# Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter

On February 28, 2013, the PM RIA is being replaced to add Appendix 3

. A, which was inadvertently left out and to correct the document number to EPA

- 452/R

- 12

- 005

.EPA

- 452/R

- 12

- 00 5 December 201 2

Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter

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

Section Page

Executive Summary

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

ES.1 Overview

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

ES.2 Existing and Re vised PM Air Quality Standards

.............................................. ES -1 ES.2.1 Overview of the Analytical Steps in this RIA

....................................... ES -2 ES.2.2 Health and Welfare Co

- Benefits

....................................................... ES

- 10 ES.2.3 Cost Analysis Approach

..................................................................... ES

- 13 ES.2.4 Comparison of Benefits and Costs

.................................................... ES

- 14

ES.3 Discussion and Conclusions

.......................................................................... ES

- 17

ES.4 Caveats and Limitations

................................................................................ ES

- 21 ES.4.1 Benefits Caveats

................................................................................ ES

- 21 ES.4.2 Control Strategy and Cost Analysis Caveats and Limitations

........... ES

- 22

ES.5 Important Updates and Analytical Differences Between the PM NAAQS Proposal RIA and the Final RIA

..................................................................... ES

- 23

ES.6 References

.................................................................................................... ES

- 25

Chapter 1 Introduction and Background

............................................................................... 1-1

1.1 Synopsis

............................................................................................................. 1-1

1.2 Background

....................................................................................................... 1-1 1.2.1 NAAQS

................................................................................................... 1-1
1.2.2 2006 PM NAAQS

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

1.3 Role of this RIA in the Process of Setting the NAAQS

....................................... 1-2 1.3.1 Legal Requirement

................................................................................ 1-2 1.3.2 Role of Statutory and Executive Orders

................................................ 1-3 1.3.3 The Need for National Ambient Air Quality Standards

........................ 1-4 1.3.4 Illustrative Nature of the Analysis

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

1.4 Overview and Design of the Final RIA

............................................................... 1-5iv 1.4.1 Important Updates and Differences Between the PM NAAQS Proposal RIA and the Final RIA

.............................................................. 1-5
1.4.2 Existing and Revised PM Air Quality Standards

.................................... 1-7 1.4.3 Health and Welfare Co

- Benefits Analysis Approach

.......................... 1- 11
1.4.4 Cost Analysis Approach

....................................................................... 1- 14

1.5 Organization of this Regulatory Impact Analysis

............................................ 1- 16

1.6 References for Chapter 1

................................................................................ 1- 17

Chapter 2 Defining the PM Air Quality Problem

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

2.1 Synopsis

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

2.2 Particulate Matter (PM) Properties

.................................................................. 2-1
2.2.1 PM 2.5 ...................................................................................................... 2-4

2.3 References

...................................................................................................... 2- 11

Chapter 3 Air Quality Modeling and Analysis

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

3.1 Synopsis

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

3.2 Modeling PM 2.5 Levels in the Future

................................................................ . 3-1 3.2.1 Air Quality Modeling Platform

.............................................................. 3-2

3.3 PM 2.5 Modeling Results and Analyses

............................................................. 3- 11 3.3.1 Calculating Future

- year Design Values for 2020 Base and Control Cases

................................................................................................... 3- 15 3.3.2 Calculating Future

- year Design Values for Meeting the Existing Standards, the Revised Annual S tandard, and Alternative Annual Standard Levels

................................................................................... 3- 22 3.3.3 Estimating Changes in Annual Average PM 2.5 for Benefits Inputs

...... 3- 29 3.3.4 Limitations of Using Adjusted Air Quality Data

.................................. 3- 31 3.3.5 Weight

- of

- Evidence Approach for Lincoln County, MT and Santa Cruz , NM

............................................................................................. 3- 32 3.3.6 Estimating Changes in Visibility for Analyzing Welfare Benefits

........ 3- 33

3.4 References

...................................................................................................... 3- 35

Appendix 3.A Additional Air Quality Modeling Information

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

3.A.1 Air Quality Modeling and Analysis

................................................................ . 3.A -1v 3.A.1.1 Development of Air Quality Respons e Ratios

.................................... 3.A -1 3.A.1.2 Estimating Area NO x Emission Contributions to NH 4 NO 3 PM 2.5 in the San Joaquin Valley

..................................................................... 3.A

- 12

Chapter 4 Control Strategies

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

4.1 Synopsis

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

4.2 PM 2.5 Control Strategy Analysis

........................................................................ 4-2 4.2.1 Identify Geographic Areas

.................................................................... 4-2
4.2.2 Developing the Control Scenario

.......................................................... 4-6 4.2.3 Identify Known Controls Needed to Meet the Analytical Baseline

...... 4-9
4.2.4 Identify Known Controls Needed to Meet the Revised and Alternative Standards

......................................................................... 4- 12 4.2.5 Identify Emission Reductions Beyond Known Controls Ne eded to Meet the Revised and Alternative Standards

..................................... 4- 13

4.3 Limitations and Uncertainties

......................................................................... 4- 14

4.4 References

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

Appendix 4.A Additional Control Strategy Information

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

4.A.1 Control Measures for Stationary Sources

...................................................... 4.A -1
4.A.1.1 PM Emissions Control Technologies

.................................................. 4.A -1
4.A.1.2 SO 2 Control Measures

........................................................................ 4.A -4 4.A.1.3 NO x Emissions Control Measures

...................................................... 4.A -5

Chapter 5 Human Health Benefits Analysis Approach and Results

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

5.1 Synopsis

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

5.2 Overview

........................................................................................................... 5-3

5.3 Updated Methodology Presented in this RIA

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

5.4 Hum an Health Benefits Analysis Methods

....................................................... 5-8
5.4.1 Health Impact Assessment

.................................................................... 5-9 5.4.2 Economic Valuation of Health Impacts

............................................... 5- 11

5.5 Uncertainty Characterization

.......................................................................... 5- 13
5.5.1 Monte Carlo Assessment

.................................................................... 5- 14 5.5.2 Concentration Benchmark Analysis for PM 2.5 ..................................... 5- 15vi 5.5.3 Alternative Concentration

- Response Functions for PM 2.5 – Related Mortality

................................................................................ 5- 15
5.5.4 Sensitivity Analyses

............................................................................. 5- 16
5.5.5 Distributional Assessment

.................................................................. 5- 17 5.5.6 Influence Analysis

— Quantitative Assessment of Uncertainty

........... 5- 18
5.5.7 Qualitative Assessment of Uncertai nty and Other Analysis Limitations

........................................................................................... 5- 18

5.6 Benefits Analysis Data Inputs

.......................................................................... 5- 21 5.6.1 Demographic D ata

.............................................................................. 5- 21 5.6.2 Baseline Incidence and Prevalence Estimates

.................................... 5- 22 5.6.3 Effect Coefficients

............................................................................... 5- 26 5.6.4 Unquantified Human Health Benefits

................................................. 5- 46 5.6.5 Economic Valuation Estimates

............................................................ 5- 48 5.6.6 Hospital Admissions and Emergency Department Valuation

............. 5- 60 5.6.7 Minor Restricted Activity Days Valuation

........................................... 5- 62 5.6.8 Growth in WTP Reflecting National Income Growth Over Time

........ 5- 62

5.7 Benefits Results

............................................................................................... 5- 65
5.7.1 Benefits of the Revised and Alternative Annual Primary PM 2.5 Standards

............................................................................................ 5- 65 5.7.2 Uncertainty in Benefits Results

........................................................... 5- 73 5.7.3 Estimated Life Years Gained and Reduction in the Percentage of Deaths Attributable to PM 2.5 .............................................................. 5- 74
5.7.4 Evaluation of Mortality Impacts Relative to Various Concentration Benchmarks

................................................................ 5- 80
5.7.5 Additional Sensitivity Analyses

........................................................... 5- 87

5.8 Discussion

........................................................................................................ 5- 87

5.9 References

...................................................................................................... 5- 90

Appendix 5.A Additional Sensitivity Analyses Related to the Health Benefits Analysis

..... 5.A -1

5.A.1 Cessation Lag Structure for PM 2.5 - Related Premature Mortality

.................. 5.A -1

5.A.2 Income Elasticity of Willingness to Pay

.......................................................... 5.A -9

5.A.3 Analysis of Cardiovascular Emergency Department Visits, Cerebrovascular Events and Chronic Bronchitis

.......................................... 5.A

- 10

5.A.4 Updating Basis for Population Projections to 201 0 Census

........................ 5.A

- 11vii 5.A.5 Long- term PM 2.5 Mortality Estimates Using Cohort Studies in California

... 5.A

- 12

5.A.6 Analysis of Health Benefits Estimated for 2025

.......................................... 5.A

- 14

5.A.7 References

................................................................................................... 5.A

- 14

Appendix 5.B Comprehensive Characterization of Uncertainty in Benefits A nalysis

......... 5.B -1

5.B.1 Description of Classifications Applied in the Uncertainty Characterization

............................................................................................. 5.B -1
5.B.1.1 Direction of Bias

................................................................................. 5.B -2 5.B.1.2 Magnitude of Impact

......................................................................... 5.B -2 5.B.1.3 Confidence in Analytic Approach

....................................................... 5.B -3
5.B.1.4 Uncertainty Quantification

................................................................ 5.B -4

5. B.2 Organization of the Qualitative Uncertainty Table

........................................ 5.B -5

5.B.3 References

................................................................................................... 5.B

- 15

Chapter 6 Welfare co

- Benefits of the Primary Standard

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

6.1 Synopsis

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

6.2 Introduction to Welfare Benefits

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

6.3 Visibility Co

- Benefits Approach

......................................................................... 6-6 6.3.1 Visibility and Light Extinction Background

............................................ 6-6 6.3.2 Quantifying Light Extinction for Assessing Visibility Co

- benefits

....... 6- 11 6.3.3 Visibility Valuation Overview

.............................................................. 6- 12 6.3.4 Recreational Visibility

......................................................................... 6- 14 6.3.5 Residential Visibility

............................................................................ 6- 23 6.3.6 Discussion of Visibility Co

- benefits

..................................................... 6- 32

6.4 Materials Damage Co

- benefits

....................................................................... 6- 33

6.5 Climate Co

- benefits

......................................................................................... 6- 36 6.5.1 Climate Effects of Short Lived Climate Forcers

................................... 6- 37 6.5.2 Climate Effects of Long

- Lived Greenhouse Gases

.............................. 6- 40

6.6 Ecosystem Co

- benefits and Services

............................................................... 6- 40 6.6.1 Ecosys tem Co

- benefits for Metallic and Organic Constituents of PM

....................................................................................................... 6- 42 6.6.2 Ecosystem Co

- benefits from Reductions in Nitrogen and Sulfur Emissions

............................................................................................. 6- 46viii 6.6.3 Ecosystem Co

- benefits from Reductions in Mercury Emissions

........ 6- 64 6.6.4 Vegetation Co

- benefits from Reductions in Ambient Ozone

............. 6- 67

6.7 References

...................................................................................................... 6- 74

Appendix 6.A Additional Details Regarding the Visibility Benefits Methodology

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

6.A.1 Introduction

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

6.A.2 Converting Modeled Light Extinction Estimates

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

6.A.3 Basic Utility Model

......................................................................................... 6.A -2

6.A.4 Measure of Visibility: Environmental “Goods” Versus “Bads”

...................... 6.A -5

6.A.5 Estimating the Parameters for Visibility at Class I Areas: the γ’s and δ’s

...... 6.A -7 6.A.5.1 Estimating Region

- Specific Recreational Visibility Parameters for the Region Covered in the Chestnut and Rowe Study (Regions 1, 2, and 3)

........................................................................................... 6.A

- 10 6.A.5.2 Inferring Region

- Specific Recreational Visibility Parameters for Regions Not Covered in the Chestnut and Rowe Study (Regions 4, 5, and 6)

....................................................................................... 6.A

- 11 6.A.5.3 Estimating Park - and Wilderness Area

- Specific Parameters

........... 6.A

- 13 6.A.5.4 Derivation of Region

- Specific WTP for National Parks and Wilderness Areas

............................................................................. 6.A

- 14 6.A.5.5 Derivation of Park - and Wilderness Area

- Specific WTPs, Given Region

- Specific WTPs for National Parks and Wilderness Areas

..... 6.A

- 14 6.A.5.6 Deri vation of Park - and Wilderness Area

- Specific Parameters, Given Park - and Wilderness

- Specific WTP

....................................... 6.A

- 15

6.A.6 Estimating the Parameter for Visibility in Residential Areas: θ

................... 6.A

- 17

6.A.7 Putting It All Together: The Household Utility and WTP Functions

............ 6.A

- 18

6.A.8 Income Elasticity and Income Growth Adjustment for Visibility Benefits

... 6.A

- 19

6.A.9 Summary of Parameters

.............................................................................. 6.A

- 20

6.A.10 References

................................................................................................... 6.A

- 23

Appendix 6.B Illustrative Scenario of Recreational and Residential Visibility Benefits

...... 6.B -1

6.B.1 Synopsis

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

6.B.2 Recreational Benefits Results

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

6.B.3 Residential Benefits Results

........................................................................... 6.B -5ix 6.B.4 References

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

Chapter 7 Engineering Cost Analysis

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

7.1 Synopsis

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

7.2 Estimation of Engineering Control Costs

.......................................................... 7-2
7.2.1 Data and Methods

— Identified Control Costs (non

- EGU Point and Area Sources)

................................................................................. 7-2
7.2.2 Identified Control Costs

........................................................................ 7-3 7.2.3 Extrapolated Costs

................................................................................ 7-6
7.2.4 Total Cost Estimates

............................................................................ 7- 12

7.3 Changes in Regulatory Cost Estimates over Time

.......................................... 7- 15 7.3.1 Examples of Technological Advances in Pollution Control

................. 7- 16 7.3.2 Influence on Regulatory Cost Estimates

............................................. 7- 19 7.3.3 Influence of Regulation on Technological Change

............................. 7- 21

7.4 Uncertainties and Limitations

......................................................................... 7- 21

7.5 References

...................................................................................................... 7- 22

Appendix 7.A Data to Calculate Costs to Meet 12 μ g/m 3 and Sensitivity Anal yses of Extrapolated Cost Estimates

...................................................................................... 7.A -1

7.A.1 PM 2.5 Emission Reductions and Costs to Meet 12 μ g/m 3

............................... 7.A -1

7.A.2 Sensitivity Analyses of Extrapolated Cost Estimates

..................................... 7.A -2 7.A.2.1 Sensitivity Analysis of Fixed

- Cost Methodology

................................ 7.A -2
7.A.2.2 Sensitivity Analysis of Alternative Hybrid Methodology

................... 7.A -3

Chapter 8 Comparison of Benefits and Costs

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

8.1 Synopsis

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

8.2 Comparison of Benefits and Costs

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

8.3 Discussion and Conclusions

.............................................................................. 8-6

8.4 References

........................................................................................................ 8-9

Chapter 9 Statutory and Executive Order Reviews

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

9.1 Synopsis

............................................................................................................. 9-1x 9.2 Executive Order 12866: Regulatory Planning and Review

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

9.3 Paperwork Reduction Act

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

9.4 Regulatory Flexibility Act

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

9.5 Unfunded Mandates Reform Act

...................................................................... 9-2

9.6 Executive Order 13132: Federalism

.................................................................. 9-2

9.7 Executive Order 13175: Consultation and Coordination with Indian Tribal Governments

.......................................................................................... 9-3

9.8 Executive Order 13045: Protection of Children from Environmental Health and Safety Risks

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

9.9 Executive Order 13211: Actions that Significantly Affect Energy Supply, Distribution or Us e

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

9.10 National Technology Transfer and Advancement Act

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

9.11 Executive Order 12898: Federal Actions to Address Environmental Justice in Minority Populations and Low

- Income Populations

........................ 9-5

Chapter 10 Qualitative Discussion of Employment Impacts of A ir Quality Regulations

..... 10 -1

10.1 Introduction

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

10.2 Influence of NAAQS Controls on Employment

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

10.3 The Current State of Knowledge Based on the Peer

- Reviewed Literature

.... 10 -2 10.3.1 Immediate and Short

- Run Employment Impacts

............................... 10 -3 10.3.2 Long- Term Employment Impacts on the Regulated Industry

............. 10 -4

10.4 Conclusion

....................................................................................................... 10 -6

10.5 References

...................................................................................................... 10 -7xi LIST OF FIGURES

Number Page

ES

- 1. PM RIA Analytical Flowchart

.......................................................................................... ES -3 ES

- 2. Annual NAAQS Exceedances in “Adjusted 2020 Base Case” Scenari o

.......................... ES -5 ES

- 3. Annual NAAQS Exceedances in “Analytical Baseline” Scenario

.................................... ES -6 ES

- 4. Monetized Benefit to Cost Comparison for 12 μg/m 3 in 2020 (7% Discount Rate)

.... ES

- 16

1- 1. PM RIA Analytical Flow Diagram

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

2- 1. Detailed Source Categorization of Anthropogenic Emissions of Primary PM 2.5 , PM 10 and Gaseous Precursor Species SO 2 , NO x , NH 3 and VOCs for 2008

....................... 2-3 2- 2. Regional and Local Contributions to Annual Average PM 2.5 by Particulate SO 4 2–

, Nitrate and Total Carbon (i.e., organic plus EC) for Select Urban Areas Based on Paired 2000

– 2004 IMPROVE and CSN Monitoring Sites

................................................. 2-5 2- 3. Regional and Seasonal Trends in Annual PM 2.5 Composition from 2002 to 2007 Derived Using the SANDWICH Method. Data from the 42 monitoring locations shown on the map were stratified by region and season including cool months (October

– April) and warm months (May

– September)

................................................... 2-6 2- 4. RCFM (left) versus SANDWICH (right) Pie Charts Comparing the Ambient and PM 2.5 FRM Reconstructed Mass Protocols on an Annual Average Basis

......................... 2-8 2- 5. Ma ximum County

- level PM 2.5 Annual Design Values Calculated Using 2006

– 2008 FRM 24

- hr Average PM 2.5 Measurements.

.................................................................... 2- 10 2- 6. Maximum County

- level PM 2.5 24

- hour Design Values Calculated Using 2006

– 2008 FRM 24

- hr Average PM 2.5 Measurements.

.................................................................... 2- 11

3- 1. Map of the CMAQ Modeling Domain Used for PM NAAQS RIA

...................................... 3-3 3- 2. Flow Diagram of Process Used to Determine Future

- Year Design Values and Associated Emission Reductions for Meeting the Current, Revised and Alternative Standard Levels

........................................................................................... 3- 14 3- 3. Counties that Exceed the Revised and/or Alternative Annual Standard Levels of 13, 12 and 11 μ g/m 3 in the Analytical Baseline

............................................................. 3- 25 3- 5. Three

- year Annual and 24

- hr Design Values for the Monitor in Libby, MT

.................. 3- 33

3.A

- 1. Location of Kings County Monitor Relative to Tulare County Border.

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

- 2. California Modeling Domain for 12

- km Simulations

.................................................. 3.A

- 13

4- 1. Counties Projected to Exceed the Current PM 2.5 Standard (15/35) in 2020

................... 4-3 4- 2. Counties Projected to Exceed the 13 ug/m 3 Alternative Standard in the Analytical Baseline

............................................................................................................................ 4-4xii 4- 3. Counties Projected to Exceed the 12 ug/m 3 Revised Standard in the Analytical Baseline

............................................................................................................................ 4-5 4- 4. Counties Projected to Exceed the 11 ug/m 3 Alternative Standard in the Analytical Baseline

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

5- 1. Illustration of BenMAP Approach

.................................................................................. 5- 11 5- 2. Data Inputs and Outputs for the BenMAP Model

......................................................... 5- 12 5- 3. Estimated PM 2.5 - Related Premature Mortalities Avoided According to Epidemiology or Expert

- Derived PM 2.5 Mortality Risk Estimate for 12 μg/m 3 in 2020

............................................................................................................................... 5- 72 5- 4. Total Monetized Benefits Using 2 Epidemiology

- Derived and 12

- Expert Derived Relationships Between PM 2.5 and Premature Mortality for 12 μg/m 3 in 2020

............. 5- 73 5- 5. Estimated Life Years Gained as a Result of the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020, Partitioned by the Age When Exposure Reduction Occurred, Not Necessarily Age at Death

....................................................................... 5- 78 5- 6. Estimate of Avoided Premature Deaths Attributed to the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020, Partitioned by the Age When Exposure Reduction Occurred, Not Necessarily Age at Death

...................................................... 5- 80 5- 7. Relationship between the Size of the PM Mortality Estimates and the PM 2.5 Concentration Observed in the Epidemiology Study

..................................................... 5- 82 5- 8. Number of Premature PM 2.5 - related Deaths Avoided for the Revise d Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020 According to the Baseline Level of PM 2.5 and the Lowest Measured Air Quality Levels of Each Mortality Study

................ 5- 84 5- 9. Number of Premature PM 2.5 - related Deaths Avoided for the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020 According to the Baseline Level of PM 2.5 and the Lowest Measured Air Quality Levels of Each Mortality Study

................ 5- 85 5- 10. Illustration of Relative Size of County with Exceeding Monitor and Modeled Grid Cells

................................................................................................ ................................ 5- 86

5.A

- 1. Alternate Lag Structures for PM 2.5 Premature Mortality (Cumulative)

........................ 5.A -8 5.A

- 2. Exponential Lag Structures for PM 2.5 Premature Mortality (Cumulative)

.................... 5.A -8

6- 1. Important Factors Involved in Seeing a Scenic Vista (Malm, 1999)

................................ 6-7 6- 2. Visibility in Selected National Parks and Wilderness Areas in the U.S., 1992

– 2008

....... 6-9 6- 3. Estimated Improvement in Annual Average Visibility Levels Associated with the CAAA Provisions in 2020

................................................................................................ 6- 10 6- 4. Mandatory Class 1 Areas in the U.S.

.............................................................................. 6- 15 6- 5. Visitation Rates and Park Regi ons for Class 1 Areas

...................................................... 6- 20 6- 6. Residential Visibility Study City Assignment

.................................................................. 6- 29 6- 7. Linkages between Categories of Ecosystem Services and Components of Human Well- Being from Millennium Ecosystem Assessment (MEA, 2005)

.............................. 6- 41 6- 8. Schematic of the Benefits Assessment Process (U.S. EPA, 2006c)

................................ 6- 42 6- 9. Schematics of Ecological Effects of Nitrogen and Sulfur Deposition

............................. 6- 47 6- 10. Nitrogen and Sulfur Cycling, and Interactions in the Environment

............................... 6- 48xiii 6- 11. Areas Potentially Sensitive to Aquatic Acidification

...................................................... 6- 51 6- 12. Areas Potentially Sensitive to Terrestrial Acidification

.................................................. 6- 53 6- 13. Distribution of Red Spruce (pink) and Sugar Maple (green) in the Eastern U.S.

........... 6- 54 6- 14. Spatial and Biogeochemical Factors Influencing MeHg Production

.............................. 6- 62 6- 15. Preliminary USG S Map of Mercury Methylation

– Sensitive Watersheds

...................... 6- 63 6- 16. Visible Foliar Injury to Forest Plants from Ozone in U.S. by EPA Regions

..................... 6- 71 6- 17. Presence and Absence of Visible Foliar Injury, as Measured by U.S. Forest Service, 2002

.................................................................................................................. 6- 72

6.B

- 1. Recreation al Visibility Benefits in Class I Areas for the Illustrative Scenario in 2020

.............................................................................................................................. 6.B -5 6.B

- 2. Residential Visibility Benefits for the Illustrative Scenario in 2020 (2010$)

................ 6.B -7

7- 1. Technological Innovation Reflected by Marginal Cost Shift

.......................................... 7- 16

8- 1. Monetized Benefit to Cost Comparison for the Revised Annual Standard of 12 μg/m 3 in 2020 (7% Discount Rate)

................................................................................... 8-3 8- 2. Net Benefits for Revised Annual Standard of 12 μg/m 3 in 2020 at a 7% Discount Rate

................................................................................................ .................................. 8-4xiv LIST OF TABLES

Number Page

ES

- 1. Emission Reductions in Illustrative Emission Reduction Strategies for the Revised and Alternative Annual Primary PM 2.5 Standards, by Pollutant and Region in 2020 (tons)

.............................................................................................................................. ES -8 ES

- 2. Total Monetized Benefits, Total Costs, and Net Benefits in 2020 (millions of 2010$)

— Full Attainment

............................................................................................. ES

- 15 ES

- 3. Benefit

- to

- Cost Ratios for Alternative Standards at 3% and 7% Based on Projected Benefits and Costs in 2020

.......................................................................... ES

- 15 ES

- 4. Estimated Number of Avoided PM 2.5 Health Impacts for Standard Alternatives

— Full Attainm e nt in 2 020

............................................................................................... ES

- 17

2- 1. Annual Average FRM and CSN PM 2.5 NO 3 – and NH 4 NO 3 Concentrations at Six Sites during 2003

...................................................................................................................... 2-8

3- 1(a). Control Strategies and Growth Assumptions for Creating 2020 Base Case Emissions Inventories from the 2007 Base Case for Non

- EGU Poi nt Sources

................. 3-9 3- 1(b). Control Strategies and Growth Assumptions for Creating 2020 Base Case Emissions Inventories from the 2007 Base Case for Nonpoint and Onroad Mobile Sources

........................................................................................................................... 3- 10 3- 1(c). Control Strategies and Growth Assumptions for Creating 2020 Base Case Emissions Inventories from the 2007 Base Case for Nonroad Mobile Sources

............ 3- 11 3- 2. Air Quality Model Simulations Used in this Regulatory Impact Analysis

...................... 3- 15 3- 3. Nonattainment Areas Where Episodic Residential Wood Burning Curtailment was Applied

........................................................................................................................... 3- 17 3- 4. Estimated Number of Burn Ban Days by County Based on 2005

- 2009 FRM Data

........ 3- 19 3- 5. Tons of Direct PM 2.5 Emission Reductions beyond the Adjusted 2020 Control Case to Meet the Current Standard Level for Counties that Exceed the Revised or Alternative An nual Standard Levels in the Adjusted 2020 Base Case

........................... 3- 23 3- 6. Annual Design Values and Exceedance Category for the Highest County Monitor in the Analytical Baseline for Counties with at Least one Monitor Above the Revised and/or Alternative Standard Levels

................................................................ . 3- 26 3- 7. Tons of Direct PM 2.5 Emission Reductions Beyond the Analytical Baseline to Meet the 13 μ g/m 3 Level

......................................................................................................... 3- 27 3- 8. Tons of Direct PM 2.5 Emission Reductions Beyond the Analytical Baseline to Meet the 12 μ g/m 3 Level

......................................................................................................... 3- 28 3- 9. Tons of Direct PM 2.5 Emission Reductions Beyond the Analytical Baseline to Meet the Alternative Standard 11 μ g/m 3 Level

...................................................................... 3- 29xv 3.A

- 1. Annual NO x Air Quality Ratios for Monitors in California Counties that Received a 2025 Mobile NO x Emission Adjustment

........................................................................ 3.A -5 3.A

- 2. Annual and D aily SO 2 Air Quality Ratios for Monitors in Counties where Ratios were Used in Adjusting Daily Design Values to Remove the Impact of SO 2 Controls

......................................................................................................................... 3.A -8 3.A

- 3. Annual and Daily Direct PM 2.5 Air Quality Ratios for Monitors in Counties with at Least One Monit or having an Annual Design Value Above 11 μ g/m 3 in the 2020 Base Case

...................................................................................................................... 3.A -9 3.A

- 4. Contribution Weighting Factors for NO x Emissions in Counties or County Groups in California’s Central Valley as Calculated According to Equation 3.A.2

................... 3.A

- 14

4- 1. Controls Applied in the Revised and Alternative Standard Analysis

............................... 4-7 4- 2. Number of Counties with Exceedances and Number of Additional Counties Where Reductions Were Applied

.................................................................................. 4- 13 4- 3. Emission Reductions from Known Controls for the Revised and Alternative Standard s

....................................................................................................................... 4- 13 4- 4. Emission Reductions Needed Beyond Known Controls for the Revised and Alternative Standards

.................................................................................................... 4- 14 4- 5. Summary of Qualitative Uncertainty for Elements of Control Strategies

..................... 4- 15

4.A

- 1. Example PM Control Measures for Non

- EGU Point Source Categor ies

....................... 4.A -3 4.A

- 2. Example PM Control Measures for Nonpoint Sources

................................................. 4.A -4 4.A

- 3. Example SO 2 Control Measures for Non

- EGU Point

..................................................... 4.A -5 4.A

- 4. Example NO x Control Measures for Non

- EGU Source Categories

................................ 4.A -7

5- 1. Estimated Monetized Benefits of the of Revised and Alternative Annual PM 2.5 Standards in 2020 Incremental to the Analytical Baseline (billions of 2010$)

................ 5-2 5- 2. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain the Primary PM 2.5 Standards

............................................................................................ 5-4 5- 3. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact Functions, General Population

...................................................................................... 5- 24 5- 4. Asthma Prevalence Rates

.............................................................................................. 5- 25 5- 5. Criteria Used When Selecting C

- R Functions

................................................................ . 5- 28 5- 6. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts in the Core Analysis

....................................................................................................... 5- 29 5- 7. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts in the Sensitivity Analysis

............................................................................................... 5- 31 5- 8. Summary of Effect estimates from Associated With Change in Long

- Term Exposure to PM 2.5 in Recent Cohort Studi es in North America

..................................... 5- 35 5- 9. Unit Values for Economic Valuation of Health Endpoints (2010$)

................................ 5- 50 5- 10. Influence of Applied VSL Attributes on the Size of the Economic Benefits of Reductions in the Risk of Premature Death (U.S. EPA, 2006a)

...................................... 5- 55 5- 11. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart Attacks

......... 5- 60xvi 5- 12. Estimated Costs Over a 5

- Year Period of a Nonfatal Myocardial Infarction (in 2010$)

............................................................................................................................ 5- 60 5- 13. Unit Values for Hospital Admissions

.............................................................................. 5- 61 5- 14. Elasticity Values Used to Account for Projected Real Income Growth

......................... 5- 64 5- 15. Adjustment Factors Used to Account for Projected Real Income Growth

.................... 5- 65 5- 16. Population

- Weighted Air Quality Change for the Revised and Alternative Annual Primary PM 2.5 Standards Relative to Analytic al Baseline

............................................... 5- 66 5- 17. Emission Reductions in Illustrative Emission Reduction Strategies for the Revised and Alternative Annual Primary PM 2.5 Standards, by Pollutant and Region in 2020 (tons)

.............................................................................................................................. 5- 67 5- 18. Estimated number of Avoided PM 2.5 Health Impacts for the Revised and Alternative Annual Primary PM 2.5 Standards (Incremental to the Analytical Baseline)

......................................................................................................................... 5- 68 5- 19. Monetized PM 2.5 Health Benefits for the Revised and Alternative Annual Primary PM 2.5 Standards (Incremental to Analytical Baseline) (Millions of 2006$, 3% discount rate)

................................................................................................................. 5- 69 5- 20. Monetized PM 2.5 Health Benefits for the Revised and Alternative Annual Primary PM 2.5 Standards (Incremental to Analytical Baseline) (Millions of 2006$, 7% discount rate)

................................................................................................................. 5- 70 5- 21. Total Estimated Monetized Benefits of t he for Revised and Alternative Annual Primary PM 2.5 Standards (Incremental to the Analytical Baseline) (billions of 2006$)

........................................................................................................................... 5- 71 5- 22. Regional Breakdown of Monetized Benefits Results

..................................................... 5- 71 5- 23. Sum of Life Years Gained by Age Range Attributed to the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020

.............................................................................. 5- 77 5- 24. Estimated Reduction in the Percentage of All

- Cause Mortality Attributed to the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020

....................................... 5- 79 5- 25. Estimated Reduction in Incidence of Adult Premature Mortality Occurring Above and Below Various Concentration Benchmarks in the Underlying Epidemiology Studies

............................................................................................................................ 5- 83

5.A

- 1. Values of the Time Constant (k) for the Exponential Decay Lag Function

................... 5.A -6 5.A

- 2. Sensitivity of Monetized PM 2.5 - Related Premature Mortality Benefits to Alternative Cessation Lag Structures, Using Effect Estimate from Krewski et al. (2009)

............................................................................................................................ 5.A -7 5.A

- 3. Ranges of Elasticity Values Used to Account for Projected Real Income Growth

........ 5.A -9 5.A

- 4. Ranges of Adjustment Factors Used to Account for Projected Real Income Growth

.......................................................................................................................... 5.A -9 5.A

- 5. Sensitivity of Monetized Benefits to Alternative Income Elasticities

......................... 5.A

- 10 5.A

- 6. Avoided Cases of Cardiovascular Emergency Department Visits, Stroke and Chronic Bronchitis in 2020 (95 th percentile confidence intervals)

............................. 5.A

- 11 5.A

- 7. Change in Total Monetized Benefits for 2000 and 2010 Census projections for 12 μg/m 3 in 2020 (2010$)

................................................................................................ 5.A

- 12xvii 5.A -8 Summary of Effect Estimates From Associated With Change in Long

- Term Exposure to PM 2.5 in Rece nt Cohort Studies in California

.......................................... 5.A

- 13 5.A

- 9. Comparison of Health Benefits Estimated for 2000 and 2025 for 12 μg/m 3

............ 5.A

- 14

5.B

- 1. Summary of Qualitative Uncertainty for Key Modeling Elements in PM 2.5 Benefits

.... 5.B -6

6- 1. Welfare Effects by Pollutants Po tentially Affected by Attainment of the PM NAAQS

.............................................................................................................................. 6-3 6- 2. Quantified and Unquantified Welfare Co

- Benefits

......................................................... 6-4 6- 3. Key Assumptions in the Light Extinction Estimates Affecting the Visibility Co

Benefits Approach

.......................................................................................................... 6- 12 6- 4. WTP for Visibility Improvements in Class 1 Areas in Non

- Studied Park Regions

.......... 6- 19 6- 5. Summary of Key Assumptions in Estimating Recreational Visibility Co

- benefits

.......... 6- 22 6- 6. Summary of Residential Visibility Valuation Estimates

................................................. 6- 26 6- 7. Summary of Key Assumptions in the Residential Visibil ity Co

- benefits

........................ 6- 30 6- 8. Materials Damaged by Pollutants Affected by this Rule (U.S. EPA, 2011b)

.................. 6- 36 6- 9. Aquatic Status Categories

.............................................................................................. 6- 50

6.A

- 1. Available Information on WTP for Visibility Improvements in National Parks

............. 6.A -8 6.A

- 2. Summary of Region

- Specific Recreational Visibility Parameters to be Estimated in Household Utility Functions

.......................................................................................... 6.A -9 6.A

- 3. Mean Annual Household WTP for Changes in Visual Range for Recreational Visibility (1990$)

......................................................................................................... 6.A

- 21 6.A

- 4. Region

- Specific Parameters for Recreational Visibility Benefits

................................ 6.A

- 21 6.A

- 5. Mean Annual Household WTP for Changes in Visual Range for Residential Visibility

....................................................................................................................... 6.A

- 22 6.A

- 6. Parameters for Income Growth Adjustment for Visibility Benefits

............................ 6.A

- 23

6.B

- 1. Annual Average Visibility Improvements in the Top 10 Most Visited Class I Areas for the Illustrative Scenario in 2020

.............................................................................. 6.B -2 6.B

- 2. Recreational Visibility Benefits in Studied Regions for the Illustrative Scenario in 2020 (in millions of 2010$)

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

- 3. Sensitivity Analysis for Recreational Visibi lity Benefits outside Studied Park Regions for the Illustrative Scenario in 2020 (in millions of 2010$)

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

- 4. Sensitivity Analysis for Incorporating Coarse Particles into Recreational Visibility Benefits for the Illustrative Scenario (in millions of 2010$)

......................................... 6.B -4 6.B

- 5. Monetized Residential Visibility Benefits in Studied Areas in 2020 for the Illustrative Scenario (millions of 2010$, 2020 income)

................................................ 6.B -6 6.B

- 6. Sensitivity Analysis for Monetized Residential Visibility Benefits in Other Areas for the Il lustrative Scenario in 2020 (in millions of 2010$)

.......................................... 6.B -6 6.B

- 7. Sensitivity Analysis for Incorporating Coarse Particles into Residential Visibility Benefits (in millions of 2010$)

...................................................................................... 6.B -7xviii 7- 1. Summary of Sectors, Emissions Reductions, and Known Annualized Control Costs (millions of 2010$)

........................................................................................................... 7-5 7- 2. Partial Attainment Known Annualized Control Costs in 2020 for Revised and Alternative Standards Analyzed (millions of 2010$)

....................................................... 7-5 7- 3. Extrapolated Costs by Revised and Alternative Standard Analyzed(millions of 201 0$)

............................................................................................................................ 7- 10 7- 4. Total Costs by Revised and Alternative Standard Analyzed (millions of 2010$), Fixed

- Cost Methodology

................................................................................................ 7- 14 7-5 Total Costs by Revised and Alternative Standard Analyzed (millions of 2010$), Hybrid Methodology

...................................................................................................... 7- 15 7- 6. Phase 2 Cost Estimates

.................................................................................................. 7- 20 7- 7. Summary of Qualitative Uncertainty for Modeling Elements of PM Engineering Costs

............................................................................................................................... 7- 22

7.A.1 PM 2.5 Emission Reductions and Costs to Meet 12 μ g/m 3

............................................. 7.A -1 7.A.2 Sensitivity Analysis of Fixed

- Cost Methodology for Unknown Controls by Revised and Alter native Standard Analyzed (millions of 2010$)

............................................... 7.A -2 7.A.3 Sensitivity Analysis of Hybrid Methodology for Unknown Controls by Revised and Alternative Standard Analyzed (millions of 2010$)

...................................................... 7.A -3

8- 1. Total Monetized Benefits, Total Costs, and Net Benefits in 2020 (millions of 2010$)

— Full Attainment

................................................................................................ . 8-2 8- 2. Human Health Effects of Ambient PM 2.5 ......................................................................... 8-5 8- 3. Welfare Co

- Benefits of PM 2.5 ........................................................................................... 8-6ES -1 EXECUTIVE SUMMARY

ES.1 Overview Based on its review of the air quality criteria and the national ambient air quality standards (NAAQS) for particulate matter (PM), the U.S. Environmental Protection Agency ( EPA ) is making revisions to the primary standards for PM to provide requisite protection of public health and welfare. The EPA is revis ing the primary annual (health

- based) standard , retaining the primary 24

- hour standard, and retaining the secondary (welfare

- based) NAAQS for fine particles (generally referring to particles less than or equal to 2.5 micrometers [μm ] in diameter

— PM 2.5 ). T he EPA is retain ing the current primary and secondary 24

- hour PM 10 standards.

As has trad itionally been done in NAAQS rulemaking s , the EPA has conducted a Regulatory Impact Analysis (RIA) to provide the public with illustrative estimates of the potential costs and health and welfare benefits of attaining the revised annual standard along with two alternat ive standards. In NAAQS rulemaking s , the RIA is prepared for informational purposes only, and the decisions related to the setting of the PM NAAQS standards are not in any way based on consideration of the information or analyses in the RIA. Th e RIA fulfills the requirements of Executive Order s 12866 and 13563 and guidelines of the Office of Management and Budget ’ s ( OMB ) Circular A -4. 1

The control strategies presented in this RIA are illustrative and represent one set of control strategies Stat e s might choose to implement in order to meet the final standards. As a result, b enefit and cost estimates provided in th is RIA cannot be added to benefits and costs f rom other regulations because each regulation is based on a different set of analytical assumptions and policy decisions . T he costs and benefits identified in this RIA will not be realized until specific controls are mandated by future State Implementation Plans ( SIPs ) or other Federal regulations

.
ES.2 Existing and Revised PM Air Quality Sta ndards

T wo primary PM 2.5 standards provide public health protection from effects associated with fine particle exposures : the annual standard and the 24

- hour standard . The current annual standard is set at a level of 15.0 μg/m 3 , based on the 3

- year average of annual arithmetic mean
PM 2.5 concentrations. The current 24

- hour standard is set at a level of 35 μg/m 3 , based on the

1 U.S. Office of Management and Budget. Circular A

- 4, September 17, 2003. Available at http://www.whitehouse.gov/omb/circulars/a004/a

- 4.pdf

.ES -2 3- year average of the 98 th percentile of 24

- hour PM 2.5 concentration. In the RIA , the current primary PM 2.5 standard s, including both annual and 24

- hour standard s, are denoted as 15/35 μg/m 3

. Attainment of the 24

- hour standard is analyzed only in developing the scenario of attainment with the existing standards of 15/35 μg/m 3

. All other scenarios evaluate additional emissions reduc tions needed to attain alternative annual standards only

.
In th is PM NAAQS review, the EPA has revis ed and lowered the level of the primary annual PM 2.5 standard to 12 μ g/m 3 in conjunction with retaining the level of the 24

- hour standard at 35 μg/m 3 and th is standard is denoted as 12 μg/m 3

. In addition to the revised annual standard of 12 μg/m 3 , the RIA also analyzes the benefits and costs of incremental control strategies for two alternative annual standards of 13 μg/m 3 and 11 μg/m 3

. Currently, t he existing secondary (welfare

- based) PM 2.5 standards are identical in all respects to the primary standards. In this PM NAAQS review , the EPA i s retaining the current suite of secondary standards for 24

- hour and annual PM 2.5 . Thus, while the new primary a nnual standard will be revised to 12 μg/m 3 , the secondary annual standard will remain at 15 μ g/m 3

. Non

- visibility welfare effects are addressed by this suite of secondary standards, and PM

related visibility impairment is addressed by the secondary 24

- hour PM 2.5 standard , which EPA is leaving unchanged at 35 μ g/m 3

. The secondary standards will thus remain at 15/35 μg/m 3

.

W ith regard to the primary and secondary standards for particles less than or equal to 10 μm in diameter (PM 10 ), the EPA is retain ing the current primary and secondary 24

- hour PM 10 standards , which are both set at a level of 150 μg/m 3 , not to be exceeded more than once per year on average over 3 years ( U.S. EPA, 1997 ). 2 Because the benefit

- cost analysis of the alt ernative PM 10 standards was conducted when the standard was promulgated in 19 97 , this RIA does not repeat that analysis here.

ES.2.1 Overview of the Analytical Steps in this RIA The goal of this RIA is to provide the best estimates of the costs and benefits of an illustrative attainment strategy to the meet the revised annual standard. The flowchart below (Figure ES

- 1) outlines the analytical steps taken to illustrate attainment with the revised annual standard of 12 μg/m 3 , and the following discussi on, by primary flowchart section, describes each of the steps taken. For important updates and analytical differences between proposal and final, see section ES.5 of this Executive Summary.

2 U.S. Environmental Protection Agency. 1997. Regulatory Impact Analyses for the Particulate Matter and Ozone National Ambient Air Quality Standards and Proposed Regional Haze Rule. Available at: http://www.epa.gov/ttn/oarpg/naaqsfin/ria.html

.ES -3 Figure ES

- 1. PM RIA Analytical Flowchart

Establishing the Analytical Baseline ( Flowchart Section 1 ) This section of the flowchart reflects the analytical steps taken to account for Federal and State rules and programs currently underway, as well as to reflect attainment of the current annual and daily standards o f 15/35 μg/m 3 for the purpose of estimating the incremental costs and benefits of attaining the revised annual standard

. Detailed discussions of the elements of this section of the flowchart are in Chapters 1 and 3 of the final RIA.

 Adjusted 2020 Base Case with “On the Books” Federal and State Rules and Programs

— The adjusted 2020 base case includes reductions expected to occur between 2007 and 2020 from existing (i.e., “on

- the

- books”) Federal and State control programs. This projection reflects air quality modeling of 2020 that accounts for major Federal and State programs along with adjustments to these national

- level modeling results to account for PM 2.5 reductions expected, largely in the westernES -4 US, from the implementation of episodic burn ban programs i n certain counties and to remove the effects of atypical events such as wildfires and fireworks displays. (See Figure ES -2 below for illustration.) Below is a list of some of the major national rules reflected in the base case. Refer to Chapter 3, Section 3.2.1.4 for a more detailed discussion of the rules reflected in the 2020 base case emissions inventory.

– Light

- Duty Vehicle Tier 2 Rule (U.S. EPA, 1999 )

– Heavy Duty Diesel Rule (U.S. EPA, 2000 )

– Clean Air Nonroad Diesel Rule (U.S. EPA, 2004 )

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

– NO x Emission Standard for New Commercial Aircraft Engines (U.S. EP A, 2005 a)

– Emissions Standards for Locomotives and Marine Compression

- Ignition Engines (U.S. EPA, 2008 a)

– Control of Emissions for Nonroad Spark Ignition Engines and Equipment (U.S. EPA, 2008 b)

– C3 Oceangoing Vessels (U.S. EPA, 2010 a)

– Boiler MACT (U.S. EPA, 2011d)

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

– Reciprocating Internal Combustion Engines (RICE) NESHAPs (U.S. EPA, 2010 b)

– Mercury and Air Toxics Standards (U.S. EPA, 2011 b)

– Cross

- State Air Pollution Rule (U.S. EPA, 2 011 a) 3

3 See Chapter 3, Section 3.2.1.4 for a discussion of the role CSAPR plays in the PM 2.5 RIA and the reasons we believe CSAPR remains an appropriate proxy for this analysis

.ES -5 Figure ES -2

. A nnual NAAQS Exceedances * in “Adjusted 2020 Base Case” Scenario

* 24- hr PM 2.5 NAAQS Exceedances as Follows:

• San Joaquin Valley (6 counties); South Coast (2 counties); Imperial, CA; Allegheny, PA; Salt Lake, UT; Lake, OR; and Sacramento, CA

 Analytical Baseline with Attainment of 15/35

— The analytical baseline includes reductions from additional controls that the EPA estimates are needed to attain the current standard s (15/35 μg/m 3 ) for the purpose of estimating th e incremental costs and benefits of attaining the revised annual standard of 12 μg/m 3

. Determining the level of emissions reductions needed to meet both the current annual and daily standards is done through adjusting a county’s projected design value (DV) concentration using the geographic area and pollutant specific air quality ratios (as described in Chapter 3 of the final RIA)

. In addition, for each area, it is necessary to determine which of the standards will be “controlling ,” i.e., which standard wil l require the most reductions to reach attainment of current standard

. This is important for establishing the analytical baseline because when the daily standard is controlling (as is the case in much of California), the emissions reductions required to me et the daily standard will result in reductions in the annual design value to below the annual standard of 15 μg/m 3

. As a result, the annual PM 2.5 increment needed to attain alternative standards for each county will not be relative to an annual design val ue of 15 μg/m 3 , rather, the increment will vary by county based on the annual design value in that county that resulted from applying emissions controls to meet the 35 μg/m 3 daily standard. As shown in the map below, for counties labeled as exceeding the 1 2 μg/m 3 annual standard, the baseline design values can be any value between 12 and 15 μg/m 3

. Similarly, exceedances of the 11 μg/m 3 and 13 μg/m 3 standards can be anywhere between 11 or 13 and 15 μg/m 3

.ES -6 The analytical baseline reflects attainment of 15/35 μg/m 3 by 2020

. We also modified the analytical baseline for counties in the South Coast Air Quality Management District and the San Joaquin Valley Air Pollution Control District to reflect reductions in mobile NO x emissions that th e se areas are expect ed to achieve between 2020 and 2025 due to fleet turnover. These reductions in NO x emissions are not attributable to attainment of the current or revised PM standards, but reflect the impacts of other ”on

- the

- books” mobile programs so that these reductions are not included as either an incremental cost nor benefit to these area’s attaining the revised annual standard

. Figure ES

- 3. Annual NAAQS Exceedances in “Analytical Baseline” Scenario

• 13 Level: 2 Counties (Tulare, CA and Riverside, CA)

• 12 Level: 7 Co unties (All in CA)

• 11 Level: 23 Counties

Control Strategies ( Flowchart Section 2 ) This section of the flowchart reflects analytical steps taken to analyze controls and emissions reductions needed beyond the current standard (15/35 μg/m 3 ) and other existing major rules to achieve the revised standard (12 μg/m 3 )

. We apply control options that might be available to State s for application by 2020. Detailed discussion of the elements of this section of the flowchart is in Chapter 4 of the final RIA.

 Sele ct Controls by Emissions Sector and Pollutant

— Non

- EGU point and nonpoint control measures were applied for the revised and alternative standards ’ control strategies. These controls were identified using the U.S. EPA’s Control Strategy Tool.ES -7 Additional control measures were not applied to EGUs because of the extensive nature of controls resulting from the inclusion of MATS.

 Adjust 2020 Control Scenario to Meet Alternate Standards

— After identifying the known controls in the control scenario that were needed to meet the analytical baseline, additional known controls needed to meet the revised and alternative standards were identified. The EPA used air quality modeling results to determine whether the control scenario was sufficient to meet the revised and alte rnative standards for each geographic area. Where the control scenario modeling resulted in design value reductions below the level needed for the revised or alternative standards for specific geographic areas, county

- specific ratios of air quality respons e to emission reductions were used to determine the subset of controls that were needed to attain the standard. Where it was determined that the control scenario was not sufficient in attaining the standard, these same response factors were used to calcula te the amount of additional emission reductions beyond known controls needed to meet the standard. For the revised and alternative control strategy analysis, known controls for two sectors were used: non

- EGU point and area sources. Onroad mobile source controls were not used in the revised and alternative standards analysis because they were applied previously in the analytical baseline analysis where they were deemed to be most cost effective. Emission reductions were calculated for the known control strat egy analysis and the cost analysis for emission reductions needed beyond known controls (“extrapolated” costs) for each alternative standard being analyzed. The EPA estimates the national

- scale emission reductions for revised annual standard of 12 μg/m 3 an d two alternative annual standards (13 μg/m 3 and 11 μg/m 3 ) as shown in Table ES

- 1.

Because the rules listed above and other emissions reductions should have substantially reduced ambient PM 2.5 concentrations by 2020 in the East, no additional controls are anticipated to be needed outside of California (assuming the absence of new sources). Specifically, our analysis estimates that in 2020 only 7 counties, all in California, will be out of attainment with the revised annual standard of 12ug/m 3 . Emissions reductions are needed in more locations for the alternative standard of 11ug/m 3

.ES -8 Table ES

- 1. Emission Reductions in Illustrative Emission Reduction Strategies for the Revised and Alternative Annual Primary PM 2.5 Standards, by Pollutant and Re gion in 2020 (tons) a

13 μg/m 3 12 μg/m 3 11 μg/m 3
Directly emitted PM 2.5

East 0 0 8,200

West 0 0 160

CA 730 4,000 10,600

SO 2

East 0 0 21,000

West 0 0 43

CA 0 0 0

NOx

East 0 0 9

West 0 0 0

CA 0 0 0

a See Chapter 4 for more information on the illustrative emission reduction strategies. The emissions in this table reflect both known and unknown controls. Estimates are rounded to two significant figure. Estimates are rounded to two significant figures. Es timates are rounded to two significant figures Analysis of Costs ( Flowchart Section 3) This section of the flowchart reflects analytical steps taken to estimate the costs of both known and unknown controls

. Detailed discussion of the elements of this section of the flowchart is in Chapter 7 of the final RIA.

 Estimate Known Controls Engineering Costs, Estimate Extrapolated Costs

— We provide engineering cost estimates for the control strategies identified in Chapter 4 that include control technologies on non

- EGU point sources and area sources. Engineering costs generally refer to the capital equipment expense, the site preparation costs for the application, and annual operating and maintenance costs. For this analysis, we included known controls for all of the geographic areas likely to exceed the revised and/or alternative standards. We also provide estimates for the engineering costs of the additional emissions reductions that are needed beyond the application of known controls to reach full attainment of the alternative standards analyzed; the cost estimates derived from this approach are referred to as “extrapolated” costs. By definition, no cost data currently exists for the additional emissions reductions needed beyond known controls

. We employ twoES -9 met hodologies for estimating the costs of unidentified future controls, and both approaches assume that innovative strategies and new control options make possible the emissions reductions needed for attainment by 2020.
Full Attainment (Flowchart Section 4) This section of the flowchart reflects analytical steps taken to estimate the costs of attainment, the benefits of attainment and the net benefits for the revised standard of 12 μg/m 3 . Detailed discussions of the elements of this section of the flowchart a re in Chapter 5, Chapter 7, and Chapter 8 of the final RIA.

 Calculate Full Attainment Costs

— In Chapter 7 we present a summary of the total national costs of attaining the revised annual standard of 12 μg/m 3 and the alternative annual standards of 13 μg/m 3 and 11 μg/m 3 in 2020. This summary includes the known and extrapolated costs. The total cost estimates are $53 million (2010$) and $350 million (2010$) for the revised annual standard of 12 μg/m 3 ; $11 million and $100 million for the alternative annual sta ndard of 13 μg/m 3 ; and $320 million and $1,700 million for the alternative annual standard of 11 μg/m 3

.

 Calculate Full Attainment Benefits

— C hapter 5 presents the estimated human health benefits for the revised NAAQS

. W e quantify the health

- related benefits of the fine particulate matter (PM 2.5 )- related air quality improvements resulting from the illustrative emission s control scenarios that reduce emissions of directl y emitted particles and precursor pollutants including SO 2 and NO x to reach alternative PM 2 .5 NAAQS levels in 2020. These benefits are relative to an analytical baseline reflecting nationwide attainment of the current primary PM 2.5 standards (i.e., annual standard of 15 μg/m 3 and 24

- hour standard of 35 μg/m 3 ) that includes promulgated national regulations and illustrative emission s controls to simulate attainment with 15/35 as well as a NO x emission adjustment to reflect expected reductions in mobile NO x emissions between 2020 and 2025.

The estimated benefits for the revised and alternative st andards are in addition to the substantial benefits estimated for several recent implementation rules. Rules such as the Mercury and Air Toxics Standard (MATS) and other emission reductions will have substantially reduced ambient PM 2.5 concentrations by 20 20 in the East, such that no additional controls would be needed in the East for the revised annual standard of 12 μg/m 3 . Thus, all of the estimated benefits occur in California because this is the only State that needs additional air quality improvement b eyond the analytical baseline after accounting for air quality improvements from recent rules.

 Net Benefits

— Chapter 8 compares estimates of the benefits with costs and summarizes the net benefits of revised annual standard of 12 μg/m 3 and the alternative a nnual standards of 13 μg/m 3 and 11 μg/m 3 relative to the analytical baseline that includes recently promulgated national regulations and additionalES

- 10 emissions reductions needed to attain the existing 15/35 μg/m 3 standards, as well as adjustments to NO x emis sions in the San Joaquin and South Coast areas.

ES.2. 2 Health and Welfare Co

- Benefits

The EPA estimated impacts on human health (e.g., mortality and morbidity effects) under full attainment of the three alternative annual PM 2.5 standards. We considered an array of health impacts attributable to changes in PM 2.5 exposure and estimated these benefits using the BenMAP model (Abt Associates, 2012 ), which has been used in many recent RIAs ( e.g., U.S. EPA, 2006, 2011a, 2011b ), and The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011c ). The monetized benefits estimated in the core analysis include avoided premature deaths (deriv ed from effect coefficients in tow cohort studies [Krewski et al. (2009) and Lepeule et al. (2012)] for adults and one for infants [Woodruff et al. (1997)] ) as well as avoided morbidity effects for 10 non

- fatal endpoints ranging in severity from lower respiratory symptoms to heart attacks. As noted above, because California is the only state that needs additional air quality improvement beyond the analytical baseline after accounting for expected air quality improvements expected from recent rules, all of the benefits associated with the revised standard of 12ug/m3 occur in California.

Since the proposed rule, t he 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 follow

- up to the Harvard Six Cities cohort study ( Lepeule et al., 2012 ), more recent demographic data projections, additional hospitalization and emergency department visit studies, inflation adjustment to 2010 dollars, and an expanded uncertainty assessment. Each of these updates is fully described in the health benef its chapter (Chapter 5) and summarized below in section ES.5

. Compared with the proposal benefits, the estimated benefits for the revised standard are about double due to a combination of updates to the analytic baseline

Even though the primary standards are designed to protect against adverse effects to human health, the emission reductions will 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, such as reductions in visibility impairment, materials damage, and ecosystem damage. Despite our attempts to quantify and monetize as many of the benefits as possible, the welfare co

- benefits associated with meeting the alternative standards ar e not quantified or monetized in this analysis. Unquantified health benefits are discussed in Chapter 5, and unquantified welfare co

- benefits are discussed in Chapter 6.ES

- 11 It is important to note that estimates of the health benefits from reduced PM 2.5 exposure reported here contain uncertaint ies, which are described in detail in Chapter 5 and Appendix 5b. Below are two key assumptions in the benefits analysis:

1. We assume that all fine particles, regardless of their chemical composition, are equally po tent in causing premature mortality. This is an important assumption, because PM 2.5 varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The Inte grated Science Assessment for Particulate Matter (PM ISA) , which was twice reviewed by 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, 2009 ). These uncertainties are likely to be magnified in the current analysis to the extent that the emissions controls are less diverse when focusi ng on one small region of the country rather than a broader geography with more diverse emissions sources and the application of a more diverse set of controls.

2. We assume that health impact functions based on national studies are representative for exposures and populations in California. I n addition to the national risk coefficients we use as our core estimates , the EPA considered the cohort studies conducted in California specifically. Although we have not calculated the benefits results using the coh ort studies conducted in California , we provided the se risk coefficients to show how much the monetized benefits could have changed. Most of the California cohort studies report central effect estimates similar to the (nation

- wide) all

- cause mortality risk estimate we applied from Krewski et al. (2009) and Lepeule et al. (2012) albeit with wider confidence intervals. Three cohort studies conducted in California indicate statistically significant higher risks than the risk estimates we applied from Lepeule e t al. (2012), and four studies showed insignificant results.

3. 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.

In general, we are more confident in the magnitude of the risks we e stimate from simulated PM 2.5 concentrations that coincide with the bulk of the observed PM concentrations in the epidemiological studies th at 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. As noted in the preamble to the rule, the range from theES

- 12 25 th to 10 th percentiles of the air quality data in the epidemiology studies is a reasonable range below which we start to have appreciably less confidence in the magnitude of the associations observed in the epidemiological studies. C oncentration benchmark analyses (e.g., 25 th percentile, 10 th percentile, one standard deviation below the mean , 4 and lowest measured level [ LML ] ) provide some insight into the level of uncertainty in the estimated PM 2.5 mortality benefits

. Most of the estimated avoided premature deaths for this rulemaking occur at or above the lowest measured PM 2.5 concentration in the two studies that are used to estimate mortality benefits. T here are uncertainties inherent in identifying any particular point at which our confidence in reported associations becomes appreciably less, and the scientific evidence provi des no clear dividing line

. However , the EPA does not view these concentration benchmarks as a concentration threshold below which we would not quantify health benefits of air quality improvements. Rather, t he core benefits estimates reported in this RIA ( i.e., those based on Krewski et al. [2 009 ] and Lepeule et al. [ 2012 ]) are the best measures because they reflect the full range of modeled air quality concentrations associated with the emission

r eduction strategies and because t he current body of scientific literature indicates that a no

threshold model provides the best estimate of PM

- related long

- term mortality. It is important to emphasize that “less confidence” does not mean “no confidence.”

The estimated benefits reflect illustrative control measures and emission reductions to lower PM 2.5 concentrations at monitors projected to exceed the revised and alternative annual standards. The result is that air quality is expected to improve in counties that exceed these standards as well as surrounding areas that do not exceed the alternative standards. In order to make a direct comparison between the benefits and costs of the emission reduction strategies, it is appropriate to include all the benefits occurring as a result of the emission reduction strategies applied regardless of where they occur. Therefore, it is not appropriate to estimate the fraction of benefits that occur only in the counties that exceed the standards because it would omit benef its attributable to emission reduction in exceeding counties. In addition, we estimate benefits using modeled air quality data with 12 km grid cells, which is important because the grid cells are often substantially smaller than counties , and PM 2.5 concent rations can vary spatially within a county. Some grid cells in a county can be below the level of the alternative standard even though the highest monitor exceeds the alternative standard. Thus, emission reductions can lead to benefits in grid cells that a re below the alternative standards within an exceeding county.

4 A range of one standard deviation around the mean represents approximately 68% of normally distributed data and below the mean falls between the 25 th and 10 th percentiles.ES

- 13 ES.2. 3 Cost Analysis Approach

The EPA estimated total costs under partial and full attainment of the alternative PM 2.5 standards. The engineering costs generally include the costs of purchasin g, installing, and operating the referenced control technologies. The technologies and control strategies selected for analysis are illustrative of one way in which nonattainment areas could meet a revised standard. There are numerous ways to construct and evaluate potential control programs that would bring areas into attainment with alternative standards, and the EPA anticipates that State and local governments will consider programs that are best suited for local conditions.

The partial attainment cost a nalysis reflects the costs associated with app lying known controls

. Costs for full attainment include estimates for the engineering costs of the additional tons of emissions reductions that are needed beyond identified controls, referred to as extrapolated costs. By definition, no cost data currently exist for the additional emissions reductions needed beyond known controls. W e e mploy two methodologies for estimating the costs of un identified future controls: a fixed

- cost methodology and a hybrid methodolog y ; b oth approaches assume either that existing technologies can be applied in particular combinations or to specific sources that we currently can’t predict or that innovative strategies and new control options make possible the emissions reductions needed for attainment by 2020. T he two approaches, however, implicitly reflect different assumptions about technological progress and innovation in emissions reductions strategies. The fixed

- cost methodology uses a $15,000/ton estimate for each ton of PM 2.5 redu ced, and t he hybrid methodology generates a total annual cost curve for PM 2.5 for unknown future controls that might be applied in order to move toward 2020 attainment. The hybrid methodology has the advantage of incorporating information about how significant the needed reductions from unspecified control technologies are relative to the known control measures and matching that information with expected increasing per

- ton cost for applying unknown controls. Employing the fixed

- cost methodology, approximate ly 90% of total costs for attaining the revised annual standard of 12 μg/m 3 are from unspecified control technologies

. Employing the hybrid methodology, approximately 97% of total costs for attaining the revised annual standard of 12 μg/m 3 are from unspeci fied control technologies

. The EPA recognizes that the extrapolated portion of the engineering cost estimates reflects substantial uncertainty about which sectors and which technologies might become available for cost

- effective application in the future.

T he engineering cost estimates are limited in their scope. Our analysis focuses on the emissions reductions needed for attainment of the revised and alternative standards . Also, t he amendments to the ambient air monitoring regulations will revise the network designES

- 14 requirements for PM 2.5 monitoring sites, resulting in moving 21 monitors to established near

road monitoring stations by January 1, 2015. The incremental cost associated with moving these 21 monitors is a one

- time cost of $28 ,570. Lastly, the EPA understands that some State s will incur costs designing SIPs and implementing new control strategies to meet the revised standard. However, the EPA does not know what specific actions State s will take to design their SIPs to meet the revised standards ; therefore , we do not include estimated costs that government agencies may incur for managing the requirement, implementing these (or other) control strategies, or for offering incentives that may be necessary to encourage the implementat ion of specific technologies, especially for technologies that are not necessarily market driven.

ES.2. 4 Comparison of Benefits and Costs The EPA’s illustrative analysis has estimated the health and welfare benefits and costs associated with the revised a nnual PM NAAQS. The results for 2020 suggest there will be significant health and welfare benefits and these benefits will outweigh the costs associated with the illustrative control strategies in 2020. In the analysis , we estimate the net benefits of the revised annual PM 2.5 standard of 12 μg/m 3 and al ternative annual standards of 13 μg/m 3
and 11 μg/m 3 , incremental to the 2020 analytical baseline. For the revised annual standard of
12 μg/m 3

, net benefits are estimated to be $ 3.7 billion to $ 9 billion at a 3% discount rate and $ 3. 3 billi on to $ 8.1 billion at a 7% discount rate in 2020 (20 10 dollars ). For an alternative annual standard of 13 μg/m 3

, net benefits are estimated to be $ 1. 2 b illion to $2 .9 b illion at the 3% discount rate and $ 1.1 b illion to $2 .6 b illion at the 7% discount rate. N et benefits of an alternative annual PM 2.5 standard of 11 μg/m 3 are estimated to be $ 11 billion to $ 29 bill i on at a 3% discount rate and $ 10 billion to $ 26 billion at a 7% discount rate in 2020

. See Table ES

- 2.

For the revised annual s tandard of 12 μg/m 3 , the EPA estimates that the benefits of full attainment exceed the costs of full attainment by 12 to 171 times at a 3 % discount rate. For the alternative annual standard of 13 μg/m 3 , the EPA estimates that the benefits of full attainment exceed the costs of full attainment by 13 to 272 times at a 3 % discount rate. For the alternative annual standard s of 11 μg/m 3

, the EPA estimates that the benefits of full attainment exceed the costs of full attainment by 8 to 90 times at a 3 % discount rate.ES

- 15 Table ES

- 2. Total Monetized Benefits, Total Costs, and Net Benefits in 20 20 (m illions of 2010$)

— Full Attainment a

Alternative

Annual Standard ( μg/m 3 ) Total Costs b Monetized Benefits d Net Benefits

3% Discount Rate c 7% Discount Rate 3% Discount Rate 7% Discount Rate 3% Discount Rate b 7% Discount Rate

13 $11 to $100 $11 to $100 $1,300 to $2,900 $1,200 to $2,600 $1, 2 00 to $2, 9 00 $1,100 to $2, 6 00

12 $53 to $350 $53 to 350 $4,000 to $9,100 $3,600 to $8,200 $ 3,7 00 to $ 9,000 $3,3 00 to $ 8,100

11 $320 to $1,700 $320 to $1,700 $13,000 to $29,000 $12,000 to $26,000 $1 1 ,000 to $2 9 ,000 $10 ,000 to $2 6 ,000

a These estimates reflect incremental emissions reductions from an analytical baseline that gives an “ adjustment ” to the San Joaquin and South Coast areas in California for NO x emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.
b The two cost estimates do not represent lower - and upper

- bound estimates but represent estimates generated by two different methodologies. The lower estimate is generated using the fixed

- cost methodology, which

assumes that technological change and innovation will result in the availability of additional controls by 2020 that are similar in cost to the higher end of the cost range for current, known controls. The higher estimate is generated using the hybrid methodology, which assumes that while additional controls may become available by 2020, they become available at an increasing cost and the increasing cost varies by geographic area and by degree of difficulty associated with obtaining the needed emissions reductions.

c Due to data limitations, we were unable to discount compliance costs for all sectors at 3%. See Chapter 7, Section 7.2.2 for additional details on the data limitations. As a result, the net benefit calculations at 3% were computed by subtracting the co sts at 7% from the monetized benefits at 3%.
d The reduction in premature deaths each year accounts for over 90% of total monetized benefits. Mortality risk valuation assumes discounting over the SAB

- recommended 20

- year segmented lag structure. Not all pos sible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all unquantified benefits. Data limitations prevented us from quantifying these endpoints, and as such, these benefits are inherently more uncertain than those ben efits that we were able to quantify. The range of benefits reflects the range of the central estimates from two mortality cohor t studies (i.e., Krewski et al., 2009 and Lepeule et al., 20 12 ).

Tabl e ES

- 3. Benefit

- to

- Cost Ratios for Alternative Standards at 3% and 7% Based on Projected Benefits and Costs in 2020

13 μ g/m 3 12 μ g/m 3 11 μ g/m 3

Benefit

- Cost Ratio 3% a 13 to 272 12 to 171 8 to 90

Benefit

- Cost Ratio 7% 11 to 246 11 to 154 7 to 81

a Due to data limitations, we were unable to discount compliance costs for all sectors at 3%. See Chapter 7, Section 7.2.2 for additional details on the data limitations. As a result, the net benefit calculations at 3% were computed by subtracting the costs at 7% from the monetized benefits at 3%.ES

- 16 Figure ES -4 reflects the range of costs based on the calculation of costs using the fixed

cost approach and the hybrid approach. Additionally, we see the difference in the calculation of benefits based on using various st udies.

Figure ES -4. Monetized Benefit to Cost Comparison for 12 μg/m 3 in 2020 (7% Discount Rate)ES

- 17 T able ES -4. Estimated Number of Avoided PM 2.5 Health Impacts for Standard Alternatives

— Full Attainment in 2020 a

Health Effect Alternative Annual Standards

13 μg/m 3 12 μg/m 3 11 μg/m 3
Adult Mortality Krewski et al. (2009) (adult) 140 460 1,500 Lepeule et al. (2012) (adult) 330 1,000 3,300 Woodruff et al. (1997) (infant) 0 1 4

Non

- Fatal Heart Attacks (age >18)

Peters et al. (2001) 160 480 1,600
Pooled estimate of 4 studies 17 52 170 Hospital admissions

— respiratory (all ages) 31 110 380 Hospital admissions

— cardiovascular (age > 18) 43 140 480 Emergency department visits for asthma ( all ages) 67 230 810 Acute bronchitis (age 8

– 12) 280 870 2,700 Lower respiratory symptoms (age 7

– 14) 3,500 11,000 34,000 Upper respiratory symptoms (asthmatics age 9

– 11) 5,100 16,000 49,000 Asthma exacerbation (age 6

– 18) 13,000 40,000 120,000 Lost work days (age 18

– 65) 22,000 71,000 230,000
Minor restricted

- activity days (age 18

– 65) 130,000 420,000 1,300,000

a Incidence estimates are rounded to whole numbers with no more than two significant figures.

ES. 3 Discussion and Conclusions An extensive body of scientific evidence documented in PM ISA indicates that PM 2.5 can penetrate deep into the lungs and cause serious health effects, including premature death and other non

- fatal illnes ses (U.S. EPA, 2009 ). As described in the preamble to the rule , the revisions to the standards are based on an integrative assessment of an extensive body of new scientific evidence (U.S. EPA, 2009 ). Health studies published since the PM ISA (e.g., Pope et al. [2009] ) confirm that recent levels of PM 2.5 have had a significant impact on public health. Based on the air quality analysis in this RIA, the EPA projects that nearly all coun ties with PM 2.5 monitors in the U nited States would meet an annual standard of 12 μg/m 3 without additional
F ederal , S tate , or local PM control programs. This demonstrates the substantial progress that the United States has made in reducing air pollution em issions over the last several decades.ES

- 18 Regulations such as the EPA’s recent Mercury a nd Air Toxics Standards (MATS) and other
F ederal programs such as diesel standards will provide substantial improvements in regional concentrations of PM 2.5 . Our analysis shows a few areas would still need additional emissions reductions to address local sources of air pollution, including ports and uncontrolled industrial emissions. For this reason, we have designed the RIA analysis to focus on local controls in these few areas. We estimate that these additional local controls would yield benefits well in excess of costs.

The setting of a NAAQS does not compel specific pollution reductions and as such does not directly result in costs or benefits. For this reason, NAAQS RIAs are merely illustrative. The NAAQS RIAs illustrate the potential costs and benefits of additional steps States could take to attain a revised air quality standard nationwide beyond rules already on the books. We base our illustrative estimates on an arra y of emission control strategies for different sources. T he costs and benefits identified in this RIA will not be realized until specific controls are mandated by SIPs or other F ederal regulations. In short, NAAQS RIAs hypothesize, but do not prescribe, th e control strategies that States may choose to enact when implementing a revised NAAQS.

It is important to emphasize that the EPA does not “double count” the costs or the benefits of our rules. E mission reductions achieved under rules that require specific actions from sources

— such as MATS

— are in the baseline of this NAAQS analysis, as are emission reductions needed to meet the current NAAQS. For this reason, the cost and benefits estimates provided in this RIA and all other NAAQS RIAs should not be added to the estimates for implementation rules.

In calculating the costs, the EPA assume d the application of a significant number of

un identified future controls that would make possible the additional emission s reductions needed for attainment in 2020. EPA use d two methodologies

— the fixed

- cost and hybrid methodologies

— for estimating the costs of un identified future controls , and b oth approaches

assume either that existing technologies can be applied in particular combinations or to specific sources that we curr ently can’t predict or that innovative strategies and new control options make possible the emissions reductions needed for attainment by 2020. Estimates generated by the two approaches do not represent lower - and upper

- bound estimates but simply represent estimates generated by two different methodologies. The fixed

- cost methodology implicitly assumes that technological change and innovation will result in the availability of additional controls by 2020 that are similar in cost to the higher end of the cos t range for current controls. The hybrid methodology implicitly assumes that while additional controls become available by 2020, they become available at an increasing cost and the increasing costES

- 19 varies by geographic area and by degree of difficulty assoc iated with obtaining the needed emissions reductions.

F or the revised annual standard of 12 μg/m 3 , the total cost estimates comprise between 90 and 97 % extrapolated cost estimates , and the estimated total cost using the hybrid methodology is roughly 6.5 t imes more than the estimated total cost using the fixed

- cost methodology. Because the hybrid methodology reflects increasing marginal costs in areas needing a higher ratio of emission s reductions from unknown to known controls, it could be more representative of total costs. In an effort to consider the potential fitness of the

e xtrapolated cost estimates, we reviewed the South Coast Air Quality Management District’s (SCAQMD) 2012 Air Quality Management Plan (AQMP) , and we locate d data on recent emission reduction credit (ERC) transaction s in both the SCAQMD and San Joaquin Valley Air Pollution Control District (SJV APCD). While this information provides context for the extrapolated cost estimates, the current relationship between available controls and costs to reduce emissions may or may not be applicable in 2020 because of changes in innovation and advances in technology.

The SCAQMD’s 2012 AQMP includes information on control measures to meet the current 24

- hour standard of 35 μg/m 3 , including further PM 2.5 controls for under

- fired charbroilers at a cost per ton reduced of $15,000. This control cost matches the parameter used in the fixed

- cost methodology, as well as the initial value used for the hybrid methodology and is suppo rtive of our selection of that value. In addition, the California Air Resources Board’s 2009 and 2010 Emission Reduction Offset Transaction Costs, Summary Report included PM 10 ERC prices in both the SCAQMD and the SJV APCD. To some degree, ERC transaction prices reflect a choice between installing a more stringent control and purchasing ERCs. Between 2009 and 2010 PM 10 ERC prices in SJV APCD ranged from $40,000 per ton per year (tpy) to $70,000/tpy, and PM 10 ERC prices in the SCAQMD ranged from $575,000/tpy to more than $1.9 million/tpy. These prices reflect both marginal costs that are higher than the fixed

- cost estimates and marginal costs that are not inconsistent with the higher cost estimates generated using the hybrid methodology. For further discussio n of the total cost estimates, refer to Section 7.2.4 in Chapter 7 of this RIA.

Furthermore, the monetized benefits estimates presented in this RIA are not intended to capture the full burden of PM to public health but rather represent the incremental benefits expected upon attaining the revised annual primary standard of 12 μg/m 3

. In comparison , modeling by Fann et al. (2012) estimated that 2005 levels of air pollution were responsible fo r between 130,000 and 320,000 PM 2.5 - related deaths, or between 6.1% and 15% of total deathsES

- 20 from all causes in the continental United States. The monetized benefits associated with attaining the proposed range of standards appear modest when viewed within the context of the potential overall public health burden of PM 2.5 and ozone air pollution estimated by Fann et al. (2012) , but this is primarily because regulations already on the books will make great strides toward reducing fu ture levels of PM. One important distinction between the total public health burden estimated for 2005 air pollution levels and the estimated benefits in this RIA is that ambient levels of PM 2.5 will have improved substantially by 2020 , due to major emissi ons reductions resulting from implementation of Federal regulations

. For example, we estimate that SO 2 emissions (an important PM 2.5 precursor) in the U nited States would fall from 14 million tons in 2005 to less than 5 million tons by 2020 (a reduction of 66%). For this reason, States will only need to achieve small air quality improvements to reach the proposed PM standards. As shown in the recent RIA for MATS (U.S . EPA, 2011b ), implementing other Federal and State air quality actions will address a substantial fraction of the total public health burden of PM 2.5 and ozone air pollution.

The NAAQS are not set at levels that eliminate the risk of air pollution. 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 PM 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, 2010 c ). While benefits occurring below the standard are assumed to be more uncertain than those occurring above the standard, the EPA considers these to be legitimate components of the total benefits estimate. Alt hough there are greater uncertainties at lower PM 2.5 concentrations, there is no evidence of a population

- level threshold in PM 2.5 - related health effects in the epidemiology literature.

Lastly, the EPA recognizes that there are uncertainties in both the cost and benefit estimates provided in this RIA. T he EPA was unable to monetize fully all of the benefits associated with reaching these standards in this RIA, including other health effects of PM, visibility effects, ecosystem effects, and climate effects. If the EPA were able to monetize all of the benefits, the benefits would exceed the estimated costs by an even greater margin.ES

- 21 ES. 4 Caveats and Limitations EPA acknowledges several important limitations of th is analysis. These include the following :

ES. 4.1 Benefits Caveats

 PM 2.5 mortality benefits represent a substantial proportion of tota l monetized benefits (over 98 %)

. To characterize the uncertainty in the relationship between PM 2.5 and premature mortality, we include a set of 12 estimates of the concentration

- response function based on results of the PM 2.5 mortality expert elicitati on study in addition to our core estimates. Even these multiple characterizations omit the uncertainty in air quality estimates, baseline incidence rates, populations exposed , chemical composition, transferability of the effect estimate to diverse location s , and additional uncertainty around the mean estimates expressed by the experts. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the PM 2.5 estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire analysis.

 Most of the estimated avoided premature deaths occur at or above the lowest measured PM 2.5 concentration in the two studies used to estimate mortality benefits. In general, we have greater confidence in risk estimates based on PM 2.5 concentrations where the bulk of the health and air quality data reside and somewhat less confidence where data density is lower.

 We a naly zed full attainment in 2020 , and projecting ke y variables introduces uncertainty. Inherent in any analysis of future regulatory programs are uncertainties in projecting atmospheric conditions and source

- level emissions, as well as population, health baselines, incomes, technology, and other factors.

 T here are uncertainties related to the health impact functions used in the analysis. These include within

- study variability; pooling across stud ies ; the application of C

- R functions nationwide and for all particle species; extrapolation of impact functions across population s ; and various uncertainties in the C

- R function, including causality and shape of the function at low concentrations. Therefore, benefits may be under or over

- estimates.

 T his analysis omits certain unquantified effects due to lack of dat a, time , and resources. These unquantified endpoints include other health and ecosystem effects. The EPA will continue to evaluate new methods and models and select those most appropriate for estimating the benefits of reductions in air pollution.

 Full ben efits of the revised standards in San Joaquin and South Coast will not be realized until 2025 when those areas are expected to demonstrate attainment with the revised standards. If we were to estimate the monetized benefits for 2025, thoseES

- 22 benefits would be higher due to population growth, aging of the population, and income growth over time.

ES. 4.2 Control Strategy and Cost Analysis Caveats and Limitations

 The control technologies applied as part of the illustrative control strategies represent technologie s that are available currently and may not reflect emerging devices that may be available in future years to aid in attainment of the revised standard. In addition , the emission reductions calculated from the known controls (control efficiencies) assume th at 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.

 The illustrative control strategy analysis estimates only one potential pathwa y to attainment. The control strategies are not recommendations for how the revised PM 2.5 standard should be implemented, and States will make all final decisions regarding implementation strategies for the revised NAAQS.

 The application of known controls is based on source information obtained from the emissions inventory. To the extent the inventory is lacking data on baseline controls from S IPs, we may analyze control options that are currently in place.

 The future

- year emissions used as a basis for developing the control strategies in this RIA have implicit assumptions regarding emissions growth and control, which differ by sector. For some emission sectors , these future

- year emissions may not reflect new sources locating in thes e areas

.

 For two area s in California (South Coast Air Quality Management District and San Joaquin Valley Air Pollution Control District ) the degree of projected nonattainment with the revised annual standard of 12 μg/m 3 is such that th ese areas are not expected to be able to d emonstrate attainment with the new standard by 2020. These areas may qualify for up to a 5- year extension of their attainment date and are likely to have until 2025 to demonstrate attainment with the revised annual standard

.

 The control technologies appli ed do not reflect potential effects of technological change that may be available in future years and the effects of “learning by doing” are not accounted for in the emissions reduction estimates. In our analysis , we do not have the necessary data for cumulative output, fuel sales, or emission reductions for all sectors included in order to properly generate control costs that reflect learning

- curve impacts. We believe the effect of including these impacts would be to lower our estimates of costs for our co ntrol strategies in 2020.

 In addition to the application of known controls, the EPA assume s the application of

un identified future controls that make possible the additional emission reductions needed for attainment in 2020. By definition, no cost data currently exist forES

- 23 unidentified future technologies or innovative strategies and the cost estimat es for

un identified future controls reflect some uncertainty

.

 Because we obtain control cost data from many sources, we are not always able to obtain consiste nt data across original data sources. If disaggregated control cost data are unavailable (i.e., where capital, equipment life value, and operation and maintenance [ O&M ] costs are not separated out), the EPA typically assumes that the estimated control cost s are annualized using a 7% discount rate. When disaggregated control cost data are available (i.e., where capital, equipment life value, and O&M costs are explicit) , we can recalculate costs using a 3% discount rate. In general, we have some disaggregated data available for non

- EGU point source controls , and we do not have any disaggregated control cost data for area source controls. In this analysis, for the revised annual standard of 12 μg/m 3 and the alternative standard of 13 μg/m 3 we did not have any d isaggregated known control cost data ; therefore, we were not able to recalculate known control costs using a 3% discount rate.

 The EPA understands that some States will incur costs designing SIPs and implementing new control strategies to meet the revised annual standard. However, the EPA does not know what specific actions States will take to design their SIPs to meet the revised standards ; therefore we do not present estimated costs that government agencies may incur for managing the requirement , impleme nting these (or other) control strategies, or for offering incentives that may be necessary to encourage the implementation of specific technologies, especially for technologies that are not necessarily market driven.

ES. 5 Important Updates and Analytical Differences Between the PM NAAQS Proposal RIA and the Final RIA There have been several major improvements in the analytical components the EPA used to estimate benefits and costs between the proposal RIA (June 2012) and this RIA

ac companying the final PM NAAQS. Important updates to our emissions, air quality modeling and ambient data, air quality ratios, population projections, as well as currency year valuation resulted in an improved analytical base for our analysis for the final rule. Based on the complexity and magnitude of the updates and improvements made between the PM RIA proposal and final RIA, it would not be appropriate to perform a simple direct comparison of results. Therefore, each analysis stands alone and must be evaluated independently as such.

Below is a list summarizing some of the analytical changes between the proposal RIA and the final RIA. Between the proposal RIA and the final RIA, we developed the control strategies based on an improved modeling platform and u pdated the approach in designing the control strategies. The improved modeling platform updated the current and projected airES

- 24 quality levels for each area across the nation and the updated approach allowed for more effective emissions reductions for each a rea in both attaining the current annual and daily standards for our analytical baseline and in attaining the revised annual standard. For example, in the proposal RIA we controlled precursor emissions of NO x and SO 2 over a broader region and these emissio ns reductions were not as effective in reducing design value for each area as the direct PM 2.5 emissions reductions targeted in the final RIA

. These analytical improvements resulted in different estimates of costs and benefits between the proposal RIA and the final RIA in which we have more confidence in reflecting the approaches that State s will pursue to attain the current and revised standards.

 New modeling platform

— In the modeling platform for the final rule we included key updates to the current ambient data that generally show improved air quality when compared to modeling for the proposed rule, although daily design values (DVs) for some areas in California increased due largely to wildfires in 2008. To address the increases in these areas we adjusted the ambient data for these atypical events.

 Future air quality with “on

- the

- books” controls

— We also project PM 2.5 air quality levels between 2007 and the 2020 base case which were generally lower in the final RIA compared to the proposal RIA largely bec ause the starting values for the ambient data were lower (i.e., final RIA air quality projections showed more improvements than proposal RIA)

.

 Analytical baseline with attainment of 15/35

— For the final RIA, EPA’s approach to attaining the existing standar ds of 15/35 μg/m 3 was improved with “ air quality adjustment ratios ” that were based on more focused sensitivity model runs for (i) specific areas like California counties; (ii) influential sources like residential wood combustion; and (iii) specific PM emission species like directly emitted PM 2.5 . In the proposal RIA, we conducted the analysis using more general air quality ratios that reflected multiple sources such as point and area and precursor PM emissions like NO x and SO 2 . As a result, in the final RIA, the daily standard of 35 μg/m 3 was attained more effectively and had less impact on annual DVs because of episodic, direct PM 2.5 reductions, while in the proposal RIA, the daily standard was attained less effectively because we pursued year

- around NO x and SO 2 reductions that necessitated more emissions reductions and had more impact on the annual DVs.

 Incremental air quality changes

— In the final RIA, the use of improved air quality adjustment ratios resulted in more incremental air quality improvement needed to attain the annual standard of 12 μg/m 3 in California. Thus, these larger incremental air quality improvements needed to attain the revised standard resulted in higher estimated health benefits in the final rule compared to the proposal RIA. As described above, this is because the daily standard of 35 μg/m 3 was attained moreES

- 25 effectively and had less impact on annual DVs acr oss the counties in California. In addition, the focus on direct PM 2.5 emissions reductions allowed for more effective controls to attain the revised standard with fewer PM 2.5 emissions reductions thereby resulting in similar fixed costs estimates. 5

 Benefits analysis

— EPA incorporated several updates that affected the core benefits estimates in this RIA. Specifically, the EPA incorporated the most recent follow

- up to the Harvard Six Cities cohort study (Lepeule et al., 2012), which decreases the high end of the mo netized benefits range by 13%. The EPA also updated the demographic data projections to reflect the 2010 Census, which increased the monetized benefits by 4% percent for the revised standard. Additional epidemiology studies for hospitalizations and emergency department visits and updated survival rates for

non

- fatal heart attacks did not affect the rounded benefits estimates.

 Cost estimates

— In the final RIA, the EPA presents a range of costs using both the fixed and hybrid methodologies to estimate the costs associated with unknown controls.

 Inflation

— The EPA updated the currency year in this RIA to use 2010 dollars, which increased both the costs and the monetized benefits by approximately 8% since the proposal.

ES. 6 References Abt Associates, Inc. 2012. BenMAP User ’ s Manual Appendices

. Prepared for U.S. Environmental Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. September.
Fann, N., A. Lamson, K. Wesson, D. Risley, S.C. Anenberg, and B.J. Hubbell. 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.
Krewski, D., R.T. Burnett, M.S. Goldbert, K. Hoover, J. Siemiatycki, M. Jerrett, M. Abrahamowicz, and W.H. White. 2009. “ Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air Pollution and Mortality. ” Special Report to the Health Effects Institute. Cambridge , MA. July.
Lepeule , J. , F

. Laden, D

. Dockery, and J. Schwartz

. 2012. “ Chronic Exposure to Fine Particles and Mortality: An Extended Follow

- Up of the Harvard Six Cities Study from 1974 to 2009. ” Environ Health Perspect. In press. Available at: http://dx.doi.org/10.1289/ehp.1104660

. Peters, A., D. W. Dockery, J. E. Muller and M. A. Mittleman. 2001. “Increased particulate air pollution and the triggering of myocardial infarction.” Circulation 103 (23): 2810

- 5.

5 The hybrid methodology cost estimates increased between the proposal RIA and the final RIA largely because a large amount of emissions reductions were needed from one cou nty with a low amount of known controls.ES

- 26 Pope, C .A. III, E

. Majid, and D. Dockery. 2009. “ Fine Particle Air Pollution and Life Expect ancy in the United States. ” New England Journal of Medicine 360: 376 -3 86.
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 . Available at: http://www.epa.gov/ttn/oarpg/naaqsfin/ria.html

. U.S. Environmental Protection Agency (U.S. EPA). 1999

. Regulatory Impact Analysis

— Control of Air Pollution from New Moto r Vehicles: Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements

. EPA 420 -R- 99

- 023

. Available at: http://www.epa.gov/otaq/regs/ld

- hwy/tier

- 2/documents/r99023.pdf

. U.S. Environmental Protection Agency (U.S. EPA). 2000

. Regulator y Impact Analysis: Control of Emissions of Air Pollution from Highway Heavy

- Duty Engines

. EPA 420 -R- 00

- 010

. Available at: http://www.epa.gov/otaq/regs/hd

- hwy/2000frm/r00010.pdf

. U.S. Environmental Protection Agency (U.S. EPA). 2004. Final Regulatory Analysis: Control of Emissions from Nonroad Diesel Engines . EPA420 -R- 04

- 007. Available at: http://www.epa.gov/otaq/documents/nonroad

- diesel/420r04007.pdf

. U.S. Environmental Protection Agency ( U.S. EPA). November 17, 2005a. Federal Register . Vol. 70, No. 2 21. “ Control of Air Pollution From Aircraft and Aircraft Engines; Emission Standards and Test Procedures.” 40 CFR Part 87. Available at: http://www.epa.gov/fedrgstr/EPA

- AIR /2005/November/Day

- 17/a22704.pdf

.
U.S. Environmental Protection Agency ( U.S. EPA ). 2005b

. Regulatory Impact Analysis for the Final Clean Air Visibility Rule or the Guidelines for Best Available Retrofit Technology (BART) Determinations Under the Regional Haze Regulations

. EPA

- 452/R

- 05

- 004. Available at: http://www.epa.gov/oar/visibility/pdfs/bart_ria_2005_6_15.pdf

. U.S. Environmental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards for Particulate Matter, Chapter 5

. Office of Air Quality Planning and Standards, Research Triangle Park, NC. October. Available at: http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205

— Benefits.pdf

. U.S. Environmental Protection Agency (U.S. EPA)

. 2008a

. Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters Per Cylinder

. Available at: http://www.regulations.gov/#!documentDetail;D=EPA

- HQ

- OAR

- 2003

- 0190

- 0938

. U.S. Environmental Protection Agency (U.S. EPA) . 2008b. Control of Emissions from Marine SI and Small SI Engines, Vessels, and Equipment. Final Regulatory Impact Analysis

. Available at: http://www.regulations.gov/#!documentDetail;D=EPA

- HQ

- OAR

- 2004

- 0008

- 0929

.ES

- 27 U.S. Environmental Protection Agency (U.S. EPA). 2009a. Economic Impacts of Revised MACT Standards for Hospital/Medical/Infectious Waste Incinerators. Available at: http://www.epa.gov/ttn/atw/129/hmiwi/hmiwi_eia_090803.pdf

.
U.S. Environme ntal Protection Agency (U.S. EPA). 2009 b

. Integrated Science Assessment for Particulate Matter (Final Report)

. EPA

- 600 -R- 08

- 139F. National Center for Environmental Assessment

— RTP Division. December. Available at: http://cfpub.epa.gov/ncea/c fm/recordisplay.cfm?deid=216546

. U.S. Environmental Pr otection Agency (U.S. EPA). 2010 a

. Regulatory Impact Analysis: Control of Emissions of Air Pollution from Category 3 Marine Diesel Engines

. Available at: http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09019.pdf

. U.S. Environmental Protection Agency (U.S. EPA). 2010b. Regulatory Impact Analysis: Regulatory Impact Ana lysis (RIA) for Existing Stati onary Spark Ignition (SI) RICE NESHAP. Available at: http://www.epa.gov/ttn/atw/rice/fnl_si_rice_ria.pdf

. U.S. Environmental Protection Agency (U.S. EPA). 201 0c

. Quantitative Health Risk Assessment for Particulate Matter

— Final Repor t . EPA

- 452/R

- 10

- 005. Office of Air Quality Planning and Standards, Research Triangle Park, NC. September. Available on the Internet at <http://www.epa.gov/ttnnaaqs/standards/pm/data/ PM_RA_FINAL_June_2010.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2011a. Regulatory Impact Analysis for the Federal Implementation Plans to Reduce Interstate Transport of Fine Particulate Matter and Ozone in 27 States; Correction of SIP Approval s for 22 States . June. Available at: http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2011 b

. 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). 2011 c

. The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress . Office of Air and Radiation, Office of Policy, Washin gton, DC. March. Available at: http://www.epa.gov/oar/sect812/feb11/fullreport.pdf

. U.S. Environmental Protection Agency (U.S. EPA). 2011 d

. Regulatory Impact Analysis: National Emission St andards for Hazardous Air Pollutants for Industrial, Commercial, and Institutional Boilers and Process Heaters

. February

. Available at: http://www.epa.gov/ttnecas1/regdata/RIAs/boilersriafinal110221_psg.pdf

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

. Available at : http://www.whitehouse.gov/omb/circulars/a004/a

- 4.pdf

.ES

- 28 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “ The Relationship Between Selected of po stneonatal infant mortality and particulate air pollution in the United States. ” Environmental Health Perspectives 105 (6): 608

- 612.1-1 CHAPTER 1 INTRODUCTION AND BAC KGROUND

1.1 Synopsis This chapter summarizes the purpose and results of this Regulatory Impact Analysis (RIA). This RIA estimates the human health and welfare benefits and costs of attaining the revised and two alternative annual particulate matter ( PM ) National Ambient Air Quality Standards (NAAQS) nationwide. According to the Clean Air Act (“ the Act ”) , the Environmental Protection Agency ( EPA ) must use health

- based criteria in setting the NAAQS and cannot consider estimates of co mpliance cost. The EPA is producing this RIA both to provide the public a sense of the benefits and costs of meeting a revised annual PM NAAQS and to meet the requirements of Executive Order s 12866 and 13563

.
1.2 Background

1.2.1 NAAQS
Two sections of the Act govern the establishment and revision of NAAQS. Section 108 (42 U.S.C. 7408) directs the Administrator to identify pollutants that “ may reasonably be anticipated to endanger public health or welfare ” and to issue air quality criteria for them. These ai r 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. ” PM 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 10 9(b)(1) defines a primary standard as “ the attainment and maintenance of which in the judgment of the Administrator, based on [the] criteria and allowing an adequate margin of safety, [are] requisite to protect the public health. ” A secondary standard, as defined in section 109(b)(2), must “ specify a level of air quality the attainment and maintenance of which in the judgment of the Administrator, based on [the] criteria, [are] requisite to protect the public welfare from any known or anticipated adverse ef fects 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. ”1-2 Section 109(d) of the Act directs the Administrator to review existing criteria and standards at 5

- year intervals. When warranted by such review, the Administrator is to retain or revise the NAAQS. After promulgation or revision of the NAAQS, the standards are implemented by the State s.

1.2.2 2006 PM NAAQS
In 2006, the EPA ’ s final PM rule established a 24

- hour standard of 35 μg/m 3 and retained the annual standard of 15 μg/m 3

. The EPA revised the secondary standards for fine particles by making them identical in all respects to the primary standards. Following promulgation of the final rule in 2006, several parties filed petitions for its review. On February 24, 2009, the U.S. Court of Appeals for the District of Columbia Circuit remanded the primary annual P M 2.5 NAAQS to the EPA citing that the EPA failed to adequately explain why the standard s provided the requisite protection from both short - and long

- term exposures to fine particles, including protection for at

- risk populations. The court remanded the seco ndary standards to the EPA citing that the Agency failed to adequately explain why setting the secondary PM standards identical to the primary standards provided the required protection for public welfare, including protection from visibility impairment. I n 2006, the EPA also retain ed the primary and secondary 24

- hour PM 10 standards, both set at a level of 150 μg/m 3

, not to be exceeded more than once per year on average over 3 years ( U.S. EPA, 1997 ).

1.3 Role of this RIA in the Process of Setting the NAAQS

1.3.1 Legal Requirement 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 new standard s. The Act requires the EPA, for each criteria pollutant, to set standard s that protect public health with “ an adequate margin of safety. ” As interpreted by the Agency and the courts, the Act requires the EPA to create standards based on health consideration s only.

The prohibition against the consideration of cost in the setting of the primary air quality standard s , 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 implement ing these standards. The impact of cost and efficiency is considered by State s during this process, as they decide what timelines, strategies, and policies make th e most sense. This RIA is intended to inform the public about the potential costs and benefits that may result when new standard s are implemented, but it is not relevant to establishing the standards themselves.1-3 1.3.2 Role of Statutory and Executive Orders

This RIA is separate from the NAAQS decision

- making process , but several statutes and executive orders still apply to any public documentation. The analysis required by these statutes and executive orders is presented in Chapter 9.

The EPA presents this RIA pursuant to Executive Order s 12866 and 13563 and the guidelines of Office of Management and Budget ( OMB ) Circular A

- 4. 1 In accordance with these guidelines, the RIA analyzes the benefits and costs associated with emissions controls to attain the revise d annual PM standard, incremental to attainment of the existing standards. In addition, this RIA analyses two alternative primary annual PM 2.5 standards : one that is more stringent than the existing standards but less stringent than the revised annual stan dard and another that is more stringent than the revised annual standard

. In the current PM NAAQS review, the EPA is revising and lowering the level of the primary annual PM 2.5 standard from 15 μg/m 3 to 12 μg/m 3 in conjunction with retaining the level of the 24

- hour standard at 35 μg/m 3

. Thus, the incremental benefits and costs analyzed in this RIA result from emissions controls needed to attain a more protective annual standard, rather than the 24

- hour standard of 35 μg/m 3

. In addition to the revised annual standard of 12 μg/m 3 , the RIA also analyzes the benefits and costs of incremental control strategies for two alternative annual standards (1 3 μg/m 3 and 11 μg/m 3 ).

