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-12-005 

December 2012 

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 27711
iii

CONTENTS

Section Page

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

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

ES.2 Existing and Revised 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............................................................... 1-5
iv

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 PM2.5...................................................................................................... 2-4

2.3 References ...................................................................................................... 2-11

Chapter 3 Air Quality Modeling and Analysis........................................................................ 3-1

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

3.2 Modeling PM2.5 Levels in the Future................................................................. 3-1

3.2.1 Air Quality Modeling Platform.............................................................. 3-2

3.3 PM2.5 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 Standard, and Alternative Annual 

Standard Levels................................................................................... 3-22

3.3.3 Estimating Changes in Annual Average PM2.5 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-1
v 

3.A.1.1Development of Air Quality Response Ratios....................................3.A-1

3.A.1.2 Estimating Area NOx Emission Contributions to NH4NO3 PM2.5 in 

the San Joaquin Valley .....................................................................3.A-12

Chapter 4 Control Strategies.................................................................................................. 4-1

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

4.2 PM2.5 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 Needed 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 SO2 Control Measures........................................................................4.A-4

4.A.1.3NOx 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 Human 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 PM2.5..................................... 5-15
vi

5.5.3 Alternative Concentration-Response Functions for PM2.5–

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 Uncertainty and Other Analysis 

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

5.6 Benefits Analysis Data Inputs.......................................................................... 5-21

5.6.1 Demographic Data .............................................................................. 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 PM2.5

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 PM2.5 .............................................................. 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 PM2.5-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 2010 Census ........................5.A-11
vii

5.A.5 Long-term PM2.5 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 Analysis......... 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 Ecosystem Co-benefits for Metallic and Organic Constituents of 

PM....................................................................................................... 6-42

6.6.2 Ecosystem Co-benefits from Reductions in Nitrogen and Sulfur 

Emissions............................................................................................. 6-46
viii

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.5.4Derivation of Region-Specific WTP for National Parks and 

Wilderness Areas.............................................................................6.A-14

6.A.5.5Derivation of Park- and Wilderness Area-Specific WTPs, Given 

Region-Specific WTPs for National Parks and Wilderness Areas.....6.A-14

6.A.5.6Derivation 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-5
ix

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 μg/m3 and Sensitivity Analyses of 

Extrapolated Cost Estimates......................................................................................7.A-1

7.A.1 PM2.5 Emission Reductions and Costs to Meet 12 μg/m3 ...............................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............................................................................................................. 9-1
x 

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 Use ............................................................................................ 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 Air 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 ...................................................................................................... 10-7
xi

LIST OF FIGURES

Number Page

ES-1. PM RIA Analytical Flowchart.......................................................................................... ES-3

ES-2. Annual NAAQS Exceedances in “Adjusted 2020 Base Case” Scenario .......................... ES-5

ES-3. Annual NAAQS Exceedances in “Analytical Baseline” Scenario .................................... ES-6

ES-4. Monetized Benefit to Cost Comparison for 12 μg/m3 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 PM2.5, 

PM10 and Gaseous Precursor Species SO2, NOx, NH3 and VOCs for 2008 ....................... 2-3

2-2. Regional and Local Contributions to Annual Average PM2.5 by Particulate SO4

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 PM2.5 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 

PM2.5 FRM Reconstructed Mass Protocols on an Annual Average Basis......................... 2-8

2-5. Maximum County-level PM2.5 Annual Design Values Calculated Using 2006–2008 

FRM 24-hr Average PM2.5 Measurements..................................................................... 2-10

2-6. Maximum County-level PM2.5 24-hour Design Values Calculated Using 2006–2008 

FRM 24-hr Average PM2.5 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 μg/m3 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 PM2.5 Standard (15/35) in 2020................... 4-3

4-2. Counties Projected to Exceed the 13 ug/m3 Alternative Standard in the Analytical 

Baseline............................................................................................................................ 4-4
xii

4-3. Counties Projected to Exceed the 12 ug/m3 Revised Standard in the Analytical 

Baseline............................................................................................................................ 4-5

4-4. Counties Projected to Exceed the 11 ug/m3 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 PM2.5-Related Premature Mortalities Avoided According to 

Epidemiology or Expert-Derived PM2.5 Mortality Risk Estimate for 12 μg/m3 in 

2020 ............................................................................................................................... 5-72

5-4. Total Monetized Benefits Using 2 Epidemiology-Derived and 12-Expert Derived 

Relationships Between PM2.5 and Premature Mortality for 12 μg/m3 in 2020............. 5-73

5-5. Estimated Life Years Gained as a Result of the Revised Annual Primary PM2.5 

Standard of 12 μg/m3 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 

PM2.5 Standard of 12 μg/m3 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 PM2.5

Concentration Observed in the Epidemiology Study..................................................... 5-82

5-8. Number of Premature PM2.5-related Deaths Avoided for the Revised Annual 

Primary PM2.5 Standard of 12 μg/m3 in 2020 According to the Baseline Level of 

PM2.5 and the Lowest Measured Air Quality Levels of Each Mortality Study................ 5-84

5-9. Number of Premature PM2.5-related Deaths Avoided for the Revised Annual 

Primary PM2.5 Standard of 12 μg/m3 in 2020 According to the Baseline Level of 

PM2.5 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 PM2.5 Premature Mortality (Cumulative)........................5.A-8

5.A-2. Exponential Lag Structures for PM2.5 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 Regions 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............................... 6-48
xiii

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 USGS 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. Recreational 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/m3 in 2020 (7% Discount Rate)................................................................................... 8-3

8-2. Net Benefits for Revised Annual Standard of 12 μg/m3 in 2020 at a 7% Discount 

Rate .................................................................................................................................. 8-4
xiv

LIST OF TABLES

Number Page

ES-1. Emission Reductions in Illustrative Emission Reduction Strategies for the Revised 

and Alternative Annual Primary PM2.5 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 PM2.5 Health Impacts for Standard Alternatives—

Full Attainment in 2020 ............................................................................................... ES-17

2-1. Annual Average FRM and CSN PM2.5 NO3

– and NH4NO3 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 Point 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 PM2.5 Emission Reductions beyond the Adjusted 2020 Control 

Case to Meet the Current Standard Level for Counties that Exceed the Revised or 

Alternative Annual 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 PM2.5 Emission Reductions Beyond the Analytical Baseline to Meet 

the 13 μg/m3 Level......................................................................................................... 3-27

3-8. Tons of Direct PM2.5 Emission Reductions Beyond the Analytical Baseline to Meet 

the 12 μg/m3 Level......................................................................................................... 3-28

3-9. Tons of Direct PM2.5 Emission Reductions Beyond the Analytical Baseline to Meet 

the Alternative Standard 11 μg/m3 Level ...................................................................... 3-29
xv

3.A-1. Annual NOx Air Quality Ratios for Monitors in California Counties that Received a 

2025 Mobile NOx Emission Adjustment........................................................................3.A-5

3.A-2. Annual and Daily SO2 Air Quality Ratios for Monitors in Counties where Ratios 

were Used in Adjusting Daily Design Values to Remove the Impact of SO2

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

3.A-3. Annual and Daily Direct PM2.5 Air Quality Ratios for Monitors in Counties with at 

Least One Monitor having an Annual Design Value Above 11 μg/m3 in the 2020 

Base Case ......................................................................................................................3.A-9

3.A-4. Contribution Weighting Factors for NOx 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 

Standards ....................................................................................................................... 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 Categories.......................4.A-3

4.A-2. Example PM Control Measures for Nonpoint Sources.................................................4.A-4

4.A-3. Example SO2 Control Measures for Non-EGU Point.....................................................4.A-5

4.A-4. Example NOx Control Measures for Non-EGU Source Categories................................4.A-7

5-1. Estimated Monetized Benefits of the of Revised and Alternative Annual PM2.5

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 PM2.5 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 PM2.5 in Recent Cohort Studies 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 ......... 5-60
xvi

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 PM2.5 Standards Relative to Analytical Baseline............................................... 5-66

5-17. Emission Reductions in Illustrative Emission Reduction Strategies for the Revised 

and Alternative Annual Primary PM2.5 Standards, by Pollutant and Region in 2020 

(tons).............................................................................................................................. 5-67

5-18. Estimated number of Avoided PM2.5 Health Impacts for the Revised and 

Alternative Annual Primary PM2.5 Standards (Incremental to the Analytical 

Baseline)......................................................................................................................... 5-68

5-19. Monetized PM2.5 Health Benefits for the Revised and Alternative Annual Primary 

PM2.5 Standards (Incremental to Analytical Baseline) (Millions of 2006$, 3% 

discount rate)................................................................................................................. 5-69

5-20. Monetized PM2.5 Health Benefits for the Revised and Alternative Annual Primary 

PM2.5 Standards (Incremental to Analytical Baseline) (Millions of 2006$, 7% 

discount rate)................................................................................................................. 5-70

5-21. Total Estimated Monetized Benefits of the for Revised and Alternative Annual 

Primary PM2.5 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 

PM2.5 Standard of 12 μg/m3 in 2020.............................................................................. 5-77

5-24. Estimated Reduction in the Percentage of All-Cause Mortality Attributed to the 

Revised Annual Primary PM2.5 Standard of 12 μg/m3 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 PM2.5-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 (95th percentile confidence intervals) .............................5.A-11

5.A-7. Change in Total Monetized Benefits for 2000 and 2010 Census projections for 12 

μg/m3 in 2020 (2010$)................................................................................................5.A-12
xvii

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

Exposure to PM2.5 in Recent Cohort Studies in California ..........................................5.A-13

5.A-9. Comparison of Health Benefits Estimated for 2000 and 2025 for 12 μg/m3 ............5.A-14

5.B-1. Summary of Qualitative Uncertainty for Key Modeling Elements in PM2.5 Benefits.... 5.B-6

6-1. Welfare Effects by Pollutants Potentially 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 CoBenefits 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 Visibility 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 Visibility 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 Illustrative 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-7
xviii

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 

2010$) ............................................................................................................................ 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 PM2.5 Emission Reductions and Costs to Meet 12 μg/m3.............................................7.A-1

7.A.2 Sensitivity Analysis of Fixed-Cost Methodology for Unknown Controls by Revised 

and Alternative 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 PM2.5 ......................................................................... 8-5

8-3. Welfare Co-Benefits of PM2.5........................................................................................... 8-6
ES-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 revising 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—PM2.5). The EPA is retaining the current primary and secondary 24-hour PM10

standards.

As has traditionally been done in NAAQS rulemakings, 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 alternative standards. In NAAQS rulemakings, 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. The RIA fulfills the 

requirements of Executive Orders 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 States might choose to implement in order to meet the final standards. As a 

result, benefit and cost estimates provided in this RIA cannot be added to benefits and costs 

from other regulations because each regulation is based on a different set of analytical 

assumptions and policy decisions. The 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 Standards

Two primary PM2.5 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/m3, based on the 3-year average of annual arithmetic mean 

PM2.5 concentrations. The current 24-hour standard is set at a level of 35 μg/m3, 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 98th percentile of 24-hour PM2.5 concentration. In the RIA, the current 

primary PM2.5 standards, including both annual and 24-hour standards, are denoted as 15/35

μg/m3. Attainment of the 24-hour standard is analyzed only in developing the scenario of 

attainment with the existing standards of 15/35 μg/m3. All other scenarios evaluate additional 

emissions reductions needed to attain alternative annual standards only. 

In this PM NAAQS review, the EPA has revised and lowered the level of the primary 

annual PM2.5 standard to 12 μg/m3 in conjunction with retaining the level of the 24-hour 

standard at 35 μg/m3 and this standard is denoted as 12 μg/m3. In addition to the revised 

annual standard of 12 μg/m3, the RIA also analyzes the benefits and costs of incremental 

control strategies for two alternative annual standards of 13 μg/m3 and 11 μg/m3. 

Currently, the existing secondary (welfare-based) PM2.5 standards are identical in all 

respects to the primary standards. In this PM NAAQS review, the EPA is retaining the current 

suite of secondary standards for 24-hour and annual PM2.5. Thus, while the new primary annual 

standard will be revised to 12 μg/m3, the secondary annual standard will remain at 15 μg/m3. 

Non-visibility welfare effects are addressed by this suite of secondary standards, and PMrelated visibility impairment is addressed by the secondary 24-hour PM2.5 standard, which EPA 

is leaving unchanged at 35 μg/m3. The secondary standards will thus remain at 15/35 μg/m3. 

With regard to the primary and secondary standards for particles less than or equal to 

10 μm in diameter (PM10), the EPA is retaining the current primary and secondary 24-hour PM10

standards, which are both set at a level of 150 μg/m3, 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 

alternative PM10 standards was conducted when the standard was promulgated in 1997, 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/m3

, and the following discussion, 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 of 15/35 μg/m3 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 PM2.5 reductions expected, largely in the western 
ES-4 

US, from the implementation of episodic burn ban programs in 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) 

– NOx Emission Standard for New Commercial Aircraft Engines (U.S. EPA, 

2005a) 

– Emissions Standards for Locomotives and Marine Compression-Ignition 

Engines (U.S. EPA, 2008a) 

– Control of Emissions for Nonroad Spark Ignition Engines and Equipment (U.S. 

EPA, 2008b) 

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

– 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, 2010b) 

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

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

 

3 See Chapter 3, Section 3.2.1.4 for a discussion of the role CSAPR plays in the PM2.5 RIA and the reasons we believe 

CSAPR remains an appropriate proxy for this analysis. 
ES-5 

Figure ES-2. Annual NAAQS Exceedances* in “Adjusted 2020 Base Case” Scenario

*

24-hr PM2.5 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 standards (15/35 μg/m3

) for the purpose of estimating the incremental costs 

and benefits of attaining the revised annual standard of 12 μg/m3

. 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 will 

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 meet the daily standard will result in reductions in the annual design value to 

below the annual standard of 15 μg/m3

. As a result, the annual PM2.5 increment 

needed to attain alternative standards for each county will not be relative to an 

annual design value of 15 μg/m3, 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/m3 daily standard. As shown in the map below, for 

counties labeled as exceeding the 12 μg/m3 annual standard, the baseline design 

values can be any value between 12 and 15 μg/m3. Similarly, exceedances of the 11 

μg/m3 and 13 μg/m3 standards can be anywhere between 11 or 13 and 15 μg/m3

. 
ES-6 

The analytical baseline reflects attainment of 15/35 μg/m3 by2020. 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 NOx emissions that these areas are expected to achieve

between 2020 and 2025 due to fleet turnover. These reductions in NOx 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 Counties (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/m3) and other existing 

major rules to achieve the revised standard (12 μg/m3

). We apply control options that might be 

available to States for application by 2020. Detailed discussion of the elements of this section of 

the flowchart is in Chapter 4 of the final RIA.

 Select 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 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 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 calculate 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 strategy 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/m3 and two alternative annual 

standards (13 μg/m3 and 11 μg/m3

) as shown in Table ES-1.

Because the rules listed above and other emissions reductions should have 

substantially reduced ambient PM2.5 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/m3. Emissions reductions are needed in more locations for the alternative 

standard of 11ug/m3

. 
ES-8 

Table ES-1. Emission Reductions in Illustrative Emission Reduction Strategies for the Revised 

and Alternative Annual Primary PM2.5 Standards, by Pollutant and Region in 2020 

(tons)a

13 μg/m3 12 μg/m3 11 μg/m3

Directly emitted PM2.5

East 0 0 8,200

West 0 0 160

CA 730 4,000 10,600

SO2

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.Estimates 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 two 
ES-9 

methodologies 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/m3. Detailed discussions of the elements of this section of the flowchart are 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/m3 and the 

alternative annual standards of 13 μg/m3 and 11 μg/m3 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/m3

; $11 

million and $100 million for the alternative annual standard of 13 μg/m3

; and $320 

million and $1,700 million for the alternative annual standard of 11 μg/m3.

 Calculate Full Attainment Benefits—Chapter 5 presents the estimated human health 

benefits for the revised NAAQS. We quantify the health-related benefits of the fine 

particulate matter (PM2.5)-related air quality improvements resulting from the 

illustrative emissions control scenarios that reduce emissions of directly emitted 

particles and precursor pollutants including SO2 and NOx to reach alternative PM2.5

NAAQS levels in 2020. These benefits are relative to an analytical baseline reflecting 

nationwide attainment of the current primary PM2.5 standards (i.e., annual standard 

of 15 μg/m3 and 24-hour standard of 35 μg/m3

) that includes promulgated national 

regulations and illustrative emissions controls to simulate attainment with 15/35 as 

well as a NOx emission adjustment to reflect expected reductions in mobile NOx

emissions between 2020 and 2025. 

The estimated benefits for the revised and alternative standards 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 PM2.5 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/m3. 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 rules.

 Net Benefits—Chapter 8 compares estimates of the benefits with costs and 

summarizes the net benefits of revised annual standard of 12 μg/m3 and the 

alternative annual standards of 13 μg/m3 and 11 μg/m3 relative to the analytical 

baseline that includes recently promulgated national regulations and additional 
ES-10

emissions reductions needed to attain the existing 15/35 μg/m3 standards, as well as 

adjustments to NOx emissions 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 PM2.5 standards. We considered an array 

of health impacts attributable to changes in PM2.5 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 (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 of 12ug/m3 occur in California.

Since the proposed rule, the EPA has incorporated an array of policy and technical 

updates to the benefits analysis approach applied in this RIA, including incorporation of the 

most recent 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 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 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 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.
ES-11

It is important to note that estimates of the health benefits from reduced PM2.5 

exposure reported here contain uncertainties, 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 PM2.5 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 PM2.5 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 

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 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 

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 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 PM2.5, 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 estimate from 

simulated PM2.5 concentrations that coincide with the bulk of the observed PM concentrations 

in the epidemiological studies that are used to estimate the benefits. Likewise, we are less 

confident in the risk we estimate from simulated PM2.5 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 
ES-12

25th to 10th 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. Concentration benchmark analyses (e.g., 

25th percentile, 10th percentile, one standard deviation below the mean,

4 and lowest 

measured level [LML]) provide some insight into the level of uncertainty in the estimated PM2.5 

mortality benefits. Most of the estimated avoided premature deaths for this rulemaking occur 

at or above the lowest measured PM2.5 concentration in the two studies that are used to 

estimate mortality benefits. There are uncertainties inherent in identifying any particular point 

at which our confidence in reported associations becomes appreciably less, and the scientific 

evidence provides no clear dividing line. However, the EPA does not view these concentration 

benchmarks as a concentration threshold below which we would not quantify health benefits 

of air quality improvements. 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 emission 

reduction strategies and because the current body of scientific literature indicates that a nothreshold 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 PM2.5 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 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 than counties, and PM2.5 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 to benefits in grid cells that are 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 25th and 10th percentiles.
ES-13

ES.2.3 Cost Analysis Approach

The EPA estimated total costs under partial and full attainment of the alternative PM2.5

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.

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 controls, referred to as 

extrapolated costs. By definition, no cost data currently exist for the additional emissions 

reductions needed beyond known controls. We employ two methodologies for estimating the 

costs of unidentified 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. The 

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 PM2.5 reduced, and the hybrid methodology generates a 

total annual cost curve for PM2.5 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, 

approximately 90% of total costs for attaining the revised annual standard of 12 μg/m3 are from 

unspecified control technologies. Employing the hybrid methodology, approximately 97% of 

total costs for attaining the revised annual standard of 12 μg/m3 are from unspecified 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.

The 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, the 

amendments to the ambient air monitoring regulations will revise the network design 
ES-14

requirements for PM2.5 monitoring sites, resulting in moving 21 monitors to established nearroad 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 States 

will incur costs designing SIPs and implementing new control strategies to meet the revised 

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 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 

implementation 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 annual 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 PM2.5 standard of 12 μg/m3 and alternative annual standards of 13 μg/m3

and 11 μg/m3, incremental to the 2020 analytical baseline. For the revised annual standard of 

12 μg/m3, 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 (2010 dollars). For an alternative annual 

standard of 13 μg/m3, net benefits are estimated to be $1.2 billion to $2.9 billion at the 3% 

discount rate and $1.1 billion to $2.6 billion at the 7% discount rate. Net benefits of an

alternative annual PM2.5 standard of 11 μg/m3 are estimated to be $11 billion to $29 billion 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 standard of 12 μg/m3, 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/m3, 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 standards of 11 μg/m3, 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 2020 (millions of 

2010$)—Full Attainmenta

Alternative 

Annual 

Standard

(μg/m3

) 

Total Costsb Monetized Benefitsd Net Benefits

3% Discount 

Ratec

7% Discount 

Rate

3% Discount 

Rate

7% Discount 

Rate

3% Discount 

Rateb

7% Discount 

Rate

13 $11 to $100 $11 to $100 $1,300 to 

$2,900

$1,200 to 

$2,600

$1,200 to 

$2,900

$1,100 to 

$2,600

12 $53 to $350 $53 to 350 $4,000 to 

$9,100

$3,600 to 

$8,200

$3,700 to 

$9,000

$3,300 to 

$8,100

11 $320 to 

$1,700

$320 to 

$1,700

$13,000 to 

$29,000

$12,000 to

$26,000

$11,000 to 

$29,000

$10,000 to 

$26,000

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 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 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 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 those 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 and Lepeule et al., 2012).

