were still positive and close to significance; consequently, we decided to include this endpoint, along with shortness of breath and wheeze, in generating asthma exacerbation benefits. 148



### Mar et al. (2004) studied the effects of various size fractions of particulate matter on respiratory symptoms of adults and children with asthma, monitored over many months. The study was conducted in Spokane, Washington, a semi-arid city with diverse sources of particulate matter. Data on respiratory symptoms and medication use were recorded daily by the study’s subjects, while air pollution data was collected by the local air agency and Washington State University. Subjects in the study consisted of 16 adults—the majority of whom participated for over a year—and nine children, all of whom were studied for over eight months. Among the children, the authors found a strong association between cough symptoms and several metrics of particulate matter, including PM 2.5 

### . However, the authors found no association between respiratory symptoms and exposure to PM for any metric in adults. Mar et al. therefore concluded that the discrepancy in results between children and adults was due either to the way in which air quality was monitored, or a greater sensitivity of children than adults to increased levels of PM air pollution.

We employed the following pooling approach in combining estimates generated using effect estimates from the two studies to produce a single estimate for PM-related asthma exacerbation incidence. First, we used random/fixed effects pooling to combine the Ostro and Mar estimates for shortness of breath and cough. Next, we pooled the Ostro estimate of wheeze with the pooled cough and shortness of breath estimates to derive an overall estimate of asthma
exacerbation in children.

Acute Respiratory Symptoms. We estimate one type of acute respiratory symptom related to ozone exposure. Minor Restricted Activity Days (MRADs) result when individuals reduce most usual daily activities and replace them with less strenuous activities or rest, yet not

148 The random effects pooling used in BenMAP (U.S. EPA, 2015b) to generate a single estimate for asthma exacerbations weights estimates based on variance, and consequently those estimates with less statistical significance and wider confidence intervals will be down-weighted in generating the total aggregated estimate.

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6-50 to the point of missing work or school. For example, a mechanic who would usually be doing physical work most of the day will instead spend the day at a desk doing paper work and phone work because of difficulty breathing or chest pain.

For ozone, we modeled MRADs using Ostro and Rothschild (1989). This study provides a co-pollutant model (with PM 2.5 

### ) based on a national sample of 18-64 year olds. The original study used a 24-hour average metric and included control for PM 2.5 

### , which necessitated the use of an air metric ratio to convert the effect estimate to an 8-hour max equivalent.

We estimate three types of acute respiratory symptoms related to PM 2.5 

### exposure: lower respiratory symptoms, upper respiratory symptoms, and MRAD. Incidences of lower respiratory symptoms (e.g., wheezing, deep cough) in children aged 7 to 14 were estimated for PM 2.5 

### using an effect estimate from Schwartz and Neas (2000). Incidences of upper respiratory symptoms in asthmatic children aged 9 to 11 are estimated for PM 2.5 

### using an effect estimate developed from Pope et al. (1991). Because asthmatics have greater sensitivity to stimuli (including air pollution), children with asthma can be more susceptible to a variety of upper respiratory symptoms (e.g., runny or stuffy nose; wet cough; and burning, aching, or red eyes). Research on the effects of air pollution on upper respiratory symptoms has focused on effects in asthmatics.

The effect of PM 2.5 

### on MRAD was estimated using an effect estimate derived from Ostro and Rothschild (1989). More recently published literature examining the relationship between
short-term PM 2.5 

### exposure and acute respiratory symptoms was available in the PM ISA (U.S. EPA, 2009b), but proved to be unsuitable for use in this benefits analysis. In particular, the best available study (Patel et al., 2010) specified a population aged 13–20, which overlaps with the population in which we assess asthma exacerbation. As we describe in detail below, to avoid the chance of double-counting impacts, we do not estimate changes in acute respiratory symptoms and asthma exacerbation among populations of the same age.

School loss days (absences). Children may be absent from school due to respiratory or other acute diseases caused, or aggravated by, exposure to air pollution. Several studies have found a significant association between ozone levels and school absence rates. We use two studies (Gilliland et al., 2001; Chen et al., 2000) to estimate changes in school absences resulting from changes in ozone levels. The Gilliland et al. study estimated the incidence of new periods

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6-51 of absence, while the Chen et al. study examined daily absence rates. We converted the Gilliland et al. estimate to days of absence by multiplying the absence periods by the average duration of an absence. We estimated 1.6 days as the average duration of a school absence, the result of dividing the average daily school absence rate from Chen et al. (2000) and Ransom and Pope (1992) by the episodic absence duration from Gilliland et al. (2001). Thus, each Gilliland et al. period of absence is converted into 1.6 absence days. Following advice from the National Research Council (NRC, 2002), we calculated reductions in school absences for the full population of school age children, ages five to 17. This is consistent with recent peer-reviewed literature on estimating the impact of ozone exposure on school absences (Hall et al., 2003). We estimate d the change in school absences using both Chen et al. (2000) and Gilliland et al. (2001) and then pooled the results using the random effects pooling procedure.
Acute Bronchitis. Approximately 4% of U.S. children between the ages of 5 and 17 experience episodes of acute bronchitis annually (ALA, 2002). Acute bronchitis is characterized by coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a number of days. According to the MedlinePlus medical encyclopedia, 149 

### with the exception of cough, most acute bronchitis symptoms abate within 7 to 10 days. Incidence of episodes of acute bronchitis in children between the ages of 5 and 17 were estimated using an effect estimate developed from Dockery et al. (1996).

Work Loss Days. Health effects from air pollution can also result in missed days of work (either from personal symptoms or from ca ring for a sick family member). Days of work lost resulting from exposure to PM 2.5 

### were estimated using an effect estimate developed from Ostro (1987). Ostro (1987) estimated the impact of PM 2.5 

### on the incidence of work loss days in a national sample of the adult working population, ages 18 to 65 living in metropolitan areas. Ostro reported that two-week average PM 2.5 

### levels were significantly linked to work loss days, but there was some year-to-year variability in the results.

149 See http://www.nlm.nih.gov/medlineplus/ency/article/001087.htm, accessed April 2012.

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6-52 6.6.3.6 Nonfatal Acute Myocardial Infarctions (AMI) (Heart Attacks)
Nonfatal heart attacks have been linked with short-term exposures to PM 2.5 

### in the United States (Mustafi ć et al., 2012; Peters et al., 2001; Sullivan et al., 2005; Pope et al., 2006; Zanobetti and Schwartz, 2006; Zanobetti et al., 2009) and other countries (Poloniecki et al., 1997; Barnett et al., 2006; Peters et al., 2005). In previous health impact assessments, we have relied upon a study by Peters et al. (2001) as the basis for the impact function estimating the relationship between PM 2.5 

### and nonfatal heart attacks. The Peters et al. (2001) study exhibits a number of strengths. In particular, it includes a robust characterization of populations experiencing AMIs. The researchers interviewed patients within 4 days of their AMI events and, for inclusion in the study, patients were required to meet a series of criteria including minimum kinase levels, an identifiable onset of pain or other symptoms, and the ability to indicate the time, place and other characteristics of their AMI pain in an interview.

Since the publication of Peters et al. (2001), a number of other single and multi-city studies have appeared in the literature, including: Sullivan et al. (2005), which considered the
risk of PM 2.5 

### -related hospitalization for AMIs in King County, Washington; Pope et al. (2006), based in Wasatch Range, Utah; Zanobetti and Schwartz (2006), based in Boston, Massachusetts; and, Zanobetti et al. (2009), a multi-city study of 26 U.S. communities. Each of these single and multi-city studies, with the exception of Pope et al. (2006), measure AMIs using hospital discharge rates. Conversely, the Pope et al. (2006) study is based on a large registry with angiographically characterized patients—arguably a more precise indicator of AMI. Because the Pope et al. (2006) study reflected both myocardial infarctions and unstable angina, this produces a more comprehensive estimate of acute ischemic heart disease events than the other studies. However, unlike the Peters study (Peters et al., 2006), Pope and colleagues did not measure the time of symptom onset, and PM 2.5 

### data were not measured on an hourly basis.

As a means of recognizing the strengths of the Peters study while also incorporating the newer evidence found in the four single and multi-city studies, we present a range of AMI estimates. The upper end of the range is calculated using the Peters study, while the lower end of the range is the result of an equal-weights pooling of these four newer studies. It is important to note that when calculating the incidence of nonfatal AMI, the fraction of fatal heart attacks is

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6-53 subtracted to ensure that there is no double-counting with premature mortality estimates. Specifically, we apply an adjustment factor in the C-R function to reflect the probability of surviving a heart attack. Based on recent data from the Agency for Healthcare Research and Quality’s Healthcare Utilization Project National Inpatient Sample database (AHRQ, 2009), we identified death rates for adults hospitalized with acute myocardial infarction stratified by age (e.g., 1.852% for ages 18–44, 2.8188% for ages 45–64, and 7.4339% for ages 65+). These rates show a clear downward trend over time between 1994 and 2009 for the average adult and replace the 7% survival rate previously applied across all age groups from Rosamond et al. (1999).

6.6.3.7 Worker Productivity
The EPA last quantified the effect of ozone on outdoor agricultural worker productivity in the final Regulatory Impact Analysis accompanying the Transport Rule (U.S. EPA, 2011c); that analysis reported the value of worker productivity in the core benefits analysis. That RIA applied information reported in Crocker and Horst (1981), which observed that reducing ozone by 10 percent translated to a 1.4 increase in income among outdoor citrus workers. The RIA accompanying the proposed Ozone NAAQS (US EPA, 2014c) noted that, due in part to the vintage of the data used in this study, the Agency omitted this analysis in subsequent RIA’s.

A recent study by Graff Zivin, and Neidell (2012) provides new evidence of the effect of ozone exposure on productivity among outdoor agricultural workers which we use in a supplemental analysis. Specifically, the study combined individual-level daily harvest rates foroutdoor agricultural workers on a 500-acre farm in the Central Valley of California, with groundlevel ozone data. The authors observed that a 10 ppb increase in work-day ozone concentrations (from 6:00 am to 3:00 pm) was associated with a 5.5% decrease in productivity among outdoor agricultural workers on a given day. Additional detail on the worker productivity supplemental analysis (including results) is presented in Appendix 6B (section 6B.8).

6.6.3.8 Unquantified Human Health Effects
Attaining a revised ozone NAAQS would reduce emissions of NOx and VOC. Although we have quantified many of the health benefits associated with reducing exposure to ozone and
PM 2.5 

### , as shown in Table 6-3, we are unable to quantify the health benefits associated with reducing direct exposure to NO 2 

### or VOC because of the absence of air quality modeling data for

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6-54 these pollutants. In addition, we are unable to quantify the effects of VOC emission reductions
on ambient PM 2.5 

### and associated health effects. Below we provide a qualitative description of these health benefits. In general, previous analyses have shown that the monetized value of these
additional health benefits is much smaller than ozone and PM 2.5 

### -related benefits (U.S. EPA, 2010a, 2010c, 2010d).

Epidemiological researchers have associated NO 2 

### exposure with adverse health effects in numerous toxicological, clinical and epidemiological studies, as described in the Integrated Science Assessment for Oxides of Nitrogen—Health Criteria (NO 2 

### ISA) (U.S. EPA, 2008b). The
NO 2 

### ISA provides a comprehensive review of the current evidence of health and environmental
effects of NO 2 

### . The NO 2 

### ISA concluded that the evidence “is sufficient to infer a likely causal relationship between short-term NO 2 

### exposure and adverse effects on the respiratory system.” These epidemiologic and experimental studies encompass a number of endpoints including emergency department visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway inflammation, and lung function. Effect estimates from epidemiologic studies conducted in the United States and Canada generally indicate a 2– 20 percent increase in risks for ED visits and hospital admissions and higher risks for respiratory symptoms. The NO 2 

### ISA concluded that the relationship between short-term NO 2 

### exposure and premature mortality was “suggestive but not sufficient to infer a causal relationship” because it is difficult to attribute the mortality risk effects to NO 2 

### alone. Although the NO 2 

### ISA stated that studies consistently reported a relationship between NO 2 

### exposure and mortality, the effect was generally smaller than that for other pollutants such as PM. We did not quantify these benefits
due to data constraints.

6.6.4 Economic Valuation Estimates
Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects for a large population. Therefore, the appropriate economic measure is willingness-to-pay (WTP) for changes in risk of a health effect rather than WTP for a health effect that would occur with certainty (Freeman, 1993). Epidemiological studies generally provide estimates of the relative risks of a particular health effect that is avoided because of a reduction in air pollution. We converted those changes in risk to units of avoided statistical

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6-55 incidence for ease of presentation. We calculated the value of avoided statistical incidences by dividing individual WTP for a risk reduction by the related observed change in risk. For example, suppose a measure is able to reduce the risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then the WTP for an avoided statistical premature mortality amounts to $1 million ( $100/0.0001 change in risk). Using this approach, the size of the affected population is automatically taken into account by the number of incidences predicted by epidemiological studies applied to the relevant population. The same type of calculation can produce values for statistical incidences of other health endpoints.

WTP estimates generally are not available for some health effects, such as hospital admissions. In these cases, we instead used the cost of treating or mitigating the effect to estimate the economic value. Cost-of-illness (COI) estimates generally (although not necessarily in all cases) understate the true value of reducing the risk of a health effect, because they reflect the direct expenditures related to treatment, but not the value of avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987).

We provide unit values for health endpoints (along with information on the distribution of the unit value) in Table 6-10. All values are in constant year 2011$, adjusted for growth in real income for WTP estimates out to 2024 using projections provided by Standard and Poor’s, which
is discussed in further detail below. 150 

### Economic theory argues that WTP for most goods (such as environmental protection) will increase if real income increases. Several of the valuation studies used in this analysis were conducted in the late 1980s and early 1990s, and we are in the process of reviewing the literature to update these unit values. The discussion below provides additional details on valuing specific PM 2.5 

### -related related endpoints.

150 Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038

estimates use income growth through 2024 and are therefore likely underestimates. Currently, BenMAP does nothave an inflation adjustment to 2011$. We ran BenMAP for a currency year of 2010$ and calculated the benefitper-ton estimates in 2010$. We then adjusted the resulting benefit-per-ton estimates to 2011$ using the Consumer Price Index (CPI-U, all items). This approach slightly underestimates the inflation for medical index and wage index between 2010 and 2011, which affects COI estimates and wage-based estimates.

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6-56 6.6.4.1 Mortality Valuation
Following the advice of the SAB’s Environmental Economics Advisory Committee (SAB-EEAC), the EPA currently uses the value of statistical life (VSL) approach in calculating the core estimate of mortality benefits, because we believe this calculation provides the most reasonable single estimate of an individual’s willingness to trade off money for reductions in mortality risk (U.S. EPA-SAB, 2000). The VSL a pproach is a summary measure for the value of small changes in mortality risk experienced by a large number of people. For a period of time (2004–2008), the Office of Air and Radiation (OAR) valued mortality risk reductions using a VSL estimate derived from a limited analysis of some of the available studies. OAR arrived at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta-analyses of the wage-risk literature. The $1 million value represented the lower end of the interquartile range from the Mrozek and Taylor (2002) meta-analysis of 33 studies. The $10 million valuerepresented the upper end of the interquartile range from the Viscusi and Aldy (2003) metaanalysis of 43 studies. The mean estimate of $5.5 million (2000$) was also consistent with the mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. However, the EPA neither changed its official guidance on the use of VSL in rule-makings nor subjected the interim estimate to a scientific pee r-review process through SAB or other peer-review group.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***



## 6-57Table 6-10. Unit Values for Economic Valuation of Health Endpoints (2011$)aHealthEndpointCentralEstimateofValuePerStatisticalIncidenceDerivationofDistributionsofEstimates1990IncomeLevel2024IncomeLevelPrematureMortality(ValueofaStatisticalLife)$8,300,000$10,000,000TheEPAcurrentlyrecommendsacentralVSLof$4.8million(1990$,1990income)basedonaWeibulldistributionfittedto26publishedVSLestimates(5contingentvaluationand21labormarketstudies).Theunderlyingstudies,thedistributionparameters,andotherusefulinformationareavailableinAppendixBoftheEPA’sGuidelinesforPreparingEconomicAnalyses(U.S.EPA,2010e).NonfatalMyocardialInfarction(heartattack)3%discountrateAge0–24Age25–44Age45–54Age55–64Age65andover7%discountrateAge0–24Age25–44Age45–54Age55–64Age65andover$100,000$110,000$120,000$210,000$100,000$100,000$110,000$120,000$190,000$100,000$100,000$110,000$120,000$210,000$100,000$100,000$110,000$120,000$190,000$100,000Nodistributionalinformationavailable.Age‐specificcost‐of‐illnessvaluesreflectlostearningsanddirectmedicalcostsovera5‐yearperiodfollowinganonfatalMI.LostearningsestimatesarebasedonCropperandKrupnick(1990).DirectmedicalcostsarebasedonsimpleaverageofestimatesfromRusselletal.(1998)andWittelsetal.(1990).Lostearnings:CropperandKrupnick(1990).Presentdiscountedvalueof5yearsoflostearningsin2000$:ageofonset:at3%at7%25–44$9,000$8,00045–54$13,000$12,00055–65$77,000$69,000Directmedicalexpenses(2000$):Anaverageof:1.Wittelsetal.(1990)($100,000—nodiscounting)2.Russelletal.(1998),5‐yearperiod($22,000at3%discountrate;$21,000at7%discountrate)(continued)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***



## 6-58Table 6-10. Unit Values for Economic Valuation of Health Endpoints (2011$)a(continued)HealthEndpointCentralEstimateofValuePerStatisticalIncidenceDerivationofDistributionsofEstimates2000IncomeLevel2024IncomeLevelHospitalAdmissionsChronicLungDisease(18–64)$22,000$22,000Nodistributionalinformationavailable.TheCOIestimates(lostearningsplusdirectmedicalcosts)arebasedonICD‐9code‐levelinformation(e.g.,averagehospitalcarecosts,averagelengthofhospitalstay,andweightedshareoftotalchroniclungillnesses)reportedinAgencyforHealthcareResearchandQuality(2007)(www.ahrq.gov).AsthmaAdmissions(0–64)$16,000$16,000Nodistributionalinformationavailable.TheCOIestimates(lostearningsplusdirectmedicalcosts)arebasedonICD‐9code‐levelinformation(e.g.,averagehospitalcarecosts,averagelengthofhospitalstay,andweightedshareoftotalasthmacategoryillnesses)reportedinAgencyforHealthcareResearchandQuality(2007)(www.ahrq.gov).AllCardiovascularAge18–64Age65–99$44,000$42,000$44,000$42,000Nodistributionalinformationavailable.TheCOIestimates(lostearningsplusdirectmedicalcosts)arebasedonICD‐9code‐levelinformation(e.g.,averagehospitalcarecosts,averagelengthofhospitalstay,andweightedshareoftotalcardiovascularcategoryillnesses)reportedinAgencyforHealthcareResearchandQuality(2007)(www.ahrq.gov).Allrespiratory(ages65+)$37,000$37,000Nodistributionsavailable.TheCOIpointestimates(lostearningsplusdirectmedicalcosts)arebasedonICD‐9codelevelinformation(e.g.,averagehospitalcarecosts,averagelengthofhospitalstay,andweightedshareoftotalrespiratorycategoryillnesses)reportedinAgencyforHealthcareResearchandQuality,2007(www.ahrq.gov).EmergencyDepartmentVisitsforAsthma$440$440Nodistributionalinformationavailable.SimpleaverageoftwounitCOIvalues(2000$):(1)$310,fromSmithetal.(1997)and(2)$260,fromStanfordetal.(1999).(continued)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***



## 6-59Table 6-10. Unit Values for Economic Valuation of Health Endpoints (2011$)a(continued)HealthEndpointCentralEstimateofValuePerStatisticalIncidenceDerivationofDistributionsofEstimates2000IncomeLevel2024IncomeLevelRespiratoryAilmentsNotRequiringHospitalizationUpperRespiratorySymptoms(URS)$35$32CombinationsofthethreesymptomsforwhichWTPestimatesareavailablethatcloselymatchthoselistedbyPopeetal.resultinsevendifferent“symptomclusters,”eachdescribinga“type”ofURS.AdollarvaluewasderivedforeachtypeofURS,usingmid‐rangeestimatesofWTP(IEc,1994)toavoideachsymptomintheclusterandassumingadditivityofWTPs.IntheabsenceofinformationsurroundingthefrequencywithwhicheachoftheseventypesofURSoccurswithintheURSsymptomcomplex,weassumedauniformdistributionbetween$9.2and$43(2000$).LowerRespiratorySymptoms(LRS)$22$21CombinationsofthefoursymptomsforwhichWTPestimatesareavailablethatcloselymatchthoselistedbySchwartzetal.resultin11different“symptomclusters,”eachdescribinga“type”ofLRS.AdollarvaluewasderivedforeachtypeofLRS,usingmid‐rangeestimatesofWTP(IEc,1994)toavoideachsymptomintheclusterandassumingadditivityofWTPs.ThedollarvalueforLRSistheaverageofthedollarvaluesforthe11differenttypesofLRS.Intheabsenceofinformationsurroundingthefrequencywithwhicheachofthe11typesofLRSoccurswithintheLRSsymptomcomplex,weassumedauniformdistributionbetween$6.9and$25(2000$).AsthmaExacerbations$56$60Asthmaexacerbationsarevaluedat$45perincidence,basedonthemeanofaverageWTPestimatesforthefourseveritydefinitionsofa“badasthmaday,”describedinRoweandChestnut(1986).ThisstudysurveyedasthmaticstoestimateWTPforavoidanceofa“badasthmaday,”asdefinedbythesubjects.Forpurposesofvaluation,anasthmaexacerbationisassumedtobeequivalenttoadayinwhichasthmaismoderateorworseasreportedintheRoweandChestnut(1986)study.Thevalueisassumedtohaveauniformdistributionbetween$16and$71(2000$).(continued)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***



## 6-60Table 6-10. Unit Values for Economic Valuation of Health Endpoints (2011$)a(continued)HealthEndpointCentralEstimateofValuePerStatisticalIncidenceDerivationofDistributionsofEstimates2000IncomeLevel2024IncomeLevelRespiratoryAilmentsNotRequiringHospitalization(continued)AcuteBronchitis$460$500Assumesa6‐dayepisode,withthedistributionofthedailyvaluespecifiedasuniformwiththelowandhighvaluesbasedonthoserecommendedforrelatedrespiratorysymptomsinNeumannetal.(1994).Thelowdailyestimateof$10isthesumofthemid‐rangevaluesrecommendedbyIEc(1994)fortwosymptomsbelievedtobeassociatedwithacutebronchitis:coughingandchesttightness.Thehighdailyestimatewastakentobetwicethevalueofaminorrespiratoryrestricted‐activityday,or$110(2000$).WorkLossDays(WLDs)Variable(U.S.median=$150)Variable(U.S.median=$150)Nodistributionavailable.Pointestimateisbasedoncounty‐specificmedianannualwagesdividedby52andthenby5—togetmediandailywage.U.S.Year2000Census,compiledbyGeolytics,Inc.(Geolytics,2002)SchoolLossDays$98$98Nodistributionavailable.Basedon(1)theprobabilitythat,ifaschoolchildstayshomefromschool,aparentwillhavetostayhomefromworktocareforthechild,and(2)thevalueoftheparent’slostproductivity.MinorRestrictedActivityDays(MRADs)$64$68MedianWTPestimatetoavoidoneMRADfromTolleyetal.(1986).Distributionisassumedtobetriangularwithaminimumof$22andamaximumof$83,withamostlikelyvalueof$52(2000$).RangeisbasedonassumptionthatvalueshouldexceedWTPforasinglemildsymptom(thehighestestimateforasinglesymptom—foreyeirritation—is$16)andbelessthanthatforaWLD.Thetriangulardistributionacknowledgesthattheactualvalueislikelytobeclosertothepointestimatethaneitherextreme.aAll estimates are rounded to two significant digits. Unrounded estimates in 2000$ are available in the Appendix J of the BenMAP user manual (U.S. EPA,2015). Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 estimates use income growth through 2024and are therefore likely underestimates. Currently, BenMAP does nothave an inflation adjustment to 2011$. We ran BenMAP for acurrency year of 2010$ andcalculated the benefit-per-ton estimates in 2010$. We then adjusted the resulting benefit-per-ton estimates to 2011$ using theConsumer Price Index (CPI-U, allitems). This approach slightly underestimates the inflation for medical index and wage index between 2010 and 2011, which affects COI estimates and wagebased estimates.

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6-61 During this time, the Agency continued work to update its guidance on valuing mortality risk reductions, including commissioning a report from meta-analytic experts to evaluate methodological questions raised by the EPA and the SAB on combining estimates from the various data sources. In addition, the Agency consulted several times with the SAB-EEAC on the issue. With input from the meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific, appropriate meta-analytic techniques to combine estimates from unique data sources and different studies, includ ing those using different methodologies (i.e., wage-risk and stated preference) (U.S. EPA-SAB, 2007).

Until updated guidance is available, the Agency determined that a single, peer-reviewed estimate applied consistently best reflects the SAB-EEAC advice it has received. Therefore, the Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) 151 

### while the Agency continues its efforts to update its guidance on this issue. This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation studies published between 1974 and 1991. The mean VSL across these studies is $4.8 million (1990$) or $6.3 million (2000$). 152 

### The Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to the SAB-EEAC’s specific recommendations. In the process, the Agency has identified a number of important issues to be considered in updating its mortality risk valuation estimates. These are detailed in a white paper on “Valuing Mortality Risk Reductions in Environmental Policy,” which underwent review by the SAB-EEAC. A meeting with the SAB on this paper was held on March 14, 2011 and formal recommendations were transmitted on July 29, 2011 (U.S. EPA-SAB, 2011). EPA is taking SAB’s recommendations under advisement.

The economics literature concerning the appropriate method for valuing reductions in premature mortality risk is still developing. The adoption of a value for the projected reduction in the risk of premature mortality is the subject of continuing discussion within the economics and

151 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), EPA retained the VSL endorsed

by the SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the near future.

152 In this analysis, we adjust the VSL to account for a different currency year (2011$) and to account for income

growth to 2024. After applying these adjustments to the $6.3 million value, the VSL is $10 million.

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6-62 public policy analysis communities. The EPA strives to use the best economic science in its analyses. Given the mixed theoretical finding and empirical evidence regarding adjustments to VSL for risk and population characteristics (e.g., Smith et al., 2004; Alberini et al., 2004; Aldy and Viscusi, 2008), we use a single VSL for all reductions in mortality risk.

Although there are several differences between the studies the EPA uses to derive a VSL
estimate and the ozone and PM 2.5 

### air pollution context addressed here, those differences in the affected populations and the nature of the risks imply both upward and downward adjustments. Table 6-11 lists some of these differences and the expected effect on the VSL estimate for air pollution-related mortality. In the absence of a comprehensive and balanced set of adjustment factors, the EPA believes it is reasonable to continue to use the $4.8 million (1990$) value adjusted for inflation and income growth over time while acknowledging the significant
limitations and uncertainties in the available literature.

Table 6-11. Influence of Applie d VSL Attributes on the Size of the Economic Benefits of Reductions in the Risk of Premature Death (U.S. EPA, 2006a) Attribute Expected Direction of Bias Age Uncertain, perhaps overestimate Life Expectancy/Health Status Uncertain, perhaps overestimate Attitudes Toward Risk Underestimate Income Uncertain Voluntary vs. Involuntary Uncertain, perhaps underestimate Catastrophic vs. Protracted Death Uncertain, perhaps underestimate



### The SAB-EEAC has reviewed many potential VSL adjustments and the state of the economics literature. The SAB-EEAC advised the EPA to “continue to use a wage-risk-based VSL as its primary estimate, including appropriate sensitivity analyses to reflect the uncertainty of these estimates,” and that “the only risk characteristic for which adjustments to the VSL can be made is the timing of the risk” (U.S. EPA-SAB, 2000). In developing our core estimate of the benefits of premature mortality reductions, we followed this advice.

For PM 2.5 

### -related premature mortality, we assume that there is a “cessation” lag between exposures and the total realization of changes in health effects. For PM 2.5 

### , we assumed that some of the incidences of premature mortality related to PM 2.5 

### exposures occur in a distributed fashion over the 20 years following exposure and discounted over the period between exposure and

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6-63 premature mortality. Although the structure of the lag is uncertain, the EPA follows the advice of the SAB-HES to assume a segmented lag structure characterized by 30 percent of mortality reductions in the first year, 50 percent over years 2 to 5, and 20 percent over the years 6 to 20
after the reduction in PM 2.5 

### (U.S. EPA-SAB, 2004c). To take this into account in the valuation of reductions in premature mortality, we discount the value of premature mortality occurring in future years using rates of 3 percent and 7 percent. 153 

### Changes in the cessation lag assumptions do not change the total number of estimated deaths but rather the timing of those deaths. As such, the monetized PM 2.5 

### co-benefits using a 7 percent discount rate are only approximately 10 percent less than the monetized benefits using a 3 percent discount rate. Further discussion of this topic appears in the EPA’s Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e).

For ozone, we acknowledge substantial uncertainty associated with specifying the lag for long-term respiratory mortality. As stated earlier, it is this uncertainty related to specifying a lag structure that prevented us monetizing these benefits. In the sensitivity analysis, we include both an assumption of zero lag and the PM lag structure (i.e., the SAB 20-year segmented lag). Inclusion of the zero lag reflects consideration of the possibility that the long-term respiratory mortality estimate primarily captures an accumulation of short-term mortality effects across the

ozone season. 154 

### The use of the 20-year segmented lag reflects consideration of advice provided by the SAB-HES (U.S. EPA-SAB, 2010a), where they state that, “[i]f Alternative estimates are derived using cohort mortality evidence, there is no evidence in the literature to support a different cessation lag between ozone and particulate matter. The HES therefore recommends using the same cessation lag structure and assumptions as for particulate matter when utilizing

153 The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the

federal government. To comply with OMB Circular A-4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003). A 3% discount reflects reliance on a “social rate of time preference” discounting concept. A 7% rate is consistent with an “opportunity cost of capital” concept to reflect the time value of resources directed to meet regulatory requirements.

154 The ozone HREA noted,: “The effect estimates used in modeling long-term O 3 -attributable mortality, utilize a

seasonal average of peak (1-hr maximum) measurements. These long-term exposure metrics can be viewed as long-term exposures to daily peak O 3 over the warmer months, as compared with annual average levels such as are used in long-term PM exposure calculations. This increases the need for care in interpreting these long-term O 3 -attributable mortality estimates together with the short-term O 3 -attributable mortality estimates, in order to avoid double counting.” (U.S. EPA, 2014b).

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6-64 cohort mortality evidence for ozone.” Monetized benefit estimates generated using both lag assumptions are presented as sensitivity analyses (see section 6.7.3.1).

Uncertainties Specific to Premature Mortality Valuation. The economic benefits associated with reductions in the risk of premature mortality are the largest category of monetized benefits in this RIA. In addition, in prior analyses, the EPA identified valuation of mortality-related benefits as the largest contributor to the range of uncertainty in monetized benefits (Mansfield et al., 2009). 155 

### Because of the uncertainty in estimates of the value of reducing premature mortality risk, it is important to adequately characterize and understand the various types of economic approaches available for valuing reductions in mortality risk. Such an assessment also requires an understanding of how alternative valuation approaches reflect that some individuals may be more susceptible to air pollution-induced mortality or reflect differences in the nature of the risk presented by air pollution relative to the risks studied in the
relevant economics literature.

The health science literature on air pollution indicates that several human characteristics affect the degree to which mortality risk affects an individual. For example, some age groups appear to be more susceptible to air pollution than others (e.g., the elderly and children). Health status prior to exposure also affects susceptibility. An ideal benefits estimate of mortality risk reduction would reflect these human characteristics, in addition to an individual’s WTP to improve one’s own chances of survival along with WTP to improve other individuals’ survival rates. The ideal measure would also take into account the specific nature of the risk reduction commodity that is provided to individuals, as well as the context in which risk is reduced. To measure this value, it is important to assess how reductions in air pollution reduce the risk of dying from the time that reductions take effect onward and how individuals value these changes. Each individual’s survival curve, or the probability of surviving beyond a given age, should shift as a result of an environmental quality improvement. For example, changing the current probability of survival for an individual also shifts future probabilities of that individual’s

155 This conclusion was based on an assessment of uncertainty based on statistical error in epidemiological effect

estimates and economic valuation estimates. Additional sources of model error such as those examined in the PM 2.5 mortality expert elicitation (Roman et al., 2008) may result in different conclusions about the relative contribution of sources of uncertainty.

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6-65 survival. This probability shift will differ across individuals because survival curves depend on such characteristics as age, health state, and the current age to which the individual is likely to
survive.

Although a survival curve approach provides a theoretically preferred method for valuing the benefits of reduced risk of premature mortality associated with reducing air pollution, the approach requires a great deal of data to implement. The economic valuation literature does not yet include good estimates of the value of this risk reduction commodity. As a result, in this analysis we value reductions in premature mortality risk using the VSL approach.

Other uncertainties specific to premature mortality valuation include the following:

 Across-study variation: There is considerable uncertainty as to whether the available literature on VSL provides adequate estimates of the VSL for risk reductions from air pollution reduction. Although there is considerable variation in the analytical designs and data used in the existing literature, the majority of the studies involve the value of risks to a middle-aged working population. Most of the studies examine differences in wages of risky occupations, using a hedonic wage approach. Certain characteristics of both the population affected and the mortality risk facing that population are believed to affect the average WTP to reduce the risk. The appropriateness of a distribution of WTP based on the current VSL literature for valuing the mortality-related benefits of reductions in air pollution concentrations therefore depends not only on the quality of the studies (i.e., how well they measure what they are trying to measure), but also on the extent to which the risks being valued are similar and the extent to which the subjects in the studies are similar to the population affected by changes in pollution concentrations.

 Level of risk reduction: The transferability of estimates of the VSL from the wage-risk studies to the context of this analysis rests on the assumption that, within a reasonable range, WTP for reductions in mortality risk is linear in risk reduction. For example, suppose a study provides a result that the average WTP for a reduction in mortality risk of 1/100,000 is $50, but that the actual mortality risk reduction resulting from a given pollutant reduction is 1/10,000. If WTP for reductions in mortality risk is linear in risk reduction, then a WTP of $50 for a reduction of 1/100,000 implies a WTP of $500 for a risk reduction of 1/10,000 (which is 10 times the risk reduction valued in the study). Under the assumption of linearity, the estimate of the VSL does not depend on the particular amount of risk reduction being valued. This assumption has been shown to be reasonable provided the change in the risk being valued is within the range of risks evaluated in the underlying studies (Rowlatt et al., 1998).