The control strategies presented in this RIA are illustrative and represent one set of control strategies State s might choose to implement in order to meet the final standards. As a result, benefit and cost estimates provided in the RIA are cannot be added to benefits and costs from other regulations , and , further, the costs and benefits identified in this RIA will not be realized until specific controls are mandated by State Implementation Plans (SIPs) or other Federal regulations.

The analytical baseline for this analysis does not assume emissions controls that might be implemented to meet the other NAAQS for O 3 , NO x , or SO 2 . To the extent that some of the estimated emissions reductions needed to meet the revised annual PM standard would be needed to meet the current standards for O 3 , NO x , or SO 2 , the costs and benefits of meeting the revised PM annual PM standard will be overstated. We did not conduct this analysis incremental to controls applied as part of previous NAAQS analyses (e.g., O 3 , NO x , or SO 2 )

1 U.S. Office of Management and Budget. Circular A

- 4, September 17, 2003, available at

< http://www.whitehouse.gov/omb/circulars/a004/a

- 4.pdf>.1-4 because the data and modeling on which these previous analyses were based are now considered outdated and are not compatible with the current PM 2.5 NAAQS analysis.

1.3.3 The Need for National Ambient Air Quality Standards

OMB Circular A

- 4 indicates that one of the reasons a regulation such as the NAAQS may be issued is to address existing “ externalities .” An externality occurs when parties to a transaction do not bear its full consequences. An e nvironmental problem , such as pollution generated from production of a good , which imposes health costs on those who neither produce nor consume it, is a classic case of an externality. In the presence of externalities, a free market does not ensure an efficient allocation of resources. Setting and implementin g primary and secondary air quality standard s is one way the government can address an externality and increase air overall public health and welfare.

1.3.4 Illustrative Nature of the Analysis This NAAQS RIA is an illustrative analysis that provides useful insights into a limited number of emissions control scenarios that State s might implement to achieve revised NAAQS.
Because State s are ultimately responsible for implementing strategies to mee t any revised standard, the control scenarios in this RIA are necessarily hypothetical in nature. Important uncertainties and limitations are documented in the relevant portions of the analysis.

Because t he illustrative goals of this RIA are somewhat diffe rent from other EPA analyses of national rules or the implementation plans State s develop, the distinctions are worth brief mention. This RIA does not assess the regulatory impact of an EPA

- prescribed national rule, nor does it attempt to model the specific actions that any State would take to implement a revised standard. This analysis attempts to estimate the costs and human and welfare benefits of cost

- effective implementation strategies that might be undertaken to achieve national attainment of new stan dards. These hypothetical strategies represent a scenario where State s use one set of cost

- effective controls to attain a revised NAAQS. Because State s— not the EPA

— will implement any revised NAAQS, they will ultimately determine appropriate emissions contr ol scenarios. SIPs would likely vary from the EPA ’ s estimates due to differences in the data and assumptions that State s use to develop these plans.

The illustrative attainment scenarios presented in this RIA were constructed with the understanding that th ere are inherent uncertainties in projecting emissions and controls.1-5 1.4 Overview and Design of the Final RIA The RIA evaluates the costs and benefits of hypothetical national strategies to attain the revised annual standard of 12 μg/m 3 and two alternative annual PM standards , incremental to attainment of the existing 15/35 μg/m 3 standards

.

T he EPA is retain ing the current primary and secondary 24

- hour PM 10 standards , which are both set at a level of 150 μg/m 3 , not to be exceeded more than once per year on average over 3 years ( U.S. EPA, 1997 ). Because the benefit

- cost analysis of the alternative PM 10 standards was conducted when the standard was promulgated in 1997 , this RIA does not rep eat that analysis here.

1.4.1 Important Updates and Differences Between the PM NAAQS Proposal RIA and the Final RIA There have been several major improvements in the analytical components the EPA used to estimate benefits and costs between the proposal RI A (June 2012) and this RIA accompanying the final PM NAAQS

. Important updates to our emissions, air quality modeling and ambient data, air quality ratios, population projections, and currency

- year valuation resulted in an improved analytical base for our a nalysis for the final rule. Based on the complexity and magnitude of the updates and improvements made between the PM RIA proposal and final RIA, it would not be appropriate to perform a simple direct comparison of results. Therefore, each analysis stands alone and must be evaluated independently as such.

Below is a list summarizing some of the analytical changes between the proposal RIA and the final RIA

. Between the proposal RIA and the final RIA, we developed the control strategies based on an improved m odeling platform and updated the approach in designing the control strategies

. The improved modeling platform updated the current and projected air quality levels for each area across the nation and the updated approach allowed for more effective emissions reductions for each area in both attaining the current annual and daily standards for our analytical baseline and in attaining the revised annual standard

. For example, in the proposal RIA we controlled precursor emissions of NO x and SO 2 over a broader re gion and these emissions reductions were not as effective in reducing design value for each area as the direct PM 2.5 emissions reductions targeted in the final RIA

. These analytical improvements resulted in different estimates of costs and benefits between the proposal RIA and the final RIA which we have more confidence in reflecting the approaches that states will pursue to attain the current and revised standards.1-6  New modeling platform

— In the modeling platform for the final rule we included key updates to the current ambient data that generally show improved air quality when compared to modeling for the proposed rule, although daily design values (DVs) for some areas in California increased due largely to wildfires in 2008

. To address the increases in these areas we adjusted the ambient data for these atypical events.

 Future air quality with “ on - the

- books ” controls

— We also project PM 2.5 air quality levels between 2007 and the 2020 base case which were generally lower in the final RIA compared to the proposal RIA largely because the starting values for the ambient data were lower (i.e., final RIA air quality projections showed more improvements than proposal RIA)

.

 Analytical baseline with attainment of 15/35

— For the final RIA, the EPA’s approach to attaining the existing standards of 15/35 μg/m 3 was improved with “ air quality adjustment ratios ” that were based on more focused sensitivity model runs for (i) specific areas like California counties; (ii) influential sources like residential wood combustion; and (iii) specific PM emission species like directly emitted PM 2.5 . In the proposal RIA, we conducted the analysis using more general air quality ratios that reflected multiple sources such as point and area and precursor PM emissions like NO x and SO 2 . As a re sult, in the final RIA, the daily standard of 35 μg/m 3 was attained more effectively and had less impact on annual DVs because of episodic, direct PM 2.5 reductions, while in the proposal RIA, the daily standard was attained less effectively because we pursued year

- around NO x and SO 2 reductions that necessitated more emissions reductions and had more impact on the annual DVs.

 Incremental air quality changes

— In the final RIA, the use of improved air quality adjustment ratios resulted in more incremental air quality improvement needed to attain the annual standard of 12 μg/m 3 in California

. Thus, these larger incremental air quality improvements needed to attain the revised standard resulted in higher estimated health benefits in the final rule compared to the proposal RIA

. As described above, this is because the daily standard of 35 μg/m 3 was attained more effectively and had less impact on annual DVs across th e counties in California

. In addition, the focus on direct PM 2.5 emissions reductions allowed for more effective controls to attain the revised standard with fewer PM 2.5 emissions reductions thereby resulting in similar fixed costs estimates. 2

 Benefits ana lysis

— The EPA incorporated several updates that affected the core benefits estimates in this RIA. Specifically, the EPA incorporated the most recent follow

- up to the Harvard Six Cities cohort study (Lepeule et al., 2012), which decreases the high end of th e monetized benefits range by 13%

. The EPA also updated the demographic data projections to reflect the 2010 Census, which

2 The hybrid methodology cost estimates increased between the proposal RIA and the final RIA largely because a large amount of emissions reductions were needed from one county with a low amount of know n controls

.1-7 increased the monetized benefits by 4% percent for the revised standard. Additional epidemiology studies for hospitalizations and emergency department visits and updated survival rates for non

- fatal heart attacks did not affect the rounded benefits estimates.

 Cost e stimates

— In the final RIA, the EPA presents a range of costs using both the fixed and hybrid methodologies to estimate the costs associated with unknown controls.

 Inflation

— The EPA updated the currency year in this RIA to use 2010 dollars, which increased both the costs and the monetized benefits by approximately 8% since the proposal.

1.4. 2 Existing and Revised PM Air Quality Standards

T wo primary PM 2.5 standards provide public health protection from effects associated with fine particle exposures: the annual standard and the 24

- hour standard. The existing annual standard is set at a level of 15.0 μg/m 3 , based on the 3

- year average of the annual arithmetic

mean of PM 2.5 concentrations. The existing 24

- hour standard is set at a level of 35 μg/m 3 , based on the 3

- year average of the 98th percentile of 24

- hour PM 2.5 concentration s . In th is RIA, the existing primary PM 2.5 standard s, including both the annual standard and 24

- hour standard , are denoted as 15/35 μg/m 3

. In th is current PM NAAQS review, the EPA has revis ed the level of the primary annual PM 2.5 standard to 12 μg/m 3 in conjunction with retaining the level of the 24

- hour standard at 35 μg/m 3

. Currently, the existing secondary (welfare

- based) PM 2.5 standards are identical in all respects to the primary standards. In this PM NAAQS review, the EPA i s retaining the curren t suite of secondary standards for 24

- hour and annual PM 2.5 . Thus, while the new primary annual standard will be revised to 12 μ g/m 3 , the secondary annual standard will remain at 15 μ g/m 3

. Non

- visibility welfare effects are addressed by this suite of secondary standards, and PM

related visibility impairment is addressed by the secondary 24

- hour PM 2.5 standard, which EPA is leaving unchanged at 35 μ g/m 3 . The secondary standards will thus remain at 15/35 μg/m 3

.

W ith regard to the primary and secondary standards for particles less than or equal to 10 μm in diameter (PM 10 ), the EPA is retaining the current primary and secondary 24

- hour PM 10 standards. Both standards are the same. The current primary and secondary 24

- hour standards are set at a level of 15 0 μg/m 3 , not to be exceeded more than once per year on average over 3
years ( U.S. EPA, 1997 ).1-8 1.4. 2 .1 Graphical Overview of the RIA Analysis

Figure 1

- 1. PM RIA Analytical Flow Diagram

1.4. 2.2 Establishing the Analytical Baseline

The RIA is intended to evaluate the costs and benefits of reaching attainment with alternative PM 2.5 standards. In order to develop and evaluate control strategies for attaining a more stringent primary standard, it is important to account for Federal and stat e rules and programs currently underway, as well as to reflect attainment of the current annual and daily standards of 15/35 μg/m 3

. Estimating the 2020 levels after attainment of the current standards of 15/35 μg/m 3 then allows us to estimate the increment al costs and benefits of attaining any alternative primary standard. EPA anticipates attainment with 15/35 μg/m 3 by 2020 in all but two areas in California, not expected to attain the current standards until 2025.1-9 The analytical baseline includes reductions already achieved as a result of national regulations and reductions expected prior to 2020 from recently promulgated national regulations , referred to as the base case. Reductions achieved as a result of State and local agency regulations and vo luntary programs are reflected to the extent that they are represented in emission inventory information submitted to the E PA by State and local agencies. Below is a list of some of the major national rules reflected in the base case. Refer to Chapter 3, S ection 3.2. 1.4 for a more detailed discussion of the rules reflected in the 2020 base case emissions inventory

.

 Light

- Duty Vehicle Tier 2 Rule (U.S. EPA, 1999 )

 Heavy Duty Diesel Rule (U.S. EPA, 2000 )

 Clean Air Nonroad Diesel Rule (U.S. EPA, 2004 )

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

 NO x Emission Standard for New Commercial Aircraft Engines ( U.S. EPA, 2005 a)

 Emissions Standards for Locomotives and Marine Compression

- Ignition Engines (U.S. EPA, 2008 a)

 Control of Emissions for Nonroad Spark Ignition Engines and Equipment (U.S. EPA, 2008 b)

 C3 Oceangoing Vessels (U.S. EPA, 2010 a)

 Boiler MACT (U.S. EPA, 2011d)

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

 Reciprocating Internal Combustion Engines (RICE) NESHAPs (U.S. EPA, 2010 b)

 Mercury and Air Toxics Standards (U.S. EPA, 2011 b)

 Cross

- State Air Pollution Rule (CSAPR) (U.S. EPA, 2011 a) 3

The analytical baseline for this analysis does not assume emissions controls that might be implemented to meet the other NAAQS for O 3 , NO x , or SO 2 . To the extent that some of the

3 See Chapter 3, Section 3.2.1.4 for a discussion of the role CSAPR plays in the PM 2.5 RIA and the reasons we believe CSAPR remains an appropriate proxy for this analysis

.1- 10 estimated emissions reductions needed to meet the revised annual PM standard are also needed to meet the current standards for O 3 , NO x , or SO 2 , the costs and benefits of meeting the revised PM annual standard will be oversta ted. We did not conduct this analysis incremental to controls applied as part of previous NAAQS analyses (e.g., O 3 , NO x , or SO 2 ) because the data and modeling on which these previous analyses were based are now considered outdated and are not compatible wi th the current PM 2.5 NAAQS analysis. In addition, all control strategies analyzed in all NAAQS RIAs are hypothetical. Th e analysis present ed here is just one scenario that State s may employ but does not prescribe how attainment must be achieved.

Most areas of the U nited States will be required to demonstrate attainment with the new standard by 2020. As a result, for these areas, the correct baseline for estimating the incremental emissions reductions that would be needed to attain the more protective standa rd is a baseline with emissions projected to 2020 and adjusted to reflect the additional emissions reductions that would be needed to attain the current 15/35 standards. For two areas in Southern California (South Coast and San Joaquin), the degree of proj ected non

- attainment with the revised annual standard 12 μg/m 3 is high enough that those counties are not expected to be able to demonstrate attainment of the new standard by 2020. Instead, those two areas are likely to qualify for an extension of their at tainment date of up to 5 years. If the areas are granted an attainment date extension, they will have until 2025 to demonstrate attainment of the revised annual standard of 12 μg/m 3 . As a result, for these two areas, the correct baseline for estimating the incremental emissions reductions that would be needed to attain the more protective standard is a baseline with emissions projected to 2025 adjusted to reflect the additional emissions reductions that would be needed to attain the current 15/35 μg/m 3 stand ards.

This difference in attainment year is important because between 2020 and 2025 emissions from mobile sources in California are expected to be reduced because of continued fleet turnover from older, higher emitting vehicles to newer, lower emitting vehicles. These reductions in emissions will occur as a result of previous State rules for which costs and benefits have already been counted and thus will not be costs and benefits attributable to meeting the revised annual standard of 12 μg/m 3

. For California, the provided future

- year 2020 and 2025 emissions included most on

- the

- books regulations such as those for low sulfur fuel, idling of heavy

- duty vehicles, chip re flash, public fleets, trash trucks, drayage trucks, and heavy duty trucks and buses. See Chapter 3, Section 3.2.1.4 for further details on California emission inventories

.1- 11 For the purposes of this analysis, we have constructed an analytical baseline that

r eflects attainment of 15/35 μg/m 3 in 2020

. This analytical baseline is modified in the South Coast Air Quality Management District and the San Joaquin Valley Air Pollution Control District to reflect an “ adjustment ” for the reductions in NO x emissions that those areas are expected to see between 2020 and 2025. These reductions in NO x are not attributable to attainment of the current or revised PM standards but rather reflect the impacts of other programs. These NO x emissions changes will affect baseline PM concentrations but will not affect costs or benefits of attaining the revised annual or the a lternative annual standards.

To provide the most reasonable and reliable estimates of costs and benefits of full attainment for the nation, we construct an analytical baseline for estimating the costs and benefits of attaining the revised annual standard of 12 μ g/m 3 , 13 μ g/m 3

, and 11 μ g/m 3 with the following characteristics: ( 1) reflects on

- the

- books regulations as implemented through 2020 plus additional emissions reductions needed to meet the 15/35 μg/m 3 standard levels , and ( 2) additional mobile source emissions reductions expected to occur between 2020 and 2025 for California’s South Coast and San Joaquin areas, which are likely to not demonstrate attainment until 2025. Essentially, we are modifying the baseline in those two areas to reflect an

“ adjustment ” for the reductions in NO x emissions that those areas are expected to see between 2020 and 2025. This allows us to generate costs and benefits of full attainment without overstating the costs and benefits in those two areas, which would occur if we forced costly emissions reductions in 2020 in areas that would not have to occur until 2025 and that will be offset because of the expected reductions in mobile source emissions due to other programs. See Chapter 3, Section 3.2.1.4 for details on emission inventories from California.

Benefits for all areas are estimated using 2020 population data fo r consistency, recognizing that full attainment costs and benefits will not actually be realized until 2025 for a portion of the costs and benefits. The 2020 estimates of full attainment costs and benefits will be an underestimate of benefits in 2025 because of population growth and changes in the age distribution of the population between 2020 and 2025.

1.4. 3 Health and Welfare Co

- Benefits Analysis Approach

The EPA estimated impacts on human health (e.g., mortality and morbidity effects) under full attainment of the three alternative annual PM 2.5 standards. We considered an array of health impacts attributable to changes in PM 2.5 exposure and estimated these benefits using the BenMAP mod el (Abt Associates, 2012 ), which has been used in many recent RIAs (e.g., U.S. EPA, 2006, 2011a, 2011b ), and The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S.1- 12 EPA, 2011c ). The monetized benefits estimated in the core analysis include avoided premature deaths (derived from effect coefficients in tow cohort studies [Krewski et al. (2009) and Lepeule et al. (2012)] for adults and one for infants [Woodruff et al. (1997)] ) as well as avoided morbidity effects for 10 non

- fatal endpoints ranging in severity from lower respiratory symptoms to heart attacks. As noted above, because California is the only state that needs additional air quality improvement beyond the analytical baseline after accounting for expected air quality improvements expected from recent rules, all of the benefits associated with the revised standard o f 12ug/m3 occur in California.

Since the proposed rule, t he 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 follow

- up to the Harvard Six Cities cohort study ( Lepeule et al., 2012 ), more recent demographic data projections, additional hospitalization and emergency department visit studies, inflation adjustment to 2010 dollars, and an expanded uncertainty assessment. Eac h of these updates is fully described in the health benefits chapter (Chapter 5) and summarized below in section ES.5

. Compared with the proposal benefits, the estimated benefits for the revised standard are about double due to a combination of updates to the analytic baseline

Even though the primary standards are designed to protect against adverse effects to human health, the emission reductions will have welfare co

- benefits in addition to the direct human health benefits. The term welfare co

- benefits co vers both environmental and societal benefits of reducing pollution, such as reductions in visibility impairment, materials damage, and ecosystem damage. Despite our attempts to quantify and monetize as many of the benefits as possible, the welfare co

- bene fits associated with meeting the alternative standards are not quantified or monetized in this analysis. Unquantified health benefits are discussed in Chapter 5, and unquantified welfare co

- benefits are discussed in Chapter 6.

It is important to note that estimates of the health benefits from reduced PM 2.5 exposure reported here contain uncertaint ies , which are described in detail in Chapter 5 and Appendix 5b. Below are two key assumptions in the benefits analysis:

1. 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 varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The Integrated Science Assessment for Particulate Matter (PM ISA) , which was twice reviewed by CASAC, concluded that “many constituents of PM 2.5 can be linked with multiple health effects, and the evidence is not yet sufficient to allow1- 13 differentiation of those constituents or sources that are more closely related to specific outcomes” (U.S. EPA, 2009 ). These uncertainties are likely to be magnified in the current analysis to the extent that the emissions controls are less diverse when focusing on one small region of the country rather than a broader geography with more diverse emissions sources and the application of a more diverse set of controls.

2. We assume that health impact functions based on national studies are representative for exposures and populations in California. I n addition to the national risk coefficients we use as our core estimates , the EPA considered the cohort studies conducted in California specifically. Although we have not calculated the benefits results using the cohort studies conducted in California , we provided the se risk coefficients to show how much the monetized benefits could have changed. Most of the California cohort studies report central effect estimate s similar to the (nation

- wide) all

- cause mortality risk estimate we applied from Krewski et al. (2009) and Lepeule et al. (2012) albeit with wider confidence intervals. Three cohort studies conducted in California indicate statistically significant higher risks than the risk estimates we applied from Lepeule et al. (2012), and four studies showed insignificant results.

3. 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.

In general, w e 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

co nfident in the risk we estimate from simulated PM 2.5 concentrations that fall below the bulk of the observed data in these studies. As noted in the preamble to the rule, the range from the 25 th to 10 th percentiles of the air quality data in the epidemiology studies is a reasonable range below which we start to have appreciably less confidence in the magnitude of the associations observed in the epidemiological studies. C oncentration benchmark analyses (e.g., 25 th percentile, 10 th percentile, one standard deviation below the mean, 4 and lowest measured level [ LML ] ) provide some insight into the level of uncertainty in the estimated PM 2.5 mortality benefits

. Most of the estimated avoided premature deaths for this rulemaking occur at or above the lowest measured PM 2.5 concentration in the two studies that are used to estimate mortality benefits. T here are uncertainties inherent in identifying any particular point

4 A range of one standard deviation around the mean represents approximately 68% of normally distributed data and below the mean falls between the 25 th and 10 th percentiles.1- 14 at which our confidence in reported associations becomes appreciably less , and the sc ientific 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 benefits of air quality improvements. Rather, t he core benefits estimates reported in this RIA (i.e., those based on Krewski et al. [2 009] and Lepeule et al. [2012] ) are the best measures because they reflect the full range of modeled air quality concentrations associated with the emission reduction strategies and because t he current body of scientific literature indicates that a no

threshold model provides the best estimate of PM

- related long

- term mortality. It is important to emphasize that “less confidence” does not mean “no confidence.”

The estimated benefits reflect illustrative control measures and emission reductions to lower PM 2.5 concentrations at monitors projected to exceed the revised and alternative annual standards. The result is that air quality i s expected to improve in counties that exceed these standards as well as surrounding areas that do not exceed the alternative standards. In order to make a direct comparison between the benefits and costs of the emission reduction strategies, it is appropriate to include all the benefits occurring as a result of the emission reduction strategies applied regardless of where they occur. Therefore, it is not appropriate to estimate the fraction of benefits that occur only in the counties that exceed the standards because it would omit benefits attributable to emission reduction in exceeding counties. In addition, we estimate benefits using modeled air quality data with 12 km grid cells, which is important because the grid cells are often substantially smaller t han counties , and PM 2.5 concentrations can vary spatially within a county. Some grid cells in a county can be below the level of the alternative standard even though the highest monitor exceeds the alternative standard. Thus, emission reductions can lead t o benefits in grid cells that are below the alternative standards within an exceeding county.

1.4. 4 Cost Analysis Approach The EPA estimated total costs under partial and full attainment of the alternative PM 2.5 standards. The engineering costs generally include the costs of purchasing, installing, and operating the referenced control technologies. The technologies and control strategies selected for analysis are illustrative of one way in which nonattainment areas could meet a revised standard. There are numerous ways to construct and evaluate potential control programs that would bring areas into attainment with alternative standards, and the EPA anticipates that State and local governments will consider programs that are best suited for local conditions.1- 15 The partial attainment cost analysis reflects the costs associated with applying known controls. Costs for full attainment include estimates for the engineering costs of the additional tons of emissions reductions that are needed beyond identified control s, referred to as extrapolated costs. By definition, no cost data currently exist for the additional emissions reductions needed beyond known controls. W e e mploy two methodologies for estimating the costs of un identified future controls: a fixed

- cost metho dology and a hybrid methodology ; b oth approaches assume either that existing technologies can be applied in particular combinations or to specific sources that we currently can’t predict or that innovative strategies and new control options make possible the emissions reductions needed for attainment by 2020. The two approaches, however, implicitly reflect different assumptions about technological progress and innovation in emissions reductions st rategies. The fixed

- cost methodology uses a $15,000/ton estimate for each ton of PM 2.5 reduced, and t he hybrid methodology generates a total annual cost curve for PM 2.5 for unknown future controls that might be applied in order to move toward 2020 attainme nt. The hybrid methodology has the advantage of incorporating information about how significant the needed reductions from unspecified control technologies are relative to the known control measures and matching that information with expected increasing pe r- ton cost for applying unknown controls. Employing the fixed

- cost methodology, approximately 90% of total costs for attaining the revised annual standard of 12 μg/m 3 are from unspecified control technologies . Employing the hybrid methodology, approximatel y 97% of total costs for attaining the revised annual standard of 12 μg/m 3 are from unspecified control technologies

. The EPA recognizes that the extrapolated portion of the engineering cost estimates reflects substantial uncertainty about which sectors an d which technologies might become available for cost

- effective application in the future.

T he engineering cost estimates are limited in their scope. Our analysis focuses on the emissions reductions needed for attainment of the revised and alternative stand ards. Also, t he amendments to the ambient air monitoring regulations will revise the network design requirements for PM 2.5 monitoring sites, resulting in moving 21 monitors to established near

road monitoring stations by January 1, 2015. The incremental co st associated with moving these 21 monitors is a one

- time cost of $28,570. Lastly, the EPA understands that some States will incur costs designing SIPs and implementing new control strategies to meet the revised standard. However, the EPA does not know wha t specific actions States will take to design their SIPs to meet the revised standards ; therefore, we do not include estimated costs that government agencies may incur for managing the requirement, implementing these (or other) control strategies, or for offering incentives that may be necessary to encourage the1- 16 implementation of specific technologies, especially for technologies that are not necessarily market driven.

1.5 Organization of this Regulatory Impact Analysis

This RIA includes the follow ing 1 0 chapters:

 Chapter 1: Introduction and Background . This chapter introduces the purpose of the RIA.

 Chapter 2: Defining the PM Air Quality Problem . This chapter characterizes the nature, scope , and magnitude of the current

- year PM 2.5 problem.

 Chapter 3 : Air Quality Modeling and Analysis . 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 costs and benefits.

 Chapter 4: Control Strategies . This chapter presents the hypothetical control strategies, the geographic areas where controls were applied, and the results of the modeling that predicted PM 2.5 concentrations in 2020 after appl ying the control strategies.

 Chapter 5: Health Benefits Analysis Approach and Results . This chapter quantifies and monetizes the health benefits of the PM 2.5 - related air quality improvements associated with the hypothetical control strategies

.

 Chapter 6 : Welfare Co

- Benefits of the Primary Standard . This chapter describes the welfare effects, including changes in visibility, materials damage, ecological effects from PM deposition, ecological effects from nitrogen and sulfur emissions, vegetation effects from ozone ex posure, ecological effects from mercury deposition, and climate effects.

 Chapter 7 : Engineering Cost Analysis . This chapter summarizes the data sources and methodology used to estimate the engineering costs of partial and full attainment of several alternative standards.

 Chapter 8: Comparison of Benefits and Costs . This chapter compares estimates of the total benefits with total costs and summarizes the net benefits of several alternative standards.

 Chapter 9 : Statutory and Executive Order Impact Ana lyses . This chapter summarizes the Statutory and Executive Order impact analyses.1- 17  Chapter 10 : Qualitative Discussion of Employment Impacts of Air Quality Regulations

. T his chapter provides a q ualitative di scussion of e mployment impacts of a ir q uality r egul ations

.
1.6 References for Chapter 1 Abt Associates, Inc. 2012. BenMAP User ’ s Manual Appendices

. Prepared for U.S. Environmental Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. September.
Krewski, D., R.T. Burnett, M.S. Goldbert, K. Hoover, J. Siemiatycki, M. Jerrett, M. Abrahamowicz, and W.H. White. 200 9 “ Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air Pollution and Mortality. ” Special Report to the Health Effects Institute. Cambridge MA. July.
Lepeule , J. , F

. Laden, D

. Dockery, and J. Schwartz

. 2012. “ Chronic Exposure to Fine Particles and Mortality: An Extended Follow

- Up of the Harvard Six Cities Study from 1974 to 2009. ” Environ Health Perspect. In press

. Available at: http://dx.doi.org/10.1289/ehp.1104660

. U.S. Environmental Protection Agency. 1997. Regulatory Impact Analyses for the Particulate Matter and Ozone National Ambient Air Quality Standards and Proposed Regional Haze Rule . Available at: http://www.epa.gov/ttn/oarpg/naaqsfin/ria.html

. U.S. Environmental Protection Agency (U.S. EPA). 1999

. Regulatory Impact Analysis

— Control of Air Pollution from New Motor Vehicles: Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements

. EPA 420 -R- 99

- 023

. Available at: http://www.epa.gov/otaq/regs/ld

- hwy/tier

- 2/documents/r99023.pdf

. U.S. Environmental Protection Agency (U.S . EPA). 2000

. Regulatory Impact Analysis: Control of Emissions of Air Pollution from Highway Heavy

- Duty Engines

. EPA 420 -R- 00

- 010

. Available at: http://www.epa.gov/otaq/regs/hd

- hwy/2000frm/r00010.pdf

. U.S. Environmental Protection Agency (U.S. EPA). 2004. Final Regulatory Analysis: Control of Emissions from Nonroad Diesel Engines . EPA420 -R- 04

- 007. Available at: http://www.epa.gov/otaq/documents/nonroad

- diesel/420r04007.pdf

. U.S. Environmental Protection Agency ( U.S. EPA). November 17, 2005a. Federal Registe r . Vol. 70, No. 221. “ Control of Air Pollution From Aircraft and Aircraft Engines; Emission Standards and Test Procedures. ” 40 CFR Part 87. Available at: http://www.epa.gov/fedrgstr/EPA

- AIR/2005/November/Day

- 17/a22704.pdf

.
U.S. Environmental Protection Agency ( U.S. EPA ). 2005b

. Regulatory Impact Analysis for the Final Clean Air Visibility Rule or the Guidelines for Best Available Retrofit Technology (BART) Determinations Under the Regional Haze Regulations

. EPA

- 452/R

- 05

- 004. Available at: http://www.epa.gov/oar/visibility/pdfs/bart_ria_2005_6_15.pdf

.1- 18 U.S. Environmental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards for Particulate Matter, Chapter 5

. Office of Air Quality Planning and Standards, Research Triangle Park, NC. October. Available at: http://www.epa.gov/ttn/ecas /regdata/RIAs/Chapter%205

— Benefits.pdf

. U.S. Environmental Protection Agency (U.S. EPA)

. 2008a

. Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters Per Cylinder

. Available at: http://www.regulations.gov/#!documentDetail;D=EPA

- HQ

- OAR

- 2003

- 0190

- 0938

. U.S. Environmental Protection Agency (U.S. EPA) . 2008b

. Control of Emissions from Marine SI and Small SI Engines, Vessels, and Equipment. Final Regulatory Impa ct Analysis

. Available at: http://www.regulations.gov/#!documentDetail;D=EPA

- HQ

- OAR

- 2004

- 0008

- 0929

. U.S. Environmental Protection Agency (U.S. EPA). 2009 a. Economic Impacts of Revised MACT Standards for Hospital/Medical/Infectious Waste Incinerators

. Avail able at: http://www.epa.gov/ttn/atw/129/hmiwi/hmiwi_eia_090803.pdf

. U.S. Environmental Protection Agency (U.S. EPA). 2009 b

. Integrated Science Assessment for Particulate Matter (Final Report)

. EPA

- 600 -R- 08

- 139F. National Center for Environmental Assessment

— RTP Division. December. Available at: http://cfpub.epa.gov/ncea/c fm/recordisplay.cfm?deid=216546

. U.S. Environmental Pr otection Agency (U.S. EPA). 2010a

. Regulatory Impact Analysis: Control of Emissions of Air Pollution from Category 3 Marine Diesel Engin es

. Available at: http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09019.pdf

. U.S. Environmental Protection Agency (U.S. EPA). 2010b. Regulatory Impact Analysis: Regulatory Impa ct Analysis (RIA) for Existing Statio nary Spark Ignition (SI) RICE NESHAP

. Ava ilable at: http://www.epa.gov/ttn/atw/rice/fnl_si_rice_ria.pdf

. U.S. Environmental Protection Agency (U.S. EPA). 2011a. 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). 2011 b

. 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). 2011 c

. 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

.1- 19 U.S. Environmental Protection Agency (U.S. EPA). 2011 d

. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants for Industrial, Commercial, and Institutional Boilers and Process Heaters

. February

. Available at: http://www.epa.gov/ttnecas1/regdata/RIAs/boilersriafinal110221_psg.pdf

. U.S. Office of Management and Budget. Circular A

- 4, September 17, 2003 . Av ailable at http://www.whitehouse .gov/omb/circulars/a004/a

- 4.pdf

. Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “ The Relationship Between Selected of postneonatal infant mortality and particulate air pollution in the United States. ” Environmental H ealth Perspectives 105 (6): 608

- 612.2-1 CHAPTER 2 DEFINING THE PM AIR QUALITY PROBLEM

2.1 Synopsis This chapter characterizes the nature, scope and magnitude of the current year
p articulate matter ( PM ) problem. It includes a summary of the spatial and temporal distribution of PM 2.5 and the likely origin from direct emissions or atmospheric transformations of gaseous precursors and recent design values for PM 2.5 .
2.2 Particulate Matter (PM) Properties

PM is a highly complex mixture of solid particles and liquid droplets distributed a mong numerous atmospheric gases which interact with solid and liquid phases. Particles range in size from those smaller than 1 nanometer (10 -9 meter) to over 100 micrometer (μm, or 10 -6 meter) in diameter (for reference, a typical strand of human hair is 70 μm in diameter and a grain of salt is about 100 μm). Atmospheric particles can be grouped into several classes according to their aerodynamic and physical sizes, including ultrafine particles (<0.1 μm), accumulation mode or “ fine ” particles (0.1 to ~3 μm ), and coarse particles (>1 μm). For regulatory purposes, fine particles are measured as PM 2.5 and inhalable or thoracic coarse particles are measured as
PM 10

- 2.5 , corresponding to their size (diameter) range in micrometers and referring to total particle mass under 2.5 and between 2.5 and 10 micrometers, respectively. The EPA currently has standards that measure PM 2.5 and PM 10 . Particles span many sizes and shapes and consist of hundreds of different chemicals. Particles are emitted directly from sources and are also formed through atmospheric chemical reactions; the former are often referred to as “ primary ” particles, and the latter as “ secondary ” particles. Particle pollution also varies by time of year and location and is affected by several weather

- rela ted factors, such as temperature, clouds, humidity, and wind. A further layer of complexity comes from particles ’ ability to shift between solid/liquid and gaseous phases, which is influenced by concentration and meteorology, especially temperature.

Partic les are made up of different chemical components. The major chemical components include carbonaceous materials (carbon soot and organic compounds ) , and inorganic compounds including, sulfate and nitrate compounds that usually include ammonium, and a mix of substances often apportioned to crustal materials such as soil and ash. As mentioned above, particulate matter includes both “ primary ” PM, which is directly emitted into the air, and “ secondary ” PM, which forms indirectly from emissions from fuel combusti on and2-2 other sources. Primary PM consists of carbonaceous materials (soot and accompanying organics) and includes:

 Elemental carbon, organic carbon, and crustal material directly emitted from cars, trucks, heavy equipment, forest fires, some industrial pro cesses and burning waste.

 Both combustion and process related fine metals and larger crustal material from unpaved roads, stone crushing, construction sites, and metallurgical operations.

Secondary PM forms in the atmosphere from gases. Some of these react ions require sunlight and/or water vapor. Secondary PM includes:

 Sulfates formed from sulfur dioxide (SO 2 ) emissions from power plants and industrial facilities;

 Nitrates formed from nitrogen oxide (NO x ) emissions from cars, trucks, industrial facilities, and power plants; and

 Ammonium formed from ammonia (NH 3 ) emissions from gas

- powered vehicles and fertilizer and animal feed operations. These contribute to the formation of sulfates and nitrates that exist in the atmosphere as ammonium sulfate and ammonium nitrate

. 1

 Organic carbon (OC) formed from reactive organic gas emissions, including volatile organic compounds (VOCs), from cars, trucks, industrial facilities, forest fires, and biogenic sources such as trees

. 1
As described above, organic carbon has both primary and secondary components. The percentage contribution to total OC from directly emitted OC versus secondarily formed OC varies based on location. In an urban area, near direct sources of OC such as cars, trucks, and industrial sources, the percentage of primary OC may dominate, whereas, in a rural area with more biogenic sources, OC may be mostly secondarily formed. In addition, emissions f rom sources such as power plants and industrial facilities may have small amounts of directly emitted PM 2.5 speciated into sulfate. Figure 2- 1 shows, in detail, the sources contributing to directly emitted PM 2.5 and PM 10 , as well as PM precursors: SO 2 , NO x , NH 3 , and VOC according to the 2008 NEI, version 2 (EPA, 2012)

. In Figure 2

- 1, EGUs stands for Electric Generating Utilities.

1 Direct NH 3 and VOC emissions are not controlled as part of the control strategy analysis. Emissions of PM 2.5 , NO x , and SO 2 are controlled in the control strategies, for a complete discussion please refer to Chapter 4.2-3 PM 2.5 (5.1 million short tons) PM 10 (20.5 million short tons)

SO 2 (10.7 million short tons) NO X (18.0 million short tons)

NH 3 (4.2 million short tons) VOC (15.0 million short tons)
Figure 2- 1. Detailed Source Categorization of Anthropogenic Emissions of Primary PM 2.5 , PM 10 and Gaseous Precursor Species SO 2 , NO x , NH 3 and VOCs for 2008 Agriculture, 18%

Construction Dust, 4%

Road Dust,

21%
Comm/Instit Boilers, 0.3%

EGUs, 6% Industrial Boilers, 2% Industrial Processes, 8%

Residential Fossil, 0.2% Residential Wood, 7%

Misc, 6% Onroad, 6% Nonroad equipment, 4% Aircraft, 0.1% Commercial Marine, 2%
Railroad, 0.5% Ag and Prescribed Fires, 15% 

# PM25

Agriculture, 23%

Construction

Dust, 10%

Road Dust,

47% Comm/Instit Boilers, 0.1% EGUs, 2% Industrial Boilers, 1% Industrial Processes, 6%

Residential Fossil, 0.1% Residential Wood, 2% Misc, 2% Onroad, 2% Nonroad equipment, 1% Aircraft, 0.05% Commercial Marine, 0.5% Railroad,

0.1% Ag and Prescribed Fires, 4% 

# PM10

Comm/Instit Boilers, 1%

EGUs, 72% Industrial Boilers, 9% Industrial Processes, 8%

Residential Fossil, 1% Residential Wood, 0.1% Misc, 0.2% Onroad, 1% Nonroad equipment,

0.3% Aircraft, 0.1%
Commercial Marine, 6%

Railroad,

0.1% Ag and Prescribed Fires, 1%



### SO2 Comm/Instit Boilers, 1%

EGUs, 17%

Industrial Boilers, 7%

Industrial Processes, 6%

Residential Fossil, 2% Residential Wood, 0.2% Misc, 1% Onroad, 40% Nonroad equipment, 11% Aircraft, 1% Commercial Marine, 9%

Railroad, 5% Ag and Prescribed Fires, 1% 

# NOX

Agriculture, 87% Comm/Instit Boilers, 0.1% EGUs, 1% Industrial Boilers, 0.3% Industrial Processes, 2% Residential Fossil, 1% Residential Wood, 0.5% Misc, 2% Onroad, 3% Nonroad equipment,

0.1% Commercial Marine, 0.02% Railroad, 0.01% Ag and Prescribed Fires, 3%



## NH3 Agriculture, 1% Comm/Instit Boilers, 0.1% EGUs, 0.3%

Industrial Boilers, 1% Industrial Processes,

17% Residential Fossil, 0.1%

Residential Wood, 2%
Misc, 8%

Solvent Use, 22% Onroad, 20% Nonroad equipment, 17% Aircraft, 0.2% Commercial Marine, 0.3% Railroad,

0.3% Ag and Prescribed Fires, 12%



# VOC2-4 2.2.1 PM 2.5

“ Fine particles ” or PM 2.5 are particles with diameters that are less than 2.5 micrometers. As discussed above, these particles are composed of both primary (derived directly from emissions) and secondary (derived from atmospheric reactions involving gaseous precursors) components.

2.2.1.1 Geographical Scale and Transport Both local and regional sources contribute to particle pollution. Fine particles can be transported long distances by wind and weather and can be found in the air thousands of miles from where they formed. Nitrates and sulfates formed from NO x and SO 2 are generally transported over wide areas leading to substantial background contributions in urban areas. Organic carbon, which has both a primary and secondary component, can also be transported but to a far lesser degree. In general, higher concentrations of elemental carbon and crustal matter are found closest to the sources of these emissions.

Figure 2- 2 shows how much of the PM 2.5 mass can be attributed to local versus regional sources for 13 selected urban areas ( EPA, 2004). 2 In each of these urban areas, monitoring sites were paired with nearby rural sites. When the average rural concentration is subtracted from the measured urban concentration, the estimated local and regional contributions become apparent. We ob serve a large urban excess across the U.S. for most PM 2.5 species but especially for total carbon mass with Fresno, CA having the highest observed measure. Larger urban excess of nitrates is seen in the western U.S. with Fresno, CA and Salt Lake City, UT s ignificantly higher than all other areas. These results indicate that local sources of these pollutants are indeed contributing to the PM 2.5 air quality problem in these areas. As expected for a predominately regional pollutant, only a modest urban excess is observed for sulfates.

In the East, regional pollution contributes to more than half of total PM 2.5 concentrations. Rural background PM 2.5 concentrations are high in the East and are somewhat uniform over large geographic areas. These regional concentrations come from emission sources such as power plants, natural sources, and urban pollution and can be transported hundreds of miles and reflect to some extent the denser clustering of urban areas in the East as compared to the West. In the West, much of t he measured PM 2.5 concentrations tend to be local in nature. These concentrations come from emission sources such as wood combustion and mobile sources. In general, these data indicate that reducing regional SO 2 and local sources

2 The measured PM 2.5 concentration is not necessarily the maximum for each urban area.2-5 of carbon in the East, and local sources of nitrate and carbon in the West will be most effective in reducing PM 2.5 concentr ations.

Figure 2- 2. Regional and Local Contributions to Annual Average PM 2.5 by Particulate SO 4 2–
, Nitrate and Total Carbon (i.e., organic plus EC) for Select Urban Areas Based on Paired 2000

– 2004 IMPROVE a and CSN b Monitoring Sites

a Interagency Monitoring of Protected Visual Environments (IMPROVE) http://vista.cira.colostate.edu/improve

b Chemical Speciation Network (CSN)

2.2.1.2 Regional and Seasonal Pat terns The chemical makeup of particles varies across the United States, as illustrated in Figure 2- 3. For example, the higher regional emissions of SO 2 in the East result in higher2-6 absolute and relative amounts of sulfates as compared to the western U.S. Fine particles in southern California generally contain more nitrates than other areas of the country. Carbon is a substantial component of fine particl es everywhere.

Figure 2- 3. Regional and Seasonal Trends in Annual PM 2.5 Composition from 2002 to 2007 Derived Using the SANDWICH Method. Data from the 42 monitoring locations shown on the map were stratified by region and season including cool months (Oc tober

– April) and warm months (May

– September)

Fine particles can also have a seasonal pattern. As shown in Figure 2- 3, PM 2.5 values in the eastern half of the United States are typically higher in warmer weather when meteorological conditions are more favo rable for the formation and build up of sulfates from2-7 higher sulfur dioxide (SO 2 ) emissions from power plants in that region. Fine particle concentrations tend to be higher in the cooler calendar months in urban areas in the West, in part because fine part icle nitrates and carbonaceous particles are more readily formed in cooler weather, and wood stove and fireplace use increases direct emissions of carbon.

2.2.1.3 Composition of PM 2.5 as Measured by the Federal Reference Method The speciation measurements in the preceding analyses represented data from EPA ’ s Speciation Trends Network, along with adjustments to reflect the fine particle mass associated with these ambient measurements. In order to more accurately predict the change in PM 2.5 design values for particular emission control scenarios, EPA characterizes the composition of
PM 2.5 as measured by the Federal Reference Method (FRM). The current PM 2.5 FRM does not capture all ambient particles measured by speciation samplers as presented in the previous

s ections. The FRM

- measured fine particle mass reflects losses of ammonium nitrate (NH 4 NO 3 ) and other semi

- volatile organic compounds (SVOCs; negative artifacts). It also includes particle

bound water (PBW) associated with hygroscopic species (positive artif acts) (Frank, 2006). Comparison of FRM and co

- located speciation sampler NO 3 - values in Table 2- 1 show that annual average NO 3 retention in FRM samples for six cities varies from 15% in Birmingham to 76% in Chicago, with an annual average loss of 1 μ g/m 3

. The volatilization is a function of temperature and relative humidity (RH), with more loss at higher temperatures and lower RH. Accordingly, nitrate is mostly retained during the cold winter days, while little may be retained during the hot summer days.

PM 2.5 FRM measurements also include water associated with hygroscopic aerosol. This is because the method derives fine particle concentrations from sampled mass equilibrated at 20

– 23 °C and 30

– 40% RH. At these conditions, the hygroscopic aerosol collected at more humid environments will retain their particle

- bound water. The water content is higher for more acidic and sulfate

- dominated aerosols. Combining the effects of reduced nitrate and hydrated aerosol causes the estimated nitrate and sulfate FRM mass to differ from the measured ions simply expressed as dry ammonium nitrate and ammonium sulfate. The composition of FRM mass is denoted as SANDWICH based on the Sulfate, Adjusted Nitrate Derived Water and Inferred Carbon approach from which they are derived. T he PM 2.5 mass estimated from speciated measurements of fine particles is termed ReConstructed Fine Mass (RCFM). The application of SANDWICH adjustments to speciation measurements at six sites is illustrated in Table 2- 1 and Figure 2- 4. EPA ’ s modeling incor porates these SANDWICH adjustments in the Model Attainment Test Software (MATS) (Abt, 2010).2-8 Table 2- 1. Annual Average FRM and CSN PM 2.5 NO 3 – and NH 4 NO 3 Concentrations at Six Sites during 2003

NO 3 – ( μ g/m 3 ) NH 4 NO 3 ( μ g/m 3 ) Percent of NH 4 NO 3 in PM 2.5 FRM Mass Sampling Site Location No. of Observations FRM Mass CSN a FRM b Difference (CSN − FRM) CSN FRM CSN FRM Mayville, WI 100 9.8 2.5 1.5 1.0 3.2 1.9 33% 19% Chicago, IL 76 14.4 2.8 2.1 0.7 3.7 2.8 25% 19% Indianapolis, IN 92 14.8 2.5 1.3 1.3 3.2 1.6 22% 11%
Cleveland, OH 90 16.8 2.9 1.7 1.2 3.7 2.2 22% 13%
Bronx, NY 108 15.0 2.4 1.1 1.3 3.1 1.4 21% 9% Birmingham, AL 113 17.0 1.1 0.2 0.9 1.4 0.2 8% 1%

a On denuded nylon

- membrane filters for al sites except for Chicago, where denuded Teflon

- membrane followed

by nylon filters were used.
b On undenuded Teflon

- membrane filters.

Figure 2- 4. RCFM (left) versus SANDWICH (right) Pie Charts Comparing the Ambient and PM 2.5 FRM Reconstructed Mass Protocols on an Annual Average Basis a

a Estimated NH 4 * and PBW for SANDWICH are included with their respective sulfate and nitrate mass slices.

Circles are scaled in proportion to PM 2.5 FRM mass.2-9 2.2.1.4 200 6– 200 8 Design Values

A nnual and 24

- hour PM 2.5 design values for 2006

- 2008 are shown in Fi gures 2

- 5 and 2

- 6, respectively. These design values were calculated using 200 6– 200 8 FRM 24

- hour average PM 2.5 concentration measurements in a manner consistent with CFR Part 50. 3 For the most part, counties in the center of the U.S. have PM 2.5 design values that are above both 11 μg/m 3 for the annual standard and 3 5 μg/m 3 for the 24

- hour standard. In the East, the counties above the current NAAQS (i.e., 15 μg/m 3 annual and 35 μg/m 3 24

- hour standards) are similar. In the West, there are fewer counties above the annual level of 15 μg/m 3 than exceed the 24

- hour standard of 35 μg/m 3

.

3 These years of ambient measurements are presented here since they fram e the air quality model year of 2007. As discussed in Chapter 3, ambient measurement for the period 2005 through 2008 were used to construct 5

- year weighted average concentrations for use as the starting point for future year projections in conjunction wit h air quality model predictions.2- 10 Figure 2- 5. Maximum County

- level PM 2.5 Annual Design Values Calculated Using 200 6– 200 8 FRM 24

- hr Average PM 2.5 Measurements

.2- 11 Figure 2- 6. Maximum County

- level PM 2.5 24

- hour Design Values Calculated Using 200 6– 200 8 FRM 24

- hr Average PM 2.5 Measurements

.
2.3 References Abt Associates, 2010. User ’ s Guide: Modeled Attainment Test Software. http://www.epa.gov/scram001/modelingapps_mats.htm

. Frank, N.H., Retained Nitrate, H ydrated Sulfates, and Carbonaceous Mass in Federal Reference Method Fine Particulate Matter for Six Eastern U.S. Cities, J. Air & Waste Manage. Assoc

. 2006 , 56, 500

- 511.
U.S. Environmental Protection Agency (EPA). 2004. Air quality criteria for particulate matter. U.S. Environmental Protection Agency. Research Triangle Park, NC. EPA/600/P

99/002aF

- bF.
U.S. Environmental Protection Agency (EPA). 2008. National Air Quality Status and Trends Through 2007. U.S. Environmental Protection Agency. Research Triangle Park. EPA

454/R

- 08

- 006

.2- 12 U.S. Environmental Protection Agency (EPA). 2009. Integrated Science Assessment for Particulate Matter . U.S. Environmental Protection Agency. Research Triangle Park. EPA/600/R

- 08/139F

. U.S. Environmental Protection Agency (EPA). 2011. Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards. Office of Air Quality Planning and Standards, Research Triangle Park, NC.
U.S. Environmental Protection Agency (EPA). 201 2

. 2008 National Emissions Inventory, Version 2 Data and Documentation available from http://www.epa.gov/ttn/chief/net/2008inventory.html

. Office of Air Quality Planning and Standards, Research Triangle Park, NC.3-1 CHAPTER 3 AIR QUALITY MODELING AND ANALYSIS

3.1 Synopsis In order to evaluate the health and environmental impacts of trying to reach the alterative primary standards in this final RIA, it was necessary to use models to predict concentrations in the future. The data, tools and methodology used for projecting future

- year air quality 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 costs and benefits.

3.2 Modeling PM 2.5 Levels in the Future A national scale air quality modeling analysis was performed to estimate PM 2.5 concentrations for the annual and 24

- hour primary standards for the future year of 2020

. 1 Air quality ratios were then developed using model responsi veness to emissions changes based on “sensitivity” air quality modeling that was designed to determine the response of PM 2.5 concentrations to reductions in emissions of SO 2 , NO x , and directly emitted PM 2.5 . The air quality ratios were used to determine th e amount of emissions reductions needed to attain the revised annual standard of 12 μg/m 3 and two alternative annual standards . The emissions reductions were then used to estimate how air quality would change under each set of emissions scenarios . These da ta were used as inputs to the calculation of expected costs and benefits associated with the emissions and air quality changes resulting from just attaining the revised and alternative annual standards

. As described in section 3.3, air quality modeling wa s used in a relative sense to project future concentrations of PM 2.5 . As part of this approach air quality model predictions from a base year simulation are coupled with predictions from the future case to calculate the relative change (between base year a nd future case) in each species component of PM 2.5 . These species

- specific relative response factors (RRFs) are applied to the corresponding measured concentrations to estimate future species concentrations. The future case PM 2.5 annual and daily design va lues are then calculated using the projected species concentrations. We used 2007 as the base year and 2020 as the future year for air quality

- related analyses in this RIA. For 2020 we modeled two emissions scenarios, a 2020 base case and a 2020 control ca se. The 2007 and 2020 scenarios were modeled as annual model simulations. In addition to these emissions scenarios, we also performed several emissions sensitivity model runs to quantify the response

1 In addition, we used air quality modeling to estimate light extinction in 2020 to support the analysis of the welfare benefits of this rule.3-2 of PM 2.5 to various precursor emissions. The modeling fo r the 2020 base case, the 2020 control case, and sensitivity scenarios were used to inform the development of design values for the baseline which provides for attainment of the 15/35 NAAQS and the incremental emissions reductions needed to attain the revi sed 12 μg/m 3 annual standard and two alternative annual standards, 13 μg/m 3 and 11 μg/m 3 . Details on the 2007

- based air quality modeling platform, the 2007 base year and 2020 base case scenarios, and the methods and results for attaining these NAAQS levels are provided below. Information on the 2020 control case can be found in Chapter 4 of this RIA.

3.2.1 Air Quality Modeling Platform

The 2007

- based Community Multi

- scale Air Quality (CMAQ) modeling platform was used as the tool to project future

- year air q uality for 2020 and to estimate the costs and benefits for attaining the current and revised alternative NAAQS presented in this assessment. This platform provides the most recent, complete set of base year emissions information currently available for nat ional scale modeling. In addition to the CMAQ model and the emissions data , the modeling platform includes the meteorology, and initial and boundary condition data for 2007 which are inputs to this model. The CMAQ model is a three

- dimensional grid

- based
Eu lerian 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.) (Appel et al., 2008; Appel et al., 2007; Byun and Schere, 2006). Consideration of the different processes (e.g., transport and deposition) that affect primary (directly emitted) and secondary (formed by atmospheric processes) PM at the regional scale in different locations is fundamental to understanding and assessing the effects of pollution control measures that affect PM, ozone and deposition of pollutants to the surface. Because it accounts for spatial and temporal variations as well as differences in the reactivity of emis sions, CMAQ is useful for evaluating the impacts of the control strategies on PM 2.5 concentrations. Version 4.7.1 of CMAQ was employed for this RIA modeling

. 2 CMAQ is applied with the AERO5 aerosol module, which includes the ISORROPIA inorganic chemistry ( Nenes et al., 1998) and a secondary organic aerosol module (Carlton et al., 2010). The CMAQ model is applied with sulfur and organic oxidation aqueous phase chemistry (Carlton et al., 2008) and the carbon

- bond 2005 (CB05) gas

- phase chemistry module (Yarwood et al., 2005).

2 More information is available online at: www.cmaq

- model.org3-3 3.2.1.1 Air Quality Modeling Domain
Figure 3- 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 contain s 2 4 vertical layers with a top at about 17,600 meters , or 50 millibars (mb). A horizontal resolution of 12 x 12 km was used for modeling the 200 7 base year and the 2020 base and control strategy scenarios. The model simulations produce gridded air quality concentrations on an hourly basis for the entire modeling domain.

Figure 3- 1. Map of the CMAQ Mode ling Domain Used for PM NAAQS RIA

3.2.1.2 Air Quality Model Inputs CMAQ requires a variety of input files that contain information pertaining to the modeling domain and simulation period. These include gridded, hourly emissions estimates and meteorologica l data, and initial and boundary conditions. Separate emissions inventories were prepared for the 200 7 base year and the future year of 2020 base case and control strategy 

## Domain Boundary3-4 scenarios . All other inputs were specified for the 200 7 base year model application and remained unchanged for each future

- year modeling scenario.
CMAQ 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 or precursors to secondary pollutants. The annual emission inventories, described in Section 3.2.2, were preprocessed into CMAQ

- ready inputs using the SMOKE emissions preprocessing system 3 . Meteorological inputs reflecting 200 7 condit ions across the contiguous U.S. were derived from Version 3.1 of the Weather Research Forecasting Model ( WRF ). 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 200 7 meteorological model simulation and evaluation are provided in a separate technical support document (EPA, 2011a).
The lateral boundary and initial species concentrations are provided by a three

dimensional global atmospheric chemistry model, the GEOS

- CHEM model version 8

- 02

- 03 (Yantosca, 2004) 4 . 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). This model was run for 2007 with a grid resolution of 2.0 degrees x 2.5 degrees (latitude

- longitude) and 47 vertical layers. The predictions were used to provide

one

- way dynamic boundary conditions at three

- hour intervals and an initial concentration field for the CMAQ simulations. A GEOS

- Chem evaluation w as conducted for the purpose of val idating the 2007 GEOS

- Chem simulation for predicting selected measurements relevant to their use as boundary conditions for CMAQ. This evaluation included reproducing GEOS

- Chem evaluation plots reported in the literature for previous versions of the model (Lam, 2010)

.

3.2.1.3 Air Quality Model Evaluation An operational model performance evaluation for PM 2.5 and its related speciated components (e.g., sulfate, nitrate, elemental carbon, organic carbon) was performed to estimate the ability of the CMAQ modeling system to replicate 200 7 measured concentrations 5

. This evaluation principally comprises statistical assessm ents of model predictions versus observations paired in time and space depending on the sampling period of measured data. Details on the evaluation methodology and the calculation of performance statistics are

3 More information is available online at: www. smoke

- model.org

. 4 More information is available online at: http://www

- as. harvard.edu/chemistry/trop/geos.
5 This operational evaluation for CMAQ included statistical and graphical comparisons of model predictions for select PM 2.5 component species to the corresponding measured data from monitoring sites in the Continuous Speciation Network (CSN), the I nteragenc y M onitoring of PRO tected V isual E nvironments (IMPROVE) network, and the C lean A ir S tatus and T rends Network (CASTNet).3-5 provided in the Technical Support Document: Ai r Quality Modeling for the Final PM NAAQS (AQMTSD, EPA, 2012a ). Overall, the model performance statistics for sulfate, nitrate, organic carbon, and elemental carbon from the CMAQ 200 7 simulation are within or close to the ranges found in other recent appli cations. These model performance results give us confidence that our applications of CMAQ using this 200 7 modeling platform provide a scientifically credible approach for assessing PM 2.5 concentrations for the purposes of the RIA.

3.2.1.4 Emissions Invento ry The 2007 emissions inventory and t he 2020 base case emissions inventory were developed using the 2007 Version 5.0 emissions modeling platform (documentation and data files available from http: //www.epa.gov/ttn/chief/emch/index.html ). The starting point for the 2007v5 platform was Version 2 of the 2008 National Emissions Inventory ( http://www.epa.gov/ttn/chief/net/2008inventory.html ). The 2008 NEI v2 is the most recently available NEI. The next NEI will be developed for 2011. Data collection for the 2011 NEI is ongoing through the end of 2012, with the inventory due to be published in 2013. Some data in the 2008 NEI v2 were adjusted to better represent 2007 for this analysis. For example , MOVES 2010b was used to compute onroad emissions and duplicate emissions values were removed where they were identified. For additional details, see t he Technical Support Document: Preparation of Emissions Inventories for the Version 5.0, 2007 Emissions Modeling Platform ( EITSD, EPA, 201 2b ) . The 2020 base case inventory is the starting point for the baseline and control strategy modeling performed for this assessment . The above

- referenced EITSD (EPA, 2012b) describes the development of the 2007 base year inventory in detail for all emissions sectors, along with the projection methodology applied to develop the 2020 base case inventory.

The 2020 EGU projected inventory represents demand growth, fuel resource availability, generating technology cost and per formance, and other economic factors affecting power sector behavior. It also reflects the expected 2020 emissions effects due to environmental rules and regulations, consent decrees and settlements, plant closures, units built or with control devices upda ted since 2007, and forecast unit construction through the calendar year 2020. In this analysis, the projected EGU emissions include the Final Mercury and A ir T oxics (MATS) rule announced on December 21, 2011 and the Final Cross

- State Air Pollution Rule (CSAPR) issued on July 6, 2011.

On August 21, 2012, the D.C. Circuit Court of Appeals issued an opinion vacating CSAPR. In its decision, the Court also instructed EPA to “continue administering CAIR [the 2005 Clean3-6 Air Interstate Rule] pending the promulgat ion of a valid replacement.” In the interim, the EPA and the states are continuing to implement CAIR to address regional transport of air pollution, as directed by the Court. The EPA has filed a petition for rehearing of the Court’s decision on CSAPR. In l ight of the Court’s instructions, the EPA believes that it is appropriate to rely on CAIR emission reductions as permanent and enforceable reductions until such a time as the EPA issues a replacement transport rule.

Because of the similarity in emissions r eductions assoc iated with CSAPR and CAIR 6
, and the inclusion of MATS in the RIA baseline, EPA has determined that it remains appropriate that CSAPR continue to be used in the RIA baseline as a proxy for representing the emission reductions required by CAIR for the purposes of the rulemaking's modeling projections for 2020.
Regarding the impact of MATS on this determination, t he MATS emission rate standard for hydrogen chloride (HCl) is expected to result in a substantial amount of new pollution controls (s crubbers and dry sorbent injection) and upgrading of existing scrubbers that will also significantly reduce SO2 emissions from power plants. MATS implementation is projected to reduce nationwide SO2 emissions from power plants to a level more than 40 perce nt lower than the SO2 emissions projected under CSAPR without MATS in place (EPA

- HQ

- OAR

- 2009

- 0234

- 20131).

In addition to these conclusions, the EGU baseline used in modeling the PM NAAQS was based on EIA’s AEO 2010 and represents a conservative approach to emission projections, given that more recent trends in power sector economics suggest a likelihood of lower future EGU emissions. This is supported by the results of a sensitivity analysis conducted using the electricity demand forecast from EIA’s AEO 201 2 that shows slightly lower EGU emissions. It is reasonable to expect that recent reductions in gas prices and increases in coal prices would yield yet lower estimations of future EGU emissions in the context of this rule’s analysis. The details of this an alysis can be found in the memo titled AEO 2012 Demand Sensitivity , which is available in the docket.
The EGU emissions were developed using the Integrated Planning Model (IPM) Version 4.10 Final MATS and are documented in detail at http://www.epa.gov/airmarkt/progsregs/epa

ipm/toxics.html. IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power sector. Note that for this analysis, no further EGU control measures

6 U.S. EPA, Cross

- State Air Pollution Rule Presentation, December 15, 2011, available at http://www.epa.gov/airtransport/pdfs/CSAPRPresentation.pdf , p. 15.3-7 were selected for illustrating attainment of the current and propo sed alternative standard levels discussed in Chapter 4. Thus, the EGU emissions are unchanged between the future

- year base

- case and the control strategies.

Table 3

- 1 provides a comprehensive list of all the control programs, growth assumptions, and facility and unit closures information in the future year base case. The future

year base 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. Many state and local control programs are also applied where those programs were finalized and enough details were available to apply reductions to the 2007 emissions data.

The 2007 and 2020 onroad mobile source emissions were developed using emissions factors derived from the MOtor Vehicle Emission Simulator (MOVES) 7 Version 2010b. The emissions were computed by using the Sparse Matrix Operator Kernel Emissions system (SMOKE) to combine the county

- , vehicle type -, and temperature

- specific emission factors and vehicle miles traveled a nd vehicle population activity data while taking into account hourly gridded temperature data. For California we received onroad emissions directly from the California Air Resources Board (CARB) in July 2012 for 2007, 2020, and 2025. These emissions were b ased on the latest available data and models from their SIP development process. We allocated the California onroad emissions down to the hourly, grid

- cell, and CMAQ model

species level using ratios derived from the MOVES

- based emissions data output from S MOKE.

The MOVES

- based 2020 onroad emissions account for changes in activity data and the impact of on

- the

- books national rules including: the Light

- Duty Vehicle Tier 2 Rule, the Heavy Duty Diesel Rule, the Mobile Source Air Toxics Rule, the Renewable Fuel Standard, the L ight
D uty G reen H ouse G as /C orporate Average Fuel Efficiency (CAFE) standards for 2012

- 2016, and the Heavy

- Duty Vehicle Gree nhouse Gas Rule. The emissions do not account for the 2017 and Later Model Year Light

- Duty Vehicle Greenhouse Gas Emi ssions and Corporate Average Fuel Economy Standards; Final Rule (LD GHG) , issued October 15, 2012. The LD GHG rule was not included in this analysis because the rule was not signed at the time the modeling was performed , and it is expected to have little i mpact on particulate matter emissions. The RIA for the LD GHG (EPA, 2012c) shows that in 2030 counties are showing decreases in PM 2.5 design values of up to 0.16 μg/m 3

. The modeling indicates that the majority of the modeled counties

7 More information is available online at: http://www.epa.gov/otaq/models/moves/index.htm

.3-8 will experience small changes of between 0.05 μg/m 3 and

- 0.05 μg/m 3 in their annual PM2.5 design values due to the vehicle standards . The impacts of the rule in 2020 should be even less than the 2030 impacts. The MOVES

- based 2020 emissions include state ru les related to the adoption of LEV standards, inspection and maintenance programs, Stage II refueling controls, and local fuel restrictions. For California, the provided future year 2020 and 2025 emissions included most on

- the

- books regulations such as those for low sulfur fuel, idling of heavy

- duty vehicles, chip reflash, public fleets, trash trucks, drayage trucks, and heavy duty trucks and buses. The zero emission vehicle program prior to adoption of Advanced Clean Cars is included but has a very small i mpact. The California emissions do not reflect the impacts of the GHG/Smartway regulation, Advanced Clean Cars, nor the low carbon fuel standard because it is assumed that there is no impact on criteria pollutants.

Table 3

- 1 provides details on the nation al rules included to develop all categories of mobile source emissions. The nonroad mobile 2020 base emissions, including railroads and commercial marine vessel emissions also include all national control programs. These control programs include the Locomo tive

- Marine Engine rule, the Nonroad Spark Ignition rule and the Class 3 commercial marine vessel “ECA

- IMO” program. The nonroad, locomotive, and class 1 and 2 commercial marine emissions used for California were obtained from CARB, and include nonroad rules reflected in the December 2010 Rulemaking Inventory ( http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf ), those in the March 2011 Rule Inventory, the Off

- Road Con struction Rule Inventory for “In

- Use Diesel”, cargo handling equipment rules in place as of 2011 (see http://www.arb.ca.gov/ports/cargo/cargo.htm ), rules through 2011 related to Transportation Ref rigeration Units, the Spark

- Ignition Marine Engine and Boat Regulations adopted July 24, 2008 for pleasure craft, and the 2007 and 2010 regulations to reduce emissions from commercial harbor craft. For ocean

- going vessels, the data represents the 2005 volu ntary 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 ECA
zone, the 2012

- 2015 Tier 2 NOx controls, the 2016 0.1% sulfur fuel regulation in ECA zone, and the 2016 IMO Tier 3 NOx controls. Control and growth

- related assumptions in the 2020 base case are described in more detail in the EITSD.

All modeled 2007 and 2020 sc enarios use the same year 2006 Canada emissions data. Note that 2006 is the latest year for which Canada provided data, and no accompanying future

year projected inventories were provided in a form suitable for this study. For Mexico , different emissions w ere used for 2008 and 2018 as described in the Development of Mexico National3-9 Emissions Inventory Projections for 2008, 2012, and 2030 (ERG, 2009) and the associated technical memorandum titled Mexico 2018 Emissions Projections for Point, Area, On

- Road
Mot or Vehicle and Nonroad Mobile Sources (ERG, 2009) . All base year and projected emissions inventories are available on the EPA ’ s Emissions Modeling Clearinghouse website at http://www. epa.gov/ttn/chief/emch/index.html

. Table 3- 1(a). Control Strategies and Growth Assumptions for Creating 2020 Base Case Emissions Inventories from the 200 7 Base Case for Non

- EGU Point Sources

Control Strategies and/or Growth Assumptions (Grouped by Affected Pollutants or Standard and Approach) Pollutants Affected

Non

- EGU Point (ptnonipm) Controls and Growth Assumptions Boat Manufacturing MACT rule, national, VOC: national applied by SCC VOC Consent decrees on companies (based on information from the Office of Enforcement and Compliance Assurance

— OECA) apportioned to plants owned/operated by the companies VOC, CO, NO x , PM, SO 2 Refinery Consent Decrees: plant/SCC controls NO x , PM, SO 2 Commercial/Institutional/ Hospital/Medical/Infectious Waste Incinerator Regulations NO X , PM, SO 2 NESHAP: Portland Cement (09/09/10)

— plant level based on Industrial Sector Integrated Solutions (ISIS) policy emissions in 2013. The ISIS results are from the ISIS

- Cement model runs for the NESHAP and NSPS analysis of J uly 28, 2010 and include closures. Hg, NO X , SO 2 , PM, HCl

New York ozone SIP controls VOC, NO X , HAP VOC Additional plant and unit closures provided by state, regional, and the EPA agencies and additional consent decrees. Includes updates from CSAPR comments. All

Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsideration NO X , CO, PM, SO 2 Ethanol plants that account for increased ethanol production due to RFS2 mandate All
State fuel sulfur content rules for fuel oil

— as of July, 201 2, effective only in Maine, Massachusetts, New Jersey, New York and Vermont. SO 2

Emission reductions resulting from controls put on specific boiler units (not due to MACT) after 2005, identified through analysis of the control data gathered from the Information Collection Request (ICR) from the Industrial/Commercial/Institutional Boiler NESHAP. NO X , SO 2 , HCl

Emissions reductions resulting from Boiler MACT controls to specific boiler units NO X , CO, PM, SO 2 , VOC, HCl Plant and unit closures resulting from state submissions and industry and web postings effective prior to January 2012 All

Aircraft growth via Itinerant (ITN) operations at airports to 20 20 All Livestock Emissions Growth from year 200 8 to year 20 20 (some farms in the point inventory) NH 3 , PM Upstream adjustments to year 2020 for refineries and gasoline distribution via the Energy Information and Security Act/Renewable Fuel Standards 2 (EISA/RFS2) impacts All3- 10 Table 3- 1(b). Control Strategies and Growth Assumptions for Creating 2020 Base Case Emissions Inventories from the 200 7 Base Case for Nonpoint and Onroad Mobile Sources

Control Strategies and/or Growth Assumptions (Grouped by Affected Pollutants or Standard and Approach Used to Apply to the Inventory) Pollutants Affected Nonpoint (nonpt sector) Controls and Growth Assumptions Residential Wood Combustion Growth and Change

- outs from year 200 8 to 2020 All

State fuel sulfur content rules for fuel oil

— as of July, 2012, effective only in Maine, Massachusetts, New Jersey, New York and Vermont. SO 2

Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsideration NO X , CO, PM, SO 2 New York, Connecticut, and Virginia ozone SIP controls VOC Livestock Emissions Growth from year 200 8 to year 20 20 (some farms in the point inventory) NH 3 , PM Upstream adjustments to year 2020 for refineries and gasoline distribution via the Energy Information and Security Act/Renewable Fuel Standards 2 (EISA/RFS2) impacts All

Portable Fuel Container Mobile Source A ir Toxics Rule 2 (MSAT2) inventory growth and control from year 200 7 to 20 20 VOC

Texas oil and g as projections to year 2020 VOC, SO 2 , NO X , CO, PM
Onroad Mobile Controls (list includes all key mobile control strategies but is not exhaustive)
National Onroad Rules: Tier 2 Rule: Signature date February 2000 2007 Onroad Heavy

- Duty Rule: February 2009 Final Mobile Source Air Toxics Rule (MSAT2): February 2007 Renewable Fuel Standard: March 2010 Light

- Duty Greenhouse Gas Emissions Standards and Corporate Average Fuel Efficiency Standards : May 2010 Heavy (and Medium)

- Duty Greenhouse Gas Emissions Standards and Fuel Efficiency Standards: August 2011 Corporate Average Fuel Economy standards for 2008

– 2011 All

Local Onroad Programs: National Low Emission Vehicle Program (NLEV): March 1998 Ozone Transport Commission (OTC) LEV Program: January 1995 VOC3- 11 Table 3- 1(c). Control Strategies and Growth Assumptions for Creating 2020 Base Case Emissions Inventories from the 200 7 Base Case for Nonroad Mobile Sources

Control Strategies and/or Growth Assumptions (Grouped by Affected Pollutants or Standard and Approach Used to Apply to the Inventory) Pollutants Affected
Nonroad Mobile Controls (list includes all key mobile control strategies but is not exhaustive) (continued)
National Nonroad Controls: Clean Air Nonroad Diesel Final Rule

— Tier 4: June 2004 Control of Emissions from Nonroad Large

- Spark Ignition Engines and Recreational Engines (Marine and Land Based): “ Pentathalon Rule ” : November 2002 Cl ean Bus USA Program: October 2007 Control of Emissions of Air Pollution from Locomotives and Marine Compression

- Ignition Engines Less than 30 Liters per Cylinder: October 2008 Locomotive and marine rule (May 6, 2008) Marine SI rule (October 4, 1996) Nonroad large SI and recreational engine rule (November 8, 2002) Nonroad SI rule (October 8, 2008) Phase 1 nonroad SI rule (July 3, 1995) Tier 1 nonroad diesel rule (June 17, 2004) All

Locomotives: Energy Information Administration (EIA) fuel consumption projections for freight rail Clean Air Nonroad Diesel Final Rule

— Tier 4: June 2004 Locomotive Emissions Final Rulemaking, December 17, 1997 Locomotive rule: April 16, 2008 Control of Emissions of Air Pollution from Locomotives and Marine: May 2008 All

Commercial Marine: Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards (April 30, 2010)

— also includes CSAPR comments.
EIA fuel consumption projections for diesel

- fueled vessels Clean Air Nonroad Diesel Final Rul e— Tier 4 Emissions Standards for Commercial Marine Diesel Engines, December 29, 1999 Locomotive and marine rule (May 6, 2008) Tier 1 Marine Diesel Engines, February 28, 2003 All

3.3 PM 2.5 M odeling Results and Analyses

The air quality modeling results were use d in the RIA to estimate future PM 2.5 concentrations for the 2020 base case and 2020 control case as well as to calculate the air quality ratios that were used in determining the emissions reductions to attain the exis ting3- 12 standards of 15/35, the revised annual standard of 12 μg/m 3 and the two alternative annual standards . These data are then used to estimate the costs and benefits of attaining these existing and revised NAAQS levels . Consistent with EPA guidance (EPA, 2007 and EPA, 2011b) , the air quality modeling results are applied in a relative sense to estimate 2020 future design values for PM 2.5 for the 2020 base case and 2020 control case . Air quality response ratios are calculat ed and used to estimate the tons of emissions reductions needed beyond the 2020 control case needed to show attainment of the existing, revised, and alternative NAAQS levels. Based on the tons of emissions needed in each county, design values are calculated for attaining the revised and alt ernative annual standard levels for input into the benefits
assessment.
The flow diagram shown in Figure 3

- 2 summarizes our approach for calculating future

year design values for meeting the existing standards, the revised annual standard, and alternative annual standard levels. Table 3

- 2 describes the specific air quality modeling simulations that informed this approach. The 2020 base case simulation (Box 1) was performed to estimate which monitors would exceed the current and alternative standard levels in 2020 based on emissions reductions expected from existing (i.e., “on

- the

- books”) state and federal control programs. The 2020 control case simulation (Box 3) was performed to estimate the impact of emission reductions from additional controls beyond tho se of the 2020 base case in areas with design values above the revised and alternative standard levels. As discussed below, the 2020 base case and 2020 control case design values were adjusted to reflect PM 2.5 reductions expected from the implementation of existing burn ban programs in certain counties and to remove the effects of atypical events such as wildfires and fireworks displays(Boxes 2 and 4). To calculate future

- year design values at the different standard levels, and the associated emissions redu ctions, these 2020 base and control case design values were adjusted downward using air quality response ratios, which give the change PM 2.5 design value ( μ g/m 3 ) per change in emissions by species (Boxes 5 through 9).

The air quality response ratios (hereafter referred to as air quality ratios) used to adjust the 2020 cases to meet the standard levels were calculated based on results of several sensitivity simulations. The sensitivity simulations, as described in Table 3

- 2, were defined to isolate the changes in the (NH 4 ) 2 SO 4 , NH 4 NO 3 and direct PM 2.5 associated with changes in emissions of SO 2 , NO x and direct PM 2.5 , respectively. These PM 2.5 component species were selected for reduction to meet the standard levels because they dominate the mass of PM 2. 5 in the areas of concern in the 2020 cases. The sensitivity simulation referred to as “2020 NO x

_ PM 2.5 ” was used in calculating the air quality ratios associated with changes in NO x and3- 13 direct PM 2.5 emissions. This simulation was based on anthropogenic NO x and direct PM 2.5 emission reductions from non

- EGU sources of 25% and 50%, respectively, relative to the 2020 base case. The sensitivity simulation referred to as “2020 SO 2 _RWC” was used in calculating the air quality ratios associated with changes in SO 2 emissions. This simulation was based on anthropogenic SO 2 and residential wood combustion emissions reductions from non

- EGU sources of 25% and 100%, respectively, relative to the 2020 base case 8 . In the sensitivity runs, emissions reductions for direct PM 2 .5 were generally applied in counties with monitors with annual design values above 11 μg/m 3 level in the 2020 base case, while emission reductions for NO x and SO 2 were generally applied in those counties as well as their adjacent counties. This approach reflects the local impacts of direct PM 2.5 emissions on air quality and the broader geographic impacts on PM 2.5 of SO 2 and NO x emissions reductions.

The development of the air quality response ratios used in the process of adjusting the air quality modeling results to meet the current and alternative standard levels is described in Appendix 3.A.1.1. The remainder of this section describes the procedure s and the results from the 2020 base case modeling and the development of the adjusted 2020 base case (Box 1 and Box 2, respectively in Figure 3

- 2) and the identification of the emissions reductions estimated to be needed to attain the 15/35 standard and a nnual standards of 13, 12, and 11 μg/m 3 (Boxes 4 through 9 in Figure 3

- 2)

.

8 The re sults of this sensitivity run were also used in the method to quantify the impacts on design values of existing burn ban programs, as described in Section 3.3.1.1.3- 14 Figure 3 -2. Flow Diagram of Process Used to Determine Future

- Year Design Values and Associated Emission Reductions for Meeting the Current, Revised and Alternative Standard Levels3- 15 Table 3 -2. Air Quality Model Simulations Used in this Regulatory Impact Analysis

Simulation Description Purpose
2020 base case Simulation of 2020 that accounts for expected controls due to existing state and federal programs. Provides estimate of future

year design values based on existing controls
2020 control case Simulation of 2020 that includes emissions controls beyond the controls of the 2020 base case in areas with design values above the alternative standard levels in the 2020 base case. Provides impact of additional known controls on design values in target areas; provides basis for meeting the existing, revised and alternative standard levels with emission controls beyond known controls
2020 NO x _PM 2.5 sensitivity Simulation of 2020 where anthropogenic NO x and PM 2.5 emissions are decreased by 25% and 50%, respectively, relative to the 2020 base case in selected counties. Used in estimating the response of air quality to changes in emissions of NO x and direct PM 2.5 2020 SO 2 _RWC sensitivity Simulation of 2020 where an thropogenic SO2 and residential wood combustion emissions are decreased by 25% and 100%, respectively, relative to the 2020 base case in selected counties. Used in estimating the response of air quality to changes in emissions of SO 2 and residential wood combustion
2020 SJV sensitivity Nine simulations of January 2020. Each simulation has emission reductions relative to the 2020 base case in a

one - or two

- county group in California’s Central Valley. The emission reductions in each county group are the same as those in the 2020 NO x _PM 2.5 sensitivity case. This series of simulations is used to estimate the contributions of emissions from counties in the California’s Central Valley on air quality in other counties in the Central Valley

3.3. 1 Calculating Future

- year Design Values for 2020 Base and Control Cases

To predict the impact of the control strategies on future

- year attainment , the air quality mode l results are used in a relative sense by estimating future

- year PM 2.5 relative re sponse factors (RRFs ). RRFs are ratios that are calculated from the modeled changes in PM 2.5 species concentrations between the base year (2007) and future

- year (2020 base case and 2020 control case) air quality modeling results. RRFs are calculated for each PM 2.5 component ( i.e. sulfates, nitrates, organic carbon, etc.) . Future

- year estimates of the PM 2.5 annual and 24

- hour standard design values at monitor locations are then calculated by applying the species

- specific RRFs to ambient PM 2.5 concentrations from the Federal Ref erence Method (FRM) Network , which are disaggregated into species concentrations through processing and interpolation of PM 2.5 species data from the CSN and IMPROVE monitoring networks

.3- 16 To more easily apply this methodology, EPA has created software, calle d Modeled Attainment Test Software (MATS) (Abt, 201 2 ) to calculate future

- year PM 2.5 annual and 24

- hour standard design values. For this RIA, design values are projected from ambient Federal Reference Method measurements during the period 2005

- 2009 9 coupled with PM 2.5 species data from IMPROVE and CSN sites for the 2006

– 200 8 time period. In addition to calculating projected future

- year annual and 24

- hour standard design values, MATS provides the amounts of sulfate, nitrate, ammonium, elemental carbon, organic carbon and crustal matter that comprise the annual and 24

- hour standard design values for each site. These data are useful for understand ing the PM species contributing to high PM 2.5 concentrations which is informative for designing control strat egies to reduce the future

- year design values to the proposed standard levels.

In order to derive 2020 design values for the purposes of the RIA , we made two additional adjustments to the design value calculations at those monitoring sites that 1) had obs erved ambient data in the base year period that reflects atypical events or highly variable events that are difficult to predict in the future year, and 2) would be affected by existing local episodic residential wood burning curtailment programs (e.g. “bu rn ban” programs) that we were not able to simulate in the 2020 base case and control case air quality modeling. These adjustments are described below.

3.3.1.1 Future

- year Design Values Adjustments for Episodic Residential Wood Curtailment Programs

A num ber of Western nonattainment areas have existing rules in place that require the curtailment of residential wood burning (from fireplaces and woodstoves) on an episodic basis. The burning curtailment programs (“burn bans”) are implemented at the local leve l based on local air quality forecasts of high PM 2.5 days. The burn ban programs vary by area, but are similar in many ways. They generally have “stage 1” (lower concentration PM 2.5 days) and “stage 2” (higher concentration PM 2.5 days) level “burn ban” day s with mandatory compliance on stage 2 days. The forecast trigger level also varies by area. When the daily PM 2.5 NAAQS was lowered to 35 μg/m 3 in 2006 most areas implemented a trigger level at or below 35 μg/m 3 for a mandatory burn ban 10 . There are also a number of exemptions in each area for residents who use firewood as their sole source of heat. These programs have been strengthened in the last few years to become mandatory and also to address the 35 μg/m 3 NAAQS. Since all of the

9 The 2005

- 2007 period includes design values 2005

- 2007, 2006

- 2008, and 2007

- 2009. 10 Some a reas previously (before 2007) had voluntary burn ban programs with relatively high trigger levels based on the 1997 daily PM 2.5 NAAQs (65 μg/m 3 ).3- 17 identified areas have im plemented or significantly strengthened their burn ban programs since 2007, we are assuming little or no reductions from a burn ban program in our 2007 base case and large reductions (on an episodic basis) in the 2020 future year cases.

Due to the complexity of accounting for “burn bans” on specific days in the future year modeling, we were not able to simulate the effects of “burn bans” in the 2020 base case modeling. In this regard, the 2020 model

- based design value were adjusted to reflect the expected effects on design values of the episodic residential wood burning curtailment programs. Using the best available information, we estimated the impacts of episodic residential wood burning programs as a post

- modeling adjustment to the 2020 base case. For th is analysis, episodic residential wood burning adjustments were made for the areas identified in Table 3 -3 11 :

Table 3

- 3. Nonattainment Areas Where Episodic Residential Wood Burning Curtailment was Applied
Nonattainment Areas Where Episodic Residential Wood Burning Adjustments Were Applied State Chico CA Los Angeles

- South Coast Air Basin CA Sacramento CA San Francisco Bay Area CA San Joaquin Valley CA Yuba City

- Marysville CA Klamath Falls OR Oakridge OR Provo UT Salt Lake City UT Seattle

- Tacoma WA

We applied two slightly different methodologies employed to adjust the annual average and daily average design values for burn bans in the selected areas. In both cases, the adjustments were based on a modeling sensitivity run that zeroed

- out all emissi ons from the residential wood combustion category on all days of the year. Since the vast majority of residential wood combustion emissions impacts are from primary PM 2.5 emissions, we calculated the total change in primary organic carbon, elemental carbon , and crustal PM 2.5

11 These areas were all predicted to violate the daily NAAQS in the 2020 base case and are known to have mandatory episodic curtailment programs. Adjustments were not applied to areas that solely violated the annual NAAQS or did not have an existing curtailm ent program. The specific counties in which episodic residential wood burning curtailment programs were applied are listed in Table 3

- 4.3- 18 species between the base and zero

- out cases to estimate the impact on PM 2.5 from residential wood combustion controls

. 12

Since the zero

- out model run reduced all residential wood combustion emissions, we had to scale the results of the sensitivity run to provide a realistic estimate of emissions reduction from a burn ban program. To quantify the compliance rate of wood burning curtailment programs we relied upon information from the Sacramento and South Coast Air Quality Management Districts. The South Coast Air Quality Management District (SCAQMD, 2012) estimated a 75% rule effectiveness for their curtailment rule and the Sacramento Metropolitan Air Quality Management District (SCMAQMD, 2009) estimated a 70% reduction in residential wood combustion emissions on burn ban days in their area. Based on this information we assumed a 70% reduction in residential wood combustion emissions on episodic burn ban days in all areas with a mandatory burn ban program. This implies a relatively high level of compliance, but recognizes that the program will provide less than a 100% reduction due to non

- compliance and exemptions from the rule.

For the annual NAAQS, we assumed that the burn ban programs provide reductions in
PM 2.5 concentrations that are c ommensurate with the reduction in primary PM 2.5 emissions 13

. We also assumed that burn bans are applicable on certain days in the 1 st and 4 th quarters of the year (i.e., during the residential wood combustion season). The number of days for which we applied the burn ban was based on the observed fraction 14 of measured days above 35 μg/m 3 in the 1 st and 4 th quarters in the 2005

- 2009 base period in each affected county. For multi

- county areas, it was assumed that the burn ban control program would be applied by county (i.e. there may be a forecasted burn ban in only a portion of a large nonattainment area). The number of burn ban days applied per year by county is provided in Table 3 -4 15

.

12 The sensitivity run also included SO2 emissions reductions. The SO2 reductions have no impact on the organic carbon, elemental carbon, and crustal primary PM 2.5 species concentrations. 13 Since all of the adjustments are for primary PM 2.5 , it is assumed that emissions reductions and the change in concentration are linear (i.e. a 50% reduction in residential wood combusti on PM 2.5 emissions leads to a 50% reduction in the primary PM 2.5 concentrations from residential wood combustion.)
14 FRM monitoring sites operate on different schedules (1 in 3 day, 1 in 6 day, or every day). The calculation was based on the fraction of ex ceedence days during the 1 st and 4 th quarters. This proportionality approach normalizes the number of high days between monitoring sites and allows a percentage of days to be applied to the modeled days (which include all days of the year). 15 The number of burn ban days was based on the monitoring site in the county with the maximum percentage of exceedence days (days > 35 μg/m 3 ).3- 19 Table 3

- 4. Estimated Number of Burn Ban Days by County Based on 2005

- 2009 FRM Data

State Nonattainment Area County Total Number of Burn Ban Days in 1st plus 4th Quarters California Chico Butte 18

California Los Angeles

- South Coast Air Basin Los Angeles 16

California Los Angeles

- South Coast Air Basin Riverside 20

California Los Angeles

- South Coast Air Basin San Bernardino 16 California Sacramento Sacramento 20 California San Francisco Bay Area Alameda 4 California San Francisco Bay Area Santa Clara 8 California San Francisco Bay Area Solano 8 California San Joaquin Valley Fresno 42 California San Joaquin Valley Kern 48 California San Joaquin Valley Kings 40 California San Joaquin Valley Merced 30 California San Joaquin Valley San Joaquin 20 California San Joaquin Valley Stanislaus 30 California San Joaquin Valley Tulare 38 California Yuba City

- Marysville Sutter 4 Oregon Klamath Falls Klamath 16 Oregon Oakridge Lane 20 Utah Salt Lake City Salt Lake 16 Utah Provo Utah 10 Washington Seattle

- Tacoma Pierce 16

The 2020 base case model output files were modified to replace the base case modeled concentrations with the burn ban day concentrations on the identified number of days per year (from Table 3

- 4) at each monitoring site in the 21 counties. The burn ban adjustment was applied to an equal number of high days per quarter in the 1 st and 4 th quarters (i.e., half of the burn ban days were applied to the high modeled days in the 1 st quarter and half to the high modeled days in the 4 th quarter). This approach provided burn ban RRFs for the 1 st and 4 th
quarters. The modified 2020 base case predictions were re

- run through the MATS tool to calculate adjusted annual average design values which account for the episodic residential wood burning curtailment programs.

A similarly representative burn ban RRF was calculated to adjust the daily design values to account for episodic residential wood burning curtailment programs. Due to the nature of the future year daily design value calculations, the methodology differed slightly from the3- 20 annual average design value calculations. The daily design value modeled RRFs were calculated from the change in modeled PM 2.5 species on the 10% highest modeled PM 2.5 days in each quarter (i.e., the 9 highest modeled days per quarter). In this approach we assume that a burn ban wi ll apply to all high PM 2.5 days (days > 35 μg/m 3 ) in the 1 st and 4 th quarters at each site. Therefore, we performed the calculation by applying the 70% burn ban adjustment on the 10% highest modeled days in the 1 st and 4 th quarters. The revised model data were re

- run through the MATS tool to calculate an adjusted 2020 base case daily design values which account for the episodic residential wood burning curtailment programs. The impact on the 2020 base case annual design values (where the burn ban adjustments were applied) ranged from 0.03 to 0.68 μg/m 3

. The impact on the 2020 base case daily design values ranged from 0.1 to 13.1 μg/m 3 .The procedures for calculating 2020 control case design values that reflect the effects of the burn ban programs are describe d in Section 3.3.3. Additional details on the procedures for treating burn ban programs are provided in the AQMTSD.

3.3.1.2 Future

- year Design Values Adjustments for Atypical or Unpredictable Events
Concentrations of PM 2.5 at a number of monitoring sites may be influenced by atypical or unpredictable events such as wildfires or fireworks. In the base year 2005

- 2009 FRM data, all design value calculations at all sites reflect adjustments to data that EPA officially determined have met the criteria for exclu sion under the Exceptional Events Rule (EPA, 2007) during that base year period. However, under a future year scenario it is possible that some atypical events would qualify as exceptional events even though they did not qualify in the base 2005

- 2009 perio d. This is due to the nature of the “but for” test in the Exceptional Events Rule. The rule states that exceptional events cannot be removed from the design value calculations unless the monitor would not violate the NAAQS, “but for” the exceptional events

. There are a number of sites that are above the current daily PM 2.5 NAAQS in the 2005

- 2009 period and would also continue to violate the current NAAQS even if certain atypical event days were removed. Therefore, those days cannot be removed from the offic ial design value calculations for 2005

- 2009 period because they do not meet the “but for” test. However, in the future year 2020 projections, we assume for analysis purposes that the impact of certain atypical or highly variable events would meet the “but for” test. This is a reasonable assumption because at a certain point in our modeling analysis the design value at each violating site is reduced to a value that is slightly above the NAAQS level such that the site would attain the NAAQS “but for” the atyp ical events days.

The identification of atypical event data that could affect future design value calculations could involve an extensive data analysis exercise. It would be difficult to identify3- 21 every potentially important past event for each monitoring site in the country and completely characterize the exact nature of those days as part of this RIA. Therefore, we limited the analysis to a small group of monitoring sites where a few atypical event days may have an important impact on the future year desi gn value calculations. In our analysis of potentially important atypical events we included only monitoring sites that have 24

- hour design values predicted to violate the 35 ug/m3 daily NAAQS in the 2020 base case. At these sites we examined the concentrations on days with daily average measurements > 35 μg/m 3

. There were several categories of potentially important atypical event days that we identified:

1. Wildfires

— Summer days with high concentrations at sites in the West which normally do not exceed the NAAQS in the summer 16 [62 site

- days];

2. Fireworks

— High PM 2.5 concentrations predominantly on July 4 th or 5 th [37 site

- days];

3. Other unusual high data

— Other site

- days with very high measured PM 2.5 concentrations that were much higher than concentrations on the same days at surrounding sites [2 site

- days].

Based on this assessment, we identified 101 site

- days in the above categories at 25 monitoring sites (23 of them in California) in the period 20 05

- 2009 17 , 18

. In all of the subsequent future year design value calculations (for both the annual and daily NAAQS), the design values have been adjusted to reflect the removal of these days. The impact on the 2005

- 2009 annual design values at these 25 sites ranged from 0.08 to 0.97 μg/m 3

. The impact on the daily design values ranged from 0 to 12.9 μg/m 3

. We recalculated the 2020 base case and 2020 control case annual and daily PM 2.5 design values to reflect the removal of potential future atypical event days from the starting point 2005

- 2009 ambient measured data. Additional details on the methodology for adjusting the future year design values for the purposes of this analysis are provided in the AQMTSD.

16 The vast majority of the wildfires days occurred during a well documented summer 2008 wildfire period in Central California. 17 These were site days that were not already identified and removed from the ambient data as EPA

- concurred exceptional events. 18 The adjustments are made to the base year design values for the sole purpose of projecting ambient data to the future year (2020 ). It is not appropriate to adjust the base year 2005

- 2009 data for the purpose of examining current or past attainment of the NAAQS.3- 22 3.3.2 Calculating Future

- year Design Values for Meetin g the Existing Standards, the Revised Annual Standard, and Alternative Annual Standard Levels The air quality ratios were used in the process of adjusting the air quality modeling results to meet the current and alternative standard levels. The 2020 base c ase modeling and the development of the adjusted 2020 base case (Box 1 and Box 2, Figure 3

- 2) were described above. The procedures for determining the emissions reductions estimated to be needed to attain the 15/35 standard and annual standards of 13, 12, and 11 μg/m 3 are identified in boxes 4 through 9 in Figure 3

- 2. These procedures and the results are described below.
Adjusted 2020 Control Case (Box 4)

. Adjust design values of 2020 control case to account for episodic wood burning curtailments and to acc ount for atypical events and inappropriate emissions controls. The impact of atypical events on design values was removed from 2020 control case design values by removing these days from the ambient data used in the future

- year design value calculations in the MATS tool, as described in section 3.3.1.2, above. To account for the impacts of wood burning curtailments in the 2020 control case, we started with the fractional change (i.e., RRF) in speciated design values between the 2020 base case and the 2020 c ontrol case (both cases without the effects of wood burning curtailment programs). We then applied these species

- specific RRFs to adjust the corresponding speciated design values in the 2020 base case that reflects the application of wood burning curtailme nts 19

. Attainment of the 15/35 Level (Box 5)

. Estimate future

- year design values and emission reductions beyond the adjusted 2020 control case to meet the existing standard level (15/35)

. For monitors with design values greater than 15/35 in the adjusted 2020 control case (Box 4, Figure 3

- 2), additional direct PM 2.5 emission reductions were applied to meet this level. The additional direct PM 2.5 emission reduction amounts were estimated using air quality ratios. The direct PM 2.5 emissions reductions needed to attain the 15/35 standard were also applied to reduce PM 2.5 design values at all attaining monitoring sites in the same county as the nonattainment monitor. For example, the highest 24

- hr design value in San Bernardino County in the adjusted 2020 contr ol case was 36.4 μg/m 3 at monitor 60719004 . Additional emissions reductions of 585 tons of direct PM 2.5 were estimated to be required for this monitor to meet

19 We also had to adjust the 2020 modeled control case design values in certain counties to remove the impacts from a subs et of control measures . These control measures were deemed to be inappropriate for the purposes of the 2020 control case after the 2020 base case air quality modeling was completed . To remove the impact of these inappropriate emissions controls, the design values were adjusted either based on the air quality ratios or on the change in the design value between the 2020 base case and 2020 control case scaled by the fraction of the total emission reduction associated with the inappropriate controls.3- 23 the 24

- hr standard level 20 as follows: (36.4

− 35.4) / 1.710 × 1000 = 585 tons, where 1.710 is the
24

- hr direct PM 2.5 air quality ratio for the monitor 60719004 (Table 3.A

- 3). The 585 tons of direct PM 2.5 emissions reductions in this county were estimated to reduce the highest annual design value in San Bernardino at monitor 60710025 from 13.41 to 12.9 9 μg/m 3 as follows: 13.41 – (585 × 0.710 / 1000) = 12.99 μg/m 3 , where 0.710 is the annual direct PM 2.5 air quality ratio for the 60710025 monitor (Table 3.A

- 3). The direct PM 2.5 emission reduction amounts beyond the adjusted 2020 control case that are necessary to meet the current standard level for individual counties are listed in Table 3

- 5.