Table ES-3. Benefit-to-Cost Ratios for Alternative Standards at 3% and 7% Based on 

Projected Benefits and Costs in 2020

13 μg/m3 12 μg/m3 11 μg/m3

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 fixedcost approach and the hybrid approach. Additionally, we see the difference in the calculation of 

benefits based on using various studies. 

Figure ES-4. Monetized Benefit to Cost Comparison for 12 μg/m3 in 2020 (7% Discount Rate)
ES-17

Table ES-4. Estimated Number of Avoided PM2.5 Health Impacts for Standard Alternatives—

Full Attainmentin 2020a

Health Effect

Alternative Annual Standards

13 μg/m3 12 μg/m3 11 μg/m3

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 PM2.5 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 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 PM2.5 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 PM2.5 

monitors in the United States would meet an annual standard of 12 μg/m3without additional 

Federal, State, 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. 
ES-18

Regulations such as the EPA’s recent Mercury and Air Toxics Standards (MATS) and other 

Federal programs such as diesel standards will provide substantial improvements in regional 

concentrations of PM2.5. 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 array of emission control strategies for different sources. The 

costs and benefits identified in this RIA will not be realized until specific controls are mandated 

by SIPs or other Federal 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. Emission 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 assumed the application of a significant number of 

unidentified future controls that would make possible the additional emissions reductions 

needed for attainment in 2020. EPA used two methodologies—the fixed-cost and hybrid 

methodologies—for estimating the costs of unidentified future controls, and 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. 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 cost 
ES-19

varies by geographic area and by degree of difficulty associated with obtaining the needed 

emissions reductions. 

For the revised annual standard of 12 μg/m3, 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 emissions reductions from unknown to known controls, it could be 

more representative of total costs. In an effort to consider the potential fitness of the

extrapolated 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) transactions 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/m3, including further PM2.5 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. In addition, the California Air Resources Board’s 

2009 and 2010 Emission Reduction Offset Transaction Costs, Summary Report included PM10

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 PM10 ERC prices in SJV APCD ranged from $40,000 per ton per year (tpy) to 

$70,000/tpy, and PM10 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 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/m3

. In comparison, 

modeling by Fann et al. (2012) estimated that 2005 levels of air pollution were responsible for 

between 130,000 and 320,000 PM2.5-related deaths, or between 6.1% and 15% of total deaths 
ES-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 PM2.5 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 PM2.5 will have improved substantially by 2020, due to major emissions 

reductions resulting from implementation of Federal regulations. For example, we estimate 

that SO2 emissions (an important PM2.5 precursor) in the United 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 PM2.5 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, 2010c). 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. Although there are greater 

uncertainties at lower PM2.5 concentrations, there is no evidence of a population-level 

threshold in PM2.5-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 

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 this analysis. These include the 

following: 

ES.4.1 Benefits Caveats

 PM2.5 mortality benefits represent a substantial proportion of total monetized 

benefits (over 98%). To characterize the uncertainty in the relationship between 

PM2.5 and premature mortality, we include a set of 12 estimates of the 

concentration-response function based on results of the PM2.5 mortality 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, chemical composition, transferability of the effect 

estimate to diverse locations, 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 PM2.5 

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 PM2.5 concentration in the two studies used to estimate mortality 

benefits. In general, we have greater confidence in risk estimates based on PM2.5 

concentrations where the bulk of the health and air quality data reside and 

somewhat less confidence where data density is lower.

 We analyzed full attainment in 2020, and projecting key variables introduces 

uncertainty. Inherent in any analysis of future regulatory programs are uncertainties 

in projecting atmospheric conditions and source-level emissions, as well as 

population, health baselines, incomes, technology, and other factors.

 There are uncertainties related to the health impact functions used in the analysis. 

These include within-study variability; pooling across studies; the application of C-R 

functions nationwide and for all particle species; extrapolation of impact functions 

across populations; and various uncertainties in the C-R function, including causality 

and shape of the function at low concentrations. Therefore, benefits may be underor over-estimates.

 This analysis omits certain unquantified effects due to lack of data, 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 benefits 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, those 
ES-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 technologies 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 that the control devices are properly installed and 

maintained. There is also variability in scale of application that is difficult to reflect 

for small area sources of emissions.

 The illustrative control strategy analysis estimates only one potential pathway to 

attainment. The control strategies are not recommendations for how the revised 

PM2.5 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 SIPs, 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 these areas. 

 For two areas 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/m3 is such that these areas are not 

expected to be able to demonstrate 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 applied 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 control strategies in 2020.

 In addition to the application of known controls, the EPA assumes the application of 

unidentified future controls that make possible the additional emission reductions 

needed for attainment in 2020. By definition, no cost data currently exist for 
ES-23

unidentified future technologies or innovative strategies and the cost estimates for

unidentified future controls reflect some uncertainty. 

 Because we obtain control cost data from many sources, we are not always able to 

obtain consistent data across original data sources. If disaggregated control cost 

data are 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 costs 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/m3 and the 

alternative standard of 13 μg/m3 we did not have any disaggregated 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, 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.

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 

accompanying 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 updated the approach in designing the 

control strategies. The improved modeling platform updated the current and projected air 
ES-24

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 NOx and SO2 over a broader region 

and these emissions reductions were not as effective in reducing design value for each area as 

the direct PM2.5 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 States 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 PM2.5 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, EPA’s approach to 

attaining the existing standards of 15/35 μg/m3 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 PM2.5. 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 

NOx and SO2. As a result, in the final RIA, the daily standard of 35 μg/m3 was attained 

more effectively and had less impact on annual DVs because of episodic, direct PM2.5

reductions, while in the proposal RIA, the daily standard was attained less effectively 

because we pursued year-around NOx and SO2 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/m3 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/m3 was attained more 
ES-25

effectively and had less impact on annual DVs across the counties in California. In 

addition, the focus on direct PM2.5 emissions reductions allowed for more effective 

controls to attain the revised standard with fewer PM2.5 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 monetized 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 PM2.5 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 county 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 Expectancy in 

the United States.” New England Journal of Medicine 360: 376-386.

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 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 Register. 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. 

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. Environmental Protection Agency (U.S. EPA). 2009b. 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/cfm/recordisplay.cfm?deid=216546. 

U.S. Environmental Protection Agency (U.S. EPA). 2010a. 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 Analysis (RIA) for Existing Stationary 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). 2010c. Quantitative Health Risk Assessment 

for Particulate Matter—Final Report. 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 Approvals for 22 States. June. Available at: 

http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2011b. Regulatory Impact Analysis for the 

Final Mercury and Air Toxics Standards. EPA-452/R-11-011. December. Available at: 

http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf. 

U.S. Environmental Protection Agency (U.S. EPA). 2011c. The Benefits and Costs of the Clean Air 

Act 1990 to 2020: EPA Report to Congress. Office of Air and Radiation, Office of Policy, 

Washington, DC. March. Available at:

http://www.epa.gov/oar/sect812/feb11/fullreport.pdf. 

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

postneonatal infant mortality and particulate air pollution in the United States.” 

Environmental Health Perspectives 105(6): 608-612.
1-1 

CHAPTER 1

INTRODUCTION AND BACKGROUND

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 compliance 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 Orders 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 air quality criteria are intended to “accurately reflect the latest scientific knowledge 

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

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

109(b)(1) defines a primary standard as “the attainment and maintenance of which in the 

judgment of the Administrator, based on [the] criteria and allowing an adequate margin of 

safety, [are] requisite to protect the public health.” A secondary standard, as defined in section 

109(b)(2), must “specify a level of air quality the attainment and maintenance of which in the 

judgment of the Administrator, based on [the] criteria, [are] requisite to protect the public 

welfare from any known or anticipated adverse effects associated with the presence of [the] 

pollutant in the ambient air.” Welfare effects as defined in section 302(h) [42 U.S.C. 7602(h)] 

include but are not limited to “effects on soils, water, crops, vegetation, manmade materials, 

animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and 

hazards to transportation, as well as effects on economic values and on personal comfort and 

well-being.” 
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 States. 

1.2.2 2006 PM NAAQS

In 2006, the EPA’s final PM rule established a 24-hour standard of 35 μg/m3 and 

retained the annual standard of 15 μg/m3. 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 PM2.5 NAAQS to the EPA citing that the EPA failed to adequately explain why the 

standards provided the requisite protection from both short- and long-term exposures to fine 

particles, including protection for at-risk populations. The court remanded the secondary 

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. In 2006, the EPA also 

retained the primary and secondary 24-hour PM10 standards, both set at a level of 150 μg/m3, 

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 standards. The Act requires the EPA, for each criteria pollutant, to set standards 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 considerations only.

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

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

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

is essential to making efficient, cost-effective decisions for implementing these standards. The 

impact of cost and efficiency is considered by States during this process, as they decide what 

timelines, strategies, and policies make the most sense. This RIA is intended to inform the 

public about the potential costs and benefits that may result when new standards are

implemented, but it is not relevant to establishing the standards themselves.
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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 Orders 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 revised annual PM standard, incremental to attainment of the existing standards. In 

addition, this RIA analyses two alternative primary annual PM2.5 standards: one that is more 

stringent than the existing standards but less stringent than the revised annual standard 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 PM2.5 standard from 15 μg/m3 to 12 μg/m3 in conjunction with retaining the 

level of the 24-hour standard at 35 μg/m3. 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/m3. In addition to the revised annual standard of 12

μg/m3, the RIA also analyzes the benefits and costs of incremental control strategies for two 

alternative annual standards (13 μg/m3 and 11 μg/m3). 

The control strategies presented in this RIA are illustrative and represent one set of 

control strategies States 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 O3, NOx, or SO2. 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 O3, NOx, or SO2, 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., O3, NOx, or SO2) 

 

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 PM2.5 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 environmental 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 implementing 

primary and secondary air quality standards 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 States might implement to achieve revised NAAQS. 

Because States are ultimately responsible for implementing strategies to meet any revised 

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

uncertainties and limitations are documented in the relevant portions of the analysis.

Because the illustrative goals of this RIA are somewhat different from other EPA 

analyses of national rules or the implementation plans States 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 standards. These hypothetical strategies represent a 

scenario where States use one set of cost-effective controls to attain a revised NAAQS. Because 

States—not the EPA—will implement any revised NAAQS, they will ultimately determine 

appropriate emissions control scenarios. SIPs would likely vary from the EPA’s estimates due to 

differences in the data and assumptions that States use to develop these plans.

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

understanding that there are inherent uncertainties in projecting emissions and controls.
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/m3 and two alternative annual PM standards, incremental to 

attainment of the existing 15/35 μg/m3 standards. 

The EPA is retaining the current primary and secondary 24-hour PM10 standards, which 

are both set at a level of 150 μg/m3, 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 PM10 

standards was conducted when the standard was promulgated in 1997, this RIA does not repeat 

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 RIA (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 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 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 NOx and SO2 over a broader region 

and these emissions reductions were not as effective in reducing design value for each area as 

the direct PM2.5 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 PM2.5 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/m3 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 PM2.5. 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 

NOx and SO2. As a result, in the final RIA, the daily standard of 35 μg/m3 was attained 

more effectively and had less impact on annual DVs because of episodic, direct PM2.5

reductions, while in the proposal RIA, the daily standard was attained less effectively 

because we pursued year-around NOx and SO2 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/m3 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/m3 was attained more 

effectively and had less impact on annual DVs across the counties in California. In 

addition, the focus on direct PM2.5 emissions reductions allowed for more effective 

controls to attain the revised standard with fewer PM2.5 emissions reductions 

thereby resulting in similar fixed costs estimates.2

 Benefits analysis—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 the 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 known 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 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.

1.4.2 Existing and Revised PM Air Quality Standards

Two primary PM2.5 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/m3, based on the 3-year average of the annual arithmetic 

mean of PM2.5 concentrations. The existing 24-hour standard is set at a level of 35 μg/m3, based 

on the 3-year average of the 98th percentile of 24-hour PM2.5 concentrations. In this RIA, the 

existing primary PM2.5 standards, including both the annual standard and 24-hour standard, are

denoted as 15/35 μg/m3. In this current PM NAAQS review, the EPA has revised the level of the 

primary annual PM2.5 standard to 12 μg/m3 in conjunction with retaining the level of the 24-

hour standard at 35 μg/m3. 

Currently, the existing secondary (welfare-based) PM2.5 standards are identical in all 

respects to the primary standards. In this PM NAAQS review, the EPA is retaining the current 

suite of secondary standards for 24-hour and annual PM2.5. Thus, while the new primary annual 

standard will be revised to 12 μg/m3, the secondary annual standard will remain at 15 μg/m3. 

Non-visibility welfare effects are addressed by this suite of secondary standards, and PMrelated visibility impairment is addressed by the secondary 24-hour PM2.5 standard, which EPA 

is leaving unchanged at 35 μg/m3. The secondary standards will thus remain at 15/35 μg/m3. 

With regard to the primary and secondary standards for particles less than or equal to 

10 μm in diameter (PM10), the EPA is retaining the current primary and secondary 24-hour PM10

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

are set at a level of 150 μg/m3, 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 PM2.5 standards. In order to develop and evaluate control strategies for attaining a 

more stringent primary standard, it is important to account for Federal and state rules and 

programs currently underway, as well as to reflect attainment of the current annual and daily 

standards of 15/35 μg/m3. Estimating the 2020 levels after attainment of the current standards

of 15/35 μg/m3 then allows us to estimate the incremental costs and benefits of attaining any 

alternative primary standard. EPA anticipates attainment with 15/35 μg/m3 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 voluntary programs are reflected to the extent that they are 

represented in emission inventory information submitted to the EPA 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, 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) 

 NOx Emission Standard for New Commercial Aircraft Engines (U.S. EPA, 2005a) 

 Emissions Standards for Locomotives and Marine Compression-Ignition Engines (U.S. 

EPA, 2008a) 

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

2008b) 

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

 Boiler MACT (U.S. EPA, 2011d)

 Hospital/Medical/Infectious Waste Incinerators: New Source Performance Standards 

and Emission Guidelines: Final Rule Amendments (U.S. EPA, 2009a) 

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

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

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

The analytical baseline for this analysis does not assume emissions controls that might 

be implemented to meet the other NAAQS for O3, NOx, or SO2. 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 PM2.5 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 O3, NOx, or SO2, the costs and benefits of meeting the 

revised PM annual standard will be overstated. We did not conduct this analysis incremental to 

controls applied as part of previous NAAQS analyses (e.g., O3, NOx, or SO2) because the data 

and modeling on which these previous analyses were based are now considered outdated and 

are not compatible with the current PM2.5 NAAQS analysis. In addition, all control strategies 

analyzed in all NAAQS RIAs are hypothetical. The analysis presented here is just one scenario 

that States may employ but does not prescribe how attainment must be achieved.

Most areas of the United 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 standard 

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 projected non-attainment 

with the revised annual standard 12 μg/m3 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 attainment 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/m3. 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/m3standards. 

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/m3

. 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. 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 

reflects attainment of 15/35 μg/m3 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 NOx emissions that those areas are expected to 

see between 2020 and 2025. These reductions in NOx are not attributable to attainment of the 

current or revised PM standards but rather reflect the impacts of other programs. These NOx

emissions changes will affect baseline PM concentrations but will not affect costs or benefits of 

attaining the revised annual or the alternative 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 μg/m3, 13 μg/m3, and 11 μg/m3 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/m3 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 NOx 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 for 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 PM2.5 standards. We considered an array 

of health impacts attributable to changes in PM2.5 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. 
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 of 12ug/m3 occur in California.

Since the proposed rule, the EPA has incorporated an array of policy and technical 

updates to the benefits analysis approach applied in this RIA, including incorporation of the 

most recent 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 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 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 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 PM2.5 

exposure reported here contain uncertainties, 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 PM2.5 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 PM2.5 can be linked 

with multiple health effects, and the evidence is not yet sufficient to allow 
1-13

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. In 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 

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 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 PM2.5, 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 estimate from 

simulated PM2.5 concentrations that coincide with the bulk of the observed PM concentrations 

in the epidemiological studies that are used to estimate the benefits. Likewise, we are less 

confident in the risk we estimate from simulated PM2.5 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 

25th to 10th 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. Concentration benchmark analyses (e.g., 

25th percentile, 10th percentile, one standard deviation below the mean,4 and lowest 

measured level [LML]) provide some insight into the level of uncertainty in the estimated PM2.5 

mortality benefits. Most of the estimated avoided premature deaths for this rulemaking occur 

at or above the lowest measured PM2.5 concentration in the two studies that are used to 

estimate mortality benefits. There 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 25th and 10th percentiles.
1-14

at which our confidence in reported associations becomes appreciably less, and the scientific 

evidence provides no clear dividing line. However, the EPA does not view these concentration 

benchmarks as a concentration threshold below which we would not quantify health benefits 

of air quality improvements. 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 emission 

reduction strategies and because the current body of scientific literature indicates that a nothreshold 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 PM2.5 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 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 than counties, and PM2.5 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 to 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 PM2.5

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 controls, referred to as 

extrapolated costs. By definition, no cost data currently exist for the additional emissions 

reductions needed beyond known controls. We employ two methodologies for estimating the 

costs of unidentified 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. The 

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 PM2.5 reduced, and the hybrid methodology generates a 

total annual cost curve for PM2.5 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, 

approximately 90% of total costs for attaining the revised annual standard of 12 μg/m3 are from 

unspecified control technologies. Employing the hybrid methodology, approximately 97% of 

total costs for attaining the revised annual standard of 12 μg/m3 are from unspecified 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.

The 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, the 

amendments to the ambient air monitoring regulations will revise the network design 

requirements for PM2.5 monitoring sites, resulting in moving 21 monitors to established nearroad 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 States 

will incur costs designing SIPs and implementing new control strategies to meet the revised 

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 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 
1-16

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 following 10 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 PM2.5 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 postprocessing 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 PM2.5 concentrations in 2020 after applying the control 

strategies.

 Chapter 5: Health Benefits Analysis Approach and Results. This chapter quantifies 

and monetizes the health benefits of the PM2.5-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 exposure, 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 Analyses. This chapter summarizes 

the Statutory and Executive Order impact analyses.
1-17

 Chapter 10: Qualitative Discussion of Employment Impacts of Air Quality 

Regulations. This chapter provides a qualitative discussion of employment impacts 

of air quality regulations. 

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. 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. 

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 Register. 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 Impact Analysis. 

Available at: http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OAR-2004-

0008-0929. 

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. Environmental Protection Agency (U.S. EPA). 2009b. 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/cfm/recordisplay.cfm?deid=216546. 

U.S. Environmental Protection Agency (U.S. EPA). 2010a. 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 Analysis (RIA) for Existing Stationary 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). 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). 2011b. Regulatory Impact Analysis for the 

Final Mercury and Air Toxics Standards. EPA-452/R-11-011. December. Available at:

http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf. 

U.S. Environmental Protection Agency (U.S. EPA). 2011c. 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). 2011d. 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. Available 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 Health 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 

particulate matter (PM) problem. It includes a summary of the spatial and temporal distribution 

of PM2.5 and the likely origin from direct emissions or atmospheric transformations of gaseous 

precursors and recent design values for PM2.5. 

2.2 Particulate Matter (PM) Properties

PM is a highly complex mixture of solid particles and liquid droplets distributed among 

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 PM2.5 and inhalable or thoracic coarse particles are measured as 

PM10-2.5, 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 PM2.5 and PM10. 

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-related 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.

Particles 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 combustion and 
2-2 

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 processes 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 reactions require 

sunlight and/or water vapor. Secondary PM includes:

 Sulfates formed from sulfur dioxide (SO2) emissions from power plants and industrial 

facilities;

 Nitrates formed from nitrogen oxide (NOx) emissions from cars, trucks, industrial 

facilities, and power plants; and

 Ammonium formed from ammonia (NH3) 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 from 

sources such as power plants and industrial facilities may have small amounts of directly 

emitted PM2.5 speciated into sulfate. Figure 2-1 shows, in detail, the sources contributing to 

directly emitted PM2.5 and PM10, as well as PM precursors: SO2, NOx, NH3, and VOC according to 

the 2008 NEI, version 2 (EPA, 2012) . In Figure 2-1, EGUs stands for Electric Generating Utilities.