 Voluntariness of risks evaluated: Although job-related mortality risks may differ in several ways from air pollution-related mortality risks, the most important difference may be that job-related risks are incurred voluntarily, or generally assumed to be, whereas air

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6-66 pollution-related risks are incurred involuntarily. Some evidence suggests that people will pay more to reduce involuntarily incurred risks than risks incurred voluntarily (e.g., Lichtenstein and Slovic, 2006). If this is the case, WTP estimates based on wage-risk studies may understate WTP to reduce involuntarily incurred air pollution-related mortality risks.

 Sudden versus protracted death: A final important difference related to the nature of the risk may be that some workplace mortality risks tend to involve sudden, traumatic events, whereas air pollution-related risks tend to involve longer periods of disease and suffering prior to death. Some evidence suggests that WTP to avoid a risk of a protracted death involving prolonged suffering and loss of dignity and personal control is greater than the WTP to avoid a risk (of identical magnitude) of sudden death (e.g., Tsuge et al., 2005; Alberini and Scasny, 2011). To the extent that the mortality risks addressed in this assessment are associated with longer periods of illness or greater pain and suffering than are the risks addressed in the valuation literature, the WTP measurements employed in the present analysis would reflect a downward bias.

 Self-selection and skill in avoiding risk : Recent research (Shogren and Stamland, 2002) suggests that VSL estimates based on hedonic wage studies may overstate the average value of a risk reduction. This is because the risk-wage trade-off revealed in hedonic studies reflects the preferences of the marginal worker (i.e., that worker who demands the highest compensation for his risk reduction for a given job). This worker must have either a higher workplace risk than the average worker in a given occupation, a lower risk tolerance than the average worker in that occupation, or both. Conversely, the marginal worker should have a higher risk tolerance than workers employed in less-risky sectors. However, the risk estimate used in hedonic studies is generally based on average risk, so the VSL may be biased, in an ambiguous direction, because the wage differential and risk measures do not match.

 Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 2006) finds that because individuals reevaluate their baseline risk of death as they age, the marginal value of risk reductions does not decline with age as predicted by some lifetime consumption models. This research supports fi ndings in recent stated preference studies that suggest only small reductions in the value of mortality risk reductions with increasing age (e.g., Alberini et al., 2004).

6.6.4.2 Hospital Admissions and Emergency Department Valuation
In the absence of estimates of societal WTP to avoid hospital visits/admissions for specific illnesses, we derive COI estimates for use in the benefits analysis. The International Classification of Diseases (ICD) (WHO, 1977) code-specific COI estimates used in this analysis consist of estimated hospital charges and the estimated opportunity cost of time spent in the hospital (based on the average length of a hospital stay for the illness). We based all estimates of

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6-67 hospital charges and length of stays on statistics provided by the Agency for Healthcare Research and Quality’s Healthcare Utilization Project National Inpatient Sample (NIS) database (AHRQ, 2007). We estimated the opportunity cost of a day spent in the hospital as the value of the lost daily wage, regardless of whether the hospitalized individual is in the workforce. To estimate the lost daily wage, we divided the median weekly wage reported by the 2007 American Community Survey (ACS) by five and deflated the result to the correct currency year using the CPI-U “all items” (U.S. EPA, 2015). The resulting national average lost daily wage is $150 (2011$). The total cost-of-illness estimate for an ICD code-specific hospital stay lasting n days, then, was the mean hospital charge plus daily lost wage multiplied by n . In general, the mean length of stay has decreased since the 2000 database used in the previous version of BenMAP, while the mean hospital charge has increased. We provide the rounded unit values in 2011$ for the COI functions used in this analysis in Table 6-12.

Table 6-12. Unit Values for Hospital Admissions a

End Point ICD Codes Age Range Mean Hospital Charge (2011$) Mean Length of Stay (days) Total Cost of Illness (unit value in 2011$) min. max. HA, Chronic Lung Disease 490–496 18 64 $20,000 3.9 $22,000 HA, Asthma 493 0 64 $15,000 3.0 $16,000 HA, All Cardiovascular 390–429 18 64 $41,000 4.1 $44,000 HA, All Cardiovascular 390–429 65 99 $38,000 4.9 $42,000 HA, All Respiratory 460–519 65 99 $32,000 6.1 $37,000

a All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in Appendix J of the BenMAP user manual (U.S. EPA, 2015). 

### To value asthma emergency department visits, we used a simple average of two estimates from the health economics literature. The first estimate comes from Smith et al. (1997), who reported approximately 1.2 million asthma-related emergency department visits in 1987, at a total cost of $186 million (1987$). The average cost per visit that year was $155; in 2011$, that cost is $480 (using the CPI-U for medical care to adjust to 2011$). The second estimate comes from Stanford et al. (1999), wh o reported the cost of an average asthma-related emergency department visit based on 1996–1997 data at $400 (using the CPI-U for medical care to adjust to 2011$). A simple average of the two estimates yields a unit value of $440 (2011$).

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6-68 6.6.4.3 Nonfatal Myocardial Infarctions Valuation
We were not able to identify a suitable WTP value for reductions in the risk of nonfatal heart attacks. Instead, we use a COI unit value with two components: the direct medical costs and the opportunity cost (lost earnings) associated with the illness event. Because the costs associated with a myocardial infarction extend beyond the initial event itself, we consider costs incurred over several years. Using age-specific annual lost earnings estimated by Cropper and Krupnick (1990) and a 3% discount rate, we estimated a rounded present discounted value in lost earnings (in 2000$) over 5 years due to a myocardial infarction of $8,800 for someone between the ages of 25 and 44, $13,000 for someone between the ages of 45 and 54, and $75,000 for someone between the ages of 55 and 65. The rounded corresponding age-specific estimates of lost earnings (in 2000$) using a 7% discount rate are $7,900, $12,000, and $67,000, respectively. Cropper and Krupnick (1990) do not provide lost earnings estimates for populations under 25 or over 65. As such, we do not include lost earnings in the cost estimates for these age groups.

We found three possible sources in the literature of estimates of the direct medical costs of myocardial infarction, which provide significantly different values (see Table 6-13):

 Wittels et al. (1990) estimated expected total medical costs of myocardial infarction over 5 years to be $51,000 (rounded in 1986$) for people who were admitted to the hospital and survived hospitalization. (There does not appear to be any discounting used.) This estimated cost is based on a medical cost model, which incorporated therapeutic options, projected outcomes, and prices (using “knowled geable cardiologists” as consultants). The model used medical data and medical decision algorithms to estimate the probabilities of certain events and/or medical procedures being used. The authors note that the average length of hospitalization for acute myocardial infarction has decreased over time (from an average of 12.9 days in 1980 to an average of 11 days in 1983). Wittels et al. used 10 days as the average in their study. It is unclear how much further the length of stay for myocardial infarction may have decreased from 1983 to the present. The average length of stay for ICD code 410 (myocardial infarction) in the year-2000 Agency for Healthcare Research and Quality (AHRQ) HCUP database is 5.5 days (AHRQ, 2000). However, this may include patients who died in the hospital (not included among our nonfatal myocardial infarction cases), and as a result whose length of stay was substantially shorter.

 Eisenstein et al. (2001) estimated 10-year costs of $45,000 in rounded 1997$ (using a 3% discount rate) for myocardial infarction patients, using statistical prediction (regression) models to estimate inpatient costs. Only inpatient costs (physician fees and hospital costs) were included.

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6-69 Table 6-13. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart Attacks a Study Direct Medical Costs (2011$) Over an x-Year Period, for x = Wittels et al. (1990) $170,000 b 5 Russell et al. (1998) $34,000 c 5 Average (5-year) costs $100,000 5 Eisenstein et al. (2001) $76,000 c 10

a All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in appendix J of the BenMAP user manual (U.S. EPA, 2015). b Wittels et al. (1990) did not appear to discount costs incurred in future years.

c Using a 3% discount rate. Discounted values as reported in the study. 

### As noted above, the estimates from these three studies are substantially different, and wehave not adequately resolved the sources of differences in the estimates. Because the wagerelated opportunity cost estimates from Cropper and Krupnick (1990) cover a 5-year period, we used estimates for medical costs that similarly cover a 5-year period (i.e., estimates from Wittels et al. (1990) and Russell et al. (1998). We used a simple average of the two 5-year estimates, or rounded to $85,000, and added it to the 5-year opportunity cost estimate. The resulting estimates are given in Table 6-14.

Table 6-14. Estimated Costs Over a 5-Year Period of a Nonfatal Myocardial Infarction (in 2011$) a Age Group Opportunity Cost Medical Cost b Total Cost 0–24 $0 $100,000 $100,000 25–44 $12,000 c $100,000 $110,000 45–54 $18,000 c $100,000 $120,000 55–65 $100,000 c $100,000 $210,000 > 65 $0 $100,000 $100,000

a All estimates rounded to two significant digits, so estimates may not sum across columns. Unrounded estimates in 2000$ are available in appendix J of the BenMAP user manual (U.S. EPA, 2015).
b An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998).

c From Cropper and Krupnick (1990), using a 3% discount rate for illustration. 

### 6.6.4.4 Valuation of Acute Health Events

Asthma Exacerbation Valuation. Several respiratory symptoms in asthmatics or characterizations of an asthma episode have been associated with exposure to air pollutants. All of these can generally be taken as indications of an asthma exacerbation when they occur in an asthmatic. Therefore, we apply the same set of unit values for all of the variations of “asthma exacerbation”. Specifically, we use a unit value based on the mean WTP estimates for a “bad asthma day,” described in Rowe and Chestnut (1986). This study surveyed asthmatics to estimate WTP for avoidance of a “bad asthma day,” as defined by the subjects.

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6-70 Minor Restricted Activity Days Valuation. No studies are reported to have estimated WTP to avoid a minor restricted activity day. However, Neumann et al. (1994) derived an estimate of willingness to pay to avoid a minor respiratory restricted activity day, using estimates from Tolley et al. (1986) of WTP for avoiding a combination of coughing, throat congestion and sinusitis. This estimate of WTP to avoid a minor respiratory restricted activity day is $38 (1990$), or about $71 (2011$). Although Ostro and Rothschild (1989) statistically linked ozone and minor restricted activity days, it is likely that most MRADs associated with ozone and PM 2.5 

### exposure are, in fact, minor respiratory restricted activity days. For the purpose of valuing this health endpoint, we used the estimate of mean WTP to avoid a minor respiratory restricted activity day.

School Loss Days Valuation. To value a school absence, we: (1) estimated the probability that if a school child stays home from school, a parent will have to stay home from work to care for the child; and (2) valued the lost productivity at the parent’s wage. To do this, we estimated the number of families with school-age children in which both parents work, and we valued a school-loss day as the probability that such a day also would result in a work-loss day. We calculated this value by multiplying the proportion of households with school-age children by a measure of lost wages.

We used this method in the absence of a preferable WTP method. However, this approach suffers from several uncertainties. First, it omits willingness to pay to avoid the symptoms/illness that resulted in the school absence; second, it effectively gives zero value to school absences that do not result in work-loss days; and third, it uses conservative assumptions about the wages of the parent staying home with the child. Finally, this method assumes that parents are unable to work from home. If this is not a valid assumption, then there would be no lost wages. For this valuation approach, we assumed that in a household with two working parents, the female parent will stay home with a sick child. From the Statistical Abstract of the United States (U.S. Census Bureau, 2001), we obtained: (1) the numbers of single, married and “other” (widowed, divorced or separated) working women with children; and (2) the rates of participation in the workforce of single, married and “other” women with children. From these two sets of statistics, we calculated a weighted average participation rate of 72.85 percent. Our

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6-71 estimate of daily lost wage (wages lost if a mother must stay at home with a sick child) is based on the year 2000 median weekly wage among women ages 25 and older (U.S. Census Bureau, 2001). This median weekly wage is $551 (2000$). Dividing by five gives an estimated median daily wage of $103 (2000$). To estimate the expected lost wages on a day when a mother has to stay home with a school-age child, we first estimated the probability that the mother is in the workforce then multiplied that estimate by the daily wage she would lose by missing a workday: 72.85 percent times $103, for a total loss of $75 (2000$). This valuation approach is similar to that used by Hall et al. (2003).

Work Loss Days Valuation. Work loss days are valued at a day’s wage. BenMAP-CE calculates county-specific median daily wages from county-specific annual wages (by dividing the annual wage by 52 weeks multiplied by 5 work days per week), on the theory that a worker’s vacation days are valued at the same daily rate as work days.

Upper and Lower Respiratory Symptoms Valuation. Lower and upper respiratory symptoms are each considered a complex of symptoms. A dollar value was derived for clusters of these symptoms that most closely match the studies used to calculate incidence (Schwartz and Neas, 2000; Pope et al., 1991) based on mid-range estimates from each cluster (IEc, 1994).

6.6.4.5 Growth in WTP Reflecting National Income Growth over Time
Our analysis accounts for expected growth in real income over time. This is a distinct concept from inflation and currency year. Economic theory argues that WTP for most goods (such as environmental protection) will increase if real incomes increase. There is substantial empirical evidence that the income elasticity 156 

### of WTP for health risk reductions is positive, although there is uncertainty about its exact value. Thus, as real income increases, the WTP for environmental improvements also increases. Although many analyses assume that the income elasticity of WTP is unit elastic (i.e., a 10% higher real income level implies a 10% higher WTP to reduce risk changes), empirical evidence suggests that income elasticity is substantially less

156 Income elasticity is a common economic measure equal to the percentage change in WTP for a 1 percent change

in income.

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6-72 than one and thus relatively inelastic. As real income rises, the WTP value also rises but at a
slower rate than real income.

The effects of real income changes on WTP estimates can influence benefits estimates in two different ways: through real (national average) income growth between the year a WTP study was conducted and the year for which benefits are estimated, and through differences inincome between study populations and the affected populations at a particular time. The SABEEAC advised the EPA to adjust WTP for increases in real income over time but not to adjust WTP to account for cross-sectional income differences “because of the sensitivity of making such distinctions, and because of insufficient evidence available at present” (U.S. EPA-SAB, 2000). An advisory by another committee associated with the SAB, the Advisory Council on Clean Air Compliance Analysis (SAB-Council), has provided conflicting advice. While agreeing with “the general principle that the willingness to pay to reduce mortality risks is likely to increase with growth in real income” and that “[t]he same increase should be assumed for the WTP for serious nonfatal health effects,” they note that “given the limitations and uncertainties in the available empirical evidence, the Council does not support the use of the proposed adjustments for aggregate income growth as part of the primary analysis” (U.S. EPA-SAB, 2004b). More recently, in response to questions related to the adjustment for income growth over time, the SAB-EEAC noted that “EPA should adjust for changes in income in evaluating benefits of risk reduction” (U.S. EPA-SAB, 2011). Until these conflicting advisories can be reconciled, the EPA will continue to adjust valuation estimates to reflect income growth using the methods described below, while providing sensitivity analyses for alternative income growth adjustment
factors.

Based on a review of the available income elasticity literature, we adjusted the valuation of human health benefits upward to account for projected growth in real U.S. income. Faced with a dearth of estimates of income elasticities derived from time-series studies, we applied estimates derived from cross-sectional studies in our analysis. Details of the procedure can be found in Kleckner and Neumann (1999). We note that the literature has evolved since the publication of this memo and that an array of newer studies identifying potentially suitable income elasticity estimates are available (IEc, 2012). The EPA anticipates seeking a peer review in the near future of these studies and its approach to adjusting WTP estimates to account for changes in personal

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6-73 income. As such, these newer studies have not yet been incorporated into the benefits analysis. An abbreviated description of the procedure we used to account for WTP for real income growth between 1990 and 2024 is presented below.

Reported income elasticities suggest that the severity of a health effect is a primary determinant of the strength of the relationship between changes in real income and WTP. As such, we use different elasticity estimates to adjust the WTP for minor health effects, severe and chronic health effects, and premature mortality. Note that because of the variety of empirical sources used in deriving the income elasticities, there may appear to be inconsistencies in the magnitudes of the income elasticities relative to the severity of the effects (a priori one might expect that more severe outcomes would show less income elasticity of WTP). We have not imposed any additional restrictions on the empirical estimates of income elasticity. One explanation for the seeming inconsistency is the difference in timing of conditions. WTP for minor illnesses is often expressed as a short-term payment to avoid a single episode. WTP for major illnesses and mortality risk reductions are based on longer-term measures of payment (such as wages or annual income). Economic theo ry suggests that relationships become more elastic as the length of time grows, reflecting the ability to adjust spending over a longer time period (U.S. EPA, 2010e, p. A-9). Based on this theory, it would be expected that WTP for reducing long-term risks would be more elastic than WTP for reducing short-term risks. The relative magnitude of the income elasticity of WTP for visibility compared with those for health effects suggests that visibility is not as much of a necessity as health, thus, WTP is more elastic with respect to income. The elasticity values used to adjust estimates of benefits in 2024 are presented in Table 6-15.

Table 6-15. Elasticity Values Used to Account for Projected Real Income Growth a Benefit Category Central Elasticity Estimate Minor Health Effect 0.14 Severe and Chronic Health Effects 0.45 Premature Mortality 0.40

a Derivation of estimates can be found in Kleckner and Neumann (1999). COI estimates are not adjusted for income growth. 

### In addition to elasticity estimates, projections of real gross domestic product (GDP) and populations from 1990 to 2024 are needed to adjust benefits to reflect real per capita income growth. For consistency with the emissions and benefits modeling, we used national population

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6-74 estimates for the years 1990 to 1999 based on U.S. Census Bureau estimates (Hollman, Mulder, and Kallan, 2000). These population estimates are based on application of a cohort-component model applied to 1990 U.S. Census data projections (U.S. Bureau of Census, 2000). For the years between 2000 and 2024, we applied growth rates based on the U.S. Census Bureau projections to the U.S. Census estimate of nati onal population in 2000. We used projections of real GDP provided in Kleckner and Neumann (1999) for the years 1990 to 2010. 157 

### We used projections of real GDP (in chained 1996 dollars) provided by Standard and Poor’s (2000) for the years 2010 to 2024. 158



### Using the method outlined in Kleckner and Neumann (1999) and the population and income data described above, we calculated WTP adjustment factors for each of the elasticity estimates listed in Table 6-16. Benefits for each of the categories (minor health effects, severe and chronic health effects, premature mortality, and visibility) are adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor. For premature mortality, we applied the income adjustment factor specific to the analysis year, but we do not adjust for income growth over the 20-year cessation lag. Our approach could underestimate the benefits for the later years of the lag.

There is some uncertainty regarding the total costs of illness in the future. Specifically, the nature of medical treatment is changing, including a shift towards more outpatient treatment. Although we adjust the COI estimates for inflation, we do not have data to project COI estimates for the cost of treatment in the future or income growth over time, which leads to an inherent though unavoidable inconsistency between COI- and WTP-based estimates. This approach may under predict benefits in future years because it is likely that increases in real U.S. income would also result in increased COI (due, for example, to increases in wages paid to medical workers) and increased cost of work loss days and lost worker productivity (reflecting that if worker

157 U.S. Bureau of Economic Analysis, Table 2A—Real Gross Domestic Product (1997) and U.S. Bureau of Economic Analysis, The Economic and Budget Outlook: An Update , Table 4— Economic Projections for Calendar Years 1997 Through 2007 (1997) . Note that projections for 2007 to 2010 are based on average GDP growth rates between 1999 and 2007.

158 In previous analyses, we used the Standard and Poor’s projections of GDP directly. This led to an apparent

discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the relative growth rates for GDP derived from the Standard and Poor’s projections to the 2010 projected GDP based on the Bureau of Economic Analysis projections.

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6-75 incomes are higher, the losses resulting from reduced worker production would also be higher). In addition, cost-of-illness estimates do not include sequelae costs or pain and suffering, the value of which would likely increase in the future. To the extent that costs would be expected to increase over time, this increase may be partially offset by advancement in medical technology that improves the effectiveness of treatment at lower costs. For these reasons, we believe that the cost-of-illness estimates in this RIA may underestimate (on net) the total economic value of avoided health impacts.

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

Benefit Category 2024 Minor Health Effect 1.07 Severe and Chronic Health Effects 1.22 Premature Mortality 1.20

a Based on elasticity values reported in Table 6-15, U.S. Census population projections, and projections of real GDP per capita.



### 6.6.5 Benefit per Ton Estimates Used in Modeling PM 2.5 

### -Related Co-benefits
This section presents the benefit-per-ton estimates (dimensioned by mortality study and simulation year) used as inputs in generating PM 2.5 

### co-benefits estimates including (Table 6-17). Benefit-per-ton estimates were generated for each of the long-term exposure-related mortality studies used in generating core benefits estimates for this RIA including Krewski et al. (2009) and Lepeule et al. (2012) (see section 6.6.3.3). Estimates were available for 2025 and 2030, with those being used to model co-benefits for the 2025 scenario and post-2025 scenario, respectively. For additional detail on the approach used to generate PM 2.5 

### co-benefits estimates and the role played by these two types of inputs, see Section 6.4.4. 159

159 Sector-level NOx reductions (for each alternative standard level) were generated using methods described in

Chapter 4, section 4.2 and 4.3. As noted in section 5.4.4, NOx emissions reductions associated with alternative standard levels considered for this NAAQS review involved seven of the 17 sectors for which we had benefit-per ton values and consequently, the co-benefits PM 2.5 estimates are based on simulated benefits for those seven sectors.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***6-76Table 6-17. Summary of PM2.5Benefit-per-ton EstimatesaaBenefit-per-ton estimates reflect application of the 20-year segmented lag at either a 3% or 7% discount rate. In addition, separate sets of benefit-per-tonestimates were generate for 2025 and 2030, reflecting applicationof appropriate projected demographic and baseline incidence data.bBenefit-per-ton estimates for the non-specified source category were generated as a weighted average of values for the 17 source categories, with weightingbased on sector-specific NOx emissions for 2005 obtained from Fann et al. (2012b).air,locamotiveandmarinecementkilnscokeovensEGUpointelectricarcfurnacesferroalloysintegratedironandsteelironandsteelnon‐EGUpointothernon‐pointothernonroad onroadpulpandpaper refineriesresidentialwoodtaconiteminingoceangoingvesselsNon‐specifiedsourceb2025at7%socialdiscountKrewskietal.,2009 $7,200 $5,700 $10,000 $5,200 $9,600 $4,400 $13,000 $17,000 $6,300 $7,900 $7,000 $7,600 $3,700 $6,900 $14,000 $6,000 $2,000 $6,400Lepeuleetal.,2012 $16,000 $13,000 $24,000 $12,000 $22,000 $9,800 $30,000 $38,000 $14,000 $18,000 $16,000 $17,000 $8,500 $16,000 $31,000 $14,000 $4,600 $15,0002025at3%socialdiscountKrewskietal.,2009 $8,000 $6,300 $12,000 $5,800 $11,000 $4,800 $15,000 $19,000 $7,000 $8,700 $7,700 $8,400 $4,200 $7,700 $15,000 $6,600 $2,300 $7,100Lepeuleetal.,2012 $18,000 $14,000 $26,000 $13,000 $24,000 $11,000 $34,000 $42,000 $16,000 $20,000 $17,000 $19,000 $9,400 $17,000 $34,000 $15,000 $5,100 $16,0002030at7%socialdiscountKrewskietal.,2009 $7,800 $6,100 $11,000 $5,600 $10,000 $4,600 $14,000 $18,000 $6,800 $8,500 $7,600 $8,200 $4,000 $7,500 $15,000 $6,400 $2,300 $6,900Lepeuleetal.,2012 $18,000 $14,000 $25,000 $13,000 $23,000 $10,000 $32,000 $41,000 $15,000 $19,000 $17,000 $19,000 $9,100 $17,000 $33,000 $14,000 $5,100 $16,0002030at3%socialdiscountKrewskietal.,2009 $8,700 $6,800 $12,000 $6,200 $11,000 $5,100 $16,000 $20,000 $7,600 $9,400 $8,400 $9,100 $4,500 $8,300 $16,000 $7,100 $2,500 $7,700Lepeuleetal.,2012 $20,000$15,000$28,000$14,000$26,000$12,000$36,000$46,000$17,000$21,000$19,000$21,000$10,000$19,000$37,000$16,000$5,600 $17,000Long‐termmortalitystudyEmissionssector

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6-77 6.7 Benefits Results We estimated the benefits of attaining the revised and alternative ozone standard levels across the U.S. in 2025 except California. We estimated the benefits of attaining these standard levels in California in 2038. We report the 2025 and 2038 estimates separately because deriving a summed estimate would require us to calculate the Present Value (PV) of the stream of benefits occurring between those two years, which is not possible with the available data. Additional analyses (section 6.7.3) inform the interpretation of these core analyses.

Applying the impact and valuation functions described above to the estimated changes in ozone concentrations yields estimates of the changes in physical damages (e.g., premature deaths, cases of hospital admissions) and the associated monetary values for those changes. Not all known ozone and PM health effects could be quantified or monetized, and the monetized value of these unquantified effects is represented by adding an unknown “B” to the aggregate total. Values are rounded to two significant figures and so totals may not sum across columns or

rows.

6.7.1 Benefits of Attaining a Revised Ozone Standard in 2025
This section presents the avoided health impacts and monetized benefits of attaining a more stringent ozone standard in 2025. Table 6-18 shows the population-weighted air quality change for the revised and alternative standard levels averaged across the continental U.S. Table 6-19 summarizes the tons of NOx emissions required to simulate attainment of the revised and alternative standard levels (further differentiated by geographic region including east, west and California). Tables 6-20 through 6-25 present the benefits results for the standard levels analyzed. Table 6-25 summarizes total benefits by geographic region (including east, west minus California, and California).

In addition, Figure 6-4 presents a sensitivity analysis for short-term ozone-related benefits using additional C-R functions for premature mortality, and Figure 6-5 presents a sensitivity analysis for PM 2.5 

### -related co-benefits using additional C-R functions for premature mortality. See sections 6.6.3.2 and 6.6.3.3, respectively for additional discussion of the alternative effect estimates used in each sensitivity analysis.

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6-78 Table 6-18. Population-Weighted Air Quality Change for the Revised and Alternative Annual Primary Ozone Standards Relative to the Analytical Baseline in 2025 a

Standard Population

‐ Weighted Summer Season Ozone Concentration Change (8

‐ hour max) b 70 ppb 0.2574 65 ppb 1.278

a Because we used benefit-per-ton estimates for the PM 2.5 co-benefits, population-weighted PM 2.5 changes are not available. b Population weighting based on all ages (demographic used in modeling short-term exposure-related mortality for ozone) for 2025.



### Table 6-19. Sector-Specific NO x 

### Emissions Reductions for the Revised and Alternative Standard Levels a

a All values are tons of NOx reductions (75 ppb vs revised and alternative standard levels). Results are presented both for “CA NOx” (emissions in CA only – used in post-2025 scenario PM 2.5 cobenefits modeling) and “nonCA NOx” (emissions reductions outside of CA – used in 2025 scenario PM 2.5 cobenefits modeling). 

### CA NOX Emis Rdxn nonCA NOX Emis Rdxn CA NOX Emis Rdxn nonCA NOX Emis Rdxn Aircraft, locomotives and marine vessels

‐ ‐ ‐ ‐ Area sources
‐ 32,224 ‐ 69,576 Cement kilns

‐ 19,285 ‐ 31,963 Electricity Generating Units

‐ 47,507 ‐ 113,678 Industrial point sources
‐ 134,763 ‐ 319,484 Non

‐ road mobile sources
‐ 2,832 ‐ 8,791 On

‐ road mobile sources
‐ Pulp and paper facilities

‐ 42 ‐ 265 Refineries 3,134 ‐ 7,735 Residential wood combustion Unknown sector 51,000 46,542 99,500 862,803 TOTAL 51,000 286,330 99,500 1,414,296 Emissions Sector 70ppb 65ppb Revised and Alternative Standard Levels

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6-79 Table 6-20. Estimated Number of Avoide d Ozone-Related Health Impacts for the Revised and Alternative Standard Levels (Incremental to the Baseline) for the 2025 Scenario (nationwide benefits of attaining the standards in the U.S. except California) a, b

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. b All incidence estimates are based on ozone-only models unless otherwise noted. d The negative estimates at the 5 th percentile confidence estimates for these morbidity endpoints reflect the statistical power of the studies used to calculate these health impacts. These results do not suggest that reducing air pollution results will adversely affect health, but rather, that we are less confident in the magnitude of the expected benefits for this endpoint. 

### 70 ppb 65 ppb

Avoided Short-Term Mortality 96 490 (47 to 140) (240 to 740) 160 820 (86 to 240) (440 to 1,200) Avoided Long-term Respiratory Mortality 340 1,700 (110 to 560) (580 to 2,800) Avoided Morbidity 180 920 (-42 to 400) (-220 to 2,000) 510 2,700 (47 to 1,600) (250 to 8,300)

220,000 1,100,000 (-67,000 to 440,000) (-330,000 to 2,100,000) 450,000 2,200,000 (190,000 to 720,000) (920,000 to 3,500,000) 160,000 790,000 (57,000 to 360,000) (280,000 to 1,700,000) Health Effectb Revised and Alterative Standard Levels (95th percentile confidence intervals)

multi-city studies Smith et al. (2009) (all ages) Zanobetti and Schwartz (2008) (all ages)

multi-city study Jerrett et al. (2009) (30-99yrs) copollutants model (PM 2.5 

### )
Hospital admissions - respiratory ( a g e 65+ ) d 

### Emergency department visits for asthma (all ages) Asthma exacerbation (age 6-18) d


### Minor restricted-activity days (age 18-65) School Loss Days (age 5-17)

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6-80 Table 6-21. Total Monetized Ozone-Related Benefits for the Revised and Alternative Annual Ozone Standards (Incremental to the Baseline) for the 2025 Scenario (nationwide benefits of attaining the standards everywhere in the U.S. except California) (millions of 2011) a

a All benefits estimates are rounded to whole numbers with a maximum of two significant digits. The monetized value of the ozone-related morbidity benefits are included in the estimates shown in this table for each mortality study.



### Figure 6-4. Sensitivity Analysis for Short-Term Ozone-Related Mortality Benefits 70 ppb 65 ppb Avoided Short-Term Mortality - Core Analysis $1,000 $5,300 ($99 to $2,900) ($500 to $15,000) 1,700 8,700 ($160 to $4,800) ($800 to $24,000) Health Effectb (95th percentile confidence intervals)

multi-city studies Smith et al. (2009) (all ages) Zanobetti and Schwartz ( 2008) (all ages) Revised and Alterative Standard Levels

$0 $500 $1,000 $1,500 $2,000 $2,500 $3,000 $3,500 $4,000Millions(2011$)

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6-81 Table 6-22. Estimated Number of Avoided PM 2.5 

### -Related Health Impacts for the Revised and Alternative Annual Ozone Standards (I ncremental to the Baseline) for the 2025 Scenario (Nationwide Benefits of Attaining the Standards in the U.S. except California) a

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. Because these estimates were generated using benefit-per-ton estimates, confidence intervals are not available. In general, the 95 th percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). 

### 70ppb 65ppb Avoided PM2.5-related Mortality Krewski et al. (2009) (adult mortality age 30+) 220 1,100 Lepeule et al. (2012) (adult mortality age 25+) 500 2,500 Woodruff et al. (1997) (infant mortality) <1 2 Avoided PM2.5-related Morbidity Non-fatal heart attacks Peters et al. (2001) (age >18) 260 1,300 Pooled estimate of 4 studies (age >18) 28 140 Hospital admissions—respiratory (all ages) 66 330 Hospital admissions—cardiovascular (age > 18) 80 400 Emergency department visits for asthma (all ages) 120 600 Acute bronchitis (ages 8–12) 340 1,700 Lower respiratory symptoms (ages 7–14) 4,400 22,000 Upper respiratory symptoms (asthmatics ages 9–11) 6,300 31,000 Asthma exacerbation (asthmatics ages 6–18) 7,000 42,000 Lost work days (ages 18–65) 28,000 140,000 Minor restricted-activity days (ages 18–65) 170,000 830,000 Revised and Alterative Standard Levels Health Effectb

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6-82 Table 6-23. Monetized PM 2.5 

### -Related Health Co-Benefits for the Revised and Alternative Annual Ozone Standards (Incremental to Baseline) for th e 2025 Scenario (Nationwide Benefits of Attaining the St andards in the U.S. except California) (Millions of 2011) a,b,c

Monetized Benefits Revised and Alternative Standard Levels 70 ppb 65 ppb 3% Discount Rate Krewski et al. (2009) (adult mortality age 30+) $2,100 $10,000 Lepeule et al. (2012) (adult mortality age 25+) $4,700 $23,000 7% Discount Rate Krewski et al. (2009) (adult mortality age 30+) $1,900 $9,300 Lepeule et al. (2012) (adult mortality age 25+) $4,200 $21,000

a All estimates are rounded to two significant digits. Because these estimates were generated using benefit-per-ton estimates, confidence intervals are not available. In general, the 95th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). b The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure.