Table 3 -5. Tons of Di rect PM 2.5 Emission Reductions b eyond the Adjusted 2020 Control Case to Meet the Current Standard Level for Counties that Exceed the Revised or A lternative Annual Standard Levels in the Adjusted 2020 Base Case

FIPS Code State Name County Name Direct PM 2.5 Emissions (tons)

6019 California Fresno 497

6025 California Imperial 288

6029 California Kern 1, 496 6031/6107 California Kings/Tulare 610

6071 California San Bernardino 585

6099 California Stanislaus 346

42003 Pennsylvania Allegheny 764

Emissions were controlled in certain counties in the 2020 control case that exceeded the alternative annual standard of 11 μg/m 3 but that did not exceed the existing standard level. These emissions controls are relevant for meeting the 11 μg/m 3 level (Box 9) but are not relevant for meeting the existing standard level. Therefore a nnual design values in the 15/35 case are set to tho se of the adjusted 2020 base case for monitors in the following counties : Jefferson, AL; Shoshone, ID; Cook, IL; Madison, IL; Klamath, OR; Lake, IN; Scott, IA; Wayne, MI; Milwaukee, WI; and Harris, TX

. 21

20 A 24

- hou r design value of 35.4 μg/m 3 is the highest value that meets the 24

- hour standard. 21 Arrows point from Box 2 and Box 4 to Box 5 in Figure 3

- 2 because information from both the adjusted 2020 base case and the adjusted 2020 control case was used in developing the set of design values that correspond to attainment of the existing standard.3- 24 Analytical Baseline ( Box 6 ). Create analytical baseline for meeting alternative standards that accounts for 2025 mobile NOx emission adjustment in San Joaquin Valley and South Coast Air Basin

. The goal of this RIA is to provide the best estimates of the costs and benefits of an illust rative attainment strategy to just meet the revised annual 12 μg/m 3 standards, as well as two alternative annual standards of 13 μg/m 3 and 11 μg/m 3 , incremental to just meeting the current standards of 15/35, and reflecting emissions projected to reflect t he impact of economic growth and implementation of state and federal emissions controls. Most areas of the U.S. will be required to demonstrate attainment with the new standards by 2020. As a result, for these areas, the correct baseline for estimating the incremental emissions reductions that would be needed to attain the more protective standards is a baseline with emissions projected to 2020 and adjusted to reflect the additional emissions reductions that would be needed to attain the current 15/35 stand ards. For two areas in Southern California (South Coast and San Joaquin), the degree of projected non

- attainment with the revised annual standard of
12 μg/m 3 is high enough that those counties are not expected to be able to demonstrate attainment with the new standard by 2020. Instead, those two areas are likely to qualify for an (up to) five year extension of their attainment date. If the areas are granted an attainment date extension, they will have until 2025 to demonstrate attainment with the revised an nual standard

. As a result, for these two areas, the correct baseline for estimating the incremental emissions reductions that would be needed to attain the more protective standards is a baseline with emissions projected to 2025 adjusted to reflect the ad ditional emissions reductions that would be needed to attain the current 15/35 standards. This difference in attainment year is important because between 2020 and 2025, emissions from mobile sources in California are expected to be reduced due to continued fleet turn over from older, higher emitting vehicles to newer, lower emitting vehicles. These reductions in emissions will occur as a result of previous state rules for which costs and benefits have already been counted, and thus will not be costs and ben efits attributable to meeting the revised annual standard.

Modeling of two separate years is time prohibitive, and would result in two separate years of benefits and costs which would not provide a complete picture of the nationwide costs and benefits of just meeting the new standards in either 2020 or 2025 because of differences in the baselines between the two years. To provide the most reasonable and reliable estimates of costs and benefits of full attainment for the nation, we are constructing an analy tical baseline for estimating the costs and benefits of attaining the revised standard of 12 μg/m 3 and alternative annual standards of 13 μg/m 3 and 11 μg/m 3 with the following characteristics. The analytical baseline was developed by applying a mobile NOx emission adjustment to design values at level s attaining 15/35. This approach allows us to generate costs and benefits of full3- 25 attainment without overstating the costs and benefits in those two areas, which would occur if

we forced costly emissions reductions in 2020 in areas that would not have to be incurred until 2025, and which will be offset because of the expected reductions in mobile source emissions du e to other programs

. 22

The emissions adjustment is equal to 27,467 tons of NOx emissions reductions in the South Coast Air Basin and 14,410 tons in the San Joaquin Valley. Annual design values for the 15/35 baseline were adjusted to account for these emiss ions reductions using the air quality ratios listed in Table 3.A

- 1. Incremental costs and benefits of the revised and alternative standards are assessed relative to this set of analytic baseline design values. Annual design values and exceedance categories are provided for the analytic baseline in Table 3

- 6 and Figure 3-3 for counties with at least one monitor that exceeds a level

. 23

Figure 3 -3. Counties that Exceed the Revised and/or Alternative Annual Standard Levels of 13, 12 and 11 μ g/m 3 in the Analyt ical Baseline

22 Benefits for all areas are estimated using 2020 population data for consistency, recognizing that full attainment costs and benefits will not actually be realized until 20 25 for a portion of the costs and benefits. The 2020 estimates of full attainment costs and benefits will be an underestimate of benefits in 2025 because of population growth and changes in the age distribution of the population between 2020 and 2025. 23 T here were two counties ( Lincoln County, MT and Santa Cruz County, AZ ) that exceeded alternative standard levels in the 2020 base case for which we used a weight

- of

- evidence approach to determine how they would attain these levels , as described in Section 3 .3.5

.3- 26 Table 3- 6. Annual Design Values and Exceedance Category for the Highest County Monitor in the Analytical Baseline for Counties with at Least one Monitor Above the Revised and/or Alternative Standard Levels

FIPS Code Monitor ID State Name County Name Annual DV 13/35 12/35 11/35
6065 60658005 California Riverside 14.58 x x x 6107 61072002 California Tulare 13.23 x x x 6029 60290016 California Kern 12.7 x x 6071 60710025 California San Bernardino 12.64 x x 6025 60250005 California Imperial 12.57 x x 6037 60371002 California Los Angeles 12.34 x x 6047 60472510 California Merced 12.12 x x 55079 550790059 Wisconsin Milwaukee 12.02 x 6031 60310004 California Kings 11.79 x 17119 171191007 Illinois Madison 11.7 x 6019 60190008 California Fresno 11.61 x 26163 261630033 Michigan Wayne 11.58 x 1073 10730023 Alabama Jefferson 11.56 x 17031 170316005 Illinois Cook 11.52 x 16079 160790017 Idaho Shoshone 11.52 x 19163 191630019 Iowa Scott 11.51 x 48201 482011035 Texas Harris 11.43 x 48141 481410044 Texas El Paso 11.39 x 41035 410350004 Oregon Klamath 11.3 x 55133 551330027 Wisconsin Waukesha 11.22 x 18089 180891003 Indiana Lake 11.17 x 6063 60631009 California Plumas 11.15 x 42003 420030064 Pennsylvania Allegheny 11.12 x

13 Standard Level ( Box 7)

. Estimate future

- year design values and emission reductions beyond the analytical baseline to meet the alternative annual standard level of 13 μg/m 3

. Annual PM 2.5 design values at monitors in Tulare and Riverside Counties in California exceeded the alternative standard level of 13 μ g/m 3 in the analytical baseline (Table 3

- 6 and Figure 3 -3 ). The additional direct PM 2.5 emission reductions required for these counties to meet this standard level were estimated using air quality ratios. For example, the highest annual design value in Riverside County in the analytical baseline case was 14.58 μg/m 3 . Emission reductions of 626 tons of direct PM 2.5 were estimated to be req uired for this monitor to meet the annual standard level of 13.04 μg/m 3 as follows: (14.58

− 13.04) / 2.459 × 1000 = 626 tons, where 2.459 is the annual direct PM 2.5 air quality ratio for monitor 60658005 (Table 3.A -3 ). The3- 27 emissions reductions by county t o attain a 13 μg/m 3 standard are provided in Table 3.7. These reductions were applied to lower the a nnual PM 2.5 design values at all sites in the given county

. 24

Table 3

- 7. Tons of Direct PM 2.5 Emissio n Reductions Beyond the Analytical Baseline to Meet the 13 μ g/m 3 Level

FIPS Code State Name County Name Direct PM 2.5 Emissions Reductions (tons)

6065 California Riverside 626

6107 California Tulare 101

12 Standard Level ( Box 8)

. Estimate future

- year design values and emission reductions beyond the analytical baseline to meet the revised annual standard level of 12 μg/m 3

. Annual
PM 2.5 design values at monitors in the following 7 counties in California exceeded the revised standard level of 12 μ g/m 3 in the analytical baseline (Table 3

- 6 and Figure 3 -3 ): Los Angeles, Riverside, San Bernardino, Kern, Tulare, Merced, and Imperial. The additional direct PM 2.5 emission reductions required for these counties to meet the standard level of 12 μ g/m 3 were estimated using air quality rati os. For example, the highest annual design value in Riverside County in the analytical baseline case was 14.58 μg/m 3 . Emission reductions of 1,033 tons of direct PM 2.5 were estimated to be required for this monitor to meet the annual standard level of 12.0 4 μg/m 3 as follows: (14.58

− 12.04) / 2.459 × 1000 = 1033 tons, where 2.459 is the annual direct PM 2.5 air quality ratio for monitor 60658005 (Table 3.A -3 ). The emissions reductions by county to attain a 12 μg/m 3 standard are provided in Table 3.8. These r eductions were applied to lower the a nnual PM 2.5 design values at all sites in the given county 25

.

24 Emissions reductions needed in Tulare County were also applied to reduce the annual PM 2.5 design value at the monitor in Kings county, which is combined with Tulare in our analysis, as discussed in Appendix 3.A.1.1. 25 For Kings and Tulare Counties, the maximum of the emission reductions required for the individual counties was applied to monitors in both counties using the air quality ratios since these counties are combined in our analysis,

as discussed in Appendix 3.A.1.1.3- 28 Table 3

- 8. Tons of Direct PM 2.5 Emission Reductions Beyond the Analytical Baseline to Meet the 12 μ g/m 3 Level a

FIPS Code State Name County Name Direct PM 2.5 Emissions Reductions (tons)

6037 California Los Angeles 743

6065 California Riverside 1,033

6025 California Imperial 294

6029 California Kern 418

6107 California Tulare 635

6047 California Merced 19

6071 California San Bernardino 844

a See Appendix Chapter 7.A for additional details on known and unknown emissions reductions and costs, by county, for 12 μ g/m 3

. 11 Standard Level ( Box 9)

. Estimate future

- year design values and emission reductions beyond the analytical baseline to meet the alternative annual standard level of 11 μg/m 3

. Annual PM 2.5 design values at monitors in 23 counties exceeded the alternative standard level of 11 μ g/m 3 in the analytical baseline (Table 3

- 6 and Figure 3 -3 ). As discussed above, a nnual design values in the analytical baseline do not reflect the emission controls of the 2020 control case for counties with monitors that did not exceed the current standard level in the 2020 base case. To estimate the emission reductions beyond the known co ntrols needed to meet the alternative standard level of 11 μ g/m 3 in these counties, we started with annual design values for the adjusted 2020 control case (Box 4 of Figure 3

- 2). The additional direct PM 2.5 emission reductions required for these counties t o meet the alternative standard level were then estimated using air quality ratios. For example, the annual design value at the high monitor in Jefferson, AL was 11.56 μ g/m 3 in the adjusted 2020 base case and 11.11 μ g/m 3 in the adjusted 2020 control case. The additional direct PM 2.5 emission reductions needed beyond the emission reductions of the 2020 control case for this monitor to meet the 11 μ g/m 3 level were estimated using air quality ratios as follows: (11.11

− 11.04) / 0.561 × 1000 = 125 tons, where 0.561 is the direct PM 2.5 air quality ratio for monitor 10730023. Annual PM 2.5 design values associated with emission reductions estimated in this way in (Table 3

- 9) were calculated for the counties with exceedance monitors.3-29 Table 3 -9. Tons of Direct PM 2.5 Emission Reductions Beyond the Analytical Baseline to Meet the Alternative Standard 11 μ g/m 3 Level a

FIPS Code State Name County Name Tons of Direct PM 2.5
6037 California Los Angeles 3, 222

6065 California Riverside 1, 440

1073 Alabama Jefferson 125

6019 California Fresno 325

6025 California Imperial 850

6029 California Kern 1, 051 6031/6107 California Kings/Tulare 1, 168

6071 California San Bernardino 2, 252

6047 California Merced 255

6063 California Plumas 44

17031 Illinois Cook 427

17119 Illinois Madison 1, 687

18089 Indiana Lake 0

16079 Idaho Shoshone 61

41035 Oregon Klamath 25

42003 Pennsylvania Allegheny 154

19163 Iowa Scott 188

26163 Michigan Wayne 870

55079 Wisconsin Milwaukee 455

55133 Wisconsin Waukesha 55

48141 Texas El Paso 158

48201 Texas Harris 123

a For the following counties, the emission reductions listed are relative to the adjusted 2020 control case design values rather than the analytical baseline: Jefferson, AL; Shoshone, ID; Cook, IL; Madison, IL; Klamath, OR; Lake, IN; Scott, IA; Wayne, MI; Milwaukee, WI; and Harris , TX.

3.3.3 Estimating Changes in Annual Average PM 2.5 for Benefits Inputs

The calculation of health benefits for the revised annual standard of 12 μg/m 3 and the two alternative annual standards uses spati al surfaces of gridded annual average PM 2.5 concentrations for the analytical baseline and spatial surface reflecting attainment of each different standard. The spatial surface for each case covers the U.S. portion of the air quality3- 30 modeling domain. To create the spatial field for the analytical baseline we started with a spatial surface for the 2020 control case reflecting the removal of atypical events. The 2020 control case spatial surface was adjusted using the projected annual design values for the analytical baseline to create the spatial surface for the baseline. The spatial surface for the 2020 control case was also adjusted to reflect attainment of the different standards using the annual design values for each standard. Details of this process a re described below.
The spatial surface for the 2020 control case (with removal of potential future atypical events) was developed using MATS by calculating species

- specific RRFs at every grid cell within the modeling domain for the 2020 control case and applying these RRFs to ambient data that have been interpolated to cover all grid cells in the modeling domain. The basic spatial interpolation technique, called Voronoi Neighbor Averaging (VNA), was applied for annual design values for the 2020 control ca se and each standard to create spatial fields of annual
PM 2.5 for each of these cases. As part of this technique, VNA uses the inverse distance squar ed weighted average of the annual design values at monitoring sites that are nearest to the center of each model grid cell. We then calculate the ratio of annual PM 2.5 for each standard level to annual PM 2.5 for the 2020 control case for each grid cell in the VNA fields. These gridded ratios are then multiplied by the gridded annual concentrations from the MATS outputs for the 2020 control case. That is, a spatial surface wa s calculated by adjusting the 2020 control case using a multiplicative factor calculated as the ratio of the gridded design values for attainment of each standard to the gridded design values of the 2020 control case where the design value gridded spatial fields are based on the nearest neighbor monitor locations (weighted by distance). This approach is shown mathematically in the equation below.

where ij refers to column i and row j of the modeling domain

. This approach aims to estimate the change in population exposure associated with attaining an alternate NAAQS, relying on data from the existing monitoring ne twork and the inverse distance squared variant of the VNA interpolation method to adjust the MATS gridded concentrations such that each area attains the standard alternatives. Using the VNA spatial averaging technique, the annual average PM 2.5 spatial surf ace s are smoothed to minimize sharp gradients in PM 2.5 conce ntrations in the spatial fields due to changes in the monitor concentrations 26 . Because the VNA approach interpolates

26 For the purposes of es timating benefits, this smoothed surface was then clipped to grid cells within 50 km of monitors whose design values were changed as a result of the standard level. VNA Interpolated AQ ij from Alternative VNA Interpolated AQ ij from 2020 Adjusted AQ ij = x MATS AQ ij3- 31 monitor values, it is most reliable in areas with a denser monitoring network. In areas with a sparser monitoring network, there is less observed monitoring data to support the VNA interpolation and we have less confidence in the air quality values further away from the location of monitoring sites . T o the extent that any bias in the interpolated values i s present , the ratio of the interpolated values should be relatively insensitive to this bias and the adjusted air quality values should be unaffected.

3.3.4 Limitations of Using Adjusted Air Quality Data Due to time constraints, design values and PM 2.5 surfaces at the analytical baseline level and the alternative standard levels were based on adjusted fields derived from the modeled 2020 base case and 2020 control case, rather than directly on air quality simulation results. While a credible technical basis exists for the adjustment procedures used in this analysis, t here

are important limitations to the approaches used to estimate the response of air quality to emissions changes. For instance, air quality ratios are calculated with r esults from a limited number of CMAQ sensitivity runs and are based on the assumption that the monitor design values would decrease with additional emissions reductions of SO 2 , NO x and direct PM 2.5 s imilar to how the CMAQ sensitivity runs predicted changes in air quality concentrations. The uncertainty of this assumption will increase with increasing emissions reductions needed to estimate attainment. In addition, the model response to emissions changes are analyzed at a county

- level or within a small group of counties, and we assume that air quality concentrations at a monitor will decrease linearly with emissions reductions in a county (e.g., direct PM 2.5 emission reductions) or a group of counties (e.g., SO 2 and NO x emissions reductions). Because of the m ore local influence of changes in directly emitted PM 2.5 emissions on air quality, it is also particularly difficult for the air quality ratio approach to estimate well how the design value at a monitor in a county would respond to changes in direct PM 2.5 emissions in a county without knowing the location of the source (e.g., extrapolated emissions reductions) relative to the location of the monitor.

The exact impact of using this methodology to estimate the emissions reductions needed for attainment and th e associated effect on the cost and benefits is uncertain and may vary from monitor

- to

- monitor. We do not believe that this methodology tends towards any general trend and does not always result in either an underestimation or overestimation of the
costs a nd benefits of attaining the alternative standards.3- 32 3.3.5 Weight

- of

- Evidence Approach for Lincoln County, MT and Santa Cruz, NM

There were two counties that exceeded alternative standard levels in the 2020 base case for which we used a weight

- of

- evidence approach to determine how they would attain these levels. These counties are Lincoln County, MT and Santa Cruz County, AZ.

Lincoln County’s PM 2.5 air quality problem is dominated by residential wood combustion emissions of PM 2.5 , and the County has few additional emissions sources to control. The Lincoln County monitor is situated in the City of Libby in a valley that is subject to wintertime temperature inversions (Figure 3 -4 ). These temperature inversions, which suppress air mixing and dilution of PM 2.5 , combined with resident’s reliance on wood burning for home heating can produce poor PM 2.5 air quality. However, since 2005, Libby has successfully impl emented a woodstove change

- out program that has resulted in consistent improvements in PM 2.5 air quality in recent years (Figure 3 -5 ). The success of this program and the downward trend in annual design values at the Libby monitor suggests that Libby will meet the revised and alternative standard levels in 2020. Since residential wood combustion emissions in Libby and the emission reductions due to the wood

- stove change

- out program are not fully captured in our emission inventory, our modeled estimates of f uture

- year design values are not reliable at this site. However, our weight

- of

- evidence considerations suggest that Lincoln County would likely attain the alternative standard levels in 2020 based on on

- the

- books control programs.

Figure 3 -4. City of Lib by in Lincoln County, Montana ( Image taken from Google Earth )3- 33 Figure 3 -5. Three

- year Annual and 24

- hr Design Values for the Monitor in Libby, MT

Santa Cruz, AZ had a 24

- hr design value of 29.7 μ g/m 3 and an annual design value of 12.65 μ g/m 3 in the 2020 base case. However, Santa Cruz has few local emissions sources and therefore relatively low emissions available for control

. Total emissions of SO 2 , NOx and direct
PM 2.5 were 65, 688, and 542 tons, respectively, in Santa Cruz County in the 202 0 base case. Total emissions of SO 2 , NOx and direct PM 2.5 for the Mexican State of Sonora, which borders Santa Cruz, were much greater: 100,089, 53,518 and 27,641 tons, respectively. The lack of substantial local controllable emissions in Santa Cruz and th e large impact of emissions from Sonora, Mexico on air quality in Santa Cruz suggest that emissions from Mexico make meeting the alternative standards for this county impractical in our analysis. Cross

- border impacts of Mexican emissions on Santa Cruz Coun ty have been recognized previously. O n September 25, 2012, in a Federal Register Notice, EPA Region IX approv ed a State Implementation Plan ( SIP ) revision submitted by the Arizona Department of Environmental Quality. As indicated in the Notice, EPA Region IX reviewed three years of air quality data from Arizona and determined that the Nogales nonattainment area in Santa Cruz County is attaining the National Ambient Air Quality Standard for PM 10 , but for international emissions sources in Nogales, Sonora, Mexico

. Our weight

- of

- evidence considerations suggest that Santa Cruz would likely not require emissions reductions in addition to those of on on

- the

- books control programs to attain the alternative standard levels.

3.3.6 Estimating Changes in Visibility f or Analyzing Welfare Benefits Changes in visibility were calculated in order to assess both recreational and residential visibility welfare benefits. The visibility calculations for the welfare benefits assessment are based on annual average light extincti on (bext) values, converted to units of visual range (km). Since we are interested in providing visibility estimates throughout the US, we utilize gridded, speciated PM 2.5 data that is produced by MATS (Abt, 2012) along with future

- year design values for t he annual NAAQS.3- 34 The gridded species data used to calculate the visibility values is somewhat different than the gridded data used to calculate health benefits. The gridded PM 2.5 data used as input to BenMAP for health benefits is based on adjusted species data using the SANDWICH technique (Frank, 2006). The PM 2.5 species data is adjusted to match the nature of the PM 2.5 FRM filter data that is used as the basis for determining attainment of the PM 2.5 NAAQS. For example, in the spatial fields used in BenMAP, the nitrate data has been adjusted to account for volatilization, a particle bound water component is added to the sulfate and nitrate concentrations, and the organic carbon is calculated as the difference between the measured
FRM PM 2.5 mass and the sum of the rest of the PM 2.5 species. For visibility calculations, we use the “raw” PM 2.5 species data, as measured by IMPROVE and CSN monitors. Equation 3.1 shows the “old” IMPROVE equation which is used to calculate visibility in Mm -1

. Note that the coarse PM component of the “old” IMPROVE equation was excluded here because this term is not used in calculating visibility spatial fields.

b ext = 3 x f ( RH ) x [ Sulfate ] + 3 x f ( RH ) x [ Nitrate ] + 4 x [ Organic Mass ]

+ 10 x [ Elemental Carbon ] + 1 x [ Fine Soil ] + 10
The mass concentrations of the components indicated in brackets are in units of μg/m 3 , and f

(RH) is the unitless water growth term that depends on relative humidity. The final term in the equation is known as the Rayleigh scattering term and accounts for light scattering by the natural gases in unpolluted air. Since IMPROVE does not include ammonium ion monitoring, the assumption is made that all sulfate is fully neutralized ammonium sulfate and all nitrate is assumed to be ammonium nitrate.

The visibility values are calculated from observed concentrations for each of the PM species for each calendar quarter. Using the “old” IMPROVE equation (without the coarse mass component), and with quarterly averaged climatological average relative humidity [f(RH)] values, we calculate a quarterly average light extinction (bext) value from the IMPROVE and CSN data for the 2006 -2 008 base period which has been interpolated to the CMAQ grid using gradient adjusted spatial fields (eVNA). The observed sulfate and nitrate concentrations are assumed to be fully neutralized by ammonium and the organic carbon is multiplied by 1.4 to deriv e organic carbon mass. The interpolated gridded 2006

- 2008 ambient data is projected to 2020 using modeled RRFs. CMAQ derived quarterly average RRFs for sulfate, nitrate, elemental carbon, organic carbon, and crustal components are multiplied by the gridded light extinction components to get future year quarterly average visibility. The four quarterly average total light3- 35 extinction values (for each grid cell) are then averaged together to get annual average visibility. The procedure wa s repeated for both the 2020 base case and 2020 control case scenarios.

The gridded field of 2020 base case and control case annual average visibility is used to calculate residential visibility benefits in the following manner

. The visibility data at Class I areas is extracted from the gridded data to calculate recreational visibility benefits. The Class I area visibility is based on the visibility calculated at the grid cell which contains the centroid of each of the 149 Class I areas in the continental U nited S tates

.
3.4 References Abt Associates, 2010. User ’ s Guide: Modeled Attainment Test Software. http://www.epa.gov/scram001/modelingapps_mats.htm
Appel, K.W., Bhave, P.V., Gilliland, A.B., Sarwar, G., Roselle, S.J., 2008. Evaluation of the community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II

— particulate matter. Atmospheric Environment 42, 6057

- 6066.
Appel, K.W., Gilliland, A.B., Sarwar, G., Gilliam, R.C., 2007. Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5: Sensitivities impacting model performance Part I

— Ozone. Atmospheric Environment 41, 9603

- 9615.
Byun, D.W., and K.L. Schere. 2006. “ Review of the Governing Equations, Compu tational Algorithms, and Other Components of the Models

- 3 Community Multiscale Air Quality (CMAQ) Modeling System. ” J. Applied Mechanics Reviews 59(2):51

- 77.
Carlton, A.G., Bhave, P.V., Napelenok, S.L., Edney, E.D., Sarwar, G., Pinder, R.W., Pouliot, G.A., Houyoux, M., 2010. Model Representation of Secondary Organic Aerosol in CMAQv4.7. Environmental Science & Technology 44, 8553

- 8560. Carlton, A.G., Turpin, B.J., Altieri, K.E., Seitzinger, S.P., Mathur, R., Roselle, S.J., Weber, R.J., 2008. CMAQ Model Performance Enhanced When In

- Cloud Secondary Organic Aerosol is Included: Comparisons of Organic Carbon Predictions with Measurements. Environmental Science & Technology 42, 8798

- 8802.
ERG. 2009. Development of Mexico National Emissions Inventory Projectio ns for 2008, 2012, AND 2030

. ERG. 2009. Technical memorandum: “Mexico 2018 Emissions Projections for Point, Area, On

Road Motor Vehicle and Nonroad Mobile Sources .”
IMPROVE, (2006), “Spatial and Seasonal Patterns and Temporal Variability of Haze and its Co nstituents in the United States: Report IV.” Cooperative Institute for Research in the3- 36 Atmosphere, ISSN: 0737

- 5352

- 74. Also available at: http://vis ta.cira.colostate.edu/improve/Publications/Reports/2006/PDF/IMPROVE_Report_IV.p df
Lam, Y

- F., Fu, J.S., Jacob, D.J., Jang, C., Dolwick, P., 2010. 2006

- 2008 GEOS

- Chem for CMAQ Initial and Boundary Conditions. 9 th Annual CMAS Conference, October 11

- 13, 2010, Chapel Hill, NC.
Nenes, A., Pandis, S.N., Pilinis, C., 1998. ISORROPIA: A new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosols. Aquatic Geochemistry 4, 123

- 152.
South Coast Air Quality Management District, 2012. Revised Draft 2012 Air Quality Management Plan. Also available at: http://www.aqmd.gov/aqmp/2012aqmp/RevisedDraft/RevisedDraft2012AQMP

- Main

- clean.pdf

Sacramento Metro politan Air quality Management District, 2009. Staff Report: Rule 421, Mandatory Episodic Curtailment of Wood and Other Solid Fuel Burning. Also available at: http://www.airquality.org/notices/Rules2009/200907Rule421WorkshopsStaffReport.pdf
U.S. Environmental Protection Agency (EPA). 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze. Office of Air Quality Planning and Standards, Research Triangle Park, NC.
U.S. Environmental Protection Agency (EPA). 2011a. Meteorological Model Performance for Annual 2007 Simulations. Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA

- 454/R

- 11

- 007.
U.S. Environmental Protection Agency (EPA). 20 11b

. Update to the 24 Hour PM2.5 NAAQS Modeled Attainment Test. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Also available at: http://www.epa.gov/ttn/scram/guidance/guide/Update_to_the_24

- hour_PM25_Modeled_Attainment_Test.pdf

U.S. Environmenta l Protection Agency (EPA). 2012a

. Technical Support Document: Air Quality Modeling for the Final PM NAAQS RIA . Office of Air Quality Planning and Standards, Research Triangle Park, NC. November 2012.
U.S. Environmental Projections Agency (EPA). 201 2b . Technical Support Document: Pr eparation of Emissions Inventories for the Version 5, 2007

- based Platform. Office of Air Quality Planning and Standards, Research Triangle Park, NC. November, 2012

. Yantosca, B., 2004. GEOS

- CHEMv7

- 01

- 02 User ’ s Guide, Atmospheric Chemistry Modeling Group, H arvard University, Cambridge, MA, October 15, 2004.
Yarwood, G., Rao, S., Yocke, M., Whitten, G.Z., 2005. Updates to the Carbon Bond chemical mechanism: CB05. Final Report to the US EPA, RT

- 0400675, December 8, 2005.3.A -1 APPENDIX 3 .A ADDITIONAL AIR QUALITY MODELING INFORMATION

3 .A.1 Air Quality Modeling and Analysis This appendix provides supplemental information for the air quality modeling analysis in Chapter 3.

3.A.1.1 Development of Air Quality Response Ratios

The air quality response ratios (hereafter referred to as air quality ratios) used to adjust the 2020 cases to meet the standard levels were calculated based on results of several sensitivity simulations. The sensitivity simulations, as described in Table 3

- 4, were defined to isolate the changes in the (NH 4 ) 2 SO 4 , NH 4 NO 3 and direct PM 2.5 associated with changes in emissions of SO 2 , NO x and direct PM 2.5 , respectively. These PM 2.5 component species were selected for reduction to meet the standard levels because th ey dominate the mass of PM 2.5 in the areas of concern in the 2020 cases. The sensitivity simulation referred to as “2020 NO x

_ PM 2.5 ” was used in calculating the air quality ratios associated with changes in NO x and direct PM 2.5 emissions. This simulation wa s based on anthropogenic NO x and direct PM 2.5 emission reductions from non

- EGU sources of 25% and 50%, respectively, relative to the 2020 base case. The sensitivity simulation referred to as “2020 SO 2 _RWC” was used in calculating the air quality ratios ass ociated with changes in SO 2 emissions. This simulation was based on anthropogenic SO 2 and residential wood combustion emissions reductions from non

- EGU sources of 25% and 100%, respectively, relative to the 2020 base case

. 1 In the sensitivity runs, emissio ns reductions for direct PM 2.5 were generally applied in counties with monitors with annual design values above 11 μg/m 3 level in the 2020 base case, while emission reductions for NO x and SO 2 were generally applied in those counties as well as their adjacent counties. This approach reflects the local impacts of direct PM 2.5 emissions on air quality and the broader geographic impacts on PM 2.5 of SO 2 and NO x emissions reductions.

In calculating air quality ratios, a “county group” associated with each monitor was defined for estimating the change in emissions associated with a given change in the design value at the monitor. For the development of direct PM 2.5 air quality ratios, the county group included just the county containing the monitor because of the relatively local nature of the impacts of direct PM 2.5 emissions on ambient PM 2.5 concentrations. For the development of NO x and SO 2 air quality ratios, the county group was generally defined as the county containing

1 The results of this sensitivity run was also used in the method to quantify the impacts on design values of existing burn ban programs, as described in Section 3.3.1.1

.3.A -2 the nonattainment monitor plus the adjacent counties (i.e., counties that border the county with the nonattainment monitor). This multi

- county group approach was used for NOx and SO 2 in view of the more widespread impacts on (NH 4 ) 2 SO 4 , NH 4 NO 3 of local emissions reductions of NOx and SO 2 compared to direct PM 2.5

. Note that this same general approach was used in the design of the 2020 sensitivity simulations and in the 2020 control case (see Chapter 4). However, there were exceptions to this approach in certain areas in California where meteorological conditions affect the relationships between emissions and pollutant concentrations on a broader geographic scale within the South Coast Air Basin and within the San Joaquin Valley Air Ba sin. In the South Coast Air Basin, the county group for NO x and SO 2 emission reductions was defined to include all counties in the air basin (i.e., Orange,
Los Angeles, Riverside, and San Bernardino). For counties in the San Joaquin Valley Air Basin, the t otal NO x emission change that contributed to PM 2.5 changes at a monitor in a given county was estimated using the weighted contribution of emissions changes in area counties as derived from the 2020 SJV s imulations (see Appendix 3.A.1.2 for details).

In a djusting design values of the 2020 control case to meet different standard levels, Kings County and Tulare County in California were considered as a single area. 2 These counties share an east

- west border and experience similar air quality due to their rela tive positions in the San Joaquin Valley. Also, direct PM 2.5 emissions are much smaller in Kings than in Tulare, and the Kings County monitor is close to the Tulare border (Figure 3 .A -1 ) such that Tulare emissions have a large impact on design values in Ki ngs County.

2 To group these counties into a single area, the emission reductions needed for the Kings and Tulare monitors to meet the standard individually was first determined. Then the maximum of the individual emission reductions was selected and was used to adjust the design values at monitors in both counties using the air quality ratios.3.A -3 Figure 3.A -1. Location of Kings County Monitor Relative to Tulare County Border.

Air quality ratios for emissions of direct PM 2.5 , SO 2 and NO x we re calculated using information from the sensitivity simulations on the response of air quality at monitors to emission changes within the county groups. Below are the steps we followed in calculating the air quality ratios:

Step 1: Calculate the fractional change in speciated annual and 24

- hr design values for the 2020 sensitivity cases rela tive to the 2020 base case

. Speciated annual and quarterly 24

- hr RRFs were calculated for the 2020 NO x _PM 2.5 and 2020 SO 2 _RWC sensitivity simulations relative to the 2020 base case using MATS (Abt, 2010) for configurations where the 2020 base case was used as the reference case and the 2020 sensitivity cases were used as the control

cases

. The fractional change in the direct PM 2.5 , ( NH 4 ) 2 SO 4 and NH 4 NO 3 3 co mponents of the des ign value for the 2020 sensitivity cases relative to the 2020 base case was then calculated as (RRF

- 1) 4 f or a given monitoring site

. Step 2: Calculate the fractional change in emissions in the relevant county group for the 2020 sensitivity cases relative to the 2020 base case

. The fractional changes in emissions of

3 The (NH 4 ) 2 SO 4 and NH 4 NO 3 components are computed using the SO 4 , NO 3 , NH 4 and water fraction from MATS as described in EPA guidance (EPA, 2007) . The direct PM 2.5 design value component is computed by summing the elemental carbon, organic carbon and crustal portions of the design value

. 4 For daily air quality ratios, a representative RRF was calculated as a weighted average of the quarterly 24

- hr RRFs, where the weighting factors were the fractions of high 24

- hr concentration days that occurred in the quarter in the 2020 control case.3.A -4 direct PM 2.5 5 , SO 2 a nd NO x between the 2020 base case and 2020 sensitivity cases were determined for the county group relevant to a given monitor

. County emission groups for NO x and SO 2 for the monitors considered are list ed in Tables 3.A

- 1 and 3.A -2

. Step 3: Calculate the r atio of fractional change in speciated design value to fractional change in emissions for the sensitivity cases. The ratio of the fractional change in speciated design values (Step 1) to fractional change in county group emissions (Step 2) was calculated. Specifically, we calculated the fractional change in the direct PM 2.5 , ( NH 4 ) 2 SO 4 and NH 4 NO 3
co mponents of the annual and daily standard design value s per fractional change in direct
PM 2.5 , SO 2 and NO x emissions, respectively, in the county group between the 2020 sensitivity

cases and the 2020 base case

. Step 4 : Calculate the ratio of the speciated design values to emissions for the 2020 control case. Using air quality and emission data from the 2020 control case, we calculated t he ratio of direct PM 2.5 , ( NH 4 ) 2 SO 4 and NH 4 NO 3 to the emissions of direct PM 2.5 , SO 2 and NO x , respectively, in the relevant county group for the 2020 control case.

Step 5: Calculate air quality ratios using results of Steps 3 and 4. Air quality ratios were calculated by multiplying the ratios from Step 3 by the ratios from Step 4 for each 2020 sensitivity case, individually. The overall calculation of air quality ratios for PM 2.5 component specie i and emission specie j is given by equation 3

- 1, where DV i indicates the PM 2.5 component design value.

Air Quality Ratio= 1000 1 ×                

−

e ControlCas j i

yCase Sensitivit j j i Emission DV Emission Emission RRF ∆ (3.A. 1)

Air quality ratios give an estimate of how PM 2.5 design value components (μg/m 3 ) would change if 1000 tons of direct PM 2.5 , SO 2 and/or NO x emissions were reduced in the county group in which the monitor is located

. Annual air quality ratios that relate changes in the NH 4 NO 3 component of the design value to changes in NO x emi ssions are listed in Table 3.A -1 for counties in the South Coast Air Basin and San Joaquin Valley of California that received a mobile NO x emission adjustment equal to the change in mobile NO x emissions from the year 2020 to 2025. Annual and daily air quality ratios that relate changes in the (NH 4 ) 2 SO 4 component of the design value to changes in SO 2 emi ssions are listed in Table 3.A -2 for monitors in counties where air quality ratios were used in adjusting daily design values for remove the impact of

5 Direct PM 2.5 emissions are computed as the sum of emissions of elemental carbon, primary organic carbon, an d unspeciated PM 2.5 mass.3.A -5 inappropriate SO 2 c ontrols . Annual and daily air quality ratios that relate changes in the direct
PM 2.5 component of the design value to changes in direct PM 2.5 emi ssions are listed in Table 3.A -3 for monitors in counties with at least one monitor with an a nnual design value above 11 μg/m 3 in the 2020 base c ase

Table 3.A -1. Annual NO x Air Qual ity Ratios for Monitors in California Counties that Received a 2 025 Mobile NO x Emission Adjustment

Monitor ID FIPS Code State Name County Name Annual NO x Air Quality Ratio ( μ g /m 3 Change in NO 3 per 1000 tons NO x ) County Emission Group

60190008 6019 California Fresno 0.047 Weighted contributions from Kern, Kings/Tulare, Fresno/Madera, Merced, Stanislaus, San Joaquin, Alameda, and Sacramento
60195001 6019 California Fresno 0.046 Weighted contributions from Kern, Kings/Tulare, Fresno/Madera, Merced, Stanislaus, San Joaquin, Alameda, and Sacramento
60195025 6019 California Fresno 0.046 Weighted contributions from Kern, Kings/Tulare, Fresno/Madera, Merced, Stanislaus, San Joaquin, Alameda, and Sacramento
60290010 6029 California Kern 0.043 Weighted contributions from Kern, Kings/Tulare, Fresno/Madera, Merced, Stanislaus, San Joaquin, Alameda, and Sacramento
60290014 6029 California Kern 0.042 Weighted contributions from Kern, Kings/Tulare, Fresno/Madera, Merced, Stanislaus, San Joaquin, Alameda, and Sacramento
60290016 6029 California Kern 0.042 Weighted contributions from Kern, Kings/Tulare, Fresno/Madera, Merced, Stanislaus, San Joaquin, Alameda, and Sacramento (continued)3.A -6 T able 3.A -1. Annual NO x Air Qual ity Ratios for Monitors in California Counties that Received a 2 025 Mobile NO x Emission Adjustment (continued)

Monitor ID FIPS Code State Name County Name Annual NO x Air Quality Ratio ( μ g /m 3 Change in NO 3 per 1000 tons NO x ) County Emission Group

60310004 6031 California Kings 0.049 Weighted contributions from Kern, Kings/Tulare, Fresno/Madera, Merced, Stanislaus, San Joaquin, Alameda, and Sacramento
60370002 6037 California Los Angeles 0.007 Los Angeles, Orange, Riverside, San Bernardino

60371002 6037 California Los Angeles 0.006 Los Angeles, Orange, Riverside, San Bernardino

60371103 6037 California Los Angeles 0.006 Los Angeles, Orange, Riverside, San Bernardino

60371201 6037 California Los Angeles 0.003 Los Angeles, Orange, Riverside, San Bernardino

60371301 6037 California Los Angeles 0.005 Los Angeles, Orange, Riverside, San Bernardino

60371602 6037 California Los Angeles 0.007 Los Angeles, Orange, Riverside, San Bernardino
60372005 6037 California Los Angeles 0.005 Los Angeles, Orange, Riverside, San Bernardino

60374002 6037 California Los Angeles 0.004 Los Angeles, Orange, Riverside, San Bernardino
60374004 6037 California Los Angeles 0.004 Los Angeles, Orange, Riverside, San Bernardino

60379033 6037 California Los Angeles 0.003 Los Angeles, Orange, Riverside, San Bernardino
60472510 6047 California Merced 0.029 Los Angeles, Orange, Riverside, San Bernardino

60590007 6059 California Orange 0.006 Los Angeles, Orange, Riverside, San Bernardino

60592022 6059 California Orange 0.005 Los Angeles, Orange, Riverside, San Bernardino

60651003 6065 California Riverside 0.012 Los Angeles, Orange, Riverside, San Bernardino

60652002 6065 California Riverside 0.000 Los Angeles, Orange, Riverside, San Bernardino (continued)3.A -7 Table 3.A -1. Annual NO x Air Qual ity Ratios for Monitors in California Counties that Received a 2 025 Mobile NO x Emission Adjustment (continued)

Monitor ID FIPS Code State Name County Name Annual NO x Air Quality Ratio ( μ g /m 3 Change in NO 3 per 1000 tons NO x ) County Emission Group

60655001 6065 California Riverside 0.000 Los Angeles, Orange, Riverside, San Bernardino
60658001 6065 California Riverside 0.012 Los Angeles, Orange, Riverside, San Bernardino

60658005 6065 California Riverside 0.011 Los Angeles, Orange, Riverside, San Bernardino

60710025 6071 California San Bernardino 0.009 Los Angeles, Orange, Riverside, San Bernardino

60710306 6071 California San Bernardino 0.004 Los Angeles, Orange, Riverside, San Bernardino

60712002 6071 California San Bernardino 0.011 Los Angeles, Orange, Riverside, San Bernardino

60718001 6071 California San Bernardino 0.000 Los Angeles, Orange, Riverside, San Bernardino

60719004 6071 California San Bernardino 0.009 Los Angeles, Orange, Riverside, San Bernardino
60771002 6077 California San Joaquin 0.018 Weighted contributions from Kern, Kings/Tulare, Fresno/Madera, Merced, Stanislaus, San Joaquin, Alameda, and Sacramento
60990005 6099 California Stanislaus 0.041 Weighted contributions from Kern, Kings/Tulare, Fresno/Madera, Merced, Stanislaus, San Joaquin, Alameda, and Sacramento
61072002 6107 California Tulare 0.055 Weighted contributions from Kern, Kings/Tulare, Fresno/Madera, Merced, Stanislaus, San Joaquin, Alameda, and Sacramento3.A -8 Table 3.A -2. Annual and Daily SO 2 Ai r Quality Ratios for Monitors in Counties where Ratios

w ere Used in A djusting Daily Design Values to Remove the Impact of SO 2 Controls

Monitor ID FIPS Code State Name County Name Annual SO 2 Air Quality Ratio ( μ g /m 3 Change in SO 4 per 1000 tons SO 2 ) Daily SO 2 Air Quality Ratio ( μ g /m 3 Change in SO 4 per 1000 tons SO 2 ) County Emission Group

60990005 6099 California Stanislaus 0.123 0.468 Stanislaus, San Joaquin, Merced
420030008 42003 Pennsylvania Allegheny 0.026 0.084 Allegheny, Armstrong, Beaver, Butler, Washington, Westmoreland
420030064 42003 Pennsylvania Allegheny 0.028 0.151 Allegheny, Armstrong, Beaver, Butler, Washington, Westmoreland

420030067 42003 Pennsylvania Allegheny 0.017 0.050 Allegheny, Armstrong, Beaver, Butler, Washington, Westmoreland

420030095 42003 Pennsylvania Allegheny 0.021 0.066 Allegheny, Armstrong, Beaver, Butler, Washington, Westmoreland

420031008 42003 Pennsylvania Allegheny 0.019 0.033 Allegheny, Armstrong, Beaver, Butler, Washington, Westmoreland

420031301 42003 Pennsylvania Allegheny 0.027 0.062 Allegheny, Armstrong, Beaver, Butler, Washington, Westmoreland

420033007 42003 Pennsylvania Allegheny 0.021 0.068 Allegheny, Armstrong, Beaver, Butler, Washington, Westmoreland3.A -9 Table 3.A -3. Annual and Daily D irect PM 2.5 Air Quality Ratios for Monitors in Counties with at Least One Mo nitor having an Annual Design Value A bove 11 μ g/m 3 in the 2020 Base Case

Monitor ID FIPS Code State Name County Name Annual PM 2.5 Air Quality Ratio ( μ g /m 3 Change in Direct PM 2.5 per 1000 tons PM 2.5 ) Daily PM 2.5 Air Quality Ratio ( μ g /m 3 Change in Direct PM 2.5 per 1000 tons PM 2.5 )

10730023 1073 Alabama Jefferson 0.561 N/A

10731005 1073 Alabama Jefferson 0.257 N/A

10731009 1073 Alabama Jefferson 0.107 N/A

10731010 1073 Alabama Jefferson 0.221 N/A

10732003 1073 Alabama Jefferson 0.602 N/A

10732006 1073 Alabama Jefferson 0.383 N/A

10735002 1073 Alabama Jefferson 0.257 N/A

10735003 1073 Alabama Jefferson 0.195 N/A

60190008 6019 California Fresno 1.751 5.714
60195001 6019 California Fresno 1.534 4.825
60195025 6019 California Fresno 1.717 4.921
60250005 6025 California Imperial 1.801 6.594
60250007 6025 California Imperial 1.523 5.309
60251003 6025 California Imperial 1.612 5.270
60290010 6029 California Kern 1.341 4.344
60290014 6029 California Kern 1.531 4.475
60290016 6029 California Kern 1.579 4.892
60310004 6031 California Kings 1.277 4.919
603700 02 6037 California Los Angeles 0.367 N/A

60371002 6037 California Los Angeles 0.404 N/A

60371103 6037 California Los Angeles 0.404 N/A

60371201 6037 California Los Angeles 0.279 N/A

60371301 6037 California Los Angeles 0.419 N/A

60371602 6037 Ca lifornia Los Angeles 0.401 N/A

60372005 6037 California Los Angeles 0.322 N/A

60374002 6037 California Los Angeles 0.325 N/A

60374004 6037 California Los Angeles 0.299 N/A

60379033 6037 California Los Angeles 0.119 N/A (continued)3.A

- 10 Table 3.A -3. Annual and Daily D irect PM 2.5 Air Quality Ratios for Monitors in Counties with at Least One Mo nitor having an Annual Design Value A bove 11 μ g/m 3 in the 2020 Base Case (continued)

Monitor ID FIPS Code State Name County Name Annual PM 2.5 Air Quality Ratio ( μ g /m 3 Change in Direct PM 2.5 per 1000 tons PM 2.5 ) Daily PM 2.5 Air Quality Ratio ( μ g /m 3 Change in Direct PM 2.5 per 1000 tons PM 2.5 )

60472510 6047 California Merced 4.233 17.925
60631006 6063 California Plumas 2.428 N/A

60631009 6063 California Plumas 2.518 N/A

60651003 6065 California Riverside 1.620 3.223
60652002 6065 California Riverside 0.930 2.463
60655001 6065 California Riverside 0.797 1.885
60658001 6065 California Riverside 2.089 3.627
60658005 6065 California Riverside 2.459 5.039
60710025 6071 California San Bernardino 0.710 1.423

60710306 6071 California San Bernardino 0.305 0.439

60712002 6071 California San Bernardino 0.619 1.180

60718001 6071 California San Bernardino 0.353 1.674

60719004 6071 California San Bernardino 0.606 1.710

60771002 6077 California San Joaquin 1.789 8.486
60990005 6099 California Stanislaus 2.449 8.955
61072002 6107 California Tulare 1.875 4.222
160790017 16079 Idaho Shoshone 7.675 N/A

170310022 17031 Illinois Cook 0. 330 N /A

170310050 17031 Illinois Cook 0.298 N/A

170310052 17031 Illinois Cook 0.356 N/A

170310057 17031 Illinois Cook 0.324 N/A

170310076 17031 Illinois Cook 0.281 N/A

170312001 17031 Illinois Cook 0.256 N/A

170313301 17031 Il linois Cook 0.307 N/A (continued)3.A

- 11 Table 3.A -3. Annual and Daily D irect PM 2.5 Air Quality Ratios for Monitors in Counties with at Least One Mo nitor having an Annual Design Value A bove 11 μ g/m 3 in the 2020 Base Case (continued)

Monitor ID FIPS Code State Name County Name Annual PM 2.5 Air Quality Ratio ( μ g /m 3 Change in Direct PM 2.5 per 1000 tons PM 2.5 ) Daily PM 2.5 Air Quality Ratio ( μ g /m 3 Change in Direct PM 2.5 per 1000 tons PM 2.5 )

170314007 17031 Illinois Cook 0.200 N/A

170314201 17031 Illinois Cook 0.205 N/A

170316005 17031 Illinois Cook 0.374 N/A

171191007 17119 Illinois Madison 0.332 N/A

171192009 17119 Illinois Madison 0.443 N/A

171193007 17119 Illinois Madison 0.417 N/A

180890006 18089 Indiana Lake 0.419 N/A

180890027 18089 Indiana Lake 0.320 N/A

180890031 18089 Indiana Lake 0.367 N/A

180891003 18089 Indiana Lake 0.401 N/A

180892004 18089 Indiana Lake 0.404 N/A

180892010 18089 Indiana Lake 0.397 N/A

191630015 19163 Iowa Scott 1.106 N/A

191630018 19163 Iowa Scott 1.051 N/A

191630019 19163 Iowa Scott 1.492 N/A

261630001 26163 Michigan Wayne 0.404 N/A

261630015 26163 Michigan Wayne 0.502 N/A

261630016 26163 Michigan Wayne 0.423 N/ A
261630019 26163 Michigan Wayne 0.335 N/A

261630025 26163 Michigan Wayne 0.241 N/A

261630033 26163 Michigan Wayne 0.483 N/A

261630036 26163 Michigan Wayne 0.336 N/A

261630038 26163 Michigan Wayne 0.381 N/A

261630039 26163 Mi chigan Wayne 0.406 N/A

410350004 41035 Oregon Klamath 3.994 N/A

420030008 42003 Pennsylvania Allegheny 0.358 1.463
420030064 42003 Pennsylvania Allegheny 0.519 4.060
420030067 42003 Pennsylvania Allegheny 0.222 0.657 (continued)3.A

- 12 Table 3.A -3. Annual and Daily D irect PM 2.5 Air Quality Ratios for Monitors in Counties with at Least One Mo nitor having an Annual Design Value A bove 11 μ g/m 3 in the 2020 Base Case (continued)

Monitor ID FIPS Code State Name County Name Annual PM 2.5 Air Quality Ratio μ g /m 3 Change in Direct PM 2.5 per 1000 tons PM 2.5 ) Daily PM 2.5 Air Quality Ratio ( μ g /m 3 Change in Direct PM 2.5 per 1000 tons PM 2.5 )

420030095 42003 Pennsylvania Allegheny 0.263 0.931
420031008 42003 Pennsylvania Allegheny 0.172 0.645
420031301 42003 Pennsylvania Allegheny 0.405 1.409
420033007 42003 Pennsylvania Allegheny 0.397 1.752
481410037 48141 Texas El Paso 1.608 N/A

481410044 48141 Texas El Paso 2.209 N/A

482010058 48201 Texas Harris 0.188 N/A

482011035 48201 Texas Harris 0.408 N/A

550790010 55079 Wisconsin Milwaukee 1.566 N/A

550790026 55079 Wisconsin Milwaukee 1.602 N/A

550790043 55079 Wisconsin Milwaukee 1.674 N/A

550790059 55079 Wisconsin Milwaukee 1.869 N/A

550790099 55079 Wisconsin Milwaukee 1.689 N/A

551330027 55133 Wisconsin Waukesha 3.297 N/A

3.A.1.2 Estimating Area NO x Emission Contributions to NH 4 NO 3 PM 2.5 in the San Joaquin Valley We conducted 9 air quality model simulations for January 2020 for a domain centered

on California (Figure 3.A -2 ) that is a subset of the continental U.S. domain used for the 2020 base case modeli ng. One of the simulations reflected the 2020 base case emission scenario, and the other 8 simulations had NO x and direct PM 2.5 emissions reduction s relative to the 2020 base case in a one - or two

- county group that matched the emissions reductions in that group in the 2020 NO X _PM 2.5 sensitivity run. The purpose of these simulations was to estimate the impact of NO x emission reductions in a given area of California’s Central Valley on NH 4 NO 3 PM 2.5 in other areas of the valley. The month of January was selected for this analysis because high NH 4 NO 3 PM 2.5 episodes occur during winter months in the Central Valley.3.A

- 13 Figure 3.A -2 . California Modeling D omain for 12

- km Simulations Equation 3.A. 2 was used to estimate the fractional contribution of NO x emissions from one area of California’s Central Valley on another (i.e., W igrp,jgrp ).

W

igrp , jgrp = ∆C NO 3 , igrp / ∆ Emiss NOx , jgrp

ma x � ∆C NO 3 , igrp / ∆ Emiss NOx , jgrp � (3.A. 2)

where ∆ C

NO3,igrp is the c hange in average nitrate PM 2.5 concentration at a given monitor in the igrp county group between the simulati on with 2020 base case emissions and the simulation with NO x emissions reductions in the jgrp county group , and ∆ Emis s NOx,jgrp is the change in NO x emissions in the jgrp county group between the simulations with 2020 base case emissions and the simulation with NO x emissions reductions in the jgrp county group. Note that Equation 3.A.2 normalizes each ∆ concentration

- to

- ∆ e mission ratio for a given county group (numerator) by the maximum ∆ concentration

- to

- ∆ e mission ratio associated with that county group (denominator). The fraction of NO x emissions from a given county or county group that impacts
PM 2.5 nitrate in another county or county group as estimated according to Equation 3.A. 2 is given in Table 3.A -4.3.A

- 14 Table 3.A -4. Contribu tion Weighting Factors for NO x Emissions in Counties or Coun ty G roups in California’s Central Valley as Calculated A ccording to Equation 3.A.2

County or

County Group Weight of Kern Emissions Weight of Kings/ Tulare Emissions Weight of Fresno/ Madera Emissions Weight of Merced Emissions Weight of Stanislaus Emissions Weight of San Joaquin Emissions Weight of Sacramento

Emissions Weight of Alameda Emissions

Kern 1 0.94 0.6 0.47 0.4 0.35 0.29 0.07 Kings/Tulare 0.11 1 0.89 0.56 0.4 0.31 0.24 0.05 Fresno/ Madera 0.06 0.41 1 0.65 0.48 0.38 0.35 0.04

Merced 0.02 0.07 0.23 1 0.92 0.51 0.4 0.04
Stanislaus 0.01 0.03 0.05 0.37 1 0.56 0.39 0.06 San Joaquin 0.02 0.04 0.08 0.39 0.7 0.66 1 0.09
Sacramento 0.01 0.03 0.04 0.12 0.22 0.25 1 0.01
Alameda 0.04 0.14 0.13 0.24 0.59 1 0.66 0.033- 374-1 CHAPTER 4 CONTROL STRATEGIES

4.1 Synopsis As discussed in previous chapters, the EPA is revising the PM 2.5 annual standard to a level of 12 μg/m 3 and is retaining the 24

- hour standard of 35 μg/m 3 . P ursuant to Executive Order 12866 and 13563 as well as the guidelines of OMB Circular A

- 4.1 , the EPA assessed the incremental costs of hypothetical control strategies to attain the revised standard

. EPA also estimated the incremental costs of attaining a less stringent alternative annual standard of 13 μg/m 3 and a more stri ngent alternative annual standard of 11 μg/m 3

. This chapter documents the emission control measures we applied to simulate attainment with the revised and alternative PM 2.5 annual standards.

T he EPA analyzed the impact that additional emissions controls across numerous sectors would have on predicted ambient PM 2.5 concentrations incremental to an analytical baseline, which includes the current PM 2.5 standard of 15/35 μg/m 3 as well as other major rules such as
M ATS . Thus, the analysis for the revised and alternative standard s focuses specifically on incremental improvements beyond the current standard and other existing major rules , and uses control options that might be available to states for application by 2 020 . The hypothetical control strategies presented in this RIA represent illustrative options for emissions reductions
t hat achieve national attainment of the revised standard as well as the alternative standards

. The hypothetical control strategies are not recommendation s for how the revised PM 2.5 standard should be implemented, and states will make all final decisions regarding implementation strategies for the revised NAAQS.

The traditional analytical approach to a NAAQS RIA is to perform air quality modeling for:

 base case projections, then

 baseline attainment strategy for the current standard , then

 revised and alternative control strategies incremental to the baseline strategy

Each subsequent model run would build on what was learned from the previous run. Because of the short timeframe for this analysis, we were limited to performing air quality modeling for the base case in parallel with air quality modeling for a single control scenario, along with several limited sensitivity runs (see Chapter 3 for a detailed description of the air quality modeling runs performed for this analysis). The following steps were taken by the EPA to4-2 analyze the impacts and costs of the control scenario incremental to attainment of the current standard of 15/35 μg/m 3 and beyond other existing major rules:

1. Identify geographic areas in the U.S. likely to exceed the revised or alternative standards in the year 2020 using the b ase c ase projections

.
2. Develop a hypothetical control scenario for these areas and generate a control case 2020 emission s inventory . These control measures will serve as the basis for the “known” controls in this analysis.

3. Perform air quality modeling to assess the air quality impacts of the hypothetical control scenario (as mentioned, the analyses were performed in parallel with base case air quality modeling)

.
4. Adjust results to remove controls deemed as inappropriate for application to a specific source (e.g., controls likely to already be in place or controls estimated to have a ve ry high cost but little impact on emissions ).

5. Calculate the portion of the hypothetical control scenario control measure s that are attri b uted to meeting the current standard of 15/35 μg/m 3 . These are the known

“ analytical baseline” reductions. Estimate any additional emission reductions beyond the known controls that are needed to meet the current standard (15/35 μg/m 3 ). Costs of known controls incremental to (i.e., over and above) the analytical baseline reductions are attributed to the costs of meeting the revised and alternative standards.

6. Estimate the additional emission reductions incremental to the analytical baseline and beyond the known controls that are needed to meet the revised and/or alternative standards. These are referred to as emission reductions needed beyond known controls (i.e., extrapolated tons)

.
7. Calculate the total costs of reductions from emission reductions from known controls and emission reductions needed beyond known controls (extrapolated costs) incremental to the analytical baseline

. This chapter discusse s the steps listed above that were key to conducting the control strategy analysis for year 2020 for the revised and alternative standards

.
4.2 PM 2.5 Control Strategy Analysis

4.2.1 Identify Geographic Areas

The fi rst step in the analysis was to identify the geographic areas likely to exceed the revised and/or alternative standards in 2020 for the base case. For a detailed description of the process used to identify these geographic areas, see Chapter 3. For the rev ised standard, there4-3 we re seven counties projected to exceed the standard , all in California

. For the hypothetical control scenario for the revised standard we also identified and applied control measure s in one county adjacent to one of the projected exceedance counties in order to reduce transport emissions. For a description of the areas included in the final analysis of the revised and alternative standards, see Figures 4

- 1 through 4

- 4.

Figure 4- 1. Counties Projected to Exceed the Current PM 2.5 Standard (15/35) in 20204-4 Figure 4-2. Counties Projected to Exceed the 13 ug/m 3 Alternative Standard in the Analytical Baseline4-5 Figure 4-3. Counties Projected to Exceed the 12 ug/m 3 Revised Standard in the Analytical Baseline4-6 Figure 4-4. Counties Projected to Exceed the 11 ug/m 3 Alternative Standard in the Analytical Baseline

4.2.2 Develop ing the Control Scenario The U.S. EPA used monitoring and emission s inventory information, including monitoring speciation, to identify the pollutants t hat were the primary contributors to PM 2.5 exceedance s at the subject monitors. This allowed us to select a set of control measures tailored to the conditions for each area.

Non

- EGU point , nonpoint (area), and onroad mobile control measures were applied for the control strategy for demonstrating attainment of the current standard (15/35 μg/m 3 ). Non

EGU point and nonpoint control measures were applied for the revised and alternative standards control strategies . These controls were identified using the U.S. EPA’s Control4-7 Strategy Tool 1 (CoST). These controls are summarized in Appendix 4.A. Additional control measures were not applied to electric generating units (EGUs) due to the extensive nature of controls resulting from the inclusion of MATS.

Non point and onroad mobile source emissions data a re generated at the county level, and therefore controls for th ese emission s sector s were applied at the county level

. Non

- EGU point source controls are applied to individual point sources. Nonpoint source controls were applied to NO x , SO 2 , and PM 2.5 . The analysis for non

- EGUs applied NO x , SO 2 , and PM 2.5 controls to the following source categories: industrial boilers, commercial and institutional boilers, sulfuric acid plants (both stand a lone and at other facilities such as copper and lead smelters), primary metal plants (iron and steel mills, lead smelters), mineral products (primarily cement kilns), and petroleum refineries. Among the control measures applied were: wet FGD scrubbers and spray dryer absorbers (SDA) for SO 2 reductions, fabric filters for PM 2.5 reductions, and SCR and low NO x burners for NO x . Table 4- 1 lists the major controls applied to each sector.

Table 4-1. Controls Applied in the Revised and Alternative Standard Analysis

Sector/Pollutant Control Measure 15/35 13 12 11

Non

- EGU Point

PM 2.5 Diesel Particulate Filter X X X Dry Electrostatic Precipitator (ESP) X
Fabric Filters X X X
Venturi Scrubber X
Wet Electrostatic Precipitator (ESP)
SO 2 Coal Washing X Flue Gas Desulfurization (FGD) X Spray Dry Absorber Sulfur Recovery and/or Tail Gas Treatment

Wet FGDs X X
NO x Biosolid Injection X
Low NO x Burner (LNB) X
Low NO x Burner (LNB) + Selective Catalytic Reduction (SCR) X
Non

- Selective Catalytic Reduction (NSCR) X
OXY

- Firing X

1 See http://www.epa.gov/ttn/ecas/cost.htm for a description of CoST.4-8 Sector/Pollutant Control Measure 15/35 13 12 11 SCR + Steam Injection X Selective Catalytic Reduction (SCR) X
Selective Non

- Catalytic Reduction (SNCR) X

Non -P oint (Area)

PM 2.5 ESPs for Commercial Cooking X X X X
Low NO x Burners for Residential Natural Gas X Substitute chipping for open burning X X X Substitute landfilling for open burning X
SO 2 Chemical Additives to Waste X X Fuel Switching for Stationary Source Fuel Combustion X X Low Sulfur Home Heating Fuel X
NO x Low NO x Burners for Residential Natural Gas X
Water heater + Low NO x Burner Space Heaters X
On r oad Mobile
NO x Elimination of Long Duration Truck Idling (diesel trucks) X Continuous Inspection and Maintenance (gasoline cars) X
*Note that control measures indicated in the table for 13, 12, and 11 μg/m 3 levels are incremental to control

measures indicated for the 15/35 μg/m 3 analysis.
To more accurately depict available controls, the EPA employed a decision rule in which controls were not applied to any non

- EGU or area sources with 50 tons/year of emissi ons or less. Furthermore, controls were not applied to sources unless at least 5 tons/year of emission reductions were achieved. This decision rule is the same rule we employed for sources in the previous PM 2.5 NAAQS RIA completed in 2006. The reason for applying this decision rule is based on a finding that most point sources with emissions of this level or less had controls already in place. This decision rule helps fill gaps in information regarding existing controls on

non

- EGU sources. An additional co nstraint was applied in the control strategy selection that concerned cost of control. No control measures were applied that cost great er than $20,000/ton of emission reduction

. We did not include known controls at an annual cost of more than $20,000 per t on because either (i) the r emaining emissions sources were relatively small sources , or we believe they are already controlled, or (ii) the equations in CoST were not applicable to these remaining emissions sources. Note that there were potential controls available at an annual cost of more than $20,000 per ton for ten of the geographic areas included in the analysis. The application of these control strategies results in some, but not all, geographic areas reaching attainment for the alternative PM 2.5 stan dards.4-9 As stated above, because of the tight time constraints for this analysis, we performed air quality modeling for a single control scenario and then used the results of sensitivity analyses to identify the subset of controls and associated emission reductions in the control scenario that were needed to meet the current baseline . We then used a similar approach to determine the subset of additional controls and emission s reductions incremental to those applied in the analytical baseline that were needed to meet the revised and alternative standards.

4.2. 3 Identify Known Controls Needed to Meet the Analytical Baseline

The analytical baseline includes reductions already achieved as a result of national regulations, reductions expected prior to 20 20 from recently promulgated national regulations, adjustment s for expected emission reductions in two areas (South Coast and San Joaquin Valley, CA) not expected to reach attainment until 2025, and reductions from additional controls which the U.S. EPA esti mates need to be included to attain the current standard (15/35 ). Reductions achieved as a result of state and local agency regulations and voluntary programs are reflected to the extent that they are represented in emission inventory information submitted to the U.S. EPA by state and local agencies. Two steps were used to develop the analytical baseline . First, the reductions expected in national PM 2.5 concentrations from national rules promulgated prior to this analysis were considered (referred to as the base case)

. Below is a list of some of the major national rules reflected in the base case . Refer to Chapter 3, Section 3.2. 1.4 for a more detailed discussion of the rules reflected in the 2020 base case emissions inventory

.

 Light

- Duty Vehicle Tier 2 Rule ( U.S. EPA , 1999)

 Heavy Duty Diesel Rule ( U.S. EPA , 2000)

 Clean Air Nonroad Diesel Rule ( U.S. EPA , 2004)

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

 NO x Emission Standard for New Commercial Aircraft Engines ( U.S. EPA , 2005)

 Emissions Standards for Locomotives and Marine Compression

- Ignition Engines ( U.S. EPA , 2008)

 Control of Emissions for Nonroad Spark Ignition Engines and Equipment ( U.S. EPA, 2008)

 C3 O ceangoing Vessels (U.S. EPA, 2010 )4- 10  Hospital/Medical/Infectious Waste Incinerators: New Source Performance Standards and Emission Guidelines: Final Rule Amendments ( U.S. EPA, 2009)

 Reciprocating Internal Combustion Engines (RICE) NESHAP s (U.S. EPA, 2010)

 Me rcury and Air Toxics Standards (U.S. EPA, 2011)

 Cross

- State Air Pollution Rule (U.S. EPA, 2011) 2

Most areas of the U.S. will be required to demonstrate attainment with the new standards by 2020. As a result, for these areas, the correct baseline for estima ting the incremental emissions reductions that would be needed to attain the more protective standards is a baseline with emissions projected to 2020 and adjusted to reflect the additional emissions reductions that would be needed to attain the current 15/ 35 μg/m 3 standard. For two areas in Southern California (South Coast and San Joaquin), the degree of projected non

attainment with the revised annual standard of 12 μg/m 3 is high enough that those counties are not expected to have to demonstrate attainment with the new standards by 2020. Instead, those two areas will likely have until 2025 to demonstrate attainment with the revised annual standard of 12 μg/m 3

. As a result, for these two areas, the correct baseline for estimating the incremental emissions re ductions that would be needed to attain the more protective standards is a baseline with emissions projected to 2025 adjusted to reflect the additional emissions reductions that would be needed to attain the current 15/35 μg/m 3 standard. The following para graphs describe the steps we followed for this analysis.

This difference in attainment year is important because between 2020 and 2025, emissions from mobile sources in California are expected to be reduced due to continued fleet turn over from older, high er emitting vehicles to newer, lower emitting vehicles. These reductions in emissions will occur as a result of previous EPA and California rules for which costs (and benefits) have already been counted and thus will not be attributed to meeting the revise d or alternative standards 3

. Modeling of two separate years is time prohibitive and would result in two separate years of benefits and costs which would not provide a complete picture of the nationwide costs and benefits of just meeting the new standards in either 2020 or 2025 because of differences in the baselines between the two years. To provide the most reasonable and reliable estimates of

2 See Chapter 3, Section 3.2.1.4 for a discussion of the role CSAPR plays in the PM 2.5 RIA. 3 See Chapter 3, Section 3.2.1.4 for a listing of the EPA and California rules reflected in the 15/35 analysis.4- 11 costs and benefits of full attainment for the nation, we constructed an analytical 2020 baseline for estimating t he costs and benefits of attaining the selected annual standard of 12 μg/m 3 and alternative standards of 13 μg/m 3 and 11 μg/m 3 with the following characteristics: ( 1) reflects “on the books” regulations as implemented through 2020 for all nonattainment cou nties, and ( 2) mobile source emissions reductions expected to occur between 2020 and 2025 for California’s South Coast and San Joaquin areas, which are likely to not demonstrate attainment until 2025. Essentially, we are adjusting emissions for the two are as in California to reflect future emissions reductions that they will achieve prior to their 2025 attainment date. This allows us to generate costs and benefits of full attainment without overstating the costs and benefits in those two areas which would occur if forced to apply costly emissions reductions in 2020 in areas that would not have to be incurred until 2025.

The 2020 analytical baseline for this analysis presents one scenario of future year air quality based upon specific control measures, promul gated federal rules such as MATS, years of air quality monitoring and emissions data. This analysis presents one illustrative strategy relying on the identified federal measures and other strategies that states may employ. States may ultimately employ othe r strategies and/or other federal rules may be adopted that would also help in achieving attainment. The number of counties that will be part of the designations process may be different than the number of counties projected to exceed as part of this analy sis.

A map of the country is presented in Figure 4- 1 which show s the counties projected to exceed the current PM 2.5 standard of 15/35 μg/m 3 in the year 2020. Control measures were identified in the control scenario run that were needed for these counties in the analytical baseline analysis to meet the current PM 2.5 standard. In addition, control measures were applied to five California counties adjacent to exceed a nce counties in order to address transport coming from these adjacent counties.

The additional known controls included in the analytical baseline to simulate attainment with the current PM 2.5 NAAQS are listed in Table 4- 1; details regarding the individual controls are provided in Appendix 4.A. Controls were applied to directly emitted PM 2 .5 and the PM 2.5 precursors of NO x and SO 2 given that nitrate, sulfate , and primary PM 2.5 species usually dominate measured PM 2.5 based on speciation data measured at the Chemical Speciation Network (CSN) sites. Control measures that directly reduced emissions of PM 2.5 in proximity to the exceeding monitors were determined to be most effective at bringing areas into attainment , with NO x and SO 2 controls supplementing the PM 2.5 controls depending upon the monitor speciation data. PM 2.5 control measures were applied in the county containing the4- 12 exceeding monitor for the non

- EGU point and area source emissions. If additional emission s control was needed, SO 2 and NO x control measures were applied within the county exceeding and in a small number of cases, a n adjacent county or counties.

For the analytical baseline, there were several geographic areas that did not reach attainment with known controls. For these geographic areas, we estimated the additional emission reductions needed beyond identified known c ontrols for PM 2.5 to attain the standard.

4.2. 4 Identify Known Controls Needed to Meet the Revised and Alternative Standard s
After identifying the known controls in the control scenario that were needed to meet the analytical baseline , additional known controls needed to meet the revised and alternative standards were identifi ed

. The EPA used air quality modeling results to determine whether the control scenario was sufficient to meet the revised and alternative standards for each geographic area. Where the control scenario modeling resulted in design value reductions below the level needed for the revised or alternative standards for specific geographic areas , county

- specific ratios of air quality response to emission reductions were used to determ ine the subset of controls that were needed to attain the standard . Where it was determined that the control scenario was not sufficient in attaining the standard , these same response factors were used to calculate the amount of additional emission reducti ons beyond known controls needed to meet the standard. For the revised and alternative control strategy analysis , known controls for two sectors were used: non

- EGU point and area sources. Onroad mobile source controls were not used in the revised and alter native standards analysis because they were applied previously in the analytical baseline analysis where they were deemed to be most cost effective.

In th e revised and alternative standards analysis, PM 2.5 controls were sought first because they were gener ally more cost

- effective. If it was determined that additional control s

were needed , SO 2 and NO x control measure s were selected depending on the chemistry of each specific geographic area

.
It should be noted that while PM 2.5 controls were applied only within the counties with monitors projected to exceed the alternative standard being analyzed , SO 2 and NO x controls were applied in the exceeding county as well as a small number of adjacent counties because of the transport of NO x and SO 2 across counties. Table 4-2 shows the number of exceeding counties and the number of adjacent counties to which controls were applied for the revised and alternative standards

. Table 4-3 shows the emission reductions from known controls for the revised and alternative standards analyzed.4- 13 Table 4- 2. Number of Counties with Exceedances and Number of Additional Counties Where Reductions Were Applied

Revised / Alternative Standard Number of Counties with Exceedances Number of Additional Counties Where Reductions Were Applied

13 2 1

12 7 1

11 23 1

Table 4-3. Emission Reductions from Known Controls for the Revised and Alternative Standards a

Emission Reductions in 2020 (annual tons/year) Revised/ Alternative

Standard Region PM 2.5 SO 2 NO x 13 East — — —
West — — —
CA 53 — — Total 53 — —
12 East — — —
West — — —
CA 803 — — Total 803 — —
11 East 3,400 21,000 9

West 75 43 —
CA 930 — — Total 4,400 21 ,000 9

a Estimates are rounded to two significant figures.

4.2. 5 Identify Emission Reductions Beyond Known Controls Needed to Meet the Revised and Alternative Standards There were several areas where known controls did not achieve enough emission reductions to attain the revised or alternative standards in 2020. To complete the analysis, the EPA then estimated the addi tional emission reductions beyond known controls needed to reach attainment. For information on the methodology used to develop those estimates , see Chapter4- 14 3 , Section 3.3. 2

. Table 4-4 shows the emission reductions needed from un known controls for the alte rnative standards analyzed.

Table 4-4. Emission Reductions Needed Beyond Known Controls for the Revised and Alternative Standards a

Emission Reductions in 2020 (annual tons/year)
Revised / Alternative

Standard Region PM 2.5 SO 2 NO x 13 East — — — West — — — CA 674 — — Total 674 — — 12 East — — — West — — — CA 3,190 — — Total 3,190 — —
11 East 4,800 — — West 86 — — CA 9,700 — — Total 15,000 — —

a Estimates are rounded to two significant figures.

4.3 Limitations and Uncertainties

The 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

r easonable modeling framework for analyzing the cost, emission s changes, and other impacts of regulatory controls. However, the estimates of emission s reductions associated with our control strategies above are subject to important limitations and uncertainties. We outline, and qualitatively assess the impact of, those limitations and uncertainties that are most significant.

A number of limitations and uncertainties are associated with the analysis of emission control measures are listed in Table 4 -5.4- 15 Tabl e 4-5. Summary of Qualitative Uncertainty for Elements of Control Strategies

Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact

on Monetized

Costs a Degree of Confidence in Our Analytical Approach b Ability to Assess Uncertainty c
Uncertainties Associated with PM Concentration Changes
Projections of future levels of emissions and emissions reductions necessary to achieve the NAAQS Both d Medium Medium Tier 1

Responsiveness of air quality model to changes in precursor emissions from control scenarios Both Medium

high Medium Tier 1

Air quality model chemistry, particularly for formation of ambient nitrate concentrations Both Medium High Tier 1

Post

- processing of air quality modeled concentrations to estimate future

- year PM 2.5 design value and spatial fields of PM 2.5 concentrations Both High High Tier 1

Uncertainties Associated with Control Strategy Development
Control Technology Data

 Technologies applied may not reflect most current emerging devices that may be available in future years

 Control efficiency data is dependent upon equipment being well maintained.

 Area source controls assume a constant estimate of emission reductions, despite variability in extent and scale of application. Both Medium

high High Tier 2

Control Strategy Development

 States may develop different control strategies than the ones illustrated

 Lack of data on analytical baseline controls from current SIPs

 Timing of control strategies may be different than envisioned in RIA

 Controls are applied within the county with the exceed ing monitor. In some cases, additional known controls are also applied in adjacent contributing counties.

 Emissions growth and control from new sources locating in these analysis areas is not included. Both Medium

high Medium

high Tier 04- 16 Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact

on Monetized

Costs a Degree of Confidence in Our Analytical Approach b Ability to Assess Uncertainty c
Technological Change

 Emission reductions do not reflect potential effects of technological change that may be available in future years

 Effects of “ learning by doing ” are no accounted for in the emission reduction estimates Likely over

estimate Medium

high Low Tier 0

Emission Reductions from Unidentified Controls

 emission control cut points for each pollutant Both High Low Tier 1

a Magnitude of Impact High

— If error could influence the total costs by more than 25% Medium

— If error could influence the total costs by 5%

– 25% Low

— If error could influence the total costs by less than 5%
b Degree of Confidence in Our Analytic Approach 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 Low

— Limited data exists to support the selected approach

c Ability to Assess Uncertainty (using WHO Uncertainty Framework) Tier 0

— Screening level, generic qualitative characterization Tier 1

— Scenario

- specific qu alitative characterization Tier 2

— Scenario

- specific sensitivity analysis Tier 3

— Scenario

- specific probabilistic assessment of individual and combined uncertainty
d Future expected emissions are difficult to predict because they depend on many independent f actors. Emission

inventories are aggregated from many spatially and technically diverse sources of emissions, so simplifying assumptions are necessary to make estimating the future tractable.

4. 4 References U.S. Environmental Protection Agency ( U.S. EPA ). 1999

. Control of Air Pollution from New Motor Vehicles : Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements

. Office of Transportation and Air Quality. Available at http ://www.epa.gov/tier2/frm/fr

- t2reg.pdf

. U.S. Environmental Protection Agency ( U.S. EPA ). 2000. Heavy

- Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements . Office of Transportation and Air Quality. Available at http://www.epa.gov/otaq/highway

diesel/index.htm

.4- 17 U.S. Environmental Protection Agency (U.S. EPA). 2004. Control of Emissions of Air Pollution from Nonroad Diesel Engines and Fuel. Office of Transportation and Air Quality. Available at http://www.regulations.gov/search/Regs/contentStreamer?objectId=09000064800be20 3&disposit ion=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 ). 2005b

. Regional Haze R egulations and Guidelines for Best Available Retrofit Technology Determinations. Office of Air Quality P lanning 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). 2005. Clean Air Interstate Rule. Office of Atmospheric Programs , Washington , D.C. Available at http://edocket.access.gpo.gov/2005/pdf/05

- 5723.pdf

. U.S. Environmental Protection Agency ( U.S. EPA ). 2007. Guidance on the Use of Models and other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at http://www.epa.gov/ttn/scram/guidance/guide/final

- 03

- pm

- rh

guidance.pdf

.
U.S. Environmenta l Protection Agency ( U.S. EPA ). 2008. Control of Emissions for Nonroad Spark Ignition Engines and Equipment. Office of Transportation and Air Quality. Available at http://www .epa.gov/otaq/regs/nonroad/marinesi

- equipld/bondfrm.pdf

. U.S. Environmental Protection Agency ( U.S. EPA ).2008. Emissions Standards for Locomotives and Marin e Compression

- Ignition Engines. Office of Transportation and Air Quality. Available at http://www.epa.gov/nonroad/420f08004.pdf

.
U .S. Environmental Protection Agency (U.S. EPA). 2009. Standards of Performance for New Stationary Sources and Emission Guidelines for Existing Sources: Hospit al/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. Envir onmental Protection Agency ( U.S. EPA ). 2010

. Control of Emissions from New Marine Compression

- Ignition Engines at or Above 30 Liters per Cylinder

. Office of Transportation and Air Quality. Available at http://www.regulations.gov/search/Regs/contentStreamer?objectId=0900006480ae43a 6&disposition=attachment&contentType=pdf

.4- 18 U.S. Environmental Protection Agency ( U.S. EPA ). 2011. 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). 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). 2011. Mercury and Air Toxics Standards. Office of Atmospheric Programs , Washington, D.C. Available at www.gpo.gov/f dsys/pkg/FR

- 2012

- 02

- 16/pdf/2012

- 806.pdf

.4.A -1 APPENDIX 4.A ADDITIONAL CONTROL S TRATEGY INFORMATION

4.A.1 Control Measures for Stationary Sources This appendix describes measures that were employed in this analysis to illustrate a hypothetical scenario for controlling emissions of PM and precursors from non

- EGU point and nonpoint ( area ) source categories to attain the baseline or to attain the revis ed or alternative standards for PM 2.5 . Most of the control measures available are add

- on technologies but some other technologies and practices that are not add

- on in nature can reduce emissions of PM and PM precursors.

4.A.1.1 PM Emissions Control Technol ogies 1

This section summarizes control measures focused on reduction of PM 2.5 from non

- EGU point and nonpoint sources. However, it should be noted that PM 10 will also be reduced by these measures. The amount of PM 10 reduction varies by the fraction of PM 10 in the inlet stream to the control measure and the specific design of the measure.

4.A.1.1.1 PM Control Measures for Non

- EGU Point Sources
Most control measures for non

- EGU point sources are add

- on technologies. These technologies include: fabric filters (baghouses), ESPs, and wet PM scrubbers. Fabric filters collect particles with sizes ranging from below 1 micrometer to several hundred micrometers in diameter at efficiencies in excess of 99%, and this device is used where high

- efficiency particle collect ion is required. A fabric filter unit consists of one or more isolated compartments containing rows of fabric bags in the form of round, flat, or shaped tubes, or pleated cartridges. Particle

- laden gas passes up (usually) along the surface of the bags then radially through the fabric. Particles are retained on the upstream face of the bags, and the cleaned gas stream is vented to the atmosphere. The filter is operated cyclically, alternating between relatively long periods of filtering and short periods of cleaning. Dust that accumulates on the bags is removed from the fabric surface when cleaning and deposited in a hopper for subsequent disposal.

ESPs use electrical forces to move particles out of a flowing gas stream and onto collector plates. The particles are given an electrical charge by forcing them to pass through a corona, a region in which gaseous ions flow. The electrical field that forces the charged particles to the plates comes from electrodes maintained at high voltage in the center of the flow lane.

1 The descriptions of add

- on technologies throughout this section are taken from the EPA Air Pollut ion Control Cost Manual, Sixth Edition. This is found on the Internet at http://epa.gov/ttn/catc/products.html#cccinfo

.4.A -2 Once particles are on the collector plates, they must be removed without re

- entraining them into the gas stream. This is usually accomplished by rapping the plates mechanically which loosens the collected particles from the collector plates, allowing the particles to slide down into a hopper from which they are evacuated. This removal of collected particles is typical of a

“ dry ” ESP. A “ wet ” ESP operates by having a water flow applied intermittently or continuously to wash away the collected particles for disposal. The advantage of wet ESPs is that there are no problems with rapping re

- entrainment or with “ back coronas ” (unintended injection of positively charged ions which reduces the charge on particles and lowers the collection efficiency). The disa dvantage is that the collected slurry must be handled more carefully than a dry product, adding to the expense of disposal. ESPs capture particles with sizes ranging from below 1 micrometer to several hundred micrometers in diameter at efficiencies from 95 to up to 99% and higher.

Wet PM scrubbers remove PM and acid gases from waste gas streams of stationary point sources. The pollutants are removed primarily through the impaction, diffusion, interception and/or absorption of the pollutant onto droplets of liquid. The liquid containing the pollutant is then collected for disposal. Collection efficiencies for wet scrubbers vary by scrubber type, and with the PM size distribution of the waste gas stream. In general, collection efficiency decreases as the PM si ze decreases. Collection efficiencies range from in excess of 99% for venturi scrubbers to 40% to 60% for simple spray towers. Wet scrubbers are generally smaller and more compact than fabric filters or ESPs, and have lower capital cost and comparable oper ation and maintenance (O&M) costs. Wet scrubbers, however, operate with a higher pressure drop than either fabric filters or ESPs, thus leading to higher energy costs. In addition, they are limited to lower waste gas flow rates and operating temperatures than fabric filters or ESPs, and also generate sludge that requires additional treatment or disposal. This RIA only applies wet scrubbers to fluid catalytic cracking units (FCCUs) at petroleum refineries.

In addition, we also examined additional add

- on cont rol measures specifically for steel mills. Virtually all steel mills have some type of PM control measure, but there is additional equipment that in many cases could be installed to further reduce emissions. Capture hoods that route PM emissions from a blast furnace casthouse to a fabric filter can provide 80% to 90% additional emission reductions from a steel mill. Other capture and control systems at blast oxygen furnaces (BOFs) can also provide 80% to 90% additional reductions.4.A -3 Table 4.A

- 1 lists some of these technologies. For more information on these technologies, refer to the EPA Air Pollution Control Cost Manual. 2

Table 4.A

- 1. Example PM Control Measures for Non

- EGU Point Source Categories

Control Measure Sector(s) to which Control Measure Can Apply C ontrol Efficiency (percent) Average Annualized

Cost/Ton

Fabric Filters a Industrial Boilers, Iron and Steel Mills, Pulp and Paper Mills 98 to 99.9 $2,000

– $100,000

ESPs

— wet or dry a Industrial Boilers, Iron and Steel Mills, Pulp and Paper Mills 95 to 99.9 $1,000

– $20,000

Wet Scrubbers Industrial Boilers, Iron and Steel Mills 40 to 99 $750

– $2,800

Secondary Capture and Control Systems

— Capture Hoods for Blast Oxygen Furnaces Coke Ovens 80 to 90 $5,000

CEM Upgrade and Increased Monitoring Frequency Non

- EGU s with an ESP 5 to 7 $600

– $5,000

a CoST contains equations to estimate capital and annualized costs for ESP and FF installation and operation. The

average annualized cost/ton estimates presented here for these control measures are outputs from our modeling, not inputs. They also reflect ap plications of control where there is no PM control measure currently operating except if the control measure is an upgrade (e.g., ESP upgrades).

4.A.1.1.2 PM Control Measures for Nonpoint Sources Specific controls exist for a number of stationary nonpoint sources. Nonpoint source PM controls at stationary sources include:

 catalytic oxidizers on conveyorized charbroilers at restaurants (up to 80% reduction of PM), and

 diesel particulate filters, applied to existing diesel

- fueled compression

- ignition (C

- I)

en gines ( up to a 90reduction in fine PM ).

D iesel particulate filters are being applied to new C

- I engines as part of a NSPS that was implemented beginning in 2006.

2 Please refer to Footnote 1.4.A -4 Table 4.A

- 2. Example PM Control Measures for Nonpoint Sources a

Control Measures Sectors to whi ch These Control Measures Can Apply Control Efficiency (percent) Average Annualized

Cost/ton
Catalytic oxidizers for conveyorized charbroilers Restaurants 83 $1,300 Replace open burning of wood waste with chipping for landfill disposal Residential waste

sources Near 100 $3,500

a The estimates for these control measures reflect applications of control where there is no PM nonpoint source

control measure currently operating. Also, the control efficiency is for total PM, and thus accounts for PM 10 and PM 2.5 . Data for these measures is available in the CoST Control Measures Documentation Report at http://www.epa.gov/ttn/ecas/models/CoST_CMDB_Document_2010

- 06

- 09.pdf

.
4.A.1.2 SO 2 Control Measures

4.A.1.2.1 SO 2 Control Measures for Non

- EGU Point Sources
The SO 2 emission control measures used in this analysis are similar to those used in the
PM 2.5 RIA prepared about four years ago. Flue gas desulfurization (FGD) scrubbers can ach ieve 95

– 98% control of SO 2 for Non

- EGU point sources and for utility boilers. Spray dryer absorbers (SDA) are another commonly employed technology, and SDA can achieve up to 90% or more control of SO 2 . For specific source categories, other types of control technologies are available that are more specific to the sources controlled. Table 4.A

- 3 lists some of these technologies. For more information on these technologies, please refer to the CoST control measures documentation report. 3

3 For a complete description of the con trol technologies used in CoST, please refer to the report at http://www.epa.gov/ttn/ecas/models/CoST_CMDB_Document_2010

- 06

- 09.pdf

.4.A -5 Table 4.A

- 3. Example SO 2 Control Measures for Non

- EGU Point a

Control Measure Sectors to Which These Control Measures Can Be Applied Control Efficiency (percent) Average Annualized Cost/Ton (2006$)
Wet and Dry FGD scrubbers and SDA ICI boilers

— all fuel types, kraft pulp mills, Mineral Products (e.g., Portland cement plants (all fuel types), primary metal plants, petroleum refineries 95

— FGD scrubbers, 90

— for SDA $800

– $8,000

— FGD $900

– $7,000

— SDA

Increase percentage sulfur conversion to meet sulfuric acid NSPS (99.7% reduction) Sulfur recovery plants 75

– 95 $4,000

Sulfur recovery and/or tail gas treatment Sulfuric Acid Plants 95

– 98 $1,000

– $4,000

Cesium promoted catalyst Sulfuric Acid Plants with Double

Absorption process 50% $1,000

a Sources: CoST control measures documentation report, May 2008, NESCAUM Report on Applicability of NO x ,

SO 2 , and PM Control Measures to Industrial Boilers, November 2008 available at http://www.nescaum.org/do cuments/ici

- boilers

- 20081118

- final.pdf , a nd Comprehensive Industry Document on Sulphuric Acid Plant, Govt. of India Central Pollution Control Board, May 2007. The estimates for these control measures reflect applications of control where there is no SO 2 control measure currently operating except for the Cesium promoted catalyst.

4.A.1.2.2 SO 2 Control Technology for Nonpoint Sources Fuel switching from high to low

- sulfur fuels is the predominant control measure available for SO 2 nonpoint sources. For home heating oil users, our analyses included switching from a high

- sulfur oil (approximately 2,500 parts per million (ppm) sulfur content) to a low

sulfur oil (approximately 500 ppm sulfur). A similar control measure is available for oil

- fired industrial boilers. For more information on these measures, please refer to the CoST control measures documentation report. 4

4.A.1.3 NO x Emissions Control Measures

4.A.1.3.1 NO x Control Measures for Non

- EGU Point Sources This section describes available measures for controlling emissions of NO x from non

- EGU point sources. In general, low

- NO x burners (LNB) are often applied as a control technology for industrial boilers and for some other non

- EGU sources because of their wide applicability and
cost

- effectiveness. While all controls presented in this analysis are considered generally

4 Please refer to Footnote 3.4.A -6 technically feasible for each class of sources, source

- specific cases may exist where a control technology is in fact not technically feasible.

Several types of NO x control technologies exist fo r non

- EGU sources: selective catalytic reduction (SCR), selective noncatalytic reduction (SNCR), natural gas reburn (NGR), coal reburn, and low

- NO x burners. The two control measures chosen most often were LNB and SCR because of their breadth of application. 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 are not feasible 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 source categories covered in this RIA include petroleum refineries, kraft pulp mills, cement kilns, stationary interna l combustion engines, glass manufacturing, combustion turbines, and incinerators. NO x control measures available for petroleum refineries, particularly process heaters at these plants, include LNB, SNCR, FGR, and SCR along with combinations of these techno logies. NO x control measures available for kraft pulp mills include those available to industrial boilers, namely LNB, SCR, SNCR, along with water injection (WI). NO x control measures available for cement kilns include those available to industrial boilers, namely LNB, SCR, and SNCR. In addition, mid

- kiln firing (MKF), ammonia

based SNCR, and biosolids injection can be used on cement kilns where appropriate. 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 emissions at such plants. LNB, SCR, and SCR combined with steam injection (SI) are available measures for combustion turbines. Finally, SNCR is an available c ontrol technology at incinerators. Table 4.A

- 4 lists typical examples of the control measures available for these categories. For more information on these measures, please refer to the CoST control measures documentation report. 5

5 Please refer to Footnote 3.4.A -7 Table 4.A

- 4. Example NO x Control Measures for Non

- EGU Source Categories a

Control Measures Sectors to Which These Control Measures Apply Control Efficiency (percent) Average Annualized Cost/ton

LNB Industrial boilers

— all fuel types, Petroleum refineries, Cement manufacturing, Pulp and Paper mills 25 to 50% $200 to $1,000

LNB + FGR Petroleum refineries 55 $4,000 SNCR (urea

- based or not) Industrial boilers

— all fuel types, Petroleum refineries, Cement manufacturing, pulp and paper mills, incinerators 45 to 75 $1,000 to $2,000

SCR Industrial boilers

— all fuel types, Petroleum refineries, Cement manufacturing, pulp and paper mills, Combustion turbines 80 to 90 $2,000 to 7,000

OXY

- Firing Glass manufacturing 85 $2,500 to 6,000

NSCR Stationary internal combustion engines 90 500

MKF Cement manufacturing

— dry 25 −$460 to 720
Biosolids Injection Cement manufacturing

— dry 23 $300

SCR + SI Industrial boilers

— all fuel types 95 $2,700

a Source: CoST control measures documentation report (June 2010). Note: a negative sign indicates a cost savings

from application of a control measure. The estimates for these control measures reflect applications of control where there is no NO x control measure currently operating except for post

- combustion controls such as SCR and SNCR. For these measu res, the costs presume that a NO x combustion control (such as LNB) is already operating on the unit to which the SCR or SNCR is applied.5-1 CHAPTER 5 HUMAN HEALTH BENEFIT S ANALYSIS APPROACH AND RESULTS

5.1 Synopsis This chapter present s the estimated human health benefits for the revised National Ambient Air Quality Standards (NAAQS) for particulate matter (PM) . In this chapter , we quantify the health

- related benefits of the fine particulate matter ( PM 2.5 )- related air quality improvements resulting from the illustrative emission control scenarios that reduce emissions of directly emitt ed particles and precursor pollutants including SO 2 and NO x to reach alternative
PM 2.5 NAAQS levels in 2020

. 1

T hese benefits are relative to an analytical baseline reflecting nationwide attainment of the current primary PM 2.5 standards (i.e., annual standard of 15 μg/m 3 and 24

- hour standard of 35 μg/m 3 ) that includes promulgated national regulations and illustrative emission controls to simulate attainment with 15/35 as well as a NO x emission adjustment to reflect expected reductions in mobile NO x emissions between 2020 and 2025

. W e project PM 2.5 levels in certain

areas that would exceed the revised annual standard of 12 μg/m 3 as well as alternative annual standards of 13 and 11 μg/m 3 after simulate d attainment with 15/35 in the analytical baseline

. Table 5

- 1 summarizes the total monetized benefits of the revised and alternative PM 2.5 standards in 2020. These estimates reflect the sum of the economic value of estimated PM 2.5 mortality impacts identified and the value of all morbidity impacts.

The estimated benefits for the revised and alternative standards are in addition to the substantial benefits estimated for several recent implementation rules (U.S. EPA, 2009a, 2011c, 20 11d, 2011e). Rules such as the Mercury and Air Toxics Standard (MATS) and other emission reductions will have substantially reduced ambient PM 2.5 concentrations by 2020 in the East, such that no additional controls would be needed in the East for the revised annual standard of 12 μg/m 3 . Thus, all of the estimated benefits occur in California because this is the only state that needs additional air quality improvement beyond the analytical baseline after accounting for air quality improvements from recent ru les. Because of the national focus of many of the inputs to the benefits model, benefits estimated for any particular location have greater uncertainty than benefits occurring nationally. Compared with the proposal benefits, the estimated benefits for the revised standard are about double due to a combination of updates

1 The estimates in this chapter reflect incremental emissions reductions from an analytical baseline that gives an

adjustment to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.5-2 to the analytic baseline (See Chapter 3). We believe that all of these updates improve our estimate of benefits for the revised annual primary standard.

Table 5- 1. Estimated Monetized Benefits of the of Revised and Alternative Annual PM 2.5 Standards in 2020 Incremental to the Analytical Baseline ( billions of 2010$ ) a

Benefits Estimate 13 μg/m 3 12 μg/m 3 11 μg/m 3
Economic value of avoided PM 2.5

- related morbidities and premature deaths using PM 2.5 mortality estimate from Krewski et al. (2009)

3% discount rate $1.3 + B $ 4.0 +B $13 + B 7% discount rate $1.2 + B $3.6 +B $12 + B Economic value of avoided PM 2.5

- related morbidities and premature deaths using PM 2.5 mortality estimate from Lepeule et al. (2012)

3% discount rate $2.9 + B $ 9.1 +B $ 29 + B 7% discount rate $2.6 + B $ 8.2 +B $ 26 + B

a Rounded to two significant figures. Avoided premature deaths account for over 98 % of monetized benefits here ,

which are discount ed over the SAB

- recommended 20

- year segmented lag. It was not all possible to quantify all benefits due to data limitations in this analysis. “B” is the sum of all unquantified health benefits and welfare co

benefits

. As we describe in detail below, we estimate PM

- related health impacts using concentration

- response relationships drawn from the epidemiological literature. In general, we are more confident in the magnitude of the risks we estimate from simulated PM 2.5 concentrations that coincide wit h the bulk of the observed PM concentrations in the epidemiological studies that are used in th e benefits estimates

. Likewise, we are less confiden t in the risk we estimate from simulated PM 2.5 concentrations that fall below the bulk of the observed data in these studies. As noted in the preamble to the rule, the range from the 25 th to 10 th percentiles of the air quality data in the epidemiology studies is a reasonable range below which we start to have appreciably less confidence in the magnitude of the a ssociations observed in the epidemiological studies. M ost of the estimated avoided premature deaths occur at or above the lowest measured PM 2.5 concentration in the two studies that are used to estimate mortality benefits.

In addition to PM 2.5 benefits, implementation of emissions controls to reach some of the alternative PM 2.5 standards would reduce other ambient pollutants , such as SO 2 , NO 2 , and

ozone

. However, because the method used in this analysis to simulate attainment does not account fo r changes in ambient concentrations of other pollutants, we were not able to quantify the co

- benefits of reduced exposure to other pollutants

. In addition, due to data and methodology limitations, we were unable to estimate additional health benefits associated with exposure to PM 2.5 or the additional co

- benefits from improvements in welfare effects (i.e.,5-3 non

- health effects) associated with emission reductions to attain the primary standard , such as visibility

. We describe the unquantified health ben efits in this chapter and the unquantified welfare co

- benefits in Chapter 6

.
5.2 Overview This chapter contains a subset of the estimated health benefits of the revised and alternative PM 2.5 standards in 2020 that the EPA was able to quantify, given the available resources and methods. The analysis in this chapter aims to characterize the benefits of the air quality changes resulting from the implementation of new PM standard s by answering two key questions:
1. What are the health effects of changes in ambient particulate matter (PM 2.5 ) resulting from reductions in directly emitted PM 2.5 and precursors due to the attainment of a new PM 2.5 standard ?
2. What is the economic value of these effects? In this analysis , we consider an arra y of health impacts attributable to changes in PM 2.5 . The Integrated Science Assessment for Particulate Matter (“PM ISA”) (U.S. EPA, 2009b ) identif ies the human health effects associated with ambient particles , which include premature mortality and a variety of morbidity effects associated with acute and chronic exposures. Table 5 -2 summarizes human health categories contained within the core benefits estimate as well as those categories that were unquantified due to limited data or resources. It is important to emphasize that the list of unquantified benefit categories is not exhaustive, nor is quantification of each effect complete. To identify human health benefits that we could quantify with confidence we selected endpoints that were classified as c ausal or likely causal in the PM ISA , and we excluded effects not identified as having at least a causal, likely causal, or suggestive relationship with the affected pollutants in the most recent comprehensive scientific assessment, such as an ISA. This de cision does not imply that additional relationships between these and other human health and environmental co

- benefits and the affected pollutants do not exist. Due to this decision criterion, we excluded some effects that were identified in previous lists of unquantified benefits in other RIAs (e.g., UVb exposure).