 

1 Direct NH3 and VOC emissions are not controlled as part of the control strategy analysis. Emissions of PM2.5, NOx, 

and SO2 are controlled in the control strategies, for a complete discussion please refer to Chapter 4. 
2-3 

PM2.5 (5.1 million short tons) PM10 (20.5 million short tons)

SO2 (10.7 million short tons) NOX (18.0 million short tons)

NH3 (4.2 million short tons) VOC (15.0 million short tons)

Figure 2-1. Detailed Source Categorization of Anthropogenic Emissions of Primary PM2.5, PM10

and Gaseous Precursor Species SO2, NOx, NH3 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%

VOC
2-4 

2.2.1 PM2.5

“Fine particles” or PM2.5 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 NOx and SO2 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 PM2.5 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 observe a large urban excess across the U.S. for most PM2.5 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 significantly 

higher than all other areas. These results indicate that local sources of these pollutants are 

indeed contributing to the PM2.5 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 PM2.5

concentrations. Rural background PM2.5 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 the measured PM2.5 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 SO2 and local sources 

 

2 The measured PM2.5 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 PM2.5 concentrations.

Figure 2-2. Regional and Local Contributions to Annual Average PM2.5 by Particulate SO4

2–, 

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

2004 IMPROVEa and CSNb 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 Patterns

The chemical makeup of particles varies across the United States, as illustrated in 

Figure 2-3. For example, the higher regional emissions of SO2 in the East result in higher 
2-6 

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 particles everywhere.

Figure 2-3. Regional and Seasonal Trends in Annual PM2.5 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)

Fine particles can also have a seasonal pattern. As shown in Figure 2-3, PM2.5 values in 

the eastern half of the United States are typically higher in warmer weather when 

meteorological conditions are more favorable for the formation and build up of sulfates from 
2-7 

higher sulfur dioxide (SO2) 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 particle 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 PM2.5 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 PM2.5

design values for particular emission control scenarios, EPA characterizes the composition of 

PM2.5 as measured by the Federal Reference Method (FRM). The current PM2.5 FRM does not 

capture all ambient particles measured by speciation samplers as presented in the previous 

sections. The FRM-measured fine particle mass reflects losses of ammonium nitrate (NH4NO3) 

and other semi-volatile organic compounds (SVOCs; negative artifacts). It also includes particlebound water (PBW) associated with hygroscopic species (positive artifacts) (Frank, 2006).

Comparison of FRM and co-located speciation sampler NO3

-

values in Table 2-1 show that 

annual average NO3 retention in FRM samples for six cities varies from 15% in Birmingham to 

76% in Chicago, with an annual average loss of 1 μg/m3. 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.

PM2.5 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. The PM2.5 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 incorporates these SANDWICH adjustments in the Model 

Attainment Test Software (MATS) (Abt, 2010).
2-8 

Table 2-1. Annual Average FRM and CSN PM2.5 NO3

– and NH4NO3 Concentrations at Six Sites 

during 2003

NO3

–

 (μg/m3) NH4NO3 (μg/m3) 

Percent of 

NH4NO3 in PM2.5

FRM Mass

Sampling Site 

Location

No. of 

Observations

FRM 

Mass CSNa FRMb

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 PM2.5

FRM Reconstructed Mass Protocols on an Annual Average Basisa

a Estimated NH4* and PBW for SANDWICH are included with their respective sulfate and nitrate mass slices. 

Circles are scaled in proportion to PM2.5 FRM mass.
2-9 

2.2.1.4 2006–2008 Design Values

Annual and 24-hour PM2.5 design values for 2006-2008 are shown in Figures 2-5 and 2-6, 

respectively. These design values were calculated using 2006–2008 FRM 24-hour average PM2.5

concentration measurements in a manner consistent with CFR Part 50.3 For the most part, 

counties in the center of the U.S. have PM2.5 design values that are above both 11 μg/m3 for the 

annual standard and 35 μg/m3 for the 24-hour standard. In the East, the counties above the 

current NAAQS (i.e., 15 μg/m3 annual and 35 μg/m3 24-hour standards) are similar. In the West, 

there are fewer counties above the annual level of 15 μg/m3 than exceed the 24-hour standard 

of 35 μg/m3. 

 

3 These years of ambient measurements are presented here since they frame 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 with air 

quality model predictions.
2-10

Figure 2-5. Maximum County-level PM2.5 Annual Design Values Calculated Using 2006–2008 

FRM 24-hr Average PM2.5 Measurements. 
2-11

Figure 2-6. Maximum County-level PM2.5 24-hour Design Values Calculated Using 2006–2008 

FRM 24-hr Average PM2.5 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, Hydrated 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/P99/002aF-bF.

U.S. Environmental Protection Agency (EPA). 2008. National Air Quality Status and Trends 

Through 2007. U.S. Environmental Protection Agency. Research Triangle Park. EPA454/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). 2012. 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 PM2.5 Levels in the Future

A national scale air quality modeling analysis was performed to estimate PM2.5

concentrations for the annual and 24-hour primary standards for the future year of 2020.

1 Air 

quality ratios were then developed using model responsiveness to emissions changes based on 

“sensitivity” air quality modeling that was designed to determine the response of PM2.5

concentrations to reductions in emissions of SO2, NOx, and directly emitted PM2.5. The air 

quality ratios were used to determine the amount of emissions reductions needed to attain the

revised annual standard of 12 μg/m3 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 data 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 was used in a relative sense to project 

future concentrations of PM2.5. 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 and future case) in each species component of PM2.5. These 

species-specific relative response factors (RRFs) are applied to the corresponding measured 

concentrations to estimate future species concentrations. The future case PM2.5 annual and 

daily design values 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 case. 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 PM2.5 to various precursor emissions. The modeling for 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 revised 12 μg/m3 annual standard and two alternative annual 

standards, 13 μg/m3 and 11 μg/m3. 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 quality 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 national 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 

Eulerian air quality model designed to estimate the formation and fate of oxidant precursors, 

primary and secondary particulate matter concentrations and deposition over regional and 

urban spatial scales (e.g., over the contiguous U.S.) (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 

emissions, CMAQ is useful for evaluating the impacts of the control strategies on PM2.5 

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.org
3-3 

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 

contains 24 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 2007 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 Modeling 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 

meteorological data, and initial and boundary conditions. Separate emissions inventories were 

prepared for the 2007 base year and the future year of 2020 base case and control strategy 

Domain Boundary
3-4 

scenarios. All other inputs were specified for the 2007 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 system3. Meteorological inputs reflecting 2007 

conditions 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 2007 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 threedimensional 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 was conducted for the purpose of 

validating 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 PM2.5 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 2007 measured concentrations5

. 

This evaluation principally comprises statistical assessments 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 PM2.5 component species to the corresponding measured data from monitoring sites in the Continuous 

Speciation Network (CSN), the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network, and 

the Clean Air Status and Trends Network (CASTNet).
3-5 

provided in the Technical Support Document: Air 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 2007 simulation are within or close to the ranges 

found in other recent applications. These model performance results give us confidence that 

our applications of CMAQ using this 2007 modeling platform provide a scientifically credible 

approach for assessing PM2.5 concentrations for the purposes of the RIA.

3.2.1.4 Emissions Inventory

The 2007 emissions inventory and the 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 the Technical Support Document: 

Preparation of Emissions Inventories for the Version 5.0, 2007 Emissions Modeling Platform 

(EITSD, EPA, 2012b). 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 performance, 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 updated since 2007, and forecast unit construction through the calendar year 2020. In 

this analysis, the projected EGU emissions include the Final Mercury and Air Toxics (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 Clean 
3-6 

Air Interstate Rule] pending the promulgation 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 light 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 reductions associated with CSAPR and CAIR6

, 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, the MATS emission rate standard 

for hydrogen chloride (HCl) is expected to result in a substantial amount of new pollution 

controls (scrubbers 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 percent 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 2012 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 analysis 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/epaipm/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 proposed 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 futureyear 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 and 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 based 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 modelspecies level using ratios derived from the MOVES-based emissions data output from SMOKE. 

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 Light 

Duty Green House Gas/Corporate Average Fuel Efficiency (CAFE) standards for 2012-2016, and 

the Heavy-Duty Vehicle Greenhouse Gas Rule. The emissions do not account for the 2017 and 

Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions 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 impact 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/m3

. 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/m3 and -0.05 μg/m3 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 rules 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 impact. 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 national 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 Locomotive-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 Construction 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 Refrigeration 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 voluntary Vessel Speed Reduction (VSR) within 20 nautical miles, the 

2007 and 2008 auxiliary engine rules, the 40 nautical mile VSR program, the 2009 Low Sulfur 

Fuel regulation, the 2009-2018 cold ironing regulation, the use of 1% sulfur fuel in the 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 scenarios use the same year 2006 Canada emissions data. 

Note that 2006 is the latest year for which Canada provided data, and no accompanying futureyear projected inventories were provided in a form suitable for this study. For Mexico, different 

emissions were used for 2008 and 2018 as described in the Development of Mexico National 
3-9 

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 

Motor 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 2007 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, NOx, 

PM, SO2

Refinery Consent Decrees: plant/SCC controls NOx, PM, SO2

Commercial/Institutional/Hospital/Medical/Infectious Waste Incinerator Regulations NOX, PM, SO2

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 July 28, 2010 and include closures.

Hg, NOX, SO2, 

PM, HCl

New York ozone SIP controls VOC, NOX, 

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 NOX, CO, PM, 

SO2

Ethanol plants that account for increased ethanol production due to RFS2 mandate All

State fuel sulfur content rules for fuel oil—as of July, 2012, effective only in Maine, 

Massachusetts, New Jersey, New York and Vermont.

SO2

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.

NOX, SO2, HCl

Emissions reductions resulting from Boiler MACT controls to specific boiler units NOX, CO, PM, 

SO2, 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 2020 All

Livestock Emissions Growth from year 2008 to year 2020 (some farms in the point inventory) NH3, 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
3-10

Table 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

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 2008 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.

SO2

Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsideration NOX, CO, PM, 

SO2

New York, Connecticut, and Virginia ozone SIP controls VOC

Livestock Emissions Growth from year 2008 to year 2020 (some farms in the point inventory) NH3, 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 Air Toxics Rule 2 (MSAT2) inventory growth and control 

from year 2007 to 2020 

VOC

Texas oil and gas projections to year 2020 VOC, SO2, 

NOX, 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

VOC
3-11

Table 3-1(c). Control Strategies and Growth Assumptions for Creating 2020 Base Case 

Emissions Inventories from the 2007 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

Clean 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 Rule—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 PM2.5 Modeling Results and Analyses

The air quality modeling results were used in the RIA to estimate future PM2.5

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 existing 
3-12

standards of 15/35, the revised annual standard of 12 μg/m3 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 PM2.5 for the 2020 base case and 2020 control case. Air quality response ratios are 

calculated 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 alternative annual standard levels for input into the benefits 

assessment. 

The flow diagram shown in Figure 3-2 summarizes our approach for calculating futureyear 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 those 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 PM2.5

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 reductions, these 2020 base and control case design values were adjusted 

downward using air quality response ratios, which give the change PM2.5 design value (μg/m3) 

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 (NH4)2SO4, NH4NO3 and direct PM2.5 associated with changes in 

emissions of SO2, NOx and direct PM2.5, respectively. These PM2.5 component species were 

selected for reduction to meet the standard levels because they dominate the mass of PM2.5 in 

the areas of concern in the 2020 cases. The sensitivity simulation referred to as “2020 

NOx_PM2.5” was used in calculating the air quality ratios associated with changes in NOx and 
3-13

direct PM2.5 emissions. This simulation was based on anthropogenic NOx and direct PM2.5

emission reductions from non-EGU sources of 25% and 50%, respectively, relative to the 2020 

base case. The sensitivity simulation referred to as “2020 SO2_RWC” was used in calculating the 

air quality ratios associated with changes in SO2 emissions. This simulation was based on 

anthropogenic SO2 and residential wood combustion emissions reductions from non-EGU 

sources of 25% and 100%, respectively, relative to the 2020 base case8. In the sensitivity runs, 

emissions reductions for direct PM2.5 were generally applied in counties with monitors with 

annual design values above 11 μg/m3 level in the 2020 base case, while emission reductions for 

NOx and SO2 were generally applied in those counties as well as their adjacent counties. This 

approach reflects the local impacts of direct PM2.5 emissions on air quality and the broader 

geographic impacts on PM2.5 of SO2 and NOx 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 procedures 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 annual standards of 13, 12, and 11 μg/m3 (Boxes 4 

through 9 in Figure 3-2). 

 

8 The results 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 

Levels
3-15

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 futureyear 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 NOx_PM2.5

sensitivity

Simulation of 2020 where anthropogenic NOx and 

PM2.5 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 NOx and direct 

PM2.5

2020 SO2_RWC 

sensitivity

Simulation of 2020 where anthropogenic 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 SO2 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 NOx_PM2.5 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 

model results are used in a relative sense by estimating future-year PM2.5 relative response

factors (RRFs). RRFs are ratios that are calculated from the modeled changes in PM2.5 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 PM2.5 component (i.e. sulfates, 

nitrates, organic carbon, etc.). Future-year estimates of the PM2.5 annual and 24-hour standard 

design values at monitor locations are then calculated by applying the species-specific RRFs to 

ambient PM2.5 concentrations from the Federal Reference Method (FRM) Network, which are 

disaggregated into species concentrations through processing and interpolation of PM2.5

species data from the CSN and IMPROVE monitoring networks. 
3-16

To more easily apply this methodology, EPA has created software, called Modeled 

Attainment Test Software (MATS) (Abt, 2012) to calculate future-year PM2.5 annual and 24-hour 

standard design values. For this RIA, design values are projected from ambient Federal 

Reference Method measurements during the period 2005-20099 coupled with PM2.5 species 

data from IMPROVE and CSN sites for the 2006–2008 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

understanding the PM species contributing to high PM2.5 concentrations which is informative 

for designing control strategies 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 

observed 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. “burn 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 number 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 level based on 

local air quality forecasts of high PM2.5 days. The burn ban programs vary by area, but are 

similar in many ways. They generally have “stage 1” (lower concentration PM2.5 days) and 

“stage 2” (higher concentration PM2.5 days) level “burn ban” days with mandatory compliance 

on stage 2 days. The forecast trigger level also varies by area. When the daily PM2.5 NAAQS was 

lowered to 35 μg/m3 in 2006 most areas implemented a trigger level at or below 35 μg/m3 for a 

mandatory burn ban10. 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/m3 NAAQS. Since all of the 

 

9 The 2005 -2007 period includes design values 2005-2007, 2006-2008, and 2007-2009. 10 Some areas previously (before 2007) had voluntary burn ban programs with relatively high trigger levels based 

on the 1997 daily PM2.5 NAAQs (65 μg/m3

).
3-17

identified areas have implemented 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 

this analysis, episodic residential wood burning adjustments were made for the areas identified 

in Table 3-311:

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 emissions 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 PM2.5 emissions, we 

calculated the total change in primary organic carbon, elemental carbon, and crustal PM2.5

 

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 curtailment 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 PM2.5 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 

PM2.5 concentrations that are commensurate with the reduction in primary PM2.5 emissions13. 

We also assumed that burn bans are applicable on certain days in the 1st and 4th 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 fraction14 of measured days above 35 μg/m3 in 

the 1st and 4th 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 3-415. 

 

12 The sensitivity run also included SO2 emissions reductions. The SO2 reductions have no impact on the organic 

carbon, elemental carbon, and crustal primary PM2.5 species concentrations. 13 Since all of the adjustments are for primary PM2.5, it is assumed that emissions reductions and the change in 

concentration are linear (i.e. a 50% reduction in residential wood combustion PM2.5 emissions leads to a 50% 

reduction in the primary PM2.5 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 exceedence days during the 1st and 4th 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/m3

).
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 1st and 4th quarters (i.e., half of the 

burn ban days were applied to the high modeled days in the 1st quarter and half to the high 

modeled days in the 4th quarter). This approach provided burn ban RRFs for the 1st and 4th

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 the 
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annual average design value calculations. The daily design value modeled RRFs were calculated 

from the change in modeled PM2.5 species on the 10% highest modeled PM2.5 days in each 

quarter (i.e., the 9 highest modeled days per quarter). In this approach we assume that a burn 

ban will apply to all high PM2.5 days (days > 35 μg/m3) in the 1st and 4th quarters at each site. 

Therefore, we performed the calculation by applying the 70% burn ban adjustment on the 10% 

highest modeled days in the 1st and 4th 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/m3. The impact on the 2020 base case daily design values ranged from 0.1 to 13.1 μg/m3.The 

procedures for calculating 2020 control case design values that reflect the effects of the burn 

ban programs are described 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 PM2.5 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 exclusion 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 

period. 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 PM2.5 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 official 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 atypical 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 identify 
3-21

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 design 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/m3

. 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 summer16 [62 site-days];

2. Fireworks—High PM2.5 concentrations predominantly on July 4th or 5th [37 site-days];

3. Other unusual high data—Other site-days with very high measured PM2.5

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 2005-200917,18. 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/m3

. The impact on the daily design values 

ranged from 0 to 12.9 μg/m3. 

We recalculated the 2020 base case and 2020 control case annual and daily PM2.5 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 Meeting 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 case 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/m3 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 account 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 control 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 

curtailments19. 

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 PM2.5 emission reductions were applied to meet this level. The 

additional direct PM2.5 emission reduction amounts were estimated using air quality ratios. The 

direct PM2.5 emissions reductions needed to attain the 15/35 standard were also applied to 

reduce PM2.5 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 control case was 36.4 μg/m3 at monitor 60719004. Additional emissions 

reductions of 585 tons of direct PM2.5 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 subset 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 level20 as follows: (36.4 − 35.4) / 1.710 × 1000 = 585 tons, where 1.710 is the

24-hr direct PM2.5 air quality ratio for the monitor 60719004 (Table 3.A-3). The 585 tons of 

direct PM2.5 emissions reductions in this county were estimated to reduce the highest annual 

design value in San Bernardino at monitor 60710025 from 13.41 to 12.99 μg/m3 as follows: 

13.41 – (585 × 0.710 / 1000) = 12.99 μg/m3, where 0.710 is the annual direct PM2.5 air quality 

ratio for the 60710025 monitor (Table 3.A-3). The direct PM2.5 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 Direct PM2.5 Emission Reductions beyond the Adjusted 2020 Control Case 

to Meet the Current Standard Level for Counties that Exceed the Revised or 

Alternative Annual Standard Levels in the Adjusted 2020 Base Case

FIPS Code State Name County Name

Direct PM2.5 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/m3 but that did not exceed the existing standard level. 

These emissions controls are relevant for meeting the 11 μg/m3 level (Box 9) but are not 

relevant for meeting the existing standard level. Therefore annual design values in the 15/35 

case are set to those 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-hour design value of 35.4 μg/m3 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 

illustrative attainment strategy to just meet the revised annual 12 μg/m3 standards, as well as 

two alternative annual standards of 13 μg/m3 and 11 μg/m3, incremental to just meeting the 

current standards of 15/35, and reflecting emissions projected to reflect the 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 standards. 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/m3 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 annual 

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 additional 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 benefits 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 analytical baseline 

for estimating the costs and benefits of attaining the revised standard of 12 μg/m3 and 

alternative annual standards of 13 μg/m3 and 11 μg/m3 with the following characteristics. The 

analytical baseline was developed by applying a mobile NOx emission adjustment to design 

values at levels attaining 15/35. This approach allows us to generate costs and benefits of full 
3-25

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 

due 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 emissions 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 μg/m3 in the Analytical 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 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. 23 There 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/m3. 

Annual PM2.5 design values at monitors in Tulare and Riverside Counties in California exceeded 

the alternative standard level of 13 μg/m3 in the analytical baseline (Table 3-6 and Figure 3-3). 

The additional direct PM2.5 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/m3. Emission reductions 

of 626 tons of direct PM2.5 were estimated to be required for this monitor to meet the annual 

standard level of 13.04 μg/m3 as follows: (14.58 − 13.04) / 2.459 × 1000 = 626 tons, where 

2.459 is the annual direct PM2.5 air quality ratio for monitor 60658005 (Table 3.A-3). The 
3-27

emissions reductions by county to attain a 13 μg/m3 standard are provided in Table 3.7. These 

reductions were applied to lower the annual PM2.5 design values at all sites in the given 

county.

24 

Table 3-7. Tons of Direct PM2.5 Emission Reductions Beyond the Analytical Baseline to Meet 

the 13 μg/m3 Level

FIPS Code State Name County Name Direct PM2.5 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/m3 . Annual 

PM2.5 design values at monitors in the following 7 counties in California exceeded the revised 

standard level of 12 μg/m3 in the analytical baseline (Table 3-6 and Figure 3-3): Los Angeles, 

Riverside, San Bernardino, Kern, Tulare, Merced, and Imperial. The additional direct PM2.5

emission reductions required for these counties to meet the standard level of 12 μg/m3 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/m3. Emission reductions of 1,033 tons of 

direct PM2.5 were estimated to be required for this monitor to meet the annual standard level 

of 12.04 μg/m3 as follows: (14.58 − 12.04) / 2.459 × 1000 = 1033 tons, where 2.459 is the 

annual direct PM2.5 air quality ratio for monitor 60658005 (Table 3.A-3). The emissions 

reductions by county to attain a 12 μg/m3 standard are provided in Table 3.8. These reductions 

were applied to lower the annual PM2.5 design values at all sites in the given county25. 

 

24 Emissions reductions needed in Tulare County were also applied to reduce the annual PM2.5 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 PM2.5 Emission Reductions Beyond the Analytical Baseline to Meet 

the 12 μg/m3 Levela

FIPS Code State Name County Name Direct PM2.5 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 μg/m3

. 

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/m3 . 