### Figure 6-5. Sensitivity Analysis Long-Term PM 2.5 

### -Related Mortality Co-Benefits $0 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 $7,000 $8,000Millions(2011$)



### Dollar benefit (7%) Dollar benefit (3%)

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6-83 Table 6-24. Estimated Monetized Ozone and PM 2.5 

### Benefits for Revised and Alternative Annual Ozone Standards Incremental to the Baseline for the 2025 Scenario (Nationwide Benefits of Attaining the Standards in the U.S. Except California) – Identified + Unidentified Control Strategies (combined) and Identified Control Strategies Only (billions of 2011$) a
Discount

Rate Revised and Alternative Standard Levels 70 ppb 65 ppb Identified + Unidentified Control Strategies Ozone-only Benefits (range reflects Smith et al. (2009) to Zanobetti and Schwartz (2008)) b $1.0 to $1.7 $5.3 to $8.7

PM 2.5 Co-benefits ( range reflects Krewski et al. (2009) to Lepeule et al. (2012) 3% $2.1 to $4.7 $10 to $23 7% $1.9 to $4.2 $9.3 to $21

Total Benefits 3% $3.1 to $6.4 +B $16 to $32 +B 7% $2.9 to $5.9 +B $15 to $30 +B Identified Control Strategies Only Ozone-only Benefits (range reflects Smith et al. (2009) to Zanobetti and Schwartz (2008)) b $0.86 to $1.4 $2.2 to $3.5

PM 2.5 Co-benefits ( range reflects Krewski et al. (2009) to Lepeule et al. (2012) 3% $1.7 to $3.9 $4.0 to $9.0 7% $1.6 to $3.5 $3.6 to $8.1 Total Benefits 3% $2.6 to $5.3 +B $6.1 to $12 +B 7% $2.4 to $4.9 +B $5.7 to $12 +B

a Rounded to two significant figures. The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. Mortality risk valuation for PM 2.5 assumes discounting over the SABrecommended 20-year segmented lag structure. Not all possible benefits are quantified and monetized in this analysis. B is the sum of all unquantified health and welfare co-benefits. Data limitations prevented us from quantifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that we were able to quantify. These estimates reflect the economic value of avoided morbidities and premature deaths using risk coefficients from the studies noted. b Ozone-only benefits reflect short-term exposure impacts and as such are assumed to occur in the same year as ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category.



### Table 6-25. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 2025 Scenario (Nationwide Benefits of Attaining the Standards in the U.S. except California) – Identified + Unidentified Control Strategies a, b
Region Revised and Alterative Standard Levels 70 ppb 65 ppb East c 98% 96% California ~0% ~0% Rest of West 2% 4%

a Because we use benefit-per-ton estimates to calculate the PM 2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits. b These regional breakdown results reflect application of identified and unidentified control strategies. Regional breakdown results are the same for benefits based on application of identified control strategies only.

c Includes Texas and those states to the north and east.

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6-84 6.7.2 Benefits of the Post-2025 Scenario
This section presents the estimated number and economic value of avoided ozone- and
PM 2.5 

### -related effects associated with attaining a revised ozone standard after 2025 (note, sectorspecific NOx emissions reductions levels used in modeling benefits for the post-2025 scenario are presented earlier in Table 6-19 - see entries under “CA NOx”). In addition, general trends and observations drawn from the sensitivity analyses presented in Figures 6-4 and 6-5 hold for the post-2025 scenario and for that reason separate plots for these sensitivity analyses (for the post-2025 scenario) are not presented (see section 6.7.3 for further discussion). While simulated attainment of standard levels for the 2025 scenario involved application of both identified andunidentified controls outside of California, simulated attainment of standard levels for the post2025 scenario involved application exclusively of unidentified controls within California (see Table 6-18 and section 2.2.2). For that reason, results tables in this section represent exclusively, benefits based on application of unidentified controls.

Table 6-26. Population-Weighted Air Quality Change for the Revised and Alternative Annual Primary Ozone Standards Rela tive to Baseline for Post-2025 a

Standard Population-Weighted Ozone Season Ozone Concentration Change (8-hour max) b 70 ppb 0.1708 65 ppb 0.3464

a Because we used benefit-per-ton estimates for the PM 2.5 co-benefits, population-weighted PM 2.5 changes are not available. b Population weighting based on all ages (demographic used in modeling short-term exposure-related mortality for ozone) for 2025.

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6-85 Table 6-27. Estimated Number of Avoided Ozone-Related Health Impacts for the Revised and Alternative Annual Ozone Stan dards (Incremental to the Baseline) for the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in California) a, b

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. b All incidence estimates are based on ozone-only models unless otherwise noted. d The negative estimates at the 5 th percentile confidence estimates for these morbidity endpoints reflect the statistical power of the studies used to calculate these health impacts. These results do not suggest that reducing air pollution results in additional health impacts. 

### 70 ppb 65 ppb

Avoided Short-Term Mortality 72 150 (35 to 110) (71 to 220) 120 240 (64 to 180) (130 to 350) Avoided Long-term Respiratory Mortality 290 590 (98 to 480) (200 to 970) Avoided Morbidity 140 270 (-32 to 300) (-65 to 610) 360 720 (33 to 1,100) (67 to 2,200)

160,000 330,000 (-49,000 to 320,000) (-100,000 to 650,000) 320,000 660,000 (130,000 to 510,000) (270,000 to 1,000,000) 120,000 240,000 (42,000 to 260,000) (85,000 to 530,000) Asthma exacerbation (age 6-18) d


### Minor restricted-activity days (age 18-65) School Loss Days (age 5-17) Hospital admissions - respiratory (age 65+) d 

### Emergency department visits for asthma (all ages) multi-city study Health Effectb Revised and Alterative Standard Levels (95th percentile confidence intervals)

multi-city studies Smith et al. (2009) (all ages) Zanobetti and Schwartz ( 2008) (all ages)
Jerrett et al. (2009) (30-99yrs) copollutants model (PM 2.5 

### )

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6-86 Table 6-28. Total Monetized Ozone-Only Benefits for the Revised and Alternative Annual Ozone Standards (Incremental to the Baseline) for the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in California) (millions of 2011) a

a All benefits estimates are rounded to whole numbers with a maximum of two significant digits. The monetized value of the ozone-related morbidity benefits are included in the estimates shown in this table for each mortality study 

### 70 ppb 65 ppb Avoided Short-Term Mortality - Core Analysis $790 $1,600 ($74 to $2,200) ($150 to $4,500) 1,300 2,600 ($120 to $3,600) ($240 to $7,200) multi-city studies Smith et al. (2009) (all ages) Zanobetti and Schwartz (2008) (all ages) Health Effectb (95th percentile confidence intervals) Revised and Alterative Standard Levels

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6-87 Table 6-29. Estimated Number of Avoided PM 2.5 

### -Related Health Impacts for the Revised and Alternative Annual Ozone Standards (I ncremental to the Baseline) for the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in California) a

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. Because these estimates were generated using benefit-per-ton estimates, confidence intervals are not available. In general, the 95 th percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). 

### 70 ppb 65 ppb Avoided PM2.5-related Mortality Krewski et al. (2009) (adult mortality age 30+) 43 84 Lepeule et al. (2012) (adult mortality age 25+) 98 190 Woodruff et al. (1997) (infant mortality) <1 <1 Avoided PM2.5-related Morbidity Non-fatal heart attacks Peters et al. (2001) (age >18) 51 100 Pooled estimate of 4 studies (age >18) 6 11 Hospital admissions—respiratory (all ages) 13 26 Hospital admissions—cardiovascular (age > 18) 16 31 Emergency department visits for asthma (all ages) 23 44 Acute bronchitis (ages 8–12) 64 130 Lower respiratory symptoms (ages 7–14) 820 1,600 Upper respiratory symptoms (asthmatics ages 9–11) 1,200 2,300 Asthma exacerbation (asthmatics ages 6–18) 1,900 3,600 Lost work days (ages 18–65) 5,300 10,000 Minor restricted-activity days (ages 18–65) 31,000 61,000 Revised and Alterative Standard Levels Health Effectb

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6-88 Table 6-30. Monetized PM 2.5 

### -Related Health Co-Benefits for the Revised and Alternative Annual Ozone Standards (Incremental to Baseline) fo r the Post-2025 Scenario (Nationwide Benefits of Attain ing the Standards just in California) (Millions of 2011$) a,b
Revised and Alterative Standard Levels Monetized Benefits 70 ppb 65 ppb 3% Discount Rate Krewski et al. (2009) (adult mortality age 30+) $400 $790 Lepeule et al. (2012) (adult mortality age 25+) $910 $1,800 7% Discount Rate Krewski et al. (2009) (adult mortality age 30+) $370 $710 Lepeule et al. (2012) (adult mortality age 25+) $820 $1,600

a All estimates are rounded to two significant digits. Because these estimates were generated using benefit-per-ton estimates, confidence intervals are not available. In general, the 95 th percentile confidence interval for monetized 

### PM 2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). b The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure.



### Table 6-31. Estimate of Monetized Ozone and PM 2.5 

### Benefits for Revised and Alternative Annual Ozone Standards Incremental to the Baseline for the Post-2025 Scenario (Nationwide Benefits of Attain ing the Standards just in California) – Identified + Unidentified Control Strategies (billions of 2011$) a
Discount

Rate Revised and Alternative Standard Levels 70 ppb 65 ppb Identified + Unidentified Control Strategies Ozone-only Benefits (range reflects Smith et al. (2009) to Zanobetti and Schwartz (2008)) b $0.79 to $1.3 $1.6 to $2.6

PM 2.5 Co-benefits ( range reflects Krewski et al. (2009) to Lepeule et al. (2012) 3% $0.40 to $0.91 $0.79 to $1.8 7% $0.37 to $0.82 $0.71 to $1.6

Total Benefits 3% $1.2 to $2.2 +B $2.4 to $4.4 +B 7% $1.2 to $2.1 +B $2.3 to $4.2 +B

a Rounded to two significant figures. The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. Mortality risk valuation for PM 2.5 assumes discounting over the SABrecommended 20-year segmented lag structure. Not all possible benefits are quantified and monetized in this analysis. B is the sum of all unquantified health and welfare co-benefits. Data limitations prevented us from quantifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that we were able to quantify. These estimates reflect the economic value of avoided morbidities and premature deaths using risk coefficients from the studies noted. b Ozone-only benefits reflect short-term exposure impacts and as such are assumed to occur in the same year as ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category.

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6-89 Table 6-32. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in California) – Identified + Unidentified Control Strategies a
Region Revised and Alterative Standard Levels 70 ppb 65 ppb East b 3% 2% California 90% 91% Rest of West 7% 7%

a Because we use benefit-per-ton estimates to calculate the PM 2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits. b Includes Texas and those states to the north and east.



### 6.7.3 Uncertainty in Benefits Results (including Results of Sensitivity Analyses and Supplemental Analyses)

Avoided ozone and PM 2.5 

### related premature deaths account for 94% to 96% of the total monetized benefits. This is true in part because we are unable to quantify many categories of benefits. The next largest benefit is for reducing the incidence of nonfatal heart attacks. The remaining categories each account for a small percentage of total monetized benefits; however, these outcomes occur among a significantly larger population. Comparing an incidence table to
the monetized benefits table reveals that the number of incidences avoided and the unit value for that endpoint do not always closely correspond. For example, for ozone we estimate almost 1,000 times more cases of exacerbated ozone would be avoided than premature deaths, yet cases of exacerbated asthma account for only a very small fraction (<1%) of total monetized benefits (see Table 6-20). This is because many of the less severe health effects, while more common, are valued at a lower level than the more severe health effects. Also, some effects, such as hospital admissions, are valued using a proxy measure of WTP. As such, the true value of these effects may be higher than that reported in the tables above. Sources of uncertainty associated with both the modeling of ozone-related benefits and PM 2.5 

### -related co-benefits are discussed qualitatively in Appendix 6A.

PM 2.5 

### mortality co-benefits represent a substantial proportion of total monetized benefits (over 98% of the co-benefits), and these estimates have the following key assumptions and
uncertainties.

 We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5

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6-90 produced varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differential effects estimates by particle type. The PM ISA, which was twice reviewed by SAB-CASAC, concluded that “many constituents of PM 2.5 

### can be linked with multiple health effects, and the evidence is not yet sufficient to allow differentiation of those constituents or sources that are more closely related to specific outcomes” (U.S. EPA, 2009b).

 We assume that the health impact function for fine particles is log-linear without a threshold in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM 2.5 

### , including both areas that do not meet the fine particle standard and those areas that are in attainment, down to the lowest modeled concentrations.

 We assume that there is a “cessation” lag between the change in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of premature mortality related to PM 2.5 

### exposures occur in a distributed fashion over the 20 years following exposure based on the advice of the SAB-HES (U.S. EPA-SAB, 2004c), which affects the valuation of mortality benefits at different discount rates.

 We recognize uncertainty associated with application of the benefit-per-ton approach used in modeling PM 2.5 

### co-benefits. The benefit-per-ton estimates used here reflect specific geographic patterns of emissions reductions and specific air quality and benefits modeling assumptions associated with the derivation of those estimates (see the TSD describing the calculation of the national benefit-per-ton estimates (U.S. EPA, 2013b) and Fann et al. (2012b). Consequently, these estimates may not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other localfactors associated with the current ozone NAAQS review. Therefore, use of these benefitper-ton values to estimate co-benefits may lead to higher or lower benefit estimates than if co-benefits were calculated based on direct air quality modeling.
6.7.3.1 Sensitivity Analyses
This RIA includes a number of sensitivity analyses. Given that the size of the monetized
benefits is sensitive to the number of estimated ozone and PM 2.5 

### related premature deaths, the sensitivity analyses focus largely on the mortalit y endpoint. Each sensitivity analysis is briefly described below, including an overview of the approach used in conducting the analysis and key observations. See Appendix 6B for more detailed results.

 Short-term ozone-exposure related mortality (alternative epidemiological studies and C-R functions): We estimated the number of premature deaths using seven additional effect estimates including four multi-city studies and three meta-analysis studies. This sensitivity analysis showed that the two core incidence and benefits

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6-91 estimates fall within (and towards the lower end of) the broader range resulting from application of the seven alternative effect estimates (see Figure 6-4).

 Economic value of avoided premature deaths from long-term exposure to ozone: We estimated the economic value of long-term ozone mortality using two cessation lag structures: 20-year segment lag (as used for PM 2.5 

### ) and a zero lag. The sensitivityanalysis suggests that if included in the core benefit estimate, long-term ozone exposurerelated mortality could add substantially to the overall benefits. Additionally, use of a 20- year segment lag can reduce benefits by 10-20% (relative to a zero lag) depending on the discount rate applied.

 Long-term ozone-exposure related respirat ory mortality and po tential thresholds: We evaluated the impact of several assumed thresholds ranging from 40-60 ppb. This sensitivity analysis suggested that a threshold of 50 ppb or greater could have a substantial impact on estimated benefits, while thresholds below this range have a relatively minor impact.

 Long-term PM 2.5 

### exposure-related mortality and alternative C-R functions (based on the Expert Elicitation): We applied the set of expert elicitation-based functions to generate an alternative set of PM 2.5 

### benefit estimates (see Figure 6-5). The estimates based on Krewski et al. (2009) and Lepeule et al. (2012) fall within the range of estimates based on the functions from the 2006 expert elicitation.

 Income elasticity for mortality and certain morbidity endpoints: We examined the impact of alternative assumptions regarding income elasticity (i.e., the degree to which WTP changes as income changes) and the degree of impact on WTP functions used for mortality and for morbidity endpoints. That sensitivity analysis suggests that alternative assumptions regarding income elasticity could result in a moderate impact on mortality benefits (values ranging from ~90% to ~130% of the core estimate depending on the assumption regarding elasticity). Income elasticity was found to have a far more modest impact on morbidity endpoints modeled using WTP functions.
6.7.3.2 Supplemental Analyses
In addition to the sensitivity analyses, we also included a number of supplemental analyses intended to examine other attributes of the core risk estimates. These are briefly described below with additional detail found in Appendix 6B.

 Ozone-related premature deaths by age: These analyses report: (a) the number of avoided ozone-related deaths by age range; (b) the estimated number of life years gained by age range; and (c) the percent reduction in baseline mortality by age range. We observe that about half of the ozone-attributable deaths occur in populations age 75–99, but half of the life years would occur in populations younger than 65. This is because the younger populations have the potential to lose more life years per death than older populations. The percent reduction in total all-cause mortality attributable to simulated

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6-92 attainment of both the revised and alternative standards are relatively small and fairly consistent across age ranges (see Appendix B, Table 6B-11). However, estimates of total ozone-attributable mortality associated with attainment of both standards is not evenly distributed across age ranges with the vast majority of projected avoided deaths occurring in older age groups (see Appendix B, Table 6B-9).

 Avoided deaths according to base line pollutant concentrations: We describe the summer season average 8-hour max ozone levels at which the avoided deaths occurred. We found that: (a) the vast majority of avoided short-term ozone-related deaths occur in grid cells with mean 8-hour max baseline levels between 35 and 55 ppb; and (b) virtually all of the avoided deaths are associated with ozone levels above the 20-40 ppb range, where we are less confident in the shape of the C-R function for ozone mortality (ozone ISA, section 2.5.4.4). For PM 2.5 

### mortality, we evaluated the lowest measured level (LML) associated with the epidemiological studies providing the effect estimates. We are less confident in specifying the shape of the C-R function at exposure levels below the range observed in the epidemiological study (i.e., below the LML). In the supplemental analysis, we found that, depending on the mortality study, between 67% and 93% of the mortality estimate is based on modeling involving PM 2.5 

### levels above the LML.

 Value of increased productivity among outd oor agricultural workers due to reduced exposure to ozone: Using information from the Graff Zivin and Neidell (2012) study, we estimate the economic value of improved productivity among outdoor non-livestock workers for the ozone standards in 2025. We estimate the monetized worker productivity benefits of attaining a 70 ppb standard would be about $1.7 million and a 65 ppb standard would yield monetized benefits of about $8.9 million.

6.8 Discussion This analysis demonstrates the potential for significant health benefits of the illustrative emissions controls applied to simulate attainment with the revised and alternative primary ozone standard levels. We estimate that by 2025, the emissions reductions to reach the revised and alternative standard levels everywhere except California, would have reduced the number of
ozone- and PM 2.5 

### -related premature mortalities and produce substantial non-mortality benefits. Furthermore, emissions reductions required to meet alternative standards in California post-2025 are also likely to produce substantial reductions in these same endpoints. This rule would also yield significant welfare impacts as well (see Chapter 7). Even considering the quantified and unquantified uncertainties identified in this chapter, we believe that the revised and alternative standards would have substantial public health benefits that are likely to outweigh the costs of the control strategies for the revised and alternative standard levels analyzed (see Chapter 4).

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6-93 Inherent in any complex RIA such as this one are multiple sources of uncertainty. Some of these we characterized through our quantification of statistical error in the C-R relationships and our use of alternate mortality functions. Others, including the projection of atmospheric conditions and source-level emissions, the projection of baseline morbidity rates, incomes and technological development are unquantified. When evaluated within the context of these uncertainties, the health impact and monetized benefits estimates in this RIA can provide useful information regarding the public health benefits associated with the revised and alternative
standard levels.

As discussed in Chapter 1, there are important differences worth noting in the design and analytical objectives of NAAQS RIAs compared to RIAs for implementation rules, such as the Tier 3 (U.S. EPA, 2014a). Settin g a NAAQS does not directly resu lt in costs or benefits. The NAAQS RIAs illustrate the potential costs and benefits of the revised and alternative air quality standards nationwide based on an array of emissions reduction strategies for different sources, incremental to implementation of existing regulations and controls needed to attain the current standards. In short, NAAQS RIAs hypothesize, but do not predict, the emissions reduction strategies that States may choose to enact when implementing a revised NAAQS, and as such, bycontrast, the emissions reductions from implementation rules are generally for specific, wellcharacterized sources, such as the recent MATS rule (U.S. EPA, 2011c). In general, the EPA is more confident in the magnitude and location of the emissions reductions for implementation rules. As such, emissions reductions achieved under promulgated implementation rules, such as MATS, have been reflected in the baseline of this NAAQS analysis. 160 

### For this reason, the benefits estimated provided in this RIA and all other NAAQS RIAs should not be added to the benefits estimated for implementation rules. Subsequent implementation rules will be reflected in the baseline for the next ozone NAAQS review.

In setting the NAAQS, the EPA considers that ozone concentrations vary over space and time. While the standard is designed to limit concentrations at the highest monitor in an area, it is understood that emissions controls put in place to reduce concentrations at the highest monitor will simultaneously result in lower ozone concentrations throughout the entire area. In fact, the

160 The full set of rules reflected in the baseline are presented in Chapter 2, Section 2.1.3.

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6-94 ozone HREA (U.S. EPA, 2014b) shows how different standard levels would affect the entire distribution of ozone concentrations, and thus people’s exposures and risk, across a selected set of urban areas. For this reason, it is inappropria te to use the NAAQS level as a bright line for
health effects.

The NAAQS are not set at levels that eliminate the risk of air pollution completely. Instead, the Administrator sets the NAAQS at a level requisite to protect public health with an adequate margin of safety, taking into consideration effects on susceptible populations based on the scientific literature. The risk analysis prepared in support of this ozone NAAQS reported risks below these levels, while acknowledging that the confidence in those effect estimates is higher at levels closer to the standard (U.S. EPA, 2014b). While benefits occurring below the standard may be somewhat more uncertain than those occurring above the standard, the EPA considers these to be legitimate components of the total benefits estimate. Though there are greater uncertainties at lower ozone and PM 2.5 

### concentrations, there is no evidence of a threshold
in short-term ozone or PM 2.5 

### -related health effects in the epidemiology literature. Given that the epidemiological literature in most cases has not provided estimates based on threshold models, there would be additional uncertainties imposed by assuming thresholds or other non-linear C-R functions for the purposes of benefits analysis.

The estimated benefits shown here are in addition to the substantial benefits estimated for several recent air quality rules (U.S. EPA, 2009a, 2011c, 2014a). Emissions reductions from rules such as Tier 3 will have substantially reduced ambient ozone concentrations by 2025 in the East, such that few additional controls would be needed to reach 70 ppb. These rules that have already been promulgated have tremendous combined benefits that explain why the number of avoided premature deaths associated with this NAAQS revision are smaller than were estimated in the previous ozone NAAQS RIA (U.S. EPA, 2006) for the year 2020 and even smaller than the mortality risks estimated for the current year in the ozone HREA (U.S. EPA, 2014b).

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U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2004a. Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Anal ysis—Benefits and Costs of the Clean Air Act, 1990–2020 . EPA-SAB-COUNCIL-ADV-04-002 . March. Available at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400433D5D/$File/counc il_adv_04002.pdf>.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2004b. Review of the Draft Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of the Clean Air Act, 1990-2020: An Advisory by the Advisory Council for Clean Air Compliance Analysis . EPA-SAB-COUNCIL-ADV-04-004. May. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/7CCBBFE15CD4C8B185256F17005E3079/$File/council_adv_04 004.pdf>.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2004c. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag . EPA-COUNCIL-LTR-05-001. December. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44B098DB49F3C85257170005293E0/$File/council_ltr_05_ 001.pdf>.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2007. SAB Advisory on EPA’s Issues in Valuing Mortality Risk Reduction . EPA-SAB-08-001. October. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/$File/sab-08-001.pdf>.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2009a. Review of Integrated Science Assessment for Particulate Matter (First External Review Draft, May 2009) . EPA-CASAC-09-008. Available at < <http://yosemite.epa.gov/sab/ sabproduct.nsf/264cb 1227d55e02c85 257402007446a4/73ACCA834AB44A10852 575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf >.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2009b. Review of Integrated Science Assessment for Particulate Matter (Second External Review Draft, July 2009) . EPA-CASAC-10-001. November. Available at

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6-105 <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/151B1F83B0231455 85257678006836B9/$File/EPA-CASAC-10-001-unsigned.pdf>.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010a. Review of EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air Act . EPA-COUNCIL-10-001. June. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/59e06b6c5ca66597852575 e7006c5d09!OpenDocument&TableRow=2.3#2.>.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010b . Review of Risk Assessment to Support the Review of the Particulate Matter (PM) Primary National Ambient Air Quality Standards—External Review Draft (September 2009). EPA-CASAC-10-003 . Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/BC1ECC5D539EF723852 57678006D5754/$File/EPA-CASAC-10-003-unsigned.pdf>.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2011. Review of Valuing Mortality Risk Reductions for Environmental Policy: A White Paper (December 10, 2010). EPA-SAB-11-011 July. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/$File/EPA-SAB-11- 011-unsigned.pdf>.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2012. CASAC Review of the EPA’s Health Risk and Exposure Assessment for Ozone (First External Review Draft - Updated August 2012) and Welfare Risk and Exposure Assessment for Ozone (First External Review Draft - Updated August 2012). U.S. Environmental Protection Agency Science Advisory Board. EPA-CASAC-13-002.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2014. CASAC Review of the EPA’s Health Risk and Exposure Assessment for Ozone (Second External Review Draft – February, 2014). U.S. Environmental Protection Agency Science Advisory Board. EPA-CASAC-14-005.
U.S. Environmental Protection Agency, 2006. Final Regulatory Impact Analysis: PM2.5 NAAQS. Prepared by Office of Air and Radiation. Available: http://www. epa.gov/ttn/ecas/ria.html [accessed 10 March 2008].
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6-106 World Health Organization (WHO). 2008. Part 1: Guidance Document on Characterizing and Communicating Uncertainty in Exposure Assessment, Harmonization Project Document No. 6. Published under joint sponsorship of the World Health Organization, the International Labour Organization and the United Nations Environment Programme. WHO Press: Geneva, Switzerland. Available at <www.who.int/ipcs/methods/harmonization/areas/uncertainty%20.pdf>.
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6A-1 APPENDIX 6A: COMPREHENSIVE CHARACTERIZATION OF UNCERTAINTY IN OZONE BENEFITS ANALYSIS

Overview As noted in Chapter 6, the benefits analysis re lies on an array of data inputs—including air quality modeling, health impact functions and valuation estimates among others—which are themselves subject to uncertainty and may contribute to the overall uncertainty in this analysis. The RIA employs a variety of analytic approaches designed to reduce the extent of the uncertainty and/or characterize the impact that uncertainty has on the final estimates. We strive to incorporate as many quantitative assessments of uncertainty as possi ble (e.g., Monte Carlo assessments, sensitivity analyses); however, there are some aspects we are only able to characterize qualitatively.

To more comprehensively and systematically a ddress these uncertainties, including those we cannot quantify, we adapt the World Health Organization (WHO) uncertainty framework (WHO, 2008), which provides a means for systematically linking the characterization of uncertainty to thesophistication of the underlying health impact assessment. EPA has applied similar approaches in peerreviewed analyses of PM 2.5 

### -related impacts (U.S. EPA, 2010a, 2011, 2012) and ozone-related impacts (U.S. EPA, 2014). EPA’s Science Advisory Board (SAB) has supported using a tabular format to qualitatively assess the uncertainties inherent in the quantification and monetization of health benefits, including identifying potential bias, potential magnitude, confidence in our approach, and the level of quantitative assessment of each uncertainty (U.S. EPA-SAB, 1999, 2001, 2004a, 2004b, 2011a, 2011b). The assessments presented here are largel y consistent with those previous peer-reviewed

assessments.

This appendix focuses on uncertainties inherent in the ozone benefits estimates. For more information regarding the uncer tainties inherent in the PM 2.5 

### benefits estimates, please see the 2012
PM 2.5 

### NAAQS RIA (U.S. EPA, 2012).

6A.1 Description of Classifications Applied in the Uncertainty Characterization Table 6A-1 catalogs the most significant sources of uncertainty in the ozone benefits analysis and then characterizes four dimensions of that uncertainty briefly described below. The first two dimensions focus on the nature of the uncertainty. The third and fourth dimensions focus on the extent

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6A-2 to which the analytic approach chosen in the bene fits analysis either minimizes the impact of the uncertainty or quantitatively characterizes its impact.

1) The direction of the bias that a given unc ertainty may introduce into the benefits assessment if not taken into acco unt in the analysis approach;
2) The magnitude of the impact that uncertainty is likely to have on the benefits estimate if not taken into account in the analysis approach;
3) The extent to which the analytic approach ch osen is likely to minimize the impact of that uncertainty on the benefits estimate; and
4) The extent to which EPA has been able to quantify the residual uncertainty after the preferred analytic approach has been incorporated into the benefits model.
6A.1.1 Direction of Bias

The “direction of bias” column in Table 6A-1 is an assessment of whether, if left unaddressed, an uncertainty would likely lead to an underestimate or overestimate of the total monetized benefits. In some cases we indicate that there are reasons why the bias might go either direction, depending upon the true nature of the underlying relationship. Wh ere available, we base the classification of the “direction of bias” on the analysis in the Integrated Science Assessment for Ozone and Related
Photochemical Oxidants (Ozone ISA) (U.S. EPA, 2013). Additional sources of information include
advice from SAB and the National Academies of Sc ience (NAS), as well as studies from the peerreviewed literature. In some cases we indicate that there is not su fficient information to estimate whether the uncertainty would likely lead to under or overestimation of benefits; these cases are
identified as “unable to determine.”

6A.1.2 Magnitude of Impact
The “magnitude of impact” column in Table 6A-1 is an assessment of how much plausible alternative assumptions ab out the underlying relationship about which we are uncertain could influence the overall monetary benefits. EPA has applied similar classifications in previous risk and benefitanalyses (U.S. EPA, 2010a, 2011, 2014), but we have slightly revised the category names and the cutoffs here. 161 

### The definitions used here are provided below.

161 In The Benefits and Costs of the Clean Air Act from 1990 to 2020 (U.S. EPA, 2011), EPA applied a classification of

“potentially major” if a plausible alternative assumption or approach could influence the overall monetary benefit estimate by five percent or more and “probably minor” if an alternative assumption or approach is likely to change the

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6A-3  High—if the uncertainty associated with an assumption could influence the total monetized benefits by more than 25%.

 Medium—if the uncertainty associated with an assumption could influence the total monetized benefits by 5% to 25%.

 Low—if the uncertainty associated with an assumption could influence the total monetized benefits by less than 5%.
For each uncertainty, we provide as much quantita tive information as is available in the table to support the classification.

Although many of the sources of uncertainty could affect both morbidity and mortality endpoints, because mortality benefits comprise over 94% of the monetized benefits that we are able to quantify in this analysis, uncertain ties that affect the mortality esti mate have the potential to have larger impacts on the total monetized benefits than uncertainties affecting only morbidity endpoints. One morbidity-related uncertainty that could have a significant impact on the benefits estimate is the extent to which omitted morbidity en dpoints are included in the benefi ts analysis. Including additional morbidity endpoints that are currently not monetized would reduce the fraction of total benefits from mortality. Ultimately, the magnitude classification is determined by professional judgment of EPA staff based on the results of available information, including other U.S. EPA assessments of uncertainty (U.S. EPA, 2010a, 2011).

Based on this assessment, the uncertainties that we classified as high or medium-high impact are: the causal relationship between long-term and short-term ozone exposure and mortality, the shape of the concentration-response (C-R) function for both categories of ozone-related mortality, and the mortality valuation, specifically for long-term exposure-related mortality.

6A.1.3 Confidence in Analytic Approach
The “confidence in analytic approach” column of Table 6A-1 is an assessment of the scientific support for the analytic approach chosen (or the inherent assumption made) to account for the relationship about which we are uncertain. In other words, based on the available evidence, how

total benefit estimate by less than five percent. In the Quantitative Health Risk Assessment for Particulate Matter (U.S. EPA, 2010b), EPA applied classifications of “low” if the impa ct would not be expected to impact the interpretation of risk estimates in the context of the PM NAAQS review, “ medium” if the impact had the potential to change the interpretation; and “high” if it was likely to influence the interpretation of risk in the context of the PM NAAQS review.

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6A-4 certain are we that EPA’s selected approach is the most plausible of the potential alternatives. Similar classifications have been included in previous risk and benefits analyses (U.S. EPA, 2010a, 2011). 162 

### The three categories used to characterize the degree of confidence are:

 High—the current evidence is plentiful and strongly supports the selected approach;

 Medium—some evidence exists to support the selected approach, but data gaps are present; and

 Low—limited data exists to su pport the selected approach.
Ultimately, the degree of confidence in the analytic approach is EPA staff’s professional judgment based on the volume and consistency of supporting evidence, much of which has been
evaluated in the O 3 

### ISA (U.S. EPA, 2013) and by SAB. The O 3 

### ISA evaluated the entire body of
scientific literature on ozone science and was twice peer-reviewed by EPA’s Clean Air Scientific Advisory Committee (CASAC). In general, we regard a conclusion in the O 3 

### ISA or specific advice from SAB as supporting a high degree of confidence in the selected approach.

Based on this assessment, we have low or low-me dium confidence in the evidence available to assess exposure error in epidemiology studies, morbidity valuation, baseline incidence projections for morbidity, and omitted morbidity endpoints. However, because these uncertainties have been classified as having a low or low-medium impact on the magnitude of the benefits, further investment in improving the available evidence would not have a substantial impact on the total monetized benefits.

6A.1.4 Uncertainty Quantification
The column of Table 6A-1 labeled “uncertainty quantification” is an assessment of the extent to which we were able to use quantita tive methods to characterize the resi dual uncertainty in the benefits analysis, after addressing it to the ex tent feasible in the analytic approach for this RIA. We categorize the level of quantification using the four tiers used in the WHO uncertainty framework. The WHO uncertainty framework is a well-established approach to assess uncertainty in risk estimates that systematically links the characterization of uncertainty to the sophistication of the health impact assessment. The advantage of using this framework is that it clearly highlights the level of uncertainty

162 We have applied the same classification as The Benefits and Costs of the Clean Air Act from 1990 to 2020 (U.S. EPA, 2011) in this analysis. In the Quantitative Health Risk Assessment for Particulate Matter (U.S. EPA, 2010b), EPA assessed the degree of uncertainty (low, medium, or high) associated with the knowledge-base (i.e., assessed how well we understand each source of uncertainty), but did no t provide specific criteria for the classification.

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6A-5 quantification applied in this asse ssment and the potential sources of uncertainty that require methods development in order to assess qua ntitatively. Specifically, EPA app lied this framework in multiple risk and exposure assessments (U.S. EPA, 2010a, 2014), and it has been recommended in EPA guidance documents assessing air toxics-related risk and Superfund site risks (U.S. EPA, 2004 and 2001, respectively). Ultimately, the tier decision is the professional judgment of EPA staff based on the availability of information for this assessment. The tiers used in this assessment are defined below.

 Tier 0—screening level, generic qualitative characterization.

 Tier 1—Scenario-specific qualitative characterization.

 Tier 2—Scenario-specific sensitivity analysis.

 Tier 3—Scenario-specific probabilistic assessment of individual and combined uncertainty.
Within the limits of the data, we strive to use mo re sophisticated approaches (e.g., Tier 2 or 3) for characterizing uncertainties that have the larges t magnitudes and could not be completely addressed through the analytic approach. The uncertainties for which we have conducted probabilistic (Tier 3) assessments in this analys is are mortality causality, the shape of the concentr ation-response function, and mortality and morbidity valuation. For lower magnitude uncertainties, we include qualitative discussions of the potential impact of uncertainty on risk results (WHO Tier 0/1) and/or completed sensitivity analyses assessing the pot ential impact of sources of uncer tainty on risk results (WHO Tier 2).