The benefits analysis in this chapter relies on an array of data inputs

— including air quality modeling, health impact functions and valuation estimates among others

— which are themselves subject to uncertainty and may also in turn contribute to the overall uncertainty in this analysis. We employ several techniques to characterize this uncertainty, which are described in detail in Section 5.4.5-4 Table 5 -2. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain the Primary PM 2.5 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 PM 2.5 Adult premature mortality based on cohort study estimates and expert elicitation estimates (age >25 or age >30)   Section 5.6

Infant mortality (age <1)   Section 5.6

Non

- fatal heart attacks (age > 18)   Section 5.6 Hospital admissions

— respiratory (all ages)   Section 5.6 Hospital admissions

— cardiovascular (age >20)   Section 5.6 Emergency department visits for asthma (all ages)   Section 5.6

Acute bronchitis (age 8

– 12)   Section 5.6
Lower respiratory symptoms (age 7

– 14)   Section 5.6 Upper respiratory symptoms (asthmatics age 9– 11)   Section 5.6

Asthma exacerbation (asthmatics age 6

– 18)   Section 5.6 Lost work days (age 18

– 65)   Section 5.6

Minor restricted

- activity days (age 18

– 65)   Section 5.6 Chronic Bronchitis (age >26)

— a

— a Section 5.6 Emergency department visits for cardiovascular effects (all ages) — a

— a Section 5.6

Strokes and cerebrovascular disease (age 50

– 79) — a

— a Section 5.6

Other cardiovascular effects (e.g., other ages) — — PM ISA b
Other respiratory effects (e.g., pulmonary function, non

- asthma ER visits, non

- bronchitis 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
(continued)5-5 Table 5

- 2. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain the Primary PM 2.5 Standards (continued)

Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information

Reduced incidence of mortality from exposure to ozone Premature mortality based on short

- term study estimates (all ages) — — Ozone ISA d

Premature mortality based on long

- term study estimates (age 30

– 99) — — Ozone ISA d

Hospital admissions

— respiratory causes (age > 65) — — Ozone ISA d

Hospital admissions

— respiratory causes (age <2) — — Ozone ISA d

Emergency department visits for asthma (all ages) — — Ozone ISA d

Minor restricted

- activity days (age 18

– 65) — — Ozone ISA d
School absence days (age 5

– 17) — — Ozone ISA d
Decreased outdoor worker productivity (age 18

– 65) — — Ozone ISA d

Other respiratory effects (e.g., premature aging of lungs) — — Ozone ISA b

Cardiovascular and nervous system effects — — Ozone ISA c
Reproductive and developmental effects — — Ozone ISA c

Reduced incidence of morbidity from exposure to NO 2 Asthma hospital admissions (all ages) — — NO 2 ISA d
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

(continued)5-6 Table 5

- 2. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain the Primary PM 2.5 Standards (continued)

Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information

Reduced incidence of morbidity from exposure to SO 2 Respiratory hospital admissions (age > 65) — — SO 2 ISA d
Asthma emergency department visits (all ages) — — SO 2 ISA d

Asthma exacerbation (asthmatics age 4

– 12) — — SO 2 ISA d
Acute respiratory symptoms (age 7

– 14) — — SO 2 ISA d
Premature mortality — — SO 2 ISA b,c
Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — SO 2 ISA b,c

Reduced incidence of morbidity from exposure to methylmercury (through role of sulfate in methylation) Neurologic effects

— IQ loss — — IRIS; NRC, 2000 d
Other neurologic effects (e.g., developmental delays, memory, behavior) — — IRIS; NRC, 2000 b

Cardiovascular effects — — IRIS; NRC, 2000 b,c
Genotoxic, immunologic, and other toxic effects — — IRIS; NRC, 2000 b,c

a We quantify these benefits in a sensitivity analysis, but not in the core analysis.
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

s ignificant concerns over the strength of the association.
d We assess these benefits qualitative ly due to time and resource limitations for this analysis.
As described in Chapter 1 of this RIA, there are important differences worth noting in the design and analytical objectives of NAAQS RIAs compared to RIAs for implementation rules, such as the recent MATS rule (U.S. EPA, 2011d). The NAAQS RIAs illustrate t he potential costs and benefits of attaining a revised air quality standard nationwide based on an array of emission reduction strategies for different sources including known and unknown controls, incremental to implementation of existing regulations and controls needed to attain the current standards. In short, NAAQS RIAs hypothesize, but do not predict, the emission reduction strategies that States may choose to enact when implementing a revised NAAQS. The setting of a NAAQS does not directly result in c osts or benefits, and as such, NAAQS RIAs are merely illustrative and the estimated costs and benefits are not intended to be added to the5-7 costs and benefits of other regulations that result in specific costs of control and emission reductions. By contrast , the emission reductions from implementation rules are generally for specific, well

- characterized sources, such as the recent MATS rule addressing emissions from coal and oil

- fired electricity generating units (U.S. EPA, 2011d). In general, the EPA is mor e confident in the magnitude and location of the emission reductions for implementation rules. As such, emission reductions achieved under promulgated implementation rules such as MATS have been reflected in the baseline of this NAAQS analysis. Subsequent implementation rules will be reflected in the baseline for the next PM NAAQS review. 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.

5.3 Updated Methodology Presented in this RIA The benefits analysis presented in this chapter incorporates an array of policy and technical changes that the Agency has adopted since the previous review of the PM 2.5 standards in 2006, and since publication of the proposal RIA for this rulemaking . Below we note the aspects of this analysis that differ from the proposal RIA (U.S. EPA, 2012 a):

1. Incorporation of the newest Harvard Six Cities mortality study . In 2012 , Lepeule et al. published an extended analysis of the Six Cities cohort. Compared to the study it replaces ( Laden et al. , 200 6 ), this new analysis follows the cohort for a longer time and includes more years of PM 2.5 monitoring data . The all

- cause PM 2.5 mortality risk coefficient drawn from Lepeule et al. (20 12 ) is roughly similar to the Laden et al. (200 6 ) risk coefficient applied in the EPA’s recent analyses of long

- term PM 2.5 mortality and has narrower confidence intervals

.
2. Updated demographic data

. We updated the population demographic data in BenMAP to reflect the 2010 Census and future projections based on economic forecasting models developed by Woods and Poole, Inc. (Woods and Poole, 2012). These data replace the earlier demographic projection data from Woods and Poole (2007).

3. Incorporation of new morbidity studies . Since the publication of the PM ISA (U.S. EPA, 2009 ) the epidemiological literature has produced several new studies examining the association between short

- term PM 2.5 exposure and respiratory and cardiovascular hospitalizations, respiratory and cardiovascular emergency department visits , and stroke . Upon careful evaluation of this new literature identified in the Provisional Assessment of Recent Studies on Health Effect s of Particulate Matter Exposure (“Provisional Assessment”) (U.S. EPA, 2012b) we added several new studies to our health impact assessment.5-8 4. Updated the survival rates for non

- fatal acute myocardial infarctions

. Based on recent data from Agency for Healthcare Research and Quality ’s Healthcare Utilization Project National Inpatient Sample database ( AHRQ, 2009) , we identified death rates for adults hospitalized with acute myocardial infarction stratified by age. These rates replace the survival rates from Ro samond et al. (1999).

5. Expanded uncertainty assessment

. We clarified the comprehensive assessment of the various uncertain parameters and assumptions within the benefits analysis and expanded the evaluation of air quality benchmarks (previously the LML analysis)

. Although the list above identifies the major changes implemented since the proposal
RIA , the EPA has also updated several additional components of the benefits analysis since the 2006 PM NAAQS RIA (U.S. EPA, 2006 a ). In the Portland Cement NESHAP proposal RIA (U.S. EPA, 2009 a ), the Agency no longer assumed a concentration threshold in the concentration

response function for PM 2.5 - related health effects and began using the benefits derived from the two major cohort studies of PM 2.5 and mortality as the core benefits estimates , while still including a range of sensitivity estimates based on the EPA ’ s PM 2.5 mortality expert elicitation

. In the NO 2 NAAQS proposal RIA (U.S. EPA, 2009a ), we revised the estimate used for the value

of -a- statistical life to be consistent with Agency guidance

. In the proposed CSAPR (previously the “ Transport Rule ” ) (U.S. EPA, 2010g) , we incorporated the “ lowest measured level ”
assessment to help characterize uncertainty in estimates of benefits of reductions in PM 2.5 at lower baseline concentrations of PM 2.5 . In the final CSAPR (U.S. EPA, 2011c ), we updated the baseline incidence rates for hospital admissions and emergency department visits and asthma prevalence rates. We direct the reader to each of these RIAs for more information on these changes. In the proposal RIA for this NAAQS review (U.S. EPA, 2012 a) , we updated the American Cancer Society cohort study to Krewski et al. (2009), upd ated health endpoints in the core and sensitivity analyses, incorporated new morbidity studies, updated the median wage data in the cost

- of

- illness studies, and expan ded the uncertainty assessment.

5.4 Human Health Benefits Analysis Methods

We follow a “ damage

- function ” approach in calculating total benefits of the modeled changes in environmental quality. 2 This approach estimates changes in individual health endpoints (specific effects that can be associated with changes in air quality) and assigns value s 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

2 The damage function approach is a more comprehensive method of estimating total benefits than the hedonic

pr ice approach applied to housing prices, which requires homebuyers to be knowledgeable of the full magnitude of health risks associated with their home purchase. See discussion of hedonic studies in Chapter 6.5-9 endpoints. The “ damage

- function ” approach is the standard method for assessi ng costs and benefits of environmental quality programs and has been used in several recent published analyses (Levy et al., 2009; Fann et al ., 201 2a ; Tagaris et al., 2009)

. To assess economic value in a damage

- function framework, the changes in environmen tal 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, a 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 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 ana lyses. Thus, similar to Kunzli et al. (2000) and other, more recent health impact analyses, our estimates are based on the best available methods of benefits transfer. Benefits transfer is the science and art of adapting primary research from similar contexts to obtain the most accurate measure of benefits for the environmental quality change under analysis. Adjustments are made for the level of environmental quality change, the socio

- demographic and economic characteristics of the affected population, and other factors to improve the accuracy and robustness of benefits estimates.

5.4.1 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 suc h as the environmental Ben efits M apping and
A nalysis P rogram (BenMAP) 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 avail able, including air quality estimates and risk coefficients (Abt Associates, 2010). 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. , 201 2a) . 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 air5- 10 quality ( e.g., U.S. EPA, 201 1d ). For this assessment, the HIA is limited to those health effects that are directly linked to ambient PM 2.5 concentrations. There may be other indirect health impacts associated with implementing emissions controls, such as occupational health
exposures.

Th e HIA approach used in this analysis involves three basic steps: (1) utilizing projections of PM 2.5 air quality 3 and estimating the change in the spatial distribution of the ambient air quality; (2) determining the subsequent change in population

- level exp osure; (3) calculating health impacts by applying concentration

- response relationships drawn from the epidemiological literature (Hubbell et al. , 2009) to this change in population exposure.

A typical health impact function might look as follows:


### ∆y = 1

− � e β∙∆x 

### � y o 

### ∙ Pop (5

. 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 populati on 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 5

- 1 provides a simplified overview of this approach.

3 Projections of ambient PM 2.5 concentrations f or this analysis were generated using the Community Multiscale Air

Quality model (CMAQ). See Chapter 3 of this RIA for more information on the air quality modeling.5- 11 Figure 5

- 1. Illustration of BenMAP Approach

5.4.2 Econo mic 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 p ay (WTP) for changes in risk. E pidemiological studies generally provide estimates of the relative risks of a particular health effect for a given increment of air pollution (often per 10 μg/m 3 for PM 2.5 )

. These relative risks can be used to develop risk coefficients that relate a unit reduction in PM 2.5 to change s in the incidence of a health effect. In order to value these changes in incidence, WTP for changes in risk need to be converted into WTP per statistical incidence. This measure is calculated by dividing individual WTP for a risk reduction by the related observed change in risk. For 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 is5- 12 $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 populat ion 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.

For some health effects, such as hospital admissions, WTP estimates are generally not available. In these cases, we use the cost of treating or mitigating the effect. For example, for the valuation of hospital admissions we use the avoided medical costs as an estimate of the value of avoiding the health effects causing the admission. These cost

- of

- illness (COI) estimates generally (although not necessarily in every case) understate the true value of reductions in risk of a health effect. They tend to reflect the direct expenditures related to treatment but not the value of avoided pain and suffering from the health effect.

We use the BenMAP model version 4.0.5 2 (Abt Associates, 2010 ) to estimate the health impacts and monetized health benefits for the proposed standard

. Figure 5

- 2 shows the data inputs and outputs for the BenMAP model.

Figure 5

- 2. Data Inputs and Outputs for the BenMAP Model Modeled Baseline and Post

- Control Ambient PM 2.5

PM 2.5 Health

Functions

Economic Valuation Functions 2020 Population Projections

PM 2.5 Incremental Air Quality Change

PM 2.5 - Related Health Impacts Woods & Poole Population Projections

Background Incidence and Prevalence Rates

Monetized PM 2.5 - related Benefits Blue identifies a user

- selected input within the BenMAP model Green identifies a data input generated outside of the BenMAP Census Population Data5- 13 5.5 Uncertainty Characterization In any complex analysis using estimated parameters and inputs from numerous models, there are likely to be many sources of uncertainty . T his analysis is no exception. As outlined both in this and preceding chapters, this analysis includes many data sources as inputs, inc luding emission 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 (i.e., regulations, technology, and human behavior). Each of these inputs may be uncertain and would affect the benefits estimate. When the uncertainties from each stage of the analysis are compounded, even small uncertain ties 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 calcula ting 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 has continued work to improve the characterization of uncertainty in both health incidence and benefits estimates. In response to t hese recommendations, we have expanded our previous analyses to incorporate additional quantitative and qualitative characterizations of uncertainty. Although we have not yet been able to make as much progress towards a full, probabilistic uncertainty asse ssment as envisioned by the NAS as we had hoped, we have added a number of additional quantitative and qualitative analyses to highlight the impact that uncertain assumptions may have on the benefits estimates. In addition, for some inputs into the benefit s 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 emission inputs. Therefore, we decline to make up alternative assumptions simply for the purpo se of probabilistic uncertainty characterization when there is no scientific literature to support 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; 2011a), and uses a tiered approach developed by the World Health Organization (WHO) for characterizing uncertainty (WHO, 2008). We present this tiered assessment as well as an assessment of the potential impact and magnitude of each aspec t of uncertainty In Appendix 5c

. D ata limitations prevent us5- 14 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

coe fficients and economic valuation functions by incorporating six quantitative analyses described in more detail below :

1. A Monte Carlo assessment that accounts for random sampling error and between study variability in the epidemiological and economic valuation studies;

2. A concentration benchmark assessment that characterizes the distribution of avoided PM 2.5 - related deaths relative to specific concentrations in the long

- term epidemiological studies used to estimate PM 2.5 - related mortality;

3. The quantification of PM

- related mortality using alternative PM 2.5 mortality effect estimates drawn from two long

- term cohort studies and an expert elicitation ;

4. S ensitivity analys es of several aspects of PM

- related benefits ;

5. Distributional analyses of PM 2.5 - related benefits by locati on, race, income, and education; and

6. An analysis of the influence of various parameters on total monetized benefits

.
5.5.1 Monte Carlo A ssessment Similar to other recent RIAs, we used Monte Carlo methods for characterizing random sampling error associated with the concentration response 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 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 ep idemiology 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 transferabilit y 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.5- 15 5.5.2 Concentration Benchmark Analysis for PM 2.5 We include a concentration benchmark assessment, which identifies the baseline (i.e. , pre

- rule) annual mean PM 2.5 levels at which populations are exposed and specific concentrations in the two long

- term cohort stud ies we use to quantify mortality impacts. This analysis characterize s avoided PM 2.5 - related deaths relative to the 10 th and 25 th percentiles of the air quality data used the Krewski et al. (2009) study as well as the lowest measured level (LML) of the Krewski et al. (2009) and Lepeule et al. (20) studies.

5.5.3 Alternative Concentration

- Response Functions for PM 2.5 – Related Mortality We assign the greatest economic value to the reduction in PM 2.5 related mortality risk

. Therefore, it is particularly important to attempt to characterize the uncertainties associated with reductions in premature mortality. To better understand the concentration

- response relationship between PM 2.5 exposure and premature mortality, the EPA conducted an expert elicitation in 2006 (Roman et al., 2008; IEc, 2006). 4 In general, th e results of the expert elicitation support the conclusion that the benefits of PM 2.5 control are very likely to be substantial.

Alternative concentration

- response functions are useful for assessing uncertainty beyond random statistical error, including uncertainty in the functional form of the model or alternative study design. Thus, we include the expert elicitation results as well as standard errors approaches to provide insights into the likelihood of different outcomes and about the state of knowledge regarding the benefits estimates. In this analysis, we present the results derived from the expert elicitation as indicative of the uncertainty associated with a major component of the health impact functions, and we provide the independent estimates deriv ed from each of the twelve experts to better characterize the degree of variability in the expert
responses.
In previous RIAs, the EPA presented benefits estimates using concentration response functions derived from the PM 2.5 Expert Elicitation (Roman et al., 2008) as a range from the lowest expert value (Expert K) to the highest expert value (Expert E). However, this approach did not indicate the agency ’ s judgment on what the best estimate of PM 2.5 benefits may be, and the EPA ’ s independent Scie nce Advisory Board (SAB) recommended refinements to the way EPA presented the results of the elicitation (U.S. EPA

- SAB, 2008). Therefore, we began to

4 Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, usually

of multiple experts, are obtained (Ayyub, 2002).5- 16 present the cohort

- based studies (Krewski et al., 2009; Laden et al., 2006) 5 as our core estimates in the proposal RIA for the Portland Cement NESHAP (U.S. EPA, 2009 a ). Using alternate relationships between PM 2.5 and premature mortality supplied by experts, higher and lower benefits estimates are plausible, but most of the expert

- based estimates of the mean
PM 2.5 effect on mortality fall between the two epidemiology

- based estimates (Roman et al., 2008)

. In addition to these studies, we have included a discussion or other recent multi

- state cohort studies conducted in North America, but we have not estimated benefits using the effect coefficients from these studies. Please 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 provi ded 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.

Even these multiple characterizations with confidence intervals omit the contribution to overall uncertainty from uncertainty in air quality changes, baseline incidence rates, and populations exposed. Furthe rmore, the approach presented here does not yet include methods for addressing correlation between input parameters and the identification of reasonable upper and lower bounds for input distributions characterizing uncertainty in additional model elements. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire ana lysis.

5.5.4 Sensitivity Analyses For some aspects of uncertainty, we have sufficient data to conduct sensitivity analyses. In this analysis, we performed five such analyses for the revised standard level . In particular,

we:

1. Assessed the sensitivity of the economic value of reductions in the risk of PM 2.5 - related death according to differing assumptions regarding the lag between PM 2.5 exposure and premature death. The timing of such premature deaths affects the magnitude of the discounted PM 2.5 - related morta lity benefits . I n this sensitivity assessment , we consider 6 alternative cessation lags.

2. Characterized the sensitivity of the economic value of the health endpoints valued using willingness

- to

- pay estimates to a higher and a lower assumption regarding

5 We have since updated the the Harvard Six Cities cohort study from Laden et al. (2006) to use the most recent follow

- up publication of this cohort (Lepeule et al, 2012).5- 17 i ncome elasticity. As we discuss below, economic theory argues that individual willingness to pay increases as personal income grows. The relationship between growth in personal income and willingness

- to

- pay to reduce mortality and morbidity risk is charact erized by the income growth factor.

3. Summarize d the avoided cases of certain health endpoints for which we either lacked an appropriate economic value (cardiovascular hospital admissions and stroke) or in which we no longer had sufficient confidence to retain in our primary benefits estimate (chronic bronchitis).

4. Assessed the sensitivity of the benefits results to the new population data from the 2010 C ensus

.
5. Assessed the sensitivity of the benefits results to an analysis year of 2025.

5.5.5 Distribution al Assessment

In an Appendix to the proposal RIA , we characterize d the distribution of PM 2.5 - related benefits based on the geographic distribution of race and education in areas where the illustrative emission reduction strategies would reduce PM 2.5 concentrations

. In that
assessment , we aim ed to answer two key questions:

1. What wa s the estimated distribution of PM 2.5 - related mortality risk based on the

race a nd education characteristics of the population living within areas projected to exceed alter native combinations of the proposed primary PM 2.5 standards ?

2. How would air quality improvements within these counties change the distribution of risk among populations of different races and educational attainment ? 6

Th at assessment wa s generally consistent with the distributional assessments performed in support of MATS ( U.S. EPA, 2011 c ), with one key difference. The e nvironmental j ustice analyses accompanying the MATS RIA applied CMAQ

- modeled PM 2.5 predictions that represent the chang e in air quality after the implementation of each rule. By contrast, the proposal RIA aim ed to illustrate the potential benefits and costs of attaining alternative primary PM 2.5 standards ; the states will ultimately implement attainment strategies, which m ay differ greatly from the least

- cost strategy the EPA modeled here. Alternative emission reduction strategies

— particularly those that maximize benefits to human health and provide a more equitable distribution of risk

— are also available to the states, though not modeled here (Fann

6 In this analysis we assess the change in risk among populations of different race and educational attainment. As

we discuss further in the methodology, we consider this last variable because of the availability of education

modified PM 2.5 mortality risk estimates.5- 18 et al. , 2012b). Due in part to time constraints, the EPA did not perform such an analysis for the final RIA, and instead refers readers to the Appendix noted above.

5.5.6 Influence Analysis

— Quantitative Assessment of Uncertainty In the past few years, the EPA has initiated several projects to improve the characterization of uncertai nty for benefits analysis. In particular, the EPA recently completed the first phase of a quantitative uncertainty analysis of benefits, hereafter referred to as the

“ Influence Analysis ” (Mansfield et al., 2009). The Influence Analysis diagramed the uncert ain components of each step within the benefits analysis process, identified plausible ranges for a sensitivity analysis, and assessed the sensitivity to total benefits to changes in each component. Although this analysis does not quite fulfill the goal of a full probabilistic assessment, it accomplished the necessary first steps and identified the challenges to accomplishing that goal. Below are some of the preliminary observations from the first phase of the project.

 The components that contribute the most to uncertainty of the monetized benefits and mortality incidence (in order of importance) are the value

- of -a- statistical

- life ( VSL ) , the concentration

- response ( C-R) function for mortality, and change in PM 2.5 concentration.

 The components that contribute the least to uncertainty of the monetized benefits and mortality incidence are population, morbidity valuation, and income elasticity.

 The choice of a C

- R function for mortality affects the mortality incidence and monetized benefits more than other sources of uncertainty within each C

- R function.

 Alternative cessation lag structures for mortality have a moderate effect on the monetized benefits.

 Because the health impact function is essentially linear, the key components show the same sensitivity across all mortality C

- R functions even if the midpoints differ significantly from one expert to another.

5.5.7 Qualitative Assessment of Uncertainty and Other Analysis Limitations Although we strive to incorporate as many quantitative assessments of uncertaint y as possible , there are several aspects we are only able to address qualitatively. These aspects are important factors to consider when evaluating the relative benefits of the emission reduction strategies for the revised and alternative annual standards:5- 19 T he total monetized benefits presented in this chapter are based on our interpretation of the best available scientific literature and methods and supported by the EPA ’ s independent SAB (Health Effects Subcommittee) ( SAB

- HES ) (U.S. EPA - SAB, 2010 a ) and the National Academies of Science ( NAS ) (NRC, 2002). The benefits estimates are subject to a number of assumptions and uncertainties. For example, the key assumptions underlying the estimates for premature mortality, which account for over 98 % of the total monetized benefits in this analysis , include the following:

1. 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 varies con siderably in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The PM ISA, which was twice reviewed by Clean Air Scientific Advisory Committee (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). These unce rtainties are likely to be magnified in the current analysis to the extent that the emissions controls are less diverse when focusing on one small region of the country rather than a broader geography with more diverse emissions sources and the application of a more diverse set of controls.

2. We assume that health impact functions based on national studies are representative for exposures and populations in California. In addition to the national risk coefficients we use as our core estimates, the EPA co nsidered the cohort studies conducted in California specifically. Although we have not calculated the benefits results using the cohort studies conducted in California, we provided these risk coefficients to show how much the monetized benefits could have changed. Most of the California cohort studies report central effect estimates similar to the (nation

- wide) all

- cause mortality risk estimate we applied from Krewski et al. (2009) and Lepeule et al. (2012) albeit with wider confidence intervals. Three coho rt studies conducted in California indicate statistically significant higher risks than the risk estimates we applied from Lepeule et al. (2012), and four studies showed insignificant results.

3. 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 tha t are in attainment, down to the lowest modeled concentrations.

4. 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 that5- 20 some of the incidences o f 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.

5. To characterize the uncertainty in the relationship between PM 2.5 and premature mortality (which account for over 98 % of total monetized benefits in this analysis ), we include a set of twelve estimates based on results of the expert elicitation study in addition to our core estimates. Even these multiple characterizations omit the uncertainty in air quality estimates, baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. As a result, the reported co nfidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the PM 2.5 estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire analysis.

As previously de scribed, we strive to monetize as many of the benefits anticipated from the revised and alternative standards as possible given data and resource limitations , but the monetized benefits estimated in this RIA inevitably only reflect a portion of the benefit s. Specifically, only certain benefits attributable to the health impacts associated with exposure to ambient fine particles have been monetized in this analysis. Data and m ethodological limitations prevented the EPA from quantifying or monetizing the bene fits from several important health benefit categories from emission reduction strategies to reach the revised annual standard in this RIA , including potential co

- benefits from reducing ozone exposure , NO 2 exposure, SO 2 exposure, and methylmercury exposure (see section 5.6.5 for more information)

. If we could fully monetize all of the benefit categories, the total monetized benefits would exceed the costs by an even greater margin than we currently estimate

.

To more fully address all these uncertainties including those we cannot quantify, we apply a four

- tiered 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 analyses (U.S. EPA, 2010b, 2011a). 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 uncertainty characterization are available in Appendix 5 B.5- 21 5.6 Benefits Analysis Data Inputs

In Figure 5

- 2, 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 MATS RIA (U.S. EPA, 2011d ) and the proposal RIA (U.S. EPA, 2012 a).

5.6.1 Demographic D ata Quantified and monetized human health impacts depend on the demographic characteristics of the population, including age, location, and income. We use population projections based on economic forecasting models developed by Woods and P oole, Inc

. (Woods and Poole, 2012 ). The Woods and Poole (WP) database contai ns county

- level projections of population by age, sex, and race out to 2040, relativ e to a baseline using the 2010 C ensus data; the proposal RIA incorporated WP projections relative to a baseline using 2000 C ensus data. An analysis exploring the sensitivity of population and health impact estimates to this update can be found in Appendix 5 .A

. Projections in each county are determined simultaneously with every other county in the United States to take into acco unt 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 co unty

- 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 17 9 economic areas defined by the Bureau of Economic Analysis (U.S. BEA, 2004 ) , using an “ export

- base ” approach, which relies on linking industrial

- sect or 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.5- 22  Fourth, employme nt 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 race for each year t hrough 20 4 0 based on historical rates of mortality, fertility, and migration.

5.6.2 Baseline Incidence and Prevalence Estimates Epidemiological studies of the association between pollution levels and adverse health effects generally provide a direct estimate of the relationship of air quality changes to the relative risk of a health effect, rather than estimating the absolute number of avoided cases. For example, a typical result might be that a 10 μg/m 3 decrease in daily PM 2.5 levels might be associated with a decrease in hospital admissions of 3% . 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 a ssessment location, as it corresponds to baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate must be multiplied by the corresponding population number. For example, if the baseline incidence rate is the nu mber of cases per year per million people, that number must be multiplied by the millions of people in the total population.

Table 5 -3 summarizes the sources of baseline incidence rates and provides average incidence rates for the endpoints included in the analysis. For both baseline incidence and prevalence data, we used age

- specific rates where available. We applied concentration

respo nse functions to individual age groups and then summed over the relevant age range to provide an estimate of total population benefits. In most cases, we used a single national incidence rate, due to a lack of more spatially disaggregated data. Whenever po ssible, the national rates used are national averages, because these data are most applicable to a national assessment of benefits. For some studies, however, the only available incidence information comes from the studies themselves; in these cases, incid ence 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 (Abt Associates, 2012 ). To perform this calculation, we began first with an average of 2004

– 2006 cause

- specific mortality rates. Using Census Bureau projec ted national

- level annual mortality rates stratified by age range, we projected these5- 23 mortality rates to 2050 in 5

- year increments (Abt Associates, 2012; U.S. Bureau of the Census 2002).

The baseline incidence rates for hospital admissions and emergency department visits reflect the revised rates first applied in the CSAPR RIA (U.S. EPA, 2011c). In addition, we have revised the baseline incidence rates for acute myocardial infarction. These revised rates are more recent (AHRQ, 2007), which provides a better representation of the rates at which populations of different ages, and in different locations, visit the hospital and emergency department for air pollution

- related illnesses. Also, the new baseline incidence rates are more spatially refined. For many lo cations 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

uns cheduled hospital admissions only, which represents a conservative assumption that most air pollution

- related visits are likely to be unscheduled. If air pollution

- related hospital admissions are scheduled, this assumption would underestimate these benefit s.
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 5

- 4 lists the prevalence rates used to determine the applicable population for asthma symptoms. Note that these reflect current asthma prevalence and assume no change in prevalence rates in future years. We updated these rates in the CSAPR RIA (U.S. EPA, 2011c).5- 24 Table 5 -3. 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 202 0 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

Cerebrovascular events Incidence of new cerebrovascular events among populations 50

– 79 0.001575I Table 3 of Miller et al. (2007)

Chronic Bronchitis c Annual prevalence rate per person

• Aged 18

– 44

• Aged 45

– 64

• Aged 65 and older • 0.0315

• 0.0549

• 0.0563 American Lung Association (2010a, Table 4).

Annual incidence rate per person 0.00378 Abbey et al. (1993, Table 3) 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

• da ily 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 (2002c, Table 11)
Lower Respiratory Symptoms Daily lower respiratory symptom incidence among children d 0.0012 Schwartz et al. (1994, Table 2)
Upper Respiratory Symptoms Daily upper respiratory symptom incidence among asthmatic children 0.3419 Pope et al. (1991, Table 2)

(continued)5- 25 Table 5

- 3. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact Functions, General Population (continued)

Endpoint Parameter Rates

Value Source 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 ICD codes (AHRQ, 2007).

c Assessed in sensitivity analysis only. The rate numbers may be slightly different from those in Table 4 because

we received more current estimates from ALA.
d Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and

wheeze.

Table 5 -4. Asthma Prevalence Rates

Population Group Asthma Prevalence Rates

Value Source All Ages 0.0780 American Lung Association (2010b, 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 (2010b, 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).5- 26 5.6.3 Effect Coefficients The first step in selecting effect coefficients is to identify the health endpoints to be quantified. We base our selection of health endpoints on consistency with the EPA ’ s Integrated Science Assessments (which replace previous Criteria Documents), with input and advice from the SAB

- HES, a scientific review panel specifically established to provide advice on the use of the scientific literature in developing benefits analys es for the EPA’s Report to Congress on The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a) . In addition , we have included more recent epidemiology studies from the PM ISA (U.S. EPA, 2009 b ) and the Provisional Assessment (U.S. EPA, 20 12 b). 7 I n general, we follow a weight of evidence approach, based on the biological plausibility of effects, availability of concentration

- response functions from well conducted peer

- reviewed epidemiological studies, cohesiveness of results across studies, and a f ocus 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 studies provide direct concentration

- response relationships that can be used to evaluate population

- level impacts of redu ctions 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 PM 2.5 and adverse human health effects. We evaluated epidemiological
st udies using the selection criteria summarized in Table 5 -5 . 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, the use of concentration

- response functions from more than a single study can provide a more representative distribution of the effect estimate

. However, there are often differences betwee n studies examining the same endpoint, making it

7 T he peer

- reviewed studies in the Provisional Assessment have not yet undergone external review by the SAB . The new studies from the PM ISA and Provisional Assessment for health endpoints not previously quantified in EPA’s RIAs are presented in a sensiti vity analysis in Appendix 5B, but these new endpoints have not been incorporated into the core benefits analysis.5- 27 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 e xamining 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 t he 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 have been selected, they are quantitatively combin ed or pooled to derive a more robust estimate of the relationship. The BenMAP Manual Technical Appendices provides details of the procedures used to combine multiple impact functions (Abt Associates, 2012). In general, we used fixed or random effects model s to pool estimates from different single city studies of the same endpoint. Fixed effects pooling simply weights each study’s estimate by the inverse variance, giving more weight to studies with greater statistical power (lower variance). Random effects
p ooling accounts for both within

- study variance and between

- study variability, due, for example, to differences in population susceptibility. We used the fixed effects 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 the random effects model. 8 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. For more details on methods used to pool incidence estimates, see the BenMAP Manual Appendices (Abt Associates, 2012).

Effect estimates selected for a given health endpoint were applied consistently across al l 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, in fact, vary from one location to another (e.g., because of di fferences in population susceptibilities or differences in the composition of PM), location

- specific effect estimates are generally not available.

8 EPA recently changed the algorithm BenMAP uses to calculate study variance, which is used in the pooling

process. Prior versions of the mode l calculated population variance, while the version used here calculated sample variance. This change did not affect the selection of random or fixed effects for the pooled incidence estimates between the proposal and final RIA.5- 28 Table 5 -5. Criteria Used When Selecting C

- R Functions

Consideration Comments

Peer

- Reviewed Research Peer

- reviewed research is preferred to research that has not undergone the peer

- review process. Study Type 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. Study Period Studies examining a relatively longer period of time (and therefore having more data) are preferred, because they have gr eater 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 w ill be included. 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. When available, multi

- city studies are preferred to single city studies because they provide a more generalizable representation of the concentration

- response function. Study Size Studies examining a relatively large sampl e are preferred because they generally have more power to detect small magnitude effects. A large sample can be obtained in several ways, including through 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 lifest yle. 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 When modeling the effects of ozone and PM (or other pollutant combinations) jointly, it is important to use properly specified impact functions that include both pollutants. Using single

- pollutant models in cases where both pollutants are expected to affect a health outcome can lead to double

- coun ting when pollutants are correlated.

Measure of PM For this analysis, 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 fracti on 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. These health effects are not quantified in this analysis.

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.5- 29 The spec ific studies from which effect estimates for the core analysis are drawn are included in Table 5

- 6. We highlight in blue those studies that have been added since the benefits analysis conducted for the MATS RIA (U.S. EPA, 2011d), and we highlight those stu dies in red that have been added since the proposal RIA (U.S. EPA, 2012a). In all cases where effect estimates are drawn directly from epidemiological studies, standard errors are used as a partial representation of the uncertainty in the size of the effec t estimate. Table 5

- 7 summarizes those health endpoints and studies we have included as in sensitivity analyses.

Table 5 -6. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts in the Core Analysis a

Endpoint Study Study Population Risk Estimate (95 th Percentile Confidence Interval) a Premature Mortality

Premature mortality

— cohort study, all

- cause Krewski et al. (2009) Premature mortality

— cohort study, all

- cause Krewski et al. (2009)
Lepeule et al. (2012) Lepeule et al. (2012)

Premature mortality, total exposures PM 2.5 Expert Elicitation (Roman et al., 2008) Premature mortality, total exposures PM 2.5 Expert Elicitation (Roman et al., 2008)

Premature mortality

— all

- cause Woodruff et al. (1997) Premature mortality

— all

cause Woodruff et al. (1997)

Chronic Illness

Nonfatal heart attacks Peters et al. (2001) Nonfatal heart attacks Peters et al. (2001) Pooled estimate:

Pope et al. (2006) Pooled estimate: Pope et al. (2006) Sullivan et al. (2005) Sullivan et al. (2005) Nonfatal heart attacks (cont’d) Zanobetti et al. (2009) Nonfatal heart attacks (cont’d) Zanobetti et al. (2009) Zanobetti and Schwartz (2006) Zanobetti and Schwartz (2006) Hospital Admissions

Respiratory Zanobetti et al. (2009)

— ICD 460

- 519 (All respiratory) Respiratory Zanobetti et al. (2009)

— ICD 460

- 519 (All respiratory) Kloog et al. (2012)

— ICD 460

- 519 (All Respiratory Kloog et al. (2012)

— ICD 460

- 519 (All Respiratory Moolgavkar (2000)

— ICD 490

– 496 (Chronic lung disease) Moolgavkar (2000)

— ICD 490

– 496 (Chronic lung disease) Babin et al. (2007)

— ICD 493 (asthma) Babin et al. (2007)

— ICD 493 (asthma) Sheppard (2003)

— ICD 493 (asthma) Sheppard (2003)

— ICD 493 (asthma) (continued)5- 30 Table 5

- 6. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts in the Core Analysis a (continued)

Endpoint Study Study Population Risk Estimate (95th Percentile Confidence Interval) a 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 ER 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 Asthma exacerbations Pooled estimate: Ostro et al. (2001) (cough, wheeze and 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 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 = 1.11 (1.58

– 1.58) per 15 μg/m 3

a Studies highlighted in blue represent updates incorporated since the RIA for MATS (U.S. EPA, 2011d). Studies

highlighted in red represent update s incorporated since the proposal RIA (U.S. EPA, 2012a).
b The original study populations were 8 to 13 for the Ostro et al. (2001) study and 7 to 12 for the Mar et al. (2004)

study. Based on advice from the SAB -H ES, we extended the applied population to 6

- 18, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. EPA

- SAB (2004) and NRC (2002).5- 31 Table 5 -7. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts in the Sensitivity Analysis a

Endpoint Study Study Population
Chronic Illness

Chronic bronchitis Abbey et al. (1995) >26 years Stroke Miller et al. (2007) 50

– 79 years Hospital Admissions

Cardiovascular ED Visits Metzger et al. (2004) 0– 99 Tolbert et al. (2007) 0– 99 Mathes et al. (2011) 40

– 99

a Studies highlighted in blue represent updates incorporated since the RIA for MATS (U.S. EPA, 2011d). Studies

highlighted in red represent updated incorporated since the proposal RIA (U.S. EPA, 2012a).

5.6.3.1 PM 2.5 Premature Mortality Effect Coefficients Core 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 this revi ew of the PM standards, which was twice reviewed by the SAB

- CASAC (U.S. EPA

- SAB, 2009b, 2009c), concluded that there is a causal relationship between mortality and both long

- term and short

- term exposure to PM 2.5 based on the entire body of scientific evide nce (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 endpo int 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 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 benefi ts, 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 short

term exposures (Kunzli et al., 2001; NRC, 2002) . The NRC stated that “ it is essential to use the cohort studies in benefits analysis to capture all important effects from air pollution exposure ”5- 32 (NRC, 2002, p. 108). The NRC further notes that “ the overall effect estimates may be a combination of effect s 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 mortali ty 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 ambi ent 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 t he fact that the initial published studies (Pope et al., 1995; Dockery et al., 1993) were subject to extensive reexamination and reanalysis by an independent team of scientific experts commissioned by the Health Effect Institute (HEI) and by a Special Pane l of the HEI Health Review Committee (Krewski et al., 2000). Publication of studies confirming and extending the findings of the 1993 Six Cities Study and the 1995 ACS study using more recent air quality and a longer follow

- up 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 reduction s, and concluded, “ the selection of these cohort studies as the underlying basis for PM mortality benefit estimates to be 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 an d Six Cities is consistent with other recent RIAs (e.g., U.S. EPA, 2006a, 2010c, 2011c, 2011d). 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 mortal ity with support from a number of additional cohort studies (described below).
The extended analyses of the ACS cohort data (Krewski et al., 2009) provides additional refinements to the analysis of PM

- related mortality 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 of5- 33 urban areas (c) addressing confounding by spatial autocorrelation by incorporating ecological, or community

- level, co

- variates; (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 agai n reviewed the choice of mortality risk coefficients for benefits analysis, concluding that “ [t]he Krewski et al. (2009) findings, while informative, have not yet undergone the same degree of peer review as have the aforementioned studies. Thus, the SAB

HES recommends that EPA not use the Krewski et al. (2009) findings for generating the Primary Estimate ” (U.S. EPA

- SAB, 2010a). Since this time, the Krewski et al. (2009) has undergone additional peer review, which we believe strengthens the support for including this study in this RIA. For example, the PM ISA (U.S. EPA, 2009b) included this study among the key mortality studies. In addition, the risk assessment supporting the PM NAAQS (U.S. EPA, 2010b) utilized risk coefficients drawn from the Krewski et al . (2009) study, the most recent reanalysis of the ACS cohort data. The 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 SAB

- CASAC also provided extensive peer review of t he risk assessment and supported the use of effect estimates from this study (U.S. EPA

- SAB, 2009a, 2010b, c).

Consistent with the Quantitative Health Risk Assessment for Particulate Matter (U.S. EPA, 2010b) which was reviewed by the SAB

- CASAC (U.S. EPA

- SA B, 2009), 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 10μg/m 3 increase in PM 2.5 ). The re lative risk estimate (1.06 per 10μg/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 Ci ties 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 tha t 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 concentration

- response relationship was linear5- 34 down to PM 2.5 concentrations of 8 μg/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.

Implicit in the calculation of PM 2.5 - related premature mortality impacts are several key assumptions, which are described in further detail later in this chapter. First, we assume that there is a “ cessation ” lag in time between the reduction in PM exposure and the full reduction in mortality risk that affects the timing (and thus discounted monetary valuation) of the resulting premature deaths (see section 5.6.6.1). Second, following conclusions of the PM ISA, we assume that all fine particles are equally potent in causing premature mortality (see section 5.7.2). Third, following conclusions of the PM ISA, we assume that the health impact function for fine part icles is linear within the range of ambient concentrations affected by these standards (see section 5.7.4).

Alternate Mortality Effect Coefficients for Adults. In addition to the ACS and Six Cities cohorts, 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, 2012 b ) (and thus not summarized here). 9 , 10 Table 5

- 8 provides the effect estimates from each of these cohort studies for all

- cause, cardiovascular, cardiopulmonary, and ischemic heart disease (IHD) mortality as well as the lowest measured air quality level (LML) and me an concentration in the study.

We also draw upon the results of the 2006 expert elicitation sponsored by the EPA (Roman et al., 2008; IEc, 2006) to demonstrate the sensitivity of the benefits estimates to 12 expert

- defined concentration

- response functions . The PM 2.5 expert elicitation and the derivation of effect estimates from the expert elicitation results are described in detail in the

9 It is important to note that the newer studies in the Provisional Assessment are published in peer

- reviewed

journals and meet our study selection criteria, but they have not been assessed in the context of an Integrated Science Assessment nor gone through review by the SAB. In addition, only the ACS and H6C cohort studies have been recommended by the SAB as appropriate for benefits analysis of national rulemakings. 10 In this chapter, we only describe multi

- state cohort studies. There are additional cohort studies that we have not

included in this list, including cohort studies that focus on single cities (e.g., Gan et al., 2012) and cohort studies focusing on methods development. In Appendix 5A, we provide additional information regarding cohort studies in California, which is the only state for which we identified single state cohorts.5- 35 2006 PM 2.5 NAAQS RIA (U.S. EPA, 2006a), the elicitation summary report (IEc, 2006) and Roman et al. (2008), and so we s ummarize here the key attributes of this study relative to the interpretation of the estimates of PM

- related mortality reported here. We describe also how the epidemiological literature has evolved since the expert elicitation was conducted in 2005 and 200 6.

Table 5

- 8. Summary of Effect estimates from Associated With Change in Long

- Term Exposure to PM 2.5 in Recent Cohort Studies in North America

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. (2011) 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 mortality5- 36 among the adult U.S. population resulting from reductions 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 proto col 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 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, the role of short - and long

- term exposures, potential confounding, and exposure misclassification. In Appendix 5c, we evaluate eac h of these uncertainties in the context of this health impact assessment. For several of these uncertainties, such as causality, we are able to use the expert

- derived functions to quantify the impacts of applying different assumptions. The elicitation received favorable peer review in 2006 (Mansfield and Patil, 2006).

Prior to providing a quantitative estimate of the risk of premature death associated with long

- term 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 ” 11 according to the experts included:

 Geographic representation of the entire U.S. (e.g., monitoring sites across the

cou ntry)

 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 ch aracterization of outcome (i.e., cause of death)

11 These criteria are substantively similar to EPA’s study selection criteria identified in Table 5

- 5 of this chapter.5- 37  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; ha ve collected data on co

- pollutants in the case of the ACS study; have accurately characterized the health outcome; include a long (and growing) follow

- up period; and, are prospective in nature. The experts also noted a series of limitations in these two cohort studies. In the case of 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 considerin g 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 ] ).

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 5

- 8. 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 th e 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 e t al. , 2012). Because the other multi

- state cohorts generally have limited geography and age/gender representativeness, these limitations preclude us from using these studies in our core benefits results, and we instead present the risk coefficients from these other multi

- state cohorts in5- 38 Table 5

- 8. In addition, we now present the full distribution s of the expert

- derived results in a probabilistic graphic (rather than cascading the expert

- derived results throughout the results tables as done in prior RIAs). We do not combine the expert results in order to preserve the breadth and diversity of opinion on the expert panel (Roman et. al., 2006). This presentation of the expert

- derived results is generally consistent with SAB advice (U.S. EPA

- SAB, 2008), which

r ecommended that the EPA empha si ze 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 o f 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.

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. 12 The PM ISA states that less evidence is available
regar ding 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 de aths 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 20 04, 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, 2004a). The SAB

- HES also cite s the study by Belanger et al. (2003) as corroborating findings linking PM exposure to increased respiratory inflammation and infections in children. A study by Chay and Greenstone (2003) found that reductions in TSP caused by the recession of 1981

– 1982 we re statistically associated with reductions in infant mortality at the county level. With regard to the cohort study conducted by Woodruff et al. (1997), the SAB

- HES notes 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

12 For the purposes of this analysis, we only calculate benefits for infants age 0– 1, not all children under 5 years

old.5- 39 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

r ecommended that the EPA incorporate infant mortality into the primary benefits estimate and that infant mortality be evaluated using an impact function developed from the Woodruff et al. (1997) study (U.S. EPA

- SAB, 2004a).

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 es timating infant mortality based on Woodruff et al. (1997) and 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 Costs and Benefits of the Clean Air
Act (U.S. EPA, 2011a) , we continue to rely on the earlier 1997 study in part due to the national

– scale of the earlier study

.
5.6.3.2 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 (Mustafić et al., 2012; Peters et al., 2001; Sullivan et al., 2005; Pope et al., 2006; Zanobetti and Schwartz, 2006; Zanobetti et al., 2009) and other countries (Poloniecki et al., 1997; Barnett et al., 2006; Peters et al., 2005). In previous health impact assessments, we have relied upon a study by Peters et al. (2001) as the basis for the impact function estimating the relationship between 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 acute myocardial infarctions (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 n umber of other single and multi

- city studies have appeared in the literature. These studies include Sullivan et al. (2005), which considered the risk of PM 2.5 - related hospitalization for AMIs in King County, WA; Pope et al. (2006), based in Wasatch Range, UT; Zanobetti and Schwartz (2006), based in Boston , MA ; 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 with5- 40 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 meas ured on an hourly basis.

As a means of recognizing the strengths of the Peters study while also incorporating the newer evidence found in the four single and multi

- city studies, we present a range of AMI estimates. The upper end of the range is calculated using the Peters study while the lower end of the range is the result of an equal

- weights pooling of these four newer studies. It is important to note that when calculating the incidence of nonfatal AMI , the fraction of fatal heart attacks is subtracted to ensure that there is no double

- counting with premature mortality estimates. Specifically, we apply an adjustment factor in the concentration

- response function to reflect the probability of surviving a heart attack. B ased 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., 1.85 2 % for age s 18

– 44, 2.8 188 % for age s 45

– 64, and 7.43 39 % for age s 65+). These rates show a clear downward trend over time between 1994 and 2009 for the average adult and thus replace the 7% survival rate previously applied across all age groups from Rosamond et al. (1999).

5.6. 3.3 Ho spital Admissions and Emergency Department Visits Because of the availability of detailed hospital admission and discharge records, there is an extensive body of literature examining the relationship between hospital admissions and air pollution. For this reason, we pool together the incidence estimates using several different studies for many of the hospital admission endpoints. In addition, some studies have examined the relationship between air pollution and emergency department visits. Since most emerge ncy 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 two main groups of hospital admissions estimated in this analysis are respiratory admissions and cardiovascular admissions. There is not much evidence linking PM 2.5 with other5- 41 types of hospital admis sions. Both asthma - and cardiovascular

- related visits have been linked 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 estimate from a study of children 18 and under by Mar et al. (2010) , Slaughter et al. ( 2005 ) , and Glad et al. (2012)

. The first two studies examine d populations 0 to 9 9 in Washington State , while Glad et al. examined populations 0

- 99 in Pittsburgh, PA . 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
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 adult s 20 to 64, while the remainder estimate risk among adults over 64. 13 Total cardiovascular hospital admissions are thus the sum of the pooled estimate for adults over 65 and the single study estimate for adults 20 to 64. Cardiovascular hospital admissions include admissions for myocardial infarctions. 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 limitati on of our approach is that while the Zanobetti et al

. (2009) study assesses all cardiovascular risk, Bell et al. (2008) , and Peng et al., (2008, 2009) studies estimate a subset of cardiovascular hospitalizations as well as certain cerebro - and peripheral

vascular diseases. To address the potential for the pooling of these four studies to produce a biased estimate, we match the pooled risk estimate with a baseline incidence rate that excludes cerebro - and peripheral

- vascular disease. An alternati ve 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.

13 Note that the Moolgavkar (2000) study has not been updated to reflect the more stringent GAM convergence

criteria. However, given that no other estimates are available for this age group, we chose to use the exist ing study. Given the very small (<5 % ) difference in the effect estimates for people 65 and older with cardiovascular hospital admissions between the original and reanalyzed results, we do not expect this choice to introduce much bias. For a discussion of t he GAM convergence criteria, and how it affected the size of effect coefficients reported by time series epidemiological studies using NMMAPS data, see: http://www.healtheffects.org/Pubs/s ttimeseries.htm

.5- 42 To estimate avoided incidences of respiratory hospital admissions associated with PM 2.5 , we u sed 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 over 65, adults 18 to 6 4 and children 0 to 1 7. For adults over 65, the multi

- city stud ie s by Zanobetti et al. (2009) and Kloog et al. (2012) provide effect coefficients for total respiratory hospital admissions (defined as ICD codes 460

– 519). We pool these two studies using equal weights. Moolgavkar et al. ( 2003 ) examines PM 2.5 and chronic lu ng disease hospital admissions (less asthma) in Los Angeles, CA among adults 18 to 6 4. 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 1 8; we adjusted the age range for this study to apply to children 0 to 1 8. The second is Sheppard et al. (2003) which assessed
PM 2.5 and asthma hospitalizations in Seattle, Washington , among children 0 to 1 8.

5.6 .3.4 Acute Health Events and School/Work Loss Days In addition to mortality, chronic illness, and hospital admissions, a number of acute health effects not requiring hospitalization are associated with exposure to PM 2.5 . The sources for the effect estimates used to quantify these effects are described below.

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 tire dness, lasting for a number of days. According to the MedlinePlus medical encyclopedia, 14 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). Health effects from air pollution can also result in missed days of work (either from personal symptoms or from caring for a sick family member). Days of work lost due to
PM 2. 5 were estimated using an effect estimate developed from Ostro (1987). Children may also be absent from school because of respiratory or other diseases caused by exposure to air pollution , but we have not quantified these effects for this rule

. We estimate three types of acute respiratory symptoms: lower respiratory symptoms, upper respiratory symptoms, and minor restricted activity days (MRAD). Incidences of lower respiratory symptoms (e.g., wheezing, deep cough) in children aged 7 to 14 were es timated using an effect estimate from Schwartz and Neas (2000). Incidences of upper respiratory symptoms in asthmatic children aged 9 to 11 are estimated using an effect estimate developed

14 See http://www.nlm.nih.gov/medlineplus/ency/article/ 001087.htm , accessed April 2012

.5- 43 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 thus focused on effects in asthmatics.
Minor restricted activity days (MRAD) result when individuals reduce most usual daily activities and replace them with less strenuous activities or rest, yet not to the point of missing work or school. For example, a mechanic who would usually be doing physical work most of the day will instead spend the day at a desk doing paper and phone work because of difficulty breathing or chest pain. 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, 2009) , but proved to be unsuitable fo r 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 asses asthma exacerbation. As we describe in detail below, to avoid the chance o f double

- counting impacts, we do not estimate changes in acute respiratory symptoms and asthma exacerbation among populations of the same age.

For this RIA, we have followed the SAB

- HES recommendations regarding asthma exacerbations in developing the core estimate (U.S. EPA

- SAB, 2004 a). Although certain studies of acute respiratory events characterize these impacts among only asthmatic populations, others consider the full population, including both asthmatics and non

- asthmatics. For this reason, incidence estimates derived from studies focused only on asthmatics cannot be added to estimates from studies that consider the full population

— to do so would double

- count impacts. To prevent such double

- counting, we estimat ed the exacerbation of asthma among childr en 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 MRADs. Finally, note also 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.

To characterize asthma exacerbations in children, we selected two studies (Ostro et al., 2001; Mar et al., 2004) that followed panels of asthmatic children. Ostro et al. (2001) followed a5- 44 group of 138 African

- American children in Los Angeles for 13 weeks, recording daily

oc currences of respiratory symptoms associated with asthma exacerbations (e.g., shortness of breath, wheeze, and cough). This study found a statistically significant association between
PM 2.5 , measured as a 12

- hour average, and the daily prevalence of shortn ess 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 incidence estimates (see below).

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 mont hs. The study was conducted in Spokane, Washington, a semiarid 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 PM of 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 generat ed using effect estimates from the two studies to produce a single asthma exacerbation incidence estimate. 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 estim ate of wheeze with the pooled cough and shortness of breath estimates to derive an overall estimate of asthma exacerbation.

5.6 .3 .5 Effect Coefficients Selected for the Sensitivity Analyses

Chronic Bronchitis. Chronic bronchitis is characterized by mucus in the lungs and a persistent wet cough for at least 3 months a year for several years in a row. C hronic bronchitis affects an estimated 5 % of the U.S. population ( ALA , 1999). A limited number of studies have estimated the impact of air pollution on new inc idences of chronic bronchitis. Schwartz (1993) and Abbey et al. (1995) provide evidence that long

- term PM 2.5 exposure gives rise to the5- 45 development of chronic bronchitis in adults in the United States; these remain the two most recent studies observing a relationship between long

- term exposure to PM 2.5 and the onset of chronic bronchitis in the U.S . The absence of newer studies finding a relationship between long

term PM 2.5 exposure and chronic bronchitis argues for moving this endpoint from the core benef its analysis to a sensitivity analysis

. In their review of the scientific literature on chronic obstructive pulmonary disease (COPD), which includes chronic bronchitis and emphysema, the American Thoracic Society concluded that air pollution is “ associated with COPD, but sufficient criteria for causation were not met ” (Eisner et al., 2010).

Stroke

. The PM ISA (U.S. EPA, 2009) includes several new studies that have examined the relationship between PM 2.5 exposure and cerebrovascular events ( U.S. EPA, 2009). Time

series studies have generally been inconsistent with several studies showing positive associations (Dominici et al. , 2006 ; Metzger et al. , 2004 ; Lippman et al. , 2000 ; Lisabeth et al. , 2008). Several other studies have demonstrated null or negative associations (Anderson et al. , 2001 ; Barnett et al. , 2006 ; Peel et al. , 2007). In general, these studies examined cerebrovascular disease as a group, though a few studies partition ischemic and hemorrhagic strokes separately (Lisabeth et al. , 2008). A key lim itation of these time

- series studies is that they use hospital discharge rates as the diagnosis and relatively short lags (0 –2 days)

— this is problematic, as discharge rates are an imperfect diagnosis and strokes may occur several days before admission
to t he hospital.

Longer

- term prospective cohort studies of PM 2.5 and stroke include Miller et al. (2007)

. Miller et al. (2007) estimated the change in risk among post

- menopausal women enrolled in the Women’ s Health Initiative ( U.S. EPA, 2009 b ). After adjusting for age, race, smoking status, educational level, household income, body

- mass index, diabetes, hypertension, and hypercholesterolemia, hazard ratios were estimated for the first cardiovascular event. Because this study considers first

- time cardiovascular events, a key challenge to incorporating this study into the core health impact assessment is match ing the baseline incidence rate correctly, and we have approximated this information using the data in the study

. In addition, Wellenius et al. ( 2012 ) examined the association of PM 2.5 with neurologist confirmed ischemic stroke in Boston adults in a time

- stratified case

- crossover study. A key feature of this study is that it included the time of stroke symptom onset for most patients. Similar to the challenge with Miller et al. (2007), we do not have baseline incidence rates, and we do not have sufficient data from the study to approximate it. Three factors argue for treating this endpoint in the sensitivity anal ysis: (1) the epidemiological literature examining
PM

- related cerebrovascular events is still evolving; (2) there are special uncertainties associated5- 46 with quantifying this endpoint; (3) we have not yet identified an appropriate method for estimating the e conomic value of this endpoint.

Cardiovascular Emergency Department Visits. A large number of recent U.S.

- based studies provide support for an association between short

- term increases in PM 2.5 and increased risk of ED visits for ischemic heart diseases ( U.S. EPA, 2009 b ). Both Metzger et al. (2004) and Tolbert et al. (2007) published interim results from the Study of Particles and Health in Atlanta (SOPHIA), finding a relationship between PM 2.5 exposure and cardiovascular emergency department visits. These cardiovascular emergency department visits are distinct from cardiovascular hospital admissions and non

- fatal heart attacks. To ensure no double

- counting, we excluded ICD -9- 411 (ischemic heart disease) from the baseline incidence rates for cardiovascular e mergency department visits. Mathes et al. (2011) find relationships between
PM 2.5 levels and cardiovascular emergency department visits in New York City. The principal challenge to incorporating these studies is the absence of readily

- available economic v aluation estimates for cardiovascular emergency department visits. Until we develop an approach for estimating the economic value of this endpoint, we will quantify these ED visits in a sensitivity analysis only

.
5.6.4 Unquantified Human Health Benefits

T he illustrative emission reduction strategies to reach the revised and alternative annual standards described in Chapter 4 would reduce emissions of directly emitted particles, as well

as SO 2 , and NO x for an alternative standard for 11 μg/m 3

. The extent to which down wind exposure to secondary pollutants ozone, and mercury would actually be reduced would depend on the specific control strategy that States would use to reduce PM 2.5 in a given area as well as local geographic and meteorological conditions. Alt hough we have quantified many of the health benefits associated with reducing exposure to PM 2.5 , as shown in Table 5

- 2, we are unable to quantify the health benefits associated with reducing the potential for ozone exposure, SO 2 exposure, NO 2 exposure or c ontamination of local water bodies with mercury due to the absence of air quality modeling data for these pollutants in this analysis. Although the method we applied simulate d the impact of attaining the revised and alternative annual standards on ambient levels of PM 2.5 , this method does not simulate how the illustrative emission reductions would affect ambient levels of ozone, SO 2 , or NO 2 . Furthermore, the air quality modeling conducted for this analysis did not assess mercury, so we are unabl e to estimate mercury deposition associated with the illustrative controls or subsequent bioaccumulation and exposure

. Below we provide a qualitative description of these health5- 47 benefits. In general, previous analyses have shown that the monetized value of these additional health benefits is much smaller than PM 2.5 - related benefits (U.S. EPA, 2010a, 2010 c, 2010 d).

Reducing NO x emissions also reduce s ozone concentrations in most areas. Reducing ambient ozone concentrations is associated with significant human health benefits, including mortality and respiratory morbidity (U.S. EPA, 2008a, 2010d ). Epidemiological researchers have associated ozone exposure with adverse health effects in nu merous toxicological, clinical and epidemiological studies (U.S. EPA, 2006b ; U.S. EPA, 2012c ). When adequate data and resources are available, the EPA generally quantifies several health effects associated with exposure to ozone (e.g., U.S. EPA, 2008a, 2010d , 2011a , 2011c ). These health effects include respiratory morbidity such as asthma attacks, hospital and emergency department visits, school loss days, as well as premature mortality. T he scientific literature suggests that exposure to ozone is also associated with chronic respiratory damage and premature aging of the lungs , but the EPA has not quantified these effects in benefits analyses previously

. Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the I ntegrated Science Assessment for Sulfur Dioxide

— Health Criteria (SO 2 ISA) concluded that there is a causal relationship between respiratory health effects and short

- term exposure to SO 2 (U.S. EPA, 2008 c ). The immediate effect of SO 2 on the respiratory syst em in humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO 2 likely resulting from preexisting inflammation associated with this disease. A clear concentration

response relationship has been demonstrated in laboratory studies fo llowing exposures to SO 2 at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adversely affected. Based on our review of this information, we identified four short

- term morbidity endpoints th at the SO 2 ISA identified as a “ causal relationship ” : asthma exacerbation, respiratory

- related emergency department visits, and respiratory

- related hospitalizations. The differing evidence and associated strength of the evidence for these different effects is described in detail in the SO 2 ISA. The SO 2 ISA also concluded that the relationship between short

- term SO 2 exposure and premature mortality was

“ suggestive of a causal relationship ” because it is difficult to attribute the mortality risk effects to SO 2 alone. Although the SO 2 ISA stated that studies are generally consistent in reporting a relationship between SO 2 exposure and mortality, there was a lack of robustness of the observed associations to adjustment for pollutants. We did not quantify these b enefits due to data constraints.

Epidemiological researchers have associated NO 2 exposure with adverse health effects in numerous toxicological, clinical and epidemiological studies, as described in the Integrated5- 48 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 betwee n short

- term NO 2 exposure and adverse effects on the respiratory system .” These epidemiologic and experimental studies encompass a number of endpoints including emergency d epartment 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 –2 0% 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 a lone. 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.

5.6.5 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 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 converte d those changes in risk to units of avoided statistical incidence for ease of presentation. We calculated the value of avoided statistical incidences by dividing individual WTP for a risk reduction by the related observed change in risk.

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. COI estimates generally ( al though 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 5 -9 . All values are in constant year 2006 dollars, adjusted for growth in real income for WTP estimates out to 2020 using projections provided by Standard and5- 49 Poor ’s , which is discussed in further detail in Section 5.6.8. 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 la te 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.

5.6. 5 .1 Mortality Valuation Following the adv ice of the SAB’s Environmental Economics Advisory Committee (SAB

EEAC) , the EPA currently uses the value of statistical life (VSL) approach in calculating the core estimate of mortality benefits, because we believe this calculation provides the most reasonable single estimate of an individual’s willingness to trade off money for reductions in mortality risk (U.S. EPA

- SAB, 2000). The VSL approach is a summary measure for the value of small changes in mortality risk experienced by a large number of people. For a period of time (2004

– 2008), the Office of Air and Radiation (OAR) valued mortality risk reductions using a VSL estimate derived from a limited analysis of some of the available studies. OAR arrived at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta

- analyses of the
wage

- risk literature. The $1 million value represented the lower end of the interquartile range from the Mrozek and Taylor (2002) meta

- analysis of 33 studies. The $10 million value represented the upper end of the interquartile range from the Viscusi and Aldy (2003) meta

analysis 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 Agency neither changed its official guidance on the use of VSL in rule

- makings nor subjected the interim estimate to a scientific peer

- review process through SAB or other peer

- review group.5-50 Table 5 -9. Unit Values for Economic Valuation of Health Endpoints ( 2010$ ) a

Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Distributions of Estimates 1990 Income Level 2020 Income Level Premature Mortality (Value of a Statistical Life) $8,0 00,000 $9,6 00,000 The EPA currently recommends a central VSL of $ 4. 8m ( 1990 $ , 1990 income ) based on a Weibull distribution fitted to 26 published VSL estimates (5 contingent valuation and 21 labor market studies). The underlying studies, the distribution parameters, and other useful information are available in Appendix B of the EPA ’ s Guidelin es for Preparing Economic Analyses (U.S. EPA, 2010 e ). Nonfatal Myocardial Infarction (heart attack)

3% discount rat e Age 0

– 24 Age 25

– 44 Age 45

– 54 Age 55

– 64 Age 65 and over

7% discount rate Age 0

– 24 Age 25

– 44 Age 45

– 54 Age 55 –64 Age 65 and over $ 98,000 $ 110,000 $ 120,000 $ 200,000

$ 98,000

$ 97,000 $ 110,000 $ 110,000 $ 190,000

$ 97,000 $98,000 $110,000 $120,000 $200,000

$98,000

$97,000 $110,000 $110,000 $190,000

$97,000 No distributional information available. Age

- specific cost

- of

- illness values reflect lost earnings and direct medical costs over a 5

- year period following a nonfatal MI. Lost earnings estimates are based on Cropper and Krupnick (1990). Direct medical costs are based on simple average of estimates from Russell et al. (1998) and Wittels et al. (1990). Lost earnings: Cropper and Krupnick (1990). Present discounted value of 5 years of lost earnings in 2000$ : age of onset: at 3% at 7% 25

– 44 $ 9,000 $ 8,000 45

– 54 $ 13,000 $12,000 55

– 65 $ 77,000 $69,000 Direct medical expenses (2000$) : An average of:

1. Wittels et al. (1990) ($ 100 , 000

— no discounting) 2. Russell et al. (1998), 5

- year period ($22, 000 at 3% discount rate;

$21,000 at 7% discount rate) (continued)5-51 Table 5 -9. Unit Values for Economic Valuation of Health Endpoints ( 2010$ ) a (continued)

Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Distributions of Estimates 2000 Income Level 2020 Income Level Hospital Admissions Chronic Lung Disease (18

– 64) $21,000 $21,000 No distributional information available. The COI estimates (lost earnings plus direct medical costs) are based on ICD

- 9 code

- level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total chronic lung illnesses) reported i n Agency for Healthcare Research and Quality (2007) (www.ahrq.gov).

Asthma Admissions (0

– 64) $ 16,000 $ 16,000 No distributional information available. The COI estimates (lost earnings plus direct medical costs) are based on ICD

- 9 code

- level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total asthma category illnesses) reported in Agency for Healthcare Research and Quality (200 7 ) (www.ahrq.gov).

All Cardiovascular Age 18

– 64 Age 65

– 99 $ 42,000 $ 41,000 $ 42,000 $ 41,000 No distributional information available. The COI estimates (lost earnings plus direct medical costs) are based on ICD

- 9 code

- level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total cardiovascular category illnesses) reported in Agency for Healthcare Research and Quality (200 7 ) (www.ahrq.gov). All respiratory (ages 65+) $ 36,000 $ 36 ,0 00 No distributions available. The COI point estimates (lost earnings plus direct medical costs) are based on ICD

- 9 code level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total respiratory category illnesses) reported in Agency for Healthcare Research and Quality, 200 7 (www.ahrq.gov).
Emergency Department Visits for Asthma $ 430 $ 430 No distributional information available. Simple average of two unit COI values (2000$) : (1) $ 310 , from Smith et al. (1997) and (2) $260, from Stanford et al. (1999). (continued)5-52 Table 5 -9. Unit Values for Economic Valuation of Health Endpoints ( 2010$ ) a (continued)

Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Distributions of Estimates 2000 Income Level 2020 Income Level Respiratory Ailments Not Requiring Hospitalization Upper Respiratory Symptoms (URS) $ 31 $ 33 Combinations of the three symptoms for which WTP estimates are available that closely match those listed by Pope et al. result in seven different “ symptom clusters, ” each describing a “ type ” of URS. A dollar value was derived for each type of URS, using mid

- range estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and assuming additivity of WTPs. In the absence of information surrounding the frequency with which each of the seven types of URS occurs within the URS symptom complex, we assumed a uniform distribution between $9.2 and $43 (2000$)

. Lower Respiratory Symptoms (LRS) $ 20 $ 21 Combinations of the four symptoms for which WTP estimates are available that closely match those listed by Schwartz et al. result in 11 different “ symptom clusters, ” each describing a “ type ” of LRS. A dollar value was derived for each type of LRS, using mi d- range estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and assuming additivity of WTPs. The dollar value for LRS is the average of the dollar values for the 11 different types of LRS. In the absence of information surrounding the frequen cy with which each of the 11 types of LRS occurs within the LRS symptom complex, we assumed a uniform distribution between $6.9 and $ 25 (2000$)

. Asthma Exacerbations $ 54 $ 58 Asthma exacerbations are valued at $45 per incidence, based on the mean of average WTP estimates for the four severity definitions of a

“ bad asthma day, ” described in Rowe and Chestnut (1986). This study surveyed asthmatics to estimate WTP for avoidance of a “ bad asthma day, ” as defined by the subjects. For purposes of valuation, an asthma exacerbation is assumed to be equivalent to a day in which asthma is moderate or worse as reported in the Rowe and Chestnut (1986) study. The value is assumed have a uniform distribution between $16 and $7 1 (2000$)

. (continued)5-53 Table 5 -9. Unit Values for Economic Valuation of Health Endpoints ( 2010$ ) a (continued)

Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Distributions of Estimates 2000 Income Level 2020 Income Level Respiratory Ailments Not Requiring Hospitalization (continued)
Acute Bronchitis $ 450 $ 480 Assumes a 6

- day episode, with the distribution of the daily value specified as uniform with the low and high values based on those recommended for related respiratory symptoms in Neumann et al. (1994). The low daily estimate of $10 is the sum of the mid

- ra nge values recommended by IEc (1994) for two symptoms believed to be associated with acute bronchitis: coughing and chest tightness. The high daily estimate was taken to be twice the value of a minor respiratory restricted

- activ ity day, or $110 (2000$)

. Work Loss Days (WLDs) Variable (U.S. median = $ 150) Variable (U.S. median = $ 150) No distribution available. Point estimate is based on county

- specific median annual wages divided by 52 and then by 5

— to get median daily wage. U.S. Year 2000 Census, compiled by Geolytics, Inc. (Geolytics, 2002)
Minor Restricted Activity Days (MRADs) $ 64 $ 68 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). Distribution is assumed to be triangular with a minimum of $22 and a maximum of $83, with a most likely value of $52 (2000$) . Range is based on assumption that value should exceed WTP for a single mild symptom (the highest estimate for a single symptom

— for eye irritation

— is $16.00) and be less than that for a WLD. The trian gular distribution acknowledges that the actual value is likely to be closer to the point estimate than either extreme.

a All estimates are rounded to two significant digits. Unrounded estimates in 2000$ are available in the Ap pendi x J of the BenMAP user manual ( Abt

Associates, 2012 ).5- 54 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 f rom 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 techni ques to combine estimates from unique data sources and different studies, including those using different methodologies (i.e.,
wage

- risk and stated preference) (U.S. EPA

- SAB, 2007).

Until updated guidance is available, the Agency determined that a single, peer

- reviewed estimate applied consistently best reflects the SAB

- EEAC advice it has received. Therefore, the Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) 15 wh ile 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 $6.3 million (2000$). 16 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 recently 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). Draft guidance responding to SAB recommendations will be developed shortly.

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 con tinuing discussion within the economics and public policy analysis community. 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.

15 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. 16 In this analysis, we adjust the VSL to account for a different currency year ( 2010$ ) and to account for income

growth to 2020 . After applying these adjustments to the $6.3 million value, the VSL is $8. 9M.5- 55 Although there are several differences between the labor market studies the EPA uses to derive a VSL estima te and the 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 5 -10 lists some of these differences and the expected effect on the VSL estim ate 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 5 -10. Influence of Applied 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 have followed this advice.

For premature mortality, we assume that there is a “cessation” lag between PM exposures and the total real ization of changes in health effects. We assumed for this analysis 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 expo sure and 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% of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the years 6 to 20 after the reduction in PM 2.5 (U.S. EPA

- SAB, 2004c). Additional cessation lag structures are described and assessed in Appendix 5.A of this RIA. To take this into account in the valuation of5- 56 reductions in premature mortality, we discount t he value of premature mortality occurring in future years using rates of 3% and 7%. 17 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 benefits using a 7% discount rate are only approximately 10% less than the monetized benefits using a 3% discount rate. Further discussion of this topic appears in the
EPA ’s Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e).

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 has identified valuation of mortality

- related benefits as the largest contributor to the range of uncertainty in monetized benefits (Mansfield et al., 2009). 18 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 pollut ion 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 plus WTP to improve other individuals ’ su rvival 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

17 The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the

fe deral government. To comply with 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 “ opport unity cost of capital ” concept to reflect the time value of resources directed to meet regulatory requirements. 18 This conclusion was based on an assessment of uncertainty based on statistical error in epidemiological effect

estimates and economic valuati on 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.5- 57 value these changes. Each individual ’ s survival curve, or the probability of surviving beyond a given age, should shift as a result of an envi ronmental quality improvement. For example, changing the current probability of survival for an individual also shifts future probabilities of that individual ’ s survival. This probability shift will differ across individuals because survival curves depend on 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 study we value reduction s 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 adeq uate 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 aver age 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 concentratio ns.

 Level of risk reduction: The transferability of estimates of the VSL from the wage

- risk studies to the context of the PM NAAQS analysis rests on the assumption that, within a reasonable range, WTP for reductions in mortality risk is linear in risk redu ction. 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 provided5- 58 the change in the risk being valued is within the range of risks evaluated in the underlying studies (Rowlatt et al., 1998).

 Voluntariness of risks evaluated: Although job

- related mortality risks may differ in several ways from air pollution

- related mortality risks, the most important difference may be that job

- related risks are incurred voluntarily, or generally assumed to be, whereas air pollution

- related risks are incurred involuntarily. Some evidence suggests that people will pay more to reduce involuntarily incurred risks than risks incurred voluntarily (e.g., 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 mortal ity risks tend to involve sudden, catastrophic 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 suf fering 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 based on the fact that the risk

- wage trade

off revealed in hedonic studies reflects the pr eferences 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 tha n the average worker in that occupation, or both. Conversely, the marginal worker should have a higher risk tolerance than workers employed in less

- risky sectors. However, the risk estimate used in hedonic studies is generally based on average risk, so the VSL may be biased, in an ambiguous direction, because the wage differential and risk measures do not match.

 Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 2006) finds that because individuals reevaluate their baseline risk of death as they age, the marginal value of risk reductions does not decline with age as predicted by some lifetime consumption models. This research supports findings in recent stated preference studies that suggest only small reductions in the value of mort ality risk reductions with increasing age (e.g., Alberini et al., 2004)

.5- 59 5.6. 5 .2 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 va lue 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 so meone 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 source s in the literature of estimates of the direct medical costs of myocardial infarction, which provide significantly different values (see Table 5 -11 ):

 Wittels et al. (1990) estimated expected total medical costs of myocardial infarction over 5 years to be $51,000 (rounded in 1986$) for people who were admitted to the hospital and survived hospitalization. (There does not appear to be any discounting used .) This estimated cost is based on a medical cost model, which incorporated therapeutic options, projected outcomes, and prices (using “ knowledgeable cardiologists ” as consultants). The model used medical data and medical decision algorithms to estimate th e 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 whose length of stay was therefore substantially shorter than it would be if they had not died.

 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.5- 60 Table 5

- 11. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart Attacks a

Study Direct Medical Costs (2010$) Over an x

- Year Perio d, for x =
Wittels et al. (1990) $1 6 0,000 b 5 Russell et al. (1998) $3 3 ,000 c 5 Average (5

- year) costs $ 98,000 5 Eisenstein et al. (2001) $7 4,000 c 10

a All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in appendix J of the

BenMAP user manual ( Abt Associates, 2012 ).
b Wittels et al. (1990) did not appear to discount costs incurred in future years.

c Using a 3% discount rate. Discounted values as reported in the study.
As noted above, the estimates from these three studies are substantially different, and we have not adequately resolved the sources of differences in the estimates. Because the
wage

- related opportunity cost estimates from Cropper and Krupnick (1990) cover a 5

- year period, we used estimates for medical costs that similarly cover a 5

- year period (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 5 -12.

Table 5 -12. Estimated Costs Over a 5

- Year Period of a Nonfatal Myocardial Infarction (in 2010$ ) a

Age Group Opportunity Cost Medical Cost b Total Cost

0– 24 $0 $ 98,000 $ 98,000

25

– 44 $1 2 ,000 c $ 98,000 $ 110,000

45

– 54 $ 17,000 c $ 98,000 $120,000

55

– 65 $ 100,000 c $ 98,000 $ 200,000

> 65 $0 $ 98,000 $ 98,000

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 ( Abt Associates, 2012 ).
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

.
5.6.6 Hospital Admissions and Emergency Department V aluation 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 International5- 61 Classification of Diseases (WHO, 1977) code

- specific COI estimates used in this analysis consist of estimated hospital charges and the estimated opportunity cost of time spent in the hospital (based on the average length of a hospital stay for the illness). We based all estimates of hospital charges and length of stays on statistics provided by the Agency for Healthcare Research and Quality ’ s Healthcare Utilization Project National Inpatient Sample (NIS) database ( AHRQ, 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 year 2010$ using the CPI

- U

“ all items ” ( Abt Associates, 2012). The resulting national average lost daily wage is $1 48 . The total cost

- of

- illness estimate for an ICD code

- specific hospital stay lasting n days, then, was the mean hospital charge plus $1 48 multiplied by n. In general, the mean length of stay has decreased since the 2000 database used in previous version of BenMAP while the mean hospital charge has increased. We provide the rounded unit values in 2 01 0$ for the COI functions used in this analysis in Table 5 -13.

T able 5 -13. Unit Values for Hospital Admissions

End Point ICD Codes Age Range Mean Hospital Charge (20 1 0$) Mean Length of Stay (days) Total Cost of Illness (unit value in 20 1 0$) min. max. HA, Chronic Lung Disease 490

– 496 18 64 $19,000 3.9 $21,000
HA, Asthma 493 0 64 $14,000 3.0 $16,000
HA, All Cardiovascular 390

– 429 18 64 $ 40,000 4.1 $ 42,000 HA, All Cardiovascular 390

– 429 65 99 $ 37,000 4.9 $ 41,000 HA, All Respiratory 460

– 519 65 99 $3 1,000 6.1 $ 36,000

* All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in Appendix J of the

BenMAP user manual ( Abt Associates, 2012 ).
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 $155; in 2010$ , that cost was $4 64 ( using the CPI

- U for medical care to adjust to 2010$ ). The second estimate comes from Stanford et al. (1999), who reported the cost of an average asthma

related emergency department visit at $335, based on 1996

– 1997 data. A simple average of the two estimates yields a unit value of $3 8 8.5- 62 5.6.7 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 respi ratory 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 $6 8 ( 2010$ ). 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.

5.6.8 Growth in WTP Reflecting National Income Growth Over Time Our analysis accounts for expected growth in real income over time. This is a di stinct 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 19 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 than one and thus relatively inelastic. As real income rises, the WTP value also rises but at a slowe r 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 ar e estimated, and through differences in income between study populations and the affected populations at a particular time. The SAB

EEAC advised the EPA to adjust WTP for increases in real income over time but not to adjust WTP to account for cross

- section al 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

19 Income elasticity is a co mmon economic measure equal to the percentage change in WTP for a 1% change in

income.5- 63 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 t he primary analysis ” (U.S. EPA

- SAB, 2004b). Until these conflicting advisories have been 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 publica tion of this memo and that an array of newer studies identifying potentially suitable income elasticity estimates are available (IE c , 2012). The EPA anticipates seeking an SAB review of these studies, and its approach to adjusting WTP estimates to account for changes in personal income, in 2013. 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 2020 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 elastic ities relative to the severity of the effects ( a priori one might expect that more severe outcomes would show less income elasticity of WTP). We have not imposed any additional restrictions on the empirical estimates of income elasticity. One explanation for the seeming inconsistency is the difference in timing of conditions. WTP for minor illnesses is often expressed as a short term payment to avoid a single episode. WTP for major illnesses and mortality risk reductions are based on longer term measures of payment (such as wages or annual income). Economic theory suggests that relationships become more elastic as the length of time grows, reflecting the ability to adjust spending over a longer time period (U.S. EPA, 2010e, p.A

- 9)

. Based on this theory, it would be expected that WTP for reducing long term risks would be more elastic than WTP for reducing short term risks. The relative magnitude of the income elasticity of WTP for visibility compared with those for health effects suggests that visibility is not as much of a necessity as health, thus,5- 64 WTP is more elastic with respect to income. The elasticity values used to adjust estimates of benefits in 2020 are presented in Table 5 -14. 20

Table 5 -14. 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 2020 are needed to adjust benefits to reflect real per capita income growth. For consistency with the emissions and benefits modeling, we used national population 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 2020 , we applied growth rates based on the U.S. Census Bureau projections to the U.S. Census estimate of national population in 2000. We used projections of real GDP provided in Kleckner and Neumann (1999) for the years 1990 to 2010. 21 We used projections of real GDP (in chained 1996 dollars) provided by Standard and Poor ’ s (2000) for the years 2010 to 2020

. 22

Using the method outlined in Kleckner and Neumann (1999) and the popul ation and income data described above, we calculated WTP adjustment factors for each of the elasticity estimates listed in Table 5 -15. Benefits for each of the categories (minor health effects, severe and chronic health effects, premature mortality, and vi sibility) 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

20 We expect that the WTP for improved visibility in Class 1 areas would also increase with growth in real income (see Chapter 6)

. 21 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 aver age GDP growth rates between 1999 and 2007. 22 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 re lative 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.5- 65 growth over the 20

- year cess ation 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 mor e 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 un derpredict 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 productivi ty (reflecting that if worker 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 th e cost

- of

- illness estimates in this RIA may underestimate ( on net ) the total economic value of avoided health impacts.

Table 5 -15. Adjustment Factors Used to Account for Projected Real Income Growth a

Benefit Category 2020 Minor Health Effect 1.07
Severe and Chronic Health Effects 1.22 Premature Mortality 1.20

a Based on elasticity values reported in Table 5

- 3, U.S. Census population projections, and projections of real GDP

per capita.

5.7 Benefits Results

5.7.1 Benefits of the Revised and Alternative Annual Primary PM 2.5 Standard s Applying the impact and valuation functions described previously in this chapter to the estimated changes in PM 2.5 yields estimates of the changes in physical damages (e.g., premature mortalities, cases of acute bronchitis and hospi tal admissions) and the associated monetary values for those changes. Not all known PM health effects could be quantified or monetized. The monetized value of these unquantified effects is represented by adding an unknown “B” to the aggregate total. The es timate of total monetized health benefits is thus equal to the subset of monetized PM

- related health benefits plus B, the sum of the non

-5- 66 monetized health benefits and welfare co

- benefits; this B represents both uncertainty and a bias in this analysis, as it reflects those benefits categories that we are unable to monetize in this analysis.

Table 5

- 16 shows the population

- weighted air quality change for the alternative standards av eraged across the continental U.S. Tables 5

- 17 through 5 -22 present the benefits results for the annual PM 2.5 standards. These benefits are relative to a 2020 analytical baseline reflecting attainment nationwide of the current primary PM 2.5 standards (i.e., 15/35) that includes promulgated national regulations and illustrative emission controls to simulate attainment with 15/35 as well as a n adjustment to NO x emission s to reflect expected reductions in mobile NO x emissions between 2020 and 2025. 23 Figur e 5

- 3 graphicall y display s the total monetized benefits of the revised annual primary standard of 12 μg/m 3

, using alternative concentration

- response functions at discount rates of 3% and 7%. Figure 5

- 4 graphically displays the cumulative distribution of to tal monetized benefits using the 2 epidemiology

derived and the 12 expert

- derived relationships between PM 2.5 and mortality for the revised standard , which provides the full range of uncertainty within and across the expert

- derived relationships

. Table 5

- 16

. Population

- Weighted Air Quality Change for the Revised and Alternative Annual Primary PM 2.5 Standards Relative to Analytical Baseline

Standard Population

- Weighted Air Quality Change

13 μg/m 3 0.014 μg/m 3

12 μg/m 3 0.04 3 μg/m 3

11 μg/m 3 0.207 μg/m 3

23 The estimates in this chapter reflect incremental emissions reductions from an analytical baselin e that gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.5- 67 Table 5 -17. Emission Reductions in Illustrative Emission Reduction Strategies for the Revised and Alternative Annual Primary PM 2.5 Standard s , by Pollutant and Region in 2020 (tons) a

13 μg/m 3 12 μg/m 3 11 μg/m 3
Directly emitted PM 2.5

East 0 0 8,200

West 0 0 160

CA 730 4,000 10,600

SO 2

East 0 0 21,000

West 0 0 43

CA 0 0 0

NOx

East 0 0 9

West 0 0 0

CA 0 0 0

a See Chapter 4 for more information on the illustrative emission reduction strategies. The emissions in this table

reflect both known and unknown controls.5- 68 Table 5 -18. Estimated number of Avoided PM 2.5 Health Impacts for the Revised and Alternative Annual Primary PM 2.5 Standards (Incremental to the Analytical Baseline ) a

Health Effect Revi sed and Alterative Annual Standards (95 th percentile confidence interval) 13 μg/m 3 12 μg/m 3 11 μg/m 3
Avoided Mortality Krewski et al. (2009) (adult mortality age 30+) 140 460 1,500 (100

-- 190) (320

-- 590) (1,000

-- 1,900) Lepeule et al. (2012) (adult mortality age 25+) 330 1,000 3,300 (180

-- 480) (560

-- 1,500) (1,800

-- 4,800) Woodruff et al. (1997) (infant mortality) 0 1 4 (0

-- 1) (1

-- 2) (2

-- 6) Avoided Morbidity Non

- fatal heart attacks

Peters et al. (2001) (age >18) 160 480 1,600 (49

-- 260) (150

-- 800) (480

-- 2,600) Pooled estimate of 4 studies (age >18) 17 52 170 (8

-- 41) (24

-- 130) (78

-- 410) Hospital admissions

— respiratory (all ages) b 31 110 380 (

- 9

-- 58) (

- 30

-- 200) (

- 100

-- 720) Hospital admissions

— cardiovascular (age > 18) 43 140 480 (20

-- 76) (66

-- 240) (230

-- 0,840) Emergency department visits for asthma ( all ages ) b 67 230 810 (

- 22

-- 140) (

- 74

-- 470) (

- 260

-- 1,600) Acute bronchitis (ages 8

– 12) b 280 870 2,700 (

- 36

-- 580) (

- 110

-- 1,800) (

- 350

-- 5,500) Lower respiratory symptoms (ages 7– 14) 3,500 11,000 34,000 (1500

-- 5500) (4,900

-- 17,000) (15,000

-- 53,000) Upper respiratory symptoms (asthmatics ages 9

– 11) 5,100 16,000 49,000 (1300

-- 8900) (4,100

-- 28,000) (12,000

-- 86,000) Asthma exacerbation (asthmatics ages 6

– 18) 13,000 40,000 120,000 (270

-- 81000) (850

-- 250,000) (2,600

-- 770,000) Lost work days (ages 18

– 65) 22,000 71,000 230,000 (19000

-- 25000) (61,000

-- 81,000) (190,000

-- 260,000) Minor restricted

- activity days (ages 18

– 65) 130,000 420,000 1,300,000 (110,000

-- 150,000) (350,000

-- 490,000) (1,100,000

-- 1,600,000)

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. These

estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those tw o areas will not be realized until 2025. Additional health endpoints , such as cardiovascular emergency department visits, are only quantified in a sensitivity analysis in Table 5.A -6 because we do not yet have a valuation estimate for this endpoint.
b The negative estimates at the 5 th percentile confidence estimates for these morbidity endpoints reflect the

statistical power of the study used to calculate these health impacts. These results do not suggest that reducing air pollution results in additional health impacts.5- 69 Table 5 -19. Monetized PM 2.5 Health Benefits for the Revised and Alternative Annual Primary PM 2.5 Standards (Incremental to Analytical Baseline ) (Millions of 2006$, 3% discount rate) a

Health Effect Revised and Annual Standards (95 th percentile confidence interval) 13 μg/m 3 12 μg/m 3 11 μg/m 3 Avoided Mortality b Krewski et al. (2009) (adult mortality age 30+) $1,300 $4,000 $13,000 ($120

-- $3,500) ($370

-- $11,000) ($1,200

-- $35,000) Lepeule et al. (2012) (adult mortality age 25+) $2,900 $9,000 $29,000 ($250

-- $8,100) ($800

-- $26,000) ($2,600

-- $82,000) Woodruff et al. (1997) (infant mortality) $3.4 $11 $35 ($0.29

-- $10) ($0.91

-- $32) ($3.0

-- $100) Avoided Morbidity Non

- fatal heart attacks

Peters et al. (2001) (age >18) $18 $55 $180 ($3.0

-- $46) ($9.1

-- $140) ($31

-- $460) Pooled estimate of 4 studies (age >18) $2.0 $6.0 $20.0 ($0.43

-- $6.8) ($1.3

-- $21) ($4.4

-- $68) Hospital admissions

— respiratory (all ages) c $0.86 $3.0 $11 (

- $0.22

-- $1.6) (

- $0.8

-- $5.5) (

- $2.7

-- $20) Hospital admissions

— cardiovascular (age > 18) $1.70 $5.3 $18 ($0.85

-- $2.8) ($2.7

-- $9.2) ($10

-- $32) Emergency department visits for asthma ( all ages ) $0.03 $0.10 $0.34 (

- $0.0052

-- $0.061) (

- $0.018

-- $0.21) (

- $0.063

-- $0.73) Acute bronchitis (ages 8

– 12) c $0.13 $0.42 $1.30 (

- $0.0060

-- $0.37) (

- $0.019

-- $1.2) (

- $0.059

-- $3.5) Lower respiratory symptoms (ages 7

– 14) $0.08 $0.24 $0.71 ($0.025

-- $0.10) ($0.078

-- $0.47) ($0.24

-- $1.4) Upper respiratory symptoms (asthmatics ages 9

– 11) $0.17 $0.54 $1.6 ($0.038

-- $0.42) ($0.12

-- $1.30) ($0.36

-- $4.0) Asthma exacerbation (asthmatics ages 6

– 18) $0.7 $2.30 $7.0 ($0.027

-- $5.2) ($0.085

-- $16) ($0.26

-- $49) Lost work days (ages 18

– 65) $3.3 $11 $35 ($2.90

-- $3.70) ($9.4

-- $12) ($30

-- $39) Minor restricted

- activity days (ages 18

– 65) $8.8 $29 $91 ($4.70

-- $13) ($15

-- $43) ($48

-- $140)

a All estimates are rounded to two significant digits. Estimates do not include unquantified health benefits noted in Table 5 -2

or Section 5.6.5 or welfare co

- benefits noted in Chapter 6. These estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.
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.

c The negative estimates at the 5 th percentile confidence estimates for this morbidity endpoint reflect s the statistical power of

the study used to calculate these health impacts. These results do not suggest that reducing air pollution results in additio nal health impacts.5- 70 Table 5

- 20

. Monetized PM 2.5 Health Benefits for the Revised and Alternative Annual Primary PM 2.5 Standards (Incremental to Analytical Baseline ) (Millions of 2006$, 7% discount rate) a

Health Effect Revised and Alterative Annual Standards (95 th percentile confidence interval) 13 μg/m 3 12 μg/m 3 11 μg/m 3 Avoided Mortality b Krewski et al. (2009) (adult mortality age 30+) $1,100 $3,600 $11,000 ($110

-- $3,100) ($330

-- $9,800) ($1,100

-- $31,000) Lepeule et al. (2012) (adult mortality age 25+) $2,600 $8,100 $26,000 ($230

-- $7,300) ($720

-- $23,000) ($2,300

-- $74,000) Woodruff et al. (1997) (infant mortality) $3.4 $11 $35 ($0.29

-- $100) ($0.91

-- $32) ($3.0

-- $100) Avoided Morbidity

Non

- fatal heart attacks

Peters et al. (2001) (age >18) $18 $54 $180 ($2.8

-- $46) ($8.4

-- $140) ($28

-- $450) Pooled estimate of 4 studies (age >18) $1.9 $5.9 $20 ($0.40

-- $6.7) ($1.2

-- $20) ($4.1

-- $67) Hospital admissions

— respiratory (all ages) c $0.86 $3.0 $11 (

- $0.22

-- $1.6) (

- $0.8

-- $5.5) (

- $2.7

-- $20) Hospital admissions

— cardiovascular (age > 18) $1.70 $5.3 $18 ($0.85

-- $2.8) ($2.7

-- $9.2) ($10

-- $32) Emergency department visits for asthma ( all ages ) $0.03 $0.10 $0.34 (

- $0.0052

-- $0.061) (

- $0.018

-- $0.21) (

- $0.063

-- $0.73) Acute bronchitis (ages 8

– 12) c $0.13 $0.42 $1.3 (

- $0.0060

-- $0.37) (

- $0.019

-- $1.2) (

- $0.059

-- $3.5) Lower respiratory symptoms (ages 7– 14) $0.08 $0.24 $0.71 ($0.025

-- $0.10) ($0.078

-- $0.47) ($0.24

-- $1.4) Upper respiratory symptoms (asthmatics ages 9

– 11) $0.17 $0.54 $1.6 ($0.038

-- $0.42) ($0.12

-- $1.30) ($0.36

-- $4.0) Asthma exacerbation (asthmatics ages 6

– 18) $0.7 $2.30 $7.0 ($0.027

-- $5.2) ($0.085

-- $16) ($0.26

-- $49) Lost work days (ages 18

– 65) $3.3 $11 $35 ($2.90

-- $3.70) ($9.4

-- $12) ($30

-- $39) Minor restricted

- activity days (ages 18

– 65) $8.8 $29 $91 ($4.70

-- $13) ($15

-- $43) ($48

-- $140)

a All estimates are rounded to two significant digits. Estimates do not include unquantified health benefits noted in Table 5 -2

or Section 5.6.5 or welfare co

- benefits noted in Chapter 6. These estimates reflect incremental emissions reductions from an analy tical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.
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 th e SAB

- recommended 20

- year segmented lag structure.

c The negative estimates at the 5 th percentile confidence estimates for this morbidity endpoint reflects the statistical power of

the study used to calculate these health impacts. These results do not sugg est that reducing air pollution results in additional health impacts.5- 71 Table 5-21. Total Estimated Monetized Benefits of the for Revised and Alternative Annual Primary PM 2.5 Standards (Incremental to the Analytical Baseline ) ( b illions of 2006$) a ,b

Benefits Estimate 13 μg/m 3 12 μg/m 3 11 μg/m

Economic value of avoided PM 2.5

- related morbidities and premature deaths using PM 2.5 mortality estimate from Krewski et al. (2009)

3% discount rate $ 1.3 + B $ 4.0 +B $ 13 + B
7% discount rate $ 1.2 + B $ 3.6 +B $ 12 + B
Economic value of avoided PM 2.5

- related morbidities and premature deaths using PM 2.5 mortality estimate from Lepeule et al. (20 12 )

3% discount rate $ 2.9 + B $ 9.1 +B $ 29 + B
7% discount rate $ 2.6 + B $ 8.2 +B $ 26 + 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 assumes discounting over the SAB

recommended 20

- year segmented lag structure. N ot 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.

b These estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment”

to the San Joaquin and South Coast areas in California for NOx emissions reductions exp ected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.

Table 5 -22. Regional Breakdown of Monetized Benefits Results

Region Revised and Alterative Annual Standards 13 μg/m 3 12 μg/m 3 11 μg/m 3

East a 0% 0% 23 %
California b 100 % 100 % 77 %
Rest of West 0% 0% <1%

a Includes Texas and those states to the north and east. Several recent rules such as MATS and CSAPR will have

substantially reduced PM 2.5 levels by 2020 in the East, thus few additional control s would be needed to reach 12 μg/m 3

. b For 12 and 13 μg/m 3

, all of the benefits occur in California because this highly populated area is where the most

air quality improvement beyond the analytical baseline is needed to reach these levels. These estimates reflect incremental emissions reductions from an analytical baseline t hat gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.5- 72 Figure 5- 3. Estimated PM 2.5 - Related Premature Mortalities Avoided According to Epidemiology or Expert

- Derived PM 2.5 Mortality Risk Estimate for 12 μg/m 3 in 2020 a

a These estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment”

to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas ar e expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025. $0 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000 $14,000

Krewski et al. Lepeuele et al.Millions (2010$)

PM 2.5 mortality benefits estimates derived from 2 epidemiology functions and 12 expert functions 3% Discount Rate
7% Discount Rate5- 73 Figure 5- 4. Total Monetized Benefits Using 2 Epidemiology

- Derived and 12

- Expert Derived Relationsh ips Between PM 2.5 and Premature Mortality for 12 μg/m 3 in 2020 a

a These estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment”

to the San Joaquin and South Coast areas in California for NOx emissions reduc tions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.