Annual PM2.5 design values at monitors in 23 counties exceeded the alternative standard level 

of 11 μg/m3 in the analytical baseline (Table 3-6 and Figure 3-3). As discussed above, annual

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 controls needed to meet the 

alternative standard level of 11 μg/m3 in these counties, we started with annual design values 

for the adjusted 2020 control case (Box 4 of Figure 3-2). The additional direct PM2.5 emission 

reductions required for these counties to 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 μg/m3 in the adjusted 2020 base case and 11.11 μg/m3 in the adjusted 

2020 control case. The additional direct PM2.5 emission reductions needed beyond the emission 

reductions of the 2020 control case for this monitor to meet the 11 μg/m3 level were estimated 

using air quality ratios as follows: (11.11 − 11.04) / 0.561 × 1000 = 125 tons, where 0.561 is the 

direct PM2.5 air quality ratio for monitor 10730023. Annual PM2.5 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 PM2.5 Emission Reductions Beyond the Analytical Baseline to Meet 

the Alternative Standard 11 μg/m3 Levela

FIPS Code State Name County Name Tons of Direct PM2.5

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 PM2.5 for Benefits Inputs

The calculation of health benefits for the revised annual standard of 12 μg/m3 and the 

two alternative annual standards uses spatial surfaces of gridded annual average PM2.5

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 quality 
3-30

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 are 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 case and each standard to create spatial fields of annual 

PM2.5 for each of these cases. As part of this technique, VNA uses the inverse distance squared 

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 PM2.5 for each standard level to 

annual PM2.5 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 was 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 network 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 PM2.5

spatial surfaces are smoothed to minimize sharp gradients in PM2.5 concentrations in the spatial 

fields due to changes in the monitor concentrations26. Because the VNA approach interpolates 

 

26 For the purposes of estimating 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 AQij from Alternative 

VNA Interpolated AQij from 2020 Adjusted AQij = x MATS AQij 
3-31

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. To the extent that any bias in the interpolated values is 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 PM2.5 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, there 

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 results 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 SO2, NOx and direct PM2.5 similar 

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 PM2.5

emission reductions) or a group of counties (e.g., SO2 and NOx emissions reductions). Because 

of the more local influence of changes in directly emitted PM2.5 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 PM2.5 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 the 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 and 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 PM2.5 air quality problem is dominated by residential wood combustion 

emissions of PM2.5, 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 PM2.5, combined with resident’s reliance on wood burning for home heating can 

produce poor PM2.5 air quality. However, since 2005, Libby has successfully implemented a 

woodstove change-out program that has resulted in consistent improvements in PM2.5 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 future-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 Libby 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 μg/m3 and an annual design value of 

12.65 μg/m3 in the 2020 base case. However, Santa Cruz has few local emissions sources and 

therefore relatively low emissions available for control. Total emissions of SO2, NOx and direct 

PM2.5 were 65, 688, and 542 tons, respectively, in Santa Cruz County in the 2020 base case.

Total emissions of SO2, NOx and direct PM2.5 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 the 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 County have been recognized previously. On September 25, 

2012, in a Federal Register Notice, EPA Region IX approved 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 PM10, 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 for 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 extinction (bext) values, converted to units of visual range (km). 

Since we are interested in providing visibility estimates throughout the US, we utilize gridded, 

speciated PM2.5 data that is produced by MATS (Abt, 2012) along with future-year design values 

for the 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 PM2.5 data used as input to 

BenMAP for health benefits is based on adjusted species data using the SANDWICH technique 

(Frank, 2006). The PM2.5 species data is adjusted to match the nature of the PM2.5 FRM filter 

data that is used as the basis for determining attainment of the PM2.5 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 PM2.5 mass and the sum of the rest of the PM2.5 species. For visibility calculations, we use 

the “raw” PM2.5 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.

bext = 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/m3

, 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 2006-2008 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 

derive 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 light 
3-35

extinction values (for each grid cell) are then averaged together to get annual average visibility.

The procedure was 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 United States. 

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, Computational 

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 Projections for 2008, 2012, 

AND 2030. 

ERG. 2009. Technical memorandum: “Mexico 2018 Emissions Projections for Point, Area, OnRoad Motor Vehicle and Nonroad Mobile Sources.”

IMPROVE, (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
3-36

Atmosphere, ISSN: 0737-5352-74. Also available at:

http://vista.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. 9th 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 Metropolitan 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, PM2.5, 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). 2011b. 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. Environmental 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). 2012b. Technical Support Document: Preparation 

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, Harvard 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 (NH4)2SO4, NH4NO3 and direct PM2.5 associated with changes in 

emissions of SO2, NOx and direct PM2.5, respectively. These PM2.5 component species were 

selected for reduction to meet the standard levels because they dominate the mass of PM2.5 in 

the areas of concern in the 2020 cases. The sensitivity simulation referred to as “2020 

NOx_PM2.5” was used in calculating the air quality ratios associated with changes in NOx and 

direct PM2.5 emissions. This simulation was based on anthropogenic NOx and direct PM2.5

emission reductions from non-EGU sources of 25% and 50%, respectively, relative to the 2020 

base case. The sensitivity simulation referred to as “2020 SO2_RWC” was used in calculating the 

air quality ratios associated with changes in SO2 emissions. This simulation was based on 

anthropogenic SO2 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, 

emissions reductions for direct PM2.5 were generally applied in counties with monitors with 

annual design values above 11 μg/m3 level in the 2020 base case, while emission reductions for 

NOx and SO2 were generally applied in those counties as well as their adjacent counties. This 

approach reflects the local impacts of direct PM2.5 emissions on air quality and the broader 

geographic impacts on PM2.5 of SO2 and NOx 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 PM2.5 air quality ratios, the county group

included just the county containing the monitor because of the relatively local nature of the 

impacts of direct PM2.5 emissions on ambient PM2.5 concentrations. For the development of 

NOx and SO2 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 SO2

in view of the more widespread impacts on (NH4)2SO4, NH4NO3 of local emissions reductions of 

NOx and SO2 compared to direct PM2.5. 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 Basin. In the South Coast Air Basin, the county group for NOx and SO2

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 total NOx emission change that contributed to PM2.5 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 simulations (see Appendix 3.A.1.2 for details). 

In adjusting 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 relative positions in 

the San Joaquin Valley. Also, direct PM2.5 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 Kings 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 PM2.5, SO2 and NOx were 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 relative to the 2020 base case. Speciated annual and quarterly 24-hr 

RRFs were calculated for the 2020 NOx_PM2.5 and 2020 SO2_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 PM2.5, (NH4)2SO4 and NH4NO3

3 components of the 

design value for the 2020 sensitivity cases relative to the 2020 base case was then calculated as 

(RRF-1)4

 for 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 (NH4)2SO4 and NH4NO3 components are computed using the SO4, NO3, NH4 and water fraction from MATS as 

described in EPA guidance (EPA, 2007). The direct PM2.5 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 PM2.5

5

, SO2 and NOx between the 2020 base case and 2020 sensitivity cases were 

determined for the county group relevant to a given monitor. County emission groups for NOx

and SO2 for the monitors considered are listed in Tables 3.A-1 and 3.A-2. 

Step 3: Calculate the ratio 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 PM2.5, (NH4)2SO4 and NH4NO3

components of the annual and daily standard design values per fractional change in direct 

PM2.5, SO2 and NOx 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 the 

ratio of direct PM2.5, (NH4)2SO4 and NH4NO3 to the emissions of direct PM2.5, SO2 and NOx, 

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 PM2.5 component 

specie i and emission specie j is given by equation 3-1, where DVi indicates the PM2.5

component design value. 

Air Quality Ratio= 1000 1

× 





























 −

ControlCase j

i

SensitivityCase j j

i

Emission

DV

Emission Emission

RRF

∆ (3.A.1)

Air quality ratios give an estimate of how PM2.5 design value components (μg/m3) would 

change if 1000 tons of direct PM2.5, SO2 and/or NOx emissions were reduced in the county group 

in which the monitor is located. Annual air quality ratios that relate changes in the NH4NO3

component of the design value to changes in NOx emissions 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 

NOx emission adjustment equal to the change in mobile NOx emissions from the year 2020 to 

2025. Annual and daily air quality ratios that relate changes in the (NH4)2SO4 component of the 

design value to changes in SO2 emissions 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 PM2.5 emissions are computed as the sum of emissions of elemental carbon, primary organic carbon, and 

unspeciated PM2.5 mass.
3.A-5 

inappropriate SO2 controls. Annual and daily air quality ratios that relate changes in the direct 

PM2.5 component of the design value to changes in direct PM2.5 emissions are listed in Table 

3.A-3 for monitors in counties with at least one monitor with an annual design value above 11 

μg/m3 in the 2020 base case

Table 3.A-1. Annual NOx Air Quality Ratios for Monitors in California Counties that Received a 

2025 Mobile NOx Emission Adjustment

Monitor 

ID

FIPS 

Code State Name County Name

Annual NOx Air 

Quality Ratio 

(μg/m3 Change 

in NO3 per 1000 

tons NOx) 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 

Table 3.A-1. Annual NOx Air Quality Ratios for Monitors in California Counties that Received a 

2025 Mobile NOx Emission Adjustment (continued)

Monitor 

ID

FIPS 

Code State Name County Name

Annual NOx Air 

Quality Ratio 

(μg/m3 Change 

in NO3 per 1000 

tons NOx) 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 NOx Air Quality Ratios for Monitors in California Counties that Received a 

2025 Mobile NOx Emission Adjustment (continued)

Monitor 

ID

FIPS 

Code State Name County Name

Annual NOx Air 

Quality Ratio 

(μg/m3 Change 

in NO3 per 1000 

tons NOx) 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 

Sacramento
3.A-8 

Table 3.A-2. Annual and Daily SO2 Air Quality Ratios for Monitors in Counties where Ratios 

were Used in Adjusting Daily Design Values to Remove the Impact of SO2

Controls

Monitor ID

FIPS 

Code State Name

County 

Name 

Annual SO2 Air 

Quality Ratio 

(μg/m3 Change 

in SO4 per 

1000 tons SO2) 

Daily SO2 Air 

Quality Ratio 

(μg/m3 Change 

in SO4 per 

1000 tons SO2) 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, 

Westmoreland
3.A-9 

Table 3.A-3. Annual and Daily Direct PM2.5 Air Quality Ratios for Monitors in Counties with at 

Least One Monitor having an Annual Design Value Above 11 μg/m3 in the 2020 

Base Case

Monitor ID

FIPS 

Code State Name County Name

Annual PM2.5 Air Quality 

Ratio (μg/m3 Change in 

Direct PM2.5 per 1000 tons 

PM2.5) 

Daily PM2.5 Air Quality 

Ratio (μg/m3 Change in 

Direct PM2.5 per 1000 tons 

PM2.5) 

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

60370002 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 California 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 Direct PM2.5 Air Quality Ratios for Monitors in Counties with at 

Least One Monitor having an Annual Design Value Above 11 μg/m3 in the 2020 

Base Case (continued)

Monitor ID

FIPS 

Code State Name County Name

Annual PM2.5 Air Quality 

Ratio (μg/m3 Change in 

Direct PM2.5 per 1000 tons 

PM2.5) 

Daily PM2.5 Air Quality 

Ratio (μg/m3 Change in 

Direct PM2.5 per 1000 tons 

PM2.5) 

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 Illinois Cook 0.307 N/A

(continued)
3.A-11

Table 3.A-3. Annual and Daily Direct PM2.5 Air Quality Ratios for Monitors in Counties with at 

Least One Monitor having an Annual Design Value Above 11 μg/m3 in the 2020 

Base Case (continued)

Monitor ID

FIPS 

Code State Name County Name

Annual PM2.5 Air Quality 

Ratio (μg/m3 Change in 

Direct PM2.5 per 1000 tons 

PM2.5) 

Daily PM2.5 Air Quality 

Ratio (μg/m3 Change in 

Direct PM2.5 per 1000 tons 

PM2.5) 

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 Michigan 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 Direct PM2.5 Air Quality Ratios for Monitors in Counties with at 

Least One Monitor having an Annual Design Value Above 11 μg/m3 in the 2020 

Base Case (continued)

Monitor ID

FIPS 

Code State Name County Name 

Annual PM2.5 Air Quality 

Ratio μg/m3 Change in 

Direct PM2.5 per 1000 tons 

PM2.5) 

Daily PM2.5 Air Quality 

Ratio (μg/m3 Change in 

Direct PM2.5 per 1000 tons 

PM2.5) 

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 NOx Emission Contributions to NH4NO3 PM2.5 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 modeling. One of the simulations reflected the 2020 base case emission scenario, and 

the other 8 simulations had NOx and direct PM2.5 emissions reductions relative to the 2020 base 

case in a one- or two-county group that matched the emissions reductions in that group in the 

2020 NOX_PM2.5 sensitivity run. The purpose of these simulations was to estimate the impact of 

NOx emission reductions in a given area of California’s Central Valley on NH4NO3 PM2.5 in other 

areas of the valley. The month of January was selected for this analysis because high NH4NO3

PM2.5 episodes occur during winter months in the Central Valley.
3.A-13

Figure 3.A-2. California Modeling Domain for 12-km Simulations

Equation 3.A.2 was used to estimate the fractional contribution of NOx emissions from 

one area of California’s Central Valley on another (i.e., Wigrp,jgrp). 

Wigrp,jgrp = ∆CNO3,igrp/∆EmissNOx,jgrp

max�∆CNO3,igrp/∆EmissNOx,jgrp� (3.A.2) 

where ∆CNO3,igrp is the change in average nitrate PM2.5 concentration at a given monitor in the 

igrp county group between the simulation with 2020 base case emissions and the simulation 

with NOx emissions reductions in the jgrp county group, and ∆EmissNOx,jgrp is the change in NOx

emissions in the jgrp county group between the simulations with 2020 base case emissions and 

the simulation with NOx emissions reductions in the jgrp county group. Note that Equation 

3.A.2 normalizes each ∆concentration-to-∆emission ratio for a given county group (numerator) 

by the maximum ∆concentration-to-∆emission ratio associated with that county group 

(denominator). The fraction of NOx emissions from a given county or county group that impacts 

PM2.5 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. Contribution Weighting Factors for NOx Emissions in Counties or County Groups 

in California’s Central Valley as Calculated According 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 0.03
3-37
4-1 

CHAPTER 4

CONTROL STRATEGIES

4.1 Synopsis

As discussed in previous chapters, the EPA is revising the PM2.5annual standard to a level 

of 12 μg/m3and is retaining the 24-hour standard of 35 μg/m3. Pursuant 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/m3 and a more stringent alternative annual standard of 11 μg/m3. This chapter documents 

the emission control measures we applied to simulate attainment with the revised and 

alternative PM2.5 annual standards.

The EPA analyzed the impact that additional emissions controls across numerous sectors 

would have on predicted ambient PM2.5 concentrations incremental to an analytical baseline, 

which includes the current PM2.5 standard of 15/35 μg/m3 as well as other major rules such as

MATS. Thus, the analysis for the revised and alternative standards 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 2020. The hypothetical 

control strategies presented in this RIA represent illustrative options for emissions reductions 

that achieve national attainment of the revised standard as well as the alternative standards. 

The hypothetical control strategies are not recommendations for how the revised PM2.5

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 to 
4-2 

analyze the impacts and costs of the control scenario incremental to attainment of the current 

standard of 15/35 μg/m3 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 base case projections. 

2. Develop a hypothetical control scenario for these areas and generate a control case 

2020 emissions 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 very high cost but little impact on emissions). 

5. Calculate the portion of the hypothetical control scenario control measures that are 

attributed to meeting the current standard of 15/35 μg/m3. 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/m3). 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 discusses the steps listed above that were key to conducting the control 

strategy analysis for year 2020 for the revised and alternative standards. 

4.2 PM2.5 Control Strategy Analysis

4.2.1 Identify Geographic Areas

The first 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 revised standard, there 
4-3 

were 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 measures 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 PM2.5 Standard (15/35) in 2020
4-4 

Figure 4-2. Counties Projected to Exceed the 13 ug/m3 Alternative Standard in the 

Analytical Baseline
4-5 

Figure 4-3. Counties Projected to Exceed the 12 ug/m3 Revised Standard in the Analytical 

Baseline
4-6 

Figure 4-4. Counties Projected to Exceed the 11 ug/m3 Alternative Standard in the 

Analytical Baseline

4.2.2 Developing the Control Scenario

The U.S. EPA used monitoring and emissions inventory information, including 

monitoring speciation, to identify the pollutants that were the primary contributors to PM2.5

exceedances 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/m3). NonEGU 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 
4-7 

Strategy Tool1 (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.

Nonpoint and onroad mobile source emissions data are generated at the county level, 

and therefore controls for these emissions sectors were applied at the county level. Non-EGU 

point source controls are applied to individual point sources. Nonpoint source controls were 

applied to NOx, SO2, and PM2.5. The analysis for non-EGUs applied NOx, SO2, and PM2.5 controls 

to the following source categories: industrial boilers, commercial and institutional boilers, 

sulfuric acid plants (both stand alone 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 SO2 reductions, fabric filters for PM2.5 reductions, and SCR 

and low NOx burners for NOx. 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 

PM2.5 Diesel Particulate Filter X X X 

 Dry Electrostatic Precipitator (ESP) X 

 Fabric Filters X X X 

 Venturi Scrubber X 

 Wet Electrostatic Precipitator (ESP) 

SO2 Coal Washing X 

 Flue Gas Desulfurization (FGD) X 

 Spray Dry Absorber 

 Sulfur Recovery and/or Tail Gas Treatment 

 Wet FGDs X X 

NOx Biosolid Injection X 

 Low NOx Burner (LNB) X 

 Low NOx 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-Point (Area)

PM2.5 ESPs for Commercial Cooking X X X X 

 Low NOx Burners for Residential Natural Gas X 

 Substitute chipping for open burning X X X 

 Substitute landfilling for open burning X 

SO2 Chemical Additives to Waste X X 

 Fuel Switching for Stationary Source Fuel Combustion X X 

 Low Sulfur Home Heating Fuel X 

NOx Low NOx Burners for Residential Natural Gas X 

 Water heater + Low NOx Burner Space Heaters X 

Onroad Mobile

NOx 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/m3 levels are incremental to control 

measures indicated for the 15/35 μg/m3 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 emissions 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 PM2.5 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 constraint was applied in the control strategy selection that 

concerned cost of control. No control measures were applied that cost greater than 

$20,000/ton of emission reduction. We did not include known controls at an annual cost of 

more than $20,000 per ton because either (i) the remaining 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 PM2.5 standards.
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 emissions 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 2020 from recently promulgated national regulations, 

adjustments 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 estimates 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 PM2.5 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, 2005b) 

 NOx 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 Oceangoing 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) NESHAPs (U.S. EPA, 2010)

 Mercury 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 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 μg/m3 standard. For 

two areas in Southern California (South Coast and San Joaquin), the degree of projected nonattainment with the revised annual standard of 12 μg/m3 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/m3. 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 additional 

emissions reductions that would be needed to attain the current 15/35 μg/m3 standard. The 

following paragraphs 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, higher 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 revised 

or alternative standards3

. 

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 PM2.5 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 the costs and benefits of attaining the selected annual standard of 12 μg/m3 and 

alternative standards of 13 μg/m3 and 11 μg/m3 with the following characteristics: (1) reflects 

“on the books” regulations as implemented through 2020 for all nonattainment counties, 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 areas 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, promulgated 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 other 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 

analysis. 

A map of the country is presented in Figure 4-1 which shows the counties projected to 

exceed the current PM2.5 standard of 15/35 μg/m3 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 PM2.5 standard. In addition, control measures were 

applied to five California counties adjacent to exceedance 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 PM2.5 NAAQS are listed in Table 4-1; details regarding the individual controls 

are provided in Appendix 4.A. Controls were applied to directly emitted PM2.5 and the PM2.5

precursors of NOx and SO2 given that nitrate, sulfate, and primary PM2.5 species usually 

dominate measured PM2.5 based on speciation data measured at the Chemical Speciation 

Network (CSN) sites. Control measures that directly reduced emissions of PM2.5 in proximity to 

the exceeding monitors were determined to be most effective at bringing areas into 

attainment, with NOx and SO2 controls supplementing the PM2.5 controls depending upon the 

monitor speciation data. PM2.5 control measures were applied in the county containing the 
4-12

exceeding monitor for the non-EGU point and area source emissions. If additional emissions 

control was needed, SO2 and NOx control measures were applied within the county exceeding 

and in a small number of cases, an 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 controls for PM2.5 to attain the standard.

4.2.4 Identify Known Controls Needed to Meet the Revised and Alternative 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 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 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 calculate 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.

In the revised and alternative standards analysis, PM2.5 controls were sought first

because they were generally more cost-effective. If it was determined that additional controls 

were needed, SO2 and NOx control measures were selected depending on the chemistry of each 

specific geographic area. 