6A.2 Organization of the Qualitative Uncertainty Table Table 6A-1 is organized as follows: the uncertainties are grouped by category (i.e., concentration-response function, valuation, population and baseline incidence, omitted benefits categories, and exposure changes).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***6A-6Table 6A-1. Summary of Qualitative Uncertainty for Key Modeling Elements in Ozone BenefitsPotential Source ofUncertaintyDirection of Potential BiasMagnitude of Impact onMonetized BenefitsConfidence in Analytical Approach Uncertainty QuantificationUncertainties Associated with Concentration-Response FunctionsCausal relationshipbetween short-termozone exposure andpremature mortalityOverestimate, if short-term ozoneexposure does not have a causalrelationship with prematuremortality.High High Tier 1 (qualitative)Mortality generally dominatesmonetized benefits, so smalluncertainties could have largeimpacts on the total monetizedbenefits.Our approach is consistent with the O3ISA,which determined that premature mortalityhas a likely causal relationship with shortterm ozone exposure based on the collectivebody of evidence (p. 6-264). In addition, theNAS recommended that EPA “should givelittle or no weight to the assumption thatthere is no causal association betweenestimated reductions in premature mortalityand reduced ozone exposure” (NRC, 2008).In 2010, the Health Effects Subcommittee ofthe Advisory Council on Clean AirCompliance Analysis, while reviewingEPA’s The Benefits and Costs of the CleanAir Act 1990 to 2020 (U.S. EPA, 2011),also confirmed the NAS recommendation toinclude ozone mortality benefits (U.S. EPASAB, 2010).Either Medium-High Medium Tier 2 (sensitivity analysis)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***6A-7Potential Source ofUncertaintyDirection of Potential BiasMagnitude of Impact onMonetized BenefitsConfidence in Analytical Approach Uncertainty QuantificationShape of the C-Rfunctions, particularly atlow concentrations forshort-term ozoneexposure-relatedmortalityThe direction of bias that assuminga linear-no threshold model oralternative model introducesdepends upon the “true” functionalfrom of the relationship and thespecific assumptions and data in aparticular analysis. For example, ifthe true function identifies athreshold below which healtheffects do not occur, benefits maybe overestimated if a substantialportion of those benefits wereestimated to occur below thatthreshold. Alternately, if asubstantial portion of the benefitsoccurred above that threshold, thebenefits may be underestimatedbecause an assumed linear nothreshold function may not reflectthe steeper slope above thatthreshold to account for all healtheffects occurring above thatthreshold.The magnitude of this impactdepends on the fraction of benefitsoccurring in areas with lowerconcentrations. Mortality generallydominates monetized benefits, sosmall uncertainties could havelarge impacts on total monetizedbenefits.The O3ISA did not find any evidence thatsupports a threshold in the relationshipbetween short-term exposure to ozone andmortality within the range of ozoneconcentrations observed in the U.S., andrecent evidence suggests that the shape ofthe ozone-mortality C-R curve remainslinear across the full range of ozoneconcentrations (p. 6-257). Consistent withthe O3ISA, we assume a log-linear nothreshold model for the C-R functions forshort-term ozone mortality. However, theISA notes that there is less certainty in theshape of the C-R function below 20 ppb dueto the low density of data in this range (p. 6-254-255).The comparison of short-termmortality against the associateddistribution of (ozone seasonaveraged) 8-hour maximumozone concentrations (seeAppendix 6B, section 6B.7)suggests that the vast majorityof predicted reductions inmortality are associated withdays having 8-hour maximumconcentrations that fall withinthe higher confidence range.Causal relationshipbetween long-termozone exposure andpremature respiratorymortalityOverestimate, if long-term ozoneexposure does not have a causalrelationship with prematuremortality.Potentially High, if included inmonetized benefitsHigh Tier 1 (qualitative)Mortality generally dominatesmonetized benefits, so smalluncertainties could have largeimpacts on the total monetizedbenefits. However, we have notincluded long-term ozonemortality in the monetized benefitsfor this analysis due touncertainties in the cessation lag.While the O3ISA concludes that evidence issuggestive of a causal associationbetweentotal mortality and long-term ozoneexposure (section 7.7.1), specifically withregard to respiratory health effects(including mortality), the ISA concludes thatthere islikely to be a causal association(section 7.2.8).Shape of the C-Rfunctions, particularly atlow concentrations forlong-term ozoneexposure-relatedrespiratory mortalityEitherPotentially High, if included inmonetized benefitsMedium Tier 2 (sensitivity analysis)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***6A-8Potential Source ofUncertaintyDirection of Potential BiasMagnitude of Impact onMonetized BenefitsConfidence in Analytical Approach Uncertainty QuantificationThe direction of bias that assuminga linear-no threshold model oralternative model introducesdepends upon the “true” functionalform of the relationship and thespecific assumptions and data in aparticular analysis. For example, ifthe true function identifies athreshold below which healtheffects do not occur, benefits maybe overestimated if a substantialportion of those benefits wereestimated to occur below thatthreshold. Alternately, if asubstantial portion of the benefitsoccurred above that threshold, thebenefits may be underestimatedbecause an assumed linear nothreshold function may not reflectthe steeper slope above thatthreshold to account for all healtheffects occurring above thatthreshold.The magnitude of this impactdepends on the fraction of benefitsoccurring in areas with lowerconcentrations. However, we havenot included long-term ozonemortality in the monetized benefitsfor this analysis due touncertainties in the cessation lag.In their memo (see Sasser 2014) clarifyingthe results of their study (Jerrett et al., 2009)regarding long-term ozone exposure-relatedrespiratory mortality, the study authors notethat in terms of goodness of fit, long-termhealth risk models including ozone clearlyperformed better than models withoutozone, indicating the improved predictionsof respiratory mortality when ozone isincluded. In the article proper, the authorsstate that, “There was limited evidence thata threshold model specification improvedmodel fit as compared with a non-thresholdlinear model...”. Furthermore, in the memoreferenced above, the authors conclude thatconsiderable caution should be exercised inusing any specific threshold, particularlywhen the more stringent statistical testindicates there is no significantly improvedprediction. The CASAC was supportive ofthe approach EPA used in the HREA ofusing a non-threshold C-R function based onthis study to generate core estimates andconsider the impact of potential thresholds(ranging from 40-60 ppb) as a sensitivityanalysis.We examined potentialthresholds (from 40 to 60 ppb)in the C-R function for longterm exposure-related morality.That analysis suggested thatthresholds between 55 and 60ppb would have a substantialimpact on overall modeledbenefits, while thresholdsbelow 50 ppb would have aminor impact on predictedbenefits (see Appendix 6B,Table 6B-3).Exposure error inepidemiology studiesUnderestimate (generally) Medium Low-Medium Tier 1 (qualitative)The O3ISA states that exposuremeasurement error can also be animportant contributor touncertainty in effect estimatesassociated with both short-termand long-term studies (p. 1xii).Together with other factors (e.g.,low data density), exposure errorcan smooth the C-R functions andobscure potential thresholds (p.lxix). In addition, the O3ISA statesthat exposure error can bias effectestimates toward or away from thenull and widen confidenceintervals (p. lxii).Recent analyses reported inKrewski et al. (2009) demonstratethe potentially significant effectthat this source of uncertainty canhave on effect estimates. Theseanalyses also illustrate thecomplexity and site-specific natureof this source of uncertainty.Although this underestimation is welldocumented, including in the O3ISA, theSAB has not suggested an approach toadjust for this bias.(No quantitative methodavailable)Unknown Medium Medium Tier 1 (qualitative)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***6A-9Potential Source ofUncertaintyDirection of Potential BiasMagnitude of Impact onMonetized BenefitsConfidence in Analytical Approach Uncertainty QuantificationAdjustment of riskcoefficients to 8-hourmaximum from 24-houraverage or 1-hourmaximum in theepidemiology studiesWe converted these metrics tomaximum 8-hour average ozoneconcentration using standardconversion functions based onobserved relationships in theunderlying studies. If therelationships between air metricsreported in the studies differsystematically from therelationships seen across themodeling domain, then bias couldbe introduced.This conversion does not affect therelative magnitude of the healthimpact function. However, thepattern of 8-hour maximumconcentrations for a particularlocation over an ozone seasoncould differ from the pattern of 1-hour max or 24-hour averagemetrics for that same location.Consequently, monetized benefitscould differ for a particularlocation depending on the metricused in modeling benefits.This practice is consistent both with theavailable exposure modeling and with theform of the current ozone standard.However, in some cases, these conversionswere not specific to the ozone “warm”season, which introduces additionaluncertainty due to the use of effect estimatesbased on a mixture of warm season and allyear data in the epidemiological studies.(No quantitative methodavailable)Confounding byindividual risk factors,other thansocioeconomic status—e.g., smoking, orecologic factors, whichrepresent theneighborhood, such asunemploymentEither, depending on the factor andstudyMedium Medium Tier 2 (sensitivity analysis)Individual, social, economic, anddemographic covariates can biasthe relationship betweenparticulate air pollution andmortality, particularly in cohortstudies that rely on regional airpollution levels.Because mortality dominatesmonetized benefits, even a smallamount of confounding could havemedium impacts on totalmonetized benefits.To minimize confounding effects, we userisk coefficients that control for individualrisk factors to the extent practical.(Quantitative methodsavailable but not assessed inthis analysis.)Confounding and effectmodification by copollutantsEither, depending upon thepollutant.Medium Medium Tier 1 (qualitative)Disentangling the health responsesof combustion-related pollutants(i.e., PM, SOx, NOx, ozone, andCO) is a challenge. The PM ISAstates that co-pollutants maymediate the effects of PM or PMmay influence the toxicity of copollutants (U.S. EPA, 2009, p. 1–16). Alternately, effects attributedto one pollutants may be due toanother.Because this uncertainty couldaffect mortality and becausemortality generally dominatesmonetized benefits, even smalluncertainties could have mediumimpacts on total monetizedbenefits.The O3ISA states that there is highconfidence that unmeasured confounders arenot producing the findings when multiplestudies are conducted in various settingsusing different subjects or exposures, suchas multi-city studies (p. lxi). When modelingeffects of pollutants jointly (e.g., PM andO3), we apply multi-pollutant effectestimates when those estimates are availableto avoid double-counting and satisfy otherselection criteria. In addition, we applymulti-city effect estimates when available.(No quantitative methodavailable)Application of C-Rrelationships only to theoriginal studypopulationUnderestimate Low High Tier 2 (sensitivity analysis)Estimating health effects for onlythe original study population mayunderestimate the wholepopulation benefits of reductionsin pollutant exposures.Mortality generally dominatesmonetized benefits, so further agerange expansions for morbidityendpoints would have a smallimpact on total monetized benefits.Following advice from the SAB (U.S. EPASAB, 2004a, pg. 7) and NAS (NRC, 2002,pg. 114), we expanded the age range forchildhood asthma exacerbations beyond theoriginal study population to ages 6-18.(Quantitative methodsavailable but not assessed inthis analysis.)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***6A-10Potential Source ofUncertaintyDirection of Potential BiasMagnitude of Impact onMonetized BenefitsConfidence in Analytical Approach Uncertainty QuantificationUncertainties Associated with Economic ValuationMortality RiskValuation/Value-of-aStatistical-Life (VSL)Unknown High Medium Tier 3 (probabilistic)Some studies suggest that EPA’smortality valuation is too high,while other studies suggest that itis too low. Differences in age,income, risk aversion, altruism,nature of risk (e.g., cancer), andstudy design could lead to higheror lower estimates of mortalityvaluation.Mortality generally dominatesmonetized benefits, so moderateuncertainties could have a largeeffect on total monetized benefits.The VSL used by EPA is based on 26 labormarket and stated preference studiespublished between 1974 and 1991. EPA isin the process of reviewing this estimate andwill issue revised guidance based on themost up-to-date literature andrecommendations from the SAB-EEAC inthe near future (U.S. EPA, 2010b, U.S.EPA-SAB, 2011c).Assessed uncertainty inmortality valuation using aWeibull distribution.Cessation lag structurefor long-term ozonemortalityUnkonwnMedium, if included in themonetized benefitsLow Tier 2 (sensitivity analysis)We included both a zero (no) lagand 20-year segmented lag modelin completing the sensitivityanalysis involving dollar benefitsfor long-term respiratory mortality.Given that available informationdoes not lead to the selection of aparticular lag model, we are not ina position to classify the directionof potential bias associated withthis source of uncertainty.Although the cessation lag doesnot affect the number of prematuredeaths attributable to long-termozone exposure, it affects thetiming of those deaths and thus thediscounted monetized benefits.Mortality generally dominatesmonetized benefits, so moderateuncertainties could have a largeeffect on total monetized benefits.However, we have not includedlong-term ozone mortality in themonetized benefits for thisanalysis due to uncertainties in thecessation lag.As discussed in section 6.7.3.1, inpresenting dollar benefit estimates as part ofthe sensitivity analysis (presenting dollarbenefits for long-term ozone-relatedmorality), we include both an assumption ofzero lag and a lag structure matching thatused for the core PM2.5estimate (the SAB20-year segmented lag). Inclusion of thezero lag reflects consideration for thepossibility that the long-term respiratorymortality estimate captures primarily anaccumulation of short-term mortality effectsacross the ozone season. The use of the 20-year segmented lag reflects consideration foradvice provided by the SAB-HES (USEPASAB, 2010, 2004c).Using the 20-year segmentedlag developed for PM2.5–related mortality results in a10-20% reduction in the totaldollar benefit (using a 3% and7% discount rate, respectively)relative to the alternativeapproach of applying no lag(i.e., assuming all of themortality reductions occur inthe same year).Income growthadjustmentsEither Medium Medium Tier 2 (sensitivity analysis)Income growth increaseswillingness-to-pay (WTP)valuation estimates, includingmortality, over time. From 1997 to2010, personal income and GDPgrowth have begun to diverge. Ifthis trend continues, theassumption that per capita GDPgrowth is a reasonable proxy forincome growth may lead to anoverstatement of benefits. (IEc,2012).Income growth from 1990 to 2020increases mortality valuation by20%. Alternate estimates for thisadjustment vary by 20% (IEc,2012). Because we do not adjustfor income growth over the 20-year cessation lag, this approachcould also underestimate thebenefits for the later years of thelag.Consistent with SAB recommendations(U.S. EPA,-SAB, 2000, pg. 16), we adjustWTP for income growth.It is difficult toforecast future income growth; however, inthe absence of readily available income dataprojections, per capita GDP is the bestavailable option.As shown in Appendix 6B(section 6B.5), the use ofalternate income growthadjustments would result in anincrease of from 8 to 75% inthe dollar benefits for shortterm ozone-related mortality.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***6A-11Potential Source ofUncertaintyDirection of Potential BiasMagnitude of Impact onMonetized BenefitsConfidence in Analytical Approach Uncertainty QuantificationMorbidity valuationUnderestimate Low LowTier 3 (probabilistic), whereavailableMorbidity benefits such as hospitaladmissions are calculated usingcost-of-illness (COI) estimates,which are generally half the WTPto avoid the illness (Alberini andKrupnick, 2000). In addition, themorbidity costs do not reflectphysiological responses orsequelae events, such as increasedsusceptibility for future morbidity.Even if we doubled the monetizedvaluation of morbidity endpointsusing COI valuations that arecurrently included in the RIA, thechange would still be less than 5%of the monetized benefits. It isunknown how much includingsequelae events could increasemorbidity valuation.Although the COI estimates forhospitalizations reflect recent data, we havenot yet updated other COI estimates such asfor school loss days. The SAB concludedthat COI estimates could be used asplaceholders where WTP estimates areunavailable, but it is reasonable to presumethat this strategy typically understates WTPvalues (U.S. EPA-SAB, 2004b, pg. 3).Assessed uncertainty inmorbidity valuation usingdistributions specified in theunderlying literature, whereavailable (see Table 6-10).Uncertainties Associated with Baseline Incidence and Population ProjectionsPopulation estimatesand projectionsEither Low–Medium Medium Tier 1 (qualitative)The monetized benefits wouldchange in the same direction as theover- or underestimate inpopulation projections in areaswhere exposure changes.Monetized benefits aresubstantially affected bypopulation density. Comparisonsusing historical Census data showthat population projections are±5% nationally, but projectionaccuracy can vary by locality.Historical error for Woods &Poole’s population projections hasbeen ±8.1% for county-levelprojections and ±4.1% for states(Woods and Poole, 2012). Themagnitude of impact on totalmonetized benefits depends on thespecific location where ozone isreduced.We use population projections for 5-yearincrements for 304 race/ethnicity/gender/agegroups (Woods and Poole, 2012) at Censusblocks. Population forecasting is wellestablished but projections of futuremigration due to possible catastrophicevents are not considered. In addition,projections at the small spatial scales used inthis analysis are inherently more uncertainthan projections at the county- or state-level.(No quantitative methodavailable)Uncertainty inprojecting baselineincidence rates formortalityUnknown Low-Medium Medium Tier 1 (qualitative)Because the mortality rateprojections for future years reflectchanges in mortality patterns aswell as population growth, theprojections are unlikely to bebiased.Because mortality generallydominates monetized benefits,small uncertainties could havemedium impacts on totalmonetized benefits.The county-level baseline mortality ratesreflect recent databases (i.e., 2004–2006data) and are projected for 5-yearincrements for multiple age groups. Thisdatabase is generally considered to haverelatively low uncertainty (CDC Wonder,2008). The projections account for bothspatial and temporal changes in thepopulation.(No quantitative methodavailable)Uncertainty inprojecting baselineEither, depending on the healthendpointLow Low-Medium Tier 1 (qualitative)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***6A-12Potential Source ofUncertaintyDirection of Potential BiasMagnitude of Impact onMonetized BenefitsConfidence in Analytical Approach Uncertainty Quantificationincidence rates andprevalence rates formorbidityMorbidity baseline incidence isavailable for current year only (i.e.,no projections available).Assuming current year levels canbias the benefits for a specificendpoint if the data has cleartrends over time. Specifically,asthma prevalence rates haveincreased substantially over thepast few years while hospitaladmissions have decreasedsubstantially.The magnitude varies with thehealth endpoint, but the overallimpact on the total benefitsestimate from these morbidityendpoints is likely to be low.We do not have a method to project futurebaseline morbidity rates, thus we assumecurrent year levels will continue. While wetry to update the baseline incidence andprevalence rates as frequently as practicable,this does not continue trends into the future.Some endpoints, such as hospitalizationsand ER visits, have more recent data (i.e.,2007) stratified by age and geographiclocation. Other endpoints, such asrespiratory symptoms, reflect a nationalaverage. Asthma prevalence rates reflectrecent increases in baseline asthma rates(i.e., 2008).(No quantitative methodavailable)Uncertainties Associated with Omitted Benefits CategoriesUnquantified ozonehealth benefitcategories, such asworker productivity andlong-term mortalityUnderestimate High Low Tier 2 (sensitivity analysis)EPA has not included monetizedestimates of these benefitscategories in the core benefitsestimate.Although the potential magnitudeis unknown, including all of theadditional endpoints associatedwith ozone exposure that arecurrently not monetized couldincrease the total benefits by alarge amount.Current data and methods are insufficient tovalue national quantitative estimates of thesehealth effects. The O3ISA determined thatrespiratory effects (including mortality) arecausally associated with long-term ozoneexposure (p. 2–17). The O3ISA alsodetermined that outdoor workers have anincreased risk of ozone-related health effects(p. 1-15), and that studies on outdoorworkers show consistent evidence thatshort-term increases in ambient ozoneexposure can decrease lung function inhealthy adults (p.6-38). Additional studieslink short-term ozone exposure to reducedproductivity in outdoor workers (Graf Zivinand Neidell, 2013; Crocker and Horst,1981).We include sensitivity analysesreflecting long-term mortality,which shows that this endpointcould add substantially to themonetized benefits (seeAppendix 6B, section 6B.2).We have also included asupplemental analysisreflecting application of anupdated worker productivityanalysis (see section 6.5.3).Uncertainties Associated withEstimated Exposure ChangesUnknown Unknown Low Tier 1 (qualitative)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***6A-13Potential Source ofUncertaintyDirection of Potential BiasMagnitude of Impact onMonetized BenefitsConfidence in Analytical Approach Uncertainty QuantificationSpatial matching of airquality estimates fromepidemiology studies toair quality estimatesfrom air qualitymodelingEpidemiology studies oftenassume one air qualityconcentration is representative ofan entire urban area whencalculating hazard ratios, whilebenefits are calculated using airquality modeling conducted at 12km spatial resolution. This spatialmismatch could introduceuncertainty.We have not controlled for this potentialbias, and the SAB has not suggested anapproach to adjust for this bias.(No quantitative methodavailable)

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6A-14 6A.3 References
Alberini, Anna and Alan Krupnick. 2000. “Cost-of-Illness and Willingness-to-Pay Estimates of the Benefits of Improved Air Quality: Evidence from Taiwan.” Land Economics Vol. 76, No. 1 (Feb), pp. 37-53.
Centers for Disease Control and Prevention (CDC). 2008. National Center for Health Statistics. National Health Interview Survey, 1999-2008.
Crocker, T. D., & Horst, R. L. (1981). Hours of work, labor productivity, and environmental conditions: A case study. The Review of Economics and Statistics, 63(3), 361-368.
Fann, N.; K.R. Baker and C.M. Fulcher. 2012. “Characterizing the PM 2.5 -related health benefits of emission reductions for 17 industrial, area and mobile emission sectors across the U.S.” Environment International 49 41–151.
Graff Zivin, J., Neidell, M. (2012). “The impact of pollution on worker productivity.” American Economic Review , 102, 3652-3673.
Industrial Economics, Incorporated (IEc). 2012. Updating BenMAP Income Elasticity Estimates—Literature Review. Memo to Neal Fann. March. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/Benefits/Income%20Elasticity%20Update_Recommendations_noAppD.p df>
Jerrett M, Burnett RT, Pope CA, III, et al. 2009. “Long-Term Ozone Exposure and Mortality.” New England Journal of Medicine 360:1085-95.
Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. “Extended follow-up and spatial analysis of the American Cancer Societ y study linking particul ate air pollution and mortality.” HEI Research Report, 140, Health Effects Institute, Boston, MA.
Mansfield, Carol; Paramita Sinha; Max Henrion. 2009. Influence Analysis in Support of Characterizing Uncertainty in Human Health Benefits Analysis: Final Report. Prepared for U.S. EPA, Office of Air Quality Planning and Standards. November. Available on the internet at <http://www.epa.gov/ttn/ecas/regdata/Benefits/influence_analysis_final_report_psg.pdf>.
National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press. Washington, DC.
National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution . National Academies Press. Washington, DC.
Sasser, E. 2014. Response to Comments Regarding the Potential Use of a Threshold Model in Estimating the Mortality Risks from Long-term Exposure to Ozone in the Health Risk and Exposure Assessment for Ozone, Second External Review Draft. Memorandum to Holly Stallworth, Designated Federal Officer, Clean Air Scientific Advisory Committee from EPA/OAQPS Health and Environmental Impacts Division.
U.S. Environmental Protection Agency (U.S. EPA). 2001. Risk assessment guidance for Superfund. Vol. III, Part A. Process for conducting probabilistic risk assessment (RAGS 3A). Washington, DC, United States Environmental Protection Agency (EPA 540-R-02-002; OSWER 9285.7-45; PB2002 963302. Available on the Internet at <http://www.epa.gov/oswer/riskassessment/rags3adt/index.htm>.
U.S. Environmental Protection Agency (U.S. EPA). 2004. EPA’s risk assessment process for air toxics: History and overview. In: Air toxics risk assessment reference library. Vol. 1. Technical resource manual. Washington, DC,

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-15 United States Environmental Protection Agency, pp. 3-1–3-30 (EPA-453-K-04-001A. Available on the Internet at <http://www.epa.gov/ttn/fera/data/risk/vol_1/chapter_03.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter (Final Report) . EPA-600-R-08-139F. National Center for Environmental Assessment—RTP Division. December. Available at <http://cfpub.epa.g ov/ncea/cfm/recordispla y.cfm?deid=216546>.
U.S. Environmental Protection Agency (U.S. EPA). 2010a. Quantitative Health Risk Assessment for Particulate Matter—Final Report. EPA-452/R-10-005. Office of Ai r Quality Planning and Standards, Research Triangle Park, NC. September. Available on the Internet at <http://www.epa.gov/ttnnaaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2010b. Valuing Mortality Risk Reductions for Environmental Policy: A White Paper (SAB Review Draft). December. Available on the Internet at <http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0563-1.pdf/$file/EE-0563-1.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2011. The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress. Office of Air and Radiation, Office of Policy, Washington, DC. March. Available on the Internet at <http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2012. Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter. EPA-452/R-12-003. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. December. Available at: <http://www.epa.gov/pm/2012/finalria.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2013. Integrated Science Assessment of Ozone and RelatedPhotochemical Oxidants (Final Report). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R10/076F. Available at: <http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download>.
U.S. Environmental Protection Agency (U.S. EPA). 2014. Health Risk and Exposure Assessment for Ozone Final Report. EPA-452/R-14-004a. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. August. Available at: < http://www.epa.gov/ttn/naaqs/standards/ozone/data/20140829healthrea.pdf>.
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 1999. An SAB Advisory: TheClean Air Act Section 812 Prospective Study Health and Ecological Initial Studies. EPA-SAB-COUNCILADV-99-005, Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/1AFDEBB45C57F6E5852571930058C1A5/$File/coua9905.pdf>.
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2000. The Clean Air Act Amendments (CAAA) Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Advisory Council on Clean Air Compliance Analysis: Costs and Benefits of the CAAA. EPA-SAB-COUNCIL-ADV-00- 002. October. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/E992F918DF32131A852571B9004571F7/$File/coua0002.pdf>.
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2001. REVIEW OF THE DRAFT ANALYTICAL PLAN FOR EPA’S SECOND PROSPECTIVE ANALYSIS - BENEFITS AND COSTS OF THE CLEAN AIR ACT 1990- 2020. EPA-SAB-COUNCIL-ADV-01-004, Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/572DC1989A3986CC8525718D006BAB8B/$File/councila01004. pdf>.
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004a. Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of the Clean Air Act, 1990–2020. EPA-SAB-COUNCIL-ADV-04-002. March. Available on the Internet at

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6A-16 <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400433D5D/$File/counc il_adv_04002.pdf>.
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004b. Review of the Draft Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of the Clean Air Act, 1990-2020: An Advisory by the Advisory Council for Clean Air Compliance Analysis. EPA-SAB-COUNCIL-ADV-04- 004. May. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/7CCBBFE15CD4C8B185256F17005E3079/$File/council_adv_04 004.pdf>.
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004c. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag. EPA-COUNCIL-LTR-05-001. December. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44B098DB49F3C85257170005293E0/$File/council_ltr_05_ 001.pdf>.
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2010. Review of EPA’sDRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air Act. EPA-COUNCIL10-001. June. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/72D4EFA39E48CDB2852 5774500738776/$File/EPA-COUNCIL-10-001-unsigned.pdf>.
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011a. CASAC Review of Quantitative Health Risk Assessment for Particulate Matter—Second External Review Draft (February 2010). EPA-CASAC-10-008. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/BC4F6E77B6385155852577070002F09F/$File/EPA-CASAC-10- 008-unsigned.pdf>.
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011b. Review of the Final Integrated Report for the Second Section 812 Prospective Study of the Benefits and Costs of the Clean Air Act (August 2010). EPA-COUNCIL-11-001. Available on the Internet at<http://yosemite.epa.gov/sab/sabproduct.nsf/1E6218DE3BFF682E852577FB005D46F1/$File/EPA-COUNCIL11-001-unsigned.pdf>.
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011c. Review of Valuing Mortality Risk Reductions for Environmental Policy: A White Paper (December 10, 2010). EPA-SAB-11-011. July. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/$File/EPA-SAB-11- 011-unsigned.pdf>.
Woods & Poole Economics, Inc. 2012. Complete Economic and Demographic Data Source (CEDDS). CD-ROM. Woods & Poole Economics, Inc. Washington, D.C.
World Health Organization (WHO). 2008. Part 1: Guidance Document on Characterizing and Communicating Uncertainty in Exposure Assessment, Harmonization Project Document No. 6. Published under joint sponsorship of the World Health Organization, the International Labour Organization and the United Nations Environment Programme. WHO Press: Geneva, Switzerland. Available on the Internet at <www.who.int/ipcs/methods/harmonization/areas/uncertainty%20.pdf>.

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6B-1 APPENDIX 6B: SENSITIVITY ANALYSES RELATED TO THE BENEFITS ANALYSIS

Overview The benefits analysis presented in Chapter 6 of this RIA is based on our current interpretation of the scientific and economic literature. That interpretation requires judgments regarding the best available data, models, and analytical methodologies and the assumptions that are most appropriate to adopt in the face of important uncertainties. The majority of the analytical assumptions used to develop the main estimates of benefits have been reviewed and supported by EPA’s independent Science Advisory Board (SAB). Both EPA and the SAB recognize that data and modeling limitations, as well as simplifying assumptions can introduce uncertainty into the estimates of benefits and that alternative choices exist for some inputs to the analysis, such as the concentration-response (C-R) functions for mortality.

This appendix assesses the sensitivity of the benefits results to: (a) alternative studies for short-term ozone exposure-related mortality (sect ion 6B.1), (b) monetized benefits associated with mortality resulting from long-term exposure to ozone (section 6B.2), (c) the potential impact of thresholds in long-term ozone exposure-related mortality in incidence and benefits estimates (section 6B.3), (d) alternative response functions developed through expert elicitation for long-term PM 2.5 

### exposure-related mortality (section 6B.4), and (e) alternative assumptions regarding income elasticity on benefits derived using willingness-to-pay (WTP) functions(section 6B.5). For the core analyses, we estimated benefits for two scenarios: 2025 and post2025. However, in conducting these sensitivity analyses, we used the 2025 scenario as the basis for making our calculations, since sensitivity analysis findings for this scenario would generally hold for the post-2025 scenario.

In addition to the sensitivity analyses described above, this appendix presents several analyses intended to supplement the core estimates generated for the RIA. These supplemental analyses give greater insight to the manner in which these impacts are distributed among populations of different ages and the ambient levels of ozone at which the avoided deaths are estimated to occur and include: (a) age grou p-differentiated aspects of short-term ozone

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6B-2 exposure-related mortality (including total avoided incidence, life years gained and percent reduction in baseline mortality - Section 6B.6), (b) evaluation of mortality impacts relative to thebaseline pollutant concentrations (used in generating those mortality estimates) for both shortterm ozone exposure-related mortality and long-term PM 2.5 

### exposure-related mortality (Section 6B.7) and (c) a detailed discussion of the methodology used in modeling ozone-related impacts on outdoor worker productivity, including presentation of results (Section 6B.8).

6B.1 Sensitivity Analysis for Alternative C-R Functions for Short-term Exposure to Ozone Table 6B-1 presents the results of applying alternative effect estimates identified for modeling short-term exposure-related mortality for ozone, along with the set of core risk estimates (these sensitivity analysis results are also reflected in Figure 6-4 presented in the body of the document). The suite of effect estimates included in the sensitivity analysis reflects consideration for both meta-analyses and multi-city epidemiology studies. The rationale for the specific mix of effect estimates included in the sensitivity analysis is presented in section 6.6.3.2.

Table 6B-1. Sensitivity Analysis for Altern ative C-R Functions fo r Short-term Exposure to Ozone a

Health Effect Revised and Alternative Standards (95 th percentile confidence intervals)

70 ppb 65 ppb Avoided Short-Term Mortality - Core Analysis

multi-city studies Smith et al. (2009) (all ages) 96 490 (47 to 140) (240 to 740) Zanobetti and Schwartz (2008) (all ages) 160 820 (86 to 240) (440 to 1,200) Avoided Short-Term Mortality - Sensitivity Analysis

multi-city studies Smith et al. (2009) (all ages) co-pollutant model with PM 10 77 390 (-21 to 170) (-110 to 890) Schwartz (2005) (all ages) 120 610 (37 to 200) (190 to 1,000) Huang et al. (2005) (cardiopulmonary) 110 580 (42 to 180) (220 to 940) Bell et al. (2004) (all ages) 78 400 (26 to 130) (130 to 660)

meta- analyses Bell et al. (2005) (all ages) 250 1,300 (120 to 380) (610 to 2,000) Ito et al. (2005) (all ages) 350 1,800 (210 to 490) (1,100 to 2,500) Levy et al. (2005) (all ages) 350 1,800

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6B-3 (240 to 470) (1,200 to 2,400)

a All estimates are rounded to two significant digits.


### This sensitivity analysis showed that the two core incidence estimates fall within (and towards the lower end of) the broader range resulting from application of the seven alternative effect estimates (note that these observations based on incidence would also hold for the matching set of dollar benefit estimates generated for this endpoint category).

6B.2 Sensitivity Analysis for Monetized Be nefits for Premature Mortality from Longterm Exposure to Ozone As discussed in section 6.3, due to uncertainty in specifying the temporal lag structure associated with reductions in long-term ozone-related respiratory mortality, we have included these estimates as a sensitivity analysis and not as part of the core dollar benefit estimate. Table 6B-2 presents the dollar benefits (2011$) associated with modeled reductions in long-term ozone-related respiratory mortality. These benefit estimates are generated using non-threshold models obtained from Jerrett et al. (2009) because we present the potential threshold analysis in the section 6B.3. Additional detail on the effect estimates used in the core analysis and this sensitivity analysis is presented in section 6.6.3.2. Section 6.6.4 provides additional detail on the approach used in valuing these mortality estimates including how uncertainty related to cessation lag was addressed.