5.7.2 Uncertainty in Benefits Results Mortality benefits account for 98% of total monetized benefits, in part because we are unable to quantify most of the non

- health benefits. The next largest benefit is for reductions in chronic illness (nonfatal heart attacks), although this value is more t han an order of magnitude lower than for premature mortality. Hospital admissions for respiratory and cardiovascular causes, MRADs and work loss days account for the majority of the remaining benefits. The remaining categories each account for a small perc entage of total benefit; however, they represent a large number of avoided incidences affecting many individuals. A comparison of the incidence table to the monetary benefits table reveals that there is not always a close correspondence between the number of incidences avoided for a given endpoint and the monetary value associated with that endpoint. For example, we estimate almost 1 , 00 0 times more work loss days would be avoided than premature mortalities, yet work loss days account 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

$0 $5 $10 $15 $20 $25 $30Cumulative Percentage of Monetized Benefits

Monetized Benefits (Billions of 2010$) Expert A Expert B Expert C Expert D Expert E Expert F Expert G Expert H Expert I Expert J Expert K Expert L Krewski et al. Lepeule et al.5- 74 for only a very small f raction of total monetized benefits. This reflects the fact that 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.

PM 2.5 mortality benefits represent a substantial proportion of total monetized benefits (over 98% in this analysis), and these estimates h ave the following key assumptions and uncertainties.

1. 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 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 mul tiple 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).

2. We assume that the health impact function for fine particles is lo g- 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.

3. 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 prematu re 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.

Given that reductions in premature mortality dominate the size of the overall monetized benefits, more focus on uncertainty in mortality

- related benefits gives us greater confidence in our uncertainty characterization surrounding total benefits.

5.7.3 Estimated Life Years Gained and Reduction in the Percentage of Deaths Attributable to PM 2.5 In their 2008 review of the EPA ’ s approach to estimating ozone

- related mortality benefits, NRC indicated, “ EPA should consider placing greater emphasis on reporting decreases5- 75 in age

- sp ecific 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 per spective, life

years lost might be more relevant than annual number of mortality cases ” (NRC, 2002). This advice is consistent with that of the 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 a). 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 PM 2.5 result ing from the illustrative emission reduction strategies to reach the revised annual primary standard . The EPA included similar estimates of life years gained in a previous assessment of PM 2.5 benefits (U.S. EPA, 2006a, 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 at the outset. Life expectancy varies by age. CDC de fines 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 t he past 50 years, average life expectancy at birth in the U.S. has increased by 8.4 years (CDC, 2001). 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 ref er 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).

5.7.3.1 Estimated Life Years Gained For estimating the potential life years gained by reducing exposure to PM 2.5 in the U.S. adult population, we use the same general approach as Hubbell (2006) and Fann et al. (2 012a). We have not estimated the change in average life expectancy at birth in this RIA. Hubbell (2006) estimated that reducing exposure to PM 2.5 from air pollution regulations may result in an average gain of 15 years of life for those adults prematurely dying from PM 2.5 exposure. In contrast, Pope et al. (2009) estimated changes in average life expectancy at birth over a twenty year period, suggesting that reducing exposure to air pollution may increase average life5- 76 expectancy at birth by approximately 7 months, which was 15% of the overall increase in life expectancy at birth from 1980 through 2000. These results are not necessarily inconsistent because they are reporting different metrics. Because life expectancy is an average of the entire population (including both those whose deaths would likely be attributed to PM exposure as well as those whose deaths would not), average life expectancy changes associated with PM exposure would be expected to always be significantly smaller than the average number of life years lost by an individual who is projected to die prematurely from PM exposure.

To estimate the potential distribution of life years gained for population subgroups defined by the age range at which their reduction in PM 2.5 exposure is modeled to o ccur we use standard life tables available from the CDC (2003) and the following formula:

Total Life Years = ∑ LE i × M i n i=1 (5

. 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 estimate the number of avoided premature deaths attributed to changes in PM 2.5 exposure in 2020), 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 r ates 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 5 -23 summarizes the modeled number of life years gained by reducing PM 2.5 exposure to 12 μg/m 3
in 2020. We then calculated the average number of life years gained per avoided premature
mort ality. Figure 5

- 5 shows the potential life years gained as a result of meeting a primary standard of 12 μg/m 3 in 2020, partitioned by the age when exposure reduction occurred, not necessarily age at death.

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, 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 based on changes in PM 2.5 exp osure in 2020. W e estimate that the average individual who would otherwise have died prematurely from PM exposure would gain 16 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 PM or whether that5- 77 susceptibility was in and of itself cause d by PM exposure (for a more complete discussion of this issue, see Kunzli et al., 2001).

Table 5 -23. Sum of Life Years Gained by Age Range Attributed to the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020 a,b

Age Range b Krewski et al. (2009) Risk Coefficient c Lepeule et al. (20 12 ) Risk Coefficient

25

– 29

— 610

30

– 34 210 470

35

– 44 550 1,200

45

– 54 1,000 2,300

55

– 64 1,600 3,500

65

– 74 1,700 3,900

75

– 84 1,300 2,900

85

– 99 560 1,300 Total life years gained 7,000 16,000 Average life years gained per individual 15.0 16.0

a Estimates rounded to two significant figures.
b Because we assume that there is a “ cessation ” lag between PM exposures and the reduction in the risk of

premature death, there is uncertainty regarding the specific ages that people die relative to the change in exposure.

c The youngest age in the population cohort of this study is 30.5- 78 Figure 5-5. Estimated Life Years Gained as a Result of the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020, Partitioned by the Age When Exposure Reduction Occurred, Not Necessarily Age at Death a

a As shown in these charts, slightly more than half of the avoided premature deaths occur in populations age 75

–

99, but slightly more than half of the avoided life years occur in populati ons age <65 due to the fact that the younger populations would lose more life years per death than older populations. Results would be similar for other standard levels on a percentage basis. Because we assume that there is a “ cessation ” lag between PM exp osures and the reduction in the risk of premature death, there is uncertainty regarding the specific ages that people die relative to the change in exposure.
b The youngest age in the population cohort of this study used to estimate PM 2.5 mortality incidence is 30.

5.7.3.2 Percent of PM

- related Mortality Reduced To estimate the percentage of all

- cause mortality attributed to reduced PM 2.5 exposure in 2020 as a result of the illustrative emission reduction strategies, we use M i from the equation above, dividing the number of excess deaths estimated for each alternative standard by the total number of deaths in each county. Table 5 -24 shows the reduction in all

- cause mortality attributed to reducing PM 2.5 exposure to the revised annual standard of 12 μg/m 3 in 2020. Figure 5 -6 shows the percentage of avoided premature deaths attributed to meeting the revised primary annual standard of 12 μg/m 3 in 2020, partitioned by the age when exposure reduction occurred, not necessarily age at death. Age 30

- 34

Age 35

- 44

Age 45

- 54

Age 55

- 64 Age 65

- 74 Age 75

- 84 Age 85

- 99 Age 25

- 29 Age 30

- 34

Age 35

- 44

Age 45

- 54

Age 55

- 64 Age 65

- 74 Age 75

- 84 Age 85

- 99

Calculated using Lepeule et al. (2012) risk coefficient Calculated using Krewski et al. (2009) risk coefficient b5- 79 Table 5 -24. Estimat ed Reduction in the Percentage of All

- Cause Mortality Attributed to the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020 a,b

Age Range b Krewski et al. (2009) Risk Coefficient c Lepeule et al. (2012) Risk Coefficient

25

– 29

— 0.80%

30

– 34 0.35% 0.78%

35

– 44 0.32% 0.73%

45

– 54 0.32% 0.73%

55

– 64 0.33% 0.73%

65

– 74 0.33% 0.73%

75

– 84 0.32% 0.73%

85

– 99 0.29% 0.65%

a Rounded to two significant figures. Results would be similar for other standard levels on a percentage basis.

These estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas ar e expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.
b Because we assume that there is a “ cessation ” lag between PM exposures and the reduction in the ri sk of

premature death, there is uncertainty regarding the specific ages that people die relative to the change in exposure.

c The youngest age in the population cohort of this study is 30.
The relative distributions of the potential number of life years ga ined (Figure 5

- 5) and the estimated avoided mortalities (Figure 5

- 6) would be similar across alternative annual standards. Because reduction in PM exposure is not associated with an immediate improvement in chronic health conditions, we assume that there i s a “cessation” lag between PM exposures and the reduction in the risk of premature death. There is uncertainty regarding the specific ages at which people “avoid” death relative to the change in exposure. While the structure of the lag is uncertain, some studies suggest that most of the premature deaths are avoided within the first 3 years after the change in exposure, while others are unable to identify conclusively a critical window of exposure (U.S. EPA, 2004c; Schwartz, 2008; Krewski et al. 2009). Thes e studies did not examine whether the cessation lag was modified by either age at the time when exposure is reduced or the extent of cumulative lifetime exposure.5- 80 Figure 5-6. Estimate of Avoided Premature Deaths Attributed to the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020 , Partitioned by the Age When Exposure Reduction Occurred, Not Necessarily Age at Death a

a As shown in these charts, slightly more than half of the avoided premature deaths occur in populations age 75

-

99, but slightly more than half of the avoided life years occur in populations age <65 due to the fact that the younger populations would lose more life years per death than older populations. Results would be similar for other standard levels on a percentage basis. Because we assume that there is a “ cessation ” lag between PM exposures and the reduction in the risk of premature death, there is uncer tainty regarding the specific ages that people die relative to the change in exposure.

b The youngest age in the population cohort of this study is 30.

5.7.4 Evaluation of Mortality Impacts Relative to Various Concentration Benchmarks In this analysis, we estimate the number of avoided PM 2.5 - related deaths occurring due
to PM 2.5 reductions down to various PM 2.5 concentration benchmarks, including the Lowest Measured Level (LML) of each long

- term PM 2.5 mortality study. This analysis is one of several sensiti vities that the EPA has historically performed that characterize the uncertainty associated with the PM

- mortality relationship and the economic value of reducing the risk of premature death (Roman et al., 2008; U.S. EPA, 2006a, 2011a; Mansfield, 2009).

Our review of the current body of scientific literature indicates that a log

- linear no

threshold model provides the best estimate of PM

- related long

- term mortality. The PM ISA (U.S. EPA, 2009b), which was twice reviewed by the EPA ’ s Clean Air Scientific Advis ory Committee (U.S. EPA

- SAB, 2009a, 2009b), concluded that the evidence supports the use of a

no

- threshold log

- linear model while also recognizing potential uncertainty about the exact Age 30

- 34 Age 35

- 44 Age 45

- 54

Age 55

- 64

Age 65

- 74 Age 75

- 84 Age 85

- 99 Age 25

- 29 Age 30

- 34 Age 35

- 44 Age 45

- 54

Age 55

- 64

Age 65

- 74 Age 75

- 84 Age 85

- 99

Calculated using Krewski et al. (2009) risk coefficient b Calculated using Lepeule et al. (2012) risk coefficient

.5- 81 shape of the concentration

- response function. 24 Consistent with this fin ding, we estimate benefits associated with the full range of PM 2.5 exposure in conjunction with sensitivity analyses to recognize the potential uncertainty at lower concentrations. Specifically, we incorporated a LML assessment, a method the EPA has employed in several recent RIAs (U.S. EPA, 2010g, 2011c, 2011d). In addition, we have incorporated an assessment using specific concentration benchmarks identified in the EPA ’ s Policy Assessment for Particulate Matter (U.S. EPA, 2011b).

These two approaches summarize the distribution of avoided PM 2.5 - related mortality impacts relative to baseline (i.e., pre

- rule) annual mean PM 2.5 levels. The LML approach compares the percentage of avoided premature deaths estimated to occur above and below the minimum observed a ir quality level of each long

- term cohort study we use to quantify PM. In the air quality benchmark approach, we summari ze the impacts occurring at different points in the distribution of the air quality data used in these same epidemiology studies.

O ur confidence in the estimated number of premature deaths avoided (but not in the existence of a causal relationship between PM and premature mortality) diminishes as we estimate these impacts in locations where PM 2.5 levels are below the LML. This interpreta tion is consistent with the Policy Assessment (U.S. EPA, 2011b) and advice from SAB

- CASAC during their peer review (U.S. EPA

- SAB, 2010d). As noted in the preamble to the final rule, the Policy Assessment (U.S. EPA, 2011b) concludes that the range from the 25 th to the 10 th percentile is a reasonable range of the air quality distribution below which we start to have appreciably less confidence in the magnitude of the associations observed in the epidemiological studies. 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 at are used to estimate the benefits. Likewise, we are less confident in the risk we estim ate from simulated PM 2.5 concentrations that fall below the bulk of the observed data in these studies. 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

. For these reasons, we consider the LML as well as one standard deviation below the

mean 25 air quality levels when characterizing the distribution of mortality impacts. It is

24 F or a summary of the scientific review statements regarding the lack of a threshold in the PM 2.5 - mortality

relationship, see the Technical Support Document (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, 2010f). 25 A range of one standard deviation around the mean represents approximately 68 percent of normally

distributed data, and, below the mean falls between the 25 th and 10 th percentiles.5- 82 important to emphasize that “ less confidence ” does not mean “ no confidence .” In addition, while we may have less confidence in the magnitude of the risk, we still have high confidence that P M 2.5 is causally associated with risk at those lower air quality concentrations. To clarify this concept, Figure 5 -7 graphically displays the spectrum of confidence using illustrative concentration benchmarks from the major epidemiology studies cited in th is chapter.

Figure 5-7. Relationship between the Size of the PM Mortality Estimates and the PM 2.5 Concentration Observed in the Epidemiology Study Although these types of concentration benchmark analyses (e.g., LML , one standard deviation below the mean , etc. ) provide some insight into the level of uncertainty in the estimated PM 2.5 mortality benefits, the EPA does not view these concentration benchmarks as a concentration threshold below which we would not quantify health benefits of air quality improvem ents Rather, the core benefits estimates reported in this RIA (i.e., those based on Krewski et al. [ 2009 ] and Lepeule et al. [ 2012 ] ) are the best measures because they reflect the full range of modeled air quality concentrations associated with the emissio n reduction strategies. In reviewing the Policy Assessment , SAB

- CASAC confirmed that “ [a]lthough there is increasing uncertainty at lower levels, there is no evidence of a threshold (i.e., a level below which there is no risk for adverse health effects) ” ( U.S. EPA

- SAB, 2010d). In addition, in reviewing the Costs and Benefits of the Clean Air Act (U.S. EPA, 2011a), the SAB

- HES noted that

“ [t]his [no

- threshold] decision is supported by the data, which are quite consistent in showing effects down to the lowest measured levels. Analyses of cohorts using data from more recent years, during which time PM concentrations have fallen, continue to report strong associations with mortality ” (U.S. EPA

- SAB, 2010a). Therefore, the best estimate of benefits includes5- 83 estima tes below and above these concentration benchmarks but uncertainty is higher in the magnitude of health benefits estimated at lower concentrations, with the lowest confidence below the LML. Estimated health impacts reflecting air quality improvements below and above these concentration benchmarks are appropriately included in the total benefits estimate. In other words, our higher confidence in the estimated benefits above these concentration benchmarks should not imply an absence of confidence in the benef its estimated below these concentration benchmarks.

We estimate that most of the avoided PM

- related impacts we estimate in this analysis occur among populations exposed at or above the LML of the Lepeule et al. (2012) study, while a majority of the impacts occur at or above the LML of the Krewski et al. (2009) study. We show the estimated reduction in incidence of premature mortality above and below the LML or air quality benchmarks of these studies in Tables 5 -25 , and we graphically display the distributio n of PM 2.5 - related mortality impacts for the selected standard in Figures 5-8 and 5 -9

. Table 5 -25. Estimated Reduction in Incidence of Adult Premature Mortality Occurring Above and Below Various Concentration Benchmarks in the Underlying Epidemiology Studies a

Revised and Alternative Standard Level Epidemiology Study Total Reduced Mortality Incidence Allocation of Reduced Mortality Incidence
Below 1 S td

. D ev . B elow AQ M ean At or Above 1 S td . D ev . B elow AQ M ean Below LML At or Above

LML
13 μg/m 3 Krewski et al. (2009) 140 79 66 6 140 (54%) (46%) (4%) (96%) Lepeule et al. (2012) 330 N/A N/A 130 200 (38%) (62%)
12 μg/m 3 Krewski et al. (2009) 460 200 260 14 440 (43%) (57%) (3%) (97%) Lepeule et al. (2012) 1,000 N/A N/A 310 720 (30%) (70%)
11 μg/m 3 Krewski et al. (2009) 1,500 690 770 27 1,400 (47%) (53%) (2%) (98%) Lepeule et al. (2012) 3,300 N/A N/A 1,000 2,300 (31%) (69%)

a Mortality incidence estimates are rounded to whole numbers and two significant digits, so estimates may not

sum across columns. One standard deviation below the mean is equivalent to the middle of the range between the 10 th and 25 th percentile. For Krewski , 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.5- 84 Of total PM 2.5 - Related deaths avoided for 12 μg/m 3 : 97 % occur among populations exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study. 70 % occur among populations exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study.
Figure 5-8. Number of Premature PM 2.5 - related Deaths Avo ided for the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020 According to the Baseline Level of PM 2.5 and the Lowest Measured Air Quality Levels of Each Mortality Study 0% 5% 10% 15% 20% 25%

<1 1

- 2 2

- 3 3

- 4 4

- 5 5

- 6 6

- 7 7

- 8 8

- 9 9

- 10 10

- 11 11

- 12 12

- 13 13

- 14 14

- 15 15

- 16 16

- 17 17

- 18 18

- 19 19

- 20Percentage of Avoided PM2.5Mortalities

Baseline Annual Mean PM 2.5 Level (μg/m 3 ) LML of Lepeule et al. (2012) study LML of Krewski et al. (2009) study5- 85 Of total PM 2.5 - Related deaths avoided for 12 μg/m 3 : 97 % occur among populations exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study. 70 % occur among populations exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study.
Figure 5-9. Number of Premature PM 2.5 - related Deaths Avoided for the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020 According to the Baseline Level of PM 2.5 and the Lowest Measured Air Quality Levels of Each Mortality Study When interpreting these LML and air quality benchmarks res ults, it is important to understand that the avoided PM 2.5 deaths are estimated to occur from PM 2.5 reductions in the baseline air quality simulation, which assumes that 15/35 is already met. When simulating attainment with revised and alternative standard s, we adjust the design value at each monitor exceeding the standard alternative to equal that standard and use an air quality interpolation technique to simulate the change in PM levels surrounding that monitor. This technique tends to simulate the greate st air quality changes nearest the monitor. We estimate benefits using modeled air quality data with 12 km grid cells, which is important because the grid cells are often substantially smaller than counties and PM 2.5 concentrations vary spatially within a county. Therefore, there may be a small number of grid cells with concentrations slightly greater than 15 μg/m 3 in the gridded baseline even though all monitors could meet an annual standard of 15 μg/m 3 . In addition, some grid cells in a county can be belo w the level of a standard even though the highest monitor value is above that standard. Thus, emission 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 20Cumulative percentage of avoided PM Mortalities

Baseline Annual Mean PM 2.5 Level (μg/m 3 ) LML of Krewski et al. (2009) study LML of Lepeuele et al. (2012) study5- 86 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 simulating attainment can lead to benefits in counties that are below alternative standard. Emission reduction strategies designed to reduce PM 2.5 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 emission reduction strategies, it is appropriate to include all the benefits occurring as a result of the emission 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 alternative standards because it would omit benefits attributable to emission reductions in exceeding counties. Figure 5

- 10 provides an illustratio n of this concept.

As this illustration shows, because 12km grid cells are much smaller than counties and because PM 2.5 concentrations vary within a county, there can be modeled grid cells with PM 2.5 concentrations below an standard level even when the county exceeds that level. Because we model benefits using grid cells, this is a key reason why the LML graphs show benefits at levels below the alternative standards. In addition, emission reductions in an exceeding county can have benefits in a neighboring county that does not exceed. Figure 5- 10. Illustration of Relative Size of County with Exceeding Monitor and Modeled Grid Cells5- 87 While the LML of each study is important to consider when characterizing and interpreting the overall level of PM 2.5 - rel ated benefits, as discussed earlier in this chapter, the EPA believes that both of the cohort

- based mortality estimates are suitable for use in air pollution health impact analyses. When estimating PM

- related premature deaths avoided using risk coefficients drawn from the Lepeule et al. (2012) analysis of the Harvard Six Cities and the Krewski et al. (2009) analysis of the ACS cohorts there are innumerable other attributes that may affect the size of the reported effect estimates

— including differences in po pulation demographics, the size of the cohort, activity patterns and particle composition among others. The LML assessment presented here provides a limited representation of one key difference between the two studies.

5.7.5 Additional Sensitivity Analys es The details of these sensitivity analyses are provided in A ppendix 5 .A , and summarized here. The use of an alternate lag structure would change the PM 2.5 - related mortality benefits discounted at 3% discounted by between 10% and

– 27%; when discounted at 7%, these benefits change by between 22% and −52%. When applying higher and lower income growth adjustments, the monetary value of PM 2.5 - related premature mortality changes between 33% and −14%; the value of acute endpoints changes between 8% and −4%. Using t he updated population projection data , the rounded estimates of total monetized benefits increases by
4. 4 % for 12 μg/m 3 . These sensitivity analysis results would be similar on a percentage basis for the alternative annual standards.

5.8 Discussion

Th e analysis in this Chapter demonstrates the potential for significant health benefits of the illustrative emission controls applied to simulate attainment with the revised annual primary PM 2.5 standard. We estimate that by 2020 the emissions reductions to reach the revised annual primary standard would have reduced the number of PM 2.5 - related premature mortalities and produce substantial non

- mortality benefits. This rule promises to yield significant welfare impacts as well (see Chapter 6), though the quantification of those endpoints is absent in this RIA. Even considering the quantified and unquantified uncertainties identified in this chapter, we believe that the implementing the revised annual PM 2.5 standard of 12 μg/m 3 would have substantial public health benefits that outweigh the costs (see Chapter 7)

. 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 concentration5- 88 response relationships and our use of the expert elicitation

- derived PM 2.5 mortality functions. Others, including the projection of atmospheric conditions and source

- level emissions, the projection of baseline morbidity rates, incomes and technological development are unquantifie d. 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 PM NAAQS.

There are important differ ences worth noting in the design and analytical objectives of NAAQS RIAs compared to RIAs for implementation rules, such as the recent MATS rule (U.S. EPA, 2011d). The NAAQS RIAs illustrate the potential costs and benefits of a revised air quality standard nationwide based on an array of emission 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 emiss ion reduction strategies that States may choose to enact when implementing a revised NAAQS. The setting of a NAAQS does not directly result in costs or benefits, and as such, NAAQS RIAs are merely illustrative and are not intended to be added to the costs and benefits of other regulations that result in specific costs of control and emission reductions. By contrast, the emission reductions from implementation rules are generally for specific, well

- characterized sources, such as the recent MATS rule (U.S. EP A, 2011d). In general, the EPA is more confident in the magnitude and location of the emission reductions for implementation rules. As such, emission reductions achieved under promulgated implementation rules such as MATS have been reflected in the baselin e of this NAAQS analysis. S ubsequent implementation rules will be reflected in the baseline for the next PM NAAQS review. 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.

In setting the NAAQS, the EPA considers that PM 2.5 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 emission controls put in p lace to meet the standard of the highest monitor will simultaneously result in lower PM 2.5 concentrations throughout the entire area. In fact, the Quantitative R isk and E xposure A ssessment for Particulate Matter (U.S. EPA, 2010b) shows how different standard levels would affect the entire distribution of PM 2.5 concentrations, and thus people ’ s exposures and risk, across urban areas. For this reason, it is inappropriate to use the NAAQS level as a bright line for health effects.

The NAAQS are not set at levels that eliminate the risk of air pollution completely

. Instead, the Administrator sets the NAAQS at a level requisite to protect public health with an5- 89 adequate margin of safety, taking into consideration effects on susceptible populations based on the scientific literature

. The risk analysis prepared in support of this PM NAAQS reported risks below these levels, while acknowledging that the confidence in those effect estimates is higher at levels closer to the s tandard (U.S. EPA, 2010 b ). 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 PM 2.5 concentrations, there is no evidence of a threshold in
PM 2.5 - related health effects in the epidemiology literature. G iven that the epidemiological literature in most cases has not provided estimates based on threshold models, t here would be additional uncertainties imposed by assuming thresholds or other non

- linear concentration

response functions for the purposes of benefits analysis.

The estimated benefits for the revised and alternative annual standards are in addition to the substantial benefits estimated for several recent implementation rules (U.S. EPA, 2009a, 2011c, 2011d, 2011e). Rules such as MATS and other emission reductions will have substantially reduced ambient PM 2.5 concentrations by 2020 in the East, such that no additional controls would be needed to reach 12 μg/m 3 in the East beyond the analytical baseline

. 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 PM NAAQS RIA (U.S. EPA, 2006a) for the year 2020 and even smaller than the mortality risks estimated for the current year in the PM REA (U.S. EPA, 2010b)

. In addition, bec ause our analytical baseline excludes benefits associated with attaining the current annual and daily standards as well as the mobile NOx emissions anticipated by 2025, including th e benefits associated with those air quality improvements would result in h igher benefits than we have estimated here.

F or the revised annual standard of 12 μg/m 3 , all of the estimated benefits occur in California because this is the only state that needs additional air quality improvement beyond the analytical baseline after acc ounting for the air quality improvements from recent rules. Because all of the monetized human health benefits are projected to occur in California, we have consider ed the cohort studies conducted in California specifically in addition to the national risk coefficients we use as our core estimates . Although we have not calculated the benefits results using these cohort studies, we provided the risk coefficients from these California cohorts to show how much the monetized benefits c ould have changed if we us ed these cohort s tudies . Most of the California cohort studies report central effect estimates similar to the (nation

- wide) all

- cause mortality risk estimate we applied from Krewski et al.5- 90 (2009) and Lepeule et al. (20 12 ) albeit with wider confidence inter vals. Three cohort studies conducted in California indicate statistically significant higher risks than the risk estimates we applied from Lepeule et al. (2012) , and four studies showed insignificant results.

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- 001. November. Available on the Internet at <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86 e/151B1F83B023145585257678006836B9/$File/EPA

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- SAB). 2010 a

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— External Review Draft (September 2009)

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10

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- 003

- unsigned.pdf>.
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- 5.5.A -1 APPENDIX 5.A ADDITIONAL SENSITIVITY ANALYSES RELATED TO THE HEALTH BENEFI TS ANALYSIS

The analysis presented in Chapter 5 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 i mportant uncertainties. The majority of the analytical assumptions used to develop the main estimates of benefits have been reviewed and approved by EPA ’ s independent Science Advisory Board ( SAB ) . Both EPA and the SAB recognize that data and modeling limit ations as well as simplifying assumptions can introduce significant uncertainty into the estimates of benefits and that alternative choices exist for some inputs to the analysis, such as the concentration

- response functions for mortality

. This appendix supplements our main analysis of benefits with five additional sensitivity calculations. The supplemental estimates examine sensitivity to assumptions about both physical effects (i.e., the structure of the cessation lag; estimates of the number of avoided

ce rebrovascular events, cardiovascular emergency department visits and cases of chronic bronchitis; and alternate effect estimates for cohorts in California) and valuation issues ( i.e., the appropriate income elasticity , updated cost

- of

- illness estimates ). W e conducted these sensitivity analyses for the selected annual standard of 12 μg/m 3 as an illustrative example. These supplemental estimates are not meant to be comprehensive. Rather, they reflect some of the key issues identified by EPA or commenters as likely to have a significant impact on total benefits, or they are health endpoints for which the health data are still evolving, or for which we lack an appropriate method to estimate the economic value. The individual income growth and lag adjustments in the tables should not simply be added together because ( 1) there may be overlap among the alternative assumptions , and ( 2) the joint probability among certain sets of alternative assumptions may be low.

5.A .1 Cessation Lag Structure for PM 2.5 - Related P remature Mortality Based in part on prior advice from the EPA ’ s independent Science Advisory Board (SAB), EPA typically assumes that there is a time lag between reductions in particulate matter (PM) exposures in a population and the full realization of red uctions in premature mortality. Within the context of benefits analyses, this term is often referred to as “ cessation lag. ” The existence of such a lag is important for the valuation of reductions in premature mortality because economic theory suggests tha t dollar

- based representations of health effect incidence changes occurring in the future should be discounted to reflect time preferences for consumption in the5.A -2 population (e.g., people generally prefer to consume now rather than later and will generally give up greater consumption in the future for earlier consumption)

.
Over the last 15 years, there has been a continuing discussion and evolving advice regarding the timing of changes in health effects following changes in ambient air pollution. It has been hypothesized that some reductions in premature mortality from exposure to ambient
PM 2.5 will occur over short periods of time in individuals with compromised health status, but other effects are likely to occur among individuals who, at baseline, have rea sonably good health that will deteriorate because of continued exposure. The SAB

- HES has recognized this lack of direct evidence. However, in early advice, they also note that “ although there is substantial evidence that a portion of the mortality effect of PM is manifest within a short period of time, i.e., less than one year, it can be argued that, if no lag assumption is made, the entire mortality excess observed in the cohort studies will be analyzed as immediate effects, and this will result in an overestimate of the health benefits of improved air quality. Thus some time lag is appropriate for distributing the cumulative mortality effect of PM in the population ” (EPA

- SAB

- COUNCIL

- ADV

- 00

- 001, 1999, p. 9). In more recent advice, the SAB

- HES suggests that appropriate lag structures may be developed based on the distribution of cause

- specific deaths within the overall all

- cause estimate (EPA

- SAB

- COUNCIL

- ADV

- 04

- 002, 2004). They suggest that diseases with longer progressions should be characterized by longer -t erm lag structures, while air pollution impacts occurring in populations with existing disease may be characterized by shorter

- term lags.

A key question is the distribution of causes of death within the relatively broad categories analyzed in the long

- term cohort studies. Although it may be reasonable to assume the cessation lag for lung cancer deaths mirrors the long latency of the disease, it is not at all clear what the appropriate lag structure should be for cardiopulmonary deaths, which include both re spiratory and cardiovascular causes. Some respiratory diseases, such as chronic obstructive pulmonary disease, may have a long period of progression, while others, such as pneumonia, have a very short duration. In the case of cardiovascular disease, there is an important question of whether air pollution is causing the disease, which would imply a relatively long cessation lag, or whether air pollution is causing premature death in individuals with preexisting heart disease, which would imply very short ces sation lags (in theory, air pollution may both cause cardiovascular disease and cause premature death in those with preexisting cardiovascular disease). The SAB

- HES provides several recommendations for future research that could support the development of defensible lag structures, including using disease

- specific lag models and constructing a segmented lag distribution to combine5.A -3 differential lags across causes of death (EPA

- SAB

- COUNCIL

- ADV

- 04

- 002, 2004). The SAB

- HES indicated support for using “ a Weibull distribution or a simpler distributional form made up of several segments to cover the response mechanisms outlined above, given our lack of knowledge on the specific form of the distributions ” (EPA

- SAB

- COUNCIL

- ADV

- 04

- 002, 2004, p. 24). However, they noted that “ an important question to be resolved is what the relative magnitudes of these segments should be, and how many of the acute effects are assumed to be included in the cohort effect estimate ” (EPA

- SAB

- COUNCIL

- ADV

- 04

- 002, 2004, p. 24

- 25). Since the pub lication of that report in March 2004, EPA has sought additional clarification from this committee. In its follow

- up advice provided in December 2004, this SAB suggested that until additional research has been completed, EPA should assume a segmented lag s tructure characterized by 30 % of mortality reductions occurring in the first year, 50 % occurring evenly over years 2 to 5 after the reduction in PM 2.5 , and 20 % occurring evenly over the years 6 to 20 after the reduction in PM 2.5 (EPA

- COUNCIL -LTR- 05

- 001, 2004). The distribution of deaths over the latency period is intended to reflect the contribution of short

- term exposures in the first year, cardiopulmonary deaths in the 2 - to 5

- year period, and long

- term lung disease and lung

ca ncer in the 6 - to 20

- year period. Furthermore, in their advisory letter, the SAB

- HES recommended that EPA include sensitivity analyses on other possible lag structures. In this appendix, we investigate the sensitivity of premature mortality

- reduction relat ed benefits to alternative cessation lag structures, noting that ongoing and future research may result in changes to the lag structure used for the main analysis.

In previous advice from the SAB

- HES, they recommended an analysis of 0

- , 8

- , and 15

- year lags, as well as variations on the proportions of mortality allocated to each segment in the segmented lag structure (EPA

- SAB

- COUNCIL

- ADV

- 00

- 001, 1999, (EPA

- COUNCIL -LTR- 05

- 001, 2004). The 0

- year lag is representative of EPA ’ s assumption in previous RIAs. The 8- and 15

- year lags are based on the study periods from the Pope et al. (1995) and Dockery et al. (1993) studies, respectively. 1 However, neither the Pope et al. nor Dockery et al. studies assumed any lag structure when estimating the relative risks from P M exposure. In fact, the Pope et al. and Dockery et al. analyses do not supporting or refute the existence of a lag. Therefore, any lag structure applied to the avoided incidences estimated from either of these studies will be an assumed structure. The 8 - and 15

- year lags implicitly assume that all premature mortalities occur at the end of the study periods (i.e., at 8 and 15 years).

1 Although these studies were conducted for 8 and 15 years, respectively, the choice of the duration of the study by the authors was not likely due to observations of a lag in effects but is more likely due to t he expense of conducting long

- term exposure studies or the amount of satisfactory data that could be collected during this time period.5.A -4 In addition to the simple 8 - and 15

- year lags, we have added several additional sensitivity analyses examining the impact of assuming different allocations of mortality to the segmented lag of the type suggested by the SAB

- HES. The first alternate lag structure assumes that more of the mortality impact is associated with chronic lung diseases or lung cancer and less with acute c ardiopulmonary causes. This illustrative lag structure (“ alternate segmented ” ) is characterized by 20 % of mortality reductions occurring in the first year, 50 % occurring evenly over years 2 to 5 after the reduction in PM 2.5 , and 30 % occurring evenly over t he years 6 to 20 after the reduction in PM 2.5 . The second alternate lag structure ( “5- year distributed ”) assumes the 5

- year distributed lag structure used in previous analyses, which is equivalent to a three

segment lag structure with 50 % in the first 2

- ye ar segment, 50 % in the second 3

- year segment, and 0 % in the 6 - to 20

- year segment. The third alternate lag structure assumes a smooth negative exponential relationship between the reduction in exposure and the reduction in mortality risk, which is describe d in more detail below.

In 2004, SAB

- HES ( U.S. EPA

- SAB, 2004) urged EPA to consider using smoothed lag distributions , incorporating information from the smoking cessation literature

. In June 2010, the SAB

- HES provided additional advice regarding alternate cessation lags (U.S. EPA

- SAB, 2010).
For PM 2.5 - related benefits, the SAB

- HES continued to support the previous 20

- year distributed lag as the main estimate. while recommending that EP A further examine additional exponential decay functions. Specifically, the SAB

- HES suggested varying the rate constant with the risk coefficient from in the cohort studies. EPA intends to incorporate these new alternate cessation lag for PM 2.5 - related benefits in the final PM NAAQS RIA.

In response to these suggestions, EPA identified Röösli et al. (2005) as model that combines empirical data on the relationship between changes in exposure and changes in mortality and the timing of the cessation of those e ffects for the smooth decay function

. 2
Because an exponential model is often observed in biological systems, Röösli et al. (2005) developed a dynamic model that assumes that mortality risks decrease exponentially after exposure termination. Th is model assumes the form risk = exp - kt , where k is the time constant and t is the time after t 0 . The relative risk from air pollution (RR) at a given time (t) can b e calculated from the excess relative risk (ERR) attributable to air pollu tion from PM cohort

s tudies ( ERR = RR -R 0 ), as follows:

2 In the 2006 PM NAAQS RIA (U.S. EPA, 2006), EPA applied equations and the time constant from a conference presentation by R öö sli et al. (2004). We have updated this sensitivity analysis in this assessment to reflect the published version in R öö sli et al. (2005) and generated additional time constants.5.A -5 RR ( t ) = ERR × exp − kt + R 0 ( 5.A

. 1)

where R 0 is the baseline relative risk in the absence of air pollution (R 0 =1). After cessation of exposure, mortality will start to decline and approach the baseline level. The change i n mortality ( Δ M), in units of percent

- years, can be derived from Equation ( 5.A

. 1) as follows:

∆M = ERR × t − ∫ ERR × exp − kt dt t 0 ( 5.A

. 2)

Integrating Equation ( 5.A

. 2) gives:

∆M = ERR × t − ERR k + ERR k × exp − kt ( 5.A

. 3)

In order to calculate values for the time constant, k, we applied the Δ M values from the two intervention studies that provide data on the time course of the change in mortality along with the ERR values from cohort studies on PM 2.5 - related mortality

. We applied t he intervention studies by C lancy et al. (2002) , which analyzed the change in mortality following the ban of coal sales in Dublin , and by Pope et al. (1992) , which examined the change in mortality resulting from the closure of a steel mill in the Utah Vall ey . We applied effect estimates from the American Cancer Society (ACS) cohort by Krewski et al. (2002) 3 and the Six Cities c ohort by Laden et al. (2006)

. 4 Applying combinations of these studies to equation 5.A.3 generates four estimates of k that range from 0.05 to 1.24

. For additional context, the time constant calculated using on a smoking cessation study (i.e., Leksell and Rabl (2001)) is in the middle of this range (k=0.10). For this sensitivity analysis, we applied a time constant of k= 0.45

as a reasonable parameter for the exponential decay function, but we acknowledge the range of estimates that we could have chosen. This k constant is calculated as the average of the average k constants corresponding to each cohort study. 5 Table 5.A .1 provi des the time constants for each of these combinations and averages, and Figure 5.A .2 illustrates the exponential decay lag structure s.

3 The relative risk coefficient from Krewski et al. (2009) (1.06 per 10 μg/m 3 change in average PM 2.5 exposure for all

- cause mortality) is the same as the risk estimate from Pope et al. (2002).
4 We have not updated this analysis to reflect the newest Six Cities cohort from Lepeule et al. (2012). While the relative risk coefficient from Lepeule et al. (2012) is slightly less than the relative risk coefficient from Laden et al. (2006), this differe nce is unlikely to have a substantial difference in the value of k. 5 The general approach for calculating the time constants based on the combination of the intervention study and cohort study is consistent with the 812 analysis (U.S. EPA, 2011), which was reviewed by SAB. However, in this analysis we have applied a single time constant (k=0.45) rather than presenting the monetized benefits results for every exponential lag function applying the various time constants.5.A -6 Table 5. A- 1. Values of the Time Constant (k) for the Exponential Decay Lag Function

Value of k PM 2.5 Cohort Study Intervention Study

0.05 H6C

— Laden et al. (2006) Dublin

— Clancy et al. (2002)

0.15 ACS

— Krewski et al. (200 9) Dublin

— Clancy et al. (2002)

0.37 H6C

— Laden et al. (2006) Utah Valley

— Pope et al. (1992)

1.24 ACS

— Krewski et al. (2009) Utah Valley

— Pope et al. (1992)

0.70 Average k for ACS

— Krewski et al. (2009)

0.21 Average k for H6C

— Laden et al. (2006)

0.45 Average of average k for each cohort study

The estimated impacts of alternative lag structures on the monetary benefits associated with reductions in PM

- related premature mortality (estimated using the effect estimate from Krewski et al. (2009) ) are presented in Table 5.A -2 . These monetized estimat es are calculated using the value of a statistical life (i.e., $6.3 million per incidence adjusted for inflation and income growth ) and are presented for both a 3 and 7 % discount rate over the lag period )

. The choice of mortality risk study and mortality valuation approach are described in detail in Chapter 5 of this RIA. Figure 5.A .1 illustrat es the cumulative distributions of the cessation lags applied in this appendix. Because we applied an income adjustment factor specific to the analysis year (see sec tion 5.6.8 of this RIA) , we do not adjust for income growth over the 20

- year cessation lag. This approach could underestimate the benefits for the later years of the lag.

The results of this sensitivity analyses demonstrate that because of discounting of delayed benefits, the lag structure may also have a large impact on monetized benefits, reducing benefits by 27% if an extreme assumption that no effects occur until after 15 years is applied at a 3% discount rate and 53% at a 7% discount rate. However, fo r most reasonable distributed lag structures, differences in the specific shape of the lag function have relatively small impacts on overall benefits. For example, the overall impact of moving from the previous
5- year distributed lag to the segmented lag r ecommended by the SAB

- HES in 2004 in the main estimate is relatively modest, reducing benefits by approximately 5% when a 3% discount rate is used and 9% when a 7% discount rate is used. If no lag is assumed, benefits are increased by approximately 10% rel ative to the segmented lag at a 3% discount rate and 22% at a 7% discount rate.5.A -7 Table 5.A-2. Sensitivity of Monetized PM 2.5 - Related Premature Mortality Benefits to Alternative Cessation Lag Structures, Using Effect Estimate from Krewski et al

. (2009)

Alternative Lag Structures for PM

- Related Premature Mortality 12 μg/m 3

Value (billion 2006$) a.b Percent Difference from Base Estimate
SAB Segmented (Main estimate) 30% of incidences occur in 1st year, 50% in years 2 to 5, and 20% in years 6 to 20

3% discount rate $4.0 N/A

7% discount rate $3.6 N/A No lag Incidences all occur in the first year

3% discount rate $4.4 10.4%
7% discount rate $4.4 22.5%

8- year Incidences all occur in the 8th year

3% discount rate $3.6 −10.3%

7% discount rate $2.7 −23.7%

15

- year Incidences all occur in the 15th year

3% discount rate $2.9 −27.0%
7% discount rate $1.7 −52.5%

Alternative Segmented 20% of incidences occur in 1st year, 50% in years 2 to 5, and 30% in years 6 to 20

3% discount rate $3.8 −3.2%
7% discount rate $3.3 −6.6%

5- Year Distributed 50% of incidences occur in years 1 and 2 and 50% in years 2 to 5
3% discount rate $4.2 4.9%
7% discount rate $3.9 9.4% Exponential Decay (k=0.45) Incidences occur at an exponentially declining rate

3% discount rate $4.2 5.0%
7% discount rate $3.9 9.9%

a Dollar values rounded to two significant digits. The percent difference using the monetized benefits estimated

using the Lepeule et al. (2012) study would be identical, but the value would be approximately 2. 3 times higher.5.A -8 Figure 5. A- 1. Alternate Lag Structures for PM 2.5 Premature Mortality (Cumulative)

Figure 5. A-2. Exponential Lag Structures for PM 2.5 Premature Mortality (Cumulative) 0% 25% 50% 75% 100%

1st year 2nd year 3rd year 4th year 5th year 6th year 7th year 8th year 9th year 10th year 11th year 12th year 13th year 14th year 15th year 16th year 17th year 18th year 19th year 20th year% Premature Mortality Occuring Within Year (Cumulative)

Year Following Reduction in PM 2.5 SAB segmented lag (main estimate) Alternate Segmented Lag Exponential decay model
5

- year distributed lag

0% 25% 50% 75% 100%1st year2nd year3rd year4th year5th year6th year7th year8th year9th year10th year11th year12th year13th year14th year15th year16th year17th year18th year19th year20th year21st year22nd year23rd year24th year25th year26th year27th year28th year29th year30th year% Premature Mortality Occuring Within Year (Cumulative)

Year Following Reduction in PM 2.5 k=0.05 (H6C/Dublin) k=0.37 (H6C/Utah) k=0.15 (ACS/Dublin) k=1.24 (ACS/Utah) k=0.70 (Average ACS) k=0.21 (Average H6C) k=0.45 (Overall Average) SAB Lag (Main Estimate)5.A -9 5.A .2 Income Elasticity of Willingness to Pay As discussed in Chapter 5, our estimates of monetized benefits account for growth in real GDP per capita by adjusting the WTP for individual endpoints based on the central estimate of the adjustment factor for each of the categories (minor health effects, severe and chronic health effects, premature mortality, and visibility). We examined how sensitive the estimate of total benefits is to alternative estimates of the income elasticities. Table 5.A

- 3 lists the ranges of elasticity values used to calculate the income adjustment factors, while Table 5.A

- 4 lists the ranges of corresponding adjustment factors. The results of this sensitivity analysis, giving the monetized benefit subtotals for the four benefit categories, are presented in Table 5.A -5.

Table 5.A -3. Ranges of Elasticity Values Used to Account for Projected Real Income Growth a

Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound

Minor Health Effect 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.

Table 5.A -4. Ranges of Adjustment Factors Used to Account for Projected Real Income Growth a

Benefit Category Lower Sensitivity B ound Upper Sensitivity Bound

Minor Health Effect 1.018 1.147 Premature Mortality 1.037 1.591

a Based on elasticity values reported in Table C

- 4, U.S. Census population projections, and projections of real GDP

per capita.5.A

- 10 Table 5.A -5. Sensitivity of Monetized Benefits to Alternative Income Elasticities a

Benefit Category Benefits Incremental to Analytical Baseline (Millions of 2006$)

12 μg/m 3
No adjustment Lower Sensitivity Bound Upper Sensitivity Bound

Minor Health Effect $30 $31 $35

Premature Mortality b $3,600 $3,800 $5,800

a All estimates rounded to two significant digits.
b Using mortality effect estimate from Krewski et al

. (2009) and 3 % discount rate. Results using Lepeule et al.

(20 12 ) or 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 2020 ranges from 86% to 133 % of the main 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 96% to 108% of the main estimate for minor effects.

5.A .3 Analysis of Cardiov ascular Emergency Department Visits, Cerebrovascular Events and Chronic Bronchitis Below we summarize the results of a sensitivity analysis of three health endpoints: cardiovascular emergency department visits, cerebrovascular events (stroke) and chronic bronchitis (Table 5.A -6 ). While in the benefits chapter we provide a full description of the rationale for treating these endpoints only in a sensitivity analysis , it is worth summarizing these reasons here. In the case of cardiovascular emergency department visits, we lack the necessary economic valuation functions to quantify the monetary value of these avoided cases. We treat cerebrovascular events as a sensitivity analysis for three reasons: (1) the epidemiological literature examining PM

- related cerebr ovascular events is still evolving; (2) there are special uncertainties associated with quantifying this endpoint; (3) we have not yet identified an appropriate means for estimating the economic value of this endpoint. Finally, we

now quantify chronic bron chitis in a sensitivity analysis because of the absence of newer studies finding a relationship between long

- term PM 2.5 exposure and this endpoint , and the relative weakness of the study available to quantify this endpoint.5.A

- 11 To quantify cardiovascular hospital admissions, we apply risk coefficient drawn from three epidemiology studies: Metzger et al. (2004) (RR= 1.033, 95 th percentile c onfidence intervals 1.01

– 1.056 per 10 μg/m 3 PM 2.5 , age 0

- 99 ) , Tolbert et al. (2007) (RR= 1.005, 95 th percentile confidence i ntervals 0.993

– 1.017 per 10 μg/m 3 PM 2.5 , age 0

- 99 ) , and Mathes et al. (2011) ( excess risk = 0.8% , 95 th percentile confidence intervals 0.0%

- 1.6 % per 10 μg/m 3 PM 2.5 , age 40- 99 )

. To estimate cerebrovascular events, we apply a risk coefficient drawn from Miller et al. (2007) (RR= 1.28, 95% confidence intervals 1.02

– 1.61 per 10 μg/m 3 PM 2.5 ). To estimate chronic bronchitis, we use a risk coefficient drawn from Abbey et al. (1995) (RR = 1.81, 95% confidence intervals 0.98

– 3.25 per 45 μg/m 3 PM 2.5 ). Additional information, including the rationale for incorporating these new endpoints into the analysis, the baseline incidence rates for these endpoints, and the prevalence rate for chronic b ronchitis are described in Chapter 5 of this RIA.

Table 5.A -6. Avoided Cases of Cardiovascular Emergency Department Visits, Stroke and Chronic Bronchitis in 2020 (95 th percentile confidence intervals) a

Endpoint 12 μg/m 3

Cardiovascular emergency department visits Metzger et al. (2004) (ages 0

– 99) 440 (160– 720 ) Tolbert et al. (2007) (ages 0

– 99) 62 (

− 72

– 200 ) Mathes et al. (2011) (ages 40

- 99) 101 (8

- 193)

Stroke Miller et al. (2007) (ages 50

– 79) 130 ( 20

– 230 )

Chronic Bronchitis Abbey et al. (1995) (ages 27

– 99) 360 ( 39

– 670 )

a All estimates rounded to two significant digits.

5.A .4 Updating Basis for Population Projections to 2010 Census
In this RIA, w e updated the population demographic data in BenMAP to reflect the 2010 Census and future projections based on economic forecasting models developed by Woods and Poole, Inc. (Woods and Poole, 2012). These data replace the earlier demographic projection data from Woods and Poole (2007) that were projected from the 2000 Census

. We use5.A

- 12 projections based on economic forecasting models developed by Woods and P oole, Inc. (Woods and Poole, 2012 ). Table 5.A -7 provides the results of a sensitivity analysis using the older population projections compared to the newer projections

. Table 5.A -7. Change in To tal Monetized Benefits for 2000 and 2010 Census projections for 12 μg/m 3 in 2020 (2010$) a

Projections from 2000 Census Projections from 2010 Census Percent C hange

Total Monetized Benefits (3% discount rate) $ 3.8 to $ 8. 7 billion $ 4.0 to $ 9.1 billion +4. 4%

a Percent change is based on the unrounded estimates

.
5.A .5 Long

- term PM 2.5 Mortality Estimates U sing Cohort Studies in California In Chapter 5, we described the multi

- state cohort studies we used to estimate the PM 2.5 - related mortality (i.e., Krewski et al., 2009; Lepeule et al., 20 12 ), as well as summarized the effect estimates for additional cohort studies. In this appendix, we provide additional information about cohort studies in California. 6 As shown in Table 5. 8 in the health benefits chapter, all of the monetized human health benefits associated with the illustrative control strategy to attain the revised annual standard of 12 μg/m 3 are projected to occur in California

. For this reason, we determined that it was appropriate to consider the sensiti vity of the benefits results using effect estimates for cohorts in California specifically. Although we have not calculated the benefits results using these cohort studies, it is possible to use the effect estimates themselves to determine how much the mon etized benefits in California would have changed if we used effect estimates from the California cohorts. Each of the California cohort studies are summarized in the PM ISA (U.S. EAP, 2009) or the Provisional Assessment (U.S. EPA, 2012) . Table 5.A .8 provid es the effect estimates from each of these cohort studies for all

- cause, cardiovascular, cardiopulmonary, and ischemic heart disease (IHD) mortality for each of the California cohort studies.

6 In addition to cohorts studies conducted in California, we have also identified a cross

- sectional studie s (Hankey et al., 2012). However, we have not summarized that study here.5.A

- 13 Table 5.A -8 Summary of Effect Estimates From Associated With Change in Long

- Term Exposure to PM 2.5 in Recent Cohort Studies in California

Authors Cohort Hazard Ratios per 10 μg/m 3 Change in PM 2.5 (95 th percentile confidence interval)

All Causes Cardiopulmonary Ischemic Heart Disease McDonnell et al. (2000) a Adventist Health Study (AHS) cohort (age > 27) 1.09 (.98

– 1.24) N/A N/A

Jerrett et al. (2005) b Subset of the ACS cohort living in the Los Angeles metropolitan area (age > 30) 1.15 (1.03

– 1.29) 1.10 (0.94

– 1.28) 1.32 (1.03

– 1.29)

Chen et al. (2005) c Adventist Health Study (AHS) cohort living in San Francisco, South Coast (i.e., Los Angeles and eastward), and San Diego air basins (age > 25) N/A N/A 1.42 (1.06

– 1.90)

Enstrom et al. (2005) d California Prevention Study (age >65) 1.04 (1.01

– 1.07) N/A N/A

Krewski et al. (2009) e Subset of the ACS cohort living in the 5

- county Los Angeles Metropolitan Statistical Area (age > 30) 1.42 (1.26

– 1.27) 1.11 (0.95

– 1.23) 1.32 (1.06

– 1.64)

Ostro et al. (2010) c California Teacher ’ s study. Current and former female public school professionals (age > 22) 1.84 (1.66

– 2.05) 2.05 (1.80

– 2.36) 2.89 (2.27

– 3.67) Ostro et al. (2011) c,f 1.06 (0.96

– 1.16) 1.19 (1.05

– 1.36) 1.55 (1.24

– 1.93) Lipsett et al. (2011) c California Teacher ’ s study. Current and former female public school professionals (age > 22) 1.01 (0.95

– 1.09) N/A 1.20 (1.02

– 1.41)

a Table 3, adjusted for 10 μg/m 3 change in PM 2.5 . b Table 1. 44 individual

- level co

- variates + all social (i.e. , ecologic) factors specified (principal component analysis).

c Women only.
d Represents deaths occurring from 1973

– 1982, but no significant associations were reported with deaths in later

time periods. The PM ISA (U.S. EPA, 2009) concludes that the use of average values for California counties as exposure surrogates likely leads to significant exposure error, as many California counties are large and quite topographically variable.

e Table 23. 44 individual

- level co

- variates + all social (i.e. , ecologic) factors specified.
f Erratum Table 2.

As shown in Table 5.A .8 , most of the cohort studies conducted in California report central effect estimates similar to the (nationwide) all

- cause mortality risk estimate we applied5.A

- 14 from Krewski et al. (2009) and Lepeule et al. (20 12 ) albeit with wider confidence intervals. Three cohort studies conducted in California indicate statistically significant higher risks than the risk estimates we applied from Lepeule et al. (2012) , and four studi es showed insignificant results

.
5.A .6 Analysis of Health Benefits Estimated for 2025
In this RIA, w e assumed an analysis year of 2020 for estimating costs and benefits with an adjustment to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025. Because of population growth, population aging, and in come growth over time, the health benefits estimated for an analysis year of 2025 would be higher. We have only conducted this sensitivity analysis for the revised standard of 12 μg/m 3 , for which the health benefits occur entirely in California and are almost entirely in the South Coast and San Joaquin air basins. Table 5.A

- 9 provides the comparison of the health benefits (including avoided premature mortality and the total monetized health benefits) estimated for 2020 and 2025.

Table 5.A -9. Comparison of H ealth Benefits Estimated for 2000 and 2025 for 12 μg/m 3 a

2020 2025 Percent Difference

Avoided Premature Mortality 460 to 1,000 510 to 1,200 +12%

Total Monetized Benefits (3% discount rate, 2010$) $4.0 to $9.1 billion $4.5 to $10 billion +12%

a Percent change is based on the unrounded estimates

.
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mo rtality with the fine and coarse fractions of long

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- Term Exposure in the California Teachers Study .” Environ Health Perspect June; 119 (6) : A242

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Röösli M, Kunzli N, Braun

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- response model. ” International Journal of Epidemiology 34(5):1029

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- 17 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). 201 2. Provisional Assessment of Recent Studies on Health Effects of Particulate Matter Exposure

. EPA/600/R

- 12/056A . National Center for Environmental Assessment

— RTP Division. December

. U.S. Environmental Protection Agency

— Science Advisory Board (EPA

- SAB). 1999a. The Clean Air Act Amendments (CAAA) Section 812 Prospective Study of Cos ts and Benefits (1999): Advisory by the Health and Ecological Effects Subcommittee on Initial Assessments of Health and Ecological Effects. Part 1. EPA

- SAB

- COUNCIL

- ADV

- 99

- 012. July. Available on the Internet at

< http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF /F64A21824D19766885257193005 E51CA/$File/conadv12.pdf>.
U.S. Environmental Protection Agency

— Science Advisory Board (EPA

- SAB). 1999 b

. The Clean Air Act Amendments (CAAA) Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Health and Ecological Effects Subcommittee on Initial Assessments of Health and Ecological Effects. Part 2. EPA

- SAB

- COUNCIL

- ADV

- 00

- 001. October. Av ailable on the Internet at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/7D54675178C12703852571B9004 4B4F8/$File/coua0001.pdf>.
U.S. Environmental Protection Agency

— Science Advisory Board (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: An Advisory by a Special Panel of the Advisory Council on Clean Air Compliance Analysis. EPA

- SAB

- COUNCIL

- ADV

- 01

- 004. September. Available on the Internet at <http://yosemite.epa.gov/s ab%5CSABPRODUCT.NSF/572DC1989A3986CC8525718D006 BAB8B/$File/councila01004.pdf>.
U.S. Environmental Protection Agency

— Science Advisory Board (EPA

- SAB). 2004. Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA ’ s Second Prospective Analysis

— Benefits and Costs of the Clean Air Act, 1990

- 2020: Advisory by the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance Analysis. EPA

SAB

- COUNCIL

- ADV

- 04

- 002. March. Available on the Internet at <http://yosemite.epa.gov/sab%5C SABPRODUCT.NSF/08E1155AD24F871C85256E54004 33D5D/$File/council_adv_04002.pdf>.5.A

- 18 U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA

- SAB). 2004 . Advisory Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag. E PA

- COUNCIL -LTR- 05

- 001 December. Available on the Internet at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/39F44B098DB49F3C85257170005 293E0/$File/council_ltr_05_001.pdf>.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA

- SAB). 2010. Rev iew 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/ 59e06b6c5ca66597852575e7006c5d09!OpenDocument&TableRow=2.3#2.>.5.B -1 APPENDIX 5.B COMPREHENSIVE CHARAC TERIZATION OF UNCERTAINTY IN BENEFITS ANALYSIS
As noted in Chapter 5, the benefits analysis relies on an array of data inputs

— including air quality modeling, health impact functions and valuation estimates among others

— which are themselves subject to uncertainty and may also in turn contribute to the overall uncertainty in this analysis. The anal ysis 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 estimate. W e strive to incorporate as many quantitative assessments of uncertainty as possible ( e.g., by using monte carlo assessments, concentration benchmar k analyses, alternative concentration

- response functions, sensitivity analyses, distributional assessments, and influence analyses); however , there are some aspects we are only able to character ize qualitatively.

To more comprehensively and systematically address these uncertainties , including those we cannot quantify, we a dapt the WHO uncertainty framework (WHO, 2008), which provides a means for systematically linking the characterization of un certainty to the sophistication of the underlying health impact assessment. EPA has applied similar approaches in peer

- reviewed analyses of PM 2.5 - related impacts (U.S. EPA, 2010b, 2011 )

. 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 impacts, including identif ying potential bias, potential magnitude, confidence in our approach, and the level of quantitative assessment of each unce rtainty (U.S. EPA

- SAB, 1999, 2001, 2004, 2011 a, 2011b ).
T he assessments presented here are largely consistent with those previous peer

- reviewed
assessments.

5.B .1 Description of Classifications Applied in the Uncertainty Characterization

Table 5.B -1 catal ogs the most significant sources of uncertainty in the PM benefits analysis and then characterizes four dimensions of that uncertainty

. The first two dimensions focus on the nature of the uncertainty. The third and fourth dimensions focus on the extent to which the analytic approach chosen in the benefits analysis either minimizes the impact of the uncertainty or quantitatively characterizes its impact.
1) T he direction of the bias that a given uncertainty may introduce into the benefits assessment if not t aken into account in the analysis approach ; 2) T he magnitude of the impact that uncertainty is likely to have on the benefits estimate if not taken into account in the analysis approach ;5.B -2 3) T he extent to which the analytic approach chosen is likely to minimize the impact of that uncertainty on the benefits estimate ; and
4) The extent to which EPA has been able to quantify the residual uncertainty after the preferred analytic approach has been incorporated into the benefits model.

5.B.1 .1 Direction of Bias

The “ direction of bias ” column in Table 5.B -1 is an assessment of whether , if left unaddressed, an uncertainty would likely lead to an underestimate or overestimate 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 Particulate Matter (hereafter, “ PM ISA ”) (U.S. EPA, 2009)

. Additional sources of information include advice from SAB and the National Academies of Science (NAS), as well as studies from the peer

- reviewed literature. In some cases we indicate that there is not sufficient information to estimate whether the uncertainty would likely lead to under or overestimat ion of benefits ; these cases are identified as “ unable to determine .”

5.B.1 .2 Magnitude of Impact

The “ magnitude of impact ” column in Table 5.B -1 is a n assessment of ho w much plausible alternative assumptions about the underlying relationship about which we are uncertain could influence the overall monetary benefits. EPA has applied similar classifications in previous risk and benefit analyses (U.S. EPA, 2010b, 2011a), but we have slightly revised the category names and the cut

- offs here. 1 The definitions used here are provided below.

 High

— If th e uncertainty associated with an assumption could influence the total monetized benefits by more than 25%

.

 Medium

— If th e uncertainty associated with an assumption could influence the total monetized benefits by 5% to 25%

.

 Low

— If th e uncertainty associated with an assumption could influence the total monetized benefits by less than 5%

.

1 In The Benefits and Costs of the Clean Air Act from 1990 to 2020 (U.S. EPA, 2011a), EPA applied a classification of

“ potentially major ” if a plausible alternative assumption or approach could influence the overall monetary benefit estimate by 5% percent or more and “ probably minor ” if an alternative assumption or approach is likely to change the total benefit estimate by less than five pe rcent. In the Quantitative Health Risk Assessment for Pa rticulate Matter (U.S. EPA, 2010b), EPA applied classifications of “ low ” if the impact would not be expected to impact the interpretation of risk estimates in the context of the PM NAAQS review, “ medium ” if the impact had the potential to change the interpretation; and “ high ” if it was are likely to influence the interpretation of risk in the context of the PM NAAQS review.5.B -3 For each uncertain ty, we provide as mu ch quantitative information as is available in the table to support the classification.

Although many of the sources of uncertainty could affect both morbidity and mortality endpoints, b ecause PM 2.5 - related mortality benefits comprise over 98% of the monetized benefits that we are able to quantify in this analysis, uncertainties that affect the mortality estimate have the potential to have larger impacts on the total monetized benefits than uncertain ties affecting only morbidity endpoints. One morbidity

- related uncertainty that could have a significant impact on benefits estimate is the extent to which omitted morbidity endpoints are included in the benefits analysis

. Including additional morbidity en dpoints 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, 2010b, 2011)

Based on this assessment , the uncertainties which we classified as high or medium

- high impact are : the causal relationship between PM 2.5 and mortality, regional differences in PM 2.5 mixtures, shape of the concentration

- response function, mortality valuation, and cessation lag

. The classification of the se uncertainties as “ high magnitude ” is generally consistent with the results of EPA ’s Influence Analysis (Mansfield et al., 2008) , the Quantitat ive Health Risk Assessment for Particulate Matter (U.S. EPA, 2010b), and the Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011)

.
5.B.1 .3 Confidence in Analytic Approach

The “ confidence in analytic approach ” column of Table 5.B -1 is an as sessment of the scientific support for the analytic approach chosen (or the inherent assumption made) to account for the relationship about which we are uncertain. In other words, based on the available evidence, how certain are we that EPA ’s selected approach is the most plausible of the potential alternatives. Similar classifications have been included in previous risk and benefits analyses (U.S. EPA, 2010b, 2011 ). 2 Th e th ree categories used to characterize the degree of confidence are:

 High —t he curr ent evidence is plentiful and strongly supports the selected approach ;

2 We have applied the same classification as The Benefits and Costs of the Clean Air Act from 1990 to 2020 (U.S. EPA, 2011a) in this analysis. In the Quantitative Health Risk Assessment for Particulate Matter (U.S. EPA, 2010b), EPA assessed the degree of uncertainty (low, medium, or high) associated with the knowledge

- base (i.e., assessed how well we understand each source of uncertainty), but did not provide specific criteria for the classification

.5.B -4  Medium —s ome evidence exists to support the selected approach, but data gaps are present ; and

 Low —l imited data exists to support the selected approach

. Ultimately, the degree of confiden ce 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 PM ISA (U.S. EPA, 2009) and EPA ’ s independent Science Advisory Board (SAB) . The PM ISA eval uated the entire body of scientific literature on PM science and was twice peer

- reviewed by EPA ’ s Clean Air Scientific Advisory Committee ( CASAC )

. In general, we regard a conclusion in the PM ISA or specific advice from SAB as supporting a high degree of confidence in the selected approach.

Based on this assessment , we have low or low

- medium confidence in the evidence available to assess exposure error in epidemiology studies, morbidity valuation, baseline incidence projecti ons for morbidity, and omitted morbidity endpoints. However, because t hese uncertainties have been classified as having a low or low

- medium impact on the magnitude of the benefits , further invest ment in improving the available evidence would not have a sub stantial impact on the total monetized benefits

.
5.B.1.4 Uncertainty Quantification

The column of Table 5.B -1 labeled “ uncertainty quantification ” is an assessment of the extent to which we were able to use quantitative methods to characterize the residual uncertainty in the benefits analysis, after addressing it to the extent feasible in the analytic approach for this RIA

. We categorize the level of quantification using t he four tiers used in the WHO uncertainty framework (WHO, 2008)

. The WHO uncertainty framework is a well

established approach to assess uncertainty in risk estimates that systematically links the characterization of uncertainty to the sophistication of the health impact assessment. The advantage of using this framework is that it clearly highlights the level of uncertainty quantification applied in this assessment and the potential sources of uncertainty that require methods development in order to assess q uantitatively. Specifically, EPA applied this framework in the Quantitative Risk Assessment for Particulate Matter (U.S. EPA, 2010b), and it has been recommended in EPA guidance documents assessing air toxics

- related risk and Superfund s ite risks (U.S. EPA , 2004b and 2001, respectively). Ultimately, the tier decision is 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

.5.B -5  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 a more sophisticated approaches (e.g., Tier 2 or 3) for characterizing uncertainties that have the largest magnitudes and could not be completely addressed through the analytic approach . T he uncertainties for which we have conducted probabili stic (Tier 3) assessments in this analysis are PM

- mortality causality , the shape of the concentration

- response function, and mortality and morbidity valuation

. For lower magnitude uncertainties, we include qualitative discussions of the potential impact of uncertainty on risk results (WHO Tier 0/1) and/or completed sensitivity analyses assessing the potential impact of sources of uncertainty on risk results (WHO Tier 2).

5.B .2 Organization of the Qualitative Uncertainty Table

Table 5 .B

- 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 )

. Within each category, the uncertainties are sorted by magnitude of impact (i.e., high to low) then by confidence in our approach (i.e., low to high). In the table, red (bold) text is used to indicate the uncertainties that likely have a high magnitude of impact on the total benefits estimate. This organization highlights the uncertainty with the largest potential impact and the lowest confidence at the top of each category.5.B-6 Table 5.B -1. Summary of Qualitative Uncertainty for Key Modeling Elements in PM 2.5 Benefits
Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification
Uncertainties Associated with Concentration

- Response Functions
Variation in effect estimates reflecting differential toxicity of particle components and regional differences in PM 2.5 composition (mixtures) Either direction , depending on the species. Potentially High Medium Tier 2 (sensitivity analysis)

PM composition and the size distribution of those particles vary within and between areas due to source characteristics

. Any specific location could have higher or lower contributions of certain PM species and other pollutants than the national average , meaning potential regional differences in health impact of given control strategies

. Depending on the toxicity of each PM species reduced in the control strategies , assuming equal toxicity could over or underestimate benefits. Epidemiology studies examining regional differences in PM 2.5 - related he alth effects have found differences in the magnitude of those effects, and composition remains one potential explanatory factor (PM ISA, section 2.3.2)

. In addition to differences in the contribution of any given species to the baseline concentrations, use of different control strategies would have a differing magnitude of the effect in different regions. Dep ending on the extent of the differences in toxic ity and the exact mix if species controlled, different control strategies could have a differing magnitude of the effect in different regions. Consistent with SAB advice, w e assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality (U.S. EPA

- SAB, 2010, pg . 18)

. The PM ISA concluded many compounds can be linked with multiple health effects and the evidence is not yet sufficient to allow differentiation of effects estimates by particle type (pg

. 2-17). We also u se national risk coefficients with no local variations due to differential exposure. The PM ISA states that available evidence and the limited amount of city

- specific speciated PM 2.5 data does not allow differentiat ion of PM effects in different locations (pg . 2–17). U sing national risk coefficients is supported by SAB (U.S. EPA

SAB, 2010) and NAS (NRC, 2002). Regional differences in hazard ratios from studies conducted in California shown in Table 5.A-8 . The hazard ratios from the Cali fornia studies range from -8 3% to +1300% compared to the national estimate applied from Krewski et al. (2009).

Causal relationship between PM 2.5 exposure and premature mortality Overestimate, if PM 2.5 does not have a causal relationship with premature mortality. High High Tier 3 (probabilistic)

Mortality generally dominates monetized benefits, so small uncertainties could have large impacts on the total monetized benefits. Our approach is consistent with the PM ISA, which concluded that premature mo rtality is causally related to PM 2.5 exposure. This conclusion is based on the consistency of the effects observed across epidemiology studies and biological plausibility) (pg. 2 –9, 2– 11).In addition, in the PM 2.5 expert elicitation, 10 of 12 experts provi ded likelihood of causality of 90% or higher (Roman et al., 2008). Each expert in the PM 2.5 expert elicitation had the opportunity to specify the likelihood of a causal relationship into their function (Roman et al., 2008). Using these expert

- derived funct ions is a probabilistic assessment of causality (see Figure 5 -4).

(continued)5.B-7 Table 5.B

- 1. Summary of Qualitative Uncertainty for Key Modeling Elements in PM 2.5 Benefits (continued)
Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification
Uncertainties Associated with Concentration

- Response Functions (cont’d) Causal relationship between PM 2.5 exposure and premature mortality Overestimate, if PM 2.5 does not have a causal relationship with premature mortality

. High High Tier 3 (probabilistic) Mortality generally dominates monetized benefits, so small uncertainties could have large impacts on the total monetized benefits. Our approach is consistent with the PM ISA , which concluded that premature mortality is causally related to PM 2.5 exposure. Th is conclusion is based on the consistency of the effects observed across epidemiolog y studies and biological plausibility ) (pg . 2– 9, 2

– 11).In addition, in the PM 2.5 expert elicitation, 10 of 12 experts provided likelihood of causality of 90% or higher (Roman et al., 2008). Each expert in the PM 2.5 expert elicitation had the opportunity to specify the likelihood of a causal relationship into their function(Ro man et al., 2008). Using these expert

- derived functions is a probab ilistic assessment of causality (see Figure 5 -4).

Shape of the C

- R functions, particularly at low concentrations Either Medium

- High High Tier 3 (probabilistic) The direction of bias that assuming linear

- no threshold model or alternative model introduces depends upon the

“ true ” functional from of the relationship and the specific assumptions and data in a particular analysis

. For example, if the true function identifies a threshold below which health effects do not occur, benefits may be overestimated if a substantial portion of those benefits were estimated to occur below that threshold

. Alternately, if a substantial portion of the benefits occurred above that threshold , the benefits may be underestimated because an assumed linear no

- threshold function may not reflect the steeper slope above that threshold to account for all health effects occurring above that threshold

. Krewski et al. (2009) considered alternative mode l forms and found that the choice of functional relationship can make a considerable difference in the predicted risk at lower concentrations. Specifically, they found a 58% increase in risk at lower concentrations for all

- cause mortality. The magnitude of this impact depends on the fraction of benefits occurring in areas with lower concentrations. Mortality generally dominates monetized benefits, so small uncertainties could have large impacts on total monetized benefits. Consistent with the PM ISA, we ass ume a log

- linear no

- threshold mode l for the concentration

- response function. In previous RIAs (between 2006 and 2009), we assumed a threshold in the mortality relationship, which shifted the slope of the function to account for all health effects occurring above the threshold concentration. The PM ISA concluded that the studies

ov erall support the use of a no

- threshold log

- linear model for PM 2.5 - related mortality (pg . 2- 25). Our approach also follows recommendations from the SAB (U.S. EPA

- SAB, 2010, pg . 13). The experts in the PM 2.5 expert elicitation specified the shape of the C

- R function (Roman et al., 2008). Only expert K assumed a threshold, but experts B, F, and L included different slopes at lower concentrations. Using the expert

- derived functions is a probabilistic assessment o f the shape of the C

- R function (see Figure 5 -4 ). Also, the concentration benchmark assessment shows the premature deaths estimated at various concentrations. Specifically, 92 % of the monetized benefits occur at or above the lowest annual concentration of PM in the Krewski et al (2009) study. (continued)5.B-8 Table 5.B

- 1. Summary of Qualitative Uncertainty for Key Modeling Elements in PM 2.5 Benefits (continued)
Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Unce rtainty Quantification

Uncertainties Associated with Concentration

- Response Functions (cont’d) Exposure error in epidemiology studies Underestimate (generally) Medium Low

- Medium Tier 1 (qualitative) The PM ISA states that the results from the Krewski et al. (2009) and Jerrett et al. (2005) studies suggest that exposure error can underestimate effect estimates (pg . 7– 90). The PM ISA states that exposure error can potentially bias an estimate of a health effect endpoint towards the null or increase the size of confidence intervals (pg . 3– 152). The PM ISA states that reducing exposure error can result in stronger associations between pollutants and effect estimates than generally observed in studies having less exposure detail (pg

. 7–90). Recent analyses reported in Krewski et al. (2009) demonstrate the potentially significant effect (approximately 18% increase in hazard ratio for all

- cause mortality in Los Angeles by improving the exposure assessment) that this source of uncertainty can have on effect esti mates. These analyses also illustrate the complexity and site

specific nature of this source of uncertainty. Although this underestimation is well documented, including in the PM ISA, SAB has not suggested an approach to adjust for this bias. (No quantita tive method available)

Modification of Mortality C

- R function by socio

- economic status (SES) Underestimate for ACS cohort (Krewski et al., 2009) because of the demographics of the cohort (NRC, 2002, pg. 101). Medium for ACS cohort Medium We have not modified the function for SES in the core analysis. Tier 2 (sensitivity analysis)

Unknown for Six Cities cohort (Lepeule et al., 2012), but educational attainment in cohort is likely more representative of the general population (NRC, 2002, pg. 101). Experts suggested that the educational attainment may also modify the risk in the Six C ities cohort, but estimates are not available, (IEc, 2006, pg. 3 –16). Using both cohorts may balance any potential bias. Krewski et al. (2009) found that educational attainment, which is a surrogate for SES, modifies the risk coefficient (i.e., ranging fr om

- 8% for individuals with more than Grade 12 to +37% for individual with less than Grade 12 after controlling for ecologic covariates compared to the national estimate). The overall impact would depend on the mixture of educational attainment in the targ et population. Unknown magnitude for Six Cities cohort. The PM ISA concluded that there is evidence that SES, measured using surrogates such as educational attainment, modifies the association between PM and health effects (pg. 8

– 15), but gender (pg. 8 –6) and race (pg. 8- 7) do not seem to modify the association between PM and health effects. The PM ISA also concluded that some evidence suggests that Hispanic ethnicity may modify the relationship (pg. 8 -7). Effect modification for educational attainment

ev aluated in the distributional analysis in Appendix 5A of the proposal RIA. For 12/35, the percent of premature deaths ranged from 4.4% for individuals with more than Grade 12 education up to 6.5% for individuals with less than Grade 12 education.

(continued)5.B-9 Table 5.B

- 1. Summary of Qualitative Uncertainty for Key Modeling Elements in PM 2.5 Benefits (continued)
Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Unce rtainty Quantification
Uncertainties Associated with Concentration

- Response Functions (cont’d) Confounding by individual risk factors, other than SES

— e.g., smoking, or ecologic factors, which represent the neighborhood, such as unemployment Either, depending on the factor and study Medium Medium Tier 2 (sensitivity analysis)

Individual, social, economic, and demographic covariates can bias the relationship between particulate air pollution and mortality, particularly in cohort studies that rely on regional air pollution levels. Because mortality dominates monetized benefits, even a small amount of confounding could have medium impacts on total monetized benefits. To minimize confounding effects, to the extent possible, we use risk coefficients that control for 44 individual and 7 ecologic factors from Krewski et al. (2009). Although Krewski et al. (2000, 2009) found that ecologic covariates did not exert a signific ant confounding influence on PM

related mortality, they highlighted the “vital need for further study of the role that ecologic covariates have in the association between air pollution and mortality.” We use risk coefficients that control for 3 individual factors (e.g., BMI, smoking, and education) from Lepeule et al. (2012). (Quantitative methods available but not assessed in this analysis.)

Confounding and effect modification by

co

- pollutants Either, depending upon the pollutant. Medium Medium Tier 1 (qualitative)

D isentangling the health responses of combustion

- related pollutants (i.e., PM, SO x , NO x , ozone, and CO is a challenge

. Ambient PM may be an indicator of complex mixtures that share emission sources (e.g., traffic and power generation). The PM ISA states that co

- pollutants may mediate the effects of PM or PM may influence the toxicity of co

pollutants (pg . 1–16). Alternately , effects attributed to one pollutants may be due to another. Because this uncertainty could affect mortality and because m ortality generally dominates monetized benefits, even a small uncertainties could have medium impacts on total monetized benefits. When modeling effects of pollutants jointly,

we appl y multi

- pollutant e ffect estimates when those estimates are available to avoid double

- counting when those estimates are available and satisfy other selection criteria. The PM ISA states that evidence from the limited number of studies suggests that gaseous co

- pollutants do not confound the PM 2.5 - mortality relationship (pg . 2–11). EPA ’ s current approach to modeling has been supported during peer

- review s by SAB (U.S. EPA

- SAB, 2010) and NAS (NRC, 2002). (No quantitative method available)

(continued)5.B-10 Table 5.B

- 1. Summary of Qualitative Uncertainty for Key Modeling Elements in PM 2.5 Benefits (continued)
Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification
Uncertainties Associated with Concentration

- Response Functions ( Cont’d ) Not including short

term exposure studies in PM mortality calculations Underestimate Medium Medium Tier 1 (qualitative) Long

- term PM exposure studies likely capture a large part of the impact of short

- term peak exposure on mortality; however, the extent of overlap between the two study types is unclear (NRC, 2002, pg

. 116). If short

- term mortality is not fully captured within the cohort mortality estimates, then the benefits could b e underestimated. Consistent with the NAS , we assume that long

- term cohort studies capture most of the mortality benefits

. However, NAS acknowledges that the effects of short

- term exposures are unlikely to be fully captured in the cohort studies (NRC, 2002, pg s. 108, 116). (No quantitative method available)

Impact of historical exposure on mortality effect estimates Either Low

- Medium Medium Tier 2 (sensitivity analysis) Long

- term studies of mortality suggest that different periods of PM exposure can produce different effects estimates, raising the issue of uncertainty in relation to determining which exposure window to use when estimating mortality benefits. Krewski et al. (2009) evaluated exposure windows for PM

- related mortality and suggested that differences in effect estimates are associated with the use of different exposure periods (with the more recent period having larger estimates) but those differences were small. Lepeule et al. (2012) reported similar findings for different exposure periods . Mortality generally dominates monetized benefits, so small uncertainties could have medium impacts on total monetized benefits. We do not make adjustments for temporal variation in exposure. The PM ISA concludes that the overall evidence for determining the appropriate exposure window suggests that the health benefits from reducing air pollution do not require a long latency period and would be expected within a few years after intervention (pg. 7 –95). (Quantitative methods available but not assessed in this analysis.)

Application of C

- R relationships only to the original study population Underestimate Low High Tier 2 (sensitivity analysis) Estimating health effects for only to the original study population may underestimate the whole population benefits of reductions in pollutant exposures. Mortality generally dominates monetized benefits, so further age range expansions for morbidity would have a small impact on total monetized benefits. With respect to adult mortality

— the baseline rate is significantly lower under age 25, so lowering the age range to 18 would have minimal impact on total monetized benefits. For mortality, we estimate heal th effects only for the ages matching the original study population (i.e., 25+ or 30+)

. Following advice from the SAB (U.S. EPA

- SAB, 2004a, pg. 7 ) and NAS (NRC, 2002, pg. 114), w e expanded the age range for childhood asthma exacerbations beyond the origina l study population to ages 6 -18. (Quantitative methods available but not assessed in this analysis.)

(continued)5.B-11 Table 5.B

- 1. Summary of Qualitative Uncertainty for Key Modeling Elements in PM 2.5 Benefits (continued)
Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification
Uncertainties Associated with Economic Valuation
Mort ality Risk Valuation / Value

- of -aStatistical

- Life (VSL) Unknown High Medium Tier 3 (probabilistic) Some studies suggest that EPA ’ s mortality valuation is too high, while other studies suggest that it is too low. Differences in age, income, risk aversion, altruism, nature of risk (e.g., cancer), and study design could lea d to higher or lower estimates of mortality valuation. Mortality generally dominates monetized benefits, so moderate uncertainties could have a large effect on total monetized benefits. The VSL used by EPA is based on 26 labor market and stated preference studies published between 1974 and 1991. EPA is in the process of reviewing this estimate and will issue revised guidance based on the most up

- to

- date literature and recommendations from the SAB

- EEAC in the near future (U.S. EPA, 2010a, U.S. EPA

- SAB, 2011 c ). Assessed uncertainty in mortality valuation using a Weibull distribution.

Cessation lag structure for long term PM mortality Underestimate Medium

- High Medium Tier 2 (sensitivity analysis) Recent studies (Schwartz, 2008; Puett et al., 2009; Lepeule et al., 2012) estimate that the majority of the risk occurs within 2 years of reduced exposure. Because we do not adjust for income growth over the 20

- year cessation lag , this approach could also underestimate the benefits for the later years of the lag. Although the cessation lag does not affect the number of premature deaths attributable to PM 2.5 exposure, it affects the timing of those deaths and thus the discounted monetized benefi ts. Mortality generally dominates monetized benefits, so moderate uncertainties could have a large effect on total monetized benefits. Consistent with SAB advice, we estimate that 30% of mortality reductions in the first year, 50% over years 2 to 5, and 2 0% over the years 6 to 20 after the reduction in PM2.5 (U.S. EPA

- SAB, 2004c). The PM ISA concludes that health benefits from reducing air pollution would be expected within a few years of intervention (pg. 7

– 95). Despite recent studies providing new eviden ce of the timing of mortality risk reduction after changes in exposure, the SAB did not suggest changes to the default cessation lag applied in EPA’s main benefits estimates in the most recent review (U.S. EPA

- SAB, 2010). As shown in Appendix 5

- A, the

use of an alternate lag structure would change the monetized benefits by +10% to -52% depending on the discount rate and lag structure assumed.

(continued)5.B-12 Table 5.B

- 1. Summary of Qualitative Uncertainty for Key Modeling Elements in PM 2.5 Benefits (continued)

Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification Uncertainties Associated with Economic Valuation (cont’d) Income growth adjustments Either Medium Medium Tier 2 (sensitivity analysis) Income growth increases willingness

- to

pay (WTP) valuation estimates, including mortality, over time. From 1997 to 2010, personal income and GDP growth have begun to diverge. If this trend continues, the assumption that per capita GDP growth is a reasonable proxy for income growth may lead to an overstatement of benefits. (IEc, 2012). Income growth from 1990 to 2020 increases mortality valuation by 20%. Alternate estimates for this adjustmen t vary by 20% (IEc, 2012). Because we do not adjust for income growth over the 20

- year cessation lag , this approach could also underestimate the benefits for the later years of the lag. Consistent with SAB recommendations (U.S. EPA,

- SAB, 2000, pg

. 16), we adjust WTP for income growth. Difficult to forecast future income growth. However, in the absence of readily available income data projections, per capita GDP is the best available option. As shown in Appendix 5 -A , the use of alternate income growth adjustments would change the monetized benefits by + 33% to

− 14%.

Morbidity valuation Underestimate Low Low Tier 3 (probabilistic) , where available Morbidity benefits such as hospital admissions and heart attacks are calculated using cost

- of

- illness (COI) estimates, which are generally half the willingness

- to

- pay to avoid the illness (Alberini and Krupnick, 2000). In addition, the morbidity costs do not reflect physiological responses or sequelae events, such as increa sed susceptibility for future morbidity. Even if we doubled the monetized valuation of morbidity endpoints using COI valuation that are currently included in the RIA, the change would still be less than 5% of the monetized benefits. It is unknown how much including sequelae events could increase morbidity valuation. Although the COI estimates for hospitalizations reflect recent data, we have not yet updated other COI estimates such as for AMI. The SAB concluded that COI estimates could be used as placehol ders where WTP estimates are unavailable, but it is reasonable to presume that this strategy typically understates WTP values (U.S. EPA

- SAB, 2004b, pg. 3). Assessed uncertainty in morbidity valuation using distribution s specified in the underlying literatu re, where available (see Table 5.9)

.

(continued)5.B-13 Table 5.B

- 1. Summary of Qualitative Uncertainty for Key Modeling Elements in PM 2.5 Benefits (continued)
Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification Uncertainties Associated with Baseline Incidence and Population Projections Population estimates and projections Either Low

– Medium Medium Tier 1 (qualitative) The monetized benefits would change in the same direction as the over - or underestimate in population projections in areas where exposure changes. Monetized benefits are substantially affected by population density. Comparisons using historical census data show that population projections

are ± 5% nationally, but projection accuracy can vary by locality. Historical error for Woods & Poole ’ s population projections has been ±8.1% for county

- level projections and ±4.1% for
states (Woods and Poole, 201 2 ). The magnitude of impact on total monetized benefits depend s on the specific location where PM is reduced. We use population projections for 5- year increm ents for 304 race/ ethnicity / gender / age groups (Woods and Poole, 20 12 ) at Census blocks. Population forecasting is well

- established but projections of future migration due to possible catastrophic events are not considered

. In addition, projections at the small spatial scales used in this analysis are inherently more uncertain than projections at the county - or state

- level. (No quantitative method available)

Uncertainty in projecting baseline incidence rates for mortality Unknown Low

- Medium Medium Tier 1 (qualitative) Because the mortality rate projections for future years reflect changes in mortality patterns as well as population growth, the projections

are unlikely to be biased. B ecause m ortality generally dominates monetized benefits, small uncertainties could have medium impacts on total monetized benefits. The county

- level baseline mortality rates reflect recent databases (i.e., 2004

– 2006 data) and projected for 5

- year increments for multiple age groups. This database is generally considered to have relatively low uncertainty (CDC Wonder, 2008)

. The projections account for both spatial and temporal changes in the population

. (No quantitative method available)

Uncertainty in projecting baseline incidence rates and prevalence rates for morbidity Either, depending on the health endpoint Low Low

- Medium Tier 1 (qualitative)

Morbidity baseline incidence is available for current year only (i.e., no projections available). Assuming
current year levels can bias the benefits for a specific endpoint if the data has clear trends over time. Specifically, asthma prevalence rates have increased substantially over the past few years while hospital admissions have decreased substantially. The magnitude varies with the health endpoint, but the overall impact on the total benefits estimate from these morbidity endpoints is likely to be low. We do not have a method to project future baseline morbidity rates, thus we assume current year levels will conti nue. While we try to update the baseline incidence and prevalence rates as frequently as practicable, this does not continue trends into the future. Some endpoints such as hospitalizations and ER visits have more recent data (i.e., 2007) stratified by age and geographic location. Other endpoints, such as respiratory symptoms reflect a national average. Asthma prevalence rates reflect recent increases in baseline asthma rates (i.e., 2008). (No quantitative method available)

(continued)5.B-14 Table 5.B

- 1. Summary of Qualitative Uncertainty for Key Modeling Elements in PM 2.5 Benefits (continued)
Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification Uncertainties Associated with Omitted Benefits Categories Unquantified PM health benefit categories, such as pulmonary function, cerebrovascular
events , low birth weight , or cancer Underestimate Medium Low Tier 2 (sensitivity analysis) EPA has not included monetized estimates of these benefits categories in the core benefits estimate. Although the potential magnitude is unknown, including all of the additional morbidity endpoints associated with PM 2.5 exposure that are currently not

mone tized could increase the total benefits by a moderate amount. Current data and methods are insufficient to develop (and value) national quantitative estimates of these health effects. The PM ISA determined that respiratory morbidity (e.g., decreases in lung function) is causally associated with PM 2.5 exposure (pg . 2–12). In Table 5 .A- 6, EPA estimates avoided incidence of strokes, cardiovascular emergency department visits, and chronic bronchitis.

Uncertainties Associated with Estimated Exposure Changes Spatial matching of air quality estimates from epidemiology studies to air quality estimates from air quality modeling Unknown Unknown Low Tier 1 (qualitative) Epidemiology studies often assume one air quality concentration is representative of an entire urban area when calculating hazard ratios, while benefits are calculated using air quality modeling conducted at 12 km spatial resolution. This spatial mismatch could introduce uncertainty. We have n ot control led for this potential bias , and the SAB has not suggested an approach to adjust for this bias. (No quantitative method available)5.B

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- SAB). 2011 c. Review of Valuing Mortality Risk Reductions for Environme ntal Policy: A White Paper (December 10, 2010). EPA

- SAB

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. Available on the Internet at

< http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/ $File/EPA

- SAB

- 11

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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 Pro ject 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/meth ods/ harmonization/areas/ uncertainty %20.pdf >.6-1 CHAPTER 6 WELFARE CO

- BENEFITS OF THE PRIMARY STAND ARD

6.1 Synopsis

T he emission reductions to meet the revised primary standard will have “welfare” co

benefits in addition to human health benefits, including changes in visibility, materials damage, ecological effects from PM deposition, ecological effects from nitrogen and sulfur emissions, vegetation effects from ozone exposure, ecological effects from mercury deposition, and climate effects. 1 Despite our goal to quantify and monetize as many of the benefits a s possible for the revised primary standard, the welfare co

- benefits of the revised primary standard remain unquantified and nonmonetized in this RIA due to data, methodology, and resource limitations. Specifically, we do not have air quality model runs fo r the regulatory baseline and the alternative standard levels that would allow us to calculate the visibility co

- benefits of attaining the revised primary standard even though we have a complete methodology for estimating these co

- benefits. However, using the approach described in this chapter, we provide the results of an illustrative analysis in Appendix 6.B using the 2020 base case and 2020 control case simulation s that were used to develop the air quality ratios.

6.2 Introduction to Welfare Benefits

Illustrative emission reduction strategies to attain the revised and alternative annual primary standard have numerous documented effects on environmental quality that affect human welfare. The Clean Air Act define s welfare effects to include any non

- healt h effects, including direct damages to property, either through impacts on material structures or by soiling of surfaces, direct economic damages in the form of lost productivity of crops and trees, indirect damages through alteration of ecosystem functions, and indirect economic damages through the loss in value of recreational experiences or the existence value of important

resources. The EPA ’ s Integrated Science Assessments for Particulate Matter (hereafter, “ PM ISA ” ) (U.S. EPA, 2009b) , NO X /SO X — Ecological Criteria (U.S. EPA, 2008), and Ozone (U.S. EPA, 2012a) identify numerous physical and ecological effects known to be causally linked to these pollutants. This chapter describes these individual effects and how we would quantify and monetize them if there is enough data to do so. These welfare effects include changes in visibility, materials damage, ecological effects from PM deposition, ecological effects from

1 While we understand that “welfare” can include health and non

- health effects in the economic sense, we use the

term “welfare” in this RIA in the same context a s the definitions in the Clean Air Act for NAAQS. In practice, welfare benefits represent non

- health effects.6-2 nitrogen and sulfur emissions, vegetation effects from ozone exposure, ecological effec ts from mercury deposition, and climate effects.

These welfare co

- benefits are associated with reductions in emissions of specific pollutants resulting from illustrative emission reduction strategies to attain the revised and alternative annual primary sta ndard , not the form or intent of any specific standard. Even though the primary standards are designed to protect against adverse effects to human health, the emission reductions have welfare co

- benefits in addition to the direct human health benefits.

The impacts of the illustrative emission reduction strategies can be grouped into four categories: directly emitted PM (e.g., metals, organic compounds, dust), reductions of PM 2.5 precursors (e.g., NO x , SO x , VOCs), other ancillary reductions from the illustrative emission reduction strategies (e.g., mercury and CO 2 ), and secondary co

- pollutant formation from PM precursors (e.g., ozone from NO x and VOCs). Regardless of the category, these emission changes are anticipated to affect ambient concentrations and deposition, and consequently affect public welfare. It is therefore appropriate and reasonable to include all the benefits and

co

- benefits associated with these emission reductions to provide a comprehensive understanding of the likely public impacts of attaining the revised or alternative annual standards. Table 6- 1 shows the welfare effects associated with the various pollutants (either directly or as a precursor to secondary formation of PM or ozone) that would be reduced by the illustrative emissio n reduction strategies to attain the revised and alternative annual standard.

Based on the EPA’s previous analyses, we believe the welfare co

- benefits associated with these non

- health benefit categories could be significant (U.S. EPA, 2011b). Despite our goal to quantify and monetize as many of the benefits and co

- benefits as possible, w elfare co

benefits of the revised primary standard remain unquantified and nonmonetized in this RIA due to data, methodology, and resource limitations. Therefore, the total benefits would be larger than we have estimated

. The monetized value of these unquantified effects is represented by adding an unknown “ B, ” which includes both unmonetized health benefits and welfare co

benefits , to the aggregate total for the cost

- benefi t comparison. These unquantified benefits and co

- benefits may be substantial, although the magnitude is highly uncertain. W e include a qualitative description of the anticipated welfare effects in this chapter to characterize the type and potential extent of those co

- benefits , as identified i n Table 6- 2.6-3 Table 6- 1. Welfare Effects by Pollutants Potentially Affected by Attainment of the PM NAAQS

Pollutant Atmospheric Effects Atmospheric and Deposition Effects Deposition Effects

Visibility Impairment Vegetation Injury (SO 2 ) Vegetation Injury (Ozone) Materials Damage Climate Ecosystem Effects

— (Organics & Metals) Acidification (freshwater) Nitrogen Enrichment Mercury Methylation

Direct PM 2.5    

NO x      

SO 2      

VOCs    

PM 10   
Hg  

CO 2 

 = Welfare category affected by this pollutant.
The remainder of this chapter is organized as follows: Section 6.3 provides a qualitative discussion of the visibility co

- benefits and describes our approach to estimat e those visibility

co

- benefits if we had the data to do so . Sections 6.4 through 6.6 provide qualitative co

- benefits for the unquantified benefits categori es of materials damage, climate, and ecosystem effects

. References are provided in Section 6.7. Additional information regarding technical details of the visibility co

- benefits approach i s provided in Appendix 6A

. The illustrative visibility co

- benefits

re sults for the specific modeled scenario (not the revised standard scenario) are provided in Appendix 6.B

.6-4 (continued) Table 6-2. Quantified and Unquantified Welfare Co

- Benefits

Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information Improved Environment Reduced visibility impairment Visibility in Class 1 areas in SE, SW, and CA regions a a Section 6.3 , Appendix 6.B Visibility in Class 1 areas in other regions — a Section 6.3 , Appendix 6.B Visibility in 8 cities

— a Section 6.3 , Appendix 6.B Visibility in other residential areas — a Section 6.3 , Appendix 6.B Reduced climate effects Global climate impacts from CO 2 — — Section 6.5, SCC TSD b Climate impacts from ozone and PM — — Section 6.5, Ozone ISA, PM ISA c
Other climate impacts (e.g., other GHGs, other impacts) — — Section 6.5, IPCC c

Reduced effects on materials Household soiling — — Section 6.4, PM ISA c Materials damage (e.g., corrosion, increased wear) — — Section 6.4, PM ISA c
Reduced effects from PM deposition (metals and organics) Effects on Individual organisms and ecosystems — — Section 6.6.1, PM ISA c

Reduced vegetation and ecosystem effects from exposure to ozone Visible foliar injury on vegetation — — Section 6.6.4, Ozone ISA c
Reduced vegetation growth and reproduction — — Section 6.6.4, Ozone ISA b Yield and quality of commercial forest products and crops — — Section 6.6.4, Ozone ISA b,d Damage to urban ornamental plants — — Section 6.6.4, Ozone ISA c Carbon sequestration in terrestrial ecosystems — — Ozone ISA c

Recreational demand associated with forest aesthetics — — Ozone ISA c

Other non

- use effects Ozone ISA c Ecosystem functions (e.g., water cycling, biogeochemical cycles, net primary productivity, leaf

- gas exchange, community composition) — — Ozone ISA 26-5 Table 6- 2. Quantified and Unquantified Welfare Co

- Benefits (Cont.)

Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information Improved Environment (Cont.) Reduced effects from acid deposition Recreational fishing — — Section 6.6.2, NO x SO x ISA b Tree mortality and decline — — Section 6.6.2, NO x SO x ISA c Commercial fishing and forestry effects — — Section 6.6.2, NO x SO x ISA c Recreational demand in terrestrial and aquatic ecosystems — — Section 6.6.2, NO x SO x ISA c Other non

- use effects Section 6.6.2, NO x SO x ISA c Ecosystem functions (e.g., biogeochemical cycles) — — Section 6.6.2, NO x SO x ISA c
Reduced effects from nutrient enrichment Species composition and biodiversity in terrestrial and estuarine ecosystems — — Section 6.6.2, NO x SO x ISA c

Coastal eutrophication — — Section 6.6.2, NO x SO x ISA c Recreational demand in terrestrial and estuarine ecosystems — — Section 6.6.2, NO x SO x ISA c Other non

- use effects Section 6.6.2, NO x SO x ISA c Ecosystem functions (e.g., biogeochemical cycles, fire regulation) — — Section 6.6.2, NO x SO x ISA c

Reduced vegetation effects from ambient exposure to SO 2 and NO x Injury to vegetation from SO 2 exposure — — Section 6.6.2, NO x SO x ISA c Injury to vegetation from NO x exposure — — Section 6.6.2, NO x SO x ISA c Reduced ecosystem effects from exposure to methylmercury (through the role of sulfate in methylation) Effects on fish, birds, and mammals (e.g., reproductive effects) — — Section 6.2 and 6.6.3, Mercury Study RTC c,d Commercial, subsistence and recreational fishing — — Section 6.2 and 6.6.3, Mercury Study RTC c

a We quantify these co

- benefits in a n illustrative analysis using the methods discussed in this chapter for the specific modeled scenario. These results are provided in Appendix 6.B , but these results of that illustrative scenario are not an estimate of the co

- benefits for the revised primary standard

. b We assess these co

- benefits qualitative ly due to time and resource limitations for this RIA

. c We assess these co

- benefits qualitatively because we do not have sufficient confidence in available data or methods. d We assess these co

- benefits qualitatively because c urrent evidence is only suggestive of causality or there are

other significant concerns over the strength of the association.6-6 6.3 Visibility Co

- Benefits Approach

6.3 .1 Visibility and Light Extinction Background
The illustrative emission reduction strategies designed to attain the revised and alternative annual standards would reduce emissions of directly emitted PM 2.5 as well as precursor emissions such as NO x and SO 2 for an alternative annual standard at 11 μg/m 3

. These emission reductions would improve the level of visibility because these suspended particles and gases impair visibility by scattering and absorbing light (U.S. EPA, 2009b). Visibility is also referred to as visual air quality (VAQ) , 2 and it directly affects people ’ s enjoyment of a v ariety of daily activities (U.S. EPA, 2009b). 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, 2009b). This section discusses the economic co

- benefits associated with improved visibility as a result of emission reductions associated with the revised and alternative annual standards.

Air pollution affects light extinction, a measure of how much the components of the atmosphere scatter and absorb light. More light extinction means that the clarity of visual images and visual range is reduced, all else held constant

. Light extinction is the optical characteristic of the atmosphere that occurs when light is either scattered or absorbed, which converts the light to heat. Particulate matter and gases can both scatter and absorb light. Fine particles with significant light

- extinction efficiencies include sulfates, nitrates, organic carbon,

e lemental carbon, and soil (Sisler, 1996). The extent to which any amount of light extinction affects a person ’ s ability to view a scene depends on both scene and light characteristics. For example, the appearance of a nearby object (e.g., a building) is generally less sensitive to a change in light extinction than the appearance of a similar object at a greater distance. See Figure 6- 1 for an illustration of the important factors affecting visibility.

According to the PM ISA, there is strong and consistent evidence that PM is the overwhelming source of visibility impairment in both urban and remote areas (U.S. EPA, 2009b). After reviewing all of the evidence, the PM ISA concluded that the evidence was sufficient to conclude that a causal relationship exists between PM and visibility impairment.

2 We use the term VAQ to refer to the visibility effects caused solely by air quality conditions, excluding fog.6-7 Figure 6- 1. Important Factors Involved in Seeing a Scenic Vista (Malm, 1999)
Visibility is commonly measured as either light extinction (β ext ), which is defined as the loss of light per unit of distance in terms of inverse megameters (Mm -1 ), or using the deciview (dv) metric, which is a logarithmic function of extinction (Pitchford and Malm, 1994). Deciviews, a unitless measure of visibility, are standardized for a reference distance in such a way that one deciview corresponds to a change of about 10% in available light. 3 Pitchford and Malm (1994) characterize a change of one deciview as “ a small but perceptible scenic change under many circumstances. ” 4 Extinction and deciviews are both physical measures of the amount of visibility impairment (e.g., the amount of “ haze ” ), with both extinction and deciview increasing as the amount of haze increases. Using the relationships derived by Pitchford and Malm (1994),

3 Note that deciviews are inversely related to visual range, such that a decrease in deciviews implies an increase in

visual range (i.e., improved visibility). Conversely, an increase in deciviews implies a decrease in visual range (i.e., decreased visibility). Deciview, in effect, is a measure of the lack of visibility. 4 An instantaneous change of less than 1 deciview (i.e., less than 10% in the light extinction budget) represents a

measurable improvement in visibility but may not be perceptible to the eye. The visibility co

- benef its approach described in this chapter reflects annual average changes in visibility, which are likely made up of periods with changes less than one deciview and periods with changes exceeding one deciview. Annual averages appear to more closely correspond to the economic literature relied upon for valuation of visibility changes in this analysis.6-8 Deciviews = 10 ∗ ln � 391 VR �
= 10 ∗ ln � β ext 10 � (6.1)

where VR denotes visual range (in kilometers) and β ext denotes light extinction (in Mm -1 ). 5

Annual average visibility conditions (reflecting light extinction due to both anthropogenic and non

- anthropogenic sources) vary regionally across the U.S. and by season ( U.S. EPA, 2009b). Particulate sulfate is the dominant source of regional haze in the eastern U.S

. (>50% of the particulate light extinction) and an important contributor to haze elsewhere in the country (>20% of particulate light extinction) (U.S. EPA, 2009b). Particulate nitrate is an important contributor to light extinction in California and the upper Midwestern U.S., particularly during winter (U.S. EPA, 2009b). Smoke plumes from large wildfires dominate many of the worst haze periods in the western U.S ., while Asian dust only caused a few of the worst haze episodes, primarily in the more northerly regions of the west (U.S. EPA, 2009b). Higher visibility impairment levels in the East are due to generally higher concentrations of fine particles, particularly sulfates, and higher average relative humidity levels (U.S. EPA, 2009b). Humidity increases visibility impairment because some particles such as ammonium sulfate and ammonium nitrate absorb water and form droplets that become larger when relative humid ity increases, thus resulting in increased light scattering (U.S. EPA, 2009b).

Reductions in air pollution from implementation of various programs associated with the Clean Air Act Amendments of 1990 (CAAA) provisions have resulted in substantial improveme nts in visibility, and will continue to do so in the future. Because trends in haze are closely associated with trends in particulate sulfate and nitrate due to the simple relationship between their concentration and light extinction, visibility trends hav e improved as emissions of SO 2 and NO x have decreased over time due to air pollution regulations such as the Acid Rain Program (U.S. EPA, 2009b). For example, Figure 6- 2 shows that visual range increased nearly 50% in the eastern U.S. since 1992. 6 The EPA’ s r ecent regulations such as the Cross

- State Air Pollution Rule (U.S. EPA, 2011 c ) and the Mercury and Air Toxics Standard (U.S. EPA, 2011 d ) are anticipated to reduce SO 2 emissions down to 2 million tons nationally, which would lead to substantial further improve ment in visibility levels in the Eastern U.S

. Calculated from light extinction efficiencies from Trijonis et al. (1987, 1988), annual average visual range under natural conditions in the East is estimated to be 150 km ± 45 km (i.e., 65 to 120 miles) and 230 km ± 35 km (i.e., 120 to 165 miles) in the West (Irving, 1991). Figure 6

- 2 reflects the average

5 It has been noted that, for a given deciview value, there can be many different visual ranges, depending on the

other factors that affect visual range

— such as light angle and altitude. See Appendix 6a for more detail. 6 In Figure 6

- 2, the “ best days ” are defined as the best 20% of days, the “ mid

- range days ” are defined as the middle

20%, and the “ worst days ” are defined as the worst 20% of days (IMPROVE, 2010).6-9 trends in visual ranges at select monitors in the eastern and western areas of the U.S. since 1992 using data from the IMPROVE monitoring network (U.S. EPA (2008) updated; IMPROVE (2010)). As an illustration of the improvements in visibility attributable to the CAAA, Figure 6- 3 depicts the modeled improvements in visibility associated with all the CAAA provisions in 2020 compared to a counterfactual scenario without the CAAA (U.S. EPA, 2011b). While visibility trends have improved in most National Parks, the recent data show that these areas continue to suffer from visibility impairment beyond natural background levels (U.S. EPA, 2009b).

Figure 6- 2. Vis ibility in Selected National Parks and Wilderness Areas in the U.S., 1992

– 2008 a,b

Source: U.S. EPA (2008) updated, IMPROVE (2010).6- 10 Figure 6- 3. Estimated Improvement in Annual Average Visibility Levels Associated with the CAAA Provisions in 2020

Source: U .S. EPA, 2011b

. 7

7 It is important to note that visibility levels shown in these maps were modeled differently than the modeling

conducted for this analysis using an earlier method that we would currently use , including coarser grid resolution (i.e., 36 km instead of 12 km ). In addition, please note that these maps present annual average visibility levels, which are different than the short

- term averages being considered for the secondary standard.6- 11 6. 3.2 Quantifying Light Extinction for Assessing Visibility Co

- benefits For this RIA, we do not have air quality model runs for the regulatory baseline and the alter native standard levels that would allow us to calculate the visibility co

- benefits of attaining the revised primary standard. However, we provide an illustrative analysis in Appendix 6.B using the 2020 base case and 2020 control case simulations that were used to develop the air quality ratios. 8 In our approach , we generate light extinction estimates using the CMAQ model in conjunction with the IMPROVE (Interagency Monitoring of Protected Visual Environments) algorithm that estimates light extinction as a f unction of PM concentrations and relative humidity levels (U.S. EPA, 2009b). 9 The procedure for calculating light extinction associated with the revised and alternative annual s tandards is described in detail in Chapter 3 of th is RIA. In addition, Appendix 6.A describes how the spatial resolution of the light extinction estimates would be adjusted in our approach

. It is important to note that the light extinction estimates used in our approach represent annual averages, which is different from the averaging times currently being considered for the secondary PM NAAQS. While the annual averages are influenced by days with extremely impaired visibility, the light extinction data is not sufficient to provide higher temporal resolution than quarterly averages. While we suspect that the most impaired days would have disproportionately improved visibility from the emission reductions associated with attaining the revised or alternative primary standards, we are not able to quantify those impacts. These data gaps res ult in an underestimate of visibility co

- benefits associated with extreme days. We recognize that recent advice from the Science Advisory Board’s Advisory Council on Clean Air Compliance Analysis ( SAB

- Council ) recommends estimating visibility co

- benefits considering daytime visibility on days with severe impairment (U.S. EPA

- SAB, 2010 a ), but the available data and valuation studies do not allow such fine temporal resolution.

While our approach is a substantial improvement in the methods to estimat e light extinction nationally, we are still developing a method to estimate coarse particle concentrations for the entire continental U.S. for estimating light extinction. As an interim solution, our approach includes sensitivity analyses to show the potent ial impact of omitting coarse particles from the light extinction estimates for recreational and residential visibility. For the se sensitivity analys e s, we selected the levels of coarse particles to represent the full range of possible annual concentrations from a recent report on the IMPROVE monitoring network

8 These simulations are described in Chapter 3. 9 According to the PM ISA, t he IMPROVE algorithm performs reasonably well despite its simplicity (U.S. EPA,

2009b).6- 12 (Debell et al., 2006). Specifically, for these sensitivity analyses, we assume four levels of coarse particles: no coarse particles, 5 μg/m 3 nationwide, 15 μg/m 3 in the Southwest with 5 μg/m 3 in the rest of the country, and 15 μg/m 3 in the Southwest with 8 μg/m 3 in the rest of the country. 10 In Table 6- 3, we provide a qualitative assessment of how key assumptions in the estimation of light extinction would affect the visibility co

- benefits

. Table 6-3. Key Assumptions in the Light Extinction Estimates Affecting the Visibility Co

Benefits Approach a

Key Assumption Direction of Bias Magnitude of Effect
The light extinction estimates are annual averages to correspond with the valuation studies. People may value large changes to the haziest days differently than small changes to many days. We assume that annual average light extinction is the most appropriate temporal scale for estimating visibility benefits. Potential Underestimate Medium

Coarse particles are a component of light extinction, but we were unable to include coarse particles in the light extinction estimates. We provide sensitivity analyses with up to 15 μg/m 3 in the Southwest and 8 μg/m 3 in the rest of the country. Potential Overestimate Very Low

a A description of the classifications for magnitude of effects can be found in Appendix 5.B of this RIA

.
6.3.3 Visibility Valuation Overview In the Clean Air Act Amendments of 1977, the U.S. Government recognized visibility ’ s value to society by establishing a national goal to protect national parks and wilderness areas from visibility impairment caused by manmade pollution. 11 Air pollution impairs visibility in both residential and recreational settings, and an individual ’ s willingness to pay (WTP ) to improve visibility differs in these two settings. Benefits of residential visibility relate to the impact of visibility changes on an individual ’ s daily life (e.g., at home, at work, and while engaged in routine recreational activities). Benefits of r ecreational visibility relate to the impact of visibility changes manifested at parks and wilderness areas that are expected to be experienced by recreational visitors.

Both recreational and residential visibility benefits consist of use values and nonuse values. Use values include the aesthetic benefits of better visibility, improved road and air safety, and enhanced recreation in activities like hunting and birdwatching. Nonuse values are

10 We define “ Southwest ” for this sensitivity analysis to be the states of California, Nevada, Utah, Arizona, New

Mexico, Colorado, and Texas. 11 See Section 169(a) of the Clean Air Act.6- 13 based on a belief that the environment ought to exist free of human

- induced haze. This includes the value of better visibility for use by others now and in the future (bequest value). Nonuse values may be more important for recreational areas, particularly national parks and
monuments.
The relationship between a household ’ s WTP and changes in visibility can be derived from a number of contingent valuation (CV) studies published in the peer

- reviewed economics literature. The studies used in the approach to estimate the residential and recreational visibility co

- benefits associated with the revised and alternative annual standards are described in the following sections. In addition to CV studies, hedonic valuation studies ( Beron et al., 2001, 2004) also demonstrate that visibility has value, but we are unable to apply these valuation estimates in the context of estimating the visibility co

- benefits associated with national regulations that reduce air pollution (Leggett and Neumann, 2004)

.
6. 3. 3.1 Visibility Valuation Approach

In our approach , we assume that individuals value visibility for aesthetic reasons rather than viewing visibility as a proxy for other impacts associated with air pollution, such as health or ecological improvements. Some studies in the literature indicate that individuals may have difficulty distinguishi ng visibility from other aspects of air pollution (e.g., McClelland et al., 1993; Chestnut and Rowe, 1990c; Carson, Mitchell, and Rudd, 1990). Because visual air quality is inherently multi

- attribute, it is a challenge for all visibility valuation studies to isolate the value of visibility from the collection of intertwined benefits. Each study used in our approach attempts to isolate visibility from other effect categories, but the different studies take different approaches (U.S. EPA, 2009b). 12 However, t he degree to which the studies were successful in convincing respondents to focus solely on visibility is unclear

Similarly, it is important to try to distinguish residential visibility from recreational visibility co

- benefits, specifically whether these can these can be treated as distinct and additive benefit categories based on the available literature. In our approach , we assume that residential and recreational visibility co

- benefits are distinct and separable. It is conceivable that respondents to the recreational visibility survey may have partially included values for their own residential visibility when evaluating changes at national parks and wilderness areas located in their region of the country. In our approach, we take care to minimize the num ber of overlapping areas and their contributions. Specifically, we believe that the potential for double

counting recreational and residential visibility is minimal for several reasons. First, in our

12 See Leggett and Neumann (2004) for a more detailed discussion of this issue.6- 14 approach, we only include a subset of areas in the prima ry estimates of recreational and residential visibility co

- benefits, which overlap in only a few places. 13 Second, a number of the overlapping counties are wilderness areas, which would contribute little to the overall monetized visibility co

- benefits due t o low visitation rates, rather than highly visited national parks. For example, Los Angeles County is home to the San Gabriel Wilderness Area, which has 10 thousand annual visitors (NPS, 2008). If we were to exclude the residential visibility co

benefits that accrue to 10 million residents in Los Angeles County and only include the very small recreational visibility co

- benefits for the wilderness area, we would be substantially biasing the overall estimates downward. For these reasons, we believe that the potential for double

- counting is minimal.

In the next sections, we describe the methodology and limitations of the recreational and residential visibility approach . Consistent with the health benefits analysis, the monetized visibility co

- benefits would be adjusted for inflation and income growth. These co

- benefits would be specific to the analysis year , and as population and income increase over time, these

co

- benefits would be expected to increase each year for the same incremental change in light extinct ion.

6.3.4 Recreational Visibility

6.3.4 .1 Methodology

Our approach for estimating recreational visibility co

- benefits is well

- establis hed and has been used in numerous analyses by the EPA (U.S. EPA, 1999; 2005 ; 20 06 ; 2010; 2011b). In our approach , recreational visibility co

- benefits apply to Class 1 areas, such as National Parks and Wilderness Areas. 14 Although other recreational settings such as National Forests, state parks, or even hiking trails or roadside areas have important scenic vistas, a lack of suitable economic valuation literature to identify these other areas and/or a lack of visitation data prevents us from generating estimates for those recreational vista areas.
Under the 1999 Regional Haze Rule (64 FR 35714), states are required to set goals develop long

- term strategies to improve visibility in Class 1 areas, with the goal of achieving

13 As described in detail in Sections 6.3.3 and 6.3.4, our approach includes only a subset of visibility co

- benefits in

the main benefits estimates, while providing the rest of the visibility co

- benefits in sensitivity analyses. 14 Hereafter referred to as Class 1 areas, which are defined as areas of the country such as national parks, national

wilderness areas, and national monumen ts that have been set aside under Section 169(a) of the Clean Air Act to receive the most stringent degree of air quality protection. Mandatory Class 1 federal lands fall under the jurisdiction of three federal agencies, the National Park Service, the Fish and Wildlife Service, and the Forest Service. EPA has designated 156 areas as mandatory Class 1 federal areas for visibility protection, including national parks that exceed 6,000 acres and wilderness areas that exceed 5,000 acres (40 CFR § 81.400 ).6- 15 natural background visibility levels by 2064. In conjunction with the U.S. National Park Service (NPS), the U.S. Forest Service (USFS), other Federal land managers, and State organizations in the U.S., the EPA has supported visibility monitoring in national parks and wilderness areas since 1988. The monitoring network known as IMPROVE includes 156 sites that represent the Class 1 areas across the countr y (U.S. EPA, 2009b). 15 The IMPROVE monitoring network measures fine particles, coarse particles, and key PM 2.5 constituents that affect visibility, such as sulfate, nitrate, organic and elemental carbon, soil dust, and several other elements. Figure 6- 4 ide ntifies where each of these parks are located in the U.S.

Figure 6- 4. Mandatory Class 1 Areas in the U.S.

For recreational visibility, the EPA relies upon a contingent valuation (CV) survey conducted in 1988 ( Chestnut and Rowe , 1990a; 1990b) to estimate the recreational visibility co

benefits . Although there are several other studies in the literature on recreational visibility valuation, they are even older and use less robust methods. In the EPA ’ s judgment, despite the inherent limitations in the survey, the Chestnut and Rowe study serve d as the basis for

15 The formula used to estimate light extinction from concentrations of PM constituents and relative humidity is

referred to as the IMPROVE algorithm.6- 16 monetary estimates of the co

- benefits of visibility changes in recreational areas for a number of previous EPA rulemakings

. This study serves as an essential input to our approach for estim ating the co

- benefits from improving recreational visibility.

In our approach , we assume that the household WTP is higher if the Class 1 recreational area is located close to the person ’ s home (i.e., in the same region of the country). People appear to be willing to pay more for visibility improvements at parks and wilderness areas that are in the same region as their household than at those that are not in the same region as their household (Chestnut and Rowe, 1990a, 1990b). This is plausible, because people are more likely to visit, be familiar with, and care about parks and wilderness areas in their own part of the country. However, studies have also found many people who had never visited and never planned to visit the parks still had positive values for visibility improvements in those locations (Chestnut and Rowe, 1990b).

The Chestnut and Rowe survey measured the demand for visibility in Class 1 areas managed by the NPS in three broad regions of the country: California, the Colorado Plateau (Southwe st), and the Southeast. 16 Respondents in five states were asked about their WTP to protect national parks or NPS

- managed wilderness areas within a particular region. The survey used photographs reflecting different visibility levels in the specified recreat ional areas. The authors used the survey data to estimate household WTP values for improved visibility in each region.
The separate regions were developed to capture differences in household WTP values based on proximity to recreational areas. Chestnut (1997) also concluded that, for a given region, a substantial proportion of the WTP is attributable to one specific park within the region. This so called “ indicator park ” is the most well

- known and frequently visited park within a particular region. The indi cator parks for the three studied park regions are Yosemite National Park for the California region, the Grand Canyon National Park for the Southwest region, and Shenandoah National Park for the Southeast region. In accordance with the methodology in Chest nut (1997), our approach calculates the benefits from households for a particular region for a given change in visibility at a particular Class 1 area. In theory, summing benefits from households in all regions would yield the total monetary benefits assoc iated with a given visibility improvement at a particular park, which could then be summed with other parks and

16 The Colorado Plateau (Southwest) region is defined as the states of Colorado, New Mexico, Arizona, and Utah.

The Southeast region is defined as the states of West Virginia, Virginia, North Carolina, South Carolina, Georgia, Florida, Alabama, Mississippi, Louisiana, Tennessee, and Kentucky. The California region includes the state of California and one wilderness area in Nevada.6- 17 regions to estimate national benefits. Because recreational visibility benefits may reflect the value an individual places on visibility improve ments regardless of whether the person plans to visit the park, all households in the U.S. are assumed to derive some benefit from improvements to Class 1 areas.
To value recreational visibility improvements associated with its rulemakings, the EPA developed a valuation WTP equation function based on the baseline of visibility, the magnitude of the visibility improvement, and household income. This function requires light extinction estimates measured as visual range. The behavioral parameters of thi s equation were taken from an analysis of the survey described in Chestnut and Rowe (1990a, 1990b). These parameters were used to calibrate WTP for the visibility changes resulting from this rule. 17 As an example, household WTP for a visibility improvement at a park in its region takes the following form:

WTP ( ∆Q ik ) = m − [ m ρ + γ ik ∗ �Q 0 ik ρ − Q 1 ik ρ � ] 1 ρ (6.2)

where:
i indexes region,
k indexes park,
m = household income,
ρ = shape parameter (0. 1),
γ = parameter corresponding to the visibility at in

- region parks,

Q 0 = starting visibility, and

Q 1 = visibility after change.

As discussed in more detail in Appendix 6 .A of this RIA, our approach to valuing recreational visibility changes is an application of the Constant Elasticity of Substitution (CES) utility function approach and is based on the preference calibration method developed by Smith, Van Houtven, and Pattanayak (2002). 18 Ava ilable evidence indicates that households are willing to pay more for a given visibility improvement as their income increases (Chestnut,

17 The parameters for each region are available in Appendix 6a of this RIA. 18 The Constant Elasticity of Substitution utility function has been chosen for use in this analysis due to its flexibility

when illustrating the degree of substitut ability present in various economic relationships (in this case, the tradeoff between income and improvements in visibility).6- 18 1997). Using the income elasticity calculated by Chestnut (1997), the recreational visibility benefits assume a 1% inc rease in income is associated with a 0.9% increase in WTP for a given change in visibility. WTP responses reported in Chestnut and Rowe (1990a, 1990b) were also region

- specific, rather than park

- specific. As visibility improvements are not constant across all parks in a region, we must infer park

- specific visibility parameters in order to calculate WTP for projected visibility changes. As the quantity and quality of parks differs between regions, we apportion the regional WTP parameters based on relative vi sitation rates at the different parks, because this statistic likely captures both park quality (more people visit parks with more desirable attributes, so collective WTP is likely higher) and quantity (more people visit parks in a region if the parks are more numerous, so collective WTP is likely higher). 19 We also adjust the

co

- benefits for inflation and growth in real income.

Recreational visibility co

- benefits can be calculated as the sum of the household WTPs for changes in light extinction. We assume t hat each household is valuing the first or only visibility change that occurs in a particular area. The co

- benefits at particular areas can be calculated by assuming that the subset of visibility changes of interest is the first or the only set of changes being valued by households. Estimating benefit components in this way will yield slightly upwardly biased estimates of co

- benefits , because disposable income is not reduced by the WTPs for any prior visibility improvements. The upward bias should be extrem ely small, however, because all of the WTPs for visibility changes are very small relative to income.

In our approach, the primary estimate for recreational visibility only includes co

- benefits for 86 Class 1 areas in the original study regions (i.e., California, the Southwest, and the Southeast). 20 These co

- benefits reflect the value to households living in the same region as the Class 1 area as well as values for all households in the United States living out side the state containing the Class 1 area.

19 We use 2008 park visitation data from the National Park Service Statistical Abstracts (NPS, 2008), as this is the

most current d ata available. Where the data for a particular park was not representative of normal visitation rates at that park (for example due to fire damage that occurred during that year), we substitute data from the prior year. We use 1997 visitation data for thos e wilderness areas not included in the National Park Service Statistical Abstracts, as more current data is not readily available. As visitation rates for Wilderness Areas are small compared to visitation rates in National Parks, the inaccuracies generated by using 1997 data are likely to also be small. 20 The 86 Class 1 areas in the three studied park regions represented 68% of the total visitor days to Class 1 areas in

2008 (NPS, 2008).6- 19 The Chestnut and Rowe study did not measure values for visibility improvement in Class
1 areas in the Northwest, Northern Rockies, and Rest of U.S. regions. 21 In order to obtain estimates of WTP for visibility cha nges for the 70 additional Class 1 areas in these non

- studied regions, we have to transfer the WTP values from the studied regions. 22 This co

- benefits transfer approach introduces additional uncertainty into the estimates. However, we have taken steps to adjust the WTP values to account for the possibility that a visibility improvement in parks within one region may not necessarily represent the same visibility improvement at parks within a different region in terms of environmental improvement. This may be due to differences in the scenic vistas at different parks, uniqueness of the parks, or other factors, such as public familiarity with the park resource. To account for this potential difference, we adjusted the transferred WTP being transferred by the ratio of visitor days in the two regions. 23 A complete description of the co

- benefits transfer method used to infer values for visibility changes in Class 1 areas outside the study regions is provided in Appendix 6a of this RIA.

Table 6-4 indicates which studied park regions we used to estimate the value in the non

studied park regions in our approach

. Figure 6- 5 shows how the visitation rates vary across Class 1 areas and regions and indicates whether each Class 1 area is located within o ne of the studied regions.

Table 6- 4. WTP for Visibility Improvements in Class 1 Areas in Non

- Studied Park Regions

Park Region Source of WTP Estimate

1. Northwest Benefits transfer from California

2. Northern Rockies Benefits transfer from Colorado Plateau

3. Rest of U.S. Benefits transfer from Southeast

21 The Northwest region is defined as the states of Washington and Oregon. The Northern Rockies region include s

the states of Idaho, Montana, Wyoming, North Dakota, and South Dakota. The Rest of the U.S. region includes all other states not included in the other 5 regions. 22 The 70 additional Class 1 areas represented 32 % of the total visitor days to Class 1 areas in 2008 (NPS, 2008). 23 For example, if total park visitation in a transfer region was less than visitation in a study region, transferred WTP

would be adjusted downward by the ratio of the two.6- 20 Figure 6- 5. Visitation Rates and Park Regions for Class 1 Areas a

a The colors in this map correspond to the park regions used in the valuation study and the extrapolation to parks in other regions. Red = California, light red = Northwest (extrapolated from California), blue = Colorado Plateau, light blue = Northern Rockies (extrapolated from Colorado Plateau), green = Southeast, light green = Rest of U.S. (extrapolated from Southeast).
In a more recent study, Smith et al. (2005) conducted a contingent valuation survey that updated and expanded a portion of the 1988 survey by Chestnut and Rowe (1990). Specifically, the Smith et al. (2005) survey relied on a panel maintained by Knowledge Networks with 2,020 participants completing the survey. Similar to the Chestnut and Rowe survey, the Smith et al. survey assessed WTP for changes in summertime visibility using the base photograph of Shenandoah National Park. Unlike the Chestnut and Rowe s urvey, the Smith et al. survey only assessed the Shenandoah National Park, did not estimate in

- region estimates of WTP, and evaluated several options for incorporating budgetary constraints into the survey. The authors concluded that WTP for recreational v isibility is skewed and sensitive to information about budgetary constraints. We are still evaluating the potential error identified by Smith et al. (2005) regarding the visibility levels in the photographs for Shenandoah National Park in the Chestnut and Rowe survey (1990a,b).
Even though this survey represents several advantages over the older survey (e.g., more recent, national, demographically representative, larger sample, etc

. ), we are unable ys6- 21 incorporate the results generated by this survey into our existing method for calculating recreational visibility co

- benefits because the survey did not account for the differences between WTP for in

- region parks and out

- of

- region parks. This omission precludes us from combining this new survey for only 1 region o f the country with the WTP for the other regions of the country from Chestnut and Rowe (1990). Furthermore, Smith et al. (2005) provide a variety of WTP estimates reflecting different versions of the survey and different methods of summarizing the typical response, which makes it difficult to select estimates to incorporate into the recreational visibility benefits calculation.

6.3.4 .2 Recreational Visibility Limitations, Caveats, and Uncertainties Our approach relies upon several data sources as inputs, including emission inventories, air quality data from models (with their associated parameters and inputs), relative humidity measurements, park information, economic data and assumptions for monetizing co

- benef its

. Each of these inputs may contain uncertainty that would affect the recreational visibility co

benefits estimates. Though we are unable to quantify the cumulative effect of all of these uncertainties in our approach , we do provide information on uncert ainty based on the available data, including model evaluation 24 and sensitivity analyses to characterize major omissions ( i.e., benefits from parks in non

- studied park regions and inclusion of coarse particles). Although we strive to incorporate as many quantitative assessments of uncertainty as possible, we are severely limited by the available data, and there are several aspects that we are only able to address qualitatively. A summary of the key assumptions including direction and magnitude of bias is provided in Table 6-5.
One major source of uncertainty for the estimation of recreational visibility co

- benefits is the benefits transfer process. Choices regarding the functional form and key parameters of the estimating equation for WTP for the affected population could have significant effects on the magnitude of the estimates. Assumptions about how individuals respond to changes in visibility that are either very small or outside the range covered in the Chestnut and Rowe study could also affect the estima tes.

24 See Chapter 4 fo r more information on model evaluation.6- 22 Table 6-5. Summary of Key Assumptions in Estimating R ecreational Visibility Co

- benefits a

Key Assumption Direction of Bias Potential Magnitude of Effect
Chestnut and Rowe study covers parks in three regions: California, Southwest, and Southeast. Benefits to other regions in the U.S. are not included in the primary benefits estimate. Underestimate Medium

Benefits to other recreational settings, such as National Forests and state parks, are not included in our approach

. Underestimate Medium

- Low

Chestnut and Rowe study conducted on populations in five states. These results are applied to the entire U.S. population. Unclear Unclear

Individuals have a greater WTP for visibility changes in parks within their region. Unclear Unclear

WTP values ref lect only visibility improvements and not overall air quality improvements. Potential Overestimate Unclear

We assume that there are 2.68 people per household. Because this estimate has been decreasing over time, this may underestimate the number of house holds. Potential Underestimate Medium

- Low

a A description of the classifications for magnitude of effects can be found in Appendix 5.B of this RIA

. Since the valuation of recreational visibility co

- benefits relies upon one study (Chestnut and Rowe, 1990a; 1990b), all of the uncertainties within that study also pertain to any analysis that uses it

. In general, the survey design and implementation reflect the period in which the Chestnut and Rowe study was con ducted. Since that time, many improvements to the design of stated preference surveys have been developed (e.g., Arrow, 1993), but we are currently unaware of newer studies that we could incorporate into our visibility co

- benefits methodology

. Although Che stnut and Rowe still offers the best available WTP estimates, the study has a number of limitations, including:

 The vintage of the survey (late 1980s) invites questions whether the values would still be valid for current populations, or more importantly fo r our approach , future populations in 2020.

 The survey focused on visibility improvements in and around specific national parks and wilderness areas. Given that national parks and wilderness areas exhibit unique characteristics, it is not clear whether the WTP estimate obtained from this survey can be transferred to other national parks and wilderness areas, even other parks within the studied park regions, without introducing additional uncertainty.6- 23  The survey focused only on populations in five states, so the application of the estimated values to populations outside those states requires that preferences of populations in the five surveyed states be similar to those of non

- surveyed states.

 There is an inherent difficulty in separating values expressed for visibility improvements from an overall value for improved air quality. The survey attempted to control for this by informing respondents that “ other households are being asked about visibility, human health, and vegetation protections in urban areas and at national parks in other regions. ” However, most of the respondents did not feel that they were able to segregate recreational visibility at national parks entirely from residential visibility and health effects.

 It is not clear exactly what visibility i mprovements the respondents to the survey were valuing. The WTP question asked about changes in average visibility, but the survey respondents were shown photographs of only daytime, summer conditions, when visibility is generally at its worst. It is possi ble that the respondents believed those visibility conditions held year

- round, in which case they would have been valuing much larger overall improvements in visibility than what otherwise would be the case. In our approach , the EPA assumed that respondents provided values for changes in annual average visibility. Because most policies would result in a shift in the distribution of visibility (usually affecting the worst days more than the best days), the annual average may not be the most relevant metric f or policy analysis.

 The survey did not include reminders of possible substitutes (e.g., visibility at other parks) or budget constraints. These reminders are considered to be best practice for stated preference surveys.

6.3.5 Residential Visibility

6.3.5 .1 Methodology Residential visibility co

- benefits are those that occur from visibility changes in urban, suburban, and rural areas where people live. These co

- benefits are important because s ome people living in certain urban areas may place a high value on unique scenic resources in or near these areas that are outside of Class 1 areas. For example, the State of Colorado established a local visibility standard for the Denver metropolitan area in 1990 (Ely et al., 1991). In our approach, residential visibility improvements are defined as those that occur specifically in Metropo litan Statistical Areas (MSAs).

In the Urban

- focused Visibility Assessment (U.S. EPA, 2010b) and the Policy Assessment for the Review of the PM NAAQS (U.S. EPA, 2011a), several preference studies provide the6- 24 foundation for the secondary PM NAAQS. 25 The three completed survey studies (all in the west) included Denver, Colorado (Ely et al., 1991), one in the lower Fraser Rive r valley near Vancouver, British Columbia (BC), Canada (Pryor, 1996), and one in Phoenix, Arizona (BBC Research & Consulting, 2003). A pilot focus group study was conducted in Washington, DC on behalf of the EPA to inform the 2006 PM NAAQS review (Abt Asso ciates Inc., 2001). Although
t hese studies indicate that some individuals considered the visual air quality associated with ambient levels of air pollution in urban areas to be unacceptable, the se studies do not provide sufficient information on which to d evelop monetized co

- benefits estimates. Specifically, the public perception studies do not provide preferences expressed in dollar values, even though they do suggest that the co

- benefits associated with improving residential visibility are positive

.
Studi es in the peer

- reviewed literature support a non

- zero value for residential visibility ( e.g., Brookshire et al., 1982; Loehman et al., 1994). Furthermore, Chestnut and Rowe (1990c) conclude that residential visibility co

- benefits are likely to be at least as high as recreational visibility co

- benefits because of the quantity of time most people spend in and near their homes and the substantial number of people affected. In previous assessments, the EPA used a study on residential visibility valuation conducted in 1990 (McClelland et al., 1993). Consistent with advice from SAB

- Council , the EPA designated the McClelland et al. study as significantly less reliable for regulatory benefit

- cost analysis, alth ough it does provide useful estimates on the order of magnitude of residential visibility co

- benefits (U.S. EPA

- SAB, 1999). 26 In our approach for estimating residential visibility co

- benefits , we replaced the previous methodology with a new benefits transfer approach and incorporated additional valuation studies. This new approach was developed for The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress (U.S. EPA, 2011) 27 and reviewed by the SAB

- Council (U. S. EPA

- SAB, 2004, 2010 a, 20 10b ).

25 For more detail about these preference studies, including information about study designs and sampling

protocols, please see Section 2 of the Particulate Matter Urban

- Focused Visibility Assessment (U.S. EPA, 2010b)

. 26 EPA ’ s Advisory Council on Clean Air Compliance Analysis noted that the McClelland et al. (1993) study may not

incorporate two potentially important adjustments. First, their study does not account for the “ warm glow ” effect, in which respondents may provide higher willingness to pay estimates simply because they favor “ good

causes ” such as environmental improvement. Second, while the study accounts for non

- response bias, it may not employ the best available methods. As a result of these concerns, the Co uncil recommended that reside ntial visibility be omitted from the overall primary benefits estimate. (U. S. EPA

- SAB, 1999) 27 This report is also known as the Second Prospective 812 analysis.6- 25 To value residential visibility improvements, the new approach draws upon information reported in the Brookshire et al. (1979), Loehman et al. (1985) and Tolley et al. (198 6 ) studies. 28 Each of the studies provides estimates of household WTP to improv e visibility conditions. While uncertainty exists regarding the precision of these older, stated

- preference residential valuation studies, we believe their results support the argument that individuals have a non

zero value for residential visibility impro vements. These studies provide primary visibility values for Atlanta, Boston, Chicago, Denver, Los Angeles, Mobile, San Francisco, and Washington D.C. 29

In accordance with Chestnut and Rowe (1990c), we utilize the WTP estimates and the associated change in visual range from each study to estimate the β parameter for the eight study areas. The β parameter represents the WTP for a specific improvement in visibility in a specific location. Where studies provide multiple estimates for visual range improvements, we estimate β by regressing the natural log of the ratio of visual range following and prior to improvement against WTP. To express these value estimates in comparable terms across study locations, we express household WTP for a change in visual range in a specific MSA using the following function:

WTP ( ∆VR ) = β* ln ( VR 1 VR 0 ) (6.3)

where:
VR 0 = mean annual visual range in miles before the improvement,

VR 1 = mean annual visual range in miles after the improvement, and
β = parameter.

28 Loehman et al. (1994) and Brookshire et al. (1982) published results in peer

- reviewed journals based on the

same underlying data we obtained from Loehman et al. (1985) and Brookshire et al. (1979). While t he specific details need to compute visibility benefits using Tolley et al. ( 198 6 ) were not published in a peer

- reviewed journal , the overall work including study and survey design was subject to peer review during study development (see Leggett and Neumann, 2004 and Patterson et al., 2005). In addition, Tolley subsequently published a book (Tolley and Fa bian, 1988) based on this research, which notes in the preface that the methods were critiqued throughout by various external economists. The EPA does not claim that this external critique necessarily constituted a formal peer review process, but we provide this information for transparency regarding the review of this work. The use of these studies as the only available information to estimate residential visibility co

- benefits in the main estimate was supported by the SAB

- Council (U.S. EPA

- SAB, 1999a, 201 0a). 29 Recognizing potential fundamental issues associated with data collected in Cincinnati and Miami (e.g., see

Chestnut et al. (1986) and Chestnut and Rowe (1990c), we do not include values for these cities in our analysis. The 8 MSAs where the valuati on studies were conducted represent 15% of the total US population in 2020 (U.S. Census).6- 26 Total residential visibility co

- benefits within a particular MSA are driven by visibility improvements, population density, and the WTP value applied. Only those people living within in an MSA are assumed to receive co

- benefits from improved residential visibility. In other words, un like recreational visibility, we do not assume a non

- use value by people who live outside the MSA for residential visibility. Table 6-6 provides a summary of these valuation estimates for each study location, as well as an illustrative implied WTP value for a 10% improvement in visual range. As shown, the implied annual per

- household WTP estimates for a hypothetical 10% improvement ranges from $21 to $220, depending on the study area. It is not surprising that such a range of values exists, as these study areas all feature different landscapes and vistas, populations and prevailing visibility conditions.

Table 6-6. Summary of Residential V isibility Valuation Estimates

City Study β Estimate Implied WTP for 10% Improvement in Visual

Range (1990$, 1990 income) Implied WTP for 10% Improvement in Visual

Range (2006$, 2020 income)

Atlanta Tolley et al. ( 198 6) 321 $31 $72

Boston Tolley et al. ( 198 6) 398 $38 $89
Chicago Tolley et al. ( 198 6) 310 $30 $69

Denver Tolley et al. ( 198 6) 696 $66 $155
Los Angeles Brookshire et al. (1979) 94 $9 $21

Mobile Tolley et al. ( 198 6) 313 $30 $70

San Francisco Loehman et al. (1985) 989 $94 $220
Washington, DC Tolley et al. ( 198 6) 614 $59 $137

Similar to recreational visibility co

- benefits , we then incorporate preference calibration using the method developed by Smith, Van Houtven, and Pattanayak (2002), which is discussed in more detail in Appendix 6a of this RIA. This preference calibration is a change since The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress (U.S. EPA, 2011 ) intended to address the SAB

- Council’s concern (U.S. EPA

- SAB, 2010a) regarding the inconsistency regarding household income in the estimation of recreational and residential visibility. To express these “ preference

- calibrated ” value estimates across study locations, we express household WTP for a change in visual range in a specific MSA using the following function:6- 27 WTP ( ∆ VR ) = m − [ m ρ + θ ∗ � VR 0 ρ

− VR 1 ρ � ] 1 ρ (6

- 4)

where:
m = household income, ρ = shape parameter (0.1), θ = WTP parameter corresponding to the visibility at MSA,
VR 0 = starting visibility, and
VR 1 = visibility after change.

While the primary estimate for residential visibility includes co

- benefits in only the eight MSAs included in the valuation studies, people living in other urban areas also have non

- zero values for residential visibility. For this reason, our approach includes a sensitivity analysis for the extrapolated residential visibility in the 351 additional MSAs. 30 Because there is considerable uncertainty about the validity of this benefit transfer approach, these extrapolated co

- benefits are included in a sensitivity analysis only. This is an important distinction between the approach used in The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress (U.S. EPA, 2011 ), where all cities were included in the total benefits approach. We believe that it is appropriate to deviate from the previous approach in order to be co nsistent with the approach used to estimate recreational visibility co

- benefits and to recognize the uncertainty associated with extrapolating beyond the studied cities. Figure 6- 6 indicates the study cities as well as the assignment of the other MSAs to the study cities.

The degree to which the three studies were successful in convincing respondents to focus solely on visibility is unclear, as none of the three studies includes follow

- up questions necessary to investigate the issue. Furthermore, no other residential visibility CV studies provide evidence regarding the degree to which health effects are embedded in visibility values. Although the McClelland et al. (1991) study has a follow

- up question designed to allocate WTP across several categories, the CV question in the McClelland et al. study was focused on air pollution generally rather than visibility. As a result, we do not adjust the results from these studies to account for potentially embedded health effects.

There are many factors that could influence WTP for residential visibility, and these factors vary across urban areas. In our approach , we utilize the benefit transfer approach

30 The 351 additional MSAs plus the 8 study area MSAs represent 84% of the total US population in 2020 (U.S.

Census).6- 28 developed for The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress (U.S. EPA, 2011 ) report , but we recognize that there are alternative methods that we could have used. We assigned a valuation study area to each MSA based on two factors: geographic proximity to one of the eight study cities and elevation. Any MSA with a county elevation a bove 1, 500 meters was assigned the Denver valuation instead of the nearest study area. 31 Because residents of Denver have a dramatic view of the Rocky Mountains that is rarely obstructed by trees, it is plausible that they might have a greater interest in p rotecting visibility than a city without nearby mountains. The geographic proximity factor is constrained in two areas. The San Francisco valuation study is only assigned to the six counties in the San Francisco Bay area MSAs because the study is unique among the three regarding the temporal description of visibility conditions, landscape/vistas, and prevailing weather conditions. In addition, the Los Angeles valuation was assigned to the Riverside MSA despite exceeding the elevation threshold. 32

31 Elevation data represent the county

- level maxim um, which were calculated using the ArcGIS Spatial Analyst tool

“ Zonal Statistics ” using the geographic database HYDRO1K for North America (U.S. Geological Survey, 1997). This dataset and associated documentation are available on the Internet at DEMhttp://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30/hydro/namerica

. 32 Riverside MSA is assigned to the Los Angeles study area because a significant portion of Riverside County itself is

located in the South Coast Air Quality Management District, which can be considered by to be part of the same regulated airshed as Los Angeles. The geographic assignment is preserved despite exceeding the elevation threshold because Riverside is adjacent to one of the study cities and this region has a particular s et of location

specific characteristics that set it apart from Denver.6- 29 Figure 6-6. Residential Visibility Study City Assignment

6. 3.5 .2 Residential Visibility Limitations, Caveats, and Uncertainties Similar to recreational visibility co

- benefits , there are many data inputs into the residential visibility co

- benefits that contribute t o overall uncertainty. Our approach includes sensitivity analyses to characterize major omissions (i.e., co

- benefits in other MSAs and coarse particles). A summary of the key assumptions including direction and magnitude of bias in our approach is provided in Table 6- 7.
The valuation studies relied upon for the residential visibility co

- benefits , although representing the best available estimates, have a number of limitations. These include the following:

 The survey design and implementation reflects the pe riod in which the surveys were conducted. Since that time, many improvements to the stated preference methods have been developed.

 The vintage of the surveys (1970s and 1980s) invites questions whether the values are still valid for current populations, or more importantly for our approach , future populations in 2020.6- 30  The survey focused only on populations in eight cities, so the transfer of the WTP estimates values to populations outside those cities requires that their preferences be similar to those in n on

- surveyed cities, as well as the visibility attributes be similar across study and transfer MSAs.

 There is an inherent difficulty in separating values expressed for visibility improvements from an overall value for improved air quality. The studies attem pted to control for this, but most of the respondents did not feel that they were able to segregate residential visibility entirely from recreational visibility and health effects.

Table 6-7. Summary of Key Assumptions in the Residential Visibility Co

- bene fits a

Key Assumption Direction of Bias Magnitude of Effect
Residential and recreational visibility benefits are distinct and separable. Potential Overestimate Medium

- Low

Estimates residential visibility benefits are limited to populations within the boundaries of MSAs. Areas outside of an MSA are not included in our approach

. Underestimate Low

WTP values reflect only visibility improvements and not overall air quality improvements. Potential Overestimate Medium

- Low

WTP values from studies in Atlanta, Boston, Chicago, Denver, Los Angeles, Mobile, San Francisco, and Washington D.C. can be accurately transferred to MSAs across the U.S. based on proximity and elevation Unclear Unclear

We assume that there are 2.68 peopl e per household. Because this estimate has been decreasing over time, this may underestimate the number of households. Potential Underestimate Medium

- Low

a A description of the classifications for magnitude of effects can be found in Appendix 5.B of this RIA

.
6. 3. 5.3 Using Hedonic Economic Literature to E stimate Visibility Co

- benefits The hedonic model assumes that consumers do not value the consumption of a good directly, but rather value the characteristics contained within a good. In the contex t of property values, the consumer values both the physical attributes of the property (i.e., number of rooms, square footage, etc.) as well as geographic and environmental attributes (e.g., proximity to parks, visibility, etc.). Following the technique de veloped by Rosen (1974), property characteristics are regressed on the observed price of the properties within a given housing market to estimate the contribution of each characteristic to the overall price.

Numerous studies have applied hedonic methods t o estimate the willingness to pay (WTP) for air quality changes (see Smith and Huang (1995) and Boyle and Kiel (2001) for6- 31 literature reviews), but fewer researchers have focused specifically on visibility. Studies that have estimated the WTP to improve vis ibility have focused on specific housing markets like Los Angeles (Murdoch and Thayer, 1998; Beron et al., 2001) or San Francisco (Graves et al., 1998), and it is unclear if these results would be more broadly applicable to the rest of the county. While th e literature demonstrates a link between pollutant concentrations and home

- buying behavior, it is difficult to partition the WTP for changes in pollution between health and aesthetic concerns. Murdoch and Thayer (1998) use visibility as a surr ogate for env ironmental quality and Beron et al. (2001) acknowledge that their parameter estimates likely reflect a combination of visual aesthetics and an absence of health effects. Delucchi et al. (2002) deals with this issue by partitioning WTP estimates from a hedo nic model into health and visibility components using results from previous contingent valuation studies, and find that the estimate of visibility co

- benefits is similar to estimates based on contingent valuation alone

.
In 2004, the Advisory Council on Cl ean Air Compliance Analysis ( SAB

- Council ) recommended that the EPA evaluate the available studies addressing residential visibility and consider the possibility of using hedonic property models to estimate residential visibility co

benefits (U.S. EPA

- SAB, 2004). In response to this recommendation, the EPA evaluated the existing economic literature, and determined that there were substantial limitations that precluded the Agency from using these studies to make inferences regarding individuals ’ WTP for impro ved visibility (Leggett and Neumann, 2004). Specifically, the literature did not provide support for the assumption that market participants are aware of the spatial variation in visibility, and consider this variation when purchasing a home, and can successfully separate visibility effects from health effects (Leggett and Neumann, 2004). This conclusion is also supported by Delucchi et al

. (2004), which found that hedonic price analysis does not capture all of the health effects of air pollution because homebuyers may not be fully informed about these effects .

Research since 2004 has attempted to address limitations of the hedonic method through the use of U.S. Census microdata (Bayer et al., 2009), spatial statistic al methods (Anselin and Le Gallo, 2006 ; Anselin and L ozana

- Gracia, 2009; Beron et al., 2004; Kim et al., 2010 ) and more complete air quality data and information about nonattainment status (Chay and Greenstone, 2005). However, none of these studies specifically address visibility, and they

are therefore of limited use at this time. While the current state of the literature does not provide a basis for using hedonics

- based approaches, continued innovations in methodology and the further development of national, micro

- level housing and demographi c datasets may open possibilities for national

- scale hedonics

- based benefit analysis in the future.6- 32 Regardless of whether we use hedonic models or stated preference surveys to estimate

co

- benefits arising from improved visibility, it is important to empha size that estimates of WTP for residential or recreational visibility improvements are not substitutes for health benefits. As previously mentioned, people often have difficulty separating their health concerns from their aesthetic concerns when evaluating preferences for visibility, which could overestimate visibility co

- benefits if not properly controlled. However, because we use a damage

- function approach to estimate health benefits (see Chapter 5 of this RIA), the health benefits estimates are unaffecte d by any potential confounding with visibility preferences.

6.3.6 Discussion of Visibility Co

- benefits As described in the previous sections of this chapter, the estimation of visibility co

benefits is complex and suffers from unavoidable limitations. Whi le we are confident that the underlying scientific literature supports a non

- zero estimate for visibility co

- benefits attributable to emission reductions, we are less confident in the magnitude of those co

- benefits outside of previously studied locations. While acknowledging these limitations, it is important
to note that this general approach was included in The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress (U.S. EPA, 2011 ) , which was reviewed by the SAB

- Council (U.S. EPA

- SAB, 2010 a, 2010b ). Although the SAB

- Council highlighted concerns with the visibility approach used in the study, it did not recommend that visibility benefits be excluded. W e have addressed the SAB

- Council’s concern regarding inconsistency between est imation of residential and recreational visibility in our approach. However, we do not have the data to address the SAB

- Council’s concern regarding inclu sion of night

- time benefits of visibility improvements in our annual average, which may lead to an unde restimation of visibility benefits. To minimize uncertainties related to extrapolation and geographical double counting, our approach only include s a subset of monetized visibility co

- benefits in the core monetized visibility co

- benefits estimate to corres pond with our higher level of confidence in recreational co

- benefits within the study regions and residential co

- benefits within the study cities. Although we are confident that visibility co

- benefits extend beyond these studied areas, we are less confiden t about the magnitude of those co

- benefits

. However, it is unclear the degree to which the visibility valuation surveys were successful in contro ll ing for potential double counting embedded health benefits.

Consistent with the approach described in the pr oposal RIA , we have described a revised approach for estimating visibility co

- benefits , including light extinction estimation methods, visitation data for Class 1 areas (used in extrapolating co

- benefits ) , valuation studies for residential visibility co

- benefits , and the benefit transfer technique for residential co

-6- 33 benefits

. Including residential visibility co

- benefits in the core visibility co

- benefits estimates reflects an evolution in our understanding of the nature and importance of the effect on public welfare from visibility impairment to a more multifaceted approach that includes non

- Class 1 areas, such as urban areas. This evolution has occurred in conjunction with the expansion of available PM data and information from associated studies of public perception, valuation and personal comfort and well

- being. While visibility preference studies (Abt Associates Inc., 2001, Ely et al., 1991, Pryor, 1996, BBC Research & Consulting, 2003) also provide support for a non

zero benefits estimate, these surveys did not include questions that would enable monetization of those preferences.

Despite these improvements, we are limited by the available peer

- reviewed studies on visibility co

- benefits , which have not undergone a similar expansion as the health lite rature. Each of these valuation studies has limitations, which are identified in the section s 6.3.4.2 and 6.3.5.2. When the SAB

- Council reviewed the visibility benefits analysis for The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Co ngress (U.S. EPA, 2011 ) , they also lamented on the need for additional research to improve methods and estimates (U.S. EPA

SAB, 2010 a, 2010b ). Because of time and resource constraints, performing original research for regulatory analyses of specific policy actions is infeasible. Most importantly, we are interested in recently published national

- scale visibility valuation studies that incorporate current CV best practices, as the existing studies are limited to specific subset of geographic areas. Other important research questions that remain unresolved include identifying factors that affect valuation preferences in order to facilitate benefits transfer from the original studies to transfer sites across localities, disentangling health and ecosystem val uation from visibility valuation, usefulness of preference calibration, and potential role of hedonic valuation approaches. Many of these same research needs were identified by Cropper (2000), but they have yet to be addressed by the research community.

In Appendix 6.B, w e provide the results of an illustrative analysis of the visibility co

benefits associated with the 2020 base case 2020 control case simulation described in Chapter 3 that were used to develop the air quality ratios; however, we do not have air quality model runs for the regulatory baseline and the alternative standard levels that would allow us to calculate the visibility co

- benefits of attaining the revised primary standard.

6.4 Materials Damage Co

- benefits Building materials including metals, stones, cements, and paints undergo natural weathering processes from exposure to environmental elements (e.g., wind, moisture,6- 34 temperature fluctuations, sunlight, etc.). Pollution can worsen and accelerate these effect s. Deposition of PM is associated with both physical damage (materials damage effects) and impaired aesthetic qualities (soiling effects). Wet and dry deposition of PM can physically affect materials, adding to the effects of natural weathering processes, by potentially promoting or accelerating the corrosion of metals, by degrading paints and by deteriorating building materials such as stone, concrete and marble (U.S. EPA, 2009b). The effects of PM are exacerbated by the presence of acidic gases and can be additive or synergistic due to the complex mixture of pollutants in the air and surface characteristics of the material. Acidic deposition has been shown to have an effect on materials including zinc/galvanized steel and other metal, carbonate stone (as m onuments and building facings), and surface coatings (paints) (Irving, 1991). The effects on historic buildings and outdoor works of art are of particular concern because of the uniqueness and irreplaceability of many of these objects.

The PM ISA concludes that evidence is sufficient to support a causal relationship between PM and effects on materials (U.S. EPA, 2009b). Considerable research has been conducted on the effects of air pollutants on metal surfaces due to the economic importance of these materials, especially steel, zinc, aluminum, and copper. Moisture is the single greatest factor promoting metal corrosion; however, deposited PM can have additive, antagonistic or synergistic effects. In general, SO 2 is more corrosive than NO x although mixtures of NO x , SO 2 and other particulate matter corrode some metals at a faster rate than either pollutant alone (U.S. EPA, 2008). Metal structures are usually coated by alkaline corrosion product layers and thus are subject to increased corrosion by acidic deposition. In addition, research has demonstrated that iron, copper, and aluminum

- based products are subject to increased corrosion due to pollution (Irving, 1991). Information from both the PM ISA (U.S. EPA, 2009b) and NO x /SO x ISA (U.S. EPA , 2008) suggest that the extent of damage to metals due to ambient PM is variable and dependent upon the type of metal, prevailing environmental conditions, rate of natural weathering and presence or absence of other pollutants

In addition, the deposition of PM can cause soiling, which is the accumulation of dirt, dust, and ash on exposed surfaces such as metal, glass, stone and paint. Particles consisting primarily of carbonaceous compounds can cause soiling of commonly used building materials and culturally important items such as statues and works of art. Soiling occurs when PM accumulates on an object and alters the optical characteristics (appearance). The reflectivity of a surface may be changed or presence of particulates may alter light transmission. These effects can reduce the aesthetic value of a structure or result in reversible or irreversible damage to statues, artwork and architecturally or culturally significant buildings. Due to soiling6- 35 of building surfaces by PM, the frequency and duration o f cleaning or repainting may be increased. In addition to natural factors, exposure to PM may give painted surfaces a dirty
appearance. Pigments in works of art can be degraded or discolored by atmospheric pollutants, especially sulfates (U.S. EPA, 2008). Previous assessments estimated household soiling co

benefits based on the Manuel et al. (1982) study of consumer expenditures on cleaning and household maintenance. However, the data used to estimate household soiling damages in the Manuel et al. study is from a 1972 consumer expenditure survey and as such may not accurately represent consumer preferences in the future. In light of this significant limitation, we believe that this study cannot provide reliable estimates of the likely magnitude of the co

ben efits of reduced PM household soiling.

In order to estimate the monetized co

- benefits associated with reducing materials damage and household soiling, quantitative relationships are needed between particle size, concentration, chemical concentrations and frequency of maintenance and repair. Such an analysis would require three steps:

1. Develop a national inventory of sensitive materials;

2. Derive concentration

- response functions that relate material damage to change in pollution concentration or deposition; and,

3. Estimate the value of lost materials and/or repair of damage.

Due to data limitations and uncertainties inherent in each of these steps, we have chosen not to include a monetized estimate of materials damage and household soiling in this analysi s. The PM ISA concluded that there is considerable uncertainty with regard to interaction of co

- pollutants in regards to materials damage and soiling processes (U.S. EPA, 2009b). Previous benefits analyses by the EPA have provided quantitative estimates of materials damage (U.S. EPA, 2011b) and household soiling damage (U.S. EPA, 1999). Consistent with SAB advice (U.S. EPA, 1998), we determined that the existing data are not sufficient to calculate a reliable estimate of future year household soiling damage s (U.S. EPA, 1998). These previous analyses have shown that materials damage co

- benefits are significantly smaller than the health benefits associated with reduced exposure to PM 2.5 and ozone, or even visibility co

benefits

. However, studies of materials damage to historic buildings and outdoor artwork in Sweden (Grosclaude and Soguel, 1994) indicate that these co

- benefits could be an order of magnitude larger than household soiling co

- benefits

.6- 36 In the absence of quantified co

- benefits , we provide a qualita tive description of the avoided damage associated with reducing PM and PM precursor pollutants. Table 6-8 shows the effect of various PM 2.5 precursor pollutants and other co

- pollutants on various materials.

Table 6-8. Materials Damaged by Pollutants Affected by this Rule (U.S. EPA, 2011b)

Pollutant Unquantified Effects / Damage to:

Sulfur oxides Infrastructural materials

— galvanized and painted carbon steel Commercial buildings

— carbonate stone, metal, and painted wood surfaces Residential buildings

— carbonate stone, metal, and painted wood surfaces
Monuments

— carbonate stone and metal Structural aesthetics Automotive finishes

— painted metal Hydrogen ion and nitrogen oxides Infrastructural materials

— galvanized and painted carbon steel
Zinc

- based metal products, such as galvanized steel Commercial and residential buildings

— carbonate stone, metal, and wood surfaces
Monuments

— carbonate stone and metal Structural aesthetics Automotive finishes

— painted metal

Carbon dioxide Zinc

- based metal products, such as galvanized steel Formaldehyde Zinc

- based metal products, such as galvanized steel

Particulate matter Household cleanliness (i.e., household soiling)

Ozone Rubber products (e.g., tires)

6. 5 Climate Co

- benefits Actions taken by s tate and l ocal governments to implement the revised annual primary standard are likely to have implications for climate change because emission reductions ultimately implemented to meet the standard may have impacts on emissions of long

- lived greenhouse gas (GHG) su ch as carbon dioxide (CO 2 ), short

- lived climate forcers such as black carbon (BC), and cooling aerosols like organic carbon (OC). Our ability to quantify the climate effects of these revised standard is limited due to lack of available information on the c ocontrolled GHG emission reductions, the energy and associated climate gas implications of control technologies assumed in the illustrative regulatory alternative s, and remaining uncertainties regarding the impact of long

- lived and short

- lived climate for cer impacts on climate change. For this RIA, we discuss qualitatively the implications of potential emission reductions in warming and cooling aerosols and changes in long

- lived GHG emissions such as6- 37 CO 2 for the regulatory alternative s. Implementation stra tegies undertaken by s tate and l ocal governments to comply with the standard s may differ from the illustrative emission reduction strategies in this RIA

. It is important to note that the net climate forcing depends on the specific combinations of emission reductions chosen to meet the revised standard because of the differences in warming and cooling potential of the difference pollutants

.
6.5 .1 Climate Effects of Short Lived Climate Forcers Pollutants that affect the energy balance of the earth are referre d to as climate forcers. A pollutant that increases the amount of energy in the Earth ’ s climate system is said to exert

“ positive radiative forcing, ” which leads to warming and climate change. In contrast, a pollutant that exerts negative radiative forcing reduces the amount of energy in the Earth ’ s system and leads to cooling.

Long- lived gases such as CO 2 differ from short

- lived pollutants such as BC in the length of time they remain in the atmosphere affecting the earth ’ s energy balance. Long- lived gases remain in the atmosphere for hundreds to thousands of years. Short

- lived climate forcers (SLCFs), in contrast, remain in the atmosphere for short periods of time ranging from days to weeks. The potential to affect near

- term climate change and the rate of climate change with policies to address these emissions is gaining attention nationally and internationally ( e.g., Black Carbon Report to Congress ( U.S. EPA, 2012b ), Arctic Council Task Force, Global Methane Initiative, and Convention on Long

- Range Trans

- bo undary Air Pollution of the United Nations Economic Commission for Europe). A recent United Nations Environmental Programme (UNEP) study provides the most comprehensive analysis to date of the co

- benefits of measures to reduce SLCFs including methane, ozon e, and black carbon assessing the health, climate, and agricultural co

- benefits of a suite of mitigation technologies. The report concludes that the climate is changing now, and these changes have the potential to “ trigger abrupt transitions such as the re lease of carbon from thawing permafrost and biodiversity loss. ” While reducing long

- lived GHGs such as CO 2 is necessary to protect against long

- term climate change, reducing SLCF gases including BC and ozone is beneficial and will slow the rate of climate change within the first half of this century (UNEP , 2011).

6.5 .1.1 Climate Effects of Black Carbon

Blac k carbon is the most strongly light

- abs orbing component of PM 2.5 , and is formed by incomplete combustion of fossil fuels, biofuels, and biomass. The shor t atmospheric lifetime of BC lasting from days to weeks and the mechanisms by which BC affects climate distinguish it from long

- live d GHGs like CO 2 . This means that actions taken to reduce the BC constituents in6- 38 dire ct PM 2.5 will have almost immediate effects on climate change. Emissions sources and ambient concentrations of BC vary geographically and temporally resulting in climate effects that are more regionally and seasonally dependent than the effects of long

- li ved, well

- mixe d GHGs. Likewise, mitigation actions for BC will produce different climate impacts depending on the region, season, and emission source category affected.

BC influences climate in multiple ways: directly absorbing light , reducing the reflectivity (“ al bedo ” ) of snow and ice through deposition, and interacting with clouds. BC affects climate directly by absorbing both incoming and outgoing radiation of all wavelengths. In contrast, GHGs mainly trap outgoing infrared radiation from the earth ’ s surface. Pe r unit of mass in the atmosphere, BC can absorb a million times more energy than CO 2 (Bond and Sun 2005). This strong absorptive capacity is the property most relevant to its potential to affect the Earth ’ s climate. W hen BC is deposited on snow and ice, it darkens the surface and decreases albedo , thereby increasing absorption and accelerating melting. Finally, BC also affects climate indirectly by altering the properties of clouds, affecting cloud reflectivity, precipitation, and surface dimming. These indirect impacts of BC are associated with all ambient particles and may lead to cooling, but are not associated with long

- lived well mixed GHGs.

Re gional climate impacts of BC are highly variable, and sensitive regions such as the Arctic and the Himalayas are particularly vulnerable to the warming and melting effects of BC. Snow and ice cover in the Western U.S. has also been affected by BC. Specifically, deposition of BC on mountain glaciers and snow packs produces a positive snow and ice albedo effect,

con tributing to the melting of snowpack earlier in the spring and reducing the amount of snowmelt that normally would occur later in the spring and summer (Hadley et al. 2010). This has implications for freshwater resources in regions of the U.S. dependent on snow

- fed o r glacier

- fe d water systems. In the Sierra Nevada mountain range, Hadley et al. (2010) found BC at different depths in the snowpack, deposited over the winter months by snowfall. In the spring, the continuous uncovering of the BC contributed to the early melt. A model capturing the effects of soot on snow in the western U.S. shows significant decreases in snowpack between December and May (Qian et al., 2009). Snow water equivalent (the amount of water that would be produced by melting all the sno w) is reduced 2

- 50 millimeters (mm) in mountainous areas, particularly over the Central Rockies, Sierra Nevadas, and western Canada. A study found that biomass burning emissions in Alaska and the Rocky Mountain region during the summer can enhance snowmelt . Dust deposition on snow, at high concentrations, can have similar effects to BC (Koch et al., 2007). Similarly, a study done by Painter et al. (2007) in the6- 39 San J uan Mountains in Colorado indicated a decrease in snow cover duration of 18

- 35 days as a

res ult of dust transported from non

- lo cal desert sources.

Th e illustrative emission reduction strategies evaluated for this rule include reductions in BC emissions that will tend to have a beneficial cooling effect on the atmosphere. BC and elemental carbon ( EC ) (or particulate elemental carbon (PEC)) are used interchangeably in this report because the EPA traditionally estimates EC emissions rather than BC and for the purpose of this analysis these measures are essentially equivalent.

6.5 .1.2 Climate Effects of Nitrates, Sulfate, and Organic Carbon (excluding BC) The composition of the total emissions mixture is also relevant as to whether emissions are warming or cooling to the atmosphere . P ollutants such as SO 2 , NO X , and most OC particles
ten d to produce a cooling influence on climate

. Exceptions include OC deposition on snow and ice , which leads to increased melting.

In addition, it is important to account for the indirect effects of all PM constituents on climate: all aerosols (including BC) affect climate indirectly by changing the reflectivity and lifetime of clouds. The net indirect effect of all aerosols is very uncertain but is thought to be a net cooling influence.

6.5 .1.3 Climate Effects of Ozone Ozone changes due to this revised annual standard are not estimated for this analysis but may occur due to the NO x reductions estimated. Ozone is a well

- known SLCF (U.S. EPA, 2006). Stratospheric ozone (the upper ozone layer) is beneficial because it protects life on Earth from the sun ’ s h armful ultraviolet (UV) radiation. In contrast, tropospheric ozone (ozone in the lower atmosphere) is a harmful air pollutant that adversely affects human health and the environment and contributes significantly to regional and global climate change. Due t o its short atmospheric lifetime, tropospheric ozone concentrations exhibit large spatial and temporal variability (U.S. EPA, 2009). The discernible influence of ground level ozone on climate leads to increases in global surface temperature and changes in hydrological cycles. While reducing long

- lived GHGs such as CO 2 is necessary to protect against long

- term climate change, reducing SLCF gases including ozone is beneficial and will slow the rate of climate change within the first half of this century (UNEP , 2011).

6.5 .1.4 SLCFs Summary and Conclusions Assessing the net climate impact of SLCFs for the illustrative emission reduction strategies is outside the scope of this regulatory analysis and requires climate atmospheric6- 40 modeling not undertaken due to time and resource constraints. Information about the amount of BC relative to non

- BC constituents emitted from a source is important. In general, these non

BC constituents are emitted in greater volume than BC, counteracting the warming influence of BC. Quali tatively, it seems likely that BC emission reductions associated with direct ly emitted
PM 2.5 reductions will be beneficial for the climate in terms of reduced radiative forcing and deposition on snow and ice. Reductions in OC, sulfates and nitrates are likely to produce warming in the atmosphere. The indirect impacts of aerosols on clouds and precipitation remain the subject of great uncertainty making it more difficult to estimate the quantitative impact of aerosol reductions on climate.

6.5 .2 Climate Effe cts of Long

- Lived Greenhouse Gases

The EPA Administrator found in 2009 that elevated concentrations of the six major GHGs, including CO 2 , endanger the public health and public welfare of current and future generations (FR 77 66496). While addressing short

- lived climate forcers can result in near

- term (and sometimes regionally specific) co

- benefits as well as reductions in the rate of warming, reductions of long

- term warming would require mitigation of long

- lived GHGs. We are unable
to quantify the impact of the illustrative emission reduction strategies for this rulemaking on long

- lived climate gases due lack of available data

. However, State and Local governments may want to consider human health, welfare, and climate implications of regulatory strategies undertaken to implement the promulgated PM standards.

6.6 Ecosystem Co

- benefits and Services The effects of air pollution on the health and stability of ecosystems are potentially very important. At present, it is difficult to measure the impact of reducing air pollution in a national scale analysis across different types of ecosystems and different pollutant effects. Previous science assessments by the EPA (U.S. EPA, 2006a; 2008c; 2009b) have determined that air pollution can be direc tly linked to aquatic and terrestrial acidification, nutrient enrichment, vegetation injury, and metal bioaccumulation in animals. Ecosystem services are a useful conceptual framework for analyzing the impact of ecosystem changes on public welfare.

Ecosyst em services can be generally defined as the benefits that individuals and organizations obtain from ecosystems. The EPA has defined ecological goods and services as the “ outputs of ecological functions or processes that directly or indirectly contribute to social welfare or have the potential to do so in the future. Some outputs may be bought and sold, but most are not marketed ” (U.S. EPA, 2006c). Figure 6-7 provides the Millennium Ecosystem Assessment ’ s schematic demonstrating the connections between the categories of ecosystem6- 41 services and human well

- being. The interrelatedness of these categories means that any one ecosystem may provide multiple services. Changes in these services can affect human wel lbeing by affecting security, health, social relationships, and access to basic material goods (MEA, 2005).

Figure 6-7. Linkages between Categories of Ecosystem Services and Components of Human Well

- Being from Millennium Ecosystem Assessment (MEA, 2005)

In the Millennium Ecosystem Assessment (MEA, 2005), ecosystem services are classified into four main categories:

1. Provisioning: Products obtained from ecosystems, such as the production of food and water
2. Regulating: Benefits obtained from the regula tion of ecosystem processes, such as the control of climate and disease
3. Cultural: Nonmaterial benefits that people obtain from ecosystems through spiritual enrichment, cognitive development, reflection, recreation, and aesthetic experiences
4. Supportin g: Services necessary for the production of all other ecosystem services, such as nutrient cycles and crop pollination The monetization of ecosystem services generally involves estimating the value of ecological goods and services based on what people are willing to pay (WTP) to increase ecological services or by what people are willing to accept (WTA) in compensation for6- 42 reductions in them (U.S. EPA, 2006c). There are three primary approaches for estimating the monetary value of ecosystem services: market

- based approaches, revealed preference methods, and stated preference methods (U.S. EPA, 2006c). Because economic valuation of ecosystem services can be difficult, nonmonetary valuation using biophysical measurements and concepts also can be used. An example of a nonmonetary valuation method is the use of relative

- value indicators (e.g., a flow chart indicating uses of a water body, such as boatable, fishable, swimmable, etc.). It is necessary to recognize that in the analysis of the environmental

res ponses associated with any particular policy or environmental management action, only a subset of the ecosystem services likely to be affected are readily identified. Of those ecosystem services that are identified, only a subset of the changes can be quan tified. Within those services whose changes can be quantified, only a few will likely be monetized, and many will remain nonmonetized. The stepwise concept leading up to the valuation of ecosystems services is graphically depicted in Figure 6-8.

Figure 6-8. Schematic of the Benefits Assessment Pro cess (U.S. EPA, 2006c)

6.6 .1 Ecosystem Co

- benefits for Metallic and Organic Constituents of PM Several significant ecological effects are associated with deposition of chemical constituents of ambient PM such as metals and organics (U.S. EPA, 2009b). The trace metal constituents of PM include cadmium, copper, chromium, mercury, nickel, zinc, and lead. The organics include persistent organic pollutants (POPs), polyaromatic hydrocarbons (PAHs) and polybromiated diph enyl ethers (PBDEs). Exposure to PM for direct effects occur via deposition ( e.g., wet, dry or occult) to vegetation surfaces, while indirect effects occur via deposition to6- 43 ecosystem soils or surface waters where the deposited constituents of PM then inte racts with biological organisms. While both fine and coarse

- mode particles may affect plants and other organisms, more often the chemical constituents drive the ecosystem response to PM (Grantz et al., 2003). Ecological effects of PM include direct effects to metabolic processes of plant foliage; contribution to total metal loading resulting in alteration of soil biogeochemistry and microbiology, plant and animal growth and reproduction; and contribution to total organics loading resulting in bioaccumulation and biomagnification across trophic levels.

The PM ISA concludes that a causal relationship is likely to exist between deposition of PM and a variety of effects on individual organisms and ecosystems (U.S. EPA 2009b). Most direct ecosystem effects associ ated with particulate pollution occur in severely polluted areas near industrial point sources (quarries, cement kilns, metal smelting) (U.S. EPA, 2009b). However the PM ISA also finds, in many cases, it is difficult to characterize the nature and magnitud e of effects and to quantify relationships between ambient concentrations of PM and ecosystem response due to significant data gaps and uncertainties as well as considerable variability that exists in the components of PM and their various ecological effec ts (U.S. EPA, 2009b).

Particulate matter can adversely impact plants and ecosystem services provided by plants by deposition to vegetative surfaces (U.S. EPA, 2009b). Particulates deposited on the surfaces of leaves and needles can block light, altering th e radiation received by the plant. PM deposition near sources of heavy deposition can obstruct stomata limiting gas exchange, damage leaf cuticles and increase plant temperatures (U.S. EPA, 2009b). Plants growing on roadsides exhibit impact damage from near

- road PM deposition, having higher levels of organics and heavy metals, and accumulate salt from road de

- icing during winter months (U.S. EPA, 2009b). In addition, atmospheric PM can convert direct solar radiation to diffuse radiation, which is more uniformly distributed in a tree canopy, allowing radiation to reach lower leaves (U.S. EPA, 2009b). Decreases in crop yields (a provisioning service) due to reductions in solar radiation have been attributed to regional scale air pollution in other counties with especially severe regional haze (Chameides et al., 1999).

In addition to damage to plant surfaces, deposited PM can be taken up by plants from soil or foliage. Copper, zinc, and nickel have been shown to be directly toxic to vegetation under field conditions (U.S. EPA, 2009b). The ability of vegetation to take up heavy metals is dependent upon the amount, solubility and chemical composition of the deposited PM. Uptake of PM by plants from soils and vegetative surfaces can disrupt photosynthesis, alter pigments and mineral content, reduce plant vigor, decrease frost hardiness and impair root development.6- 44 Particulate matter can also contain organic air toxic pollutants, including PAHs, which are a class of polycyclic organic matter (POM). PAHs can accumul ate in sediments and bioaccumulate in freshwater, flora and fauna. The uptake of organics depends on the plant species, site of deposition, physical and chemical properties of the organic compound and prevailing environmental conditions (U.S. EPA, 2009b). Different species can have different uptake rates of PAHs. For example, zucchini ( Cucurbita pepo ) accumulated significantly more PAHs than related plant species (Parrish et al., 2006). PAHs can accumulate to high enough concentrations in some coastal environments to pose an environmental health threat that includes cancer in fish populations, toxicity to organisms living in the sediment and risks to those (e.g., migratory birds) that consume these organisms (Simcik et al., 1996; Simcik et al., 1999). Atmosp heric deposition of particles is thought to be the major source of PAHs to the sediments of Lake Michigan, Chesapeake Bay, Tampa Bay and other coastal areas of the U.S. (Arz avus, Dickhut, and Canuel, 2001 ).

Contamination of plant leaves by heavy metals can lead to elevated concentrations in the soil. Trace metals absorbed into the plant, frequently bind to the leaf tissue, and then are lost when the leaf drops. As the fallen leaves decompose, the heavy metals are transferred into the soil (Cotrufo et al

. , 1 995; Niklinska et al., 1998). Many of the major indirect plant responses to PM deposition are chiefly soil

- mediated and depend on the chemical composition of individual components of deposited PM. Upon entering the soil environment, PM pollutants can alter ecological processes of energy flow and nutrient cycling, inhibit nutrient uptake to plants, change microbial community structure and, affect biodiversity. Accumulation of heavy metals in soils depends on factors such as local soil characteristics, geologic origin of parent soils, and metal bioavailability. Heavy metals, such as zinc, copper, and cadmium, and some pesticides can interfere with microorganisms that are responsible for decomposition of soil litter, an important regulating ecosystem service that serves as a source of soil nutrients (U.S. EPA, 2009b). Surface litter decomposition is reduced in soils having high metal concentrations. Soil communities have associated bacteria, fungi, and invertebrates that are essential to soil nutrient cycling pr ocesses. Changes to the relative species abundance and community composition are associated with deposited PM to soil biota (U.S. EPA, 2009b).

Atmospheric deposition can be the primary source of some organics and metals to watersheds. Deposition of PM to s urfaces in urban settings increases the metal and organic component of storm water runoff (U.S. EPA, 2009b). This atmospherically

- associated pollutant burden can then be toxic to aquatic biota. The contribution of atmospherically deposited PAHs to aquatic food webs was demonstrated in high elevation mountain lakes with no other6- 45 anthropogenic contaminant sources (U.S. EPA, 2009b). Metals associated with PM deposition limit phytoplankton growth, affecting aquatic trophic structure. Long

- range atmospheric
tran sport of 47 pesticides and degradation products to the snowpack in seven national parks in the Western U.S. was recently quantified indicating PM

- associated contaminant inputs to receiving waters during spring snowmelt (Hageman et al., 2006).

The recently completed Western Airborne Contaminants Assessment Project (WACAP) is the most comprehensive database on contaminant transport and PM depositional effects on sensitive ecosystems in the Western U.S. (Landers et al., 2008). In this project, the transport, f ate, and ecological impacts of anthropogenic contaminants from atmospheric sources were assessed from 2002 to 2007 in seven ecosystem components (air, snow, water, sediment, lichen, conifer needles and fish) in eight core national parks. The study conclude d that bioaccumulation of semi

- volatile organic compounds occurred throughout park ecosystems, an elevational gradient in PM deposition exists with greater accumulation in higher altitude areas, and contaminants accumulate in proximity to individual agriculture and industry sources, which is counter to the original working hypothesis that most of the contaminants would originate from Eastern Europe and Asia.

Although there is considerable data on impacts of PM on ecological receptors, few studies link ambient PM levels to observed effect. This is due, in part, to the nature, deposition, transport and fate of PM in ecosystems. Some of the difficulties in quantifying the ecosystem

co

- benefits associated with reduced PM deposition include the following:

 PM is n ot a single pollutant, but a heterogeneous mixture of particles differing in size, origin and chemical composition. Since vegetation and other ecosystem components are affected more by particulate chemistry than size fraction, exposure to a given mass concentration of airborne PM may lead to widely differing plant or ecosystem responses, depending on the particular mix of deposited particles.

 Composition of ambient PM varies in time and space and the particulate mixture may have synergistic, antagonistic or additive effects on ecological receptors depending upon the chemical species present.

 Presence of co

- pollutants makes it difficult to attribute observed effects to ecological receptors to PM alone or one component of deposited PM.

 Ecosystem effects linked to PM are difficult to determine because the changes may not be observed until pollutant deposition has occurred for many decades. Furthermore, many PM components bioaccumulate over time in organisms or plants, making correlations to ambient levels of PM difficult.6- 46  Multiple ecological stressors can confound attempts to link specific ecosystem responses to PM deposition. These stressors can be anthropogenic ( e.g., habitat destruction, eutrophication, other pollutants) or natural ( e.g., drought, fire, di sease). Deposited PM interacts with other stressors to affect ecosystem patterns and processes.

 Each ecosystem has a unique topography, underlying bedrock, soils, climate, meteorology, hydrologic regime, natural and land use history, and species compositio n. Sensitivity of ecosystem response can be highly variable in space and time. Because of this variety and lack of data for most ecosystems, extrapolating these effects from one ecosystem to another is highly uncertain.

6.6 .2 Ecosystem Co

- benefits from Red uctions in Nitrogen and Sulfur Emissions Emissions of the PM precursors, such as n itrogen and sulfur oxides occur over large regions of North America. Once these pollutants are lofted to the middle and upper troposphere, they typically have a much longer lifetime and, with the generally stronger winds at these altitudes, can be transported long distances from their source regions. The length scale of this transport is highly variable owing to differing chemical and meteorological conditions encountered alon g the transport path (U.S. EPA, 2008c). Secondary particles are formed from NO X and SO 2 gaseous emissions and associated chemical reactions in the atmosphere. Deposition can occur in either a wet (i.e., rain, snow, sleet, hail, clouds, or fog) or dry form (i.e., gases or particles). Together these emissions are deposited onto terrestrial and aquatic ecosystems across the U.S., contributing to the problems of acidification, nutrient enrichment, and methylmercury production as represented in Figures 6-9 and 6-10. Although there is some evidence that nitrogen deposition may have positive effects on agricultural and forest output through passive fertilization, it is likely that the overall value is very small relative to other health and welfare effects. In addition to deposition effects, SO 2 can affect vegetation at ambient levels near pollution sources.6- 47 Figure 6-9. Schematics of Ecological Effects of Nitrogen and Sulfur Deposition NO X Atmospheric

Fate and Transport

Deposition

Process

Acidification

Aquatic Ecosystems Terrestrial Ecosystems Nutrient Enrichment

Aquatic Ecosystems Terrestrial Ecosystems

SO 2 Atmospheric

Fate and Transport

Deposition

Process

Acidification

Aquatic Ecosystems Terrestrial Ecosystems Methylmercury Production6- 48 Figure 6-10. Nitrogen and Sulfur Cycling , and Interactions in the Environment
Source: U.S. EPA, 2008c

. The atmospheric lifetimes of particles vary with particle size. Accumulation

- mode particles such as sulfates are kept in suspension by normal air motions and have a lower deposition velocity than coarse

- mode particles; they can be transported thousands of kilometers and remain in the atmosphere for a number of days. They are removed from the atmosphere primarily by cloud processes. Particulates affect acid deposition by serving as cloud condensation nuclei and contribute directly to the acidification of rain. In addition, the gas

phase species that lead to the dry deposition of acidity are also precursors of particles. Therefore, reductions in NO X and SO 2 emissions will decrease both acid deposition and PM concentrations, but not necessarily in a linear fashion (U.S. EPA, 2008c). Sulfuric acid is also deposited on surfaces by dry deposition and can contribute to environmental effects (U.S. EPA, 2008c).

6.6 .2.1 Ecological Effects of Acidification Deposition of nitrogen and sulfur can cause acidification, which alters biogeochemistry and affects animal and plant life in terrestrial and aquatic ecosystems across the U.S. Soil acidification is a natural process, but is often accelerated by acidifying deposition, which can decre ase concentrations of exchangeable base cations in soils (U.S. EPA, 2008c). Major6- 49 terrestrial effects include a decline in sensitive tree species, such as red spruce ( Picea rubens ) and sugar maple ( Acer saccharum ) (U.S. EPA, 2008c). 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, 2008c). Decreases in the acid neutralizing capacity and increases in inorganic aluminum concentration contribute to declines in zooplankton, macro invertebrates, and fish species richness in aquatic ecosystems (U.S. EPA, 2008c).
Geology (particularly surficial geology) is the principal factor governing the sensitivity of terrestrial and aquatic ecosystems to acidification from nitrogen and sulfur deposition (U.S. EPA, 2008c). Geologic formations having low base cation supply generally underlie the watersheds of acid

- sensitive lakes and streams. Other factors contribute to the sensitivity of soils and surface waters to acidifying deposition, including topography, soil chemistry, land use, and hydrologic flow path (U.S. EPA, 2008c).

Aquatic Acidification. Aquatic effects of acidification have been well studied in the U.S. and elsewhere at various trophic level s. These studies indicate that aquatic biota have been affected by acidification at virtually all levels of the food web in acid sensitive aquatic ecosystems. The ISA for NO x /SO x — Ecological Criteria concluded that the evidence is sufficient to infer a caus al relationship between acidifying deposition and effects on biogeochemistry related to aquatic ecosystems and biota in aquatic ecosystems (U.S. EPA, 2008c). Effects have been most clearly documented for fish, aquatic insects, other invertebrates, and alga e. Biological effects are primarily attributable to a combination of low pH and high inorganic aluminum concentrations. Such conditions occur more frequently during rainfall and snowmelt that cause high flows of water and less commonly during low

- flow cond itions, except where chronic acidity conditions are severe. Biological effects of episodes include reduced fish condition factor 33 , changes in species composition and declines in aquatic species richness across multiple taxa, ecosystems and regions. These c onditions may also result in direct fish mortality (Van Sickle et al., 1996). Biological effects in aquatic ecosystems can be divided into two major categories: effects on health, vigor, and reproductive success; and effects on biodiversity. Surface water with ANC values greater than 50 μeq/L generally provides moderate protection for most fish (i.e., brook trout, others) and other aquatic organisms (U.S. EPA, 2009c). Table 6-9 provides a summary of the biological effects experienced at various ANC levels.

33 Condition factor is an index that describes the relationship between fish weight and length, and is one measure

of sublethal acidification stress that has been used to quantify effec ts of acidification on an individual fish (U.S. EPA, 2008f).6- 50 Table 6-9. Aquatic Status Categories

Category Label ANC Levels Expected Ecological Effects

Acute Concern <0 micro equivalent per Liter (μeq/L) Near complete loss of fish populations is expected. Planktonic communities have extremely low diversity and are dominated by acidophilic forms. The number of individuals in plankton species that are present is greatly reduced.

Severe Concern 0– 20 μeq/L Highly sensitive to episodic acidification. During episodes of high acidifying deposition, brook trout populations may experience lethal effects. Diversity and distribution of zooplankton communities decline sharply.

Elevated Concern 20

– 50 μeq/L Fish species richness is greatly reduced (i.e., more than half of expected species can be missing). On average, brook trout populations experience sublethal effects, including loss of health, reproduction capacity, and fitness. Diversity and distribution of zooplankton communities decline.

Moderate Concern 50

– 100 μeq/L Fish species richness begins to decline (i.e., sensi tive species are lost from lakes). Brook trout populations are sensitive and variable, with possible sublethal effects. Diversity and distribution of zooplankton communities also begin to decline as species that are sensitive to acidifying deposition are a ffected.

Low Concern >100 μeq/L Fish species richness may be unaffected. Reproducing brook trout populations are expected where habitat is suitable. Zooplankton communities are unaffected and exhibit expected diversity and distribution.

A number of national and regional assessments have been conducted to estimate the distribution and extent of surface water acidity in the U.S. (U.S. EPA, 2008c). As a result, several regions of the U.S. have been identified as containing a large number of lakes and streams that are seriously impacted by acidification. Figure 6- 11 illustrates those areas of the U.S. where aquatic ecosystems are at risk from acidification.

Because acidification primarily affects the diversity and abundance of aquatic biota, it also affects the ecosystem services that are derived from the fish and other aquatic life found in these surface waters.

While acidification is unlikely to have serious negative effects on, for example, water supplies, it can limit the productivity of surface waters as a source of food (i.e., fish). In the northeastern United States, the surface waters affected by acidification are not a major source of commercially raised or caught fish; however, they are a source of food for some recreational and subsistence fishermen and for other consumers. For example, there is evidence that certain population subgroups in the northeastern United States, such as the Hmong and Chippewa ethnic groups, have particularly high rates of self

- caught fish consumption (Hutchison and Kraft , 1994; Peterson et al., 1994). However, it is not known if and how their consumption patterns6- 51 are affected by the reductions in available fish populations caused by surface water acidification.

Figure 6- 11. Areas Potentially Sensitive to Aquatic Acidification
Source: U.S. EPA, 2008c

. Inland surface waters support several cultural services, including aesthetic and educational services and recreational fishing. Recreational fishing in lakes and streams is among the most popular outdoor recreational activities in the northeastern United States. Based on studies conducted in the northeastern United States, Kaval and Loomis (2003) estimated average consumer surplus values per day of $36 for recreational fishing (in 2007 dollars); therefore, the implied total annual value of freshwater fishing in the northeastern United States was $5.1 billion in 2006. 34 For recreation days, consumer surplus value is most commonly measured using recreation demand, travel cost models.

Another estimate of the overarching eco logical co

- benefits associated with reducing lake acidification levels in Adirondacks National Park can be derived from the contingent valuation (CV) survey (Banzhaf et al., 2006), which elicited values for specific improvements in

34 These estimates reflect the total value of the service, not the marginal change in the value of the service as a

result of the emission reductions achieved by this rule.6- 52 acidification

- related wa ter quality and ecological conditions in Adirondack lakes. The survey described a base version with minor improvements said to result from the program, and a scope version with large improvements due to the program and a gradually worsening status
quo. Aft er adapting and transferring the results of this study and converting the 10

- year annual payments to permanent annual payments using discount rates of 3% and 5%, the WTP estimates ranged from $48 to $107 per year per household (in 2004 dollars) for the bas e version and $54 to $154 for the scope version. Using these estimates, the aggregate annual benefits of eliminating all anthropogenic sources of NO x and SO x emissions were estimated to range from $291 million to $829 million (U.S. EPA, 2009c). 35

In addition, inland surface waters provide a number of regulating services associated with hydrological and climate regulation by providing environments that sustain aquatic food webs. These services are disrupted by the toxic effects of acidification on fish and other aquatic life. Although it is difficult to quantify these services and how they are affected by acidification, some of these services may be captured through measures of provisioning and cultural services.

Terrestrial Acidification. Acidifying deposition has altered major biogeochemical processes in the U.S. by increasing the nitrogen and sulfur content of soils, accelerating nitrate and sulfate leaching from soil to drainage waters, depleting base cations (especially calcium and magnesium) from s oils, and increasing the mobility of aluminum. Inorganic aluminum is toxic to
some tree roots. Plants affected by high levels of aluminum from the soil often have reduced root growth, which restricts the ability of the plant to take up water and nutrients, especially calcium (U. S. EPA, 2008c). These direct effects can, in turn, influence the response of these plants to climatic stresses such as droughts and cold temperatures. They can also influence the sensitivity of plants to other stresses, including insect pests and disease (Joslin et al., 1992) leading to increased mortality of canopy trees. In the U.S., terrestrial effects of acidification are best described for forested ecosystems (especially red spruce and sugar maple ecosystems) with additional information on other plant communities, including shrubs and lichen (U.S. EPA, 2008c). The ISA for NO x /SO x — Ecological Criteria concluded that the evidence is sufficient to infer a causal relationship between acidifying deposition and effects on biogeochemi stry related to terrestrial ecosystems and biota in terrestrial ecosystems (U.S. EPA, 2008c).

Certain ecosystems in the continental U.S. are potentially sensitive to terrestrial acidification, which is the greatest concern regarding nitrogen and sulfur dep osition U.S. EPA

35 These estimates reflect the total value of the service, not the marginal change in the value of the service as a

result of the emission reductions achieved by this rule.6- 53 (2008c). Figure 6-12 depicts the areas across the U.S. that are potentially sensitive to terrestrial acidification.

Figure 6-12. Areas Potentially Sensitive to Terrestrial Acidification

Source: U.S. EPA, 2008c

. Both coniferous and deciduous forests throughout the eastern U.S. are experiencing gradual losses of base cation nutrients from the soil due to accelerated leaching from acidifying deposition. This change in nutrient availability may reduce the quality of forest nutrition over the long term. Evidence suggests that red spruce and sugar maple in some areas in the eastern U.S. have experienced declining health because of this deposition. For red spruce, ( Picea rubens ) dieback or decline has been observed across high elevation landscape s of the northeastern U.S., and to a lesser extent, the southeastern U.S., and acidifying deposition has been implicated as a causal factor (DeHayes et al., 1999). Figure 6-13 shows the distribution of red spruce (brown) and sugar maple (green) in the eastern U.S.

Terrestrial acidification affects several important ecological endpoints, including declines in habitat for threatened and endangered species (cultural), declines in forest6- 54 aesthetics (cultural), declines in forest productivity (provisioning), and increases in forest soil erosion and reductions in water retention (cultural and regulating).

Figure 6-13. Distribution of Red Spruce (pink) and Sugar Maple (green) in the Eastern U.S.
Source: U.S. EPA, 2008c

. Forests in the northeastern United States provide several important and valuable provisioning services in the form of tree products. Sugar maples are a particularly important commercial hardwood tree species, providing timber and maple syrup. In the United States, sugar maple saw timber was nearly 900 million board feet in 2006 (USFS, 2006), and annual production of maple syrup was nearly 1.4 million gallons, accounting for approximately 19% of worldwide production. The total annual value of U.S. production in these years was approximately $160 million (NASS, 2008). 36 Red spruce is also used in a variety of products including lumber, pulpwood, poles, plywood, and musical instruments. The total removal of red spruce saw ti mber from timberland in the United States was over 300 million board feet in 2006 (USFS, 2006).

36 These estimates reflect the total value of the service, not the marginal change in the value of the ser vice as a

result of the emission reductions achieved by this rule.6- 55 Forests in the northeastern United States are also an important source of cultural ecosystem services

— nonuse (i.e., existence value for threatened and endangere d species), recreational, and aesthetic services. Red spruce forests are home to two federally listed species and one delisted species:

1. Spruce

- fir moss spider ( Microhexura montivaga )— endangered

2. Rock gnome lichen ( Gymnoderma lineare )— endangered

3. Vir ginia northern flying squirrel ( Glaucomys sabrinus fuscus )— delisted, but important
Forestlands support a wide variety of outdoor recreational activities, including fishing, hiking, camping, off

- road driving, hunting, and wildlife viewing. Regional statisti cs on recreational activities that are specifically forest based are not available; however, more general data on outdoor recreation provide some insights into the overall level of recreational services provided by forests. More than 30% of the U.S. adult population visited a wilderness or primitive area during the previous year and engaged in day hiking (Cordell et al., 2008). From 1999 to 2004, 16% of adults in the northeastern United States participated in off

- road vehicle recreation, for an average of 27 days per year (Cordell et al., 2005). The average consumer surplus value per day of off

- road driving in the United States was $25 (in 2007 dollars), and the implied total annual value of off

- road driving recreation in the northeastern United States was

m ore than $9 billion (Kaval and Loomis, 2003). More than 5% of adults in the northeastern United States participated in nearly 84 million hunting days (U.S. FWS and U.S. Census Bureau, 2007). Ten percent of adults in northeastern states participated in wild life viewing away from home on 122 million days in 2006. For these recreational activities in the northeastern United States, Kaval and Loomis (2003) estimated average consumer surplus values per day of $52 for hunting and $34 for wildlife viewing (in 2007 dollars). The implied total annual value of hunting and wildlife viewing in the northeastern United States was, therefore, $4.4 billion and $4.2 billion, respectively, in 2006 (U.S. EPA, 2009c). 37

As previously mentioned, it is d ifficult to estimate the po rtion of these recreational services that are specifically attributabl e to forests and to the health of specific tree species. However, one recreational activity that is directly dependent on forest conditions is fall color viewing. Sugar maple trees, in particular, are known for their bright colors and are, therefore, an essential aesthetic component of most fall color landscapes. A survey of residents in the

37 These estimates reflect the total value of the service, not the marginal change in the value of the service as a

result of the emission reductions achieved by this rule.6- 56 Great Lakes area found that roughly 30% of residents reported at least one trip in the previous
ye ar involving fall color viewing (Spencer and Holecek, 2007). In a separate study conducted in Vermont, Brown (2002) reported that more than 22% of households visiting Vermont in 2001 made the trip primarily for viewing fall colors.

Two studies estimated va lues for protecting high

- elevation spruce forests in the southern Appalachian Mountains. Kramer et al. (2003) conducted a contingent valuation study estimating households ’ WTP for programs to protect remaining high

- elevation spruce forests from damages ass ociated with air pollution and insect infestation. Median household WTP was estimated to be roughly $29 (in 2007 dollars) for a smaller program, and $44 for the more extensive program. Jenkins et al. (2002) conducted a very similar study in seven Southern Appalachian states on a potential program to maintain forest conditions at status quo levels. The overall mean annual WTP for the forest protection programs was $208 (in 2007 dollars). Multiplying the average WTP estimate from these studies by the total number of households in the seven

- state Appalachian region results in an aggregate annual range of $470 million to $3.4 billion for avoiding a significant decline in the health of high

- elevation spruce forests in the Southern Appalachian region (U.S. EPA, 20 09c). 38

Forests in the northeastern United States also support and provide a wide variety of valuable regulating services, including soil stabilization and erosion control, water regulation, and climate regulation. The total value of these ecosystem services is very difficult to quantify in a meaningful way, as is the reduction in the value of these services associated with total nitrogen and sulfur deposition. As terrestrial acidification contributes to root damages, reduced biomass growth, and tree mortality, all of these services are likely to be affected; however, the magnitude of these impacts is currently very uncertain.