It should be noted that while PM2.5 controls were applied only within the counties with 

monitors projected to exceed the alternative standard being analyzed, SO2 and NOx controls 

were applied in the exceeding county as well as a small number of adjacent counties because of 

the transport of NOx and SO2 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

Standardsa

Emission Reductions in 2020 (annual tons/year)

Revised/ 

Alternative 

Standard Region PM2.5 SO2 NOx 

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 additional emission reductions beyond known controls needed to reach 

attainment. For information on the methodology used to develop those estimates, see Chapter 
4-14

3, Section 3.3.2. Table 4-4 shows the emission reductions needed from unknown controls for 

the alternative standards analyzed.

Table 4-4. Emission Reductions Needed Beyond Known Controls for the Revised and 

Alternative Standardsa

Emission Reductions in 2020 (annual tons/year)

Revised/ 

Alternative 

Standard Region PM2.5 SO2 NOx 

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 

reasonable modeling framework for analyzing the cost, emissions changes, and other impacts 

of regulatory controls. However, the estimates of emissions 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

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

Potential Source of Uncertainty

Direction of 

Potential 

Bias

Magnitude 

of Impact 

on 

Monetized 

Costsa 

Degree of 

Confidence 

in Our 

Analytical 

Approachb 

Ability to 

Assess 

Uncertaintyc 

Uncertainties Associated with PM Concentration Changes

Projections of future levels of emissions and emissions 

reductions necessary to achieve the NAAQS

Bothd Medium Medium Tier 1

Responsiveness of air quality model to changes in 

precursor emissions from control scenarios

Both MediumhighMedium 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 PM2.5design value and spatial 

fields of PM2.5 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 MediumhighHigh 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 

exceeding 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 MediumhighMediumhighTier 0
4-16

Potential Source of Uncertainty

Direction of 

Potential 

Bias

Magnitude 

of Impact 

on 

Monetized 

Costsa 

Degree of 

Confidence 

in Our 

Analytical 

Approachb 

Ability to 

Assess 

Uncertaintyc 

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 overestimateMediumhighLow 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 qualitative 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 factors. 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/highwaydiesel/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&disposition=attachment&contentType=pdf. 

U.S. Environmental Protection Agency (U.S. EPA). 2005. New Emission Standards for New 

Commercial Aircraft Engines. Office of Transportation and Air Quality. Available at 

http://www.epa.gov/oms/regs/nonroad/aviation/420f05015.htm. 

U.S. Environmental Protection Agency (U.S. EPA). 2005b. Regional Haze Regulations and 

Guidelines for Best Available Retrofit Technology Determinations. Office of Air Quality 

Planning and Standards. Available at http://www.epa.gov/fedrgstr/EPAAIR/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, PM2.5, 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-rhguidance.pdf. 

U.S. Environmental 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 Marine 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: 

Hospital/Medical/Infectious Waste Incinerators. Office of Air Quality Planning and 

Standards, Research Triangle Park, NC. Available at 

http://www.epa.gov/ttn/atw/129/hmiwi/fr06oc09.pdf. 

U.S. Environmental Protection Agency (U.S. EPA). 2010. Control of Emissions from New Marine 

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/fdsys/pkg/FR-2012-02-16/pdf/2012-806.pdf. 
4.A-1 

APPENDIX 4.A

ADDITIONAL CONTROL STRATEGY 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 revised or alternative 

standards for PM2.5. 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 Technologies1

This section summarizes control measures focused on reduction of PM2.5 from non-EGU 

point and nonpoint sources. However, it should be noted that PM10 will also be reduced by 

these measures. The amount of PM10 reduction varies by the fraction of PM10 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 

collection 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 Pollution 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 disadvantage 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 size 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 operation 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 control 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

Control 

Efficiency 

(percent)

Average 

Annualized 

Cost/Ton

Fabric Filtersa Industrial Boilers, Iron and Steel 

Mills, Pulp and Paper Mills

98 to 99.9 $2,000–$100,000

ESPs—wet or drya 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-EGUs 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 applications 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) 

engines (up to a 90reduction in fine PM). 

Diesel 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 Sourcesa

Control Measures

Sectors to which 

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 PM10 and 

PM2.5. 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 SO2 Control Measures

4.A.1.2.1 SO2 Control Measures for Non-EGU Point Sources

The SO2 emission control measures used in this analysis are similar to those used in the 

PM2.5 RIA prepared about four years ago. Flue gas desulfurization (FGD) scrubbers can achieve 

95–98% control of SO2 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 SO2. 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 control 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 SO2 Control Measures for Non-EGU Pointa

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 DoubleAbsorption process50% $1,000

a Sources: CoST control measures documentation report, May 2008, NESCAUM Report on Applicability of NOx, 

SO2, and PM Control Measures to Industrial Boilers, November 2008 available at 

http://www.nescaum.org/documents/ici-boilers-20081118-final.pdf, and 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 SO2 control measure currently operating except for 

the Cesium promoted catalyst.

4.A.1.2.2 SO2 Control Technology for Nonpoint Sources

Fuel switching from high to low-sulfur fuels is the predominant control measure 

available for SO2 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 lowsulfur 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 NOx Emissions Control Measures

4.A.1.3.1NOx Control Measures for Non-EGU Point Sources

This section describes available measures for controlling emissions of NOx from non-EGU 

point sources. In general, low-NOx 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 NOx control technologies exist for non-EGU sources: selective catalytic 

reduction (SCR), selective noncatalytic reduction (SNCR), natural gas reburn (NGR), coal reburn, 

and low-NOx 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 NOx emissions are expected to be of greater importance 

than thermal NOx 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 NOx 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 internal combustion engines, 

glass manufacturing, combustion turbines, and incinerators. NOx control measures available for 

petroleum refineries, particularly process heaters at these plants, include LNB, SNCR, FGR, and 

SCR along with combinations of these technologies. NOx control measures available for kraft 

pulp mills include those available to industrial boilers, namely LNB, SCR, SNCR, along with water 

injection (WI). NOx control measures available for cement kilns include those available to 

industrial boilers, namely LNB, SCR, and SNCR. In addition, mid-kiln firing (MKF), ammoniabased SNCR, and biosolids injection can be used on cement kilns where appropriate. Nonselective 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 NOx emissions at such plants. LNB, SCR, and SCR combined with steam injection (SI) are 

available measures for combustion turbines. Finally, SNCR is an available control 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 NOx Control Measures for Non-EGU Source Categoriesa 

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 NOx control measure currently operating except for post-combustion controls such as SCR and 

SNCR. For these measures, the costs presume that a NOx 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 BENEFITS ANALYSIS APPROACH AND RESULTS

5.1 Synopsis

This chapter presents 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 (PM2.5)-related air quality 

improvements resulting from the illustrative emission control scenarios that reduce emissions 

of directly emitted particles and precursor pollutants including SO2 and NOx to reach alternative

PM2.5 NAAQS levels in 2020.

1

These benefits are relative to an analytical baseline reflecting nationwide attainment of 

the current primary PM2.5 standards (i.e., annual standard of 15 μg/m3 and 24-hour standard of 

35 μg/m3) that includes promulgated national regulations and illustrative emission controls to 

simulate attainment with 15/35 as well as a NOx emission adjustment to reflect expected 

reductions in mobile NOx emissions between 2020 and 2025. We project PM2.5 levels in certain 

areas that would exceed the revised annual standard of 12 μg/m3 as well as alternative annual 

standards of 13 and 11 μg/m3 after simulated attainment with 15/35 in the analytical baseline. 

Table 5-1 summarizes the total monetized benefits of the revised and alternative PM2.5

standards in 2020. These estimates reflect the sum of the economic value of estimated PM2.5

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, 

2011d, 2011e). Rules such as the Mercury and Air Toxics Standard (MATS) and other emission 

reductions will have substantially reduced ambient PM2.5 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/m3

. 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 rules. 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 PM2.5

Standards in 2020 Incremental to the Analytical Baseline (billions of 2010$)

a

Benefits Estimate 13 μg/m3 12 μg/m3 11 μg/m3 

Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 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 PM2.5-related morbidities and premature deaths using PM2.5 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 discounted 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 cobenefits. 

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 PM2.5 

concentrations that coincide with the bulk of the observed PM concentrations in the 

epidemiological studies that are used in the benefits estimates. Likewise, we are less confident 

in the risk we estimate from simulated PM2.5 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 25th to 

10th 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. Most of the estimated avoided premature deaths 

occur at or above the lowest measured PM2.5 concentration in the two studies that are used to 

estimate mortality benefits.

In addition to PM2.5 benefits, implementation of emissions controls to reach some of the 

alternative PM2.5 standards would reduce other ambient pollutants, such as SO2, NO2, and 

ozone. However, because the method used in this analysis to simulate attainment does not 

account for 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 PM2.5 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 benefits 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 PM2.5 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 standards by answering two key 

questions:

1. What are the health effects of changes in ambient particulate matter (PM2.5) 

resulting from reductions in directly emitted PM2.5 and precursors due to the 

attainment of a new PM2.5 standard? 

2. What is the economic value of these effects?

In this analysis, we consider an array of health impacts attributable to changes in PM2.5. 

The Integrated Science Assessment for Particulate Matter (“PM ISA”)(U.S. EPA, 2009b) identifies

the human health effects associated with ambient particles, which include premature 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 causal 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 decision 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 PM2.5 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 PM2.5

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 ISAb

Other respiratory effects (e.g., pulmonary 

function, non-asthma ER visits, non-bronchitis 

chronic diseases, other ages and populations)

— — PM ISAb

Reproductive and developmental effects (e.g., 

low birth weight, pre-term births, etc.)

— — PM ISAb,c

Cancer, mutagenicity, and genotoxicity effects — — PM ISAb,c

(continued)
5-5 

Table 5-2. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain 

the Primary PM2.5 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 ISAd

Premature mortality based on long-term study 

estimates (age 30–99)

— — Ozone ISAd

Hospital admissions—respiratory causes (age 

> 65)

— — Ozone ISAd

Hospital admissions—respiratory causes (age 

<2)

— — Ozone ISAd

Emergency department visits for asthma (all 

ages)

— — Ozone ISAd

Minor restricted-activity days (age 18–65) — — Ozone ISAd

School absence days (age 5–17) — — Ozone ISAd

Decreased outdoor worker productivity (age 

18–65)

— — Ozone ISAd

Other respiratory effects (e.g., premature 

aging of lungs)

— — Ozone ISAb

Cardiovascular and nervous system effects — — Ozone ISAc

Reproductive and developmental effects — — Ozone ISAc

Reduced incidence 

of morbidity from 

exposure to NO2

Asthma hospital admissions (all ages) — — NO2 ISAd

Chronic lung disease hospital admissions (age 

> 65)

— — NO2 ISAd

Respiratory emergency department visits (all 

ages)

— — NO2 ISAd

Asthma exacerbation (asthmatics age 4–18) — — NO2 ISAd

Acute respiratory symptoms (age 7–14) — — NO2 ISAd

Premature mortality — — NO2 ISAb,c

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations)

— — NO2 ISAb,c

(continued)
5-6 

Table 5-2. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain 

the Primary PM2.5 Standards (continued)

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized More Information

Reduced incidence 

of morbidity from 

exposure to SO2

Respiratory hospital admissions (age > 65) — — SO2 ISAd

Asthma emergency department visits (all 

ages)

— — SO2 ISAd

Asthma exacerbation (asthmatics age 4–12) — — SO2 ISAd

Acute respiratory symptoms (age 7–14) — — SO2 ISAd

Premature mortality — — SO2 ISAb,c

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations)

— — SO2 ISAb,c

Reduced incidence 

of morbidity from 

exposure to 

methylmercury 

(through role of 

sulfate in 

methylation)

Neurologic effects—IQ loss — — IRIS; NRC, 2000d

Other neurologic effects (e.g., developmental 

delays, memory, behavior)

— — IRIS; NRC, 2000b

Cardiovascular effects — — IRIS; NRC, 2000b,c

Genotoxic, immunologic, and other toxic 

effects

— — IRIS; NRC, 2000b,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 the strength of the association.

d We assess these benefits qualitatively 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 the 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 costs or benefits, and as such, NAAQS RIAs are 

merely illustrative and the estimated costs and benefits are not intended to be added to the 
5-7 

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 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 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 PM2.5 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, 2012a): 

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., 2006), this new analysis follows the cohort for a longer time 

and includes more years of PM2.5 monitoring data. The all-cause PM2.5 mortality risk 

coefficient drawn from Lepeule et al. (2012) is roughly similar to the Laden et al. 

(2006) risk coefficient applied in the EPA’s recent analyses of long-term PM2.5

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 PM2.5 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 Effects 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 Rosamond 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, 2006a). In the Portland Cement NESHAP proposal RIA (U.S. EPA, 

2009a), the Agency no longer assumed a concentration threshold in the concentrationresponse function for PM2.5-related health effects and began using the benefits derived from 

the two major cohort studies of PM2.5 and mortality as the core benefits estimates, while still 

including a range of sensitivity estimates based on the EPA’s PM2.5mortality expert elicitation. 

In the NO2 NAAQS proposal RIA (U.S. EPA, 2009a), we revised the estimate used for the valueof-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 PM2.5 at 

lower baseline concentrations of PM2.5. 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, 2012a), we updated the 

American Cancer Society cohort study to Krewski et al. (2009), updated health endpoints in the 

core and sensitivity analyses, incorporated new morbidity studies, updated the median wage 

data in the cost-of-illness studies, and expanded 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 values 

to those changes assuming independence of the values for those individual endpoints. Total 

benefits are calculated simply as the sum of the values for all non-overlapping health 

 

2 The damage function approach is a more comprehensive method of estimating total benefits than the hedonic 

price 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 assessing costs and 

benefits of environmental quality programs and has been used in several recent published 

analyses (Levy et al., 2009; Fann et al., 2012a; Tagaris et al., 2009). 

To assess economic value in a damage-function framework, the changes in 

environmental quality must be translated into effects on people or on the things that people 

value. In some cases, the changes in environmental quality can be directly valued, as is the case 

for changes in visibility. In other cases, such as for changes in ozone and PM, 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 PM2.5. 

We note at the outset that the EPA rarely has the time or resources to perform 

extensive new research to measure directly either the health outcomes or their values for 

regulatory analyses. Thus, similar to Kunzli et al. (2000) and other, more recent health impact 

analyses, our 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 PM2.5 and ozone air quality. HIAs 

are a well-established approach for estimating the retrospective or prospective change in 

adverse health impacts expected to result from population-level changes in exposure to 

pollutants (Levy et al., 2009). PC-based tools such as the environmental Benefits Mapping and 

Analysis Program (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 available, 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., 2012a). The EPA and others have relied upon this method to predict future changes 

in health impacts expected to result from the implementation of regulations affecting air 
5-10

quality (e.g., U.S. EPA, 2011d). For this assessment, the HIA is limited to those health effects 

that are directly linked to ambient PM2.5 concentrations. There may be other indirect health 

impacts associated with implementing emissions controls, such as occupational health 

exposures.

The HIA approach used in this analysis involves three basic steps: (1) utilizing projections 

of PM2.5 air quality3 and estimating the change in the spatial distribution of the ambient air 

quality; (2) determining the subsequent change in population-level exposure; (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 − �eβ∙∆x�yo ∙ Pop (5.1)

where y0 is the baseline incidence rate for the health endpoint being quantified (for example, a 

health impact function quantifying changes in mortality would use the baseline, or background, 

mortality rate for the given population of interest); Pop is the population affected by the 

change in air quality; ∆x is the change in air quality; and β is the effect coefficient drawn from 

the epidemiological study. Figure 5-1 provides a simplified overview of this approach.

 

3 Projections of ambient PM2.5 concentrations for 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 Economic Valuation of Health Impacts

After quantifying the change in adverse health impacts, the final step is to estimate the 

economic value of these avoided impacts. The appropriate economic value for a change in a 

health effect depends on whether the health effect is viewed ex ante (before the effect has 

occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air 

pollution generally lower the risk of future adverse health effects by a small amount for a large 

population. The appropriate economic measure is therefore ex ante willingness to pay (WTP) 

for changes in risk. Epidemiological studies generally provide estimates of the relative risks of a 

particular health effect for a given increment of air pollution (often per 10 μg/m3 for PM2.5). 

These relative risks can be used to develop risk coefficients that relate a unit reduction in PM2.5 

to changes in the incidence of a health effect. In order to value these changes in incidence, WTP 

for changes in risk need to be converted into WTP per statistical incidence. This measure is 

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 is 
5-12

$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 population is 

automatically taken into account by the number of incidences predicted by epidemiological 

studies applied to the relevant population. The same type of calculation can produce values for 

statistical incidences of other health endpoints.

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 4.0.52 (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 PM2.5

PM2.5 Health 

Functions

Economic 

Valuation 

Functions

2020 Population 

Projections

PM2.5 Incremental Air 

Quality Change

PM2.5-Related Health 

Impacts

Woods & Poole 

Population 

Projections

Background 

Incidence and 

Prevalence Rates

Monetized PM2.5-

related Benefits

Blue identifies a user-selected input within the BenMAP model

Green identifies a data input generated outside of the BenMAP 

Census 

Population Data
5-13

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. This analysis is no exception. As outlined 

both in this and preceding chapters, this analysis includes many data sources as inputs, 

including 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 uncertainties can have large effects on the total 

quantified benefits.

After reviewing the EPA’s approach, the National Research Council (NRC) (2002, 2008), 

which is part of the National Academies of Science, concluded that the EPA’s general 

methodology for calculating the benefits of reducing air pollution is reasonable and informative 

in spite of inherent uncertainties. The NRC also highlighted the need to conduct rigorous 

quantitative analyses of uncertainty and to present benefits estimates to decision makers in

ways that foster an appropriate appreciation of their inherent uncertainty. Since the 

publication of these reports, the EPA has continued work to improve the characterization of 

uncertainty in both health incidence and benefits estimates. In response to these 

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 assessment 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 benefits analysis, such as the air quality 

data, it is difficult to address uncertainty probabilistically due to the complexity of the 

underlying air quality models and emission inputs. Therefore, we decline to make up alternative 

assumptions simply for the purpose 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 aspect of uncertainty In Appendix 5c. Data limitations prevent us 
5-14

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 

coefficients 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 PM2.5-related deaths relative to specific concentrations in the long-term 

epidemiological studies used to estimate PM2.5-related mortality;

3. The quantification of PM-related mortality using alternative PM2.5 mortality effect 

estimates drawn from two long-term cohort studies and an expert elicitation; 

4. Sensitivity analyses of several aspects of PM-related benefits; 

5. Distributional analyses of PM2.5-related benefits by location, race, income, and 

education; and

6. An analysis of the influence of various parameters on total monetized benefits. 

5.5.1 Monte Carlo Assessment

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 epidemiology standard errors as well as the distribution of the 

valuation function. These confidence intervals do not reflect other sources of uncertainty 

inherent within the estimates, such as baseline incidence rates, populations exposed and 

transferability of the effect estimate to diverse locations. As a result, the reported confidence 

intervals and range of estimates give an incomplete picture about the overall uncertainty in the 

benefits estimates.
5-15

5.5.2 Concentration Benchmark Analysis for PM2.5

We include a concentration benchmark assessment, which identifies the baseline (i.e., 

pre-rule) annual mean PM2.5 levels at which populations are exposed and specific 

concentrations in the two long-term cohort studies we use to quantify mortality impacts. This 

analysis characterizes avoided PM2.5-related deaths relative to the 10th and 25th 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 PM2.5–Related Mortality

We assign the greatest economic value to the reduction in PM2.5 relatedmortality 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 PM2.5 exposure and premature mortality, the EPA conducted an expert 

elicitation in 2006 (Roman et al., 2008; IEc, 2006).4 In general, the results of the expert 

elicitation support the conclusion that the benefits of PM2.5 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 derived 

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 PM2.5 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 PM2.5 benefits may be, and 

the EPA’s independent Science 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).
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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, 2009a). Using 

alternate relationships between PM2.5 and premature mortality supplied by experts, higher and 

lower benefits estimates are plausible, but most of the expert-based estimates of the mean 

PM2.5 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 provided in those studies or by experts. In addition, the experts 

provided distributions around their mean PM2.5 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. Furthermore, the approach presented here does not yet include methods 

for addressing correlation between input parameters and the identification of reasonable upper 

and lower bounds for input distributions characterizing uncertainty in additional model 

elements. As a result, the reported confidence intervals and range of estimates give an 

incomplete picture about the overall uncertainty in the estimates. This information should be 

interpreted within the context of the larger uncertainty surrounding the entire analysis.

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 PM2.5-

related death according to differing assumptions regarding the lag between PM2.5

exposure and premature death. The timing of such premature deaths affects the 

magnitude of the discounted PM2.5-related mortality benefits. In 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

income 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 characterized by the income growth factor.

3. Summarized 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 Census. 

5. Assessed the sensitivity of the benefits results to an analysis year of 2025.

5.5.5 Distributional Assessment

In an Appendix to the proposal RIA, we characterized the distribution of PM2.5-related 

benefits based on the geographic distribution of race and education in areas where the 

illustrative emission reduction strategies would reduce PM2.5 concentrations. In that 

assessment, we aimed to answer two key questions:

1. What was the estimated distribution of PM2.5-related mortality risk based on the

race and education characteristics of the population living within areas projected to 

exceed alternative combinations of the proposed primary PM2.5 standards? 