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6B-4 Table 6B-2. Sensitivity Analysis for Monetized Benefits for Mortality from Long-term Exposure to Ozone (millions of 2011$) (2025 and post-2025 scenarios) a, d

a See section 6B.3 for the sensitivity analysis for potential thresholds in the C-R function for long-term exposurerelated mortality Observations from that analysis can be applied to these results. b The zero-lag model is not affected by discounting. The values in parentheses reflect 95 th percentile confidence intervals.

c The range (outside of the parentheses) results from application of 7% and 3% discount rates after applying the 20- year segmented lag (i.e., ranging from the 7% discount rate up to the 3% discount rate). The range in parentheses reflects the 95 th percentile confidence interval in combination with the discount rate (i.e., ranging from the 2.5 th percentile and the 7% discount rate up to the 97.5 th percentile and the 3% discount rate). d All estimates rounded to two significant digits.



### This sensitivity analysis suggests that if in cluded in the core benefit estimate, long-term ozone exposure-related mortality could add substantially to the overall benefits. Additionally, use of a 20-year segment lag can reduce benefits by 10-20% (relative to a zero lag) depending on the discount rate applied.

6B.3 Threshold Sensitivity Analysis for Premature Mortality Incidence and Benefits from Long-term Exposure to Ozone In estimating long-term ozone mortality (including the benefit estimates presented in the last section), we employed a continuous non-thres hold C-R function relating ozone exposure to premature death. However, as discussed in Section 6.6.3.2, there is uncertainty regarding the 70 ppb 65 ppb Avoided Long-term Respiratory Mortality 2025 scenario $3,400 $17,000 ($280 to $10,000) ($1,400 to $52,000)
$2,800 to $3,100 $14,000 to $16,000
($250 to $8,400) ($1,200 to $47,000) post-2025 scenario $3,000 $6,000 ($240 to $8,400) ($490 to $18,000) $2,400 to $2,700 $4,900 to $5,400
($200 to $8,000) ($400 to $18,000) Revised and Alterative Standard Levels

(95th percentile confidence intervals)

multi-city study Jerrett et al. (2009) (age 30-99) copollutants model (PM 2.5 

### ) no lag b



### multi-city study Jerrett et al. (2009) (age 30-99) copollutants model (PM 2.5 

### ) no lag b 

### Jerrett et al. (2009) (age 30-99) copollutants model (PM 2.5 

### ) 20 yr segmented lag c 

### Jerrett et al. (2009) (age 30-99) copollutants model (PM 2.5 

### ) 20 yr segmented lag c 

### Health Effectb

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6B-5potential existence and location of a threshold in the C-R function relating mortality and longterm ozone concentrations. Thus, we have included a sensitivity analysis exploring the impact of potential thresholds in the C-R relationship on estimates of long-term exposure-related mortality that were evaluated in Jerrett et al. (2009), consistent with advice from the Clean Air Scientific Advisory Committee (CASAC) (U.S. EPA-SAB, 2014).

In their memo clarifying the results of their study (Sasser, 2014), the authors note that in terms of goodness of fit, long-term health risk models including ozone clearly performed better than models without ozone, indicating the improved predictions of respiratory mortality when ozone is included. In exploring different functional forms, the authors report that the model including a threshold at 56 ppb had the lowest log-likelihood value of all models evaluated (i.e., linear models and models including thresholds ranging from 40-60 ppb), and thus provided the best overall statistical fit to the data. However, they also note that it is not clear whether the 56ppb threshold model is a better predictor of respiratory mortality than when using a linear (nothreshold) model for this dataset. Using one statistical test, the model with a threshold at 56 ppb was determined to be statistically superior to the linear model. Using another, more stringent test, none of the threshold models considered were statistically superior to the linear model. Under the less stringent test, although the threshold model produces a statistically superior prediction than the linear model, there is uncertainty about the specific location of the threshold, if one exists. This is because the confidence intervals on the model predictions indicate that a threshold could exist anywhere from 0 to 60 ppb. The authors conclude that considerable caution should be exercised in using any specific threshold, particularly when the more stringent statistical test indicates there is no significantly improved prediction. Based on this additional information from the authors, we have chosen to reflect the uncertainty about the existence and location of a potential threshold by estimating mortality attributable to long-term ozone exposures using arange of threshold-based effect coefficient as sensitivity analyses. Specifically, we estimate longterm ozone mortality benefits using unique risk coefficients that include a range of thresholds from 40 ppb to 60 ppb in 5 ppb increments, while also including a model with a threshold equal to 56 ppb, which had the lowest log-likelihood value for all models examined. 163 

### Table 6B-3

163 There is a separate effect coefficient (and associated standard error) for each of the fitted threshold models

estimated in Jerrett et al. (2009). As a result, the sensitivity of estimated mortality attributable to long-term ozone

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6B-6 provides the results of these sensitivity analyses (based on modeling incidence) for 65 ppb and 70 ppb. The same pattern in terms of relative reductions across thresholds would also hold for
the monetized benefit estimates.

Table 6B-3. Long-term Ozone Mortality Incidence at Various Assumed Thresholds a Threshold Concentration 70 ppb 65 ppb No threshold 340 1,700

40 ppb 260 1,300 45 ppb 200 910 50 ppb 59 210 55 ppb 6 66 56 ppb 5 60 60 ppb 3 19

a All estimates rounded to two significant digits. 

### The results of the sensitivity analysis based on the suite of threshold-based risk coefficients suggest that threshold models can substantially lower estimates of ozone-attributable long-term mortality. For example, estimated bene fits for long-term mortality using a model that includes a 55 ppb threshold are approximately 70% less than long-term mortality benefits estimated using the co-pollutant, non-threshold model. Generally, estimated long-term mortality benefits are progressively reduced when using models with increasing thresholds, with the highest threshold considered (60 ppb) removing virtually all of the estimated incidence reduction
and associated benefits.

6B.4 Sensitivity Analysis for Alternative C-R Functions for PM 2.5 

### –Related Mortality In estimating PM 2.5 

### co-benefits, monetized benefits are driven largely by reductions in mortality. Therefore, it is particularly important to attempt to characterize the uncertainties associated with reductions in premature mortality. In addition to the ACS and Six Cities cohort studies, several recent cohort studies conducted in North America provide evidence for the relationship between long-term exposure to PM 2.5 

### and the risk of premature death. Many of these additional cohort studies are described in the PM ISA (U.S. EPA, 2009) and the Provisional
Assessment (U.S. EPA, 2012a). 164, 

### Table 6B-4 provides the effect estimates from each of these

concentrations is affected by both the assumed threshold level (below which there is no effect of ozone) and the effect coefficient applied to ozone concentrations above the threshold.

164 It is important to note that the newer studies in the Provisional Assessment are published in peer-reviewed journals and meet our study selection criteria, but they have not been assessed in the context of an Integrated

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6B-7 cohort studies for all-cause, cardiovascular, cardiopulmonary, and ischemic heart disease (IHD) mortality, as well as the lowest measured air quality level (LML) and mean concentration in the study.

We also draw upon the results of the 2006 expert elicitation 165 

### sponsored by the EPA (Roman et al., 2008; IEc, 2006) to demonstrate the sensitivity of the benefits estimates to 12 expert-defined C-R functions. The PM 2.5 

### expert elicitation and the derivation of C-R functions from the expert elicitation results are described in detail in the 2006 PM 2.5 

### NAAQS RIA (U.S. EPA, 2006), the elicitation summary report (IEc, 2006) and Roman et al. (2008), and wesummarize the key attributes of this study relative to the interpretation of the estimates of PMrelated mortality reported here. We describe also how the epidemiological literature has evolved since the expert elicitation was conducted in 2005 and 2006.

Science Assessment nor gone through review by the SAB. In addition, only the ACS and Harvard Six Cities’ cohort studies have been recommended by the SAB as appropriate for benefits analysis of national rulemakings.

165 Expert elicitation is a formal, highly-structured and well-documented process whereby expert judgments, usually

of multiple experts, are obtained (Ayyub, 2002).

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6B-8 Table 6B-4. Summary of Effect Estimates from Recent Cohort Studies in North America Associated with Change in Long-Term Exposure to PM 2.5

Study Cohort (age) LML (μg/ m 3 ) Mean (μg/ m 3 ) Hazard Ratios per 10 μg/m 3 Change in PM 2.5 (95 th percentile confidence intervals) All Causes Cardiovascular Cardiopulmonary IHD Pope et al. (2002) ACS (age >30) 7.5 18.2 1.06 (1.02–1.11) 1.12 (1.08–1.15) 1.09 (1.03–1.16) N/A

Laden et al. (2006) Six Cities (age > 25) 10 16.4 1.16 (1.07–1.26) 1.28 (1.13–1.44) N/A N/A
Lipfert et al. (2006) a Veterans (age 39–63) <14.1 14.3 1.15 (1.05–1.25) N/A N/A N/A

Miller et al. (2007) b WHI (age 50

– 79) 3.4 13.5 N/A 1.76 (1.25

– 2.47) N/A 2.21 (1.17

– 4.16) Eftim et al. (2008) Medicare (age > 65) 6 13.6 1.21 (1.15–1.27) N/A N/A N/A
Zeger et al. (2008) c Medicare (age > 65) <9.8 13.2 1.068 (1.049–1.087) N/A N/A N/A

Krewski et al. (2009) d ACS (age >30) 5.8 14 1.06 (1.04–1.08) N/A 1.13 (1.10–1.16) 1.24 (1.19–1.29) Puett et al. (2009) b NHS (age 30–55) 5.8 13.9 1.26 (1.02–1.54) N/A N/A 2.02 (1.07–3.78) Crouse et al. (2012) d,e Canadian

census 1.9 8.7 1.06 (1.01–1.10) N/A N/A N/A

Puett et al. (2011) f Health Professionals (age 40–75) <14.4 17.8 0.86 (0.70–1.00) 1.02 (0.84–1.23) N/A N/A

Lepeule et al. (2012) d Six Cities (age > 25) 8 15.9 1.14 (1.07–1.22) 1.26 (1.14–1.40) N/A N/A

a Low socio-economic status (SES) men only. Used traffic proximity as a surrogate of exposure. b Women only.

c Reflects risks in the Eastern U.S. Risks in the Central U.S. were higher, but the authors found no association in the Western U.S. d Random effects Cox model with individual and ecologic covariates.

e Canadian population. f Men with high socioeconomic status only. 

### The primary goal of the 2006 study was to elicit from a sample of health experts probabilistic distributions describing uncertainty in estimates of the reduction in mortality among the adult U.S. population resulting from re ductions in ambient annual average PM 2.5 

### levels. These distributions were obtained through a formal interview protocol using methods designed to elicit subjective expert judgments. These experts were selected through a peer-nomination process and included experts in epidemiology, toxicology, and medicine. The elicitation interview consisted of a protocol of carefully structured questions, both qualitative and quantitative, about the nature of the PM 2.5 

### -mortality relationship designed to build twelve individual distributions for the coefficient (or slope) of the C-R function relating changes in annual average PM 2.5 

### exposures to annual, adult all-cause mortality. The elicitation also provided

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6B-9 useful information regarding uncertainty characterization in the PM 2.5 

### -mortality relationship. Specifically, during their interviews, the experts highlighted several uncertainties inherent within the epidemiology literature, such as causality, concentration thresholds, effect modification, role of short- and long-term exposures, potential co nfounding, and exposure misclassification. For several of these uncertainties, such as causality, we are able to use the expert-derived functions to quantify the impacts of applying different as sumptions. The elicitation received favorable peer review in 2006 (Mansfield and Patil, 2006).

Prior to providing a quantitative estimate of the risk of premature death associated with long-term PM 2.5 

### exposure, the experts answered a series of “conditioning questions.” One such question asked the experts to identify which epidemiological studies they found most informative. The “ideal study attributes” 166 

### according to the experts included:

 Geographic representation of the entire U.S. (e.g., monitoring sites across the country)

 Collection of information on individual risk factors and residential information both at the beginning and throughout the follow-up period

 Large sample size that is representative of the general U.S. population

 Collection of genetic information from cohort members to identify and assess potential effect modifiers

 Monitoring of individual exposures (e.g., with a personal monitor)

 Collection of data on levels of several co-pollutants (not only those that are monitored for compliance purposes)

 Accurate characterization of outcome (i.e., cause of death)

 Follow-up for a long period of time, up to a lifetime

 Prospective study design Although no single epidemiological study completely satisfies each of these criteria, the experts determined that the ACS and Six Cities’ cohort studies best satisfy a majority of these ideal attributes. To varying degrees the studies examining these two cohorts are geographically representative; have collected information on individual risk factors; include a large sample size; have collected data on co-pollutants in the case of the ACS study; have accurately characterized the health outcome; include a long (and growing) follow-up period; and, are prospective in

166 These criteria are substantively similar to EPA’s study selection criteria identified in Table 6-5 of Chapter 6.

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6B-10 nature. The experts also noted a series of limitations in these two cohort studies. In the case of the Six Cities study (Laden et al., 2006), the experts identified the “small sample size, limited number of cities, and concerns about representativeness of the six cities for the U.S. as a whole” as weaknesses. When considering the ACS study (Pope et al., 2002), the experts indicated that the “method of recruitment for the study, which resulted in a group with higher income, more education, and a greater proportion of whites than is representative of the general U.S. population” represented a shortcoming. Several experts also argued that because the ACS study relied upon “...whatever monitors were available to the study...a single monitor represent[ed] exposure for an entire metropolitan area...whereas [the Six Cities study] often had exposures assigned at the county level.” Despite these limitations, the experts considered the Pope et al. (2002) extended analysis of the ACS cohort and the Laden et al. (2006) extended analysis of the Six Cities cohort to be particularly influential in their opinions (see Exhibit 3-3 of the elicitation summary report [IEc, 2006]).

It is important to note that the benefits estimates results presented are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the effect coefficients provided in those studies or by experts. In addition, the experts provided
distributions around their mean PM 2.5 

### effect estimates, which provides more information regarding the overall range of uncertainty, and this overall range is larger than the range of the mean effect estimates from each of the experts.

Since the completion of the EPA’s expert elicitation in 2006, additional epidemiology literature has become available, including 9 new multi-state cohort studies shown in Table 6B-4. This newer literature addresses some of the weaknesses identified in the prior literature. For example, in an attempt to improve its characterization of population exposure the most recent extended analysis of the ACS cohort Krewski et al. (2009) incorporates two case studies that employ more spatially resolved estimates of population exposure.

In light of the availability of this newer literature, we have updated the presentation of results in the RIA. Specifically, we focus the core analysis on results derived from the two most recent studies of the ACS and Six Cities cohorts (Krewski et al., 2009; Lepeule et al., 2012). Because the other multi-state cohorts generally have limited geography and age/gender

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6B-11 representativeness, these limitations preclude us from using these studies in our core benefits results, and we instead present the risk coefficients from these other multi-state cohorts in Table 6B-4. However, we have completed a sensitivity analysis based on application of the full set of expert-derived effect estimates. To preserve the breadth and diversity of opinion on the expert panel, we do not combine the expert results (Roman et. al., 2008). This presentation of the expert-derived results is generally consistent with SAB advice (U.S. EPA-SAB, 2008), which suggested that the EPA emphasize that “scientific differences existed only with respect to the magnitude of the effect of PM 2.5 

### on mortality, not whether such an effect existed” and that the expert elicitation “supports the conclusion that the benefits of PM 2.5 

### control are very likely to be substantial.” Although it is possible that the newer literature could revise the experts’ quantitative responses if elicited again, we believe that these general conclusions are unlikely to change.

Table 6B-5 presents the results of this sensitivity analysis using the expert elicitation results for the 2025 scenario for 70 ppb. Overall conclusions from this analysis are also applicable to the post-2025 scenario.

Table 6B-5. PM 2.5 

### Co-benefit Estimates using Two Epidemiology Studies and Functions Supplied from the Ex pert Elicitation
C

‐ R Function 70 ppb (2025 Scenario) Krewski et al. (2009) 220 Lepeule et al. (2012) 500 Expert K 50 Expert G 260 Expert L 310 Expert D 320 Expert H 330 Expert J 360 Expert F 430 Expert C 450 Expert I 450 Expert B 460 Expert A 570 Expert E 740

a All estimates rounded to two significant digits. 

### The values presented in Table 6B-5 indicate that the two core incidence estimate fall within the range of alternative C-R function-based estimates obtained through expert elicitation. Figure 6-5 in the body of the document reproduces these benefit estimates, but presents them in

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6B-12 terms of the associated dollar benefits (7% discount rate in 2011$) rather than incidence estimates (observations presented here for the sensitivity analysis results reflected modeled incidence estimates - i.e., spread in results and relative position of the two core estimates - also hold for dollar benefit estimates presented in Figure 4-6.

6B.5 Sensitivity Analysis for Income Elasticity of Willingness-to-Pay As discussed in Chapter 6, our estimates of monetized benefits account for growth in real GDP per capita by adjusting the WTP for individual endpoints based on the central estimate of the adjustment factor for each of the categories (minor health effects, severe and chronic health effects, premature mortality, an d visibility). We examined how sensitive the esti mate of total benefits is to alternative estimates of the income elasticities. Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and post-2025 scenario estimates use income growth through 2024 only and are therefore likely underestimates.

Table 6B-6 lists the ranges of elasticity values used to calculate the income adjustment factors, while Table 6B-7 lists the ranges of corresponding adjustment factors. The results of this sensitivity analysis for the two benefit categories are presented in Table 6B-8.

Table 6B-6. Ranges of Elasticity Values Used to Account for Projected Real Income Growth a Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound Minor Health Effect b 0.04 0.30 Premature Mortality 0.08 1.00

a Derivation of these ranges can be found in Kleckner and Neumann (1999). COI estimates are assigned an adjustment factor of 1.0. b Minor health effects included in this RIA and valued using WTP-based functions include: upper and lower respiratory symptoms, asthma exacerbations, minor restricted activity days, and acute bronchitis. 

### Table 6B-7. Ranges of Adjustment Factors Used to Account for Projected Real Income Growth to 2024 a

Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound Minor Health Effect b 1.021 1.170 Premature Mortality 1.043 1.705

a Based on elasticity values reported in Table C-4, U.S. Census population projections, and projections of real GDP per capita. b Minor health effects included in this RIA and valued using WTP-based functions include: upper and lower respiratory symptoms, asthma exacerbations, minor restricted activity days, and acute bronchitis.

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6B-13 Table 6B-8. Sensitivity of Monetized Ozone Bene fits to Alternative Income Elasticities in 2025 (Millions of 2011$) a
Benefit Category No adjustment Lower Sensitivity Bound Upper Sensitivity Bound 70 ppb 65 ppb 70 ppb 65 ppb 70 ppb 65 ppb Minor Health Effect b $31 $150 $33 $162 $38 $186 Premature Mortality c $950 $4,100 $1,000 $5,200 $1,700 $8,500

a All estimates rounded to two significant digits. Only reflects income growth to 2024. b For illustrative purposes, we evaluate minor restricted activity days (MRADS) resulting from short-term ozone exposure is the minor health effect here.

c Short-term mortality using Smith et al. (2009) and 3% discount rate. Results using other short-term mortality studies and a 7% discount rate would show the same proportional range.


### Consistent with the impact of mortality on total benefits, the adjustment factor for mortality has the largest impact on total benefits. The value of mortality in 2025 ranges from 8% to 76% greater than the core estimate for mortality based on the lower and upper sensitivity bounds on the mortality income adjustment factor. The effect on the value of minor health effects is much less pronounced, ranging from 5% to 21% greater than the core estimate for minor effects. These observations (in terms of relative impact from alternative elasticities) hold for the revised and alternative standard levels analyzed under both the 2025 and post-205 scenarios.

6B.6 Supplemental Analysis: Age Group-Differentiated Aspects of Short-Term Ozone Exposure-Related Mortality In their 2008 review of the EPA’s approach to estimating ozone-related mortality benefits, the National Research Council (NRC) indicated, “EPA should consider placing greater emphasis on reporting decreases in age-specific death rates in the relevant population and develop models for consistent calculation of changes in life expectancy and changes in number of deaths at all ages” (NRC, 2008). In addition, NRC noted in an earlier report that “[f]rom a public-health perspective, life-years lost might be more relevant than annual number of mortality cases” (NRC, 2002). This advice is consistent w ith that of the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance Analysis (SAB-HES), which agreed that “...the interpretation of mortality risk results is enhanced if estimates of lost life-years can be made” (U.S. EPA-SAB, 2004). To address these recommendations, we use simplifying assumptions to estimate the number of life years that might be gained. We also estimate the reduction in the percentage of deaths attributed to ozone resulting from the illustrative emissions reduction strategies to reach the revised and alternative primary standards. The EPA included similar estimates of life years gained in a previous assessment of ozone and/or PM 2.5 

### benefits (U.S. EPA,

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6B-14 2006, 2010, 2011a), the latter of which was peer reviewed by the SAB-HES (U.S. EPA-SAB, 2010a).

Changes in life years and changes in life expectancy at birth are frequently conflated, thus it is important to distinguish these two very different metrics. Life expectancy varies by age. The Centers for Disease Control and Prevention (C DC) defines life expectancy as the “average number of years of life remaining for persons who have attained a given age” (CDC, 2011). In other words, changes in life expectancy refer to an average change for the entire population, and refer to the future. Over the past 50 years, average life expectancy at birth in the U.S. has increased by 8.4 years (CDC, 2011). For example, life expectancy at birth was estimated in 2007 to be 77.9 years for an average person born in the U.S., but for people surviving to age 60, estimated life expectancy is 82.5 years (i.e., 4.6 years more than life expectancy at birth) (CDC, 2011). Life years, on the other hand, measure the amount of time that an individual loses if they die before the age of their life expectancy. Life years refer to individuals, and refer to the past, e.g., when the individual has already died. If a 60-year old individual dies, we estimate that this individual would lose about 22.5 years of life (i.e., the average population life expectancy for an individual of this age minus this person’s age at death).

Due to the use of benefit-per-ton estimates for the PM 2.5 

### co-benefits, we are unable to estimate the life years gained by reducing exposure to PM 2.5 

### in this analysis. Instead, we refer the
reader to the 2012 PM 2.5 

### NAAQs RIA for more information about the avoided life years lost
from PM 2.5 

### exposure (U.S. EPA, 2012b). The analysis performed for the PM NAAQS RIA found that about half of the avoided PM-related deaths occur in populations age 75 to 99, but half of the avoided life years lost would occur in populations younger than 65 because the younger populations have the potential to lose more life years per death than older populations. In addition, that analysis found that the average individual who would otherwise have died prematurely from PM exposure would gain 16 additional years of life.

Estimated Life Years Gained
To estimate the potential life years gained by reducing exposure to ozone in the U.S. adult population, we use the same general approach as Hubbell (2006) and Fann et al. (2012). We have not estimated the change in average life expectancy at birth in this RIA. Because life

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6B-15 expectancy is an average of the entire population (including both those whose deaths would likely be attributed to air pollution exposure as well as those whose deaths would not), we would expect average life expectancy changes associated with air pollution exposure to always be significantly smaller than the average number of life years lost by an individual projected to die prematurely from air pollution exposure.

To estimate the potential distribution of life years gained for population subgroups defined by the age range at which their reduction in air pollution exposure is modeled to occur, we use standard life tables available from the CDC (2014) and the following formula:

ൌ ݏݎܻ݁ܽ	݂݅݁ܮ	݈ܽݐ݋ܶ ∑ ܧܮ ௜ 

### ܯ	ൈ ௜ ௡ ௜ୀଵ 

### (6.2)

where LE i 

### is the average remaining life expectancy for age interval i, M i 

### is the estimated change in number of deaths in age interval i, and n is the number of age intervals.

To get M i 

### (the estimated number of avoided premature deaths attributed to changes in ozone exposure for the 2025 scenario), we use a health impact function that incorporates risk coefficients estimated for the adult population in the U.S. and age-specific mortality rates. That is, we use risk coefficients that do not vary by age, but use baseline mortality rates that do. Because mortality rates for younger populations are much lower than mortality rates for older populations, most but not all, of the avoided deaths tend to be in older populations. Table 6B-9 summarizes the number of avoided deaths (by age range) attributable to ozone for the revised
and alternative standards for the 2025 scenario. Table 6B-10 summarizes the modeled number of life years gained (for each age range) by reducing ozone for the revised and alternative standards analyzed for the 2025 scenario. We then calculated the average number of life years gained per avoided premature mortality.

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6B-16 Table 6B-9. Potential Reduction in Premature Mortality by Age Range from Attaining the Revised and Alternative Ozone Standards (2025 scenario) a, b Age Range b Revised and Alternative Standards 70 ppb 65 ppb 0-4 0.3 1.5 5-9 0.068 0.33 10-14 0.072 0.35 15-19 0.1 0.5 20-24 0.14 0.69 25–29 0.29 1.4 30–34 0.3 1.5 35–44 1.6 7.9 45–54 4.1 20 55–64 10 52 65–74 22 110 75–84 29 150 85–99 28 150 Total ozone-attributable mortality 96 490

a Estimates rounded to two significant digits. b Effects calculated using Smith et al. (2009).



### Table 6B-10. Potential Years of Life Gained by Age Range from Attaining the Revised and Alternative Ozone Standards (2025 Scenario) a,b Age Range b Revised and Alternative Standard 70 ppb 65 ppb 0-4 24 110 5-9 5 23 10-14 5 23 15-19 6 30 20-24 8 38 25–29 14 70 30–34 13 66 35–44 57 280 45–54 110 550 55–64 200 990 65–74 260 1,300 75–84 190 970 85–99 65 340 Total life years gained 960 4,800 Average life years gained per individual 10 10

a Estimates rounded to two significant digits. b Effects calculated using Smith et al. (2009).


### By comparing the projected age distribution of the avoided premature deaths with the age distribution of life years gained, we observed that about half of the deaths occur in populations age 75–99 (see Table 6B-9), but half of the life years would occur in populations younger than

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6B-17 65 (see Table 6B-10). This is because the younger populations have the potential to lose more life years per death than older populations based on changes in ozone exposure for the 2025 scenario. We estimate that the average individual who would otherwise have died prematurely from ozone exposure would gain 10 additional years of life. However, this approach does not account for whether or not people who are older are more likely to be susceptible to the health effects of air pollution or whether that susceptibility was caused by air pollution exposure (for a more complete discussion of this issue, see Kunzli et al., 2001).

Percent of Ozone-related Mortality Reduced
To estimate the percentage reduction in all-cause mortality attributed to reduced ozone exposure for the 2025 scenario as a result of th e illustrative emissions reduction strategies, we
use M i 

### from the equation above, dividing the number of excess deaths estimated for the revised and alternative standards by the total number of deaths in each county. Table 6B-11 shows the reduction in all-cause mortality attributed to reducing ozone exposure to the revised and alternative primary standards for the 2025 scenario.

Table 6B-11. Estimated Percent Reduction in All-Cause Mortality Attributed to the Proposed Primary Ozone Standards (2025 Scenario) a Age Range b Revised and Alternative Standards 70 ppb 65 ppb 0-4 0.0085% 0.0409% 5-9 0.0084% 0.0411% 10-14 0.0084% 0.0415% 15-19 0.0086% 0.0419% 20-24 0.0085% 0.0412% 25–29 0.0084% 0.0409% 30–34 0.0083% 0.0408% 35–44 0.0081% 0.0404% 45–54 0.0082% 0.0410% 55–64 0.0085% 0.0429% 65–74 0.0086% 0.0436% 75–84 0.0085% 0.0433% 85–99 0.0079% 0.0415%

a To illustrate the slight variations in percent reductions across age ranges, we present results rounded to three significant digits (rather than two as is typically done for other estimates in this RIA).



### Results presented in Table 6B-11 highlight that when reductions in ozone-attributable mortality (in going from baseline to the revised or alternative standard level) are considered as a

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6B-18 percentage of total all-cause baseline mortality, the estimates are relatively small and are fairlyconstant across age ranges. However, it is important to point out that estimates of total ozoneattributable mortality represent a substantially larger fraction of all-cause baseline mortality.

6B.7 Supplemental Analysis: Evaluation of Mortality Impacts Relative to the Baseline Pollutant Concentrations for both Short -Term Ozone Exposure-Related Mortality and Long-Term PM 2.5 

### Exposure-Related Mortality Analysis of baseline ozone levels used in modeling short-term ozone exposure-related mortality

Our review of the current body of scientific literature indicates that a log-linear nothreshold model provides the best estimate of ozone-related short-term mortality (see section 2.5.4.4, in the O 3 

### ISA, U.S. EPA, 2013). Consistent with this finding, we estimate benefits associated with the full range of ozone exposure. Our confidence in the estimated number of premature deaths avoided (but not in the existence of a causal relationship between ozone and premature mortality) diminishes as we estimate these impacts at successively lower concentrations. However, there are uncertainties inherent in identifying any particular point at which our confidence in reported associations becomes appreciably less, and the scientific evidence provides no clear dividing line. The O 3 

### ISA noted that the studies indicate reducedcertainty in specifying the shape of the C-R function specifically for short-term ozoneattributable respiratory morbidity and mortality, in the range generally below 20 ppb (for these reasons, the ≤ 20 ppb range discussed in the O 3 

### ISA should be viewed as a more generalized range to be considered qualitatively or semi-quantitatively, along with many other factors, when interpreting the risk estimates rather than as a fixed, bright-line). 167



### Figures 6B-1 and 6B-2 compare the distribution of short-term ozone exposure-related mortality to the underlying distribution of the summer season 8-hour maximum ozone concentrations. Both figures (probability and cumulative probability) are based on Smith et al.

167 While clinical studies have suggested the presence of a threshold for respiratory effects, these should not be used to support specification of population-level thresholds for use in the epidemiological-based risk assessment focusing on short-term exposure-related endpoints. The clinical studies focus on relatively small and clearly defined populations of healthy adults, which are not representative of the broader residential populations typically associated with epidemiological studies, including older individuals and individuals with existing health conditions that place them at greater risk for ozone-related effects. Therefore, the clinical studies are unlikely to have the power to capture population thresholds in a broader and more diverse urban residential population, should those thresholds exist.

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6B-19 (2009) using mortality results at each 12 km grid cell and the associated summer season 8-hour maximum concentration in the baseline. In addition, each figure includes separate plots for the revised and alternative standard levels. Figure 6B-1 shows that approximately 45% of the premature mortalities estimated for the 65 ppb alternative standard is associated with baseline ozone concentrations between 40 and 45 ppb. Because this baseline range is represents the mean across the ozone season of 8-hour max values within a given grid cell, the actual distribution of 8-hour max values on a daily basis is likely wider than the 40-45 ppb range.

Figure 6B-1. Premature Ozone-related Deaths Avoided for the Revised and Alternative Standards (2025 scenario) According to the Baseline Ozone Concentrations 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%0to1515to2020to2525to3030to3535to4040to4545to5050to5555to6060to6565to7070to7575to80Percentageofreduction inshort‐termozonerelatedmortality



### Ozone season mean daily 8hr max baseline (ppb) 75

‐ 70 75

‐ 65

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6B-20 Figure 6B-2. Cumulative Probability Plot of Premature Ozone-related Deaths Avoided for the Revised and Alternative Standards (2025 scenario) According to the Baseline Ozone ConcentrationsWhen interpreting these results, it is important to understand that the avoided ozonerelated deaths are estimated to occur from ozone reductions in the baseline air quality simulation, which assumes that 75 ppb is already met. When simulating attainment with the revised and alternative standards, we adjust the design value at each monitor exceeding the standard to equal that standard and use an air quality interpolation technique to simulate the change in ozone concentrations surrounding that monitor. This technique tends to simulate the greatest air quality changes nearest the monitor. We estimate benefits using modeled air quality data with 12 km grid cells, which is important because the grid ce lls are often substantially smaller than counties and ozone concentrations vary spatially within a county. Therefore, there may be a small number of grid cells with concentrations slightly greater than 75 ppb in the gridded baseline even though all monitors could meet an annual standard of 75 ppb. In addition, some grid cells in a county can be below the level of a standard even though the highest monitor value is above that standard. Thus, emissions reductions can lead to benefits in grid cells that are below a standard even within a county with a monitor that exceeds that standard. Furthermore, our approach to 

## 0 0.2 0.4 0.6 0.8

1 1.20to1515to2020to2525to3030to3535to4040to4545to5050to5555to6060to6565to7070to7575to80Cumulativepercentageofreduction inshort‐termozonerelatedmortality



## Ozone season mean daily 8hr max baseline (ppb) 75

‐ 70 75

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6B-21 simulating attainment can lead to benefits in counties that are below the revised or alternative standard being evaluated. Emissions reduction strategies designed to reduce ozone concentrations at a given monitor will frequently improve air quality in neighboring counties. In order to make a direct comparison between the benefits and costs of these emissions reduction strategies, it is appropriate to include all the benefits occurring as a result of the emissions reduction strategies applied, regardless of where they occur. Therefore, it is not appropriate to estimate the fraction of benefits that occur only in counties that exceed the revised or alternative standards because it would omit benefits attributable to emissions reductions in exceeding
counties.

One final caveat in interpreting the information presented in these figures is that in modeling this mortality endpoint, rather than using a true distribution of daily 8hr max ozone levels for each grid cell, due to resource limitations, we used a single mean value for the ozone season within each grid cell. While this will generate the same total ozone benefit estimate for each grid cell compared with application of a full distribution of daily 8hr max values, use of a mean daily value means that an assessment such as this one, that considers both the spatial and temporal association between mortality benefit estimates and ozone levels, will be limited somewhat in its treatment of the temporal dimension.

As shown in Figures 6B-1 and 6B-2, the vast majority of reductions in short-term exposure-related mortality for ozone occur in grid cells with mean 8-hour max baseline levels (across the ozone season) between 35 and 55 ppb. Importantly, virtually all of the mortality reductions are associated with ozone levels above the greater than 20 ppb range identified within
the O 3 

### ISA as being associated with less confidence in specifying the nature of the C-R function for ozone mortality (O 3 

### ISA, section 2.5.4.4). 168 

### Comparing patterns across the revised and

168 As noted earlier, care needs to be taken in interpreting these mortality vs. ozone air level distributions in the context of the range of reduced confidence (<20 ppb) identified in the O 3 ISA. The region of reduced confidence identified by the ISA reflects the composite monitor daily time series values (including 8-hour max values) used in short-term mortality studies, while the ozone levels summarized in Figures 6B-1 and 6B-2 are the mean (across the ozone season) of daily 8-hour max values within each grid cell. The use of these mean values, while not impacting the total mortality reductions estimated, could significantly reduce variability in the spread of values presented in these two figures.