6.6 .2.2 Ecological Effects from Nitrogen Enrichment Aquatic Enrichment

. The ISA for NO x /SO x — Ecological Criteria concluded that the evidence is sufficient to infer a causal relationship between nitrogen deposition and the alteration of species richness, species composition, and biodiversity in wetland, freshwater aquatic and coastal marine ecosystems (U.S. EPA, 2008c).

One of the main adverse ecological effects resulting from nitrogen deposition, particularly in the Mid

- Atlantic region of the United States, is the effect associated with nutrient enrichment in estuarine waters. A recent assessment of 141 estuaries nationwide by the

38 These estimates reflect the marginal value of the service for the hypothetical program described in the survey,

not the marginal change in the value of the service as a result of the emission reductions achieved by this rule.6- 57 Natio nal Oceanic and Atmospheric Administration (NOAA) concluded that 19 estuaries (13%) suffered from moderately high or high levels of eutrophication due to excessive inputs of both N and phosphorus, and a majority of these estuaries are located in the coasta l area from North Carolina to Massachusetts (NOAA, 2007). For estuaries in the Mid

- Atlantic region, the contribution of atmospheric distribution to total N loads is estimated to range between 10% and 58% (Valigura et al., 2001).

Eutrophication in estuaries is associated with a range of adverse ecological effects. The conceptual framework developed by NOAA emphasizes four main types of eutrophication effects

— 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. Low DO also degrades the aesthetic qualities of surface water. In addition to often being toxic to fish and shellfish, and leading to fish kills and aesthetic impairments of estuaries, HABs can, in some instances, also 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; therefore, declines in SAV due to nutrient enrichment are an important source of concern. Low water clarity is in part the result of accum ulations 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.

Estuaries in the eastern United States are an important so urce of food production, in particular fish and shellfish production. The estuaries are capable of supporting large stocks of resident commercial species, and they serve as the breeding grounds and interim habitat for several migratory species. To provide an indication of the magnitude of provisioning services associated with coastal fisheries, from 2005 to 2007, the average value of total catch was $1.5 billion per year. It is not known, however, what percentage of this value is directly attributable
to or dependent upon the estuaries in these states.

In addition to affecting provisioning services through commercial fish harvests, eutrophication in estuaries may also affect the demand for seafood. For example, a well

publicized toxic pfiesteria bloom in the Maryland Eastern Shore in 1997, which involved thousands of dead and lesioned fish, led to an estimated $56 million (in 2007 dollars) in lost seafood sales for 360 seafood firms in Maryland in the months following the outbreak (Lipton, 1999).6- 58 Estuaries in the United States also provide an important and substantial variety of cultural ecosystem services, including water

- based recreational and aesthetic services. The water quality in the estuary directly affects the quality of these experiences. For example, there were 26 million days of saltwater fishing coastal states from North Carolina to Massachusetts in 2006 (FWA and Census, 2007). Assuming an average consumer surplus value for a fishing day at $36 (in 2007 dollars) in the Northeast and $87 in the South east (Kaval and Loomis, 2003), the aggregate value was approximately $1.3 billion (in 2007 dollars) (U.S. EPA, 2009c). 39 In addition, almost 6 million adults participated in motorboating in coastal states from North Carolina to Massachusetts, for a total o f nearly 63 million days annually during 1999

– 2000 (Leeworthy and Wiley, 2001). Using a national daily value estimate of $32 (in 2007 dollars) for motorboating (Kaval and Loomis (2003), the aggregate value of these coastal motorboating outings was $2 billi on per year (U.S. EPA, 2009c). 40 Almost 7 million participated in birdwatching for 175 million days per year, and more than 3 million participated in visits to non

- beach coastal waterside

areas.
Estuaries and marshes have the potential to support a wide range of regulating services, including climate, biological, and water regulation; pollution detoxification; erosion prevention; and protection against natural hazards from declines in SAV (MEA, 2005). SAV can help reduce wave energy levels and thus protect shorelines against excessive erosion, which increases the risks of episodic flooding and associated damages to near

- shore properties or public infrastructure or even contribute to shoreline retreat.

We are unable to provide an estimate of the aquatic enr ichment co

- benefits associated with the revised or alternative annual standard s due to data, time, and resource limitations.

Terrestrial Enrichment

. Terrestrial enrichment occurs when terrestrial ecosystems receive N loadings in excess of natural background levels, through either atmospheric deposition or direct application. Evidence presented in the ISA for NO x /SO x (U.S. EPA, 2008c) supports a causal relationship between atmospheric N deposition and biogeochemical cycling and fluxes of N and carbon in terr estrial systems. Furthermore, evidence summarized in the report supports a causal link between atmospheric N deposition and changes in the types and number of species and biodiversity in terrestrial systems. Nitrogen enrichment occurs over a long time peri od; as a result, it may take as much as 50 years or more to see changes in

39 These estimates reflect the total value of the service, not the marginal change in the value of the service as a

result of the emission reductions achieved by this rule. 40 These estimates reflect the total value of the service, not the marginal change in the value of the service as a

result of the emission reductions achieved by this rule.6- 59 ecosystem conditions and indicators. This long time scale also affects the timing of the ecosystem service changes. The ISA for NO x /SO x — Ecological Criteria concluded that the evidence is sufficient to infer a causal relationship between nitrogen deposition and the alteration of species richness, species composition, and biodiversity in terrestrial ecosystems (U.S. EPA, 2008c).

One of the main provisioning services potentially a ffected by N deposition is grazing opportunities offered by grasslands for livestock production in the Central U.S. Although N deposition on these grasslands can offer supplementary nutritive value and promote overall grass production, there are concerns t hat fertilization may favor invasive grasses and shift the species composition away from native grasses. This process may ultimately reduce the productivity of grasslands for livestock production. Losses due to invasive grasses can be significant; for exam ple, based on a bioeconomic model of cattle grazing in the upper Great Plains, Leitch, Leistritz, and Bangsund (1996) and Leistritz, Bangsund, and Hodur (2004) estimated $130 million in losses due to a leafy spurge infestation in the Dakotas, Montana, and Wyoming. 41 However, the contribution of N deposition to these losses is still uncertain.

Terrestrial nutrient enrichment also affects cultural and regulating services. For example, in California, Coastal Sage Scrub (CSS) habitat concerns focus on a decline in CSS and an increase in nonnative grasses and other species, impacts on the viability of threatened and endangered species associated with CSS, and an increase in fire frequency. Changes in Mixed Conifer Forest (MCF) include changes in habitat suitabilit y and increased tree mortality, increased fire intensity, and a change in the forest ’ s nutrient cycling that may affect surface water quality through nitrate leaching (U.S. EPA, 2008c). CSS and MCF are an integral part of the California landscape, and together the ranges of these habitats include the densely populated and valuable coastline and the mountain areas. Numerous threatened and endangered species at both the state and federal levels reside in CSS and MCF. The value that California residents and the U.S. population as a whole place on CSS and MCF habitats is reflected in the various federal, state, and local government measures that have been put in place to protect these habitats, including the Endangered Species Act, conservation planning
programs , and private and local land trusts. CSS and MCF habitat are showcased in many popular recreation areas in California, including several national parks and monuments. In addition, millions of individuals are involved in fishing, hunting, and wildlife viewi ng in California every year (DOI, 2007). The quality of these trips depends in part on the health of the

41 These estimates reflect the total value of the service, not the marginal change in the value of the service as a

result of the emission reductions achieved by this rule.6- 60 ecosystems and their ability to support the diversity of plants and animals found in important habitats found in CSS or MCF ecosystems and the parks as sociated with those ecosystems. Based on analyses in the NO x SO x REA average values of the total benefits in 2006 from fishing, hunting, and wildlife viewing away from home in California were approximately $950 million, $170 million, and $3.6 billion, resp ectively (U.S. EPA, 2009c). 42 In addition, data from California State Parks (2003) indicate that in 2002, 69% of adult residents participated in trail hiking for an average of 24 days per year. The aggregate annual benefit for California residents from trai l hiking in 2007 was $11 billion (U.S. EPA, 2009c). 43 It is not currently possible to quantify the loss in value of services due to nitrogen deposition as those losses are already reflected in the estimates of the contemporaneous total value of these recrea tional activities. Restoration of services through decreases in nitrogen deposition would likely increase the total value of recreational services.

Fire regulation is also an important regulating service that could be affected by nutrient enrichment of the CSS and MCF ecosystems by encouraging growth of more flammable grasses, increasing fuel loads, and altering the fire cycle. Over the 5

- year period from 2004 to 2008, Southern California experienced, on average, over 4,000 fires per year burning, on averag e, over 400,000 acres per year (National Association of State Foresters [NASF], 2009). It is not possible at this time to quantify the contribution of nitrogen deposition, among many other factors, to increased fire risk.

We are unable to provide an estima te of the terrestrial nutrient enrichment co

- benefits associated with the revised or alternative annual st andards due to data, time, and resource limitations. Methods are not yet available to allow estimation of changes in ecosystem services due to nitroge n deposition.

6.7 .2.3 Vegetation Effects Associated with Gaseous Sulfur Dioxide Uptake of gaseous sulfur dioxide in a plant canopy is a complex process involving adsorption to surfaces (leaves, stems, and soil) and absorption into leaves. SO 2 penetrates into leaves through to the stomata, although there is evidence for limited pathways via the cuticle (U.S. EPA, 2008c). Pollutants must be transported from the bulk air to the leaf boundary layer in order to get to the stomata. When the stomata are closed, as occurs under dark or drought conditions, resistance to gas uptake is very high and the plant has a very low degree of

42 These estimates reflect the total value of the service, not the marginal change in the value of the service as a

result of the emission reductions achieved by this rule. 43 These estimates reflect the total value of the service, not the marginal change in the value of the service as a

res ult of the emission reductions achieved by this rule.6- 61 susceptibility to injury. In contrast, mosses and lichens do not have a protective cuticle barrier to gaseous pollutants or stomates and are generally more sensitive to gaseous sulfur and nitrogen than vascular plants (U.S. EPA, 2008c). Acute foliar injury usually happens within hours of exposure, involves a rapid absorption of a toxic dose, and involves collapse or necrosis o f plant tissues. Another type of visible injury is termed chronic injury and is usually a result of variable SO 2 exposures over the growing season. Besides foliar injury, chronic exposure to low SO 2 concentrations can result in reduced photosynthesis, grow th, and yield of plants (U.S. EPA, 2008c). These effects are cumulative over the season and are often not associated with visible foliar injury. As with foliar injury, these effects vary among species and growing environment. SO 2 is also considered the pri mary factor causing the death of lichens in many urban and industrial areas (Hutchinson et al., 1996). The ISA for NO x /SO x — Ecological Criteria concluded that the evidence is sufficient to infer a causal relationship between SO 2 injury to vegetation (U.S. E PA, 2008c).

6.6 .2.4 Mercury

- Related Co

- benefits Associated with the Role of Sulfate in Mercury Methylation Mercury is a persistent, bioaccumulative toxic metal that is emitted from in three forms: gaseous elemental Hg (Hg 0 ), oxidized Hg compounds (Hg +2 ), a nd particle

- bound Hg (Hg P ). Methylmercury (MeHg) is formed by microbial action in the top layers of sediment and soils, after Hg has precipitated from the air and deposited into waterbodies or land. Once formed, MeHg is taken up by aquatic organisms and bioaccumulates up the aquatic food web. Larger predatory fish may have MeHg concentrations many times, typically on the order of one million times, that of the concentrations in the freshwater body in which they live.

The NO x SO x ISA

— Ecological Criteria concluded that evidence is sufficient to infer a causal relationship between sulfur deposition and increased mercury methylation in wetlands and aquatic environments (U.S. EPA, 2008c). Specifically, there appears to be a relationship be tween SO 4 2- deposition and mercury methylation; however, the rate of mercury methylation varies according to several spatial and biogeochemical factors whose influence has not been fully quantified (see Figure 6-14 ). Therefore, the correlation between SO 4 2- deposition and MeHg could not be quantified for the purpose of interpolating the association across waterbodies or regions. Nevertheless, because changes in MeHg in ecosystems represent changes in significant human and ecological health risks, the associ ation between sulfur and mercury cannot be neglected (U.S. EPA, 2008c).6- 62 Figure 6-14. Spatial and Biogeochemical Factors Influencing MeHg Production

As research evolves and the computational capacity of models expands to meet the complexity of mercury me thylation processes in ecosystems, the role of interacting factors may be better parsed out to identify ecosystems or regions that are more likely to generate higher concentrations of MeHg. Figure 6-15 illustrates the type of current and forward

- looking research being developed by the U.S. Geological Survey (USGS) to synthesize the contributing factors of mercury and to develop a map of sensitive watersheds. The mercury score referenced in Figure 6-15 i s based on SO 4 2- concentrations, acid neutralizing capacity (ANC), levels of dissolved organic carbon and pH, mercury species concentrations, and soil types to gauge the methylation sensitivity (Myers et al., 2007).6- 63 Figure 6-15. Preliminary USGS Map of M ercury Methylation

– Sensitive Watersheds Source: Myers et al., 2007

. Interdependent biogeochemical factors preclude the existence of simple sulfate

- related mercury methylation models. It is clear that decreasing sulfate deposition is likely to result in decreased MeHg concentrations. Future research may allow for the characterization of a usable sulfate

- MeHg response curve; however, no regional or classification calculation scale can be created at this time because of the number of confounding factors.

De creases in SO 4 2- deposition have already shown promising reductions in MeHg. Observed decreases in MeHg fish tissue concentrations have been linked to decreased acidification and declining SO 4 2- and mercury deposition in Little Rock Lake, WI (Hrabik and
Wa tras, 2002), and to decreased SO 4 2- deposition in Isle Royale in Lake Superior, MI (Drevnick
et al., 2007). Although the possibility exists that reductions in SO 4 2- emissions could generate a pulse in MeHg production because of decreased sulfide inhibition in sulfate

- saturated waters, this effect would likely involve a limited number of U.S. waters (Harmon et al., 2007). Also, because of the diffusion and outward flow of both mercury

- sulfide complexes and SO 4 2-

, increased mercury methylation downstream may still occur in sulfate

- enriched ecosystems with increased organic matter and/or downstream transport capabilities.6- 64 Remediation of sediments heavily contaminated with mercury has yielded significant reductions of MeHg in biotic tissues. Establishing quantit ative relations in biotic responses to MeHg levels as a result of changes in atmospheric mercury deposition, however, presents difficulties because direct associations can be confounded by all of the factors discussed in this section. Current research does suggest that the levels of MeHg and total mercury in ecosystems are positively correlated, so that reductions in mercury deposited into ecosystems would also eventually lead to reductions in MeHg in biotic tissues. Ultimately, an integrated approach that involves the reduction of both sulfur and mercury emissions may be most efficient because of the variability in ecosystem responses. Reducing SO X emissions could have a beneficial effect on levels of MeHg in many waters of the United States.

MeHg is the only form of mercury that biomagnifies in the food web. Concentrations of MeHg in fish are generally on the order of a million times the MeHg concentration in water. In addition to mercury deposition, key factors affecting MeHg production and accumulation in fish include the amount and forms of sulfur and carbon species present in a given waterbody. Thus, two adjoining water bodies receiving the same deposition can have significantly different fish mercury concentrations.

Methylmercury builds up more in some types of fish and shellfish than in others. The levels of methylmercury in high and shellfish vary widely depending on what they eat, how long they live, and how high they are in the food chain. Most fish, including ocean species and local freshwater fish, contain some methylmercury. In general, higher mercury concentrations are expected in top predators, which are often large fish relative to other species in a waterbody.

The ecosystem service most directly affected by sulfate

- mediated mercury methylation is the provision of fish for consumption as a food source. This service is of particular importance to groups engaged in subsistence fishing, pregnant women and young children.

6.6 .3 Ecosystem Co

- benefits from Reductions in Mercury Emissions Deposition of mercury to waterbodies can also have an impact on ecosystems and wildlife. Mercury contamination is present in all environmental media with aquatic systems experiencing the greatest exposures due to bioaccumulation. Bioaccumulation refers to the net uptake of a contaminant from all possible pathways and includes the accumulation that may occur by direct exposure to contaminated media as well as uptake from food.

Atmospheric mercury enters freshwater ecosystems by direct deposition and through runoff from te rrestrial watersheds. Once mercury deposits, it may be converted to organic6- 65 methylmercury mediated primarily by sulfate

- reducing bacteria. Methylation is enhanced in anaerobic and acidic environments, greatly increasing mercury toxicity and potential to bi oaccumulate in aquatic foodwebs. A number of key biogeochemical controls influence the production of methylmercury in aquatic ecosystems. These include sulfur, pH, organic matter, iron, mercury “ aging ,” and bacteria type and activity (Munthe et al., 2007).

Wet and dry deposition of oxidized mercury is a dominant pathway for bringing mercury to terrestrial surfaces. In forest ecosystems, elemental mercury may also be absorbed by plants stomatally, incorporated by foliar tissues and released in litterfall (Ericksen et al., 2003). Mercury in throughfall, direct deposition in precipitation, and uptake of dissolved mercury by roots (Rea et al., 2002) are also important in mercury accumulation in terrestrial ecosystems.

Soils have significant capacity to store large quantities of atmospherically deposited mercury where it can leach into groundwater and surface waters. The risk of mercury exposure extends to insectivorous terrestrial species such as songbirds, bats, spiders, and amphibians that receive mercury depos ition or from aquatic systems near the forest areas they inhabit (Bergeron et al., 2010a, b; Cristol et al., 2008; Rimmer et al., 2005; Wada et al., 2009 & 2010).

Numerous studies have generated field data on the levels of mercury in a variety of wild
spec ies. Many of the data from these environmental studies are anecdotal in nature rather than representative or statistically designed studies. The body of work examining the effects of these exposures is growing but still incomplete given the complexities of the natural world. A large portion of the adverse effect research conducted to date has been carried out in the laboratory setting rather than in the wild; thus, conclusions about overarching ecosystem health and population effects are difficult to make a t this time. In the sections that follow numerous effects have been identified at d iffering exposure levels.

6.6 .3.1 Mercury Effects on Fish A review of the literature on effects of mercury on fish (Crump and Trudeau, 2009) reports results for numerous spe cies including trout, bass (large and smallmouth), northern pike, carp, walleye, salmon and others from laboratory and field studies. The effects studied are reproductive and include deficits in sperm and egg formation, histopathological changes in
testes and ovaries, and disruption of reproductive hormone synthesis. These studies were conducted in areas from New York to Washington and while many were conducted by adding MeHg to water or diet many were conducted at current environmental levels. While we can not determine at this time whether these reproductive deficits are affecting fish populations across the United States it should be noted that it is possible that over time reproductive deficits could6- 66 have an effect on populations. Lower fish populations would conceivably impact the ecosystem services like recreational fishing derived from having healthy aquatic ecosystems quite apart from the effects of consumption advisories due to the human health effects of mercury.

6.6 .3.2 Mercury Effects on Birds

In a ddition to effects on fish, mercury also affects avian species. In previous reports (U.S.
EPA , 1997; 2005), much of the focus has been on large piscivorous species in particular the common loon. The loon is most visible to the public during the summer breeding season on northern lakes and they have become an important symbol of wilderness in these areas (McIntyre and Barr, 1997). A multitude of loon watch, preservation, and protection groups have formed over the past few decades and have been instrumental i n promoting conservation, education, monitoring, and research of breeding loons (McIntyre and Evers, 2000; Evers, 2006). Significant adverse effects on breeding loons from mercury have been found to occur including behavioral (reduced nest

- sitting), physio logical (flight feather asymmetry) and reproductive (chicks fledged/territorial pair) effects (Evers, 2008). Additionally Evers, et al (2008) report that they believe that the weight of evidence indicates that population

- level effects occur in parts of Mai ne and New Hampshire, and potentially in broad areas of the loon ’ s range.
Recently attention has turned to other piscivorous species such as the white ibis, and great snowy egret. While considered to be fish

- eating generally these wading birds have a very wide diet including crayfish, crabs, snails, insects and frogs. These species are experiencing a range of adverse effects due to exposure to mercury. The white ibis has been observed to have decreased foraging efficiency (Adams and Frederick, 2008). Additi onally ibises have been shown to exhibit decreased reproductive success and altered pair behavior (Frederick and Jayasena, 2010). These effects include significantly more unproductive nests, male/male pairing, reduced courtship behavior (head bobbing and pair bowing) and lower nestling production by exposed males. In this study, a worst

- case scenario suggested by the results could involve up to a 50% reduction in fledglings due to MeHg in diet. These estimates may be conservative if male/male pairing in the wild it could result in a shortage of partners for females and the effect of homosexual breeding would be magnified. In egrets, mercury has been implicated in the decline of the species in south Florida (Sepulveda, et al., 1999) and Hoffman (2010) has sho wn that egrets show liver and possibly kidney effects. While ibises and egrets are most abundant in coastal areas and these studies were conducted in south Florida and Nevada the ranges of ibises and egrets extend to a large portion of the United States. I bis territory can range inland to Oklahoma, Arkansas and Tennessee. Egret range covers virtually the entire United States except the mountain west.6- 67 Insectivorous birds have also been shown to suffer adverse effects due to mercury
exposure. These songbirds such as Bicknell ’ s thrush, tree swallows and the great tit have shown reduced reproduction, survival, and changes in singing behavior. Exposed tree swallows produced fewer fledglings (Brasso, 2008), lower survival (Hallinger, 2010) and had compromised immu ne competence (Hawley, 2009). The great tit has exhibited reduced singing behavior and smaller song repertoire in an area of high contamination in the vicinity of a metallurgic smelter in Flanders (Gorissen, 2005).

6.6 .3.3 Mercury Effects on Mammals
In mammals, adverse effects have been observed in mink and river otter, both fish eating species. For otter from Maine and Vermont maximum concentrations on Hg in fur nearly equal or exceed a concentration associated with mortality and concentration in liver for mink in Massachusetts/Connecticut and the levels in fur from mink in Maine exceed concentrations associated with acute mortality (Yates, 2005). Adverse sublethal effects may be associated with lower Hg concentrations and consequently be more widespread than potential acute effects. These effects may include increased activity, poorer maze performance, abnormal startle reflex, and impaired escape and avoidance behavior (Scheuhammer et al., 2007).

6.6 .3.4 Mercury Ecological Conclusions

The studies cited here provide a glimpse of the scope of mercury effects on wildlife particularly reproductive and survival effects. These effects range across species from fish to mammals and spatially across a wide area of the United States. The literature is far from complet e however. Much more research is required to establish a link between the ecological effects on wildlife and the effect on ecosystem services (services that the environment provides to people) for example recreational fishing, bird watching and wildlife vi ewing. The EPA is not, however, currently able to quantify or monetize the co

- benefits of reducing mercury exposures affecting provision of ecosystem services.

6.6 .4 Vegetation Co

- benefits from Reductions in Ambient Ozone

Illustrative emission reduction st rategies that include NO x emission reductions would affect ambient ozone concentrations. Ozone causes discernible injury to a wide array of vegetation (U.S. EPA, 2006a; Fox and Mickler, 1996). Air pollution can affect the environment and affect eco logical systems, leading to changes in the ecological com munity and influencing the diversity, health, and vigor of individual species (U.S. EPA, 2006a). In terms of forest productivity and ecosystem diversity, ozone may be the pollutant with the greatest potent ial for regional

- scale forest impacts (U.S. EPA, 2006a). Studies have demonstrated repeatedly that6- 68 ozone concentrations commonly observed in polluted areas can have substantial impacts on plant function (De Steiguer et al., 1990; Pye, 1988).

When ozone is present in the air, it can enter the leaves of plants, where it can cause significant cellular damage. Like carbon dioxide (CO 2 ) 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, lip ids, and cellular membranes, disrupting the plant ’ s osmotic (i.e., water) balance and energy utilization patterns (U.S. EPA, 2006a; Tingey and Taylor, 1982). With fewer resources available, the plant reallocates existing resources away from root growth and storage, above ground growth or yield, and reproductive processes, toward leaf repair and maintenance, leading to reduced growth and/or reproduction. Studies have shown that plants stressed in these ways may exhibit a general loss of vigor, which 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

env ironmental 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 mycorrhiza, essential symbiotic fung i 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, 2006a).

This ozone damage may or may not be accompanied by visible injury on leaves, and likewise, visible foliar injury may or may not be a symptom of the other types of plant damage described above. Foliar injury is usually the first visible sign of injury to plants from ozone exposure and indicates impaired physiological processes in the leaves (Grul ke, 2003). When visible injury is present, it is commonly manifested as chlorotic or necrotic spots, and/or increased leaf senescence (accelerated leaf aging). Visible foliar injury reduces the aesthetic value of ornamental vegetation and trees in urban landscapes and negatively affects scenic vistas in protected natural areas.

Ozone can produce both acute and chronic injury in sensitive species depending on the concentration level and the duration of the exposure. Ozone effects also tend to accumulate

over the growing season of the plant, so that even lower concentrations experienced for a longer duration have the potential to create chronic stress on sensitive vegetation. Not all plants, however, are equally sensitive to ozone. Much of the variation in sensitivity between6- 69 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, 2006a; Winner, 1994). After injuries have occurred, plants may be capable of repairing the damage to a limited extent (U.S. EPA, 2006a). Because of the differing sensitivities among plants to ozone, ozone pollution can also exert a selective pressure that leads to changes in plant community composition. Given the range of plant sensitivities and the fact that numerous other environmental factors modify plant uptake and response to ozone, it is not possible to identify threshold values above which ozone is consistently toxic for all plants.

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 suitabilit y of habitats that support threatened or endangered species and below ground organisms living in the root zone). Ozone impacts at the community and ecosystem level vary widely depending upon numerous factors, including concentration and temporal variation of tropospheric ozone, species composition, soil properties and climatic factors (U.S. EPA, 2006a). 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, 2006a, 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, 2006a).

6.6 .4.1 Ozone Effects on Forests Air pollution can affect the environment and affect eco logical systems, leading to changes in the ecological com munity and influencing the diversity, health, and vigor of individual species (U.S. EPA, 2006a). Ozone has been shown in numerous studies to have a strong effect on the health of many plants, including a variety of co mmercial and ecologically impor tant forest tree species throughout the United States (U.S. EPA, 2007b).
In the U.S., this data comes from the U.S. Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) program. As part of its Phase 3 program (formerly known as Forest He alth Monitoring), FIA looks for visible foliar injury of ozone

- sensitive forest plant species at each ground monitoring site across the country (excluding woodlots and urban trees) that meets certain minimum criteria. Because ozone injury is cumulative ove r the course of the growing season, examinations are conducted in July and August, when ozone concentrations and associated injury are typically highest.6- 70 Monitoring of ozone injury to plants by the U.S. Forest Service has expanded over the last 15 years from monitoring sites in 10 states in 1994 to nearly 1,000 monitoring sites in 41 states in 2002. Since 2002, the monitoring program has further expanded to 1,130 monitoring sites in 45 states. Figure 6- 16 shows the results of this monitoring program for the year 2002 broken down by U.S. EPA Regions. 44 Figure 6- 17 identifies the counties that were included in Figure 6-16 , and provides the county

- level data regarding the presence or absence of ozone

related injury. As shown in Figure 6-16 , large geographic area s of EPA Regions 6, 8, and 10 were not included in the assessment. Ozone damage to forest plants is classified using a subjective five

- category biosite index based on expert opinion, but designed to be equivalent from site to site. Ranges of biosite values translate to no injury, low or moderate foliar injury (visible foliar injury to highly sensi tive or moderately sensitive plants, respectively), and high or severe foliar injury, which would be expected to result in tree

- level or ecosystem

- level responses , respectively (U.S. EPA, 2006a; Coulston, 2004). The highest percentages of observed high and severe foliar injury, which are most likely to be associated with tree or ecosystem

- level responses, are primarily found in the Mid

- Atlantic and Southeast region s. While the assessment showed considerable regional variation in ozone injury, this assessment targeted different

ozone

- sensitive species in different parts of the country with varying ozone sensitivity, which contributes to the apparent regional differen ces. It is important to note that ozone can have other, more significant impacts on forest plants ( e.g., reduced biomass growth in trees) prior to showing signs of visible foliar injury (U.S. EPA, 2006a).

Assessing the impact of ground

- level ozone on fores ts in the U.S. involves understanding the risks to sensitive tree species from ambient ozone concentrations and accounting for the prevalence of those species within the forest. As a way to quantify the risks to particular plants from ground

- level ozone, scientists have developed ozone

- exposure/tree

- response functions by exposing tree seedlings to different ozone levels and measuring reductions in growth as

“ biomass loss. ” Typical ly, seedlings are used because they are easy to manipulate and measure their growth loss from ozone pollution. The mechanisms of susceptibility to ozone within the leaves of seedlings and mature trees are identical, and the decreases predicted using the

se edlings should be related to the decrease in overall plant fitness for mature trees, but the magnitude of the effect may be higher or lower depending on the tree species (Chappelka and Samuelson, 1998). In areas where certain ozone

- sensitive species domina te the forest community, the biomass loss from ozone can be sig nificant. Experts have identified 2% annual

44 The data are based on averages of all observations collected in 2002, which is the last year for which data are

publicly available. For more information, please consult EPA ’ s 2008 Report on the Environment (U.S. EPA, 2008b).6- 71 c Degree of Injury: These categories reflect a subjective index based on expert opinion. Ozone can have other, more significant impacts on forest plants (e.g., reduced biomass growth in trees) prior to showing signs of visible foliar injury.
Figure 6- 16. Visible Foliar Injury to Forest Plants from Ozone in U.S. by EPA Regions a,b,c6- 72 Figure 6- 17. Presence and Absence of Visible Foliar Injury, as Measured by U.S. Forest Service, 2002
Source: U.S. EPA, 2007b

. biomass loss as a level of concern, which would cause long term ecological harm as the short

term negative effects on seedlings compound to affect long

- term forest health (Heck and Cowling, 1997).

Ozone damage to the plants including the trees and understory in a forest can affect the ability of the forest to sustain suitable habitat for associated species particularly threatened and endangered species that have existence value

— a nonuse ecosystem s ervice

— for the public. Similarly, damage to trees and the loss of biomass can affect the forest ’ s provisioning services in the form of timber for various commercial uses. In addition, ozone can cause discoloration of leaves and more rapid senescence (early shedding of leaves), which could negatively affect fall

color tourism because the fall foliage would be less available or less attractive. Beyond the aesthetic damage to fall color vistas, forests provide the public with many other recreational and educat ional services that may be affected by reduced forest health including hiking, wildlife viewing (including bird watching), camping, picnicking, and hunting. Another potential effect of biomass loss in forests is the subsequent loss of climate regulation se rvice in the form of reduced ability to sequester carbon and alteration of hydrologic cycles.6- 73 Some of the common tree species in the United States that are sensitive to ozone are black cherry ( Prunus serotina ), tulip

- poplar ( Liriodendron tulipifera) , and eastern white pine ( Pinus strobus)

. Ozone

- exposure/tree

- response functions have been developed for each of these tree species, as well as for aspen ( Populus tremuliodes ), and ponderosa pine ( Pinus ponderosa ) (U.S. EPA, 2007b).

6.6 .4.2 Ozone Effects on Crop s Laboratory and field experiments have shown reductions in yields for agronomic crops exposed to ozone, including vegetables (e.g., lettuce) and field crops (e.g., cotton and wheat). Damage to crops from ozone exposures includes yield losses (i.e., in ter ms of weight, number, or size of the plant part that is harvested), as well as changes in crop quality (i.e., physical appearance, chemical composition, or the ability to withstand storage) (U.S. EPA, 2007b). The most extensive field experiments, conducted under the National Crop Loss Assessment Network (NCLAN) examined 15 species and numerous cultivars. The NCLAN results show that

“ several economically important crop species are sensitive to ozone levels typical of those found in the United States ” (U.S. E PA, 2006a). In addition, economic studies have shown reduced economic co

- benefits as a result of predicted reductions in crop yields, directly affecting the amount and quality of the provisioning service provided by these crops, associated with observed oz one levels (Kopp et al., 1985; Adams et al., 1986; Adams et al., 1989). In addition, visible foliar injury by itself can reduce the market value of certain leafy crops (such as spinach, lettuce). According to the Ozone Staff Paper, there has been no eviden ce that crops are becoming more tolerant of ozone (U.S. EPA, 2007b). Using the Agriculture Simulation Model (AGSIM) (Taylor, 1994) to calculate the agricultural benefits of reductions in ozone exposure, the EPA estimated that attaining a W126 standard of 1 3 ppm

- hr would produce monetized benefits of approximately $400 million to $620 million in 2006 (inflated to 2006 dollars) (U.S. EPA, 2007b). 45

6.6 .4.3 Ozone Effects on Ornamental Plants Urban ornamental plants are an additional vegetation category likely t o experience some degree of negative effects associated with exposure to ambient ozone levels. Several ornamental species have been listed as sensitive to ozone (Abt Associates, 1995). Because ozone causes visible foliar injury, the aesthetic value of ornamental plants (such as petunia, geranium, and poinsettia) in urban landscapes would be reduced (U.S. EPA, 2007b). Sensitive

45 These estimates illustrate the value of vegetation effects from a substantial reduction of ozone concentrations,

not the marginal change in ozone concentrations anticipated a result of the emission reductions achieved by this rul e.6- 74 ornamental species would require more frequent replacement and/or increased maintenance (fertilizer or pesticide application) to mai ntain the desired appearance because of exposure to ambient ozone (U.S. EPA, 2007b). In addition, many businesses rely on healthy

- looking vegetation for their livelihoods (e.g., horticulturalists, landscapers, Christmas tree growers, farmers of leafy crops, etc.). The ornamental landscaping industry is a multi

- billion dollar industry that affects both private property owners/tenants and governmental units responsible for public areas (Abt Associates, 1995). Preliminary data from the 2007 Economic Census ind icate that the landscaping services industry, which is primarily engaged in providing landscape care and maintenance services and installing trees, shrubs, plants, lawns, or gardens, was valued at $53 billion (U.S. Census Bureau, 2010). Therefore, urban or namentals represent a potentially large unquantified benefit category. This aesthetic damage may affect the enjoyment of urban parks by the public and homeowners ’ enjoyment of their landscaping and gardening activities. In addition, homeowners may experience a reduction in home value or a home may linger on the market longer due to decreased aesthetic appeal. In the absence of adequate exposure

- response functions and economic damage functions for the potential range of effects relevant to ornamental plants, we cannot conduct a quantitative analysis to estimate these effects.

We are unable to provide an estimate of the ozone crop co

- benefits associated with the revised or alternative annual standard s due to data, time, and resource limitations.

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6.A.1 Introduction Economic benefits may result from two broad categories of changes in light extinction: (1) changes in “ residential ” visibility

— i.e., the visibility in and around the locations where people live; and (2) changes in “ recreational ” visibility at Class I areas

— i.e., visibility at Class I national parks and wilderness areas. 1 In this analysis, only those recreational and re sidential benefits in areas that have been directly studied in the valuation literature are included in the primary presentation of benefits; recreational benefits in other U.S. Class I regions and residential benefits in other metropolitan areas are presented as sensitivity analyses of visibility benefits.

In Chapter 6 of this RIA, we provide an overview of the visibility benefits methodology and results. This appendix provides additional detail regarding specific aspects of the visibility benefits methodo logy and is organized as follows. Section 6.A.2 describes the process we used to convert the modeled light extinction data to match the spatial scale of the visibility benefits assessment. We present the basic utility model in Section 6.A.3. In Section 6.A.4 we discuss the measurement of visibility, and the mapping from environmental “ bads ” to environmental

“ goods. ” In Sections 6.A.5 and 6.A.6 we summarize the methodology for estimating the parameters of the model corresponding to visibility at in

- region an d out

- of

- region Class I areas, and visibility in residential areas, respectively, and we describe the methods used to estimate these parameters. Section 6.A.7 describes the process for aggregating the recreational and residential visibility benefits. Secti on 6.A.8 describes the adjustment to reflect income growth over time. Section 6.A.9 provides all the parameters used to calculate visibility benefits.

6.A.2 Converting Modeled Light Extinction Estimates To calculate visibility benefits, we use light extinc tion estimates generated by the CMAQ model. 2 Modeled light extinction estimates are measured in units of inverse megameters (Mm -1 ). Because the valuation studies measure visibility in terms of visual range, we convert the light extinction units from Mm -1 t o visual range (in km) for both recreational and residential

1 Hereafter referred to as Class I areas, which are defined as areas of the country such as national parks, national wilderness areas, and national monuments that have been set aside under Section 169(a) of the Clean Air Act to receive the most stringent de gree of air quality protection. Class I federal lands fall under the jurisdiction of three federal agencies, the National Park Service, the Fish and Wildlife Service, and the Forest Service. 2 For more information regarding the CMAQ modeling conducted for the PM NAAQS RIA, please see Chapter 3 of this RIA.6.A -2 visibility benefits. Using the relationships derived by Pitchford and Malm (1994), the formulas for this conversion are

Deciviews = 10 ∗ ln � 391 VR �
= 10 ∗ ln � β ext 10 �

where VR denotes visual rang e (in kilometers) and β ext denotes light extinction (in Mm -1 ). Because we leverage the tools and data prepared for previous analyses (U.S. EPA, 2011), we use
a two

- step process to convert from Mm -1 to VR using deciviews as an intermediate conversion instea d of converting directly. Therefore, the full formula incorporating the two

- step conversion is



### VR = 391 ∗ e −0

. 1∗ ( 10 ∗ ln � β ext 10 � )

The spatial scale of the modeled light extinction estimates must also be adjusted to correspond with the design of the valuation studies and the underlying population and economic data. For the residential visibility benefits analysis, we convert the spatial resolution of the light extinction estimates from 12

- km grid to county

- level. We use county

- level light extinction to match the MSA boundaries, population data, and household income data. We used the geographic centroids of each 12

- km grid cell with the Veronoi Neighborhood Averaging ( VNA) interpolation method in the BenMAP model for this conversion (Abt Associates, 2010).

For the recreational visibility benefits analysis, we use the light extinction estimates from 12

- km grid cell located at the geographic center of the Class I area. Although we considered using the IMPROVE monitor location instead, we selected the park centroid for three reasons:

1. Consistency with previous method for estimating recreational visibility benefits

2. Not all Class I areas have monitors, and shared monitors may be outside park

3. Siting criteria for IMPROVE monitors do not include iconic scenic vista location

6.A.3 Basic Utility Model Within the category of recreational visibility, further distinctions have been made. There is evidence (Chestnut and Rowe, 1990) that an individual ’ s WTP for improvements in visibility at a Class I area is influenced by whether it is in the region in which the individual lives, or whether it is somewhere else. In general, people appear to be willing to pay more for visibility6.A -3 improvements at parks and wilderness areas that a re “ in

- region ” than at those that are “ out

- of

region. ” This is plausible, because people are more likely to visit, be familiar with, and care about parks and wilderness areas in their own part of the country.

To value estimated changes in visibility, we us e an approach that is consistent with economic theory. Below we discuss an application of the Constant Elasticity of Substitution (CES) utility function approach 3 to value both residential visibility improvements and visibility improvements at Class I area s in the United States. This approach is based on the preference calibration method developed by Smith, Van Houtven, and Pattanayak (2002).

We begin with a CES utility function in which a household derives utility from

1. “ all consumption goods, ” X,

2. vis ibility in the residential area in which the household is located ( “ residential visibility ” ), 4

3. visibility at Class I areas in the same region as the household ( “ in

- region recreational visibility ” ), and

4. visibility at Class I areas outside the househol d’ s region ( “ out

- of

- region recreational visibility ” ).

We have specified a total of six recreational visibility regions, 5 so there are five regions for which any household is out of region. The utility function of a household in the n th
residential area and the i th region of the country is:

3 The constant elasticity of substitution utility function has been chosen for use in this analysis because of its flexibility when illustrating the degree of substitutability present in various economic relationships (in this case, the trade

- off between income and improvements in visibility).
4 We remind the reader that, although residential and recreational visibility benefits estimation is discussed simultaneously in this section, benefits are calculat ed and presented separately for each visibility category. 5 See Section 6.3.4 of this RIA for a description of the different recreational visibility considered in this analysis. 

## U X Z Q Q
i k j k ni n ik ik k N
jk jk k N

j i

ik jk i j 

## = + + +

> > ∀ > ∀ ≤ = =≠ 

# ∑ ∑∑ ( ) ,

, , , , , , , . / ρ ρ ρ ρ ρ 

## θ γ δ
θ γ δ ρ 1 1 1



## 0 0 0 16.A -4 where

Z n = the level of visibility in the nth residential area; Q ik = the level of visibility at the k th in

- region park (i.e., the k th park in the i th region);
Q

jk = the level of visibility at the k th park in the j th region (for which the household is out of region), j ≠ i;
N i = the number of Class I areas in the i th region;
N j = the number of Class I areas in the j th region (for which the household is out of region), j ≠ i; and θ, the γ ’ s and δ ’ s are p arameters of the utility function corresponding to the visibility levels at residential areas, and at in

- region and out

- of

- region Class I areas, respectively.
In particular, the γ ik ’ s are the parameters corresponding to visibility at in

- region Class I

area s; the δ 1 ’ s are the parameters corresponding to visibility at Class I areas in region 1 (California), if i ≠ 1; the δ 2 ’ s are the parameters corresponding to visibility at Class I areas in region 2 (Colorado Plateau), if i ≠ 2, and so forth. Because the model a ssumes that the relationship between residential visibility and utility is the same everywhere, there is only one θ. The parameter ρ in this CES utility function is an important determinant of the slope of the marginal WTP curve associated with any of the environmental quality variables. When ρ=1, the marginal WTP curve is horizontal. When ρ<1, it is downward sloping.

The household ’ s budget constraint is:

where m is income, and p is the price of X. Without loss of generality, set ρ = 1. The only choice

v ariable is X. The household maximizes its utility by choosing X=m. The indirect utility function for a household in the n th residential area and the i th region is therefore

where Q denotes the vector of vectors, Q 1 , Q 2 , Q 3 , Q 4 , Q 5 , and Q 6 , and the unsubscripted γ and δ denote vectors as well. 

## m p X − ⋅ ≤ 0 ,



## V m Z Q m Z Q Q

ni n n ik ik k N
jk jk k N

j i i j 

## ( , , ; , , , ) ( ) , / 

## θ γ δ ρ θ γ δ ρ ρ ρ ρ ρ 

## = + + +

= =≠ 

# ∑ ∑∑

1 1 16.A -5 Given estimates of ρ, θ, the γ ’ s and the δ ’ s, the household ’ s utility function and the corresponding WTP functions are fully specified. The household ’ s WTP for any set of changes in the levels of visibility at in

- region Class I areas, out

- of

- region Class I areas, and the household ’ s residential area can be shown to be:

The household ’ s WTP for a single visibility improvement will depend on its order in the series of visibility improvements the household is valuing. If it is the first visibility improvement to be valued, the household ’ s WTP for it follows directly from the previous equation. For example, the household ’ s WTP for an improvement in visibility at the first in

- region park, from Q i1 = Q 0i1 to Q i1 = Q 1 i1 , is

if this is the first (or only) visibility change the household values.

6.A.4 Measure of Visibility: Environmental “ Goods ” Versus “ Bads ”
In the above model, Q and Z are environmental “ goods. ” As the level of visibility increases, utility increases. The utility function and the corresponding WTP function both have reasonable properties. The first derivative of the indirect utility function with respect to Q (or Z) is positive; the second deriva tive is negative. WTP for a change from Q 0 to a higher (improved) level of visibility, Q 1 , is therefore a concave function of Q 1 , with decreasing marginal WTP.

The measure of visibility that is currently preferred by air quality scientists is the deciview, which increases as visibility decreases . Deciview, in effect, is a measure of the lack of visibility. As deciviews increase, visibility, and therefore utility, decreases. The deciview, then, is a measure of an environmental “ bad. ” There are many examples of environmental “ bads ”— all types of pollution are environmental “ bads. ” Utility decreases, for example, as the concentration of particulate matter in the atmosphere increases.

One way to value decreases in environmental bads is to consider the “ goods ” with which they are associated, and to incorporate those goods into the utility function. In particular, if B denotes an environmental “ bad, ” such that: WTP Z Q m m Z Z Q Q Q Q ni n n ik ik ik k N
jk jk jk k N

j i i j ( , ) [ ( ) ( ) ( )] . / ∆ ∆ = − + − + − +

−

= =≠ 

# ∑ ∑∑ ρ ρ ρ ρ ρ ρ ρ ρ θ γ δ 0 1 0 1 1 0 1 1 1



### WTP Q m m Q Q i i i i 

### ( ) [ ( )] , / 

### ∆ 1 1 0 1 1 1 1 

### = − + − ρ ρ ρ ρ 

### γ



### ∂ ∂

V B < 0 ,6.A -6 and the environmental “ good, ” Q, is a function of B,

then the environmental “ bad ” can be related to utility via the corresponding environmental

“ good ”: 6

The relationship between Q and B, F(B), is an empirical relationship that must be estimated.

There is a potential problem with this approach, however. If the function relating B and Q is not the s ame everywhere (i.e., if for a given value of B, the value of Q depends on other factors as well), then there can be more than one value of the environmental good corresponding to any given value of the environmental bad, and it is not clear which value to use. This has been identified as a problem with translating deciviews (an environmental “ bad ” ) into visual range (an environmental “ good ” ). It has been noted that, for a given deciview value, there can be many different visual ranges, depending on the oth er factors that affect visual
range — such as light angle and altitude. We note here, however, that this problem is not unique to visibility, but is a general problem when trying to translate environmental “ bads ” into

“ goods. ” 7

In order to translate deciview s (a “ bad ” ) into visual range (a “ good ” ), we use a relationship derived by Pitchford and Malm (1994) in which

where DV denotes deciview and VR denotes visual range (in kilometers). Solving for VR as a function of DV yields

6 There may be more than one “ good ” related to a given environmental “ bad. ” To simplify the discussion, however, we assume only a single “ good. ”
7 Another example of an environmental “ bad ” is particulate matter air pollution (PM). The relationship between survival probability (Q) and the ambient PM level is generally taken to be of the form where ∀ denotes the mortality rate (or level) when there is no ambient PM (i.e., when PM=0). However, α is implicitly a function of all the factors other than PM that affect mortality. As these factors change (e.g., from one location to another), α will change (just as visual range changes as light angle changes). It is therefore possible to have many values of Q corresponding to a given value of PM, as the values of ∀ vary. 

### Q F B = ( ) ,

V V m Q V m F B = = ( , ) ( , ( ))

.

DV 10 * ln( 391 VR ) , =

Q e PM = − 1 α β

.6.A -7 This conversion is based on specific assumptions characterizing the “ average ” conditions of those factors, such as light angle, that affect visual range. To the extent that specific locations depart from the average conditions, the relationship will be an imperfect approximation. 8

6 .A.5 Estimating the Parameters for Visibility at Class I Areas: the γ ’ s and δ ’s As noted in Section 6.A.3, if we consider a particular visibility change as the first or the only visibility change valued by the household, the household ’ s WTP for that change in visibility can be calculated, given income (m), the “ shape ” parameter, ρ , and the corresponding recreational visibility parameter. For example, a Southeast household ’ s WTP for a change in visibility at in

- region parks (collectively) from Q 1 = Q 01 to Q 1 = Q 11 is:

if this is the first (or only) visibility change the household values.

Alternatively, if we have estimates of m as well as WTP 1 in and WTP 1 out of in

- region and
out

- of

- region households, respectively, for a given change in visibility from Q 01 t o Q 11 in Southeast parks, we can solve for γ 1 and δ 1 as a function of our estimates of m, WTP 1 in and
WTP 1 out , for any given value of ρ . Generalizing, we can derive the values of γ and δ for the j th region as follows:

and

Chestnut and Rowe (1990) and Chestnut (1997) estimated WTP (per household) for specific visibility changes at national parks in three regions of the United States

— both for households that are in

- region (in the same region as the park) and for households that are out

-

8 Ideally, we would want the location

- , time

- , and meteorological condition

- specific relationships between deciviews and visual range, which could be applied as appropriate. This is probably not feasible, however. 

### VR 391 * e

. 0.1DV 

### = −



## WTP(DQ ) m [m g (Q Q )] 1 r 1 01 r 11 r 1/ r 

## = − + −

γ ρ ρ

ρ ρ j j in

j j 

### m WTP m Q Q = − −

− ( ) ( ) 0 1

δ ρ ρ

ρ ρ j j out

j j m WTP m Q Q = − −

− ( ) ( ) . 0 16.A -8 of

- region. The Che stnut and Rowe study asked study subjects what they would be willing to pay for each of three visibility improvements in the national parks in a given region. Study subjects were shown a map of the region, with dots indicating the locations of the parks in question. The WTP questions referred to the three visibility improvements in all the parks collectively; the survey did not ask subjects ’ WTP for these improvements in specific parks individually. Responses were categorized according to whether the respondents lived in the same region as the parks in question ( “ in

- region ” respondents) or in a different region ( “ out

- of

- region ” respondents). The areas for which in

- region and out

- of

- region WTP estimates are available from Chestnut and Rowe (1990), and the sou rces of benefits transfer

- based estimates that we employ in the absence of est imates, are summarized in Table 6.A

- 1. In all cases, WTP refers to WTP per household.

Table 6.A -1. Available Information on WTP for Visibility Improvements in National Parks

Region of Park Region of Household
In Region a Out of Region b

1. California WTP estimate from study WTP estimate from study

2. Colorado Plateau WTP estimate from study WTP estimate from study

3. Southeast United States WTP estimate from study WTP estimate from study

4. Northwest United States (based on benefits transfer from California)

5. Northern Rockies (based on benefits transfer from Colorado Plateau)

6. Rest of United States (based on benefits transfer from Southeast U.S.)

a In

- region ” WTP is WTP for a visibility improvement in a park in the same region as that in which the household is

located. For example, in

- region WTP in the “ Southeast ” row is the estimate of the average Southeast household ’ s WTP for a visibility improvement in a So utheast park.
b Out

- of

- region ” WTP is WTP for a visibility improvement in a park that is not in the same region in which the

household is located. For example, out

- of

- region WTP in the “ Southeast ” row is the estimate of WTP for a visibility improvement in a park in the Southeast by a household outside of the Southeast.
In the primary calculation of visibility benefits for this analysis, only visibility changes at parks within visibility regions for which a WTP estimate was available from Chestnut and Rowe ( 1990) are considered (for both in - and out

- of

- region benefits). Primary estimates will not include visibility benefits calculated by transferring WTP values to visibility changes at parks not included in the Chestnut and Rowe study. Transferred benefits at parks located outside of the Chestnut and Rowe visibility regions will, however, be included as an alternative calculation.6.A -9 The values of the parameters in a household ’ s utility function will depend on where the household is located. The region

- specific p arameters associated with visibility at Class I areas (that is, all parameters except the residential visibility parameter) are arrayed in Table 6.A

- 2. The parameters in columns 1 through 3 can be directly estimated using WTP estimates from Chestnut and Rowe (1990) (the columns labeled “ Region 1, ” “ Region 2, ” and “ Region 3 ” ).

Table 6.A -2. Summary of Region

- Specific Recreational Visibility Parameters to be Estimated in Household Utility Functions

Region of Household Region of Park
Region 1 Region 2 Region 3 Region 4 Region 5 Region 6
Region 1 γ 1 a δ 2 δ 3 δ 4 δ 5 δ 6 Region 2 δ 1 γ 2 δ 3 δ 4 δ 5 δ 6 Region 3 δ 1 δ 2 γ 3 δ 4 δ 5 δ 6 Region 4 δ 1 δ 2 δ 3 γ 4 δ 5 δ 6 Region 5 δ 1 δ 2 δ 3 δ 4 γ 5 δ 6 Region 6 δ 1 δ 2 δ 3 δ 4 δ 5 γ 6

a The parameters arrayed in this table are region

- specific rather than park

- specific or wilderness area

- specific. For

example, δ 1 is the parameter associated with visibility at “ Class I areas in region 1 ” for a household in any region other than region 1. The benefits analysis must derive Cla ss I a rea

- specific parameters ( e.g., δ 1k , for the k th Class I area in the first region ).
For the three regions covered in Chestnut and Rowe (1990a) (California, the Colorado Plateau, and the Southeast United States), we can directly use the in

- region WTP estimat es from the study to estimate the parameters in the utility functions corresponding to visibility at in

- region parks (γ 1 ); similarly, we can directly use the out

- of

- region WTP estimates from the study to estimate the parameters for out

- of

- region parks (δ 1 ) . For the other three regions not covered in the study, however, we must rely on benefits transfer to estimate the necessary parameters.
While Chestnut and Rowe (1990) provide useful information on households ’ WTP for visibility improvements in national parks, there are several significant gaps remaining between the information provided in that study and the information necessary for the benefits analysis. First, as noted above, the WTP responses were not park specific, but only region specific. Because visibility improvements vary from one park in a region to another, the benefits analysis must value park

- specific visibility changes. Second, not all Class I areas in each of the three regions considered in the study were included on the maps shown to study s ubjects. Because6.A

- 10 the focus of the study was primarily national parks, most Class I wilderness areas were not included. Third, only three regions of the United States were included, leaving the three remaining regions without direct WTP estimates.

In additi on, Chestnut and Rowe (1990) elicited WTP responses for three different visibility changes, rather than a single change. In theory, if the CES utility function accurately describes household preferences, and if all households in a region have the same pref erence structure, then households ’ three WTP responses corresponding to the three different visibility changes should all produce the same value of the associated recreational visibility parameter, given a value of ρ and an income, m. In practice, of cours e, this is not the case.

In addressing these issues, we take a three

- phase approach:

1. We estimate region

- specific parameters for the region in the modeled domain covered by Chestnut and Rowe (1990a) (California, the Colorado Plateau, and the Southeast)

— γ 1 , γ 2 , and γ 3 and δ 1 , δ 2, and δ 3 . 2. We infer region

- specific parameters for those regions not covered by the Chestnut and Rowe study (the Northwest United States, the Northern Rockies, and the rest of the U.S.)

— γ 4 , γ 5 , and γ 6 and δ 4 , δ 5 , and δ 6. 3. We der ive park - and wilderness area

- specific parameters within each region (γ 1k and δ 1k , for k=1,

... , N 1 ; γ 2k and δ 2k , for k=1, ..., N 2 ; and so forth).

The question that must be addressed in the first phase is how to estimate a single region

- specific in

- region parameter and a single region

- specific out

- of

- region parameter for each of the three regions covered in Chestnut and Rowe (1990) from study respondents ’ WTPs for three different visi bility changes in each region. All parks in a region are treated collectively as if they were a single “ regional park ” in this first phase. In the second phase, we infer region

specific recreational visibility parameters for regions not covered in the Ches tnut and Rowe study (the Northwest United States, the Northern Rockies, and the rest of the United States). As in the first phase, we ignore the necessity to derive park

- specific parameters at this phase. Finally, in the third phase, we derive park - and wi lderness area

- specific parameters for each region.

6.A.5.1 Estimating Region

- Specific Recreational Visibility Parameters for the Region Covered in the Chestnut and Rowe Study (Regions 1, 2, and 3)

Given a value of ρ and estimates of m and in

- region and out

- of

- region WTPs for a change from Q 0 to Q 1 in a given region, the in

- region parameter, γ , and the out

- of

- region parameter, δ , for that region can be solved for. Chestnut and Rowe (1990), however,6.A

- 11 considered not just one, but three visibility changes in each region, each of which results in a different calibrated γ and a different calibrated δ , even though in theory all the γ ’ s should be the same and similarly, all the δ ’ s should be the same. For each region, however, we must have only a single γ and a singl e δ

. Denoting as our estimate of γ for the j th region, based on all three visibility changes, we chose to best predict the three WTPs observed in the study for the three visibility improvements in the j th region. First, we calculated , i=1, 2, 3, corresponding to each of the three visibility improvements considered in the study. Then, using a grid search method beginning at the average of the three ’ s , we chose to minimize the sum of the squared differences between the WTPs we predict using and the three region

- specific WTPs observed in the study. That is, we selected to minimize:

where WTP ij and WT P ij ( ) are the observed and the predicted WTPs for a change in visibility in the j th region from Q 0 = Q 0i to Q 1 = Q 1i , i=1, ..., 3. An analogous procedure was used to select an optimal δ , for each of the three regions in the Chest nut and Rowe study.

6.A.5.2 Inferring Region

- Specific Recreational Visibility Parameters for Regions Not Covered in the Chestnut and Rowe Study (Regions 4, 5, and 6) One possible approach to estimating region

- specific parameters for regions not covered by Chestnut and Rowe (1990a) (γ 4 , γ 5 , and γ 6 and δ 4 , δ 5 , and δ 6 ) is to simply assume that households ’ utility functions are the same everywhere, and that the environmental goods being valued are the same

— e.g., that a change in visibility at national parks in California is the same environmental good to a Californian as a change in visibility at national parks in Minnesota is to a Minnesotan.
For example, to estimate δ 4 in the utility function of a California household, corresponding to visibility at national p arks in the Northwest United States, we might assume that out

- of

- region WTP for a given visibility change at national parks in the Northwest United States is the same as out

- of

- region WTP for the same visibility change at national parks in California (inco me held constant). Suppose, for example, that we have an estimated mean WTP of out

- of

- region households for a visibility change from Q 01 to Q 11 at national parks in California (region 1), denoted WTP 1 out . Suppose the mean income of the out

- of

- region subjects in the  γ j

 γ j

 γ ji

 γ ji

 γ j



### ( (  ) )

WTP WTP ij j ij i 

### γ

−

= 

# ∑ 2

1 36.A

- 12 study was m. We might assume that, for the same change in visibility at national parks in the Northwest United States, WTP 4 out = WTP 1 out among out

- of

- region individuals with income m.

We could then derive the value of δ 4 , given a value of ρ as follows:

where Q 04 = Q 01 and Q 14 = Q 11 , (i.e., where it is the same visibility change in parks in region 4 that was valued at parks in the region 1).

This benefits t ransfer method assumes that (1) all h ouseholds have the sam e preference structures and (2) what is being valued in the Northwest United States (by a California household) is the same as what is being valued in the California (by all out

- of

- region households). While we cannot know the extent t o which the first assumption approximates reality, the second assumption is clearly problematic. National parks in one region are likely to differ from national parks in another region in both quality and quantity (i.e., number of parks).

One statistic tha t is likely to reflect both the quality and quantity of national parks in a region is the average annual visitation rate to the parks in that region. A reasonable way to gauge the extent to which out

- of

- region people would be willing to pay for visibility changes in parks in the Northwest United States versus in California might be to compare visitation rates in the two regions. 9 Suppose, for example, that twice as many visitor

- days are spent in California parks per year as in parks in the Northwest United States per year. This could be an indication that the parks in California are in some way more desirable than those in the Northwest United States and/or that there are more of them

— i.e., that the environmental goods being valued in the two regions ( “ visib ility at national parks ” ) are not the same.

A preferable way to estimate δ 4 , then, might be to assume the following relationship:

(income held constant), where n 1 = the average annual number of visitor

- days to California parks and n 4 = the average annual number of visitor

- days to parks in the Northwest United States. This implies that

9 We acknowledge that reliance on visitation rates does not get at nonuse value. δ ρ ρ

ρ ρ 4 4

04 14 = − −

− ( ) m WTP m Q Q out



### WTP WTP n

n out

out 4

1 4

1 

### =6.A

- 13 for the same change in visibility in region 4 parks among out

- of

- region individuals with income m. If, for example, n 1 = 2n 4 , WTP 4 out would be half o f WTP 1 out . The interpretation would be the following: California national parks have twice as many visitor

- days per year as national parks in the Northwest United States; therefore they must be twice as desirable/plentiful; therefore,
out

- of

- region people would be willing to pay twice as much for visibility changes in California parks as in parks in the Northwest United States; therefore a Californian would be willing to pay only half as much for a visibility change in national parks in the Northwest United States as an
out

- of

- region individual would be willing to pay for the same visibility change in national parks in California. This adjustment, then, is based on the premise that the environmental goods being valued (by people out of region) are not the sa me in all regions.

The parameter δ 4 is estimated as shown above, using this adjusted WTP 4 out . The same procedure is used to estimate δ 5 and δ 6. We estimate γ 4, γ , and γ 6 in an analogous way, using the in

- region WTP estimates from the transfer regions, e.g .,

6.A.5.3 Estimating Park - and Wilderness Area

- Specific Parameters

As n oted above, Chestnut and Rowe (1990) estimated WTP for a region ’ s national parks collectively, rather than providing park

- specific WTP estimates. The  ’ s and  ’ s are therefore the p arameters that would be in household utility functions if there were only a single park in each region, or if the many parks in a region were effectively indistinguishable from one another. Also noted above is the fact that the Chestnut and Rowe study did not include all Class I areas in the regions it covered, focusing primarily on national parks rather than wilderness areas. Most Class I wilderness areas were not represented on the maps shown to study subjects. In California, for example, there are 31 Class I areas, including 6 national parks and 25 wilderness areas. The Chestnut and Rowe study map of California included only 10 of these Class I areas, including all 6 of the national parks. It is unclear whether subjects had in mind “ all parks and wilderne ss areas ” when they offered their WTPs for visibility improvements, or whether they had in mind the specific number of (mostly) parks that were shown on the maps. The derivation of park - and wilderness area

- specific parameters depends on this. 

### WTP n

n WTP out out 4 4

1 1 

### = *

WTP n

n WTP in in 4 4

1 1 

### = * .6.A

- 14 6.A.5.4 Deri vation of Region

- Specific WTP for National Parks and Wilderness Areas If study subjects were lumping all Class I areas together in their minds when giving their WTP responses, then it would be reasonable to allocate that WTP among the specific parks and wi lderness areas in the region to derive park - and wilderness area

- specific γ ’ s and δ ’ s for the region. If, on the other hand, study subjects were thinking only of the (mostly) parks shown on the map when they gave their WTP response, then there are two poss ible approaches that could be taken. One approach assumes that households would be willing to pay some additional amount for the same visibility improvement in additional Class I areas that were not shown, and that this additional amount can be estimated using the same benefits transfer approach used to estimate region

- specific WTPs in regions not covered by Chestnut and Rowe (1990a).

However, even if we believe that households would be willing to pay some additional amount for the same visibility improvement in additional Class I areas that were not shown, it is open to question whether this additional amount can be estimated using benefits transfer methods. A third possibility, then, is to simply omit wilderness areas from the benefits analysis. For this analysis we calculate visibility benefits assuming that study subjects lumped all Class I areas together when stating their WTP, even if these Class I areas were not present on the map.

6.A

. 5.5 Derivation of Park - and Wilderness Area

- Specific WTPs, Given Re gion

- Specific WTPs for National Parks and Wilderness Areas The first step in deriving park - and wilderness area

- specific parameters is the estimation of park - and wilderness area

- specific WTPs. To derive park and wilderness area

- specific WTPs, we apportion the region

- specific WTP to the specific Class I areas in the region according to each area ’ s share of the region ’ s visitor

- days. For example, if WTP 1 in and WTP 1 out denote the mean household WTPs in the Chestnut and Rowe (1990) study among respondents who were in

- region

- 1 and out

- of

- region

- 1, respectively, n 1k denotes the annual average number of visitor

days to the kth Class I area in California, and n 1 denotes the annual average number of visitor

days to all Class I areas in California (that are included in the benefits analysis), then we assume that

and 

### WTP n

n WTP k in k in 1 1

1 1 

### = * ,6.A

- 15 Using WTP j in and WTP j out , either from the Chestnut and Rowe study (for j = 1, 2, and 3) or derived by the benefits transfer method (for j = 4, 5, and 6), the same method is used to derive Class I are a- specific WTPs in each of the six regions.

While this is not a perfect allocation scheme, it is a reasonable scheme, given the limitations of data. Visitors to national parks in the United States are not all from the United States, and certainly not all from the region in which the park is located. A very large proportion of the visitors to Yosemite National Park in California, for example, may come from outside the United States. The above allocation scheme implicitly assumes that the relative frequencies of visits to the parks in a region from everyone in the world is a reasonable index of the relative WTP of an average household in that region (WTP j in ) or out of that region (but in the United States) (WTP j out ) for visibility improvements at these parks. 10

A possible problem with this allocation scheme is that the relative frequency of visits is an indicator of use value but not necessarily of nonuse value, which may be a substantial component of the household ’ s total WTP for a visibility improvement at Class I areas. If park A is twice as popular (i.e., has twice as many visitors per year) as park B, this does not necessarily imply that a household ’ s WTP for an improvement in visibility at park A is twice its WTP for the same improvement at park B. Although an allocation scheme based on relative visitation frequencies has some obvious problems, however, it is still probably the best way to allocate a collective WTP.

6.A.5.6 Derivation of Park - and Wilderness Area

- Specific Parameters, Given Park - and Wilderne ss - Specific WTP
Once the Class I area

- specific WTPs have been estimated, we could derive the park - and wilderness area

- specific γ ’ s and δ ’ s using the method used to derive region

- specific γ ’ s and δ ’ s. Recal l that that method involved (1) calibrating γ and δ to each of the three visibility improvements in the Chestnut and Rowe study (producing three γ ’ s and three δ ’ s), (2) averaging the three γ ’ s and averaging the three δ ’ s, and finally, (3) using these average γ and δ as starting points for a grid search to find the optimal γ and the optimal δ

— i.e., the γ and δ

10 This might be thought of as two assumptions: (1) that the relative frequencies of visits to the parks in a region from everyone in the world is a reasonable representation of the relative frequency of visits from people in the United States

— i.e., that the parks that are most popular (receive the most visitors per year) in general are also the most popular among Americans; and (2) that the relative frequency with which Americans visit each of their parks is a good index of their relative WTPs for visibility improvements at these parks. 

### WTP n

n *WTP

. 1k out 1k

1 1 out 

### =6.A

- 16 that would allow us to reproduce, as closely as possible, the three in

- region and three out

- of

region WTPs in the study for the three visibility changes being valued.

Going through this procedure for each national park and each wilderness area separately would be very time consuming, however. We therefore used a simpler approach, which produces very close approximations to the γ ’ s and δ ’ s produced using the above approach. If:

WTP j in = the in

- region WTP for the change in visibility from Q 0 to Q 1 in the j th region;
WTP jk in = the in

- region WTP for the same visibility change (from Q 0 to Q 1 ) in the k th Class I area in the jth region (= s jk *WTP j in , where s jk is the k th area ’ s share of visitor

- days in the j th region);

m = income;

γ j * = the optimal value of γ for the jth region; and

γ jk = the value of γ jk calibrated to WTP jk in and the change from Q 0 to Q 1 ;

then 11 :

and

which implies that:

where:

11 γ j * is only approximately equal to the right

- hand side because, although it is the optimal value designed to reprod uce as closely as possible all three of the WTPs corresponding to the three visibility changes in the Chestnut and Rowe study, γ j * will not exactly reproduce any of these WTPs. 

### γ ρ ρ

ρ ρ j j in 

### m WTP m Q Q * 

### ( ) ( ) ≈ − −

− 0 1



### γ ρ ρ

ρ ρ jk jk in 

### m WTP m Q Q = − −

− ( ) ( ) 0 1



## γ γ jk jk j 

## a ≈ * , *



### a m WTP m

m WTP m jk jk in

j in 

### = − −

− − ( ) ( ) . ρ ρ

ρ ρ6.A

- 17 We use the adjustment factor, a jk , to derive γ jk from γ j *, for the k th Class I area in the j th region. We use an analogous procedure to derive δ jk from δ j * for the k th Class I area in the j th region (where, in this case, we use WTP j out and WTP jk out instead of WTP j in and WTP jk in ). 12

6.A.6 Estimating the Parameter for Visibility in Residential Areas: θ In previous assessments, EPA used a study on residential visibility valuation conducted in 1990 (McClelland et al., 1993). Consistent with advice from EPA ’ s Science Advisory Board (SAB), EPA designated the McClelland et al. study as significantly less reliable for regulatory benefit

- cost analysis, although it does provide useful estimates on the order of magnitude of residential visibility benefits (U.S. EPA

- SAB, 1999). 13 In order to estimate residential visibility benefits in this analysis, we have replaced the previous methodology with a new benefits transfer approach and incorporated additional valuation studies. This new approach was developed for The Benefits and Cost s of the Clean Air Act 1990 to 2020: EPA Report to Congress (U.S. EPA, 2011) and reviewed by the SAB (U. S. EPA

- SAB, 2010). To value residential visibility improvements, the new approach draws upon information from the Brookshire et al. (1979), Loehman et al. (1985) and Tolley et al. (1984) studies. 14 These studies provide primary visibility values for Atlanta, Boston, Chicago, Denver, Los Angeles, Mobile, San Francisco, and Washington D.C. 15

The estimation of θ is a simpler procedure for residential visibili ty benefits, involving a straightforward calibration using the study income and WTP for each study city:

12 This method uses a single in

- region WTP and a single out

- of

- region WTP per r egion. Although the choice of WTP will affect the resulting adjustment factors (the a jk ’ s) and therefore the resulting γ jk ’ s and δ jk ’ s, the effect is negligible. We confirmed this by using each of the three in

- region WTPs in California and comparing the

re sulting three sets of γ jk ’ s and δ jk ’ s, which were different from each other by about one one

- hundredth of a percent. 13 EPA ’ s Advisory Council on Clean Air Compliance Analysis noted that the McClelland et al. (1993) study may not incorporate two potentially important adjustments. First, their study does not account for the “ warm glow ” effect, in which respondents may provide higher willingness to pay estimates simply because they favor “ good

causes ” such as environmental impro vement. Second, while the study accounts for non

- response bias, it may not employ the best available methods. As a result of these concerns, the Council recommended that residential visibility be omitted from the overall primary benefits estimate. (U.S. EP A- SAB, 1999) 14 Loehman et al. (1985) and Brookshire et al. (1979) were subsequently published in peer

- reviewed journals (see Loehman et al. (1994) and Brookshire et al. (1982). The Tolley et al. (1984) work was not published, but was subject to peer review during study development. 15 Recognizing potential fundamental issues associated with data collected in Cincinnati and Miami (e.g., see Chestnut et al. (1986) and Chestnut and Rowe (1990c), we do not include values for these cities in our analysis. 

### θ ρ ρ

ρ ρ 

### = − −

− ( ) ( ) . m WTP m Z Z

0 16.A

- 18 where:

m = household income,

ρ = shape parameter (0.1),
θ = WTP parameter corresponding to the visibility at MSA,
Z 0 = starting visibility, and
Z 1 = visibility after change.

Where studies provide multiple estimates for visual range improvements for a single study city, we estimate one θ as the simple average of the θ calculated for each set of visual range improvements.

6.A.7 Putting It All Together: The Household Utility and WTP Functions Given an estimate of θ, derived as shown in Section 6.A.4, and estimates of the γ ’ s and δ ’ s, derived as shown in Section 6.A.3, based on an assumed or estimated value of ρ , the utility and WTP functions for a househo ld in any region are fully specified. We could therefore estimate the value to that household of visibility changes from any baseline level to any alternative level in the household ’ s residential area and/or at any or all of the Class I areas in the United States, in a way that is consistent with economic theory. In particular, the WTP of a household in the i th region and the n th residential area for any set of changes in the levels of visibility at in

- region Class I areas, out

- of

- region Class I areas, and the household ’ s residential area is:

The national benefits associated with any suite of visibility changes would be calcula ted as the sum of these household WTPs for those changes. The benefit of any subset of visibility changes (e.g., changes in visibility only at Class I areas in California) can be calculated by setting all the other components of the WTP function to zero (t hat is, by assuming that all other visibility changes that are not of interest are zero). This is effectively the same as assuming that the subset of visibility changes of interest is the first or the only set of changes being valued by households. Estimat ing benefit components in this way will yield slightly upward biased estimates of benefits, because disposable income, m, is not being reduced by the WTPs for any prior visibility improvements. That is, each visibility improvement (e.g., visibility at Clas s I areas in the California) is assumed to be the first, and they cannot all be the first. The upward bias



### WTP Z Q m m Z Z Q Q Q Q ni n n ik ik ik k N
jk jk jk k N

j i i j 

### ( , ) [ ( ) ( ) ( ) ] . / 

### ∆ ∆ = − + − + − +

−

= =≠ 

# ∑ ∑∑ ρ ρ ρ ρ ρ ρ ρ ρ 

### θ γ δ 0 1 0 1 1 0 1 1 16.A

- 19 should be extremely small, however, because all of the WTPs for visibility changes are very small relative to income.

Although we recognize that the approach described above is most consistent with economic theory, we have chosen to not use this function with income constraints on overall WTP. Instead, we simply add the total preference calibrated recreational visibility benefits to the preference

- cali brated residential visibility benefits. Again, because all of the WTPs for visibility changes are very small relative to income, the upward bias should be extremely small.

6.A.8 Income Elasticity and Income Growth Adjustment for Visibility Benefits

Growth in real income over time is an important component of benefits analysis. 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 16 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 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 income growth between the year a WTP study was conducted and the year for which benefi ts are estimated, and through differences in income between study populations and the affected populations at a particular time. Empirical evidence of the effect of real income on WTP gathered to date is based on studies examining the former. The Environmental Economics Advisory Committee (EEAC) of the Science Advisory Board (SAB) advised 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, 2000a). A recent advisory by another committee associated with the SAB, the Advisory Council on Clean Air Compliance Analysis, 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 (U.S. EPA

- SAB, 2004) ” and that “ The same increase should be assumed for the WTP for serious nonfatal health effects ( U.S. EPA

- SAB, 2004), ” they note that “ given the

16 Inco me elasticity is a common economic measure equal to the percentage change in WTP for a 1% change in income.6.A

- 20 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, 2 004). ” Until these conflicting advisories have been reconciled, 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 factor s.
We assume that the WTP for improved visibility would increase with growth in real income. 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.

Details of the general procedure to account for projected growth in real U.S. income between 1990 and 2020 can be found in Kleckner and Neumann (1999). Specifically, we use the elasticity for vi sibility benefits provided in Chestnut (1997).
In addition to elasticity estimates, projections of real gross domestic product (GDP) and populations from 1990 to 2020 are needed to adjust benefits to reflect real per capita income growth. We used projectio ns of real GDP provided in Kleckner and Neumann (1999) for the years 1990 to 2010. 17 We used projections of real GDP provided by Standard and Poor ’ s (2000) for the years 2010 to 2020. 18 Visibility benefits are adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor.

6.A.9 Summary of Parameters

In Tables 6.A

- 3 through 6.A

- 6, we provide the parameters used to calculate recreational and residential visibility benefits.

17 U.S. Bureau of Economic Analysis, Table 2A (available at http://www.bea.doc.gov/bea/dn/0897nip2/ tab2a.htm.) and U.S. Bureau of Economic Analysis, Economics and Budget Outlook. Note that projections for 2007 to 2010 are based on average GDP growth rates between 1999 and 2007. 18 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 proj ections.6.A

- 21 Table 6.A -3. Mean Annual Household WTP for Changes in Visual Rang e for Recreational Visibility (1990$) a

Region WTP In

- region WTP Out

- of

- region Starting Visual Range (miles) Ending Visual Range (miles) Study Household Income

California $66.41 $43.85 90 125 $48,759 $80.19 $53.88 90 150 $71.42 $51.37 45 90

Southwest $50.12 $45.11 155 200 $48,759 $72.67 $55.13 155 250 $61.40 $48.87 115 155

Southeast $66.41 $35.08 25 50 $48,759 $82.70 $53.88 25 75 $75.18 $47.61 10 25

a Based on Chestnut (1997) and adjusted for study sample income and currency year
Table 6.A -4. Region

- Specific Parameters for Recreational Visibility Benefits a

Region Optimal γ Optimal δ

California 0.00517633 0.003629603
Southwest 0.006402706 0.005092572
Southeast 0.003552379 0.002163346

Northwest 0.001172669 0.000823398
Northern Rockies 0.005263445 0.004176339
Rest of U.S. 0.001211215 0.000738149

a Calculated using methodology described in sections 6.A.3 through 6.A.46.A

- 22 Table 6.A -5. Mean Annual Household WTP for Changes in Visual Range for Residential Visibility

City WTP in Original Year ’ s $ Starting Visual Range (miles) Ending Visual Range (miles) Study Household

Income Year of Original

Data θ if ρ = 0.1 (1990$,

1990 income) θ if ρ = 0.1 (Simple Average)

Atlanta (Tolley et al., 1984) $188 12 22 $19,900 a 1982 0.033446

0.021316 $281 12 32 $19,900 a 1982 0.031661 $82 12 22 $27,600 d 1984 0.010738 $119 12 32 $27,600 d 1984 0.009417

Boston (Tolley et al., 1984) $139 18 28 $25,000 a 1982 0.026636 0.022843 $171 18 38 $25,000 a 1982 0.019049

Chicago (Tolley et al., 1984) $202 9 18 $30,000 b 1981 0.022313

0.015480 $269 9 30 $30,000 b 1981 0.016696 $121 10 20 $29,400 d 1984 0.013180 $144 10 30 $29,400 d 1984 0.009732

Denver (Tolley et al., 1984) $115 50 60 $32,000 c 1984 0.038558 0.033181 $154 50 70 $32,000 c 1984 0.027803

Los Angeles (Brookshire et al., 1979) $43 2 12 $15,200 d 1978 0.003866

0.007428 $116 2 28 $15,200 d 1978 0.006716 $71 12 28 $15,200 d 1978 0.011702

Mobile (Tolley et al., 1984) $168 10 20 $20,200 a 1982 0.026078 0.022480 $197 10 30 $20,200 a 1982 0.018882

San Francisco (Loehman et al., 1985) $71 16.3 18.6 $26,100 c 1980 0.045307 0.045307

Washington, DC (Tolley et al., 1984) $238 15 25 $27,500 a 1982 0.036866 0.032335 $303 15 35 $27,500 a 1982 0.027804

a See Chestnut et al. (1986), pages 5

- 5 through 5

- 10.
b See Tolley et al., (1984), page 127.

c See Loehman et al. (1985), page 38.
d Historical median income data by MSA from U.S. Census (1990).6.A

- 23 Table 6.A -6. Parameters for Income Growth Adjustment for Visib ility Benefits

Adjustment Step Parameter Estimate Central Estimate of Elasticity a 0.90 Adjustment Factor Used to Account for Projected Real Income Growth in 2020 b 1.517

a Derivation of estimates can be found in Kleckner and Neumann (1999) and Chestnut (1997).
b Based on elasticity values reported in Table 5

- 3, U.S. Census population projections, and projections of real GDP

per capita.

6.A.10 References Abt Associates, Inc. 2010. Environmental Benefits and Mapping Program (Version 4.0). Bethesda, MD. Prepared for U.S. Environmental Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. Available on the Internet at <http://www.epa.gov/air/ benmap>.
Brookshire, D.S., R.C. d ’ Arge, W.D. Schulze and M.A. Thayer. 1979. Methods Development for Assessing Tradeoffs in Environmental Management, Vol. II: Experiments in Valuing Non

Market Goods: A Case Study of Alternative Benefit Measures of Air Pollu tion Control in the South Coast Air Basin of Southern California . Prepared for the U.S. Environmental Protection Agency, Office of Research and Development. Available on the Internet at <http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE

- 0271B

- 1.pdf/$file/EE

- 0271B

1.pdf>.
Brookshire, D.S., Thayer, M.A., Schulze, W.D. & D ’ Arge, R.C. 1982. “ Valuing Public Goods: A Comparison of Survey and Hedonic Approaches. ” The American Economic Review. 72(1): 165

- 177.
Chestnut, L.G. 1997. Draft Memorandum: Methodology for Est imating Values for Changes in Visibility in National Parks . Prepared for Office of Policy, Planning and Evaluation, May. Available on the Internet at
< http://www.epa.gov/ttn/ecas/regdata/Benefits/background/chestnut1997.pdf >.
Chestnut, L.G., and R.D. Rowe. 1990. “ A New National Park Visibility Value Estimates. ” In Visibility and Fine Particles, Transactions of an AWMA/EPA International Specialty Conference , C.V. Mathai, ed. Air and Waste Management Association, Pittsburgh.
Chestnut, L.G., R.D. Rowe and J. Murdoch. 1986. Review of ‘ Establishing and Valuing the Effects of Improved Visibility in Eastern United States. ’ Prepared for the U.S. Environmental Protection Agency. October. Available on the Internet at
< http://www.epa.gov/ttn/ecas/regdata/Benefits/back ground/chestnut1986.pdf >.6.A

- 24 Kleckner, N., and J. Neumann. 1999. Recommended Approach to Adjusting WTP Estimates to Reflect Changes in Real Income. Memorandum to Jim Democker, U.S. EPA/OPAR. June. Available on the Internet at < http://www.epa.gov/ttn/ecas/regd ata/Benefits/ background/klecknerandneumann1999.pdf >.
Loehman, E.T., D. Boldt, K. Chaikin. 1985. Measuring the Benefits of Air Quality Improvements in the San Francisco Bay Area . From Methods Development for Environmental Control Benefits Assessment, Volum e IV. Prepared for the U.S. Environmental Protection Agency, Office of Policy, Planning and Evaluation, September. Grant #R805059

- 01

- 0 Available on the Internet at <http://yosemite.epa.gov/ee/epa/eerm.nsf/ vwGA/e85705387833398f8525644d0053bdd0!OpenDocument #_Section6>.
Loehman, E.T., S. Park, and D. Boldt. 1994. “ Willingness to Pay for Gains and Losses in Visibility and Health. ” Land Economics 70(4): 478

- 498.
McClelland, G., W. Schulze, D. Waldman, J. Irwin, D. Schenk, T. Stewart, L. Deck and M. Thayer. 1993

. Valuing Eastern Visibility: A Field Test of the Contingent Valuation Method

. Prepared for U.S. Environmental Protection Agency, Office of Policy, Planning and Evaluation. September. Available on the Internet at
< http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE

- 0008

- 1.pdf/$file/EE

- 0008

- 1.pdf >.
Pitchford, M.L., and W.C. Malm. 1994. “ Development and Applications of a Standard Visual Index. ” Atmospheric Environment 28(5):1049

- 1054.
Sisler, J.F. 1996. Spatial and Seasonal Patterns and Long Term Variability of the Composition of the Haze in the United States: An Analysis of Data from the IMPROVE Network . Colorado State University, Cooperative Institute for Research in the Atmosphere (CIRA), ISSN 0737

- 5352

- 32. Fort Collin s, CO. July. Available on the Internet at <http://vista.cira. colostate.edu/improve/Publications/Reports/1996/PDF/TBLCON1.96.pdf>.
Smith, V.K., G. Van Houtven, and S.K. Pattanayak. 2002. “ Benefit Transfer via Preference Calibration .” Land Economics 78:132

- 152.
Tolley, G., A. Randall, G. Blomquist, M. Brien, R. Fabian, G. Fishelson, A. Frankel, M. Grenchik, J. Hoehn, A. Kelly, R. Krumm, E. Mensah, and T. Smith. 1984. Establishing and Valuing the Effects of Improved Visibility in Eastern United States . Prepar ed for U.S. Environmental Protection Agency, Office of Policy, Planning and Evaluation. March. U.S. Environmental Protection Agency Grant #807768

- 01

- 0. Available on the Internet at <http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE

- 0003

- 01.pdf/$file/EE

- 0003

- 01.pdf>.

U.S. Census. 1990. “ Table 1: Median Household Income by Metropolitan Statistical Areas (MSA): 1969, 1979, and 1989. ” 1970, 1980, and 1990 Censuses of Population and Housing. Income Statistics Branch/HHES Division. Available on the Internet at

< htt p://www.census.gov/hhes/www/income/data/historical/metro/msa1.html >.6.A

- 25 U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 1999 . 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/E992F918DF32131A 852571B9004571F7/ $File/coua0002.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2011b . The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress. Office of Air and Radiation, Washington, DC. March. Available on the Internet at <http://www.epa.gov/air/sect812/feb11/fullreport.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses . EPA 240 -R- 00

- 003. National Center for Environmental Economics, Office of Policy Economics and Innovat ion. Washington, DC. September. Available on the Internet at < http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/ $file/cover.pdf >.6.B -1 APPENDIX 6.B ILLUSTR A TIVE SCENARIO OF RECREATIONAL AND RES IDENTIAL VISIBILITY BENEFITS

6.B.1 Synopsis In this Appendix, we provide an illustrative example analysis of visibility benefits that applies the visibility benefits methodology described in Chapter 6 and Appendix 6A to a specific modeled scenario

. For this illustrative example, we use the 2020 base case simulation and the 2020 control case simulation from the CMAQ model that were used to develop the air quality ratios. 1 In this Appendix, we r efer to this specific scenario as the “ illustrative scenario ,” which is not a surrogate of the revised annual primary standard. Because we do not have air quality model runs for the regulatory baseline and the revised or alternative annual standard s, we

ca nnot that calculate the visibility co

- benefits of attaining the revised annual primary standard. It is important to emphasize that this illustrative scenario does not reflect an emissions control strategy for any specific annual standard level , which is im portant because light extinction can vary substantially depending on the specific combination of SO 2 , NO X , or directly emitted particles reduced and the magnitude and location of those emission reductions. In addition, the visibility benefits in this chapter cannot be added to the health benefits of the revised or alternative standards. We provide this illustrative scenario to demonstrate the results of applying the methodology for estimating benefits for scenarios with light extinction estimates.

6.B.2 Recreational Benefits Results The modeling results indicate that visibility would improve in several Class I areas as a result of emission reductions in the illustrative scenario

. While we are unable to calculate the speci fic contribution in this analysis, the emission reductions associated with these emission reductions would help Class 1 areas to meet the goals of the Regional Haze rule. Table 6.B -1 identifies the visibility improvements in the 10 most visited parks for t he illustrative scenario using two light extinction metrics: visual range and deciview. The monetized benefits of recreational visibility improvements are provided in Table 6.B -2 for the illustrative scenario

. Because there is considerable uncertainty about the accuracy of the benefit transfer to other regions, we include the estimated recreational visibility benefits for parks in other regions as a sensitivity analysis only. The sensitivity analysis results are not considered part of the total monetized b enefits. Table 6.B -3 provides the results of this sensitivity analysis. Figure 6.B

- 1

1 See Chapter 4 of this RIA f or more information regarding the specific scenario in these modeling simulations including the magnitude, location, and type of emission reductions

. See Chapter 3 for more information regarding how these modeling simulations were used to calculate the air quality ratios.6.B -2 shows how the monetized benefits for recreational visibility are distributed across Class I areas for the illustrative scenario

. This sensitivity analysis shows that the b enefits in non

- studied park regions could be substantial. In addition, in Table 6.B.4, we provide an indication of the potential impact of omitting coarse particles from the light extinction calculation using surrogate coarse particle concentrations from D ebell et al. ( 2006 ). 2

Table 6.B -1. Annual Average Visibility Improvements in the Top 10 Most Visited Class I Areas for the Illustrative Scenario in 2020 a,b

Class I Area State Illustrative Scenario Visual Range (m) Deciviews Grand Canyon NP AZ - - Great Smoky Mountains NP NC/TN - - Yellowstone NP c WY - -
Yosemite NP CA 400 0.1
Sequoia

- Kings NP CA 990 0.3

Glacier NP c MT - - Rocky Mountain NP CO - -
Zion NP UT - - Grand Teton NP c WY - - Kings Canyon NP CA 470 0.1

a Because these benefits occur within the analysis year, the monetized benefits are the same for all discount rates.

Although the light extinction estimates do not reflect coarse particles, the rounded incremental visibility benefits are unaffected.
b Visibility measured at the c ounty of the geographic center of park. Top 10 most visited parks ranked by visitor

days in 2008 (NPS, 2008).

c Not included in the primary benefits because the parks are not located in the studied regions. Benefits for these

parks are included in the sensitivity analysis.

2 See Chapter 6 (Section 6.3.1) for more information regarding the purpose of this sensitivity analysis.6.B -3 Table 6.B -2. Recreational Visibility Benefits in Studied R egions for the Illustrative Scenario in 2020 (in millions of 2010$ ) a

Studied Park Region Illustrative Scenario Benefits

Southeast $4.3

Southwest - California $3 50

Total $ 350

a Because these benefits occur within the analysis year, the monetized benefits are the same for all discount rates.

These benefits reflect the WTP for all U.S. households for parks in these regions. Although the light extinction estimates do not reflect coarse particles, the rounded incremental visibility benefits are unaffected.

Table 6.B -3. Sensitivity Analysis for Recreational Visibility Benefits outside Studied Park Regions for the Illustrative Scenario in 2020 (in millions of 2010$ ) a

Non

- Studied Park Region Illustrative Scenario Benefits

Northwest - Northern Rockies - Rest of U.S. -
Total for Non

- Studied Parks Regions - Total including Studied Park Regions $ 350

a Because these benefits occur within the analysis year, the monetized benefits are the same for all discount rates.

These benefits reflect the WTP for all U.S. households for parks in these regions. These sensitivity analysis results are not considered part of the total monetized benefits. Although the light extinc tion estimates do not reflect coarse particles, the rounded incremental visibility benefits are unaffected.6.B -4 Table 6.B -4. Sensitivity Analysis for Incorporating Coarse Particles into Recreational Visibility Benefits for the Illustrative Scenario (in millions of 2010$ ) a

Illustrative Scenario Benefits Primary Recreational Benefits (Studied Park Regions) No coarse particles $ 350 + 5μg/m 3 coarse $ 3 30 + 5 μg/m 3 coarse with 15 μg/m 3 in Southwest $ 300 +8 μg/m 3 coarse with 15 μg/m 3 in Southwest $ 300
Sensitivity Analysis (Other Park Regions) No coarse particles - + 5μg/m 3 coarse - + 5 μg/m 3 coarse with 15 μg/m 3 in Southwest - +8 μg/m 3 coarse with 15 μg/m 3 in Southwest -

a Because these benefits occur within the analysis year, the monetized benefits are the same for all discount rates.

These benefits reflect the WTP for U.S. households who live in these regions for the parks in the study regions (primary benefits) or parks in other regions (sensitivity analysis). The levels of coarse particles represent the full range of possible annual concentrations from a recent report on the IMPROVE monitoring network (Debell et al., 2006). We define Southwest to be the states of California, Nevada, Utah, Arizona, New Mexico, Colorado, and Texas.6.B -5 Figure 6.B -1. Recreational Visibility Benefits in Class I Areas for the Illustrative Scenario in 2020 a

a The size of the circle in this map indicates the magnitude of the recreational benefits for each Class 1 Area. The

colors in this map indicate whet her the park benefits are included in the primary benefits or in the sensitivity analysis (i.e., non

- studied park regions). Blue = primary benefits (studied park regions), Green = sensitivity analysis (non

- studied park regions).

6.B.3 Residential Benefits Results The modeling results indicate that visibility would improve in many of the study areas as a result of the emission reductions associated with emission reductions in the illustrative scenario. Table 6.B -5 shows the monetized residential visibility b enefits in the eight study areas. These benefits reflect the value to households living within those MSAs, accounts for inflation, and account for growth in real income since the original WTP estimates were developed.

The results of the sensitivity analysi s for the monetized residential visibility benefits in all MSAs for the illustrative scenario are provided in Table 6.B -6 . Figure 6.B -2 shows how the sensitivity analysis results are distributed geographically for the illustrative scenario

. In addition, Table 6.B -7 provides an indication of the potential impact of omitting coarse particles in the calculation of light extinction

.6.B -6 Table 6.B -5. Monetized Residential Visibility Benefits in Studied Areas in 2020 for the Illustrative Scenario (millions of 2010 $ , 2020 income) a

Study Area Illustrative Scenario

Atlanta -
Boston - Chicago $43

Denver - Los Angeles $110

Mobile -
San Francisco $39
Washington, DC - Total $1 90

a Because these benefits occur within the analysis year, the monetized benefits are the same for all discount rates.

Although the light extinction estimates do not reflect coarse particles, the rounded incremental visibility benefits are unaffected.

Table 6.B -6. Sensitivity Analysis for Monetized Residential Visibility Benefits in Other A reas for the Illustrative Scenario in 2020 (in millions of 2010$ ) a

Extent of Benefit Transfer Illustrative Scenario Benefits

Additional MSAs in East $ 140
Additional MSAs in West $ 24
Additional MSAs in California $160

Total $ 330 Total including Study Areas $ 520

a Because these benefits occur within the analysis year, the monetized benefits are the same for all discount rates.

These sensitivity analysis results are not considered part of the total monetized benefits. Although the light extinction estimates do not reflect coarse particles, the rounded incremental visibility benefits are unaffected.6.B -7 Figure 6.B -2. Residential Visibility Benefits for the Illustrative Scenario in 2020 (2010$)

Table 6.B -7. Sensitivity Analysis for Incorporating Coarse Particles into Residential Visibility Benefits (in millions of 2010$ ) a

Illustrative Scenario Benefits Primary Residential Benefits No coarse particles $190

+ 5 μg/m 3 coarse $ 180 + 5 μg/m 3 coarse with 15 μg/m 3 in Southwest $ 150 +8 μg/m 3 coarse with 15 μg/m 3 in Southwest $ 130 Sensitivity Analysis No coarse particles $ 330

+ 5 μg/m 3 coarse $ 310 + 5 μg/m 3 coarse with 15 μg/m 3 in Southwest $250 +8 μg/m 3 coarse with 15 μg/m 3 in Southwest $ 270

a Because these benefits occur within the analysis year, the monetized benefits are the same for all discount rates.

These benefits reflect the WTP for U.S. households who live in these regions for the parks in the study regions (primary benefits) or parks in other regions (sensitivity analysis). The levels of coarse particles represent the full range of possible annual concentrations from a recent report on the IMPROVE monitoring network (Debell et al., 2006). We define Southwest to be the states of California, Nevada, Utah, Arizona, New Mexico, Colorado, and Texas.6.B -8 6.B.4 References DeBell, L. J.; Gebhart, K. A.; Hand, J. L.; Malm, W. C.; Pitchford, M. L.; Schichtel, B. A.; White, W. H. 2006. Spatial and Seasonal Patterns and Temporal Variability of Haze and its Constituents in the United States, Report IV ; Cooperative Institute for Research in the Atmosphere, Nov; ISSN 0737

- 5352

- 74. Available on the Internet at <http://vista.cira.colostate.edu/improve/Publications/Reports/2006/2006.htm>.

National Par k Service (NPS). 2008. Statistical Abstract: 2008. Department of Interior, National Park Service Social Science Program, Public Use Statistics Office, Denver, Colorado. Available on the Internet at <http://www.nature.nps.gov/stats/abstracts/abst2008.pdf>.7-1 CHAPTER 7 ENGINEERING COST ANA LYSIS

7.1 Synopsis This chapter summarizes the data sources and methodology used to estimate the engineering costs of attaining the revised annual standard and two alternative annual standards for the PM 2.5 primary standard analyzed in this RIA. This chapter provides the estimates of the annual engineering costs for illustrative control strategies designed to demonstrate attainment of the revised annual standard of 12 μg/m 3 in conjunction with retaining the 24

- hour standard of 35 μg/m 3 , as well as control strategies designed to demonstrate attainment of the a lternativ e annual standards of 13 and 11 μg/m 3 in conjunction with retaining the 24

- hour standard of 35 μg/m 3 (referred to as 1 2 , 1 3 , and 11). These illu strative control strategies are outlined in Chapter 4. The cost discussion for known controls in Section 7.2.2 is followed by a presentation of estimates for the engineering costs of the additional emission s reductions that are needed beyond the application of known controls to reach full attainment of the alternat iv e standards analyzed ; the cost estimates derived from this approach, discussed in Section 7.2.3, are referred to as “ extrapolated ” costs

. By definition, no cost data current ly exist s for the additional emissions reductions needed beyond known controls . W e e mploy two methodologies for estimating the costs of un identified future controls , and b oth approaches assume either that existing technologies can be applied in particular combinations or to specific sources that we currently can’t predict or that innovative strategies and new control options make possible the emissions reductions needed for attainment by 2020.

The engineering costs described in this chapter generally include the costs of purchasing, installing, operating , and maintaining the referenced control technologies. For a variety of reasons, actual control costs may vary from the estimates EPA presents. As discussed throughout this document , the technologies and con trol strategies selected for analysis are illustrative of one way in which nonattainment areas could meet a revised standard. There are numerous ways to construct and evaluate potential control programs that would bring areas into attainment with the revis ed and alternative standards, and EPA anticipates that local and
state governments will consider programs that are best suited for local and regional conditions

. Furthermore, based on past experience, EPA believes that it is reasonable to anticipate that the marginal cost of control will decline over time due to technological improvements and more widespread adoption of previously considered niche control t echnol ogies. Also, EPA recognizes the extrapolated portion of the engineering cost estimates are uncertain because extrapolated7-2 costs do not contain information about which sectors may be affected or which control

measures may be employed in the future.

T he eng ineering cost estimates are limited in their scope. This analysis focuses on the emission s reductions needed for attainment of the revised and alternative standards that are described earlier in this RIA

. EPA understands that some states will incur costs d esigning State Implementation Plans (SIPs) and implementing new control strategies to meet the revised standard. However, EPA does not know what specific actions states will take to design their SIPs to meet the revised standards ; therefore we do not prese nt estimated costs that government agencies may incur for managing the requirement, implementing these (or other) control strategies, or for offering incentives that may be necessary to encourage the implementation of specific technologies, especially for technologies that are not necessarily market driven. EPA generally estimates state

- level administrative costs in an information collection request (ICR) that accompanies the implementation rule or guidance for each NAAQS (as opposed to accompanying the iss uance of the NAAQS)

7.2 Estimation of Engineering Control Costs

7.2.1 Data and Methods

— Identified Control Costs (non

- EGU Point and Area Sources) After designing the hypothetical control strategy using the methodology discussed in Chapter 4, EPA used the Co ntrol Strategy Tool 1 (CoST) to estimate en gineering control costs for mobile, non

- EGU point and a rea sources. 2 CoST calculates engineering costs using either : (1) an average annualized cost

- per

- ton estimate multiplied by the total tons of a pollutant reduced to derive a total cost estimate , or (2) an equation that incorporates key emission source information (e.g., unit capacity and stack flow information). 3 Most control cost information within CoST w as developed based on the cost

- per

- ton approach because estimating engineering costs using an equation requires more data, and these data are sometimes, but not always, available

.