2. How would air quality improvements within these counties change the distribution 

of risk among populations of different races and educational attainment?6

That assessment was generally consistent with the distributional assessments 

performed in support of MATS (U.S. EPA, 2011c), with one key difference. The environmental 

justice analyses accompanying the MATS RIA applied CMAQ-modeled PM2.5 predictions that 

represent the change in air quality after the implementation of each rule. By contrast, the 

proposal RIA aimed to illustrate the potential benefits and costs of attaining alternative primary 

PM2.5 standards; the states will ultimately implement attainment strategies, which may 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 educationmodified PM2.5 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 uncertainty 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 uncertain 

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 PM2.5 

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 uncertainty 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

The 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, 2010a) 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 PM2.5 varies considerably in composition across sources, but the scientific 

evidence is not yet sufficient to allow differentiation of effect estimates by particle 

type. The PM ISA, which was twice reviewed by Clean Air Scientific Advisory 

Committee (SAB-CASAC), concluded that “many constituents of PM2.5 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 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. In 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 

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 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 PM2.5, including both areas that 

do not meet the fine particle standard and those areas that are in attainment, down 

to the lowest modeled concentrations.

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 that 
5-20

some of the incidences of premature mortality related to PM2.5 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 PM2.5 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 confidence intervals and range of estimates give an incomplete picture 

about the overall uncertainty in the PM2.5 estimates. This information should be 

interpreted within the context of the larger uncertainty surrounding the entire 

analysis.

As previously described, 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 benefits. 

Specifically, only certain benefits attributable to the health impacts associated with exposure to 

ambient fine particles have been monetized in this analysis. Data and methodological 

limitations prevented the EPA from quantifying or monetizing the benefits 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, NO2

exposure, SO2 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 5B. 
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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, 2012a). 

5.6.1 Demographic Data

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 Poole, Inc. 

(Woods and Poole, 2012). The Woods and Poole (WP) database contains county-level 

projections of population by age, sex, and race out to 2040, relative to a baseline using the 

2010 Census data; the proposal RIA incorporated WP projections relative to a baseline using 

2000 Census 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 account 

patterns of economic growth and migration. The sum of growth in county-level populations is 

constrained to equal a previously determined national population growth, based on Bureau of 

Census estimates (Hollman et al., 2000). According to WP, linking county-level growth 

projections together and constraining to a national-level total growth avoids potential errors 

introduced by forecasting each county independently. County projections are developed in a 

four-stage process:

 First, national-level variables such as income, employment, and populations are 

forecasted.

 Second, employment projections are made for 179 economic areas defined by the 

Bureau of Economic Analysis (U.S. BEA, 2004), using an “export-base” approach, 

which relies on linking industrial-sector production of non-locally consumed 

production items, such as outputs from mining, agriculture, and manufacturing with 

the national economy. The export-based approach requires estimation of demand 

equations or calculation of historical growth rates for output and employment by 

sector.

 Third, population is projected for each economic area based on net migration rates 

derived from employment opportunities and following a cohort-component method 

based on fertility and mortality in each area.
5-22

 Fourth, employment and population projections are repeated for counties, using the 

economic region totals as bounds. The age, sex, and race distributions for each 

region or county are determined by aging the population by single year of age by sex 

and race for each year through 2040 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/m3 decrease in daily PM2.5 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 assessment 

location, as it corresponds to baseline pollutant levels in that location. To derive the total 

baseline incidence per year, this rate must be multiplied by the corresponding population 

number. For example, if the baseline incidence rate is the number of cases per year per million 

people, that number must be multiplied by the millions of people in the total population.

Table 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 concentrationresponse functions to individual age groups and then summed over the relevant age range to 

provide an estimate of total population benefits. In most cases, we used a single national 

incidence rate, due to a lack of more spatially disaggregated data. Whenever possible, the 

national rates used are national averages, because these data are most applicable to a national 

assessment of benefits. For some studies, however, the only available incidence information 

comes from the studies themselves; in these cases, incidence in the study population is 

assumed to represent typical incidence at the national level. 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 

projected national-level annual mortality rates stratified by age range, we projected these 
5-23

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 locations within the U.S., these data are resolved at the county- or 

state-level, providing a better characterization of the geographic distribution of hospital and 

emergency department visits than the previous national rates. Lastly, these rates reflect 

unscheduled hospital admissions only, which represents a conservative assumption that most 

air pollution-related visits are likely to be unscheduled. If air pollution-related hospital 

admissions are scheduled, this assumption would underestimate these benefits.

For the set of endpoints affecting the asthmatic population, in addition to baseline 

incidence rates, prevalence rates of asthma in the population are needed to define the 

applicable population. Table 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).
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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 2020a

Age-, cause-, and countyspecific rateCDC WONDER (2004–

2006)

U.S. Census bureau, 2000

Hospitalizations Daily hospitalization rate Age-, region-, state-, 

county- and cause-specific 

rate

2007 HCUP data filesb

ER Visits Daily ER visit rate for asthma and 

cardiovascular events

Age-, region-, state-, 

county- and cause-specific 

rate

2007 HCUP data filesb

Cerebrovascular 

events

Incidence of new cerebrovascular 

events among populations 50–79

0.001575I Table 3 of Miller et al. 

(2007)

Chronic Bronchitisc 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

• daily shortness of breath

• 0.173

• 0.145

• 0.074

Ostro et al. (2001) 

Acute Bronchitis Annual bronchitis incidence rate, 

children

0.043 American Lung Association 

(2002c, Table 11)

Lower Respiratory 

Symptoms

Daily lower respiratory symptom 

incidence among childrend

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)
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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 RestrictedActivity DaysDaily 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 Associationa

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 analyses for the EPA’s Report to Congress on The 

Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a). In addition, we have 

included more recent epidemiology studies from the PM ISA (U.S. EPA, 2009b) and the 

Provisional Assessment (U.S. EPA, 2012b).7

 In 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 focus on endpoints reflecting public health impacts (like hospital 

admissions) rather than physiological responses (such as changes in clinical measures like 

Forced Expiratory Volume [FEV1]).

There are several types of data that can support the determination of types and 

magnitude of health effects associated with air pollution exposures. These sources of data 

include toxicological studies (including animal and cellular studies), human clinical trials, and 

observational epidemiology studies. All of these data sources provide important contributions 

to the weight of evidence surrounding a particular health impact. However, only epidemiology 

studies provide direct concentration-response relationships that can be used to evaluate 

population-level impacts of reductions in ambient pollution levels in a health impact 

assessment.

For the data-derived estimates, we relied on the published scientific literature to ascertain the 

relationship between PM2.5 and adverse human health effects. We evaluated epidemiological 

studies 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 between studies examining the same endpoint, making it 

 

7

 The 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 sensitivity 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 examining each endpoint and select a consistent subset that provides a good balance 

of population coverage and match with the pollutant of interest. In many cases, either because 

of a lack of multiple studies, consistency problems, or clear superiority in the quality or 

comprehensiveness of one study over others, a single published study is selected as the basis of 

the effect estimate.

When several effect estimates for a pollutant and a given health endpoint have been 

selected, they are quantitatively combined 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 models 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 

pooling 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 all 

locations nationwide. This applies to both impact functions defined by a single effect estimate 

and those defined by a pooling of multiple effect estimates. Although the effect estimate may, 

in fact, vary from one location to another (e.g., because of differences in population 

susceptibilities or differences in the composition of PM), location-specific effect estimates are 

generally not available.

 

8 EPA recently changed the algorithm BenMAP uses to calculate study variance, which is used in the pooling 

process. Prior versions of the model 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 greater statistical power to detect effects. Studies that are 

more recent are also preferred because of possible changes in pollution mixes, medical 

care, and lifestyle over time. However, when there are only a few studies available, 

studies from all years will 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 sample are preferred because they generally have 

more power to detect small magnitude effects. A large sample can be obtained in several 

ways, including through a large population or through repeated observations on a smaller 

population (e.g., through a symptom diary recorded for a panel of asthmatic children).

Study Location U.S. studies are more desirable than non-U.S. studies because of potential differences in 

pollution characteristics, exposure patterns, medical care system, population behavior, 

and lifestyle. 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-counting when pollutants are correlated.

Measure of PM For this analysis, impact functions based on PM2.5 are preferred to PM10 because of the 

focus on reducing emissions of PM2.5 precursors, and because air quality modeling was 

conducted for this size fraction of PM. Where PM2.5 functions are not available, PM10

functions are used as surrogates, recognizing that there will be potential downward 

(upward) biases if the fine fraction of PM10 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 specific 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 studies 

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 effect 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

(95th 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

PM2.5 Expert Elicitation (Roman et al., 

2008)

Premature 

mortality, total 

exposures

PM2.5 Expert Elicitation 

(Roman et al., 2008)

Premature 

mortality—all-cause

Woodruff et al. (1997) Premature 

mortality—allcause

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/m3

Asthma-related ER 

visits

Pooled estimate:

Mar et al. (2010)

All ages RR = 1.04 (1.01–1.07) 

per 7 μg/m3

Slaughter et al. (2005) RR = 1.03 (0.98–1.09) 

per 10 μg/m3

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/m3

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/m3 

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/m3

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/m3

Lower respiratory 

symptoms Schwartz and Neas (2000) 7–14 years OR = 1.11 (1.58–1.58) 

per 15 μg/m3

a Studies highlighted in blue represent updates incorporated since the RIA for MATS (U.S. EPA, 2011d). Studies 

highlighted in red represent updates 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-HES, 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 Analysisa

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 PM2.5 Premature Mortality Effect Coefficients

Core Mortality Effect Coefficients for Adults. A substantial body of published scientific 

literature documents the association between elevated PM2.5 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 review 

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 PM2.5 based on the entire body of scientific evidence (U.S. EPA, 2009b). 

The size of the mortality effect estimates from epidemiological studies, the serious nature of 

the effect itself, and the high monetary value ascribed to prolonging life make mortality risk 

reduction the most significant health endpoint quantified in this analysis. 

Researchers have found statistically significant associations between PM2.5 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 PM2.5 concentrations and changes in 

daily mortality rates up to several days after a period of elevated PM2.5 concentrations. Cohort 

methods have been used to examine the potential relationship between community-level PM2.5 

exposures over multiple years (i.e., long-term exposures) and community-level annual mortality 

rates that have been adjusted for individual level risk factors. When choosing between using 

short-term studies or cohort studies for estimating mortality benefits, cohort analyses are 

thought to capture more of the public health impact of exposure to air pollution over time 

because they account for the effects of long-term exposures as well as some fraction of shortterm exposures (Kunzli et al., 2001; NRC, 2002). The 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 effects from long-term exposure plus some fraction from short-term exposure. 

The amount of overlap is unknown” (NRC, 2002, p. 108-9). To avoid double counting, we focus 

on applying the risk coefficients from the long-term cohort studies in estimating the mortality 

impacts of reductions in PM2.5. 

Over the last two decades, several studies using “prospective cohort” designs have been 

published that are consistent with the earlier body of literature. Two prospective cohort 

studies, often referred to as the Harvard “Six Cities Study” (Dockery et al., 1993; Laden et al., 

2006; Lepeule et al., 2012) and the “American Cancer Society” or “ACS study” (Pope et al., 

1995; Pope et al., 2002; Pope et al., 2004; Krewski et al., 2009), provide the most extensive 

analyses of ambient PM2.5 concentrations and mortality. These studies have found consistent 

relationships between fine particle indicators and premature mortality across multiple locations 

in the United States. The credibility of these two studies is further enhanced by the fact that the 

initial published studies (Pope et al., 1995; 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 Panel 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 SABHES also supported using these two cohorts for analyses of the benefits of PM reductions, and 

concluded, “the selection of these cohort studies as the underlying basis for PM mortality 

benefit estimates 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 and 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 PM2.5 exposure and 

premature mortality with support from a number of additional cohort studies (described 

below).

The extended analyses of the ACS cohort data (Krewski et al., 2009) 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 of 
5-33

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 wellsuited for the assessment of mortality risk from long-term PM2.5 exposures for the EPA’s

benefits analyses. 

In 2009, the SAB-HES again reviewed the choice of mortality risk coefficients for benefits 

analysis, concluding that “[t]he Krewski et al. (2009) findings, while informative, have not yet 

undergone the same degree of peer review as have the aforementioned studies. Thus, the SABHES 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 the 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-SAB, 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/m3 increase in PM2.5). The relative risk estimate (1.06 per 10μg/m3 increase in 

PM2.5) is identical to the risk estimate drawn from the earlier Pope et al. (2002) study, though 

the confidence interval around the Krewski et al. (2009) risk estimate is tighter.

In the most recent Six Cities study, which was published after the last SAB-HES review, 

Lepeule et al. (2012) evaluated the sensitivity of previous Six Cities results to model 

specifications, lower exposures, and averaging time using eleven additional years of cohort 

follow-up that incorporated recent lower exposures. The authors found significant associations 

between PM2.5 exposure and increased risk of all-cause, cardiovascular and lung cancer 

mortality. The authors also concluded that the concentration-response relationship was linear 
5-34

down to PM2.5 concentrations of 8 μg/m3, and that mortality rate ratios for PM2.5 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/m3 increase in PM2.5). 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 PM2.5-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 particles 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 PM2.5 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, 2012b) (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 mean 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 PM2.5 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 PM2.5 NAAQS RIA (U.S. EPA, 2006a), the elicitation summary report (IEc, 2006) and Roman 

et al. (2008), and so we summarize 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 2006. 

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

Exposure to PM2.5 in Recent Cohort Studies in North America

Study Cohort (age)

LML 

(μg/m3) 

Mean 

(μg/m3) 

Hazard Ratios per 10 μg/m3 Change in PM2.5

(95th 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 mortality 
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among the adult U.S. population resulting from reductions in ambient annual average PM2.5 

levels. These distributions were obtained through a formal interview protocol using methods 

designed to elicit subjective expert judgments. These experts were selected through a peernomination process and included experts in epidemiology, toxicology, and medicine. The

elicitation interview consisted of a protocol of carefully structured questions, both qualitative 

and quantitative, about the nature of the PM2.5-mortality relationship designed to build twelve 

individual distributions for the coefficient (or slope) of the C-R function relating changes in 

annual average PM2.5 exposures to annual, adult all-cause mortality. The elicitation also 

provided useful information regarding uncertainty characterization in the PM2.5-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 each 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 PM2.5 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 

country)

 Collection of information on individual risk factors and residential information both 

at the beginning and throughout the follow-up period

 Large sample size that is representative of the general U.S. population

 Collection of genetic information from cohort members to identify and assess 

potential effect modifiers

 Monitoring of individual exposures (e.g., with a personal monitor)

 Collection of data on levels of several co-pollutants (not only those that are 

monitored for compliance purposes

 Accurate characterization of outcome (i.e., cause of death)

 

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; have collected data on co-pollutants in the case of the ACS study; have accurately 

characterized the health outcome; include a long (and growing) follow-up period; and, are 

prospective in nature. The experts also noted a series of limitations in these two cohort studies. 

In the case of the Six Cities study (Laden et al., 2006), the experts identified the “small sample 

size, limited number of cities, and concerns about representativeness of the six cities for the 

U.S. as a whole” as weaknesses. When considering the ACS study (Pope et al., 2002), the 

experts indicated that the “method of recruitment for the study, which resulted in a group with 

higher income, more education, and a greater proportion of whites than is representative of 

the general U.S. population” represented a shortcoming. Several experts also argued that 

because the ACS study relied upon “...whatever monitors were available to the study...a single 

monitor represent[ed] exposure for an entire metropolitan area...whereas [the Six Cities study] 

often had exposures assigned at the county level.” Despite these limitations, the experts 

considered the Pope et al. (2002) extended analysis of the ACS cohort and the Laden et al. 

(2006) extended analysis of the Six Cities cohort to be particularly influential in their opinions 

(see Exhibit 3-3 of the elicitation summary report [IEc, 2006]). 

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 the availability of this newer literature, we have updated the presentation of 

results in the RIA. Specifically, we focus the core analysis on results derived from the two most 

recent studies of the ACS and Six Cities cohorts (Krewski et al., 2009; Lepeule et al., 2012). 

Because the other multi-state cohorts generally have limited geography and age/gender 

representativeness, these limitations preclude us from using these studies in our core benefits 

results, and we instead present the risk coefficients from these other multi-state cohorts in 
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Table 5-8. In addition, we now present the full distributions 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 

recommended that the EPA emphasize that “scientific differences existed only with respect to 

the magnitude of the effect of PM2.5 on mortality, not whether such an effect existed” and that 

the expert elicitation “supports the conclusion that the benefits of PM2.5 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 

regarding the potential impact of PM2.5 exposure on infant mortality than on adult mortality 

and the results of studies in several countries include a range of findings with some finding 

significant associations. Specifically, the PM ISA concluded that evidence exists for a stronger 

effect at the post-neonatal period and for respiratory-related mortality, although this trend is 

not consistent across all studies. In addition, compared to avoided premature deaths estimated

for adult mortality, avoided premature deaths for infants are significantly smaller because the 

number of infants in the population is much smaller than the number of adults and the 

epidemiology studies on infant mortality provide smaller risk coefficients associated with 

exposure to PM2.5.

In 2004, the SAB-HES noted the release of the WHO Global Burden of Disease Study 

focusing on ambient air, which cites several recently published time-series studies relating daily 

PM exposure to mortality in children (U.S. EPA-SAB, 2004a). The SAB-HES also cites 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 were 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 

recommended 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 estimating 

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 PM2.5 and infant mortality in 

California. The SAB-HES also noted, “when PM10 results are scaled to estimate PM2.5 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 PM2.5 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 PM2.5 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 number of other single and multi-city 

studies have appeared in the literature. These studies include Sullivan et al. (2005), which 

considered the risk of PM2.5-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 with 
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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 PM2.5 data were not measured on an hourly basis.

As a means of recognizing the strengths of the Peters study while also incorporating the 

newer evidence found in the four single and multi-city studies, we present a range of AMI 

estimates. The upper end of the range is calculated using the Peters study while the lower end 

of the range is the result of an equal-weights pooling of these four newer studies. It is 

important to note that when calculating the incidence of nonfatal AMI, the fraction of fatal 

heart attacks is 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. Based on recent data from the Agency for

Healthcare Research and Quality’s Healthcare Utilization Project National Inpatient Sample 

database (AHRQ, 2009), we identified death rates for adults hospitalized with acute myocardial 

infarction stratified by age (e.g., 1.852% for ages 18–44, 2.8188% for ages 45–64, and 7.4339% 

for ages 65+). These rates show a clear downward trend over time between 1994 and 2009 for 

the average adult and thus replace the 7% survival rate previously applied across all age groups 

from Rosamond et al. (1999). 

5.6.3.3 Hospital 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 emergency 

department visits do not result in an admission to the hospital (i.e., most people going to the 

emergency department are treated and return home), we treat hospital admissions and 

emergency department visits separately, taking account of the fraction of emergency 

department visits that are admitted to the hospital. Specifically, within the baseline incidence 

rates, we parse out the scheduled hospital visits from unscheduled ones as well as the hospital

visits that originated in the emergency department. 

The two main groups of hospital admissions estimated in this analysis are respiratory 

admissions and cardiovascular admissions. There is not much evidence linking PM2.5 with other 
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types of hospital admissions. Both asthma- and cardiovascular-related visits have been linked to 

PM2.5 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 PM2.5 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 examined 

populations 0 to 99 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 

PM2.5, we used studies by Moolgavkar (2000), Zanobetti et al. (2009), Peng et al. (2008, 2009) 

and Bell et al., (2008). Only Moolgavkar (2000) provided a separate effect estimate for adults 20 

to 64, while the remainder estimate risk among adults over 64.13 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 limitation 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 peripheralvascular 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 alternative approach would be to use the 

Zanobetti et al. (2009) study alone, though this would prevent us from drawing upon the 

strengths of the three multi-city studies.

 

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 existing 

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 the 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/sttimeseries.htm.
5-42

To estimate avoided incidences of respiratory hospital admissions associated with PM2.5, 

we used a number of studies examining total respiratory hospital admissions as well as asthma 

and chronic lung disease. We estimated impacts among three age groups: adults over 65, adults 

18 to 64 and children 0 to 17. For adults over 65, the multi-city studies by Zanobetti et al. 

(2009) and Kloog et al. (2012) provide effect coefficients for total respiratory hospital 

admissions (defined as ICD codes 460–519). We pool these two studies using equal weights. 

Moolgavkar et al. (2003) examines PM2.5 and chronic lung disease hospital admissions (less 

asthma) in Los Angeles, CA among adults 18 to 64. For children 0 to 18, we pool two studies 

using random/fixed effects. The first is Babin et al. (2007) which assessed PM2.5 and asthma 

hospital admissions in Washington, DC among children 1 to 18; we adjusted the age range for 

this study to apply to children 0 to 18. The second is Sheppard et al. (2003) which assessed 

PM2.5 and asthma hospitalizations in Seattle, Washington, among children 0 to 18.