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6B-22 alternative standard levels, the upper end of the distribution shifts downwards as increasingly lower standard levels are analyzed (see Figure 6B-2).

Concentration Benchmark Analysis for PM 2.5 

### Benefit-per-ton Estimates
In general, we are more confident in the magnitude of the risks we estimate from simulated
PM 2.5 

### concentrations that coincide with the bulk of the observed PM concentrations in the epidemiological studies that are used to estimate the benefits. Likewise, we are less confident in
the risk we estimate from simulated PM 2.5 

### concentrations that fall below the bulk of the observed data in these studies. Concentration benchmark analyses (e.g., lowest measured level [LML], one standard deviation below the mean of the air quality data in the study, etc.) allow readers to determine the portion of population exposed to annual mean PM 2.5 

### levels at or above different concentrations, which provides some insight into the level of uncertainty in the estimated PM 2.5 

### mortality benefits. In this analysis, we apply two concentration benchmark approaches (LML and one standard deviation below the mean) that have been incorporated into recent RIAs and EPA’s Policy Assessment for Particulate Matter (U.S. EPA, 2011c). There are uncertainties inherent in identifying any particular point at which our confidence in reported associations becomes appreciably less, and the scientific evidence provides no clear dividing line. However, the EPA
does not view these concentration benchmarks as a concentration threshold below which we would not quantify health co-benefits of air quality improvements. 169 

### Rather, the co-benefits estimates reported in this RIA are the best estimates because they reflect the full range of air quality concentrations associated with the emissions reduction strategies. The PM ISA concluded that the scientific evidence collectively is sufficient to conclude that the relationship between long-term PM 2.5 

### exposures and mortality is causal and that overall the studies support the use of a no-threshold log-linear model to estimate PM-r elated long-term mortality (U.S. EPA, 2009).

For this analysis, policy-specific air quality data is not available, and the compliance strategies are illustrative of what states may choose to do. For this RIA, we are unable to estimate the percentage of premature mortality associated with the emissions reductions at each
PM 2.5 

### concentration, as we have done for previous rules with air quality modeling (e.g., U.S.

169 For a summary of the scientific review statements regarding the lack of a threshold in the PM 2.5 -mortality

relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the Concentration-Response Function for PM 2.5 -related Mortality (U.S. EPA, 2010b).

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6B-23 EPA, 2011b, 2012b). However, we believe that it is still important to characterize the distribution of exposure to baseline concentrations. As a surrogate measure of mortality impacts, we provide the percentage of the population exposed at each PM 2.5 

### concentration in the baseline of the source apportionment modeling used to calculate the benefit-per-ton estimates for this sector using 12 km grid cells across the contiguous U.S. 170 

### It is important to note that baseline exposure is only one parameter in the health impact function, along with baseline incidence rates, population and change in air quality. In other words, the percentage of the population exposed to air pollution below the LML is not the same as the percentage of the population experiencing health impacts as a result of a specific emissions reduction policy. The most important aspect, which we are unable to quantify without rule-specific air quality modeling, is the shift in exposure anticipated by the revised or alternative standards. Therefore, caution is warranted when interpreting the LML assessment in this RIA because these results are not consistent with results from RIAs that had air quality modeling.

Table 6B-12 provides the percentage of the population exposed above and below two concentration benchmarks in the modeled baseline for the sector modeling. Figure 6B-3 shows a bar chart of the percentage of the population exposed to various air quality levels in the baseline, and Figure 6B-4 shows a cumulative distribution function of the same data. Both figures identify the LML for each of the major cohort studies.

170 As noted above, the modeling used to generate the benefit-per-ton estimates does not reflect emissions

reductions anticipated from MATS rule. Therefore, the baseline PM 2.5 concentrations in the LML assessment are higher than would be expected if MATS was reflected.

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6B-24 Table 6B-12. Population Exposure in the Baseli ne Sector Modeling (used to generate the benefit-per-ton estimates) Above and Be low Various Concentration Benchmarks in the Underlying Epidemiology Studies a

Epidemiology Study Below 1 Standard Deviation. Below AQ Mean At or Above 1 Standard Deviation Below AQ Mean Below LML At or Above LML
Krewski et al. (2009) 89% 11% 7% 93% Lepeule et al. (2012) N/A N/A 23% 67%

a One standard deviation below the mean is equivalent to the middle of the range between the 10 th and 25 th

percentile. For Krewski et al., the LML is 5.8 μg/m 3 and one standard deviation below the mean is 11.0 μg/m 3

. For Lepeule et al., the LML is 8 μg/m 3 and we do not have the data for one standard deviation below the mean. It is important to emphasize that although we have lower levels of confidence in levels below the LML for each study, the scientific evidence does not support the existence of a level below which health effects from exposure to PM 2.5 do not occur.



### Figure 6B-3. Percentage of Adult Po pulation (age 30+) by Annual Mean PM 2.5 

### Exposure in the Baseline Sector Modeling (used to generate the benefit-per-ton estimates)*
* This graph shows the population exposure in the modeling baseline used to generate the benefit-per-ton estimates. Similar graphs for analyses with air quality modeling show premature mortality impacts at each PM 2.5 concentration. Therefore, caution is warranted when interpreting this graph because it is not consistent with similar graphs from RIAs that had air quality modeling (e.g., MATS).

Among the populations exposed to PM 2.5 in the baseline: 93% are exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study 67% are exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study 0% 5% 10% 15% 20% 25%

<1 1

‐ 2 2

‐ 3 3

‐ 4 4

‐ 5 5

‐ 6 6

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‐ 9 9

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‐ 20PercentageofPopulationExposed

Baseline Annual Mean PM 2.5 Level (μg/m 3 ) LML of Lepeule et al. (2012) study LML of Krewski et al. (2009) study

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6B-25 Figure 6B-4. Cumulative Dist ribution of Adult Population (age 30+) by Annual Mean PM 2.5 

### Exposure in the Baseline Sector Modeling (used to generate the benefitper-ton estimates)*
* This graph shows the population exposure in the modeling baseline used to generate the benefit-per-ton estimates. Similar graphs for analyses with air quality modeling show premature mortality impacts at each PM 2.5 concentration. Therefore, caution is warranted when interpreting this graph because it is not consistent with similar graphs from RIAs that had air quality modeling (e.g., MATS). 

### 6B.8 Sensitivity Analysis of Ozone-relate d Impacts on Outdoor Worker Productivity The EPA last quantified the value of ozone-related worker productivity in the final Regulatory Impact Analysis supporting the Transport Rule (U.S. EPA, 2011d). That analysis applied information reported in Crocker and Horst (1981) to relate changes in ground-level ozone to changes in the productivity of outdoor citrus workers. That study found that a 10 percent reduction in ozone translated to a 1.4 increase in income among outdoor citrus workers.Concerned that this study might not adequately characterize the relationship between groundAmong the populations exposed to PM 2.5 in the baseline: 93% are exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study 67% are exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20Cumulativepercentageofpopulationexposed

Baseline Annual Mean PM 2.5 Level (μg/ m 3 ) LML of Krewski et al. (2009) study LML of Lepeule et al. (2012) study

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6B-26 level ozone and the productivity of agricultural workers because of the vintage of the underlying data, the Agency subsequently omitted this endpoint.

In a recent study, Graff Zivin and Neidell (2012) combined data on individual-level daily harvest rates for Outdoor Agricultural Workers (O WAs) with ground-level ozone concentrations to characterize changes in worker productivity as a result of ozone exposure. The authors used
data on harvest rates from a 500-acre farm in the Central Valley of California. That farm produced three crops (blueberries and two types of grapes) and the harvesting laborers were paid through piece rate contracts. The analyses in the paper were based on 2009 and 2010 California growing seasons. The analyses were not affect ed by: (i) endogenous ozone exposure (because there were limited local sources of ozone precursors); (ii) avoidance behavior (because the work has to be performed outdoors); and (iii) shirking (due to the nature of the piece rate contract).

Table 3 in Graff Zivin and Neidell (2012) reports the main result: A 10 ppb increase in work-day ozone concentration (represented by hourly measurements averaged between 6am and 3pm) will result in a decline of 0.143 (with a standard error of 0.068) in standardized hourly pieces collected on a given work day. The standardized hourly pieces were “the average hourly productivity minus the minimum number of pieces per hour required to reach the piece rate regime, divided by the standard deviation of productivity for each crop” (Graff Zivin and Neidell, 2012; p. 3665). The range of ozone concentrations in the sample was between 10.50 ppb and 86.0 ppb (Table 1 in Graff Zivin and Neidell, 2012). This result is significant and robust under different model specifications designed to test modeling assumptions. Based on the effect estimate and individual-level information in their dataset, the authors estimated the effect of an increase in ozone concentration on worker productivity, as measured by the average number of pieces collected per hour during a given work day (rather than by standardized hourly piece rate that was used in regression modeling). They found a decline of 5.5% in worker productivity due to a 10 ppb increase in average work-day ozone concentration.

While Graff Zivin and Neidell (2012) report the information needed to quantify ozonerelated worker productivity, we are still evaluating whether and how to most appropriately apply the limited evidence from this study in a national benefits assessment. An important issue is the

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6B-27 generalizability of the results to the appropriate population. Because we received no comments on this proposed approach, we now include the results in a sensitivity analysis.

Below we provide the function, input data and results for the sensitivity analysis.

ݓ݋ݎܩ݈݌݉ܧ ∗ ݏݎ݇݁ݎ݋ܹ݃ܣݎ݋݋݀ݐݑܱ ∗ ܹܽ݃݁ݎ݋݋݀ݐݑܱݕ݈ܽ݅ܦ ∗ ܳܣܽݐ݈݁ܦ ∗ ߚ	 ൌ ܻ

Table 6B-13. Definitions of Variables Used to Calculate Changes in Worker Productivity Variable Definition ߚ Percent change in daily outdoor worker productivity per 1ppb change in ozone (Graff Zivin and Neidell, 2012) ܦ݈݁ݐܽܣܳ Summer season average of daily 9 hour average (6am to 3pm) ܦ݈ܽ݅ݕܱݐݑ݀ݎ݋݋ܹܽ݃݁ Summer season daily wage for agricultural non-livestock workers in 17 USDA-defined regions (USDA, 2012) ܱݐݑ݀ܣݎ݋݋ܹ݃ݎ݋݇݁ݏݎ Summer season number of workers employed in outdoor non-livestock agricultural per county (USDA, 2010; US Census, 2010) ܧ݉݌݈ݓ݋ݎܩ Growth in agricultural non-livestock workers to 2025 (Woods and Poole, 2012)



### Table 6B-14. Population Est imated Economic Value of Increased Productivity among Outdoor Agricultural Workers from Attaining the Revised and Alternative Ozone Standards in 2025 (millions of 2011$)

Standard Economic Value (95 th percentile confidence interval) 70 ppb $1.7 ($0.1 to $3.3) 65 ppb $8.9 ($0.6 to $17) 60 ppb $20 ($1.4 to $39)



### 6B.9 References
Centers for Disease Control and Prevention (CDC). 2011 . United States Life Tables, 2007 National Vital Statistics Reports . Volume 59, Number 9. September. Available at <http://www.cdc.gov/nchs/data/nvsr/nvsr59/nvsr59_09.pdf>.
Centers for Disease Control and Prevention (CDC). 2014. Health, United States. Table 19. Years of Potential Life Lost Before Age 75 for Selected Causes of Death, According to Sex, Race and Hispanic Origin: United States, Selected Years 1980–2013 (2014).
Crocker, T. D., & Horst, R. L. 1981. “Hours of work, labor productivity, and environmental conditions: A case study.” The Review of Economics and Statistics 63(3), 361-368.
Crouse, DL; Peters, PA; van Donkelaar, A; Goldberg, MS; Villeneuve, PJ; Brion, O; Khan, S; Atari, DO; Jerrett, M; Pope, CA; Brauer, M; Brook, JR; Martin, RV; Stieb, D; Burnett, RT. 2012.” Risk of nonaccidental and cardiovascular mortality in relation to long-term exposure to low concentrations of fine particulate matter: a Canadian national-level cohort study.” Environ Health Perspect 120: 708-714.

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6B-28 Eftim SE, Samet JM, Janes H, McDermott A, Dominici F. 2008. “Fine Particulate Matter and Mortality: A Comparison of the Six Cities and American Cancer Society Cohorts with a Medicare Cohort.” Epidemiology 19: 209- 216.
Fann N, Lamson A, Wesson K, Risley D, Anenberg SC, Hubbell BJ. 2012. “Estimating the National Public Health Burden Associated with Exposure to Ambient PM 2.5 and ozone. Risk Analysis,” Risk Analysis 32(1): 81-95.
Graff Zivin, J., Neidell, M. (2012). The impact of pollution on worker productivity. American Economic Review 102, 3652-3673.
Hubbell BL. 2006. “Implementing QALYs in the analysis of air pollution regulations.” Environmental and Resource Economics 34:365–384.
Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the ConcentrationResponse Relationship Between PM2.5 Exposure and Mortality. Prepared for: Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. September. Available at <http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_tsd_expert_interview_summaries.pdf>.
Jerrett M, Burnett RT, Pope CA, III, et al. 2009. “Long-Term Ozone Exposure and Mortality.” New England Journal of Medicine 360:1085-95.
Kleckner, N., and J. Neumann. 1999. “Recommended Approach to Adjusting WTP Estimates to Reflect Changes in Real Income.” Memorandum to Jim DeMocker, US EPA/OPAR. June 3, Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/Benefits/background/klecknerandneumann1999.pdf>.
Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality . HEI Research Report, 140, Health Effects Institute, Boston, MA.
Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr, and M. Studnicka. 2001. “Assessment of Deaths Attributable to Air Pollution: Should We Use Risk Estimates Based on Time Series or on Cohort Studies?” American Journal of Epidemiology 153(11):1050-55.
Laden F; Schwartz J; Speizer FE; Dockery DW. 2006. “Reduction in fine particulate air pollution and mortality: extended follow-up of the Harvard Six Cities study.” Am J Respir Crit Care Med , 173: 667-672.
Lepeule J, Laden F, Dockery D, Schwartz J. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspectives 120 (7):965-70.
Lipfert FW; Baty JD; Miller JP; Wyzga RE (2006). “PM 2.5 constituents and related air quality variables as predictors of survival in a cohort of U.S. military veterans.” Inhal Toxicol , 18: 645-657.
Mansfield and Patil, 2006. Peer Review of Expert Elicitation . Memo from RTI International to Ron Evans, EPA. September 26. Available at <http://www.epa.gov/ttn/ecas/regdata/Uncertainty/peer_review_summary_rti_29sept06.pdf>.
Miller KA, Siscovick DS, Sheppard L, Shepherd K, Sullivan JH, Anderson GL, Kaufman JD. (2007). “Long-term exposure to air pollution and incidence of cardiovascular events in women” N Engl J Med , 356: 447-458.
National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . Washington, DC: The National Academies Press. Washington, DC.
National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution . National Academies Press. Washington, DC.

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6B-29 Pope, C.A. III; Burnett, R.T.; Thun, M.J.; Calle, E.E.; Krewski, D.; Ito, K.; Thurston G.D. 2002. “Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution.” JAMA 287(9):1132-1141.
Puett, RC; Hart, JE; Suh, H; Mittleman, M; Laden, F. (2011). “Particulate matter exposures, mortality and cardiovascular disease in the health professionals follow-up study.” Environ Health Perspect 119: 1130-1135. http://dx.doi.org/10.1289/ehp.1002921
Puett, RC; Hart, JE; Yanosky, JD; Paciorek, C; Schwartz, J; Suh, H; Speizer, FE; Laden, F. 2009. “Chronic fine and coarse particulate exposure, mortality, and coronary heart disease in the Nurses' Health Study.” Environ Health Perspect 117: 1697-1701. http://dx.doi.org/10.1289/ehp.0900572
Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, Bryan J. Hubbell, and Patrick L. Kinney. 2008. “Expert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine Particulate Matter in the U.S.” Environmental Science & Technology 42(7):2268-2274.
Sasser, E. 2014. Response to Comments Regarding the Potential Use of a Threshold Model in Estimating the Mortality Risks from Long-term Exposure to Ozone in the Health Risk and Exposure Assessment for Ozone, Second External Review Draft . Memorandum to Holly Stallworth, Designated Federal Officer, Clean Air Scientific Advisory Committee from EPA/OAQPS Health and Environmental Impacts Division.
Smith, R.L.; B. Xu and P. Switzer. 2009. Reassessing the relationship between ozone and short- term mortality in U.S. urban communities. Inhalation Toxicology 21:37-61.
U.S. Environmental Protection Agency (U.S. EPA). 2006. Final Regulatory Impact Analysis (RIA) for the PM2.5 National Ambient Air Quality Standards (NAAQS) . Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at < http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf >.
U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter: Final . U.S. Environmental Protection Agency, Research Triangle Park, NC, EPA/600/R-08/139F. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.
U.S. Environmental Protection Agency (U.S. EPA). 2010. Final Regulatory Impact Analysis (RIA) for the SO2 National Ambient Air Quality Standards (NAAQS) . Office of Air Quality Planning and Standards, Research Triangle Park, NC. June. Available at <http://www .epa.gov/ttn/ecas/regdata/RI As/fso2ria100602full.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2011a. The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress . Office of Air and Radiation, Office of Policy, Washington, DC. March. Available at <http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2011b. Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards . EPA-452/R-11-011. December. Available at: <http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2011c. Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards. EPA-452/D-11-003. April. Available on the Internet at <http://www.epa.gov/ttnnaaqs/standards/pm/s_pm_2007_pa.html>.
U.S. Environmental Protection Agency (U.S. EPA). 2011d. Regulatory Impact Analysis for the Federal Implementation Plans to Reduce Interstate Transport of Fine Particulate Matter and ozone in 27 States; Correction of SIP Approvals for 22 States . June. Available at <http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2012a. Provisional Assessment of Recent Studies on Health Effects of Particulate Matter Exposure . Office of Research and Development, Research Triangle Park, NC.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6B-30 December 2012, EPA/600/R-12/056F. Available at <http://www.epa.gov/ttn/naaqs/standards/pm/data/20121213psa.pdf >.
U.S. Environmental Protection Agency (U.S. EPA). 2012b. Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter . EPA-452/R-12-003. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. December. Available at: <http://www.epa.gov/pm/2012/finalria.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2013. Integrated Science Assessment for Ozone and Related Photochemical Oxidants: Final . Research Triangle Park, NC: U.S. Environmental Protection Agency. (EPA document number EPA/600/R-10/076F).
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2004. Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Anal ysis—Benefits and Costs of the Clean Air Act, 1990-2020: Advisory by the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance Analysis. EPA-SAB-COUNCIL-ADV-04-002. March. Available on the Internet at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400433D5D/$File/counc il_adv_04002.pdf>.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2008. Characterizing Uncertainty in Particulate Matter Benefits Using Expert Elicitation. EPA-COUNCIL-08-002. July. Available at<http://yosemite.epa.gov/sab/sabproduct.nsf/0/43B91173651AED9E85257487004EA6CB/$File/EPACOUNCIL-08-002-unsigned.pdf>.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010. Review of EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air Act . EPA-COUNCIL-10-001. June. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/59e06b6c5ca66597852575 e7006c5d09!OpenDocument&TableRow=2.3#2.>.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2014. CASAC Review of the EPA’s Health Risk and Exposure Assessment for Ozone (Second External Review Draft – February, 2014). U.S. Environmental Protection Agency Science Advisory Board. EPA-CASAC-14-005. <http://yosemite.epa.gov/sab/sabproduct.nsf/db6b9452a00aa3bd85257242006901de/5F6FB6DEE3442C948525 7D08004C707A/$File/EPA-CASAC-14-005+unsigned.pdf>.
Zeger S; Dominici F; McDermott A; Samet J (2008). Mortality in the Medicare population and chronic exposure to fine particulate air pollution in urban centers (2000-2005). Environ Health Perspect , 116: 1614-1619.

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7-1 CHAPTER 7: IMPACTS ON PUBLIC WELF ARE OF ATTAINMENT STRATEGIES TO MEET PRIMARY AND SECONDARY OZONE NAAQS

Overview This chapter provides a discussion of the welfare-related benefits of meeting alternative primary and secondary ozone standards. Welfare benefits of reductions in ambient ozone include increased growth and/or biomass production in sensitive plant species, including forest trees, increased crop yields, reductions in visible foliar injury, increased plant vigor (e.g. decreased susceptibility to harsh weather, disease, insect pest infestation, and competition), and changes in ecosystems and associated ecosystem services. Although we have not conducted an analysis to represent the ozone improvements from emissions reductions estimated in the final RIA, we reference the analysis conducted in the proposal RIA (U.S. EPA, 2014b) as an indication of the potential magnitude of effects associated with changes in yields of commercial forests and agriculture, and carbon sequestration and storage. We did not update the analysis from the proposal RIA because the welfare co-benefits estimates (i) in the proposal analysis were small, and we anticipated that the estimates in the final analysis would be even smaller, and (ii)
are not added to the human health benefits estimates.

The EPA has also concluded that the current secondary standard for ozone, set at a level of 75 ppb, is not requisite to protect public welfare from known or anticipated adverse effects, and is revising the standard to provide increased protection against vegetation-related effects on public welfare. Specifically, the EPA is retaining the indicator (ozone), averaging time (8-hour) and form (annual fourth-highest daily maximum, averaged over 3 years) of the existing secondary standard and is revising the level of that standard to 70 ppb. The EPA has concluded that this revision will effectively curtail cumulative seasonal ozone exposures above 17 ppm-hrs, in terms of a three-year average seasonal W126 index value, based on the three consecutive month period within the growing season with the maximum index value, with daily exposures cumulated for the 12-hour period from 8:00 am to 8:00 pm. Thus, the EPA has concluded that this revision will provide the requisite protectio n against known or anticipated adverse effects to the public welfare.

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7-2 In addition to the direct welfare benefits of decreased levels of ambient ozone, the emissions reduction strategies used to demonstrate attainment with alternative ozone standards may result in additional benefits associated with reductions in nitrogen deposition and reductions
in ambient concentrations of PM 2.5 

### and its components. These additional benefits include reductions in nutrient enrichment and acidification impacts on sensitive aquatic and terrestrial ecosystems and improvements in visibility in state and national parks, wilderness areas, and in the areas where people live and work. However, we are not able to quantify or monetize these
benefits in this RIA.

7.1 Welfare Benefits of Strategies to A ttain Primary and Secondary Ozone Standards

The Clean Air Act defines welfare effects to include any non-health effects, including direct economic damages in the form of lost productivity of crops and trees, indirect damages through alteration of ecosystem functions, indirect economic damages through the loss in value of recreational experiences or the existence value of important resources, and direct damages to property, either through impacts on material stru ctures or by soiling of surfaces (Section 302(h) (42 U.S.C. § 7602(h)). For welfar e effects associated with changes to ecosystem functions, we use the concept of ecosystem services as a useful framework for analyzing the impact of ecosystem changes on public welfare. Ecosystem services can be generally defined as the benefits that individuals and organizations obtain from ecosystems. The EPA has defined ecological goods and services as the “outputs of eco logical functions or processes that directly or indirectly contribute to social welfare or have the potential to do so in the future. Some outputs may be bought and sold, but most are not marketed” (U.S. EPA, 2006). Changes in these services can affect human well-being by affecting security, health, social relationships, and access to basic material goods (MEA, 2005).

This RIA employs reductions in nitrogen oxides (NOx) and volatile organic compound (VOC) emissions to demonstrate attainment with alternative levels of the NAAQS. Reductions in these emissions will result in changes in ambient concentrations of ozone, as well as changes in ambient concentrations of NOx, PM 2.5 

### and its components, and deposition of nitrogen. It is appropriate and reasonable to include all the benefits associated with these emissions reductions to provide a comprehensive understanding of the likely public welfare impacts of attaining

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7-3 alternative standards. Table 7-1 shows the welfare effects associated with emissions of NOx and VOC. The following subsections discuss the direct benefits of reducing ambient ozone
concentrations and the additional welfare benefits associated with reduced emissions of NOx and
VOC.

Table 7-1. Welfare Effects of NO x 

### and VOC Emissions

Pollutant Atmospheric Effects Atmospheric and Deposition Effects Deposition Effects

Vegetation Injury (Ozone) Visibility Impairment Materials Damage Climate Ecosystem Effects— (Organics) Acidification (freshwater) Nitrogen Enrichment

N O x      

VOCs    



### 7.2 Welfare Benefits of Reducing Ozone Ozone can affect ecological systems, leading to changes in the ecological community and influencing the diversity, health, and vigor of individual species (U.S. EPA, 2013). Ozone causes discernible injury to a wide array of vegetation (U.S. EPA, 2013). In terms of forest productivity and ecosystem diversity, ozone may be the pollutant with the greatest potential for region-scale forest impacts (U.S. EPA, 2013). Studies have demonstrated repeatedly that ozone concentrations observed in polluted areas can have substantial impacts on plant function (De Steiguer et al,. 1990; Pye, 1988).

When ozone is present in ambient air, it can enter the leaves of plants, where it can cause significant cellular damage. Like carbon dioxide and other gaseous substances, ozone enters plant tissues primarily through the stomata in leaves in a process called “uptake” (Winner and Atkinson, 1986). Once sufficient levels of ozone (a highly reactive substance), or its reaction products, reaches the interior of plant cells, it can inhibit or damage essential cellular components and functions, including enzyme activities, lipids, and cellular membranes, disrupting the plant’s osmotic (i.e., water) balance and energy utilization patterns (U.S. EPA, 2013; Tingey and Taylor, 1982). With fewer resources available, the plant reallocates existing

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7-4 resources away from root growth and storage, above ground growth or yield, and reproductive processes, toward leaf repair and maintenance, leading to reduced growth and/or reproduction. Studies have shown that plants stressed in these ways may exhibit a general loss of vigor, which can lead to secondary impacts that modify plants' responses to other environmental factors. Specifically, plants may become more sensitive to other air pollutants, or more susceptible to disease, pest infestation, harsh weather (e.g., drought, frost) and other environmental stresses, which can all produce a loss in plant vigor in ozone-sensitive species that over time may lead to premature plant death. Furthermore, there is evidence that ozone can interfere with the formation of mycorrhizae, an essential symbiotic fungus associated with the roots of most terrestrial plants, by reducing the amount of carbon available for transfer from the host to the symbiont (U.S. EPA, 2013).

This ozone damage may or may not be accompanied by visible injury on leaves, and likewise, visible foliar injury may or may not be a symptom of the other types of plant damage described above. Foliar injury is usually the first visible sign of injury to plants from ozone exposure and indicates impaired physiological pr ocesses in the leaves (Grulke, 2003). When visible injury is present, it is commonly manifested as chlorotic or necrotic spots, and/or increased leaf senescence (accelerated leaf aging). Visible foliar injury reduces the aesthetic value of ornamental vegetation and trees in urban landscapes and negatively affects scenic vistas in protected natural areas.

Ozone can produce both acute and chronic injury in sensitive species depending on the concentration level and the duration of the exposur e. Ozone effects also tend to accumulate over the growing season of the plant, so that even lower concentrations experienced for a longer duration have the potential to create chronic stress on sensitive vegetation. Not all plants, however, are equally sensitive to ozone. Much of the variation in sensitivity between individual plants or whole species is related to the plant’s ability to regulate the extent of gas exchange via leaf stomata (e.g., avoidance of ozone uptake through closure of stomata) and the relative ability of species to detoxify ozone-generated reactive oxygen free radicals (U.S. EPA, 2013; Winner, 1994). After injuries have occurred, plants may be capable of repairing the damage to a limited extent (U.S. EPA, 2013). Because of the differi ng sensitivities among plants to ozone, ozone pollution can also exert a selective pressure that leads to changes in plant community

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7-5 composition. Given the range of plant sensitivities and the fact that numerous other environmental factors modify plant uptake and response to ozone, it is not possible to identify threshold values above which ozone is consistently toxic for all plants.

Because plants are at the base of the food web in many ecosystems, changes to the plant community can affect associated organisms and ecosystems (including the suitability of habitats that support threatened or endangered species and below ground organisms living in the root zone). Ozone impacts at the community and ecosystem level vary widely depending upon numerous factors, including concentration and temp oral variation of tropospheric ozone, species composition, soil properties and climatic factors (U.S. EPA, 2013). In most instances, responses to chronic or recurrent exposure in forested ecosystems are subtle and not observable for many years. These injuries can cause stand-level forest decline in sensitive ecosystems (U.S. EPA, 2013, McBride et al., 1985; Miller et al., 1982). It is not yet possible to predict ecosystem responses to ozone with certainty; however, considerable knowledge of potential ecosystem responses is available through long-term observations in highly damaged forests in the U.S. (U.S EPA, 2013). Biomass loss due to ozone exposure affects climate regulation by reducing carbon sequestration. More carbon stays in the atmosphere because carbon uptake by forests is reduced. The studies cited in the Ozone ISA demonstrate a consistent pattern of reduced carbon uptake because of ozone damage, with some of the largest reductions projected over North America (U.S. EPA, 2013).

Ozone also directly contributes to climate change because tropospheric ozone traps heat, leading to increased surface temperatures. Projections of radiative forcing due to changing ozone concentrations over the 21st century show wide variation, due in large part to the uncertainty of future emissions of source ga ses (U.S. EPA 2014a). However, reduction of tropospheric ozone concentrations could provide an important means to slow climate change in addition to the added benefit of improving surface air quality (U.S. EPA, 2014a).

While it is clear that increases in tropospheric ozone lead to warming, the precursors of ozone also have competing effects on methane, complicating emissions reduction strategies. A
decrease in carbon monoxide or VOC emissions would shorten the lifetime of methane, leading to an overall cooling effect. A decrease in NO X 

### emissions could lengthen the methane lifetime in

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7-6 certain regions, leading to warming (U.S. EPA, 2014a). Additionally, some strategies to reduce ozone precursor emissions could also lead to the reduced formation of aerosols (e.g., nitrates and sulfates) that currently have a cooling effect.

In the proposal RIA (U.S. EPA, 2014b), we were able to quantify only a small portion of the welfare impacts associated with reductions in ozone concentrations to meet alternative ozone standards. Using a model of commercial agriculture and forest markets, we analyzed the effects on consumers and producers of forest and agricultural products of changes in the W126 index resulting from meeting alternative standards within the proposed range of 70 to 65 ppb, as well as a lower standard level of 60 ppb. We also assessed the effects of those changes in commercial agricultural and forest yields on carbon sequestration and storage. This analysis provided limited quantitative information on the welfare benefits of meeting these alternative standards, focused only on one subset of ecosystem services. Commercial and non-commercial forests provide a number of additional services, including medicinal uses, non-commercial food and fiber production, arts and crafts uses, habitat, recreational uses, and cultural uses for Native American tribes. A more complete discussion of these additional ecosystem services is provided in the
final Welfare Risk and Exposure Assessment for Ozone (WREA) (U.S. EPA, 2014a). Because we did not update this analysis for this RIA, we refer the reader to the results in the proposal RIA for an indication of the potential magnitude of these welfare benefits.

7.3 Additional Welfare Benefits of Strategies to Meet the Ozone NAAQS

Reductions in emissions of NOx and VOC are associated with additional welfare benefits, including reductions in nutrient enrichment and acidification impacts on sensitive aquatic and terrestrial ecosystems and improvements in visibility in state and national parks, wilderness areas, and in the areas where people live and work.

Excess nitrogen deposition can lead to eutrophication of estuarine waters, which is associated with a range of adverse ecological effects. These include low dissolved oxygen (DO), harmful algal blooms (HABs), loss of submerged aquatic vegetation (SAV), and low water clarity. Low DO disrupts aquatic habitats, causing stress to fish and shellfish, which, in the short-term, can lead to episodic fish kills and, in the long-term, can damage overall growth in fish and shellfish populations. HAB are often toxic to fish and shellfish, lead to fish kills and

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7-7 aesthetic impairments of estuaries, and can in some instances be harmful to human health. SAV provides critical habitat for many aquatic species in estuaries and, in some instances, can also protect shorelines by reducing wave strength. Low water clarity is in part the result of accumulations of both algae and sediments in estuarine waters. In addition to contributing to declines in SAV, high levels of turbidity also degrade the aesthetic qualities of the estuarine
environment.

Nutrient enrichment from nitrogen deposition to terrestrial ecosystems is causally linked to alteration of species richness, species composition, and biodiversity (U.S. EPA, 2008b). Nitrogen enrichment occurs over a long time period; as a result, it may take as much as 50 years or more to see changes in ecosystem conditions, indicators, and services.

Terrestrial acidification resulting from deposition of nitrogen can result in declines in sensitive tree species, such as red spruce ( Picea rubens ) and sugar maple ( Acer saccharum ), and can also impact other plant communities including shrubs and lichen (U.S. EPA, 2008b). Biological effects of acidification in terrestrial ecosystems are generally linked to aluminum toxicity and decreased ability of plant roots to take up base cations (U.S. EPA, 2008b). Terrestrial acidification affects several important ecosystem services, including declines in habitat for threatened and endangered species, declines in forest aesthetics and productivity, and
increases in forest soil erosion and reductions in water retention.

Aquatic acidification resulting from deposition of nitrogen can result in effects on health, vigor, and reproductive success for aquatic species; and effects on biodiversity. Deposition of nitrogen results in decreases in the acid neutralizing capacity and increases in inorganic aluminum concentration, which contribute to declines in zooplankton, macro invertebrates, and fish species richness in aquatic ecosystems (U.S. EPA, 2008b).

Reductions in NOx emissions will improve visibility in parks and wilderness areas and in places where people live and work because of their impact on light extinction (U.S. EPA, 2009). Good visibility increases quality of life where individuals live and work, and where they travel for recreational activities, including sites of unique public value, such as the Great Smoky Mountains National Park (U. S. EPA, 2009). Particulate nitrate is an important contributor to light extinction in California and the upper Midwestern U.S., particularly during winter (U.S.

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7-8 EPA, 2009). While EPA typically estimates the visibility benefits associated with reductions in NOx (U.S. EPA, 2008a), we have not done so here because we do not have estimates of the changes in particulate nitrate needed to calculate changes in light extinction and the resulting changes in
economic benefits.