T he cost equations located in CoST require either unit capacity or stack flow to determine annua l ized , capital and/or operating and maintenance (O&M) costs. Capital costs

1 The Control Strateg y Tool recently underwent peer review by an ad hoc panel of experts. Responses to the peer review are currently being developed and will be available by final promulgation of this rule. 2 Area sources are not necessarily non

- urban sources. 3 Annualized cos ts represent an equal stream of yearly costs over the period the control technology is expected to operate.7-3 are converted to annual ized costs using the capital recovery factor (CRF). 4 When the cost equations and input data are available in CoST , the equations are used to calculate total a nnual control cost (TACC) , which is a function of capital costs (CC) and O&M costs. The CRF incorporates the interest rate and equipment life (in years) of the control equipment. Operating costs are calculated as a function of annual O&M and other variable costs. The resulting TACC equation is TACC = (CRF * CC) + O&M.

Engineering costs will differ between different emissions sources based upon quantity of emissions reduced, plant capacity, or stack flow. Engineering costs will also differ in nominal (i.e., not adjusted for inflation) terms by the year the costs are calculated for (i.e., 1999$ versus
2010 $). 5 For capital investment, in order to attain standards in 2020 we assume capital investment occurs at the beginning of 2020. We make this simplifying assumption because we do not know what all firms making capital investments will do and when they will do it. For 2020 , our estimate of annualized costs include s annualized capital and O&M costs for those controls included in our identified control strateg y analysis. Our engineering cost analysis uses the equivalent uniform annual costs (EUAC) method, in which annualized costs are calculated based on the equipment life for the control measure along with the interest rate of 7 % incorporated into the CRF. We make no presumption of additional capital investment in years beyond 2020 . The EUAC method is discussed in detail in the EPA Air Pollution Control Cost Manual. 6 Applied controls and their respective engineering costs are provided in the PM NAAQS docket.

7. 2.2 Identified Control Costs In this section, we provide engineering cost estimates for the control strategies identified in Chapter 4 that include control technologies on non

- EGU point sources and area

sources. Engineering costs generally refer to the cap ital equipment expense, the site preparation costs for the application, and annual operating and maintenance costs. For this analysis, we included known controls for all of the geographic areas likely to exceed the revised

4 The capital recovery factor formula is expressed as [r*(1+r)^n/(1+r)^n

- 1]*CC. Where r is the real rate of interest,

n is the number of time periods, and CC is the capital cost. For more information on this cost methodology and CoST, please refer to the documentation at http://www.epa.gov/ttn/ecas/cost.htm , the EPA Air Pollution Control Cost Manual found at http://epa.gov/ttn/catc/products.html#cccinfo , and EPA’s Guidelines for Preparing Economic Analyses, Chapter 6 found at http://yosemite.epa.gov/ee/epa /eed.nsf/webpages/Guidelines.html#download

. 5 The engineering costs will not be any different in real (inflation

- adjusted) terms if calculated in 20 10 versus other

year dollars , if the other

- year dollars are properly adjust ed. For this analysis, all costs are reported in real 20 1 0 dollars. 6 http://epa.gov/ttn/catc/products.html#cccinfo7-4 and/or alternative standards. We included all known controls at a n annual cost of $20,000 per ton or less, which included approximately 85 % of known controls in the geographic areas likely to exceed the revised and/or alternative standards

. 7 We did not include the small number of known c ontrols that had a n annual cost of more than $20,000 per ton because either (i) the

r emaining emissions sources were relatively small sources , and we believe they are already controlled, or (ii) the equations in CoST we re not applicable to these remaining emissions

sources.
Because we obtain control cost data from many sources, we are not always able to obtain consistent data across original data sources. 8 If disaggregated control cost data is unavailable (i.e., where capital, equipment life value, and O&M costs are not separated out), EPA typically assumes that the estimated control costs are annualized using a 7 % discount rate because the majority of the available disaggregated control cost data is calculated using 7%

. When disaggregated control cost data is available (i.e., where capital, equipment life value, and O&M costs are explicit) we can recalculate costs using a 3 % discount rate. The use of these two discount rates for cost estimation reflects the guidance for RIA preparation found in Circular A

4, issued by OMB in September 2003. 9 In general, we have some disaggregated data available for non

- EGU point source controls ; we do not have any disaggregated control cost data for area source controls

. 10 In this analysi s, f or the revised annual standard of 12 μg/m 3 and the alternative standard of 13 μg/m 3 we did not have any disaggregated known control cost data, and as such we were not able to recalculate known control costs using a 3 % discount rate. Because we were not able to recalculate known controls costs using a 3 % discount rate, we are not presenting known controls costs for the revised or alternative standards using that discount
rate. S ee Table 7

- 1 for a summary of sectors and known control costs.

7 For the known controls , for all of the geographic areas likely to exceed the revised and/or alternative standards we include controls at an annual cost of $20,000 per ton or less. To estimate the costs associated with unidentified future controls, or unknown controls , we employ a fixed

- cost and hybrid methodology. The fixed

cost methodology employs a primar y cost estimate of $15,000/ton (2010 dollars), and the hybrid methodology employs an initial, annual cost

- per

- ton estimate of $15,000/ton (2010 dollars). We explain the choices of these parameters in th is Section and Section 7.2.3. 8 Data sources can include states, as well as technical studies, which do not typically include references to the original data source. 9 U.S. Office of Management and Budget. Circular A

- 4, September 17, 2003. Available at http://www.whitehouse.gov/omb/circulars_a004_a

- 4/

. 10 For area source controls, total annualized costs are assumed to be calculated using a 7% discount rate.7-5 Table 7-1. Summary of Sectors , Emissions Reductions, and Known Annualized Control Costs (millions of 20 1 0$) a , b

Revised and Alternat ive Standard Emissions Sector Emissions Reductions c 7% Discount Rate

13 μg/m 3 Non

- EGU Point Sources

-- —
Area Sources 53 $0.63

Total 53 $0.63

12 μg/m 3 Non

- EGU Point Sources 3 80 $0.8 7

Area Sources 4 30 $4.3

Total 80 0 $ 5.1

11 μg/m 3 Non

- EGU Point Sources 23 , 000 $ 88

Area Sources 2,3 00 $13

Total 25, 4 00 $100

a All estimates rounded to two significant figures. Estimates may not sum due to rounding convention. b The emissions reductions and total costs are associated with partial attainment.

c The emissions reductions for the alternative standard of 11 include PM 2.5 and SO 2 emissions re ductions.

The total annualized cost of control in each sector in the control scenario is summarized by region in Table 7-2. Table 7-2 includes annualized control cost s to allow for comparison

across regions and between costs and benefits. These numbers reflect the engineering costs annualized at a discount rate of 7%. E ngineering cost estimates presented throughout this and subsequent chapters are based on a 7 % discount rate.

Table 7-2. Partial Attainment Known Annualized Control Costs in 2 020 for Revised and Alternat iv e Standards Analyzed (millions of 20 1 0$) a ,b

Revised & Alternat iv e Standard Region Known Controls

13 μg/m 3 East —
West — California $0.63

Total $0.63

12 μg/m 3 East —
West — California $5.17-6 Total $5.1

11 μg/m 3 East $96

West $0.45

California $5.3

Total $ 100

a Estimates are rounded to two significant figures . As such , numbers may not sum down columns.
b Note that the estimates provided reflect incremental emissions reductions from an analytical baseline that gives

“credit ” to the San Joaquin Valley and South Coast areas for emissions reductions expected to occur between 2020 and 2025 ( when those areas are expected to demonstrate attainment with the revised and/or alternative standards ).

The total annualized engineering costs associated with the application of known controls, incremental to the baseline and using a 7 % discount rate, are approximately $ 5.1 million for the revised annual standard of 12, $ 630,000 for the less stringent alternat iv e annu al standard of 13 μg/m 3 , and $ 100 million for a more stringent alternat iv e annual standard of 11 μg/m 3

.
7.2.3 Extrapolated Costs This section presents the methodology and results of the extrapolated engineering cost calculations for attainment of the revis ed annual PM 2.5 standard of 12 μg/m 3 , as well as alternativ e annual standards of 13 μg/m 3 and 11 μg/m 3

. All costs presented for the illustrative control strategies are calculated incrementally from the current PM 2.5 standard of 15/35 μg/m 3

, therefore, any additional emission reductions needed to attain the current 24

- hour standard of 35 μg/m 3 are part of the baseline analysis and not presented here.

As mentione d earlier in this chapter, and as described in more detail in Chapter 4, the
ap plication of the known control strategy was not successful in reaching attainment for all

areas for these alternat iv e PM 2.5 standards. Because some areas remained in nonattainment, the engineering costs detailed in Section 7.2.2 represent the costs of part ial attainment for the revised annual standard of 12 μg/m 3 and alternat iv e annual PM 2.5 standards of 1 3 μg/m 3 and 11 μg/m 3

. For the revised standard and each alternat iv e standard and geographic area that cannot reach attainment with known controls, we estimated the additional emission s reductions needed for PM 2.5 to attain the standard. See Chapter 3, Section 3.3.2 and Tables 3 -7 , 3 -8 , and 3

- 9 f or a detailed discussion of how the additional, needed emissions reductions were estimated and for a summary o f the needed emissions reductions for the revised annual standard and the alternative annual standards

.7-7 To generate estimates of the costs and benefits of meeting the revised and alternativ e standards, in addition to the application of known controls EPA assume s the application of

un identified future controls that make possible the additional emission reductions needed for attainment in 2020. By definition, no cost data currently exist s for unidentified future technologies or innovative strategies. EPA use d two methodologies for estimating the costs of

un identified future controls: a fixed

- cost methodology and a hybrid methodology. Both approaches assume either that existing technologies can be applied in particular combinations or to specific sources that we currently can’t predict or that innovative strategies and new control options make possible the emissions reductions needed for attainment by 2020. However, the two approaches reflect different assumptions about technological progress and innovation in emissions reductions strategies.

7.2.3. 1 Fixed

- Cost Methodology

The fixed

- cost methodology uses a $15,000/ton estimate for each ton of PM 2.5 reduced ; the hybrid me thodology is similar to the hybrid methodology used for the 2008 Ozone NAAQS RIA cost analysis and is presented in more detail below

. The fixed

- cost methodology was preferred by EPA ’ s Science Advisory Board over two other options, including a marginal

- cost

based methodology

. When reviewing the Office of Air and Radiation’s Direct Cost Report and Uncertainty Analysis Plan, the Science Advisory Board noted:
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.
Note that the choice of $15,000/ton for the fixed

- cost methodology was based on the precedent set in the March 2011 final report The Benefits and Costs of the Clean Air Act from 1990 to 2020

. 11 , 12 We also chose $15,000/ton for the national, initial cost

- per

- ton for use in the

11 We considered adjusting the $15,000/ton value and reviewed data on inflation between 2006 and 2010. We found that during that period inflation was sufficiently low to not warrant a $/ton value adjustment ; a ny such adjustment would be considered well with in the bounds of uncertainty in this analysis. To assess the sensitivity of the results to the value of $15,000/ton, we also include sensitivity analyses at $10,000/ton and $20,000/ton in Appendix 7A. In addition, the extrapolated costs do not rely on sp ecific underlying data sources (e.g., Census data) that periodically change and that would require updating based on those changes. As such, we currently do not have either specific data or a particular rationale to change the $15,000/ton value.
12 T he fin al report The Benefits and Costs of the Clean Air Act from 1990 to 2020 includes the following discussion for the rationale for the $15,000 per ton threshold . C ontrols that are more costly than $15,000 per ton may not be cost effective , and local air quality agencies would likely seek reductions from other unidentified control measures. This is consistent with the practice of the South Coast Air Quality Management District , which7-8 hybrid methodology to facilitate direct comparison with the fixed

- cost methodology. 13 In addition, we do not have reason to conclude that the initial cost

- per

- ton used in the hybrid methodology should be differ ent than th e value used in the fixed

- cost methodology

. In the proposal RIA, we requested comments or suggestions on methodologies for estimating the costs of unspecified future controls to provide illustrative estimates of NAAQS costs. We did not receive a ny direct comments on methodologies, but we did receive comments from the San Joaquin Valley Air Pollution Control District (SJV APCD) on the magnitude of their potential investments needed to meet the revised annual standard of 12 μg/m 3 relative to our total cost estimates. The SJV APCD commented that expenditures in their jurisdiction alone could likely be more than our total cost estimate. Their comment provides additional context for the need to improve existing or identify new methodologies for estimating the costs of unspecified future controls

.
7.2.3. 2 Hybrid Cost Methodology

The hybrid methodology generate s a total annual cost curve for PM 2.5 for unknown future controls that might be applied in order to move toward 2020 attainment. The hybrid methodology has the advantage of incorporat ing information about how significant the needed reductions from unspecified control technologies are relative to the known control measures and matching that i nformation with expected increasing per -t on cost for applying unknown controls

. 14 For PM 2.5 reductions needed in each area, the cost begins with a national constant
cost

- per

- ton for PM 2.5 and increases as emissions reduction for PM 2.5 are needed, reflect in g the expectation that average per

- ton control costs are likely to be higher in areas needing a higher ratio of emission reductions from unknown to known controls. For example, to attain a revised annual standard of 12 μg/m 3 , all of the needed emissions reductions for Los Angeles County were from known controls at an average cost of $6,000 per ton; whereas for Riverside County approximately 95 percent of the needed emissions reductions were from unknown future controls at an average cost of $290,000 per t on. For other geographical areas, the average cost

attempts to identify viable alternatives for any control requir ements with an estimated cost exceeding $16,500 per ton. When costs are above this threshold, the South Coast Air Quality Management District conducts more detailed cost

- effectiveness and economic impact analyses of the controls. 13 For the known controls , for all of the geographic areas likely to exceed the revised and/or alternative standards we include controls at an annual cost of $20,000 per ton or less. To estimate the costs associated with unidentified future controls, or unknown controls , we employ a fixed

- cost and hybrid methodology. The fixed

cost methodology employs a primary cost estimate of $15,000/ton (2010 dollars), and the hybrid methodology employs an initial, annual cost

- per

- ton estimate of $15,000/ton (2010 dollars). We explain the choice s of these parameters in th is Section and Section 7.2.2. 14 In applying the hybrid methodology, EPA reviewed the data to ensure that the estimated, additional emissions reductions selected for each geographic area were not greater than the remaining uncontr olled emissions in that geographic area. The highest percent selected was 90%.7-9 per ton for unknown controls ranged from $19,000 to $28,000 per ton. The incremental improvement in air quality for an unknown control is determined using an area

- by

- area ratio of air quality improvement to air quality change , which is discussed in more detail in Chapter 3.

EPA developed a model of increasing total annualized costs for controlling PM 2.5 emissions

. The simplest form of ax 2 + bx + c was used where x is the tons of a particular pollutant to b e reduced in a particular area and a, b , and c are constants. For the hybrid methodology b is set to be a national, initial cost

- per

- ton (N) for unknown controls for PM 2.5 , and c is set to zero because there is no cost to imposing no control. The hybrid me thodology has a different a for PM 2.5 for e ach geographic area. For a particular geographic area a is N/E where

N = national, initial annualized cost/ton (b from above) of $15,000 per ton.

E = by geographic area , is the denominator and represents all particulate emission reductions achieved (from applying known and unknown controls to obtain the 15/35 baseline, as well as known controls to achieve the alternat iv e standard) prior to estimating needed emission reductions from unknown cont rols to achieve the alternat iv e standard.

U = unknown emissions reductions by geographic area and standard

.

T = cost by geographic area and standard , or N E U 2 + NU (i.e., ax 2 + bx )

.
An example of the hybrid methodology is provided below. In this example, in Area B the percentage of total PM 2.5 reductions needed from unknown controls relative to total emissions reductions needed ( e.g., 100/150 , or 67% ) is larger than the percentage of total PM 2.5 reductions needed from unknown controls relative to total emi ssions reductions needed in Area A ( e.g., 100/200 , or 50% ). Because Area B needs a higher portion of emission s reductions from unknown controls, total cost using the hybrid methodology is higher in A rea B

. Th is illustration shows that t he relative costs of unknown controls reflect the expectation that
average per

- ton control costs for the same number of unknown tons are likely to be higher in Area B , which need s a higher ratio of emission reductions from unknown to known controls

.7- 10 7.2.3. 3 Fixed

- Cost and Hybrid Methodology Extrapolated Cost Estimates Extrapolated cost estimates are provided using a 7 % discount rate because known control measure information is available at 7 % for all measures applied in this analysis. Table 7-3 provides the extrapolated cost estimates using both the fixed

- cost and hybrid methodolog ies described above

. The extrapolated cost estimate s range from $ 48 million (20 1 0$) to $340 million (2010$) for the revise d standard of 12 μg/m 3

. We includ ed sensitivity analyses using both the alte r native fixed cost

- per

- ton and the hybrid methodologies in Appendix 7.A

. Table 7- 3. Extrapolated Costs by Revised and Alternat iv e Standard Analyzed a,b (millions of 20 1 0$)

Revised and Alternat iv e Standard Region Extrapolated Cost

Fixed

- Cost Methodology Hybrid Methodology c

7% 7%

13 μg/m 3 East — —
West — — California $10 $100

Total $10 $100 �� $15, 000 100 tttt rrrrrrr � ∗ 100 tttt 2 � + ($15, 000 ∗ 100 tttt ) = $ 3 , 000 , 000

�� $15, 000 50 tttt rrrrrrr � ∗ 100 tttt 2 � + ($15, 000 ∗ 100 tttt ) = $ 4 , 5 00 , 000 Example of Applying Hybrid Methodology

PM 2.5 Emissions Reductions Achieved With Known Controls PM 2.5 Emissions Reductions Needed From Unknown Controls Area A 100 100 Area B 50 100

Area A

— Cost Using Hybrid Methodology

Average cost/ton = $30,000

Area B

— Cost Using Hybrid Methodology

Average cost/ton = $45,0007- 11 12 μg/m 3 East — —
West — — California $48 $340

Total $48 $340

11 μg/m 3 East $71 $650

West $1.3 $3.3

California $150 $940

Total $220 $1,600

a Estimates are rounded to two significant figures

. b Note that the estimates provided reflect incremental emissions reductions from an analytical baseline that gives

“credit” to the San Joaquin Valley and South Coast areas for emissions reductions expected to occur between 2020 and 2025 ( when those areas are expected to demonstrate attainment with the revised and/or alternative standards )

. c In applying the hybrid methodology, Plumas County, CA and Shoshone County, ID did not have any known PM

controls. We took the following approach t o estimate prior emissions reductions for these two counties for use in the hybrid methodology cost calculations : for the remain ing counties , by county we summed (i) emissions reductions from known controls and (ii) extrapolated emissions reductions to meet the 15/35 baseline and divided each county ’ s sum by that county ’ s b ase c ase PM emissions. We selected the overall minimum perc entage and for each of the two counties without any known PM controls, we multiplied that overall minimum percentage by the specific county ’ s b ase c ase PM emissions.

Of note is the geographic distribution of extrapolated costs. For the revised and alternativ e standards, the above costs indicate that control measures applied in California represent a significant portion of the extrapolated costs. Using the fixed

- cost methodology, f or the revised annual standard of 12 μg/m 3 and the alternat iv e annual standards of 13 μg/m 3 and
11 μg/m 3

, the California component of the extrapolated cost estimates represents 100 % , 100 % , and 67 % , respectively, of the nationwide extrapolated cost estimates. Using the hybrid methodology, for the revised annual standard of 12 μg/m 3 and the alternat iv e annual standards of 13 μg/m 3 and 1 1 μg/m 3

, the California component of the extrapolated cost estimates represents 100 % , 100 % , and 59 % , respectively, of the nationwide extrapolated cost estimates. Because no cost data exists for unknown future controls , it is unclear whether approaches using hypothetical cost curves will be more accurate or less accurate in forecasting total national costs of unknown controls than a fixed

- cost methodology that uses a range of national cost

per

- ton values.

E stimatin g engineering costs for emission reductions needed beyond those from known controls to reach attainment in 2020 is inherently a challenging exercise . As described later in this chapter, our experience with Clean Air Act implementation shows that technological7- 12 advances and development of innovative strategies can reduce emission s and reduce the costs of emerging technologies over time. Technological change may provide new possibilities for controlling emissio ns as well as reduc ing the cost and effectiveness of known controls through technological improvements or higher control efficiencies

.
7.2.3. 4 Interpreting Extrapolated Cost Estimates The two estimates do not represent lower and upper bound estimates, but simply represent estimates generated by two different methodologies. The fixed

- cost methodology assumes that technological change and innovation will result in the availability of add itional controls by 2020 that are similar in cost to the higher end of the cost range for current known controls. The hybrid methodology assumes that while additional controls may become available by 2020, they become available at an increasing cost and the increasing cost varies by geographic area and by degree of difficulty associated with obtaining the needed emissions reductions. Without an initial parameter estimate, i.e., $15,000/ton, we are not able to predict which methodology will generate a higher cost estimate; however, with the same initial parameter estimate of $15,000/ton, the hybrid methodology will generate a higher cost
estimate.

7.2.4 Total Cost Estimates In the supporting statement for the Information Collection Request Revision for
Part iculate Matter 2.5 Ambient Air Monitoring , 40 CFR P art 58 we estimate the incremental cost of relocating 21 existing near

- roadway monitors, and those costs are included in the total national cost estimates presented below. The amendments to the ambient ai r monitoring regulations will revise the network design requirements for PM 2.5 monitoring sites, resulting in moving 21 monitors to established near

- road monitoring stations by January 1, 2015. The incremental cost associated with moving these 21 monitors is a one

- time cost of $28,570. 15

Table s 7-4 and 7

- 5 present a summary of the total national cost s of attaining the revised annual standard of 12 μg/m 3 and the alternat iv e annual standards of 13 μg/m 3 and 11 μg/m 3 in 2020

. This summary includes the known and extrapolated costs

. As discussed in Section 7.2.2, we were not able to recalculate any known control costs using a 3 % discount rate. As such, both

15 EPA is not increas ing the size of the national PM 2.5 monitoring network; the A gency anticipates that states would be able to relocate existing monitors to meet the near

- road wa y requirement. For purposes of estimating cost, only 21 monitors will be moved by 2015. Data from these monitors, along with other monitors in the area, could be used to determine whether the area is meeting both the annual and 24

- hour standards. However , data from these monitors would not be available in time for use in making initial attainment and nonattainment designations.7- 13 known and e xtrapolated costs were calculated at a 7 % discount rate only. T he total cost estimates are $53 million (2010$) and $350 million (2010$) for the revised annual standard of
12 μg/m 3 ; $11 million and $100 million for the alternative annual standard of 13 μg/m 3 ; and $ 320 million and $1,700 million for the alternative annual standard of 11 μg/m 3

. To further evaluate potential costs ranges, w e included sensitivity analyses using both the alternative fixed
cost

- per

- ton and the hybrid methodologies in Appendix 7.A

. In addition, Appendix 7.A includes costs and information needed to calculate those costs, by county, to meet 12 μ g/m 3

.

F or the revised annual standard of 12 μg/m 3 , the total cost estimates are comprised of between 90 and 97 percent extrapolated cost estimates , and the estimated total cost using the hybrid methodology is roughly 6.5 times more than the estimated total cost using the fixed

- cost methodology. 16 Because the hybrid methodology reflects increasing marginal costs in areas needing a higher ratio of emission s reductions from unknown to known controls, it could be more representative of total costs. In an effort to consider the potential fitness of the

e xtrapolated cost estimates, we reviewed the South Coast Air Quality Management District’s (SCAQMD) 2012 Air Quality Management Plan (AQMP) 17
, and we located data on recent emission reduction credit (ERC) transaction s in both the SCAQMD and SJV APCD. While this information provides context for the extrapolated cost estimates, the current relationship between avai lable controls and costs to reduce emissions may or may not be applicable in 2020 because of changes in innovation and advances in technology

.

T he SCAQMD’s 2012 AQMP include s information on control measures to meet the current 24

- hour standard of 3 5. Th is list of control measures includes further PM 2.5 controls for under

- fired charbroilers at a cost per ton reduced of $15,000. This control cost matches the parameter used in the fixed

- cost methodology , as well as the initial value used for the hybrid methodology and is supportive of our selection of that value

. To provide context for the hybrid methodology’s increasing per -t on cost format we obtained the California Air Resources Board’s 2009 and 2010 Emission Reduction Offset Transaction Costs, Summ ary Report and r eviewed the PM 10 ERC prices in both the SCAQMD and the SJV APCD. To some degree, ERC transaction prices reflect a choice between installing a more stringent control or purchasing ERCs. Between 2009 and 2010 PM 10 ERC prices in S JV APCD ranged from $ 40 ,000 per ton per year (tpy) to $ 70,000/tpy
, and PM 10 ERC prices in the SC AQMD ranged from $575,000/tpy to more than $1.9 million/tpy. T hese prices reflect both

16 Note that the extrapolated cost estimates do not represent lower and upper bound estimates, but simply represent estimates ge nerated by the fixed

- cost and hybrid methodologies. 17 Available at http://www.aqmd.gov/aqmp/2012aqmp/draft/index.html

.7- 14 marginal costs that are higher than the fixed

- cost estimates and marginal costs that are not inconsistent with the higher cost estimates generated using the hybrid methodology.

Table 7 -4. Total Costs by Revised and Alternat iv e Standard Analyzed (million s of 20 1 0$) a , Fixed

- Cost Methodology b ,c

Revised and Alternative Standard Region Known Control Costs Unknown Control Costs

— Fixed

- Cost

Methodology Total Costs

13 μg/m 3 East — — —
West — — — California $0.63 $10 $11
Total $0.63 $10 $11

12 μg/m 3 East — — —
West — — — California $5.1 $48 $53
Total $5.1 $48 $53

11 μg/m 3 East $96 $71 $170

West $0.45 $1.3 $1.8

California $5.3 $150 $160

Total $100 $220 $320

a Estimates are rounded to two significant figures . A s such , numbers may not sum down columns.
b All control costs are presented at a 7 % discount rate only.

c Note that the estimates provided reflect incremental emissions reductions from an analytical baseline that gives

“credit” to the San Joaquin Valley and South Coast areas for emissions reductions expected to occur between 2020 and 2025 ( when those areas are expected to demonstrate attainment with the revised and/or alternative standards )

.7- 15 T able 7 -5 Total Costs by Revised and Alternat iv e Standard Analyzed (millions of 2010$) a , Hybrid Methodology b,c

Revised and Alternative Standard Region Known Control Costs Unknown Control Costs

— Hybrid Methodology Total Costs

13 μg/m 3 East — — —
West — — — California $0.63 $100 $100

Total $0.63 $100 $100

12 μg/m 3 East — — —
West — — — California $5.1 $340 $350

Total $5.1 $340 $350

11 μg/m 3 East $96 $650 $750

West $0.45 $3.3 $3.8

California $5.3 $940 $950

Total $100 $1,600 $1,700

a Estimates are rounded to two significant figures . A s such , numbers may not sum down columns.
b All control costs are presented at a 7 % discount rate only.

c Note that the estimates provided reflect incremental emissions reductions from an analytical baseline that gives

“credit” to the San Joaquin Valley and South Coast areas for emissions reductions expected to occur between 2020 and 2025 ( when those areas are expected to demonstrate attainment with the revised and/or alternative standards ).

7.3 Changes in Regulatory Cost Estimates over Time There are many examples in which t echnological innovation and “ learning by doing ” have made it possible to achieve greater emission reductions than had been feasible earlier, or have reduced the costs of emission s control in relation to original estimates. Studies have conclud ed that costs of some EPA programs have been less than originally estimated , due in part to EPA ’s inability to predict and account for future technological innovation in regulatory impact analyses. 18 Additionally, technological change will affect baseline conditions for our analysis. T echnical change may lead to potential improvements in the efficiency with which

18 Harrington et al. (2000 ) and previous studies cited by Harrington. Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “ On the Accuracy of Regulatory Cost Estimates. ” Journal of Policy Analysis and Management 19(2):297

- 322.7- 16 firms produce goods and services ; for example, firms may use less energy to produce the same quantities of output.

I ncreasing marginal abatement costs could pos sibly induce the type of innovation that would result in lower costs than estimated in this chapter. By 2020, b reakthrough technologies in control equipment could result in a downward shift in the marginal abatement cost curve for such equipment (Figure 7- 1) 19 as well as a decrease in its slope, reducing marginal costs per unit of abatement. In addition, elevated abatement costs may result in significant increases in the cost of production and would likely induce production efficiencies, in particular those related to energy inputs, which would lower emissions from the production side.

Figure 7- 1. Technological Innovation Reflected by Marginal Cost Shift

7.3.1 Examples of Technological Advances in Pollution Control

There are a num ber of examples of low

- emission s technologies and pollution control equipment developed and/or commercialized over the past 15 to 20 years, such as

 Selective catalytic reduction (SCR) and ultra

- low NO x burners for NO x emissions

 Scrubbers, which achieve 95% and potentially greater SO 2 control on boilers

 Sophisticated new valve seals and leak detection equipment for refineries and chemical plants

19 Figure 7-1 shows a linear marginal abatement cost curve. It is po ssible that the shape of the marginal abatement cost curve is non

- linear. MC 0 MC 1

SO 2 Reductions Cost/ Ton

Induced Technology Shift Slope = ? 0 Slope = ? 1 MC LONG7- 17  Low- or zero

- VOC paints, c onsumer products and cleaning processes

 Chlorofluorocarbon (CFC) free air conditioners, refrigerators, and solvents

 Water- and powder

- based coatings to replace petroleum

- based formulations

 Vehicles are much cleaner than believed possible in the late 1980s due to improvements in evaporative controls, catalyst design and fuel control systems for light

- duty vehicles; and treatment devices and retrofit technologies for heavy

- duty engines

 Idle

- reduction technologies for engines, including truck stop electrificat ion efforts

 Market penetration of gas

- electric hybrid vehicles, and clean fuels

 The development of retrofit technology to reduce emissions from in

- use vehicles and non

- road equipment

These technologies were not commercially available two decades ago, and s ome did not even exist. Yet today, all of these technologies are on the market, and many are widely employed. Several are key components of major pollution control programs.

“L earning by doing ” or “ learning curve impacts, ” a distinct concept from technolo gical innovation, ha s also made it possible to achieve greater emissions reductions than had been feasible earlier or ha s reduced the costs of emission s control compared 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. Impacts such as these would manifest themsel ves as a low er expected cost to operat e technologies in the future compared to what costs may have been.

The magnitude of learning curve impacts on pollution control costs has been estimated for a variety of sectors as part of the cost analyses done for the Direct Cost Estimates for the Clean Air Act S econd Section 812 Prospective Analysis. 20 In that report, l earning curve adjustments were included for those sectors and technologies for which learning curve data

w ere available

. A typical learning curve adjustment example is to reduce either capital or O&M costs by a certain percentage given a doubling of output from that sector or for that technology. In 1936, T.P. Wright was the first to characterize the relationship between increased productivity and cumulative production. He analyzed man

- hours required to

20 E.H. Pechan and Associates , Inc. and Industrial Economics, Incorporated. Direct Cost Estimates for the Clean Air Act Second Section 812 Prospecti ve Analysis: Final Report, prepared for U.S. EPA, Office of Air and Radiation, February 20 11

. Available at http://www.epa.gov/oar/sect812/feb11/costfullreport.pdf

.7- 18 assemble successive airplane bodies. He suggested the relationship is a log linear function, since he observed a constant linear reduction in man

- hours every time the total number of airplanes assembled was doubled. The relationship he devised between number assembled and assembly time is called Wright ’ s Equa tion (Gumerman and Marnay, 2004 ). 21 This equation, shown below, has been shown to be widely applicable in manufacturing:

Wright ’ s Equation: C N = C o * N b

, (7.2)

where:

N = cumulative production

C N = cost to produce Nth unit o f capacity

Co = cost to produce the first unit

b = learning parameter = ln (1

- LR)/ln(2), where

LR = learning by doing rate, or cost reduction per doubling of capacity or output.

The percentage adjustments to costs can range from 5 to 20 % , depending on the sector and technology. Learning curve adjustments were prepared in a memo by IEc supplied to U .S. EPA and applied for the mobile source sector (both onroad and nonroad) and for application of various EGU control technologies within the Draft Direct Cost Re port. 22 Advice received from the SAB Advisory Council on Clean Air Compliance Analysis in June 2007 indicated an interest in expanding the treatment of learning curves to those portions of the cost analysis for which no learning curve impact data are currently available. Examples of these sectors are non

- EGU point sources and area sources. The memo by IEc outlined various approaches by which learning curve impacts can be addressed for those sectors. The recommended learning curve impact adjustment for virtua lly every sector considered in the Draft Direct Cost Report is a 10% reduction in O&M costs for two doubling s of cumulative output, with proxies such as cumulative fuel sales or cumulative emission s reductions being used when output data was unavailable.

21 Gumerman, Etan an d Marnay, Chris. Learning and Cost Reductions for Generating Technologies in the National Energy Modeling System (NEMS), Ernest Orlando Lawrence Berkeley National Laboratory, University of California at Berkeley, Berke ley, CA. January 2004, LBNL

- 52559. 22 Industrial Economics, Inc . Proposed Approach for Expanding the Treatment of Learning Curve Impacts for the Second Section 812 Prospective Analysis: Memorandum, prepared for U.S. EP A, Office of Air and Radiation, August 13, 2007.7- 19 F or this RIA, we do not have the necessary data for cumulative output, fuel sales, or emission reductions for all sectors included in our analysis in order to properly generate control costs that reflect learning curve impacts. Clearly, the effect of includ ing these impacts would be to lower our estimates of costs for our control strategies in 2020 , but we are not able to include such an analysis in this RIA.

7.3.2 Influence on Regulatory Cost Estimates Studies indicate that it is not uncommon for pre

- regulatory cost estimates to be higher than later estimates, in part because of an inability to predict technological advances. Over longer time horizons, the opportunity for technical advances is greater.

7 .3.2.1 Multi

- Rule Study Harrington et al. of Resources for the Future (RFF) 23 conducted an analysis of the predicted and actual costs of 28 federal and state rules, including 21 issued by U.S. EPA and the Occupational Safety and Health Administration (OSHA) , and found a tendency for predicted costs to overstate actual implementation costs. Costs were considered accurate if they fell within the analysis error bounds or if they fe ll within 25 % (greater or less than) of the predicted amount. They found that predicted total costs were overestimated for 14 of the 28 rules, while total costs were underestimated for only three rules. Differences can result because of quantity differences (e.g., overestimate of pollution reductions) or differences in per

- unit costs ( e.g., cost per unit of pollution reduction). Per

- unit costs of regulations were overestimated in 14 cases, while they were underestimated in six cases. In the case of U.S. EPA rules, the A gency overestimated per

- unit costs for five regulations, underestimated them for four regulations (three of these were relatively small pesticide rules), and accurately estimated them for four

. Based on an examination of eight rules, “ for those rules that employed economic incentive mechanisms, overestimation of per

- unit c osts seems to be the norm, ” the study said. In addition, Harrington et al. also states that overestimation of total costs can be due to error in the quantity of emission s reductions achieved, which would also cause the benefits to be overestimated.
It should be noted that many (though not all) of the U.S. EPA rules examined by Harrington et al. had compliance dates of several years, which allowed a limited period for technical innovation. In contrast, the progress demonstration and compliance dates for a

23 Harrington, W., R.D. Morgenstern, and P. Nelson. 2000

. “ On the Accuracy of Regulatory Cost Estimates. ” Journal of Policy Analysis and Management 19(2):297

- 322.7- 20 attaining a NAAQS occur over a longer time horizon and could allow for possible technical innovation.

7.3.2.2 Acid Rain SO 2 Trading Program

Recent cost estimates of the Acid Rain SO 2 trading program by RFF and MIT have been as much as 83 % lower than originally projected by EPA (see Table 7 -6). 24 As noted in the RIA for the Clean Air Interstate Rule, the 1989 ex ante numbers associated with the Acid Rain Program were an overestimate in part because of the limitation of economic modeling to predict technological improvement of pollution controls and other compliance options , such as fuel switching. In part, t he fuel switching from high

- sulfur to low

- sulfur coal was spurred by a reduction in rail transportation costs due to deregulation of rail rates during the 1990s

. Harrington et al. report that scrubbing turned out to be more efficient (95% removal vs. 80

– 85% removal) and more reliable (95% vs. 85% reliability) t han expected, and that unanticipated opportunities arose to blend low - and high

- sulfur coal in older boilers up to a 40/60 mixture, compared with the 5/95 mixture originally estimated.

Table 7 -6. Phase 2 Cost Estimates

Phase 2 Cost Estimates

Ex ante estimates $2.7 to $6.2 billion a
Ex post estimates $1.0 to $1.4 billion

a 2010 Phase II cost estimate in 1995$.

7.3.2. 3 Chlorofluorocarbon Phase

- Out EPA used a combination of regulatory, market

- based (i.e., a cap

- and

- trade system among manufacturers), and voluntary approaches to phase out the most harmful ozone depleting substances. Th e phase out was done more efficiently than either EPA or industry originally anticipated. The phase out for Class I substances was implemented
4– 6 years faster, included 13 more chemicals, and cost 30 % less than was predicted at the time the 1990 Clean Air Act Amendments were enacted. 25

24 Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L. Palmer. 2000

. “ Sulfur Dioxide Control by Ele ctric Utilities: What Are the Gains from Trade? ” Journal of Political Economy 108(#6):1292

- 1326. Ellerman, Denny. January 2003 . Ex Post Evaluation of Tradable Permits: The U.S. SO2 Cap

- and

- Trade Program. Massachusetts In stitute of Technology Center for Energy and Environmental Policy Research. 25 Holmstead, Jeffre y, 2002 . “Testimony of Jeffrey Holmstead , Assistant Administrator, Office of Air and Radiation, U.S. Environmental Protection Agenc y, Before the Subcommittee on Energy and air Quality of the committee on Energy and Commerce, U.S. House of Representatives, May 1, 2002, p. 10.7- 21 The Harrington et al. study states, “ When the original cost analysis was performed for the CFC phase

- out it was not anticipated that the hy drofluorocarbon HFC

- 134a could be substituted for CFC

- 12 in refrigeration. ” However, as Hammit 26 notes “ since 1991 most new U.S. automobile air conditioners have contained HFC

- 134a (a compound for which no commercial production technology was available in 1986) instead of CFC

- 12 ” (p. 13). H ammit cites a similar story for HCFRC

- 141b and 142b, which are currently substituting for CFC

- 11 in important foam

blowing applications.

7.3.3 Influence of Regulation on Technological Change We cannot estimate the interplay between EPA regulation and technology improvement but have reason to believe it may be significant. There is emerging research on technology

forcing polices (i.e., where a regulator specifies a policy standard that cannot be met with existing technology or cannot be met with existing technology at an acceptable cost, and over time market demand will provide incentives for industry to develop the appropriate technology). This is illustrated by Gerard and Lave (2005 ). T hey demons trate through a careful review of policy history that the 1970 CAA legislated dramatic improvements in the reduction of emissions for 1975 and 1976 automobiles. Those mandated improvements went beyond the capabilities of existing technologies. But the regu latory pressure “ pulled ” forth or “ forced ” catalytic converting technology in 1975.
Popp (2003) and Keohane (2002) have both provided empirical evidence that Title IV led to induced technological change. Popp provides evidence that since Title IV there has been technological innovations that have improved the removal efficiency of scrubbers. Keohane provides ev idence that fossil

- fuel fired electric utilities that were subject to Title IV were, for a given increase in the cost of switching to low sulfur coal, more likely to install a scrubber.

7.4 Uncertainties and Limitations

EPA base d its estimates of emission s control costs on the best available information from available engineering studies of air pollution controls and developed a reliable modeling framework for analyzing the cost, emission s changes, and other impacts of regulatory controls. However, our cost analysis is subject to uncertainties and limitations, which we document on a qualitative basis in Table 7-7 below. For additional discussion of how we assess uncertainty, see Section 5.5.7.

26 Hammit, J.K. ( 2000 ). “ Are the costs of proposed environmental regulations overestima ted? Evidence from the CFC phase out. ” Environmental and Resource Economics , 16(#3): 281

- 302.7- 22 Table 7 -7. Summary of Qualitative Uncertainty for Modeling Elemen ts of PM Engineering Costs

Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized

Costs a Degree of Confidence in Our Analytical Approach b Ability to Assess Uncertainty c
Uncertainties Associated with Engineering Costs
Engineering Cost Estimates

▪ Capital recovery factor estimates (7% and 3%)

▪ Estimates of private compliance cost

▪ Increased advancement in control

technologies as well as reduction in costs over time

▪ Cost estimates for PM 10 Both Medium

- high Medium Tier 2

Unquantified Costs

▪ Costs of federal and state administration of SIP

program

▪ Transactional costs Low Medium Medium Tier 1

Extrapolated Costs Both High Low Tier 1

a Magnitude of Impact High

— If error could influence the total costs by more than 25% Medium

— If error could influence the total costs by 5%

– 25% Low

— If error could influence the total costs by less than 5%
b Degree of Confidence in Our Analytic Approach 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 Low

— Limited data exists to support the selected approach

c Ability to Assess Uncertainty (using WHO Uncertainty Framework) Tier 0

— Screening level, generic qualitative c haracterization Tier 1

— Scenario

- specific qualitative characterization Tier 2

— Scenario

- specific sensitivity analysis Tier 3

— Scenario

- specific probabilistic assessment of individual and combined uncertainty

7.5 References Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L. Palmer. 2000. “ Sulfur Dioxide Control by Electric Utilities: What Are the Gains from Trade? ” Journal of Political Economy 108(#6):1292

- 1326.
Ellerman, Denny. January 2003. Ex Post Evaluation of Tradable Permits: The U.S. SO2 Cap

- and

Trade Program. Massachusetts Institute of Technology Center for Energy and Environmental Policy Research.7- 23 Gerard, D. and L.B. Lave (2005). “ Implementing Technology

- Forcing Policies: The 1970 Clean Air Act Amendments and the Introduction of Advanced Automotive Emissions Controls in the United States, ” Technological Forecasting and Social Change, 72: 761

- 778.
Gumerman, Etan and Marnay, Chris. Learning and Cost Reductions for Generating Technologies in the National Energy Modeling System (NEMS), Ernest Orlando Lawrence Berkeley National Laboratory, University of California at Berkeley, Berkeley, CA. January 2004, LBNL

- 52559.
Hammit, J.K. (2000). “ Are the costs of proposed environmental regulations overestimated? Evidence from the CFC phase out. ” Environmental and Resource Economics , 16(#3): 281

- 302.
Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “ On the Accuracy of Regulatory Cost Estimates. ” Journal of Policy Analysis and Management 19(2):297

- 322.
Holmstead, Jeffrey, 2002. “ Testimony of J effrey Holmstead, Assistant Administrator, Office of Air and Radiation, U.S. Environmental Protection Agency, Before the Subcommittee on Energy and air Quality of the committee on Energy and Commerce, U.S. House of Representatives, May 1, 2002, p. 10.

ICF Consulting. 2005. The Clean Air Act Amendment: Spurring Innovation and Growth While Cleaning the Air. Washington, DC. Available at http://www.icfi.com/Markets/Environment/do c_files/caaa

- success.pdf

. Industrial Economics, Inc. Proposed Approach for Expanding the Treatment of Learning Curve Impacts for the Second Section 812 Prospective Analysis: Memorandum, prepared for U.S. EPA, Office of Air and Radiation, August 13, 2007.

K eohane, Nathaniel. 2002. “ Environmental Policy and the Choice of Abatement Technique ” Evidence from Coal

- Fired Power Plants. ” February. Yale School of Management.
Office of Management and Budget (OMB). 2003. Circular A

- 4. Available at http://www.whitehouse .gov/omb/circulars_a004_a

- 4/
Popp, David. 2003. “ Pollution Control Innovations and the Clean Air Act of 1990. ” Autumn. Journal of Policy Analysis and Management, 22(4), 641

- 60.
E.H. Pechan and Associates and Industrial Economics, Direct Cost Estimates for the Clean Air Act Second Section 812 Prospective Analysis: Draft Report, prepared for U.S. EPA, Office of Air and Radiation, February 2007. Available at http://www.epa.gov/oar/sect812/mar07/direct_cost_draft.pdf

. U.S. Environmental Protection Agency (U.S. EPA). 2002. EPA Air Pollution Control Cost Manual

— Sixth Edition (EPA 452/B

- 02

- 001) . Office of Air Quality Planning and Standards, Research Triangle Park, NC. Also Available at http://epa.gov/ttn/catc/dir1/c_allchs.pdf

.7- 24 U.S. Environmental Protection Agency (U.S. EPA). 2006. AirControlNET 4.1 Control Measures Documentation Report. Office of Air Quality Planning and Standards, Re search Triangle Park, NC. Also Available at http://www.epa.gov/ttnecas1/models/ DocumentationReport.pdf

. 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.
U.S. Environmental Protection Agency (U.S. EPA). 2010. Guidelines for Preparing Economic Analysis. Office of Policy, National Center for Environmental Economics. Also Available at http://yosemite.epa.gov/ee/epa /eed.nsf/Webpages/Guidelines.html/ $file/Guidelines.pdf

U.S. Environmental Pr otection Agency (U.S. EPA). 2011

. The Benefits and Costs of the Clean Air Act from 1990 to 2020. Final Report for the U.S. Environmental Protection Agency, Office of Air and Radi ation. Also available at http://www.epa.gov/air/sect812/feb11/fullreport.pdf7.A -1 APPENDIX 7. A DATA TO CALCULATE CO STS TO MEET 12 μ G/M 3 AND SENSITIVITY ANALYSES OF EXTRAPOLATED COST ES TIMATES

7.A.1 PM 2.5 Emission Reductions and Costs to Meet 12 μ g/m 3

Table7.A.1 below includes costs and information needed to calculate those costs , by county, to meet 12 μ g/m 3

. The Table includes the PM 2.5 emissions reductions needed to reach the 1 5/35 μ g/m 3 level because the hybrid methodology includes a parameter that uses the quantity of prior emissions reductions. 1

Table 7.A.1 PM 2.5 Emission Reductions and Costs to Meet 12 μ g/m 3

FIPS Code County 12 μ g/m 3

Known Controls Unknown Controls

PM 2.5 Emissions Reductions

t o Reach 15/35 μ g/m 3 Emissions Reductions Costs Emissions Reductions Hybrid Methodology
Costs
0 6037 Los Angeles

-- 743 $4.5

-- --
0 6065 Riverside

-- 53 $0.63 980 $290

0 6025 Imperial 404

-- -- 294 $7.6

0 6029 Kern 1,769

-- -- 418 $7.8

0 6107 Tulare 726

-- -- 635 $18

0 6047 Merced 76

-- -- 19 $0.36

0 6071 San Bernardino 988

-- -- 844 $23

1 EPA developed a model of increasing total annualized costs for controlling PM 2.5 emissions

-- ax 2 + bx + c where x is the tons of a particular pollutant to be reduced in a particular area and a, b , and c are constants. For a particular geographic area a is N/E where (i) N is a national, initial annualized cost/ton of $15,000 per ton and (ii) E is, by geographic area, the denominator and represents all particulate emission reductions achieved ( from applying known and unknown controls to obtain the 15/35 baseline, as well as known controls to achieve the alternative standard) prior to estimating needed emission reductions from unknown controls to achieve the alternative standard

.7.A -2 7.A.2 S ensitivity Analyses of Extrapolated Cost Estimate s
Because of the uncertainties associated with estimating costs for the PM 2.5 NAAQS and because a significant portion of the estimated emissions reductions and related costs for attaining the NAAQS come from unknown controls, it is important to test the sensitivity of the assumptions applied t o estimat e unknown controls. The sensitivity analyses below are included
to help characterize the uncertainty for the cost estimates from unknown controls and the responsiveness of the cost estimates to varying parameter estimates and assumptions. Note that the tables below include cost estimates associated with unknown controls and not total cost estimates.

While there are many approaches to sensitivity analys is, we selected analyses below, keeping emissions estimates constant, t o show variability in the cost estimates and remain consistent with the benefits analysis. Note that the extrapolated cost estimates are provided using a 7 percent discount rate because known control measure information is available at 7 percent for all measures applied in this analysis.

7.A. 2 .1 Sensitivity Analysis of Fixed

- Cost Methodology

Table 7.A. 2 below presents the sensitivity analysis of the fixed

- cost methodology and includes , by region and revised and alternativ e standard, the primary cost estimate of $15,000/ton . The Table also includes , by region and revised and alternativ e standard, cost estimates using $10,000/ton and $20,000/ton. For the revised standard of 12/35, the total cost estimate associated with unknown control costs r anges from $ 32 million to $ 64 million, de pending on the fixed

- cost

- per

- ton assumed

. Table 7.A. 2 Sensitivity Analysis of Fixed

- Cost Methodology for Unknown Controls by Revised and Alternat iv e Standard Analyzed (millions of 2010 $) a

Revised & Alternat iv e Standard Region Extrapolated Costs $10,000/ton $15,000/ton $20,000/ton

7% 7% 7%

13 μg/m 3 East — — — West — — — California $ 6.7 $ 10 $ 13
Total $ 6.7 $ 10 $ 13

12 μg/m 3 East — — — West — — —7.A -3 California $ 32 $ 48 $ 64
Total $ 32 $ 48 $ 64

11 μg/m 3 East $ 48 $ 71 $ 95

West $ 0.86 $1.3 $ 1.7
California $ 97 $ 150 $ 190
Total $ 150 $220 $290

a Estimates are rounded to two significant figures.

7.A. 2 .2 Sensitivity Analysis of Alternative Hybrid Methodology

Table 7.A. 3 below presents the sensitivity analysis of the alternative hybrid methodology

. To be consistent with the sensitivity analysis of the fixed

- cost methodology , t he Table also includes, by region and revised and alternativ e standard, cost estimates using alternate parameter estimates for the initial cost per ton

. For the revised standard of 12/35, the total cost estimate associated with unknown control costs ranges from $ 230 million to $ 460 million.

Table 7.A .3 Sensitivity Analysis of Hybrid Methodology for Unknown Controls by Revised and Alternativ e Standard Analyzed (millions of 20 1 0$) a

Revised & Alternat iv e Standard Region Extrapolated Costs $10,000/ton $15,000/ton $20,000/ton

7% 7% 7%

13 μg/m 3 East — — — West — — — California $69 $100 $140
Total $69 $100 $140

12 μg/m 3 East — — — West — — — California $230 $340 $460
Total $230 $340 $ 460

11 μg/m 3 East $430 $650 $870

West $2.2 $3.3 $4.4

California $630 $940 $1,300

Total $ 1,100 $ 1,600 $ 2,100

a Estimates are rounded to two significant figures.8-1 CHAPTER 8 COMPARISON OF BENEFI TS AND COSTS

8.1 Synopsis This chapter compares estimates of the benefits with costs and summarizes the net benefits of the revised annual standard of 12 μg/m 3 and the alternative annual standards of 13 μg/m 3 and 11 μg/m 3 relative to the analytical baseline that includes recently p romulgated national regulations and additional emissions reductions needed to attain the existing 15/35 μg/m 3 standards, as well as ad justments to NO x emissions in the San Joaquin and South Coast

areas

.
8.2 Comparison of Benefits and Costs The EPA’s illustrative analysis has estimated the health and welfare benefits and costs associated with the revised annual PM NAAQS. The results in Ta ble 8

- 1 for 2020 suggest there will be significant health and welfare benefits and these benefits will outweigh the costs associated with the illustrative control strategies in 2020. In the analysis, we estimate the net benefits of the revised annual PM 2.5 standard of 12 μg/m 3 and alternative annual standards of 13 μg/m 3 and 11 μg/m 3 , incremental to the 2020 analytical baseline. For the revised annual standard of 12 μg/m 3 , net benefits are estimated to be $ 3.7 billion to $ 9 billion at a 3% discount rate and $ 3. 3 billion to $ 8.1 billion at a 7% discount rate in 2020 (20 10 dollars). For an alternative annual standard of 13 μg/m 3 , net benefits are estimated to be $ 1.2 b illion to $2 .9 b illion at the 3% discount rate and $ 1.1 b il lion to $2 .6 b illion at the 7% discount rate. N et benefits of an alternative annual PM 2.5 standard of 11 μg/m 3 are estimated to be $ 11 billion to $2 9 billion at a 3% discount rate and $ 10 billion to $ 26 billion at a 7% discount rate in 2020.

For the revised annual s tandard of 12 μg/m 3 , the EPA estimates that the benefits of full attainment exceed the costs of full attainment by 11 to 15 4 times at a 7% discount rate. For the alternative annual standard of 13 μg/m 3 , the EPA estimates that the benefits of full attainment exceed the costs of full attainment by 11 to 24 6 times at a 7% discount rate. For the alternative annual standard s of 11 μg/m 3

, the EPA estimates that the benefits of full attainment exceed the costs of full attainment by 7 to 81 times at a 7% discount rate.8-2 Table 8

- 1. Total Monetized Benefits, Total Costs, and Net Benefits in 2020 (millions of 2010$)

— Full Attainment a

Revised Annual Standard Total Costs b Monetized Benefits d Net Benefits

3% Discount

Rate c 7% Discount Rate 3% Discount

Rate 7% Discount Rate 3% Discount Rate b 7% Discount Rate
12 $53 to $350 $53 to 350 $ 4,0 00 to $ 9,1 00 $3, 6 00 to $8, 2 00 $ 3,700 to $9 ,0 00 $3, 3 00 to $ 8,100

Alternative Standards

13 $ 11 to $100 $ 11 to $100 $ 1,300 to $2,900 $ 1,200 to $2,600 $ 1, 2 00 to $2, 9 00 $ 1,100 to $2, 6 00

11 $ 320 to $1,700 $ 320 to $1,700 $ 13 ,0 00 to $ 29,000 $ 12 , 00 0 to $26 ,000 $11,0 00 to $29 ,000 $10, 00 0 to $2 6 ,000

a These estimates reflect incremental emissions reductions from an analytical baseline that gives an “ adjustment ” to the San Joaquin and South Coast areas in California for NO x emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two ar eas will not be realized until 2025.
b The two cost estimates do not represent lower - and upper

- bound estimates but represent estimates generated by two different methodologies. The lower estimate is generated using the fixed

- cost methodology, which assumes that technological change and innovation will result in the availability of additional controls by 2020 that are similar in cost to the higher end of the cost range for current, known controls. The higher estimate is generated using the hybrid meth odology, which assumes that while additional controls may become available by 2020, they become available at an increasing cost and the increasing cost varies by geographic area and by degree of difficulty associated with obtaining the needed emissions red uctions.

c Due to data limitations, we were unable to discount compliance costs for all sectors at 3%. See Chapter 7, Section 7.2.2 for additional details on the data limitations. As a result, the net benefit calculations at 3% were computed by subtractin g the costs at 7% from the monetized benefits at 3%.
d The reduction in premature deaths each year accounts for over 90% of total monetized benefits. Mortality risk valuation assumes discounting over the SAB

- recommended 20

- year segmented lag structure. Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all unquantified benefits. Data limitations prevented us from quantifying these endpoints, and as such, these benefits are inherently more uncertain than t hose benefits that we were able to quantify. The range of benefits reflects the range of the central estimates from two mortality cohort studies (i.e., Krewski et al. [ 2009 ] to Lepeule et al. [ 2012 ] ).

Figure 8

- 1 demonstrates the size of the benefits relative to costs for the revised annual standards of 12 μg/m 3 at the 7% discount rate. This figure shows benefits for two different mortality studies and costs using two methods for extrapolating costs to emissions reductions associated with unknown controls.8-3 Figure 8

- 1. Monetized Benefit to Cost Comparison for the Revised Annual Standard of 12 μg/m 3 in 2020 (7% Discount Rate)

Note: Relative size of benefits and costs are to scale.
Figure 8

- 2 displays the range of net benefits for the selected standards using the two epidemiology functions and 12 expert elicitation functions for PM

- related premature mortality that the EPA employs in its analysis of benefits. As shown in the figure, the benefits exceed costs in every combination analyzed.8-4 Figure 8

- 2. Net Benefits for Revised Annual Standard of 12 μg/m 3 in 2020 at a 7% Discount Rate Due to data and methodology limitations, the EPA was unable to quantify some health benefits associated with exposure to PM 2.5 , as well as the additional co

- benefits from improvements in welfare effects associated with emission reductions to attain the primary standard, such as visibility. Tables 8

- 2 and 8

- 3 summarize the human health and welfare categories contained within the core benefits estimate as well as those categories that are unquantified in the core benefits estimate. Because the illustrative emission reduction strategy for the revised annual standard at 12 μg/m 3 consisted of only directly emitted PM 2.5 , these
ta bles are limited to only those categories associated with emission reductions of directly emitted PM 2.5 . 1 It is important to emphasize that the list of unquantified benefit categories is not exhaustive, nor is quantification of each effect complete.

1 For the unquantified benefits associated with emission reductions of NO x and SO 2 , please see Chapters 5 and 6. $0.0 $1.0 $2.0 $3.0 $4.0 $5.0 $6.0 $7.0

Krewski et al. Lepeule et al.Billions of 2010$

2 Cost estimates combined with total monetized benefits estimates derived from 2 epidemiology functions and 12 expert functions8-5 Tab le 8 -2. Human Health Effects of Ambient PM 2.5

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 PM 2.5 Adult premature mortality based on cohort study estimates and expert elicitation estimates (age >25 or age >30)   Section 5.6

Infant mortality (age <1)   Section 5.6
Non

- fatal heart attacks (age > 18)   Section 5.6 Hospital admissions

— respiratory (all ages)   Section 5.6 Hospital admissions

— cardiovascular (age >20)   Section 5.6

Emergency department visits for asthma (all ages)   Section 5.6

Acute bronchitis (age 8

– 12)   Section 5.6 Lower respiratory symptoms (age 7

– 14)   Section 5.6 Upper respiratory symptoms (asthmatics age 9– 11)   Section 5.6

Asthma exacerbation (asthmatics age 6

– 18)   Section 5.6 Lost work days (age 18

– 65)   Section 5.6
Minor restricted

- activity days (age 18

– 65)   Section 5.6
Chronic bronchitis (age >26)

— a

— a Section 5.6

Emergency department visits for cardiovascular effects (all ages) — a

— a Section 5.6

Strokes and cerebrovascular disease (age 50

– 79) — a

— a Section 5.6

Other cardiovascular effects (e.g., other ages) — — PM ISA b Other respiratory effects (e.g., pulmonary function, non

- asthma ER visits, non

bronchitis 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

a We quantify these benefits in a sensitivity analysis, but not in the core analysis.
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 th e strength of the association.8-6 Table 8 -3. Welfare Co

- Benefits of PM 2.5

Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information
Improved Environment
Reduced visibility impairment Visibility in Class I areas in SE, SW, and CA regions a a Section 6.3 , Appendix 6b Visibility in Class I areas in other regions — a Section 6.3 , Appendix 6b Visibility in 8 cities

— a Section 6.3 , Appendix 6b Visibility in other residential areas — a Section 6.3 , Appendix 6b

Reduced climate effects Climate impacts from PM — — Section 6.5, PM ISA b

Reduced effects on materials Household soiling — — Section 6.4, PM ISA b
Materials damage (e.g., corrosion, increased wear) — — Section 6.4, PM ISA c

Reduced effects from PM deposition (metals and organics) Effects on Individual organisms and ecosystems — — Section 6.6.1, PM ISA b

a We quantify these co

- benefits in a n illustrative analysis, but these results of that illustrative scenario are not an estimate of the co

- benefits for the revised primary standard

. b We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.

8. 3 Discussion and Conclusions An extensive body of scientific evidence documented in PM ISA indicates that PM 2.5 can penetrate deep into the lungs and cause serious health effects, including premature death and other non

- fatal illnesses (U.S. EPA, 2009). As described in the preamble to the rule , the revisions to the standards are bas ed on an integrative assessment of an extensive body of new scientific evidence (U.S. EPA, 2009 ). Health studies published since the PM ISA (e.g., Pope et al. [2009] ) confirm that recent levels of PM 2.5 have had a significant impact on public health. Based on the air quality analysis in this RIA, the EPA projects that nearly all counties with PM 2.5 monitors in the U nited States would meet an annual standard of 12 μg/m 3 without additional
F ederal, S tate, or local PM control programs. This demonstrates the substantial progress that the United States has made in reducing air pollution emissions over the last several decades.8-7 Regulations such as the EPA’s recent Mercury a nd Air Toxics Standards (MATS) and other
F ederal programs such as diesel standards will provide substantial improvements in regional concentrations of PM 2.5 . Our analysis shows a few areas would still need additional emissions reductions to address local sources of air pollution, including ports and uncontrolled industrial emissions. For this reason, we have designed the RIA analysis to focus on local controls in these few areas. We estimate that these additional local controls would yield benefits well in excess of costs.

The setting of a NAAQS d oes not compel specific pollution reductions and as such does not directly result in costs or benefits. For this reason, NAAQS RIAs are merely illustrative. The NAAQS RIAs illustrate the potential costs and benefits of additional steps States could take to attain a revised air quality standard nationwide beyond rules already on the books. We base our illustrative estimates on an array of emission control strategies for different sources. T he costs and benefits identified in this RIA will not be realized unt il specific controls are mandated by SIPs or other F ederal regulations. In short, NAAQS RIAs hypothesize, but do not prescribe, the control strategies that States may choose to enact when implementing a revised NAAQS.

It is important to emphasize that the EPA does not “double count” the costs or the benefits of our rules. E mission reductions achieved under rules that require specific actions from sources

— such as MATS

— are in the baseline of this NAAQS analysis, as are emission reductions needed to meet the current NAAQS. For this reason, the cost and benefits estimates provided in this RIA and all other NAAQS RIAs should not be added to the estimates for implementation rules.

In calculating the costs, the EPA assume d the application of a significant number of

un identified future controls that would make possible the additional emission s reductions needed for attainment in 2020. EPA used two methodologies

— the fixed

- cost and hybrid methodologies

— for estimating the costs of un identified future controls , and b oth approaches

assume either that existing technologies can be applied in particular combinations or to specific sources that we currently can’t predict or that innovative strategies and new control options make possible the emissions reductions needed for att ainment by 2020. Estimates generated by the two approaches do not represent lower - and upper

- bound estimates but simply represent estimates generated by two different methodologies. The fixed

- cost methodology implicitly assumes that technological change and innovation will result in the availability of additional controls by 2020 that are similar in cost to the higher end of the cost range for current controls. The hybrid methodology implicitly assumes that while additional controls become available by 2020 , they become available at an increasing cost and the increasing cost8-8 varies by geographic area and by degree of difficulty associated with obtaining the needed emissions reductions.

F or the revised annual standard of 12 μg/m 3 , the total cost estimates comprise between 90 and 97 % extrapolated cost estimates , and the estimated total cost using the hybrid methodology is roughly 6.5 times more than the estimated total cost using the fixed

- cost methodology. Because the hybrid methodology reflects increasing marginal costs in areas needing a higher ratio of emission s reductions from unknown to known controls, it could be more representative of total costs. In an effort to consider the potential fitness of the

e xtrapolated cost estimates, we reviewed the South Coast Air Quality Management District’s (SCAQMD) 2012 Air Quality Management Plan (AQMP) , and we located data on recent emission reduction credit (ERC) transaction s in both the SCAQMD and San Joaquin Valley Air Pollution Control District (SJV APCD). While this information provides context for the extrapolated cost estimates, the current relationship between available controls and costs to reduce emissions may or may not be applicable in 2020 because of changes in innovation and advances in technology.

The SCAQMD’s 2012 AQMP includes information on control measures to meet the current 24

- hour standard of 35 μg/m 3 , including further PM 2.5 controls for under

- fired charbroilers at a cost per ton reduced of $15,000. This control cost matches the parameter used
i n the fixed

- cost methodology, as well as the initial value used for the hybrid methodology and is supportive of our selection of that value. In addition, the California Air Resources Board’s 2009 and 2010 Emission Reduction Offset Transaction Costs, Summar y Report included PM 10 ERC prices in both the SCAQMD and the SJV APCD. To some degree, ERC transaction prices reflect a choice between installing a more stringent control and purchasing ERCs. Between 2009 and 2010 PM 10 ERC prices in SJV APCD ranged from $4 0,000 per ton per year (tpy) to $70,000/tpy, and PM 10 ERC prices in the SCAQMD ranged from $575,000/tpy to more than $1.9 million/tpy. These prices reflect both marginal costs that are higher than the fixed

- cost estimates and marginal costs that are not in consistent with the higher cost estimates generated using the hybrid methodology. For further discussion of the total cost estimates, refer to Section 7.2.4 in Chapter 7 of this RIA.

Furthermore, the monetized benefits estimates presented in this RIA are not intended to capture the full burden of PM to public health but rather represent the incremental benefits expected upon attaining the revised annual primary standard of 12 μg/m 3

. In comparison , modeling by Fann et al. (2012) estimated that 2005 levels of air pollution were responsible for between 130,000 and 320,000 PM 2.5 - related deaths, or between 6.1% and 15% of total deaths8-9 from all causes in the continental United States. The monetized benefits associated with attaining the proposed range of standards appear modest when viewed within the context of the potential overall public health burden of PM 2.5 and ozone air pollution estimated by Fann et al. (2012) , but this is primarily because regulations already on the books will make great strides toward reducing future levels of PM. One important distinction between the total public health burden estimated for 2005 air pollution levels and the estimated benefits in this RIA is that ambient levels of PM 2 .5 will have improved substantially by 2020 , due to major emissions reductions resulting from implementation of Federal regulations

. For example, we estimate that SO 2 emissions (an important PM 2.5 precursor) in the U nited States would fall from 14 million tons in 2005 to less than 5 million tons by 2020 (a reduction of 66%). For this reason, States will only need to achieve small air quality improvements to reach the proposed PM standards. As shown in the recent RIA for MATS (U.S. EPA, 2011b), implementing other Federal and State air quality actions will address a substantial fraction of the total public health burden of PM 2.5 and ozone air pollution.

The NAAQS are not set at levels that eliminate the risk of air pollution. Instead, t he 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 PM 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, 2010 c ). While benefits occurring below the standard are assumed to be more uncertain than those occurring above the standard, the EPA considers these to be legitimate components of the total benefits estimate. Alt hough there are greater uncertainties at lower PM 2.5 concentrations, there is no evidence of a population

- level threshold in PM 2.5 - related health effects in the epidemiology literature.

Lastly, the EPA recognizes that there are uncertainties in both the cost and benefit estimates provided in this RIA. The EPA was unable to monetize fully all of the benefits associat ed with reaching these standards in this RIA, including other health effects of PM, visibility effects, ecosystem effects, and climate effects. If the EPA were able to monetize all of the benefits, the benefits would exceed the estimated costs by an even g reater margin.

8. 4 References Abt Associates, Inc. 2012. BenMAP User’s Manual Appendices . Prepared for U.S. Environmental Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. September.8- 10 Fann, N., A. Lamson, K. Wesson, D. Risley, S.C. Anenberg, and B.J. Hubbell. 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.
Krewski, D., R.T. Burnett, M.S. Goldbert, K. Hoover, J. Siemiatycki, M. Jerrett, M. Abrahamowicz, and W.H. White. 200 9 . “Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air Pollution and Mortality.” Special Report to the Health Effects Institute. Cambridge M A. July.
Lepeule , J. , F . Laden , D . Dockery , and J . Schwartz. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended Follow

- Up of the Harvard Six Cities Study from 1974 to 2009.” Environ Health Perspect. In press. Available at: http://dx.doi.org/10.1289/ehp.1104660

. Pope, C .A. III, E

. Majid, and D. Dockery. 2009. “Fine Particle Air Pollution and Life Expectancy in the United States.” New England Journal of Medicine 360: 376

– 386.

U.S. Environ mental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards for Particulate Matter, Chapter 5 . Office of Air Quality Planning and Standards, Research Triangle Park, NC. October. Available at : http://ww w.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 Report) . EPA

- 600 -R- 08

- 139F. National Center for Environmental Assessment

— RTP Divisi on. December. Available at : http://cfpub.epa.gov/ncea/c fm/recordisplay.cfm?deid=216546

. U.S. Environmental Protection Agency (U.S. EPA). 2010

. Quantitative Health Risk Assessment for Particulate Matter

— Final Repor t . EPA

- 452/R

- 10

- 005. Office of Air Quality Planning and Standards, Research Triangle Park, NC. September. Available on the Internet at <http://www.epa.gov/ttnnaaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2011a. Regulatory Impact Analysis for th e 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 Protectio n 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). 201 1c. 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/o ar/sect812/feb11/fullreport.pdf

.8- 11 U.S. Environmental Protection Ag ency (U.S. EPA). 2011d. Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards . EPA

- 452/D

- 11

- 003. April. Available at: http://www.epa.gov/ ttnnaaqs/standards/pm/s_pm_2007_pa.html

.9-1 CHAPTER 9 STATUTORY AND EXECUT IVE ORDER REVIEWS

9 .1 Synopsis This chapter summarizes the Statutory and Executive Order (EO) impact analyses relevant for the PM NAAQS Regulatory Impact Analysis. For each EO and Statutory requirement we describe both the requirements and the way in which our analysis addresses these requirements.

9 .2 Executive Order 12866: Regulatory Planning and Review Under section 3(f)(1) of Executive Order 12866 (58 FR 51735, O ctober 4, 1993), this action is an "economically significant regulatory action” because it is likely to have an annual effect on the economy of $100 million or more. The $100 million threshold can be triggered by either costs or benefits, or a combination of them. Accordingly, the EPA submitted this action to the Office of Management and Budget (OMB) for review under Executive Orders 12866 and 13563 (76 FR 3821, January 21, 2011), and any changes made in response to OMB recommendations have been documented in the docket for this action.

9 .3 Paperwork Reduction Act This action does not impose an information collection burden under the provisions of the Paperwork Reduction Act, 44 U.S.S. 3501 et seq. Burden is defined at 5 CFR 1320.3(b). There are no information collection requirements directly associated with revisions to a NAAQS under section 109 of the CAA.

9 .4 Regulatory Flexibility Act The Regulatory Flexibility Act (RFA) generally requires an agency to prepare a regulatory flexibility analysis of any rule subject to notice and comment rulemaking requirements under the Administrative Procedure Act or any other statute unless the agency certifies that the rule will not have a significant economic impact on a substantial number of small entities. Small entiti es include small businesses, small organizations, and small governmental jurisdictions.

For purposes of assessing the impacts of this rule on small entities, small entity is defined as: (1) a small business that is a small industrial entity as defined by the Small Business Administration’s (SBA) regulations at 13 CFR 121.201; (2) a small governmental jurisdiction that is a government of a city, county, town, school district or special district with a population of less than 50,000; and (3) a small organizat ion that is any not

- for

- profit enterprise which is independently owned and operated and is not dominant in its field.9-2 After considering the economic impacts of this final rule on small entities, I certify that this action will not have a significant econom ic impact on a substantial number of small entities. This final rule will not impose any requirements on small entities. Rather, this rule establishes national standards for allowable concentrations of particulate matter in ambient air as required by secti on 109 of the CAA. See also American Trucking Associations v. EPA. 175 F.3d at 1044

- 45 (NAAQS do not have significant impacts upon small entities because NAAQS themselves impose no regulations upon small entities).

9 .5 Unfunded Mandates Reform Act

This ac tion contains no Federal mandates under the provisions of Title II of the Unfunded Mandates Reform Act of 1995 (UMRA), 2 U.S.C. 1531

- 1538 for state, local, or tribal governments or the private sector. The action imposes no enforceable duty on any state, lo cal or tribal governments or the private sector. Therefore, this action is not subject to the requirements of sections 202 or 205 of the UMRA.

This action is also not subject to the requirements section 205 of the UMRA because it contains no regulatory requirements that might significantly or uniquely affect small governments. This action imposes no new expenditure or enforceable duty on any state, local, or tribal governments or the private sector, and the EPA has determined that this rule contains no regulatory requirements that might significantly or uniquely affect small governments.

Furthermore, in setting a NAAQS, the EPA cannot consider the economic or technological feasibility of attaining ambient air quality standards although such factors may be

c onsidered 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 Regulatory Impact Analysis pursuant to the Unfunded Mandates Reform Act would not furnish any information which the court could consider in reviewing the NAAQS). The EPA acknowledges, however, that any corresponding revisions to associated SIP requirements and air quality surveillance requirements, 40 CFR part 51 and 40 CFR part 58, respectively, might result in such effects. Accordingly, the EPA will address, as appropriate, unfunded mandates if and when it proposes any revisions to 40 CFR parts 51 or 58.

9 .6 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, as9-3 specified in Executive Order 13132. The rule does not alter the relationship between the Federal government and the states regarding the establishment and implementation of air qualit y improvement programs as codified in the CAA. Under section 109 of the CAA, the EPA is mandated to establish and review NAAQS; however, CAA section 116 preserves the rights of states to establish more stringent requirements if deemed necessary by a state. Furthermore, this final rule does not impact CAA section 107 which establishes that the states have primary responsibility for implementation of the NAAQS. Finally, as noted in section D (above) on UMRA, this rule does not impose significant costs on stat e, local, or Tribal governments or the private sector. Thus, Executive Order 13132 does not apply to this action.
However, as also noted in section D (above) on UMRA, the EPA recognizes that states will have a substantial interest in this rule and any corresponding revisions to associated air quality surveillance requirements, 40 CFR part 58.

9 .7 Executive Order 13175: Consultation and Coordination with Indian Tribal Governments Executive Order 13175, entitled ‘‘Consultation and Coordination with Indian Tribal Governments’’ (65 FR 67249, November 9, 2000), requires the EPA to develop an accountable process to ensure ‘‘meaningful and timely input by tribal officials in the developm ent of regulatory policies that have tribal implications.” This rule concerns the establishment of national standards to address the health and welfare effects of particulate matter. Historically, the EPA’s definition of “tribal implications” has been lim ited to situations in which it can be shown that a rule has impacts on the tribes’ ability to govern or implications for tribal sovereignty. Based on this historic definition, this action does not have Tribal implications, as specified in Executive Order 13175 (65 FR 67249, November 9, 2000), i.e. because it does not have a substantial direct effect on one or more Indian tribes, since tribes are not obligated to adopt or implement any NAAQS. Nevertheless, we were aware that many tribes would be interest in this rule and we undertook a number of outreach activities to inform tribes about the PM NAAQS review and offered to two consultations with tribes.

Although Executive Order 13175 does not apply to this rule, the EPA undertook a consultation process including: prior to proposal on March 29,2012 we sent letters to tribal leadership inviting consultation on the rule and then sent a second round of letters offering consultation after the proposal was issued on June 29, 2012. We conducted outreach and informa tion calls to tribal environmental staff on May 9, 2012; June 15, 2012; and August 1, 2012. We also participated on the National Tribal Air Association call on June 28, 2012.9-4 As a result we received comments from the National Tribal Air Association, the So uthern Ute Mountain Ute Tribe, and the Navajo Nation EPA.

9 .8 Executive Order 13045: Protection of Children from Environmental Health and Safety Risks This action is subject to Executive Order 13045 (62 FR 19885, April 23, 1997) because it is an economica lly significant regulatory action as defined by Executive Order 12866, and the EPA believes that the environmental health or safety risk addressed by this action may have a disproportionate effect on children. Accordingly, we have evaluated the environment al health or safety effects of PM exposures on children. The protection offered by these standards may be especially important for children because childhood represents a lifestage associated with increased susceptibility to PM

- related health effects. Because children have been identified as
an at

- risk population, we have carefully evaluated the environmental health effects of exposure to PM pollution among children. Discussions of the results of the evaluation of the scientific evidence and policy considerations pertaining to children are contained in sections III.B, III.D, III.E, IV.B, and IV.C of the rule’s preamble

.
9 .9 Executive Order 13211: Actions that Significantly Affect Energy Supply, Distribution or Use This action is not a “significant energy act ion” as defined in Executive Order 13211, (66 FR 28355, May 22, 2001) because it is not likely to have a significant adverse effect on the supply, distribution, or use of energy. The purpose of this action concerns the review of the NAAQS for PM. The actio n does not prescribe specific pollution control strategies by which these ambient standards will be met. Such strategies are 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.

9 .10 National Technology Transfer and Advancement Act Section 12(d) of the National Technology Transfer and Advancement Act of 1995 (NTTAA), Public Law 104

– 113, section 12(d) (15 U.S.C. 272 note) di rects the EPA to use voluntary consensus standards in its regulatory activities unless to do so would be inconsistent with applicable law or otherwise impractical. Voluntary consensus standards are technical standards (e.g., materials specifications, test methods, sampling procedures, and business practices) that are developed or adopted by voluntary consensus standards bodies. The NTTAA directs the EPA to provide Congress, through OMB, explanations when the Agency decides not to use available and applicabl e voluntary consensus standards.9-5 This final rulemaking involves technical standards for environmental monitoring and measurement. Specifically, the EPA proposes to retain the indicators for fine (PM2.5) and coarse (PM10) particles. The indicator for fine p articles is measured using the Reference Method for the Determination of Fine Particulate Matter as PM2.5 in the Atmosphere (appendix L to 40 CFR part 50), which is known as the PM2.5 FRM, and the indicator for coarse particles is measured using the Refere nce Method for the Determination of Particulate Matter as PM10 in the Atmosphere (appendix J to 40 CFR part 50), which is known as the PM10 FRM.

To the extent feasible, the EPA employs a Performance

- Based Measurement System (PBMS), which does not require the use of specific, prescribed analytic methods. The PBMS is defined as a set of processes wherein the data quality needs, mandates or limitations of a program or project are specified, and serve as criteria for selecting appropriate methods to meet those needs in a cost

- effective manner. It 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. Though the FRM defines the particular spec ifications for ambient monitors, there is some variability with regard to how monitors measure PM, depending on the type and size of PM and environmental conditions. Therefore, it is not practically possible to fully define the FRM in performance terms to account for this variability. Nevertheless, our approach in the past has resulted in multiple brands of monitors being approved as FRM for PM, and we expect this to continue. Also, the FRMs described in 40 CFR part 50 and the equivalency criteria described in 40 CFR part 53, constitute a performance

based measurement system for PM, since methods that meet the field testing and performance criteria can be approved as FEMs. Since finalized in 2006 (71 FR, 61236, October 17, 2006) the new field and performance criteria for approval of PM2.5 continuous FEMs has resulted in the approval of six approved FEMs. In summary, for measurement of PM2.5 and PM10, the EPA relies on both FRMs and FEMs, with FEMs relying on a PBMS approach for their approval. The EPA is not precluding the use of any other method, whether it constitutes a voluntary consensus standard or not, as long as it meets the specified performance criteria.

9 .11 Executive Order 12898: Federal Actions to Address Environmental Justice in Minority Populati ons and Low

- Income Populations Executive Order 12898 (59 FR 7629, February 16, 1994) establishes federal executive policy on environmental justice. Its main provision directs federal agencies, to the greatest extent practicable and permitted by law, to make environmental justice part of their mission by identifying and addressing, as appropriate, disproportionately high and adverse human health9-6 or environmental effects of their programs, policies, and activities on minority populations and low

- income popula tions in the United States.

The EPA maintains an ongoing commitment to ensure environmental justice for all people, regardless of race, color, national origin, or income. Ensuring environmental justice means not only protecting human health and the environment for everyone, but also ensuring that all people are treated fairly and are given the opportunity to participate meaningfully in the development, implementation, and enforcement of environmental laws, regulations, and policies.

The EPA has identified potential disproportionately high and adverse effects on minority and/or low

- income populations related to PM2.5 exposures. In addition, the EPA has identified persons from lower socioeconomic strata as an at

- risk population for PM

- related health effects. As a result, the EPA has carefully evaluated the potential impacts on low

- income and minority populations as discussed in section III.E.3.a of the rule’s preamble. Based on this evaluation and consideration of public comments on the proposal, the EPA is e liminating the spatial averaging provisions as part of the form of the annual standard to avoid potential disproportionate impacts on at

- risk populations. The Agency expects this final rule will lead to the establishment of uniform NAAQS for PM. The Integr ated Science Assessment and Policy Assessment contain the evaluation of the scientific evidence and policy considerations that pertain to these populations. These documents are available as described in the Supplementary Information section of the rule’s p reamble and copies of all documents have been placed in the public docket for this action.10 -1 CHAPTER 10 QUALITATIVE DISCUSSI ON OF EMPLOYMENT IMP ACTS OF AIR QUALITY REGULATIONS

10 .1 Introduction Executive Order 13563 states that federal agencies should consider the effect of regulations on employment

. According to the Executive Order, “o ur 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 ” (E xecutive Order 13563, 2011). Although a stand

- alone analysis of employment impacts is not typically included in a standard cost

- benefit analysis, 1 employment impacts are currently of particular concern due to recent economic conditions reflectin g relatively high levels of unemployment

. This chapter provide s a context for consider ing the potential influence of environmental regulation on growth and job shifts in the U.S. economy. S ection 10 .2 addresses the particular influence of this proposed rule on employment. S ection 10 .3 presents a descriptive overview of the peer

- reviewed literature relevant to evaluati ng the effect of air quality regulation on employment

. Finally, in Section 10 .4, we offer several conclusions.

10 .2 Influence of NAAQS Control s on Employment

Peer

- reviewed econometric studies that estimate the impact of air quality regulation on net overall employment and within the regulated sector converge on the finding that any net employment effects, whether positive or negative, have been small

. This finding holds for even major nationwide environmental regulations. Therefore, given the overall small e ffect environmental regulations have been shown to have on net employment in the regulated
sectors , we do not expect them to have a significa nt impact on the overall economy.

Estimating specific employment impacts from a new NAAQS standard is particularly challenging for two reasons. First, the NAAQS target s a level of public health protection that individual areas have flexibility to meet in a variety of ways, and the primary regulatory activity and implementation occur at the state or local level. Under th ese circumstances, states and localities are given considerable flexibility in choosing which strategies to adopt to meet the NAAQS target. State and local officials can consider a variety of economic impacts including employment impacts of various control strategies , as well as other factors , when designing their state implementation plans (SIPs). This mak es it challenging to predict how spec ific sectors will be impacted and how those impacts vary across regions of the country

. Analyses in the RIA are based on a particular NAAQS compliance scenario that reflects assumptions about control

1 This is the case e xcept to the extent that labor costs are p art of total costs in a cost

- benefit analysis.10 -2 measures applied across all sectors and locations , speci fic control strategies adopted by the

s tates , and associated extrapolated costs. EPA believes this compliance scenario supports reasonably illustrative quantitative estimates of the potential overall economic effects of the revised NAAQS. However, EPA does not consider this illustrative, aggregate compliance scenario to be sufficiently certain and precise to support quantitative projections of outcomes in particular locations, sectors, or markets, including labor markets, in light of the scarcity of applica ble studies that can be used to generate such estimates. Therefore, this RIA does not include quantitative projections of aggregate shifts in employment.

Second, we anticipate that national employment levels will be changing during the period that the NAA QS is being implemented, a period that may be greater than 10 years for some areas, following designations of nonattainment. Although current unemployment rates remain high relative to historical averages large ly due to the sharp increase in unemployment
t hat began in early 2008 (U.S. Department of Labor, Bureau of Labor Statistics, 2012a) , current data suggest unemployment rates have been declining in recent months (U.S. Department of Labor, Bureau of Labor Statistics, 2012b)

. Policies to meet the NAAQS in all areas will not go into effect for several years. By this time , we anticipate the economy will have had a chance to recover toward higher employment levels that more closely approximate full employment. In addition, over a period of 10 years or longer, potentially significant changes in technology, growth and distribution of economic activities, and other key determinants of local and national labor market conditions further complicate projections of future employment and the potential incremental effec t of regulatory programs

. Although a quantitative assessment of employment consequences of today ’ s proposed revision to the national ambient PM standards remains beyond the reach of available data and modeling tools, EPA is in the process of supporting the development of tools and research that could assist in the future. In the interim, some insights on the potentially relevant consequences of revising ambient air pollution standards can be gained by considering currently available literature, including its limitations. In light of these challenges, S ection 10 .3 focuses on qualitative insights from currently available peer

- reviewed literature on the impact of air quality regulations in general.

10 .3 The Current State of Knowledge Based on the Peer

- Reviewed Literature

There is limited peer

- reviewed econometric literature estimating employment effects of environmental regulations. We present an overview here, highlighting studies with particular relevance for NAAQS

. Determining the direction of employment effects in the regulated10 -3 industries is challenging because of competing effects. Complying with the new or more stringent regulation requires additional inputs, including labor, and may alter the relative proportions of labor and capital used by regulated firm s in their production processes.

When the economy is at full employment, an environmental regulation is unlikely to have a considerable impact on net employment in the long run. Instead, labor would primarily be reallocated from one productive use to anot her (e.g., from producing electricity or steel to producing pollution abatement equipment ) . Theory support s the argument that, in the case of full employment, the net national employment effects from environmental regulation are likely to be small and tran sitory (e.g., as workers move from one job to another). There is reason to believe that when the economy is operating at less than full employment environmental regulation could result in a short

- run net increase in employment. 2 Several empirical studies

s uggest that net employment impacts may be positive but small even in the regulated sector. Taken together, the peer

- reviewed literature does not contain evidence that environmental regulation would have a notable impact on net employment across the whole e conomy.

This discussion focuses on both short - and long

- term employment impacts in the regulated industries , as well as on the environmental protection sector for construction of needed pollution control equipment prior to the compliance date of the regula tion. EPA is committed to using the best available science and the relevant theoretical and empirical literature in this assessment and is pursuing efforts to support new research in this field.

10 .3.1 Immediate and Short

- Run Employment Impacts

Environment al regulations are typically phased in to allow firms time to invest in the necessary technology and process changes to meet the new standards. Whatever effects a regulation will have on employment in the regulated sector will typically occur only after a regulation takes effect or in the long term, as new technologies are introduced. However, the environmental protection sector (pollution control equipment) often sees immediate employment effects. When a regulation is promulgated, the first response of industry is to order pollution control equipment and services to comply with the regulation when it becomes effective. This can produce a short

- term increase in labor demand for specialized workers within the environmental protection sector related to design, construction, installation , and operation of the new pollution control equipment required by the regulation (see Schmalansee and Stavins , 2011 ; Bezdek, Wendling, and Di p erna , 2008).

2 See Schmalansee and Stavin (2011).10 -4 As the NAAQS are implemented, it is possible that the regulated sector will experience short

- run changes in employment. Because it is the states ’ responsibility to design the ir SIPs over the next few years, we cannot assess the short

- term effects of those SIPs on the regulated sector with sufficient precision to quantify the resulting incremental effects on employment. However, as previously noted, even in a full employment case , there may be transitory effects as workers change jobs. Some workers may need to retrain or relocate in anticipation of the new requirements or requi re time to search for new jobs, while shortages in some sectors or regions could bid up wages to attract workers.

It is important to recognize that these adjustment costs can entail local labor disruptions , and , although the net change in the national work force might be small, gross reductions in employment can still have negative impacts on individuals and communities. The
peer

- reviewed literature that is currently available is focused on medium - and long

- term employment impacts and does not offer much ins ight into the short

- term balance between increased employment in the environmental protection sector and possible decreased employment in some regulated sectors.

10 .3.2 Long

- Term Employment Impacts on the Regulated Industry Determining the direction of net employment effects in regulated industries is challenging because of competing effects. Morgenstern, Pizer , and Shih (2002) discuss how environmental regulations can be understood as requiring regulated firms to add a new output (environmental quality) to their product mix. Although legally compelled to produce this new output , regulated firms have to finance this additional production input with the proceeds of sales of their other (market) products. The current literature on employment impacts of air
qu ality regulations can be disaggregated into two types of approaches or models: 1) structural and 2) reduced

- form models. Two papers that present a formal structural model of the underlying profit

- maximizing/ cost

- minimizing problem of the firm are Berman an d Bui (2001) and Morgenstern, Pizer , and Shih (2002). Berman and Bui (2001) developed an innovative approach to estimating the effect of environmental regulations designed to meet a NAAQS (e.g. , ozone and NO X ) requirement in California on employment. Berman and Bui ’ s model allows environmental regulation to operate via two separate mechanisms: 1) the output elasticity of labor demand and 2) the effect of pollution abatement activities on demand for variable factors, combined with the marginal rates of technical substitution between abatement activity and variable factors, including labor. Berman and Bui s how how Neoclassical economic theory predicts that the output effect is, in most cases, negative, while the direction of the second, composite effect is indeterminate , making the overall net effect ambiguous

.10 -5 Morgenstern, Pizer , and Shih (2002) developed a similar structural model to Berman and Bui ’ s (2001) model

. Their model focuses on three mechanisms whereby environmental regulation may impact employ ment in regulated industries. The first mechanism is t he demand, or output, effect, where new compliance costs increase the cost of production , raising prices and thereby reducing consumer demand, which, in turn, reduces labor demand. The second mechanism is t he cost effect , which increases the demand for inputs , including labor, because more inputs are now required to produce the same amount of output. Finally, the factor

- shift effect notes how regulated firms ’ production technologies may be more or less l abor intensive after complying with the regulation (i.e., more/less labor is required relative to capital per dollar of output), implying an ambiguous overall net effect on labor demand. Conceptually, this theoretical approach, which is very similar to Berman and Bui ’ s approach , could be applied to NAAQS. However , Morgenstern et al. ’s empirical approach uses pollution abatement expenditures for only four highly polluting/regulated sectors (pulp and paper, plastics, steel, and petroleum refining) to estim ate effects on net employment ; therefore , their empirical results are not directly applicable to the full range of manufacturing and non manufacturing industries affected by NAAQS. Regardless, their work represents one of the most rigorous attempts to quantify the net employment impacts of regulation on the regulated sector. Morgenstern et al. conclude from their empirical results that increased pollution abatement expenditures generally have not caused a significant change in net employment in those four

se ctors. More specifically, their results suggest that, on average across the industries studied, each additional $1 million ($1987) spent on pollution abatement result ed in a (statistically insignificant) net increase of 1.5 jobs.

Berman and Bui (2001) use their model to empirically examine how an increase in local air quality regulation that reduces NO X emissions as a precursor to ozone and PM 10 affects manufacturing employment in the South Coast Air Quality Management District (SCAQMD), which incorporates Los Angeles and its suburbs. During the time frame of their study, 1979 to 1992, the SCAQMD enacted some of the country ’ s most stringent air quality regulations. Using SCAQMD ’ s local air quality regulations, which are more stringent than federal and state regulations, Berman and Bui identify the effect of environmental regulations on net employment in the regulated sectors. 3 They compare changes in employment in affected plants to those in other plants in the same industries but in regions not subject to the local regulations. The authors find that “ while regulations do impose large costs, they have a limited effect on employment ”— even when exit and dissuaded entry effects are considered ( Berman

3 Note , like Morgenstern, Pizer , and Shih (2002), this study does not estimate the number of jobs created in the environmental protection sector.10 -6 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 [emphasis added], even when exit and dissuaded entry effects are included ” ( Berman and Bui , 2001, p. 269)

. In their view, the limited effects likely arose because 1) the regulations applied disproportionately to capital

- intensive plants with relatively little employment , 2) the plants sold to local markets where competitors were subject to the same regulations (so that sales were relatively unaffected) , and 3) abatement inputs served as complements to employment. Although Berman and Bui focus on more sectors than Morgenstern et al. and focus specifically on air regulations, the study onl y examined impacts in Southern California and impacts may differ in other non attainment areas.

Other studies , including Henderson (1996), Becker and Henderson (2000), Greenstone (2002), and List et al

. (2003) , have taken a reduced

- form approach to ask a re lated but quite different question regarding the impact of environmental regulation on economic activity. All of these studies examined the effect of attainment status, with respect to NAAQS, on various forms of economic activity (e.g. , employment growth, plant openings and closings, investment). Polluting plants already located in and new polluting plants wanting to open in non attainment counties (counties not in compliance with one or more NAAQSs) are likely to face more stringent air pollution regulation s to help bring them into compliance. Thus, the stringency in environmental regulations may vary spatially , which may affect the spatial distribution of economic activity but not necessarily the overall level of economic activity. These studies find limite d evidence that employment grows more slowly, investment is lower, or fewer new polluting plants open in non attainment areas relative to attainment areas. However, this evidence does not mean that there is less aggregate economic activity as a result of

en vironmental regulation nor does it provide evidence regarding absolute growth rates; it simply suggests that the relative growth rate of some sectors may differ between attainment and non attainment areas. The approach used in all of these other studies is not capable of estimating net employment effects as would be necessary for a national rulemaking , only certain aspects of gross labor flows in selected areas.

10 .4 Conclusion The long

- term effects of a regulation on the environmental protection sector (which provides goods and services to the regulated sector) are difficult to assess. Employment in the industry supplying pollution control equipment is likely to increase with the increased demand from the regulated industry for the equipment. 4 According to U .S. Department of Commerce

4 See Bezdek , Wendling, and Diperna (2008), for example, and U.S. Department of Commerce (2010).10 -7 (2010) data , by 2008, there were 119,000 environmental technolog y (ET) firms generating approximately $300 billion in revenues domestically (2% of national gross domestic product ), producing $43.8 billion in exports (2% of t otal exports), and supporting nearly 1.7 million jobs (0.93% of total jobs). Air pollution control accounted for 18% of the domestic ET market and 16% of exports. Small and medium

- size companies represent 99% of private ET firms, producing 20% of total rev enue. The remaining 1% of companies are large companies supplying 49% of ET

revenue (OEEI, 2010)

. 5

As described above, d eriving estimates of how regulations will impact economy

- wide net employment is a difficult task, especially in the case of setting a ne w NAAQS, given that economic theory predicts that t he net effect of an environmental regulation on regulated sectors and the overall economy is indeterminate (not necessarily positive or negative). Peer

reviewed econometric studies that use a structural ap proach, applicable to overall net effects in the regulated sectors, converge on the finding that any net employment effects of environmental regulation in general, whether positive or negative, have been small and have not affected employment in the nation al economy in a significant way.

10 .5 References Becker, R. and V. Henderson (2000). “ Effects of Air Quality Regulations on Polluting Industries. ” Journal of Political Economy 108 (2): 379

- 421.
Berman, E. and L. T. M. Bui (2001). “ Environmental Regulation a nd Labor Demand: Evidence from the South Coast Air Basin. ” Journal of Public Economics 79 (2): 265

- 295.
Bezdek, R. H., R. M. Wendling, and P. Diperna (2008). “ Environmental Protection, the Economy, and Jobs: National and Regional Analyses. ” Journal of Environmental Management 86 (1): 63

- 79.

Executive Order 13563 (January 21, 2011). “ Improving Regulation and Regulatory Review. Section 1. General Principles of Regulation .” Federal Register 76 (14): 3821

- 3823.
Greenstone, M. (2002). “ The Impacts of Environme ntal Regulations on Industrial Activity: Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufactures. ” J ournal of Political Economy 110 (6): 1175

- 1219.
Henderson, V. (1996). “ Effects of Air Quality Regulation. ” The American Econo mic Review 86 (4): 789

- 813.

5 To calculate the percentages, total national 2008 GDP ($14,369.1 billion), exports ( $ 1,842.68 billion), and employment (181.75 million employees) were obtained from Bureau of Economic Analysis, U.S. Census Bureau , and Woods & Poole, respectively.10 -8 List, J. A., D. L. Millimet , P

. G. Fredriksson , a nd W. W. McHone (2003). “ Effects of Environmental Regulations on Manufacturing Plant Births: Evidence from a Propensity Score Matching Estimator. ” The Review of Economics and Stati stics 85 (4): 944

- 952.
Morgenstern, R. D., W. A. Pizer, and J-S. Shih (2002). “ Jobs Versus the Environment: An Industry

- Level Perspective. ” Journal of Environmental Economics and Management 43 (3): 412

- 436.
Schmalansee, R. and R. Stavins (2011). “ A Guide to Economic and Policy Analysis for the Transport Rule. ” White Paper. Boston, MA: Exelon Corp.
U.S. Department of Commerce , International Trade Administration ( 2010 , April ). Environmental Industries Fact Sheet, using 2008 data from Environmental Business International, Inc. http://web.ita.doc.go v/ete/eteinfo.nsf/068f3801d047f26e85256883006ffa54/4878b7e2 fc08ac6d85256883006c452c?OpenDocument

. U.S. Department of Labor, Bureau of Labor Statistics ( 2012a ) . Databases, Tables & Calculators by Subject. http://data.bls.gov/timeseries/LNS14000000 . Accessed April 16, 2012.
U.S. Department of Labor, Bureau of Labor Statistics (2012b, April 6). “ The Employment Situation

— March 2012. ” News Release

. http://www.bls.gov/news.release/pdf/empsit.pdf . Accessed April 16, 2012.United States Environmental Protection Agency Office of Air Quality Planning and Standards Health and Environmental Impacts Division Research Triangle Park, NC Publication No. EPA

- 452/R

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