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 PM2.5. 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 tiredness, 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 

PM2.5 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 estimated 

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 PM2.5 on MRAD was estimated using an effect estimate 

derived from Ostro and Rothschild (1989).

More recently published literature examining the relationship between short-term PM2.5 

exposure and acute respiratory symptoms was available in the PM ISA (U.S. EPA, 2009), but 

proved to be unsuitable for use in this benefits analysis. In particular, the best available study 

(Patel et al., 2010) specified a population aged 13–20, which overlaps with the population in 

which we asses asthma exacerbation. As we describe in detail below, to avoid the chance of 

double-counting impacts, we do not estimate changes in acute respiratory symptoms and 

asthma exacerbation among populations of the same age.

For this RIA, we have followed the SAB-HES recommendations regarding asthma 

exacerbations in developing the core estimate (U.S. EPA-SAB, 2004a). Although certain studies 

of acute respiratory events characterize these impacts among 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 estimated the exacerbation of asthma among

children and excluded adults from the calculation. Asthma exacerbations occurring in adults are 

assumed to be captured in the general population endpoints such as work loss days and 

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 a 
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group of 138 African-American children in Los Angeles for 13 weeks, recording daily 

occurrences of respiratory symptoms associated with asthma exacerbations (e.g., shortness of 

breath, wheeze, and cough). This study found a statistically significant association between 

PM2.5, measured as a 12-hour average, and the daily prevalence of shortness of breath and 

wheeze endpoints. Although the association was not statistically significant for cough, the 

results were still positive and close to significance; consequently, we decided to include this 

endpoint, along with shortness of breath and wheeze, in generating 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 months. 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 PM2.5. 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 generated 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 estimate 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. Chronic 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 incidences of chronic bronchitis. Schwartz (1993) 

and Abbey et al. (1995) provide evidence that long-term PM2.5 exposure gives rise to the 
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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 PM2.5 and the onset of 

chronic bronchitis in the U.S. The absence of newer studies finding a relationship between longterm PM2.5 exposure and chronic bronchitis argues for moving this endpoint from the core

benefits 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 PM2.5 exposure and cerebrovascular events (U.S. EPA, 2009). Timeseries 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 limitation 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 the hospital.

Longer-term prospective cohort studies of PM2.5 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, 2009b). 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 matching 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 PM2.5 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 analysis: (1) the epidemiological literature examining 

PM-related cerebrovascular events is still evolving; (2) there are special uncertainties associated 
5-46

with quantifying this endpoint; (3) we have not yet identified an appropriate method for 

estimating the economic 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 PM2.5 and increased 

risk of ED visits for ischemic heart diseases (U.S. EPA, 2009b). 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 PM2.5 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 emergency department visits. Mathes et al. (2011) find relationships between 

PM2.5 levels and cardiovascular emergency department visits in New York City. The principal 

challenge to incorporating these studies is the absence of readily-available economic valuation 

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

The 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 SO2, and NOx for an alternative standard for 11 μg/m3

. 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 PM2.5 in a given area as well as 

local geographic and meteorological conditions. Although we have quantified many of the 

health benefits associated with reducing exposure to PM2.5, as shown in Table 5-2, we are 

unable to quantify the health benefits associated with reducing the potential for ozone 

exposure, SO2 exposure, NO2 exposure or contamination 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 simulated the impact of attaining the revised and alternative annual 

standards on ambient levels of PM2.5, this method does not simulate how the illustrative 

emission reductions would affect ambient levels of ozone, SO2, or NO2. Furthermore, the air 

quality modeling conducted for this analysis did not assess mercury, so we are unable to 

estimate mercury deposition associated with the illustrative controls or subsequent 

bioaccumulation and exposure. Below we provide a qualitative description of these health 
5-47

benefits. In general, previous analyses have shown that the monetized value of these additional 

health benefits is much smaller than PM2.5-related benefits (U.S. EPA, 2010a, 2010c, 2010d). 

Reducing NOx emissions also reduces 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 numerous 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. The 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 Integrated Science Assessment for Sulfur Dioxide—Health Criteria (SO2 ISA) 

concluded that there is a causal relationship between respiratory health effects and short-term 

exposure to SO2 (U.S. EPA, 2008c). The immediate effect of SO2 on the respiratory system in 

humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO2 likely 

resulting from preexisting inflammation associated with this disease. A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO2

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 that the SO2 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 SO2 ISA. The SO2 ISA also 

concluded that the relationship between short-term SO2 exposure and premature mortality was 

“suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects 

to SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a 

relationship between SO2 exposure and mortality, there was a lack of robustness of the 

observed associations to adjustment for pollutants. We did not quantify these benefits due to 

data constraints.

Epidemiological researchers have associated NO2 exposure with adverse health effects 

in numerous toxicological, clinical and epidemiological studies, as described in the Integrated 
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Science Assessment for Oxides of Nitrogen—Health Criteria (NO2 ISA) (U.S. EPA, 2008b). The 

NO2 ISA provides a comprehensive review of the current evidence of health and environmental 

effects of NO2. The NO2 ISA concluded that the evidence “is sufficient to infer a likely causal 

relationship between short-term NO2 exposure and adverse effects on the respiratory system.” 

These epidemiologic and experimental studies encompass a number of endpoints including 

emergency department visits and hospitalizations, respiratory symptoms, airway 

hyperresponsiveness, airway inflammation, and lung function. Effect estimates from 

epidemiologic studies conducted in the United States and Canada generally indicate a 2–20% 

increase in risks for ED visits and hospital admissions and higher risks for respiratory symptoms.

The NO2 ISA concluded that the relationship between short-term NO2 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 NO2 alone. Although the NO2 ISA stated that studies 

consistently reported a relationship between NO2 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 converted 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 (although not necessarily in all cases) 

understate the true value of reducing the risk of a health effect, because they reflect the direct 

expenditures related to treatment, but not the value of avoided pain and suffering (Harrington 

and Portney, 1987; Berger, 1987). 

We provide unit values for health endpoints (along with information on the distribution 

of the unit value) in Table 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 and 
5-49

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 late 1980s and early 

1990s, and we are in the process of reviewing the literature to update these unit values. The 

discussion below provides additional details on valuing specific PM2.5-related related endpoints.

5.6.5.1 Mortality Valuation

Following the advice of the SAB’s Environmental Economics Advisory Committee (SABEEAC), 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) metaanalysis of 43 studies. The mean estimate of $5.5 million (2000$) was also consistent with the 

mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. However, the 

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

1990 Income Level 2020 Income Level Derivation of Distributions of Estimates

Premature Mortality (Value of a 

Statistical Life)

$8,000,000 $9,600,000 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 Guidelines for 

Preparing Economic Analyses (U.S. EPA, 2010e).

Nonfatal Myocardial Infarction 

(heart attack)

3% discount rate 

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; 

$21,000at 7% discount rate)

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

 a (continued)

Health Endpoint

Central Estimate of Value Per Statistical Incidence

2000 Income Level 2020 Income Level Derivation of Distributions of Estimates

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 in 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 (2007) 

(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 

(2007) (www.ahrq.gov). 

All respiratory (ages 65+) $36,000 $36,000 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, 2007 (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)
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Table 5-9. Unit Values for Economic Valuation of Health Endpoints (2010$)

 a (continued)

Health Endpoint

Central Estimate of Value Per Statistical Incidence

2000 Income Level 2020 Income Level Derivation of Distributions of Estimates

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 mid-range 

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster 

and assuming additivity of WTPs. The dollar value for LRS is the 

average of the dollar values for the 11 different types of LRS. In the 

absence of information surrounding the frequency with which each of 

the 11 types of LRS occurs within the LRS symptom complex, we 

assumed a uniform distribution between $6.9 and $25 (2000$). 

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 $71 (2000$). 

(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

2000 Income Level 2020 Income Level Derivation of Distributions of Estimates

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-range 

values recommended by IEc (1994) for two symptoms believed to be 

associated with acute bronchitis: coughing and chest tightness. The 

high daily estimate was taken to be twice the value of a minor 

respiratory restricted-activity 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 triangular 

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 Appendix 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 from the 

various data sources. In addition, the Agency consulted several times with the SAB-EEAC on the 

issue. With input from the meta-analytic experts, the SAB-EEAC advised the Agency to update 

its guidance using specific, appropriate meta-analytic techniques to combine estimates from 

unique data sources and different studies, 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 while the Agency continues its efforts to 

update its guidance on this issue. This approach calculates a mean value across VSL estimates 

derived from 26 labor market and contingent valuation studies published between 1974 and 

1991. The mean VSL across these studies is $4.8 million (1990$) or $6.3 million (2000$).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 continuing 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 estimate and the PM2.5 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 

estimate for air pollution-related mortality. In the absence of a comprehensive and balanced 

set of adjustment factors, the EPA believes it is reasonable to continue to use the $4.8 million 

(1990$) value adjusted for inflation and income growth over time while acknowledging the 

significant limitations and uncertainties in the available literature.

Table 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 realization of changes in health effects. We assumed for this analysis 

that some of the incidences of premature mortality related to PM2.5 exposures occur in a 

distributed fashion over the 20 years following exposure and discounted over the period 

between exposure 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 PM2.5 (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 of 
5-56

reductions in premature mortality, we discount the 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 pollution indicates that several human 

characteristics affect the degree to which mortality risk affects an individual. For example, some 

age groups appear to be more susceptible to air pollution than others (e.g., the elderly and 

children). Health status prior to exposure also affects susceptibility. An ideal benefits estimate 

of mortality risk reduction would reflect these human characteristics, in addition to an 

individual’s WTP to improve one’s own chances of survival plus WTP to improve other 

individuals’ survival rates. The ideal measure would also take into account the specific nature of 

the risk reduction commodity that is provided to individuals, as well as the context in which risk 

is reduced. To measure this value, it is important to assess how reductions in air pollution 

reduce the risk of dying from the time that reductions take effect onward and how individuals 

 

17 The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the 

federal 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 “opportunity 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 valuation estimates. Additional sources of model error such as those examined in the 

PM2.5 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 environmental 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 reductions in premature mortality risk using the VSL approach.

Other uncertainties specific to premature mortality valuation include the following:

 Across-study variation: There is considerable uncertainty as to whether the available 

literature on VSL provides adequate estimates of the VSL for risk reductions from air 

pollution reduction. Although there is considerable variation in the analytical designs 

and data used in the existing literature, the majority of the studies involve the value 

of risks to a middle-aged working population. Most of the studies examine 

differences in wages of risky occupations, using a hedonic wage approach. Certain 

characteristics of both the population affected and the mortality risk facing that 

population are believed to affect the average WTP to reduce the risk. The 

appropriateness of a distribution of WTP based on the current VSL literature for 

valuing the mortality-related benefits of reductions in air pollution concentrations 

therefore depends not only on the quality of the studies (i.e., how well they 

measure what they are trying to measure), but also on the extent to which the risks 

being valued are similar and the extent to which the subjects in the studies are 

similar to the population affected by changes in pollution concentrations.

 Level of risk reduction: The transferability of estimates of the VSL from the wage-risk 

studies to the context of the PM NAAQS analysis rests on the assumption that, 

within a reasonable range, WTP for reductions in mortality risk is linear in risk 

reduction. For example, suppose a study provides a result that the average WTP for 

a reduction in mortality risk of 1/100,000 is $50, but that the actual mortality risk 

reduction resulting from a given pollutant reduction is 1/10,000. If WTP for 

reductions in mortality risk is linear in risk reduction, then a WTP of $50 for a 

reduction of 1/100,000 implies a WTP of $500 for a risk reduction of 1/10,000 

(which is 10 times the risk reduction valued in the study). Under the assumption of 

linearity, the estimate of the VSL does not depend on the particular amount of risk 

reduction being valued. This assumption has been shown to be reasonable provided 
5-58

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 mortality 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 suffering and loss of dignity 

and personal control is greater than the WTP to avoid a risk (of identical magnitude) 

of sudden death (e.g., Tsuge et al., 2005; Alberini and Scasny, 2011). To the extent 

that the mortality risks addressed in this assessment are associated with longer 

periods of illness or greater pain and suffering than are the risks addressed in the 

valuation literature, the WTP measurements employed in the present analysis would 

reflect a downward bias.

 Self-selection and skill in avoiding risk: Recent research (Shogren and Stamland, 

2002) suggests that VSL estimates based on hedonic wage studies may overstate the 

average value of a risk reduction. This is based on the fact that the risk-wage tradeoff revealed in hedonic studies reflects the preferences of the marginal worker (i.e., 

that worker who demands the highest compensation for his risk reduction for a 

given job). This worker must have either a higher workplace risk than the average 

worker in a given occupation, a lower risk tolerance than the average worker in that 

occupation, or both. Conversely, the marginal worker should have a higher risk 

tolerance than workers employed in less-risky sectors. However, the risk estimate 

used in hedonic studies is generally based on average risk, so the VSL may be biased, 

in an ambiguous direction, because the wage differential and risk measures do not 

match.

 Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 2006) 

finds that because individuals reevaluate their baseline risk of death as they age, the 

marginal value of risk reductions does not decline with age as predicted by some 

lifetime consumption models. This research supports findings in recent stated 

preference studies that suggest only small reductions in the value of mortality risk 

reductions with increasing age (e.g., Alberini et al., 2004). 
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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 value with two components: the direct medical costs 

and the opportunity cost (lost earnings) associated with the illness event. Because the costs 

associated with a myocardial infarction extend beyond the initial event itself, we consider costs 

incurred over several years. Using age-specific annual lost earnings estimated by Cropper and 

Krupnick (1990) and a 3% discount rate, we estimated a rounded present discounted value in 

lost earnings (in 2000$) over 5 years due to a myocardial infarction of $8,800 for someone 

between the ages of 25 and 44, $13,000 for someone between the ages of 45 and 54, and 

$75,000 for someone between the ages of 55 and 65. The rounded corresponding age-specific 

estimates of lost earnings (in 2000$) using a 7% discount rate are $7,900, $12,000, and $67,000, 

respectively. Cropper and Krupnick (1990) do not provide lost earnings estimates for 

populations under 25 or over 65. As such, we do not include lost earnings in the cost estimates 

for these age groups.

We found three possible sources in the literature of estimates of the direct medical 

costs of myocardial infarction, which provide significantly different values (see Table 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 the probabilities of certain events and/or medical procedures 

being used. The authors note that the average length of hospitalization for acute 

myocardial infarction has decreased over time (from an average of 12.9 days in 1980 

to an average of 11 days in 1983). Wittels et al. used 10 days as the average in their 

study. It is unclear how much further the length of stay for myocardial infarction 

may have decreased from 1983 to the present. The average length of stay for ICD 

code 410 (myocardial infarction) in the year-2000 Agency for Healthcare Research 

and Quality (AHRQ) HCUP database is 5.5 days (AHRQ, 2000). However, this may 

include patients who died in the hospital (not included among our nonfatal 

myocardial infarction cases), and 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.
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Table 5-11. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart Attacksa

Study Direct Medical Costs (2010$) Over an x-Year Period, for x =

Wittels et al. (1990) $160,000b 5 

Russell et al. (1998) $33,000c 5 

Average (5-year) costs $98,000 5 

Eisenstein et al. (2001) $74,000c 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 Costb Total Cost

0–24 $0 $98,000 $98,000

25–44 $12,000c $98,000 $110,000

45–54 $17,000c $98,000 $120,000

55–65 $100,000c $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 Valuation

In the absence of estimates of societal WTP to avoid hospital visits/admissions for 

specific illnesses, we derive COI estimates for use in the benefits analysis. The International 
5-61

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 $148. The 

total cost-of-illness estimate for an ICD code-specific hospital stay lasting n days, then, was the 

mean hospital charge plus $148 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 2010$ for the COI 

functions used in this analysis in Table 5-13. 

Table 5-13. Unit Values for Hospital Admissions

End Point ICD Codes

Age Range

Mean Hospital 

Charge (2010$)

Mean Length 

of Stay (days)

Total Cost of Illness 

(unit value in 

min. max. 2010$)

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 $31,000 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 $464 (using the CPI-U for medical care to adjust to 2010$). The second 

estimate comes from Stanford et al. (1999), who reported the cost of an average asthmarelated emergency department visit at $335, based on 1996–1997 data. A simple average of the 

two estimates yields a unit value of $388.
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 

respiratory restricted activity day, using estimates from Tolley et al. (1986) of WTP for avoiding 

a combination of coughing, throat congestion and sinusitis. This estimate of WTP to avoid a 

minor respiratory restricted activity day is $38 (1990$), or about $68 (2010$). Although Ostro 

and Rothschild (1989) statistically linked ozone and minor restricted activity days, it is likely that 

most MRADs associated with ozone and PM2.5 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 distinct 

concept from inflation and currency year. Economic theory argues that WTP for most goods 

(such as environmental protection) will increase if real incomes increase. There is substantial 

empirical evidence that the income elasticity19 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 (national average) income growth between the year a WTP 

study was conducted and the year for which benefits are estimated, and through differences in 

income between study populations and the affected populations at a particular time. The SABEEAC advised the EPA to adjust WTP for increases in real income over time but not to adjust 

WTP to account for cross-sectional income differences “because of the sensitivity of making 

such distinctions, and because of insufficient evidence available at present” (U.S. EPA-SAB, 

2000). An advisory by another committee associated with the SAB, the Advisory Council on 

Clean Air Compliance Analysis (SAB-Council), has provided conflicting advice. While agreeing 

with “the general principle that the willingness to pay to reduce mortality risks is likely to 

increase with growth in real income” and that “[t]he same increase should be assumed for the 

 

19 Income elasticity is a common 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 the 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 

publication of this memo and that an array of newer studies identifying potentially suitable 

income elasticity estimates are available (IEc, 2012). The EPA anticipates seeking 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 elasticities relative to the severity of the 

effects (a priori one might expect that more severe outcomes would show less income elasticity 

of WTP). We have not imposed any additional restrictions on the empirical estimates of income 

elasticity. One explanation for the seeming inconsistency is the difference in timing of 

conditions. WTP for minor illnesses is often expressed as a short term payment to avoid a single 

episode. WTP for major illnesses and mortality risk reductions are based on longer term 

measures of payment (such as wages or annual income). Economic 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 Growtha

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 population 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 visibility) are adjusted by multiplying the 

unadjusted benefits by the appropriate adjustment factor. For premature mortality, we applied 

the income adjustment factor specific to the analysis year, but we do not adjust for income 

 

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 average 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 relative 

growth rates for GDP derived from the Standard and Poor’s projections to the 2010 projected GDP based on the 

Bureau of Economic Analysis projections.
5-65

growth over the 20-year cessation lag. Our approach could underestimate the benefits for the 

later years of the lag. 

There is some uncertainty regarding the total costs of illness in the future. Specifically, 

the nature of medical treatment is changing, including a shift towards more outpatient

treatment. Although we adjust the COI estimates for inflation, we do not have data to project 

COI estimates for the cost of treatment in the future or income growth over time, which leads 

to an inherent though unavoidable inconsistency between COI- and WTP-based estimates This 

approach may underpredict benefits in future years because it is likely that increases in real 

U.S. income would also result in increased COI (due, for example, to increases in wages paid to 

medical workers) and increased cost of work loss days and lost worker productivity (reflecting 

that if worker incomes are higher, the losses resulting from reduced worker production would 

also be higher). In addition, cost-of-illness estimates do not include sequelae costs or pain and 

suffering, the value of which would likely increase in the future. To the extent that costs would 

be expected to increase over time, this increase may be partially offset by advancement in 

medical technology that improves the effectiveness of treatment at lower costs. For these 

reasons, we believe that the cost-of-illness estimates in this RIA may underestimate (on net) 

the total economic value of avoided health impacts.