Strategies implemented by state and local governments to reduce emissions of ozone precursors may also impact emissions of CO 2 

### or other long-lived climate gases. Our ability to quantify the climate effects of the proposed standard levels is limited due to lack of available information on the energy and associated climate gas implications of control technologies assumed in the illustrative control strategy alternatives, remaining uncertainties regarding theimpact of ozone precursors on climate change, and lack of available information on the cocontrolled greenhouse gas (GHG) emission reductions. As a result, we do not attempt to quantify the impacts of the illustrative attainment scenarios on GHG emissions and impacts.

7.4 References
De Steiguer, J., Pye, J., Love, C. 1990. Air Pollution Dama ge to U.S. Forests. Journal of Forestry, 88(8), 17-22.
Grulke, N.E. 2003. The physiological basis of ozone injury assessment attributes in Sierran conifers. In A. Bytnerowicz, M.J. Arbaugh, & R. Alonso (Eds.), Ozone air pollution in the Sierra Nevada: Distribution and effects on forests. (pp. 55-81). New York, NY: Elsevier Science, Ltd.
McBride, J.R., Miller, P.R., Laven, R.D. 1985. Effects of oxidant air pollutants on forest succession in the mixed conifer forest type of southern California. In: Air Pollutants Effects on Forest Ecosystems, Symposium Proceedings, St. P, 1985, p. 157-167.
Millennium Ecosystem Assessment Board (MEA). 2005. Ecosystems and Human Well-being: Synthesis. Washington, DC: World Resources Institute. Available on the Internet at <http://www.who.int/globalchange/ecosystems/ecosystems05/en/index.html>.
Miller, P.R., O.C. Taylor, R.G. Wilhour. 1982. Oxidant air pollution effects on a western coniferous forest ecosystem. Corvallis, OR: U.S. Environmental Protection Agency, Environmental Research Laboratory (EPA600-D-82-276).
Pye, J.M. 1988. Impact of ozone on the growth and yield of trees: A review. Journal of Environmental Quality, 17, 347-360.
Tingey, D.T., and Taylor, G.E. 1982. Variation in plant response to ozone: a conceptual model of physiological events. In M.H. Unsworth & D.P. Omrod (Eds.), Effects of Gaseous Air Pollution in Agriculture and Horticulture. (pp.113-138). London, UK: Butterworth Scientific.
U.S. Environmental Protection Agency (U.S. EPA). 2006. Ecological Benefits Assessment Strategic Plan. EPA-240- R-06-001. Office of the Administrator. Washington, DC. October. Available on the Internet at <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/EcologBenefitsPlan.html>.

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7-9 U.S. Environmental Protection Agency (U.S. EPA). 2008a. Regulatory Impact Analysis, 2008 National Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. Office of Air Quality Planning and Standards, Research Triangle Park, NC. March. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/6- ozoneriachapter6.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2008b. Integrated Science Assessment for Oxides of Nitrogen and Sulfur—Ecological Criteria National (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/139. December. Available on the Internet at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>.
U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for Environmental Assessment—RTP Division. December. Available on the Internet at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.
U.S. Environmental Protection Agency (U.S. EPA). 2013. Integrated Science Assessment of Ozone and Related Photochemical Oxidants (Final Report). EPA-600/R-10/076F. February. Available on the Internet at <http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492>.
U.S. EPA. (U.S. Environmental Protection Agency). 2014a. Welfare Risk and Exposure Assessment (Final). August. Available on the internet at: <http://www.epa.gov/ttn/naaqs/standards/ozone/data/20141021welfarerea.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2014b. Regulatory Impact Analysis of the Proposed Revisions to the National Ambient Air Quality Standards for Ground-Level Ozone. Office of Air Quality Planning and Standards, Research Triangle Park, NC. November. Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/20141125ria.pdf >.
Winner, W.E. 1994. Mechanistic analysis of plant responses to air pollution. Ecological Applications, 4(4), 651- 661.
Winner, W.E., and C.J. Atkinson. 1986. Absorption of air pollution by plants, and consequences for growth. Trends in Ecology and Evolution 1:15-18.

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8-1 CHAPTER 8: COMPARISON OF COSTS AND BENEFITS

Overview The EPA performed an illustrative analysis to estimate the costs and human health benefits of nationally attaining revised and alternative ozone standards. The EPA Administrator is revising the level of the primary ozone standard to 70 ppb. Per Executive Order 12866 and the guidelines of OMB Circular A-4, this Regulatory Impact Analysis (RIA) presents analysis of an alternative standard level of 65 ppb. This chapter summarizes these results and discusses the implications of the analysis. The cost and benefit estimates below are calculated incremental to a 2025 baseline assuming attainment of the existing ozone standard of 75 ppb and incorporating air quality improvements achieved through the projected implementation of existing regulations.

8.1 Results In this RIA we present the primary cost and benefit estimates for full attainment in 2025. For analytical purposes, we assume that almost all areas of the country will meet each alternative standard level in 2025 through the adoption of technologies at least as effective as the control strategies used in this illustration. It is expected that some costs and benefits will begin occurring earlier, as states begin implementing control measures to attain earlier or to show progress towards attainment. For California, we provide estimates of the costs and benefits of attaining the standard in a post-2025 time frame.

In estimating the incremental costs and benefits of the revised and alternative standard levels, we recognize that there are several areas that are not required to meet the existing ozone standard by 2025. The Clean Air Act allows areas with more significant air quality problems to take additional time to reach the existing standard. Several areas in California are not required to meet the existing standard by 2025, and depending on how areas are ultimately designated for a revised standard, many areas may not be required to meet a revised standard until sometime between 2032 and December 31, 2037. Because of data and resource constraints, we were not able to project emissions and air quality beyond 2025 for California; however, we adjusted baseline air quality to reflect mobile source emi ssions reductions for California that would occur between 2025 and 2030; these emissions reductions will be the result of mobile source

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8-2 regulations expected to be fully implemented by 2030. While there is uncertainty about the precise timing of emissions reductions and related costs for California, we assume costs occur through the end of 2037 and beginning of 2038. In addition, we model benefits for California using projected population demographics for 2038.

Because of the different timing for incurring costs and accruing benefits and for ease of discussion throughout the analyses, we refer to the different time periods for potential attainment as 2025 and post-2025 to reflect that (1) we did not project emissions and air quality for any year other than 2025; (2) for California, emissions controls and associated costs are assumed to occur through the end of 2037 and beginning of 2038; and (3) for California benefits are modeled using population demographics in 2038. It is not straightforward to discount the post-2025 results for California to compare with or add to the 2025 results for the rest of the U.S. While we estimate benefits using 2038 information, we do not have good information on precisely when the costs of controls will be incurred. Because of these differences in timing related to California attaining a revised standard, the separate post-2025 costs and benefits estimates for California should not be added to the primary 2025 estimates for the rest of the U.S.

By the 2030s, various mobile source rules, such as the onroad and nonroad diesel rules, are expected to be fully implemented. Because California will likely not have all of its areas in attainment with a revised standard until sometime after its attainment date for the existing standard, it is important to reflect the impact these mobile source rules might have on the emissions that affect ozone nonattainment. To reflect the emissions reductions that are expected from these rules, we subtract them from the estimates of the emissions reductions that might be needed for California to fully attain in 2025, making our analysis more consistent with full attainment later than 2025. The EPA did the analysis this way to be consistent with the requirements in the Clean Air Act and because forcing full attainment in California in an earlier year would likely lead to overstating costs and benefits due to (1) emissions reductions those areas might enjoy from existing federal or state programs implemented between 2025 and the future potential attainment year, (2) the likelihood that energy efficiency and cleaner technologies will be further implemented, and/or (3) the potential decline in costs of existing technologies due to economies of scale or improvements in the efficiency of installing and operating controls (‘learning by doing’).

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8-3 Tables 8-1 and 8-2 summarize the total costs and benefits of the revised and alternative standard levels analyzed, and show the net benefits for each of the levels across a range of modeling assumptions related to the calculation of costs and benefits. 171 

### Tables 8-3 and 8-4 summarize the costs and benefits resulting from identified control strategies and do not include the additional costs and benefits associated with the unidentified control strategies. Tables 8-5 and 8-6 provide information on the total costs by geographic region for the U.S., except California in 2025 and on the costs for California for post-2025. Tables 8-7 and 8-8 provide a regional breakdown of benefits for 2025 and a regional breakdown of benefits for post-2025.

The estimates for benefits reflect the variability in the functions available for estimating the largest source of benefits – avoided premature mortality associated with simulated reductions
in ozone and PM 2.5 

### (as a co-benefit). The low end of the range of net benefits is constructed by subtracting the cost from the lowest benefit, while the high end of the range is constructed by subtracting the cost from the highest benefit. Following these tables is a discussion of the implications of these estimates, as well as the uncertainties and limitations that should be considered in interpreting the estimates.

In the RIA we provide estimates of costs of emissions reductions to attain the revised and alternative standards in three regions -- California, the rest of the western U.S., and the eastern U.S. In addition, we provide estimates of the benefits that accrue to each of these three regions resulting from both control strategies applied within the region and reductions in transport of ozone associated with emissions reductions in other regions.
The net benefits of emissions reductions strategies in a specific region would be the benefits of the emissions reductions occurring both within and outside of the region minus the
costs of the emissions reductions. Because the air quality modeling is done at the national level,

171 As discussed in Chapter 2, Section 2.2.5, of the 1,225 ozone monitors with complete ozone data, there were seven monitors, or 0.6 percent of the total, for which the DVs were influenced by wintertime ozone episodes. These seven monitors were removed from the analysis because the modeling tools are not currently sufficient to properly characterize ozone formation during wintertime ozone episodes. Because there was no technically feasible method for projecting DVs at these sites, these sites were not included in determining required NO x and VOC emissions reductions needed to meet the revised or alternative standard levels. There could be additional emissions reductions required to lower ozone at these locations and associated additional costs and benefits not reflected in this analysis.

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8-4 we do not estimate separately the nationwide benefits associated with the emissions reductions occurring in any specific region. 172 

### As a result, we are only able to provide net benefits estimates at the national level. The difference between the estimated benefits accruing to a specific region and the costs for that region is not an estimate of net benefits of the emissions reductions in that region because it ignores the benefits occurring outside of that region.

Table 8-1. Total Costs, Total Monetized Benefits, and Net Benefits of Control Strategies in 2025 for U.S., except California (billions of 2011$) a

Revised and Alternative Standard Levels Total Costs b Monetized Benefits Net Benefits 7% Discount Rate 7% Discount Rate 7% Discount Rate 70 $1.4 $2.9 to $5.9 $1.5 to $4.5 65 $15.5 $15 to $30 -$1.0 to $14

a Estimating multiple years of costs and benefits is not possible for this RIA due to data and resource limitations. As a result, we provide a snapshot of costs and benefits in 2025, using the best available information to approximate social costs and social benefits recognizing uncertainties and limitations in those estimates. b The numbers presented in this table reflect engineering costs annualized at a 7 percent discount rate to the extent possible. 

### Table 8-2. Total Costs, Total Monetized Benefi ts, and Net Benefits of Control Strategies Applied in California, Post-2025 (billions of 2011$) a

Revised and Alternative Standard Levels Total Costs Monetized Benefits Net Benefits 7% Discount Rate 7% Discount Rate 7% Discount Rate 70 $0.8 $1.2 to $2.1 $0.4 to $1.3
65 $1.5 $2.3 to $4.2 $0.8 to $2.7

a Estimating multiple years of costs and benefits is not possible for this RIA due to data and resource limitations. As a result, we provide a snapshot of costs and benefits in 2025, using the best available information to approximate social costs and social benefits recognizing uncertainties and limitations in those estimates. b The numbers presented in this table reflect engineering costs annualized at a 7 percent discount rate to the extent possible. 

### Table 8-3. Summary of Identified Control Strategies Annualized Control Costs by Sector for 70 ppb and 65 ppb for 2025 - U. S., except California (millions of 2011$) a Geographic Area Emissions Sector Identified Control Costs for 70 ppb Identified Control Costs for 65 ppb 7 Percent Discount Rate b 7 Percent Discount Rate b

East EGU 52 c 130 c Non-EGU Point 260 d 750 d

172 For California, we provide separate estimates of the costs and nationwide estimates of benefits, so it is appropriate to calculate net benefits. As such, we provide net benefits for the post-2025 California analysis.

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8-5 Geographic Area Emissions Sector Identified Control Costs for 70 ppb Identified Control Costs for 65 ppb Nonpoint 360 1,500 Nonroad 13 e 36 e Total 690 2,400

West EGU - - Non-EGU Point 4 d 49 d Nonpoint <1 88 Nonroad - 4 e Total 4 140 Total Identified Control Costs 690 2,600

a All values are rounded to two significant figures. b The numbers presented in this table reflect engineering costs annualized at a 7 percent discount rate to the extent possible.

c EGU control cost data is calculated using a capital charge rate between 7 and 12 percent for retrofit controls depending on the type of equipment. d A share of the non-EGU point source costs can be calculated using both 3 and 7 percent discount rates. A share of the non-EGU point source sector costs can be calculated using both 3 and 7 percent discount rates. When applying a 3 percent discount rate where possible, the total non-EGU point source sector costs are $250 million for 70 ppb and $740 million for 65 ppb.

e Non-EGU point source costs at a 3 percent discount rate are $72 million for 70 ppb and $180 million for 65 ppb.



### Table 8-4. Estimated Monetized Ozone and PM 2.5 

### Benefits for Revised and Alternative Annual Ozone Standards Incremental to the Baseline for the 2025 Scenario (Nationwide Benefits of Attaining the St andards in the U.S. except California) – Identified Control Strategies (billions of 2011$) a

Discount

Rate Revised and Alternative Standard Levels 70 pp b 65 pp b Identified Control Strategies Ozone-only Benefits (range reflects Smith et al. (2009) to Zanobetti and Schwartz (2008)) b $0.86 to $1.4 $2.2 to $3.5

PM 2.5 Co-benefits ( range reflects Krewski et al. (2009) to Lepeule et al. (2012) 3% $1.7 to $3.9 $4.0 to $9.0 7% $1.6 to $3.5 $3.6 to $8.1 Total Benefits 3% $2.6 to $5.3 +B $6.1 to $12 +B 7% $2.4 to $4.9 +B $5.7 to $12 +B

a Rounded to two significant figures. The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. Mortality risk valuation for PM 2.5 assumes discounting over the SABrecommended 20-year segmented lag structure. Not all possible benefits are quantified and monetized in this analysis. B is the sum of all unquantified health and welfare co-benefits. Data limitations prevented us from quantifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that we were able to quantify. These estimates reflect the economic value of avoided morbidities and premature deaths using risk coefficients from the studies noted. b Ozone-only benefits reflect short-term exposure impacts and as such are assumed to occur in the same year as ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category.

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8-6 Table 8-5. Summary of Total Control Costs (Identified + Unidentified) by Revised and Alternative Standard Level for 2025 - U.S., except California (millions of 2011$, 7% Discount Rate) a

Revised and Alternative Level Geographic Area Identified Control Costs Unidentified Control Costs Total Control Costs (Identified and Unidentified) 70 ppb East 690 700 1,400 West 4 - <5 Total $690 $700 $1,400

65 ppb East 2,400 12,000 15,000 West 140 610 750 Total $2,600 $12,600 $15,500

a All values are rounded to two significant figures. Unidentified control costs are based on an average cost-per-ton methodology described in Chapter 4. 

### Table 8-6. Summary of Total Control Costs (Unidentified Co ntrol Strategies) by Revised and Alternative Level for Post-2025 - California (millions of 2011$, 7% Discount Rate) a Revised and Alternative Level Geographic Area Total Control Costs (Unidentified) 70 ppb California $800 65 ppb California $1,500

a All values are rounded to two significant figures. Unidentified control costs are based on an average cost-per-ton methodology described in Chapter 4.



### Table 8-7. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 2025 Scenario (nationwide benefits of attainin g revised and alternative standard levels everywhere in the U.S. except California) – Identified + Unidentified Control Strategies a

Region Alterative Standards

70 ppb 65 ppb East b 98% 96% California 0% 0% Rest of West 2% 4%

a Because we use benefit-per-ton estimates to calculate the PM 2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits. b Includes Texas and those states to the north and east. Se veral recent rules such as Ti er 3 will have substantially reduced ozone concentrations by 2025 in the East, thus few additional controls would be needed to reach 70 ppb.

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8-7Table 8-8. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the Post2025 Scenario (nationwide benefits of attaining revised and alternative standard levels just in California) – Identifi ed + Unidentified Control Strategies a

Region Alterative Standards

70 ppb 65 ppb East 3% 2% California 90% 91% Rest of West 7% 7%

a Because we use benefit-per-ton estimates to calculate the PM 2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits. 

### In this RIA, we quantify an array of adverse health impacts attributable to ozone and
PM 2.5 

### . The Integrated Science Assessment for Ozone and Related Photochemical Oxidants (“Ozone ISA”) (U.S. EPA, 2013a) identifies the human health effects associated with ozoneexposure, which include premature death and a variety of illnesses associated with acute (dayslong) and chronic (months to years-long) exposures. Similarly, the Integrated Science Assessment for Particulate Matter (“PM ISA”) (U.S. EPA, 2009) identifies the human health effects associated with ambient particles, which include premature death and a variety of illnesses associated with acute and chronic exposures. Air pollution can affect human health in a variety of ways, and in Table 8-9 we summarize the “categories” of effects and describe those that we could quantify in our “core” benefits estimates and those we were unable to quantify because we lacked the data, time or techniques.

Table 8-9. Human Health Effects of Pollutants Po tentially Affected by Strategies to Attain the Primary Ozone Standards

Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information Improved Human Health Reduced incidence of premature mortality from exposure to ozone Premature mortality based on short-term exposure (all ages)   Section 5.6
Premature respiratory mortality based on long-term exposure (age 30–99)  a Section 5.6

Reduced incidence of morbidity from exposure to ozone Hospital admissions—respiratory causes (age > 65)   Section 5.6
Emergency department visits for asthma (all ages)   Section 5.6
Asthma exacerbation (age 6-18)   Minor restricted-activity days (age 18–65)   Section 5.6 School absence days (age 5–17)   Section 5.6 Decreased outdoor worker productivity (age 18–65) a a Section 5.6

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8-8 Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information Other respiratory effects (e.g., mediation use, pulmonary inflammation, decrements in lung functioning) — — ozone ISA c

Cardiovascular (e.g., hospital admissions, emergency department visits) — — ozone ISA c
Reproductive and developmental effects (e.g., reduced birthweight, restricted fetal growth) — — ozone ISA c

Reduced incidence of premature mortality from exposure to PM 2.5 Adult premature mortality based on cohort study estimates and expert elicitation estimates (age >25 or age >30)   Section 5.6 of PM RIA
Infant mortality (age <1)   Section 5.6 of PM RIA Reduced incidence of morbidity from exposure to PM 2.5 Non-fatal heart attacks (age > 18)   Section 5.6 of PM RIA Hospital admissions—respiratory (all ages)   Section 5.6 of PM RIA Hospital admissions—cardiovascular (age >20)   Section 5.6 of PM RIA Emergency department visits for asthma (all ages)   Section 5.6 of PM RIA Acute bronchitis (age 8–12)   Section 5.6 of PM RIA Lower respiratory symptoms (age 7–14)   Section 5.6 of PM RIA Upper respiratory symptoms (asthmatics age 9–11)   Section 5.6 of PM RIA Asthma exacerbation (asthmatics age 6– 18)   Section 5.6 of PM RIA Lost work days (age 18–65)   Section 5.6 of PM RIA Minor restricted-activity days (age 18–65)   Section 5.6 of PM RIA Chronic Bronchitis (age >26) — — Section 5.6 of PM RIA Emergency department visits for cardiovascular effects (all ages) — — Section 5.6 of PM RIA Strokes and cerebrovascular disease (age 50–79) — — Section 5.6 of PM RIA Other cardiovascular effects (e.g., other ages) — — PM ISA b
Other respiratory effects (e.g., pulmonaryfunction, non-asthma ER visits, nonbronchitis chronic diseases, other ages and populations) — — PM ISA b

Reproductive and developmental effects (e.g., low birth weight, pre-term births, etc.) — — PM ISA b ,c

Cancer, mutagenicity, and genotoxicity effects — — PM ISA b ,c
Asthma hospital admissions (all ages) — — NO 2 ISA d

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8-9 Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information
Reduced incidence of morbidity from exposure to NO 2 Chronic lung disease hospital admissions (age > 65) — — NO 2 ISA d
Respiratory emergency department visits (all ages) — — NO 2 ISA d
Asthma exacerbation (asthmatics age 4– 18) — — NO 2 ISA d
Acute respiratory symptoms (age 7–14) — — NO 2 ISA d Premature mortality — — NO 2 ISA b ,c Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — NO 2 ISA b ,c

a We are in the process of considering an update to the worker productivity analysis for ozone based on more recent literature. b We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.

c We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over the strength of the association. d We assess these benefits qualitatively due to time and resource limitations for this analysis.


### 8.2 Improvements between the Proposal and Final RIAs In the regulatory impact analyses for both the proposed and revised ozone NAAQS, there were two geographic areas outside of California where the majority of emissions reductions were needed to meet a standard level of 70 ppb – Texas and the Northeast. In analyzing 70 ppb in this RIA for the revised NAAQS, there were approximately 50 percent fewer emissions reductions needed in these two geographic areas. For an alternative standard of 65 ppb, emissions reductions needed nationwide were approximately 20 percent lower than at proposal.

The primary reason for the difference in emissions reductions estimated for attainment is that for this RIA we conducted more geographically-refined air quality sensitivity modeling to develop improved ozone response factors (see Chapter 2, Section 2.1.4 for a more detailed discussion of this air quality modeling) and focused the emissions reduction strategies on geographic areas closer to the monitors with the highest design values (see Chapter 3, Section 3.1.2 for a more detailed discussion of the emissions reduction strategies). The improvements in air quality modeling and emissions reduction strategies account for about 80 percent of the
difference in needed emissions reductions between the two RIAs.

In Texas and the Northeast, the updated response factors and more focused emissions reduction strategies resulted in larger changes in ozone concentrations in response to more

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8-10 geographically focused emissions reductions. In east Texas, the ppb/ton ozone response factors used in this RIA were 2 to 3 times more responsive than the factors used in the proposal RIA at controlling monitors in Houston and Dallas. In the Northeast, the ppb/ton ozone response factors used in this RIA were 2.5 times more responsive than the factors used in the proposal RIA at the controlling monitor on Long Island, NY.

A secondary reason for the difference is that in the time between developing the two RIAs we updated emissions inventories, models and model inputs for the base year of 2011. See Chapter 2, Section 2.1 and 2.2 for additional discussion of the updated emissions inventories, models and model inputs. When projected to 2025, these changes in inventories, models and inputs had compounding effects for year 2025, and in some areas resulted in lower projected base case design values for 2025. The updated emissions inventories, models, and model inputs account for about 20 percent of the difference in needed emissions reductions between the two
RIAs.

These differences in the estimates of emissions reductions needed to attain the revised
and alternative standard levels affect the estimates for the costs and benefits in this RIA. For a revised standard of 70 ppb, the costs were 60 percent lower than at proposal and the benefits were 55 percent lower than at proposal. The percent decrease in costs is slightly more than the percent decrease in emissions reductions because a larger number of lower cost identified controls were available to bring areas into attainment with 70 ppb. 173 

### The percent decrease in benefits is similar to the percent decrease in emissions reductions. For an alternative standard level of 65 ppb, the costs were less than three percent higher than those estimated at proposal and the benefits were 22 percent lower than at proposal. 174 

### The percent change in costs was less than the percent decrease in emissions reductions because in this analysis we applied identified controls in smaller geographic areas, resulting in fewer identified controls available within those

173 In the final RIA, outside of California all areas were projected to meet the current standard of 75 ppb. As such, no identified controls were used to bring areas into attainment with 75 ppb. In the proposal RIA, some of these lower cost controls were used to bring areas into attainment with 75 ppb, making them unavailable for application in the analysis of 70 ppb.

174 We have slightly modified our approach to estimating morbidity benefits since proposal, which had a negligible (~1%) influence on the total monetized benefits in this RIA.

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8-11 areas and an increase in higher cost unidentified controls being applied to bring areas into attainment with 65 ppb. The percent decrease in benefits is similar to the percent decrease in
emissions reductions.

8.2.1 Relative Contribution of PM Benefits to Total Benefits
Because of the relatively strong relationship between PM 2.5 

### concentrations and premature mortality, PM co-benefits resulting from reductions in NOx emissions can make up a large fraction of total monetized benefits, depending on the specific PM mortality impact function used, and on the relative magnitude of ozone benefits, which is dependent on the specific ozone mortality function assumed. PM co-benefits based on daily average concentrations are calculated over the entire year, while ozone related benefits are calculated only during the summer ozone season. Because the control strategies evaluated in this RIA are assumed to operate year round rather than only during the oz one season, this means that PM benefits will accumulate during both the ozone season and the rest of the year. For primary benefits estimates in 2025, PM 2.5 

### co-benefits account for approximately half to three-quarters of the estimated benefits, depending on the standard analyzed and on the choice of ozone and PM mortality
functions used.

8.2.2 Developing Future Control Strategies with Limited Data
Because of relatively higher ozone levels in several large urban areas (Southern California, Houston, and the Northeastern urban corridor, including New York and Philadelphia) and because of limitations associated with the data on currently identified emissions control technologies, the EPA recognized that identified and reasonably anticipated emissions controls would likely not be sufficient to bring some areas into attainment with either the existing or alternative, more stringent ozone standard level. Therefore, we designed this analysis in two stages: the first stage focused on analyzing the air quality improvements that could be achieved through application of documented, well-characterized, end-of-pipe emissions controls, and the costs and benefits associated with those controls. The second stage took the emissions reductions beyond identified controls and used an average cost-per-ton method to estimate the costs and benefits of these additional emissions reductions needed to bring all areas into full attainment with the alternative standard levels analyzed.

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8-12 The structure of the RIA reflects this two-stage analytical approach. Separate chapters are provided for the emissions, air quality, and cost impacts of identified controls. We used the information currently available to develop reasonable approximations of the costs and benefits of the unidentified portion of the emissions reductions necessary to reach attainment. However, because of the uncertainty associated with the costs of unidentified controls, we judged it appropriate to provide separate estimates of the costs and benefits for partial attainment (based on identified controls) and full attainment (based on identified controls and unidentified controls), as well as an overall estimate for reaching full attainment. There is a single chapter on benefits, because the methodology for estimating benefits does not change between stages. However, in that chapter, we again provide separate estimates of the benefits associated with the partial attainment and full attainment portions of the analysis.

In both stages of the analysis, it should be recognized that all estimates of future costs and benefits are not intended to be forecasts of the actual costs and benefits of implementing potentially revised standards. Ultimately, states and local areas will be responsible for developing and implementing emissions control programs to reach attainment with the ozone NAAQS, with the timing of attainment being determined by future decisions by states and the EPA. Our estimates are intended to provide information on the general magnitude of the costs and benefits of alternative standard levels rather than on precise predictions of control measures, costs, or benefits. With these caveats, we expect that this analysis can provide a reasonable picture of the types of emissions controls that are currently available, the direct costs of those controls, the levels of emissions reductions that may be achieved with these controls, the air quality impact that can be expected to result from reducing emissions, and the public health benefits of reductions in ambient ozone levels. This analysis identifies those areas of the U.S. where our existing knowledge of control strategies is not sufficient to allow us to model attainment, and where additional data or research may be needed to develop strategies for
attainment.

In many ways, RIAs for proposed and final actions are learning processes that can yield valuable information about the technical and policy issues that are associated with a particular regulatory action. This is especially true for RIAs for proposed and revised NAAQS, where we are required to stretch our understanding of both science and technology to develop scenarios

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8-13 that illustrate how certain we are about how economically feasible the attainment of these standards might be regionally. The ozone NAAQS RIA provided great challenges when compared to previous RIAs primarily because as we tighten standards across multiple pollutants with overlapping precursors (e.g., the recent tightening of the PM 2.5 

### standards), we move further
down the list of cost-effective identified and available controls in our database. With the more stringent NAAQS, more areas will need to employ additional ways of reducing emissions. The control measures reflected in our databases include very few abatement possibilities from energy efficiency measures, fuel switching, input or process changes, or other abatement strategies that are non-traditional in the sense that they are not the application of an end-of-pipe control. So while we can speculate on what some of the future emissions reduction strategies might look like based on new developments in energy efficiency and clean technology, the specific technological path in different nonattainment areas is not clear.

Because of the uncertainty regarding the development of future emissions reduction strategies, a significant portion of the analysis is based on extrapolating from available data on identified control technologies to generate the emissions reductions necessary to reach full attainment of an alternative ozone NAAQS and the resulting costs and benefits. Studies indicate that it is not uncommon for pre-regulatory cost estimates to be higher than later estimates, in part because of difficulty in predicting technological ch anges. Over longer time horizons, such as the time allowed for areas with high levels of ozone pollution to meet the ozone NAAQS, the opportunity for technical change is greater (See Chapter 4, Section 4.2.3 for additional discussion). Also, because of the nature of the extrapolation method for benefits (which focuses on reductions in ozone only at monitors that exceed the NAAQS), we generally understate the total benefits that would result from implementing additional emissions controls to fully attain the ozone NAAQS (i.e., assuming that the application of control strategies would result in ozone reductions both at nonattainment and attainment monitors). On the other hand, the possibility also exists that benefits are overestimated, because it is possible that new technical changes might not meet the specifications, development time lines, or cost estimates provided in this analysis.

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8-14 8.3 Net Present Value of a Stream of Costs and Benefits The EPA believes that providing comparisons of social costs and social benefits at discount rates of 3 and 7 percent is appropriate. The three different uses of discounting in the RIA – (i) construction of annualized engineering costs, (ii) adjusting the value of mortality risk for lags in mortality risk decreases, and (iii) adjusting the cost of illness for non-fatal heart attacks to adjust for lags in follow up costs -- are all appropriate. Our estimates of net benefits are the approximations of the net value (in 2025) of benefits attributable to emissions reductions needed to attain just for the year 2025.
The EPA presents annualized costs and benefits in a single year for comparison in this RIA because there are a number of methodological complexities associated with calculating the net present value (NPV) of a stream of costs and be nefits for a NAAQS. Whil e NPV analysis allows evaluation of alternatives by summing the present value of all future costs and benefits, insights into how costs will occur over time are limited by underlying assumptions and data. Calculating a present value (PV) of the stream of future benefits also poses special challenges, which we describe below. In addition, the method requires definition of the length of that future time period, which is not straightforward for this analysis and subject to uncertainty.

To estimate engineering costs, the EPA employs the equivalent uniform annual cost (EUAC) method, which annualizes costs over varying lifetimes of control measures applied in the analysis. 175 

### Using the EUAC method results in a stream of annualized costs that is equal for each year over the lifetime of control measures, resulting in a value similar to the value associated with an amortized mortgage or other loan payment. Control equipment is often purchased by incurring debt rather than through a single up-front payment. Recognizing this led the EPA to estimate costs using the EUAC method instead of a method that mimics firms paying up front for the future costs of installation, maintenance, and operation of pollution control
devices.

Further, because we do not know when a facility will stop using a control measure or change to another measure based on economic or other reasons, the EPA assumes the control

175 See Chapter 4, Section 4.1.1 for additional information on the EUAC method.

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8-15 equipment and measures applied in the illustrative control strategies remain in service for their full useful life. As a result, the annualized cost of controls in a single future year is the same throughout the lifetimes of control measures analyzed, allowing the EPA to compare the annualized control costs with the benefits in a single year for consistent comparison.

The EPA’s RIAs for air quality rules generally report the estimated net benefits of improved air quality for a single year. The estimated NPV can better characterize the stream of benefits and costs over a multi-year period. However, calculating the PV of improved air quality is generally quite data-intensive and costly. Further, the results are sensitive to assumptions regarding the time period over which the stream of benefits is discounted.

The theoretically appropriate approach for characterizing the PV of benefits is the life table approach. The life table, or dynamic population, approach explicitly models the year-to-year influence of air pollution on baseline mortality risk, population growth and the birth rate— typically for each year over the course of a 50-to-100 year period (U.S. EPA SAB, 2010; Miller, 2003). In contrast to the pulse approach 176 

### , a life table models these variables endogenously byfollowing a population cohort over time. For example, a life table will “pass” the air pollutionmodified baseline death rate and population from year to year; impacts estimated in year 50 will account for the influence of air pollution on death rates and population growth in the preceding 49 years.

Calculating year-to-year changes in mortality risk in a life table requires some estimate of the annual change in air quality levels. It is both impractical to model air quality levels for each year and challenging to account for changes in federal, state and local policies that will affect the annual level and distribution of pollutants. For each of these reasons the EPA has not generally reported the PV of benefits for air rules but has instead pursued a pulse approach. While we agree that providing the NPV of a stream of costs and benefits could be informative, based on these reasons we are not able to provide the NPV of that stream in this RIA.

176 The pulse approach assumes changes in air pollution in a single year and affects mortality estimates over a 20- year period.

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8-16 8.4 Framing Uncertainty This section includes a qualitative presentation of key factors that (1) could impact how air quality changes over time; (2) could impact the timing for meeting an alternative standard; (3) are difficult to predict and quantify; and (4) introduce additional uncertainty into this analysis. 177 

### These factors, summarized in Table 8.10 below, include energy development, distribution, and use trends; land use development patterns; economic factors; energy and research and development policies; climate signal changes; and the influence of technological change. Additional factors that could have an impact on how air quality changes over time include environmental indicators other than climate change and societal preferences and attitudes toward the environment and conservation; the potential direction and magnitude of these additional
factors is less clear.

These key factors can affect the estimated baseline air quality used in the analysis, and as a result the types of control measures and associated costs needed to meet an alternative standard. In addition, some combinations of the key factors could have significant effects beyond the effects of any individual factor. We cannot estimate the probability that any one factor or combination of factors will occur, but we do believe that they introduce additional, broader uncertainties about future trends that provide important context for the costs and benefits presented in this analysis.

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

Individual Factors Potential Implications for NAAQS Attainment Information on Trends

Energy -- Extraction, conversion, distribution and storage, efficiency, international energy trends Geopolitics, reserves, international and domestic demand, and technological breakthroughs in energy technologies can drive fuel prices up or down.

If more renewable sources of energy are employed and use of natural gas increases, then emissions may be Recent increases in domestic production of oil and a relative decrease in imported oil, in addition to policies and investments geared toward the development of alternative fuels and energy efficiency 178 have likely led to a reduction of U.S. dependence on imports of foreign oil.
Upward trends that have emerged over the last ten years in natural gas production and consumption, renewable energy installations,

177 OMB Circular A-4 indicates that qualitative discussions should be included in analyses whenever there is

insufficient data to quantify uncertainty.

178 http://energy.gov/articles/us-domestic-oil-production-exceeds-imports-first-time-18-years

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8-17 Individual Factors Potential Implications for NAAQS Attainment Information on Trends
lower, potentially lowering attainment costs. and energy efficiency technology installations are likely to continue. 179

Land Use Development Patterns – Design of urban areas, vehicle-miles travelled A move toward denser urban settlements, slowing of growth in vehicle miles travelled (VMT) and increased use of public transit could decrease emissions, potentially lowering attainment costs. 180 Recent trends in VMT illustrate some of the uncertainty around future emissions from mobile sources 181 . In 2006, projections of VMT showed a sustained increase, 182 yet VMT growth slowed in recent years and actually declined in 2008 and 2009. 183 Between 2000 and 2010 average growth in VMT was 0.8%, as compared to 2.9% from the previous decade.

The Economy An increase in economic growth, investment in technologies that have high energy use, and a return of U.S. manufacturing could lead to higher emissions making attainment potentially more costly. A slowing of the economy, investments in energy efficient technologies, and a continuation of a service-based economy could lead to lower emissions making attainment potentially less costly. 184 Affluence leads to increased consumption and energy use. However, this increase may not be proportional. Energy and materials use is not directly proportional to economic growth, but decrease or stabilize over time in spite of continued economic growth. 185

Policies a – Energy efficiency, energy security, direction of research and development, renewable energy A move toward energy security and independence would mean an increased use of domestic energy sources. If this results in a fuel mix where emissions decrease, then attainment would likely be less costly. If not, attainment would likely be more costly. A move toward investments in fuel efficiency and low emissions fuels could State and local policies related to energy efficiency, cleaner energy, energy security 186
, as well as the direction of research and development of technology can have a direct or indirect effect on emissions. Policies that result in energy efficiency, renewable electric power, the use of cleaner fuels and conservation measures would likely result in decreased emissions and likely decrease attainment costs. 187 Growth in energy demand has stayed well below growth in gross domestic product,

179 http://www.eia.gov/forecasts/aeo/er/pdf/0383er%282014%29.pdf;

http://energy.gov/sites/prod/files/2014/08/f18/2013%20Wind%20Technologies%20Market%20Report%20Present ation.pdf; http://www.eia.gov/electricity/monthly/update/archive/april2014/; http://www.ercot.com/content/news/presentations/2014/GCPA%20%2002%20Oct%202013%20FINAL.pdf; http://energy.gov/eere/sunshot/photovoltaics.
180 For example, see Cervero (1998), the Center for Clean Air Policy’s Transportation Emissions Guidebook

(http://www.trb.org/Main/Blurbs/156164.aspx). For ongoing research see http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=3092.
181 For example, see the Transportation Research Board’s National Cooperative Highway Research Program

(NCHRP) 2014.
182 https:// 

### www .fhwa.dot.gov/policy/2006cpr/chap9.htm#body
183 https://www.fhwa.dot.gov/policyinformation/travel_monitoring/13jantvt/page2.cfm
184 For example, Bo (2011), and http://www.epa.gov/region1/airquality/nox.html for manufactures contributions to

NO X emissions.
185 UNEP 2011, http://www.unep.org/resourcepanel/decoupling/files/pdf/decoupling_report_english.pdf
186 For example, http://www2.epa.g ov/laws-regulations/sum mary-energy-in dependence-and-security-act.
187 For example, see http://www.dsireusa.org/solar/solarpolicyguide/

.

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8-18 Individual Factors Potential Implications for NAAQS Attainment Information on Trends
decrease emissions and likely lower attainment costs. likely as a result of technological advances, federal, state and local energy efficiency standards and policies, and other macroeconomic factors. 188 U.S. productivity per energy expended relative to other countries suggests that additional efficiency gains are possible. 189

Intensity, Location and Outcome of the Climate Change Signal Strong climate signals that bring high temperatures could increase ozone, likely making attainment more costly. Uncertainty exists regarding how the climate signal will interact with air quality, as well as with other factors. However, research demonstrates that in areas where there are both high levels of emissions and high temperatures, attaining an ozone standard will likely be much harder. The magnitudes of these impacts will depend on atmospheric chemical and physical processes, as well as anthropogenic activities that increase or decrease NOx and/or VOC emissions. 190

Technological Change -- Including emissions reductions technologies and other technological developments Innovation in production and emissions control technologies, learning that lower costs, and breakthroughs in battery/energy storage technologies for use with renewable energy could improve air quality, reducing emissions and likely lowering attainment costs. Examples of emerging technologies include carbon capture and sequestration (CCS), battery technologies, emerging advanced biofuels, which could all have breakthroughs that could impact fuel use. Similarly, shifts in industrial production processes, such as a move from using primary metals to more recycling could impact energy use. 191

a Policies refer to any policies or regulations that are not environmental regulations set by U.S. EPA, states, tribes, or

local authorities. 

### 8.5 Key Observations from the Analysis The following are key observations about the RIA results.

 Tightening the ozone standards can incur significant, but uncertain, costs. Our estimates of costs for a set of identified NOx and VOC controls comprise only a small part of the estimated costs of full attainment. These estimated costs for the identified set of controls are still uncertain, but they are based on the best available information on control technologies, and have their basis in real, tested technologies. Estimating costs of full attainment was based on a generalized relationship between emissions and ozone levels. This introduces significant uncertainty into the calculation of the emissions reductions that might be needed to reach full attainment.

188 http://bipartisanpolicy.org/sites/default/files/BPC%20SEPI%20Energy%20Report%202013_0.pdf, p. 5.
189 http://bipartisanpolicy.org/sites/default/files/BPC%20SEPI%20Energy%20Report%202013_0.pdf, p. 69.
190 See Jacobs (2009).
191 http://www.eia.gov/todayinenergy/detail.cfm?id=16211

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8-19  Tightening the ozone standards can also result in significant benefits. Estimates of benefits are driven largely by projected reductions in ozone-related short-term mortality and co-benefits associated with reductions in PM 2.5 

### -related long-term mortality. Although using a benefit-per-ton approach in modeling PM 2.5 

### -related cobenefits (rather than direct modeling) has increased uncertainty, this approach is peer-reviewed and robust. We also modeled reductions in ozone-related long-term respiratory mortality, however due to concerns over potential double counting of benefits and limitations in our ability to project the lag-structure of reductions in this mortality endpoint, we did not include these estimates as part of the core benefit estimate. In addition to these mortality endpoints, we did quantify a wide-range of morbidity endpoints for both ozone and PM 2.5, 

### although these contribute only minimally to total monetized benefits.

 An air quality modeling approach can introduce uncertainty. Based on air quality modeling sensitivity analyses, there is significant spatial variability in the relationship between local and regional NOx emission reductions and ozone levels across urban areas. We performed a national scale air quality modeling analysis to estimate ozone concentrations for the future base case year of 2025. To accomplish this, we modeled multiple emissions cases for 2025, including th e 2025 base case and fifteen (15) 2025 emissions sensitivity si mulations. The 15 emissions sensitivity simulations were used to develop ozone sensitivity factors (ppb/ton) from the modeled response of ozone to changes in NOx and VOC emissions from various sources and locations. These ozone sensitivity factors were then used to determine the amount of emissions reductions needed to reach the 2025 baseline and evaluate revised and alternative standard levels of 70 and 65 ppb incremental to the baseline. We used the estimated emissions reductions needed to reach each of these standard levels to analyze the costs and benefits of alternative standard levels.

 NOx and VOC emissions reductions quantifie d from the technologies identified in this RIA may not be sufficient to attain alternative ozone NAAQS in some areas. In some areas of the U.S., the information we have about existing controls does not result in sufficient emissions reductions needed to meet the existing or alternative standards. Chapter 4 contains discussion of other emissions reduction measures not quantified in this RIA, as well as discussion of technological improvement over time. After applying existing rules and the illustrative identified controls across the nation (excluding California), in order to reach 70 ppb we were able to identify controls that reduce overall NOx emissions by 240,000 tons and VOC emissions by 20,000 tons. In order to reach 65 ppb we were able to identify controls that reduce overall NOx emissions by 560,000 tons and VOC emissions by 110,000 tons. After these reductions, in order to reach 70 ppb over 47,000 tons of NOx emissions remained, and in order to reach 65 ppb over 860,000 tons of NOx emissions remained.

 California costs and benefits are highly uncertain. California faces large challenges in meeting any alternative standard, but their largest challenges may be in attaining the existing standard. Because our analysis suggested that all available controls would be exhausted in attempting to meet the current 75 ppb standard, all of the benefits and costs of lower standards in California are based on the application of unidentified controls. Both

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8-20 the benefits and the costs associated with the assumed NOx and VOC reductions in California are particularly uncertain.

 Some EPA existing mobile source programs will help some areas reach attainment. These programs promise to continue to help areas reduce ozone concentrations beyond 2025. In California, continued implementation of mobile source rules, including the onroad and nonroad diesel rules and the locomotive and marine engines rule, are projected to reduce NOx emissions by an additional 14,000 tons and VOC emissions by an additional 6,300 tons between 2025 and 2030. These additional reductions will likely reduce the overall emissions reductions needed for attainment relative to what California might have needed to reduce from other sectors if attainment were to be required in 2025.

 The economic impacts (i.e., social costs) of the cost of these modeled controls were not included in this analysis. Incorporating the economic impact of the unidentified portion of the emissions reductions was too uncertain to be included as part of these estimates. Therefore, we did not include the economic impacts of either the identified control costs or costs of unidentified controls.

 Costs and benefits will depend on implementation timeframes. States will ultimately select the specific timelines for implementation as part of their State Implementation Plans. To the extent that states seek classification as extreme nonattainment areas, the timeline for implementation may be extended beyond 2025, meaning that the amount of emissions reductions that will be required in 2025 will be less, and costs and benefits in 2025 will be lower.

8.6 References
Cervero, R. (1998) The Transit Metropolis, A Global Inquiry. Island Press, Washington, D.C.
Jacob, D. J., & Winner, D. (2009). Effect of climate change on air quality. Atmospheric Environment, 43, 51-63.
Miller BG. Life table methods for quantitative impact assessments in chronic mortality. Journal of Epidemiology & Community Health, 2003; 57(3):200–206.
NCHRP (2014). The Effects of Socio-Demographics on Future Travel Demand, Transportation Research Board of

the National Academies, Washington, D.C. Strategic Issues Facing Transportation, Report 750, Volume 6.
U.S. Environmental Protection Agency (U.S. EPA). 2008. Final Ozone NAAQS Regulatory Impact Analysis. Office

of Air Quality Planning and Standards, Research Triangle Park, NC. Available at http://www.epa.gov/ttn/ecas/regdata/RIAs/452_R_08_003.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2010. Regulatory Impact Analysis (RIA) for the Proposed

Reconsideration of the ozone National Ambient Air Quality Standards (NAAQS). Office of Air Quality Planning and Standards, Research Triangle Park, NC. January. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/s1- supplemental_analysis_full.pdf>.
U.S. EPA. 2009. Integrated Science Assessment for Particulate Matter: Final. Research Triangle Park, NC: U.S. Environmental Protection Agency. (EPA document number EPA/600/R-08/139F).

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8-21 U.S. EPA. 2013a. Integrated Science Assessment for Ozone and Related Photochemical Oxidants: Final. Research Triangle Park, NC: U.S. Environmental Protection Agency. (EPA document number EPA/600/R-10/076F).

U.S. EPA Science Advisory Board. Review of EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air Act. Washington, DC, 2010.

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

Overview This section explains the statutory and executive orders applicable to EPA rules, and discusses EPA’s actions taken pursuant to these orders.

9.1 Executive Order 12866: Regula tory Planning and Review This action is an economically significant regulatory action that was submitted to the Office of Management and Budget (OMB) for review. Any changes made in response to OMB
recommendations have been documented in the docket. This RIA estimates the costs and monetized human health and welfare benefits of attaining two alternative ozone NAAQS nationwide. Specifically, the RIA examines the alternatives of 65 ppb and 70 ppb. The RIA contains illustrative analyses that consider a limited number of emissions control scenarios that states and Regional Planning Organizations might implement to achieve these alternative ozone NAAQS. However, the CAA and judicial decisions make clear that the economic and technical feasibility of attaining ambient standards are not to be considered in setting or revising NAAQS, although such factors may be considered in the development of state plans to implement the standards. Accordingly, although an RIA has been prepared, the results of the RIA have not been considered in issuing the revised NAAQS.

9.2 Paperwork Reduction Act
The information collection requirements for the revised NAAQS have been submitted for approval to the Office of Management and Budget (OMB) under the Paperwork Reduction Act (PRA). The information collection requirements are not enforceable until OMB approves them. The Information Collection Request (ICR) document prepared by the EPA for these revisions has been assigned EPA ICR #2313.0 4

. The information collected and reported under 40 CFR part 58 is needed to determine compliance with the NAAQS, to characterize air quality and associated health and ecosystems impacts, to develop emission control strategies, and to measure progress for the air pollution program. We are extending the length of the required ozone monitoring season in 32 states and

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9-2 the District of Columbia and the revised ozone monitoring seasons will become effective on January 1, 2017. We are also revising the PAMS monitoring requirements to reduce the number of required PAMS sites while improving spatial coverage, and requiring states in moderate or above ozone non-attainment areas and the ozone transport region to develop an enhanced monitoring plan as part of the PAMS requirements. Monitoring agencies will need to comply with the PAMS requirements by June 1, 2019. In addition, we are revising the ozone FRM to establish a new, additional technique for measuring ozone in the ambient air. It will be incorporated into the existing ozone FRM, using the same calibration procedure in Appendix D of 40 CFR part 50. We are also making changes to the procedures for testing performance characteristics and determining comparability between candidate FEMs and reference methods.

For the purposes of ICR number 2313.0 4 , the burden figures represent the burden estimate based on the requirements contained in this rule. The burden estimates are for the 3-year period from 201 6 through 201 8 . The implementation of the PAMS changes will occur beyond the time frame of this ICR with implementation occurring in 2019. The cost estimates for the PAMS network (including revisions) will be captured in future routine updates to the Ambient Air Quality Surveillance ICR that are required every 3 years by OMB. The addition of a new FRM in 40 CFR part 50 and revisions to the ozone FEM procedures for testing performance characteristics in 40 CFR part 53 does not add any additional information collection requirements.

The ICR burden estimates are associated with the changes to the ozone seasons in the revised NAAQS. This information collection is es timated to involve 158 respondents for a total cost of approximately $24,597,485 (total capital, labor, and operation and maintenance) plus a total burden of 339,930 hours for the support of all operational aspects of the entire ozone monitoring network. The labor costs associated with these hours are $20,209,966. Also included in the total are other costs of operations and maintenance of $2,254,334 and equipment and contract costs of $2,133,185. The actual labor cost increase to expand the ozone monitoring seasons is $2,064,707. In addition to the costs at the state, local, and tribal air quality management agencies, there is a burden to EPA of 41,418 hours and $2,670,360. Burden is defined at 5 CFR 1320.3(b). State, local, and tribal entities are eligible for state assistance grants provided by the Federal government under the CAA which can be used for related activities. An

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9-3 agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB control number. The OMB control numbers for EPA’s regulations in 40 CFR are listed in 40 CFR part 9.

9.3 Regulatory Flexibility Act This action will not have a significant econo mic impact on a substantial number of small entities under the RFA. This action will not impose any requirements on small entities. Rather,
the rule establishes national standards for allowable concentrations of ozone in ambient air as required by section 109 of the CAA. See also American Trucking Associations v. EPA , 175 F. 3d at 1044-45 (NAAQS do not have significant impacts upon small entities because NAAQS themselves impose no regulations upon small entiti es). Similarly, the revisions to 40 CFR part 58 address the requirements for states to collect information and report compliance with the NAAQS and will not impose any requirements on small entities. Similarly, the addition of a new FRM in 40 CFR part 50 and revisions to the FEM procedures for testing in 40 CFR part 53 will not impose any requirements on small entities.

9.4 Unfunded Mandates Reform Act This action does not contain any unfunded mandate as described in UMRA, 2 U. S. C. 1531 – 1538, and does not significantly or uniquely affect small governments. Furthermore, as indicated previously, in setting a NAAQS the EPA cannot consider the economic or technological feasibility of attaining ambient air quality standards, although such factors may be considered to a degree in the development of state plans to implement the standards. See also American Trucking Associations v. EPA , 175 F. 3d at 1043 (noting that because the EPA is precluded from considering costs of implementation in establishing NAAQS, preparation of a RIA pursuant to the UMRA would not furnish any information which the court could consider in reviewing the NAAQS).

9.5 Executive Order 13132: Federalism This action does not have federalism implications. It will not have substantial direct effects on the states, on the relationship between the national government and the states, or on the distribution of power and responsibilities among the various levels of government.

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9-4 9.6 Executive Order 13175: Consultation and Coordination with Indian Tribal Governments This action does not have tribal implications as specified in Executive Order 13175. It does not have a substantial direct effect on one or more Indian Tribes as tribes are not obligated to adopt or implement any NAAQS. In addition, tribes are not obligated to conduct ambient monitoring for ozone or to adopt the ambient monitoring requirements of 40 CFR part 58. Thus, Executive Order 13175 does not apply to this action.

The EPA specifically solicited comment on this rule from tribal officials. The EPA also conducted outreach consistent with the EPA Policy on Consultation and Coordination with Indian Tribes. Outreach to tribal environmental professionals was conducted through participation in the Tribal Air call, which is sponsored by the National Tribal Air Association. Consistent with the EPA Policy on Consultation and Coordination with Indian Tribes, the EPA offered formal consultation to the tribes during the public comment period. Consultation was not requested.

9.7 Executive Order 13045: Protection of Children from Environmental Health & Safety Risks This action is subject to Executive Order 13045 because it is an economically significant regulatory action as defined by Executive Order 12866, and the EPA believes that the environmental health risk addressed by this action may have a disproportionate effect on children. The rule will establish uniform NAAQS for ozone; these standards are designed to protect public health with an adequate margin of safety, as required by CAA section 109. However, the protection offered by these standards may be especially important for children because children, especially children with asthma, along with other at-risk populations192 such as all people with lung disease and people active outdoors, are at increased risk for health effects associated with exposure to ozone in ambient air. Because children are considered an at-risk lifestage, we have carefully evaluated the environmental health effects of exposure to ozone pollution among children. Discussions of the results of the evaluation of the scientific evidence,

192 As used here and similarly throughout this document, the term population refers to people having a quality or characteristic in common, including a specific pre-existing illness or a specific age or lifestage.

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9-5 policy considerations, and the exposure and risk assessments pertaining to children are contained in sections II.B and II.C of the preamble.

9.8 Executive Order 13211: Actions that Significantly Affect Energy Supply, Distribution, or Use This action is not a “significant energy action” because it is not likely to have a significant adverse effect on the supply, distribution, or use of energy. The purpose of the rule is to establish a revised NAAQS for ozone, establish an additional FRM, revise FEM procedures for testing, and revises air quality surveillance requirements. The rule does not prescribe specific pollution control strategies by which these ambient standards and monitoring revisions will be met. Such strategies will be developed by states on a case-by-case basis, and the EPA cannot predict whether the control options selected by states will include regulations on energy suppliers, distributors, or users. Thus, the EPA conc ludes that this rule is not likely to have any adverse energy effects and does not constitute a significant energy action as defined in Executive
Order 13211.

Application of the modeled illustrative control strategy containing identified controls for power plants, shown in Chapter 4, means that 4 percent of the total projected coal-fired EGU capacity nationwide in 2025 could be affected by controls for the revised standard level of 70 ppb. Similarly, 13 percent of total projected coal-fired EGU capacity in 2025 could be affected by controls for the alternative standard of 65 ppb. In addition, some fuel switching might occur that could alter these percentages, although we are not able to estimate the effect on energy impacts from fuel switching. In addition, we are not able to estimate energy impacts resulting from application of controls to non-EGUs or mobile sources. It is important to note that the estimates presented above are just one illustrative strategy and states may choose to apply controls to sources other than EGUs for the purpose of attaining a more stringent standard.

9.9 National Technology Transfer and Advancement Act This rulemaking involves environmental monitoring and measurement. Consistent with the Agency’s Performance Based Measurement System (PBMS), the EPA is not requiring the use of specific, prescribed analytical methods. Rather, the Agency is allowing the use of any method that meets the prescribed performance criteria. Ambient air concentrations of ozone are

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9-6 currently measured by the Federal reference method (FRM) in 40 CFR part 50, Appendix D (Measurement Principle and Calibration Procedure for the Measurement of Ozone in the Atmosphere) or by Federal equivalent methods (FEM) that meet the requirements of 40 CFR part 53. Procedures are available in part 53 that a llow for the approval of an FEM for ozone that is similar to the FRM. Any method that meets the performance criteria for a candidate equivalent method may be approved for use as an FEM. This approach is consistent with EPA’s PBMS. The PBMS approach is intended to be more flexible and cost-effective for the regulated community; it is also intended to encourage innovation in analytical technology and improved data quality. The EPA is not precluding the use of any method, whether it constitutes a voluntary consensus standard or not, as long as it meets the specified performance criteria.

9.10 Executive Order 12898: Federal Actions to Address Environmental Justice in Minority Populations and Lo w-Income Populations The EPA believes that this action will not have disproportionately high and adverse human health or environmental effects on minority populations, low-income populations or indigenous peoples. The action described in this notice is to strengthen the NAAQS for ozone

. 

### The primary NAAQS are established at a level that is requisite to protect public health, including the health of sensitive or at-risk groups, with an adequate margin of safety. The NAAQS decisions are based on an explicit and comprehensive assessment of the current scientific evidence and associated exposure/risk analyses. More specifically, EPA expressly considers the available information regarding health effects among at-risk populations, including that available for low-income populations a nd minority populations, in decisions on NAAQS. Where low-income populations or minority populations are among the at-risk populations, the decision on the standard is based on providing protection for these and other at-risk populations and lifestages. Where such populations are not identified as at-risk populations, a NAAQS that is established to provide protection to the at-risk populations would also be expected to provide protection to all other populations, including low-income populations and minority populations.

The Integrated Science Assessment, the Health Risk and Exposure Assessment, and the Policy Assessment for this review, which include identification of populations at risk from ozone health effects, are available in the docket EPA-HQ-OAR-2008-0699. The information on at-risk

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9-7 populations for this NAAQS review is summarized and considered in the rule preamble (see section II.A). The final rule increases the level of environmental protection for all affected populations without having any disproportionately high and adverse human health or environmental effects on any population, including any minority populations, low-income populations or indigenous peoples. The rule establishes uniform national standards for ozone in ambient air that, in the Administrator’s judgment, protect public health, including the health of sensitive groups, with an adequate margin of safety.

Although it has a separate docket and is not part of the rulemaking record for this action, EPA has prepared a RIA of this decision. As part of the RIA, a demographic analysis wasconducted. While, as noted in the RIA, the demographic analysis is not a full quantitative, sitespecific exposure and risk assessment, that analysis examined demographic characteristics of persons living in areas with poor air quality relative to the revised standard. Specifically, Appendix 9A describes the proximity and socio-demographic analysis. The analysis found that in areas with poor air quality relative to the revised standard,193 the representation of minority populations was slightly greater than in the U.S. as a whole. Because the air quality in these areas does not currently meet the revised standard, populations in these areas would be expected to benefit from implementation of the strengthened standard, and, thus, would be more affected by strategies to attain the revised standard. The analysis, which evaluates the potential implications for minority populations and low-income populations of future air pollution control actions that state and local agencies may co nsider in implementing the revised ozone NAAQS described in the decision notice, is discussed in Appendix 9A. The RIA is available on the Web, through the EPA’s Technology Transfer Network website athttp://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_index.html and in the RIA docket (EPAHQ-OAR-2013-0169). As noted above, although an RIA has been prepared, the results of the RIA have not been considered in issuing this final rule.

193 This refers to monitored areas with ozone design values above the revised and alternative standards.

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9-8 9.11 Congressional Review Act (CRA) This action is subject to the CRA, and the EPA will submit a rule report to each House of the Congress and to the Comptroller General of the United States. This action is a “major rule” as defined by 5 U.S.C. 804(2).

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9A-1 APPENDIX 9A: SOCIO-DEMOGRAPHIC CHARACTERISTICS OF POPULATIONS IN CORE BASED STATISTICAL AREAS WITH OZONE MONITORS EXCEEDING REVISED AND ALTERNATIVE OZONE STANDARDS

Overview
The Environmental Protection Agency’s (EPA) Office of Air Quality Planning and Standards (OAQPS) has conducted a limited analysis of population demographics in some areas that may be affected by the revisions to the National Ambient Air Quality Standards (NAAQS) for ozone. This appendix describes a limited screening-level analysis of the socio-demographic characteristics of populations living in areas with an ozone monitor with a current (2012-2014) design value exceeding the revised and alternative ozone standard levels of 70 and 65 parts per billion (ppb). This analysis does not include a quantitative assessment of exposure and/or risk for populations of potential interest from an environmental justice (EJ) perspective, and therefore it cannot be used to draw any conclusions regarding potential disparities in exposure or risk across populations of interest from an EJ perspective. This appendix describes the technical approach used in the analysis, discusses uncertainties and limitations associated with the analysis, and presents results. The EPA Administrator is revising the NAAQS for ozone from the current level of 75 ppb to a level of 70 ppb. The revisions will establish uniform national standards for ozone in ambient air and improve public health protection for at-risk groups, especially children. The Agency has elected to conduct a limited analysis of key socio-demographic characteristics of populations living in areas with poor ozone air quality, defined for this analysis as any Core Based Statistical Area (CBSA) with at least one county having an ozone monitor with a current (2012-2014) design value exceeding the revised and alternative ozone standard levels (70 and 65 ppb), including individual counties not included in a CBSA with a design value exceeding the revised and alternative standards. These areas are called “study areas” for the purposes of this analysis.
9A.1 Design of Analysis
To gain a better understanding of the populations within the study areas, the EPA conducted an analysis at the county level for this ozone NAAQS review. The study areas for these analyses were defined as all counties contained within any CBSA with at least one monitor

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9A-2 with a current (2012-2014) design value above the revised and alternative standard levels (70 and 65 ppb) as well as counties not in a CBSA with a current (2012-2014) design value above the revised and alternative standard levels (70 and 65 ppb). The study areas were designed to capture population and communities with poor ozone air quality, and areas most likely to benefit from improved air quality following the impl ementation of the revised ozone NAAQS. For the revised standard levels of 70 ppb, 511 counties were analyzed in 143 areas exceeding the standard levels, including 495 counties in 127 CBSAs and 16 counties outside CBSAs. For the alternative standard levels of 65 ppb, 891 counties were analyzed in 294 areas exceeding the standard level, including 847 counties in 250 CBSAs and 44 counties outside CBSAs. The population identified within these study areas made up about 52% of the U.S. population for the revised standard levels and about 69% of the U.S. population for the
alternative standard levels. Demographic data from the study areas identified for the revised and alternative standard levels were aggregated nationally for comparison with the U.S. population demographics. The demographic data used in this analysis include race, ethnicity, age, income, and education variables. Details on these demographic groups are provided in the following section (9A.1.1). The aggregated demographic values across the study areas are compared to the national data in
Table 9A-2 of Section 9A-3. This analysis identifies, on a limited basis, the populations that are most likely to experience reductions in ozone concentrations as a result of actions taken to meet the revised standard levels, and thus are expected to benefit most from this regulation. This analysis does not identify the demographic characteristics of the most highly affected individuals or communities nor does it quantify the level of risk faced by those individuals or communities. To the extent that any populations are disproportionately impacted by ozone levels because they reside in a study area, that population will also experience increased environmental and health benefits from meeting the revised standard levels.

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9A-3 9A.1.1 Demographic Variables Included in Analysis
This analysis includes race, ethnicity, and age data derived from the 2010 Census SF1
dataset 194 

### and income and education data from the Census Bureau’s 2006-2010 American Community Survey (ACS ) 5-Year Estimates. 195 

### This data is summarized in Table 9A-1.
Table 9A-1. Census Derived Demographic Data
Race, Ethnicity, and Age Data (Cen sus 2010 block-level SF1 data)*
Parameter Definition Population Total population White Number of whites (may include Hispanics) African American Number of African Americans (may include Hispanics) Native American Number of Native Americas (may include Hispanics) Other and multiracial Number of other race and multiracial (may include Hispanics) Minority Total Population less White Population Hispanic Number of Hispanics Age 0 to 4 Number of people age 0 to 4 Age 0 to 17 Number of people age 0 to 17 Age 65 and up Number of people age 65 and up Economic and Education Date (2006-2010 ACS)*
Parameter Definition

Education level Number of adults age 25 years and up without a high school diploma

Low Income Number of people living in households with income below twice the poverty line Linguistic Isolation Number of people linguistically isolated
*Census 2010 does not currently report this data for the Virgin Islands, Guam, American Samoa, and the Northern Marianas; Census 2000 data are used for these areas.

194 2010 Census Summary File 1 Delivered via FTP, http://www2.census.gov/census_2010/04-Summary_File_1/
195 U.S. Census Bureau 2006-2010 American Community Survey 5-Year Estimates, http://www.census.gov/acs/www/data_documentation/2010_release/

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9A-4 As noted above, the EPA uses population data collected by the 2010 Census. All data is stored at the block level. For those indicators available from the Census at the block group, but not block level, the EPA assigns a block the same percentage as the block group of which it is a part. For example, a block is assigned the same percentage of people living below the national poverty line as the block group in which it is contained. Nationally, a census block contains about 50 people on average; and a block group contains about 26 blocks on average, or about 1,350 people. For comparison, a census tract is larger than a block group, with each tract containing an average of 3 block groups, or about 4,300 people. For this analysis, the data was aggregated to the county level. Data on race, ethnicity and age for all census blocks in the country except for the Virgin Islands, Guam, American Samoa, and the Northern Marianas were obtained from the 2010 Census SF1 dataset. This dataset provides the population for each census block among different racial and ethnic classifications, including: White, African American or Black, Hispanic or Latino, American Indian or Native Alaskan, Asian, Native Hawaiian or other South Pacific Islander, other race, and two or more races. Data on age distributions in the U.S. and Puerto Rico were obtained at the census block level from the 2010 Census of Population and Housing Summary File 1 (SF1) short form. SF1 contains the information compiled from the questions asked of all people about every housing unit. Data on poverty status, education level, and linguistic isolation in the U.S. and Puerto Rico were obtained at the block group level from the
Census Bureau’s 2006-2010 ACS.
9A.2 Considerations in Evaluating and Interpreting Results
This analysis characterizes the demographic attributes of populations located in areas defined by a county or a CBSA containing a county with a monitored 2012-2014 design value greater than the revised standard levels of 70 ppb, or the alternative standard levels of 65 ppb. Therefore, the results of this analysis can only be used to inform whether there are differences in the composition of populations residing within these areas relative to the nation as a whole. As noted earlier, the purpose of the analysis is to determine whether populations of interest from an EJ perspective have a higher representation in areas that exceed the revised and alternative ozone standard levels, and thus may be more affected by implementation of the revised standards. This analysis does not include a quantitative assessment of exposure or risk for specific populations of

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9A-5 potential interest from an EJ-perspective, and theref ore it cannot be used to draw any conclusions regarding potential disparities in exposure or risk across populations of interest from an EJ perspective. Nor can it be used to draw conclusions about any disparities in the health and environmental benefits that could result from implementation of the revised ozone standards. The analysis simply represents a national depiction of the baseline characteristics of populations residing in areas with measured ozone air quality above the revised and alternative standard
levels. In order to clearly identify disparities in risk between populations of interest, we would need to conduct rigorous site-specific population- level exposure and risk assessments that take into account short-term mobility (daily patterns of travel linked for example to school or work) or long-term mobility (families moving into or out of specific block groups). EPA does not have the ability to conduct such a rigorous technical analysis at this time.
9A.3 Presentation of Results
This section presents a summary of the demographics of populations in areas with 2012- 2014 design values greater than the revised and alternative ozone standard levels. The results are provided in Table 9A-2. As a whole, the demographic distributions within the study areas estimated for the revised and alternative standard levels (i.e., 70 ppb and 65 ppb) are similar to the national averages. The largest difference is only 5%, between the national and study area percentages for the Minority demographic group. Overall, these qualitative results support the determination that the revised rule will tend to benefit geographic areas that have a higher proportion of minority residents than the national average.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***9A-6Table 9A-2 Summary of Population Totals and Demographic Categories for Areas of Interest and National PerspectiveDemographic Summary Population WhiteAfricanAmericanNativeAmericanOther orMultiracialMinority HispanicArea Total - 70 ppb163,900,459109,711,30523,448,5401,196,13029,544,48454,189,15433,651,929% of Area Total - 70 ppb67%14%1%18%33%21%Area Total - 65 ppb216,932,408150,701,60029,091,6021,699,45035,439,75666,230,80839,746,762% of Area Total - 65 ppb69%13%1%16%31%18%National Total312,861,256226,405,20539,475,2162,952,08744,028,74886,456,05154,181,245% of National Total72%13%1%14%28%17%Demographic Summary Population Age 0 to 4 Age 0 to 17 Age 65+No HighSchoolDiplomaLow IncomeLinguisticallyIsolatedArea Total - 70 ppb163,900,45911,028,42840,546,15019,510,55315,960,33349,392,88111,150,433% of Area Total - 70 ppb7%25%12%10%30%7%Area Total - 65 ppb216,932,40814,387,22352,913,37626,596,43220,469,63265,336,39613,050,626% of Area Total - 65 ppb7%24%12%9%30%6%National Total312,861,25620,465,06575,217,17640,830,26230,952,789101,429,43619,196,507% of National Total7%24%13%10%32%6%***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***This page intentionally left blank.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***United StatesEnvironmental ProtectionAgencyOffice of Air Quality Planning and StandardsHealth and Environmental Impacts DivisionResearch Triangle Park, NCPublication No. EPA-452/R-14-007August 2015