Table 5-15. Adjustment Factors Used to Account for Projected Real Income Growtha

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 PM2.5 Standards 

Applying the impact and valuation functions described previously in this chapter to the 

estimated changes in PM2.5 yields estimates of the changes in physical damages (e.g., 

premature mortalities, cases of acute bronchitis and hospital 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 estimate 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 averaged across the continental U.S. Tables 5-17 through 5-22 present the benefits 

results for the annual PM2.5 standards. These benefits are relative to a 2020 analytical baseline 

reflecting attainment nationwide of the current primary PM2.5 standards (i.e., 15/35) that 

includes promulgated national regulations and illustrative emission controls to simulate 

attainment with 15/35 as well as an adjustment to NOx emissions to reflect expected reductions 

in mobile NOx emissions between 2020 and 2025.23 Figure 5-3 graphically displays the total 

monetized benefits of the revised annual primary standard of 12 μg/m3, using alternative

concentration-response functions at discount rates of 3% and 7%. Figure 5-4 graphically 

displays the cumulative distribution of total monetized benefits using the 2 epidemiologyderived and the 12 expert-derived relationships between PM2.5 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 PM2.5 Standards Relative to Analytical Baseline

Standard Population-Weighted Air Quality Change

13 μg/m3 0.014 μg/m3

12 μg/m3 0.043 μg/m3

11 μg/m3 0.207 μg/m3

 

23 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.
5-67

Table 5-17. Emission Reductions in Illustrative Emission Reduction Strategies for the Revised 

and Alternative Annual Primary PM2.5 Standards, by Pollutant and Region in 2020 

(tons)a

13 μg/m3 12 μg/m3 11 μg/m3

Directly emitted PM2.5

East 0 0 8,200

West 0 0 160

CA 730 4,000 10,600

SO2

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 PM2.5 Health Impacts for the Revised and 

Alternative Annual Primary PM2.5 Standards (Incremental to the Analytical 

Baseline)

a

Health Effect

Revised and Alterative Annual Standards

(95th percentile confidence interval)

13 μg/m3 12 μg/m3 11 μg/m3 

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 two 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 5th 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 PM2.5 Health Benefits for the Revised and Alternative Annual Primary 

PM2.5 Standards (Incremental to Analytical Baseline) (Millions of 2006$, 3% 

discount rate)a

Health Effect

Revised and Annual Standards

(95th percentile confidence interval)

13 μg/m3 12 μg/m3 11 μg/m3

Avoided Mortalityb

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 5th percentile confidence estimates for this morbidity endpoint reflects 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.
5-70

Table 5-20. Monetized PM2.5 Health Benefits for the Revised and Alternative Annual Primary 

PM2.5 Standards (Incremental to Analytical Baseline) (Millions of 2006$, 7% 

discount rate)a

Health Effect

Revised and Alterative Annual Standards

(95th percentile confidence interval)

13 μg/m3 12 μg/m3 11 μg/m3

Avoided Mortalityb

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 

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 5th percentile confidence estimates for this morbidity endpoint reflects 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.
5-71

Table 5-21. Total Estimated Monetized Benefits of the for Revised and Alternative Annual 

Primary PM2.5 Standards (Incremental to the Analytical Baseline) (billions of 

2006$) a,b

Benefits Estimate 13 μg/m3 12 μg/m3 11 μg/m

Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 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 PM2.5-related morbidities and premature deaths using PM2.5 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. 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 SABrecommended 20-year segmented lag structure. Not all possible benefits are quantified and monetized in this 

analysis. B is the sum of all unquantified health and welfare co-benefits. Data limitations prevented us from 

quantifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that 

we were able to quantify. 

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 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.

Table 5-22. Regional Breakdown of Monetized Benefits Results

Region

Revised and Alterative Annual Standards

13 μg/m3 12 μg/m3 11 μg/m3 

Easta 0% 0% 23% 

Californiab 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 PM2.5 levels by 2020 in the East, thus few additional controls would be needed to reach 12 

μg/m3

. 

b For 12 and 13 μg/m3

, 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 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.
5-72

Figure 5-3. Estimated PM2.5-Related Premature Mortalities Avoided According to 

Epidemiology or Expert-Derived PM2.5 Mortality Risk Estimate for 12 μg/m3 in 2020a

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 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.

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

Krewski et al.

Lepeuele et al.

Millions (2010$)

PM2.5 mortality benefits estimates derived from 2 epidemiology functions and 12 expert functions

 

3% Discount Rate

7% Discount Rate
5-73

Figure 5-4. Total Monetized Benefits Using 2 Epidemiology-Derived and 12-Expert Derived 

Relationships Between PM2.5 and Premature Mortality for 12 μg/m3 in 2020a

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 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 than 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 percentage 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 1,000 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 $30

Cumulative 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 fraction 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.

PM2.5 mortality benefits represent a substantial proportion of total monetized benefits 

(over 98% in this analysis), and these estimates have 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 PM2.5 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 PM2.5 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).

2. 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 PM2.5, 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 premature mortality related to PM2.5 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 

PM2.5

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 decreases 
5-75

in age-specific death rates in the relevant population and develop models for consistent 

calculation of changes in life expectancy and changes in number of deaths at all ages” (NRC, 

2008). In addition, NRC noted in an earlier report that “[f]rom a public-health perspective, lifeyears 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, 

2004a). 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 PM2.5 resulting 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 PM2.5 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 defines life expectancy as the “average number of years of life remaining for 

persons who have attained a given age” (CDC, 2011). In other words, changes in life expectancy 

refer to an average change for the entire population, and refer to the future. Over the past 50 

years, average life expectancy at birth in the U.S. has increased by 8.4 years (CDC, 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 refer to individuals, and refer to the past, e.g., when the individual has 

already died. If a 60-year old individual dies, we estimate that this individual would lose about 

22.5 years of life (i.e., the average population life expectancy for an individual of this age minus 

this person’s age at death).

5.7.3.1 Estimated Life Years Gained

For estimating the potential life years gained by reducing exposure to PM2.5 in the U.S. 

adult population, we use the same general approach as Hubbell (2006) and Fann et al. (2012a). 

We have not estimated the change in average life expectancy at birth in this RIA. Hubbell (2006) 

estimated that reducing exposure to PM2.5 from air pollution regulations may result in an 

average gain of 15 years of life for those adults prematurely dying from PM2.5 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 life 
5-76

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 PM2.5 exposure is modeled to occur we use 

standard life tables available from the CDC (2003) and the following formula:

Total Life Years = ∑ LEi × Mi n

i=1 (5.2)

where LEi is the average remaining life expectancy for age interval i, Mi is the estimated change 

in number of deaths in age interval i, and n is the number of age intervals. 

To get Mi (the estimate the number of avoided premature deaths attributed to changes 

in PM2.5 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 rates that do. Because 

mortality rates for younger populations are much lower than mortality rates for older 

populations, most but not all, of the avoided deaths tend to be in older populations. Table 5-23 

summarizes the modeled number of life years gained by reducing PM2.5 exposure to 12 μg/m3

in 2020. We then calculated the average number of life years gained per avoided premature

mortality. Figure 5-5 shows the potential life years gained as a result of meeting a primary 

standard of 12 μg/m3 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 PM2.5 exposure in 2020. We 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 that 
5-77

susceptibility was in and of itself caused 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 

PM2.5 Standard of 12 μg/m3 in 2020a,b

Age Rangeb

Krewski et al. (2009) Risk 

Coefficient c

Lepeule et al. (2012) 

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 PM2.5 

Standard of 12 μg/m3 in 2020, Partitioned by the Age When Exposure Reduction Occurred, 

Not Necessarily Age at Deatha

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 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 PM2.5 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 PM2.5 exposure 

in 2020 as a result of the illustrative emission reduction strategies, we use Mi 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 PM2.5 exposure to the revised annual standard of 12 μg/m3 in 2020. 

Figure 5-6 shows the percentage of avoided premature deaths attributed to meeting the 

revised primary annual standard of 12 μg/m3 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 b
5-79

Table 5-24. Estimated Reduction in the Percentage of All-Cause Mortality Attributed to the 

Revised Annual Primary PM2.5 Standard of 12 μg/m3 in 2020a,b

Age Rangeb Krewski et al. (2009) Risk Coefficientc 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 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 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.

The relative distributions of the potential number of life years gained (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 is 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). These 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 PM2.5 Standard of 12 μg/m3 in 2020, Partitioned by the Age When Exposure 

Reduction Occurred, Not Necessarily Age at Deatha

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 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 is 30.

5.7.4 Evaluation of Mortality Impacts Relative to Various Concentration Benchmarks

In this analysis, we estimate the number of avoided PM2.5-related deaths occurring due 

to PM2.5 reductions down to various PM2.5 concentration benchmarks, including the Lowest 

Measured Level (LML) of each long-term PM2.5 mortality study. This analysis is one of several 

sensitivities 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 nothreshold 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 Advisory 

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 finding, we estimate 

benefits associated with the full range of PM2.5 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 PM2.5-related mortality 

impacts relative to baseline (i.e., pre-rule) annual mean PM2.5 levels. The LML approach 

compares the percentage of avoided premature deaths estimated to occur above and below 

the minimum observed air quality level of each long-term cohort study we use to quantify PM. 

In the air quality benchmark approach, we summarize the impacts occurring at different points 

in the distribution of the air quality data used in these same epidemiology studies.

Our 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 PM2.5 levels are below the LML. This interpretation 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 25th to the 10th 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 PM2.5 

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 estimate from simulated PM2.5 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 

mean25 air quality levels when characterizing the distribution of mortality impacts. It is 

 

24 For a summary of the scientific review statements regarding the lack of a threshold in the PM2.5-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 PM2.5-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 25th and 10th 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 PM2.5 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 this chapter.

Figure 5-7. Relationship between the Size of the PM Mortality Estimates and the PM2.5

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 PM2.5 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 

improvements 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 emission 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 includes 
5-83

estimates 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 benefits 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 distribution 

of PM2.5-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 

Studiesa

Revised and 

Alternative 

Standard Level

Epidemiology 

Study

Total Reduced 

Mortality 

Incidence

Allocation of Reduced Mortality Incidence

Below 1 Std. 

Dev. 

Below AQ Mean 

At or Above 1 

Std. Dev. Below 

AQ Mean 

Below 

LML

At or 

Above 

LML

13 μg/m3 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/m3 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/m3 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 10th and 25th percentile. For Krewski, the LML is 5.8 μg/m3 and one standard deviation below the mean is 

11.0 μg/m3

. For Lepeule et al., the LML is 8 μg/m3 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 PM2.5 do not occur.
5-84

Of total PM2.5-Related deaths avoided for 12 μg/m3 : 

97% occur among populations exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study.

70% occur among populations exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study.

Figure 5-8. Number of Premature PM2.5-related Deaths Avoided for the Revised Annual 

Primary PM2.5 Standard of 12 μg/m3 in 2020 According to the Baseline Level of PM2.5 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 19-20

Percentage of Avoided PM2.5Mortalities

Baseline Annual Mean PM2.5 Level (μg/m3)

 

LML of Lepeule et 

al. (2012) study

LML of Krewski et 

al. (2009) study
5-85

Of total PM2.5-Related deaths avoided for 12 μg/m3: 

97% occur among populations exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study.

70% occur among populations exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study.

Figure 5-9. Number of Premature PM2.5-related Deaths Avoided for the Revised Annual 

Primary PM2.5 Standard of 12 μg/m3 in 2020 According to the Baseline Level of PM2.5 and the 

Lowest Measured Air Quality Levels of Each Mortality Study

When interpreting these LML and air quality benchmarks results, it is important to 

understand that the avoided PM2.5 deaths are estimated to occur from PM2.5 reductions in the 

baseline air quality simulation, which assumes that 15/35 is already met. When simulating 

attainment with revised and alternative standards, 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 greatest air quality changes nearest the monitor. We estimate benefits using 

modeled air quality data with 12 km grid cells, which is important because the grid cells are 

often substantially smaller than counties and PM2.5 concentrations vary spatially within a 

county. Therefore, there may be a small number of grid cells with concentrations slightly 

greater than 15 μg/m3 in the gridded baseline even though all monitors could meet an annual 

standard of 15 μg/m3. In addition, some grid cells in a county can be below the level of a 

standard even though the highest monitor value is above that standard. Thus, 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 20

Cumulative percentage of avoided PM Mortalities

Baseline Annual Mean PM2.5 Level (μg/m3)

 

 

LML of Krewski et 

al. (2009) study

LML of Lepeuele et 

al. (2012) study
5-86

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 PM2.5 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 illustration of this concept.

As this illustration shows, because 12km grid cells are much smaller than counties and because PM2.5 concentrations vary within 

a county, there can be modeled grid cells with PM2.5 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 Cells
5-87

While the LML of each study is important to consider when characterizing and 

interpreting the overall level of PM2.5-related 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 population 

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 Analyses 

The details of these sensitivity analyses are provided in Appendix 5.A, and summarized 

here. The use of an alternate lag structure would change the PM2.5-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 PM2.5-related premature mortality changes between 33% 

and −14%; the value of acute endpoints changes between 8% and −4%. Using the updated 

population projection data, the rounded estimates of total monetized benefits increases by 

4.4% for 12 μg/m3. These sensitivity analysis results would be similar on a percentage basis for 

the alternative annual standards.

5.8 Discussion

The 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 PM2.5 standard. We estimate that by 2020 the emissions reductions to reach the

revised annual primary standard would have reduced the number of PM2.5-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 PM2.5 standard 

of 12 μg/m3 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 concentration 
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response relationships and our use of the expert elicitation-derived PM2.5 mortality functions. 

Others, including the projection of atmospheric conditions and source-level emissions, the 

projection of baseline morbidity rates, incomes and technological development are 

unquantified. When evaluated within the context of these uncertainties, the health impact and 

monetized benefits estimates in this RIA can provide useful information regarding the public 

health benefits associated with the revised PM NAAQS.

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 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 emission 

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. EPA, 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 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.

In setting the NAAQS, the EPA considers that PM2.5 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 place to meet the standard of the highest monitor 

will simultaneously result in lower PM2.5 concentrations throughout the entire area. In fact, the 

Quantitative Risk and Exposure Assessment for Particulate Matter (U.S. EPA, 2010b) shows how 

different standard levels would affect the entire distribution of PM2.5 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 an 
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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, 2010b). 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 PM2.5 concentrations, there is no evidence of a threshold in 

PM2.5-related health effects in the epidemiology literature. Given that the epidemiological 

literature in most cases has not provided estimates based on threshold models, there would be 

additional uncertainties imposed by assuming thresholds or other non-linear concentrationresponse 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 PM2.5 concentrations by 2020 in the East, such that no additional controls 

would be needed to reach 12 μg/m3 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, because 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 the benefits associated with those air quality improvements would result in higher 

benefits than we have estimated here.

For the revised annual standard of 12 μg/m3

, 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 the air quality improvements from recent rules.

Because all of the monetized human health benefits are projected to occur in California, we 

have considered 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 could have changed if we used 

these cohort studies. 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. 
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(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.

5.9 References

Abbey, David E et al. 1995. “Chronic Respiratory Symptoms Associated with Estimated LongTerm Ambient Concentrations of Fine Particulates less than 2.5 Microns in aerodynamic 

diameter (PM2.5) and Other Air Pollutants.” Journal of Exposure Analysis and 

Environmental Epidemiology 5(2): 137-158.

Abbey, D.E., N. Nishino, W.F. McDonnell, R.J. Burchette, S.F. Knutsen, W. Lawrence Beeson, and 

J.X. Yang. 1999. “Long-term inhalable particles and other air pollutants related to 

mortality in nonsmokers.” American Journal of Respiratory and Critical Care Medicine

159(2):373-382.

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. Available on the Internet at 

<http://www.epa.gov/air/benmap/models/BenMAPAppendicesOct2012.pdf>.

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>.

Adams PF, Hendershot GE, Marano MA. 1999. “Current Estimates from the National Health 

Interview Survey, 1996.” Vital Health Stat 10(200):1-212.

Agency for Healthcare Research and Quality (AHRQ). 2000. HCUPnet, Healthcare Cost and 

Utilization Project. Rockville, MD. Available on the Internet at 

<http://hcupnet.ahrq.gov>.

Agency for Healthcare Research and Quality (AHRQ). 2007. HCUPnet, Healthcare Cost and 

Utilization Project. Rockville, MD. Available on the Internet at 

<http://hcupnet.ahrq.gov>.

Agency for Healthcare Research and Quality (AHRQ). 2009. HCUPnet, Healthcare Cost and 

Utilization Project. Rockville, MD. Available on the Internet at 

<http://hcupnet.ahrq.gov>.American Lung Association (ALA). 1999. Chronic Bronchitis. 

Available on the Internet at <http://www.lungusa.org/diseases/lungchronic.html>.

Alberini A, Cropper M, Krupnick A, Simon NB. 2004. “Does the value of a statistical life vary with 

age and health status? Evidence from the US and Canada.” Journal of Environmental 

Economics and Management 48(1): 769-792.
5-91

Alberini, Anna & Milan Ščasný. 2011. "Context and the VSL: Evidence from a Stated Preference 

Study in Italy and the Czech Republic," Environmental & Resource Economics, European 

Association of Environmental and Resource Economists 49(4), pages 511-538.

Aldy JE, Viscusi WK. 2008. “Adjusting the value of a statistical life for age and cohort effects.” 

The Review of Economics and Statistics 90(3):573-581.

American Lung Association (ALA). 2002. Trends in Asthma Morbidity and Mortality. American 

Lung Association, Best Practices and Program Services, Epidemiology and Statistics Unit.

Available on the Internet at <http://www.lungusa.org/data/asthma/ASTHMAdt.pdf>.

American Lung Association (ALA). 2010a. Trends in COPD (Chronic Bronchitis and Emphysema): 

Morbidity and Mortality. American Lung Association Epidemiology and Statistics Unit 

Research and Program Services Division. February. Table 4. Based on NHIS data (CDC, 

2008). Available at http://www.lungusa.org/finding-cures/our-research/trendreports/copdtrend-report.pdf.

American Lung Association (ALA). 2010b. Trends in Asthma Morbidity and Mortality. American 

Lung Association Epidemiology and Statistics Unit Research and Program Services 

Division. February. Table 7. Based on NHIS data (CDC, 2008). Available at 

http://www.lungusa.org/finding-cures/our-research/trend-reports/asthma-trendreport.pdf.

Anderson HR, Bremner SA, Atkinson RW, Harrison RM, Walters S. 2001. “Particulate matter and 

daily mortality and hospital admissions in the west midlands conurbation of the UK: 

associations with fine and coarse particles, black smoke and sulphate.” Occup Environ 

Med 58:504–510.

Ayyub, B.M. 2002. Elicitation of Expert Opinions for Uncertainty and Risk. CRC Press, Florida.

Babin, S. M., H. S. Burkom, et al. 2007. “Pediatric patient asthma-related emergency 

department visits and admissions in Washington, DC, from 2001–2004, and associations

with air quality, socio-economic status and age group.” Environ Health 6: 9. 

Barnett, Adrian G. et al. 2006. “The Effects of Air Pollution on Hospitalizations for 

Cardiovascular Disease in Elderly People in Australian and New Zealand Cities.” 

Environmental Health Perspectives 114(7): 1018-1023.

Belanger, K., W. Beckett, E. Triche, M.B. Bracken, T. Holford, P. Ren, J.E. McSharry, D.R. Gold, 

T.A. Platts-Mills, and B.P. Leaderer. 2003. “Symptoms of Wheeze and Persistent Cough 

in the First Year of Life: Associations with Indoor Allergens, Air Contaminants, and 

Maternal History of Asthma.” American Journal of Epidemiology 158:195-202.

Bell, M. L., K. Ebisu, et al. 2008. “Seasonal and Regional Short-term Effects of Fine Particles on 

Hospital Admissions in 202 US Counties, 1999–2005.” American Journal of Epidemiology

168(11): 1301-1310.
5-92

Berger, M.C., G.C. Blomquist, D. Kenkel, and G.S. Tolley. 1987. “Valuing Changes in Health Risks: 

A Comparison of Alternative Measures.” The Southern Economic Journal 53:977-984.

Centers for Disease Control and Prevention (CDC). 2003. Health, United States. Table 30. Years 

of Potential Life Lost Before Age 75 for Selected Causes of Death, According to Sex, Race 

and Hispanic Origin: United States, Selected Years 1980– 2000. Available on the Internet 

at <http://www.cdc.gov/nchs/data/hus/hus03.pdf>.

Centers for Disease Control and Prevention (CDC). 2008. National Center for Health Statistics. 

National Health Interview Survey, 1999-2008.

Centers for Disease Control and Prevention (CDC). 2011. United States Life Tables, 2007 

National Vital Statistics Reports. Volume 59, Number 9. September. Available on the 

Internet at <http://www.cdc.gov/nchs/data/nvsr/nvsr59/nvsr59_09.pdf>.

Chay, K.Y., and M. Greenstone. 2003. “The Impact of Air Pollution on Infant Mortality: Evidence 

from Geographic Variation in Pollution Shocks Induced by a Recession.” Quarterly 

Journal of Economics 118(3).

Chen L, Jennison BL, Yang W, and Omaye ST. 2000. “Elementary school absenteeism and air 

pollution.” Inhal Toxicol 12(11):997-1016.

Cropper, M. L. and A. J. Krupnick. 1990. The Social Costs of Chronic Heart and Lung Disease. 

Resources for the Future. Washington, DC. Discussion Paper QE 89-16-REV.

Crouse DL, Peters PA, van Donkelaar A, Goldberg MS, Villeneuve PJ, Brion O, et al. 2012. “Risk 

of Non-accidental and Cardiovascular Mortality in Relation to Long-term Exposure to 

Low Concentrations of Fine Particulate Matter: A Canadian National-level Cohort Study.” 

Environ Health Perspect. In press. http://dx.doi.org/10.1289/ehp.1104049.

Dockery, D.W., C.A. Pope, X.P. Xu, J.D. Spengler, J.H. Ware, M.E. Fay, B.G. Ferris, and F.E. 

Speizer. 1993. “An Association between Air Pollution and Mortality in Six U.S. Cities.” 

New England Journal of Medicine 329(24):1753-1759.

Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler, P. Koutrakis, J.H. Ware, 

M. Raizenne, and F.E. Speizer. 1996. “Health Effects of Acid Aerosols On North American 

Children-Respiratory Symptoms.” Environmental Health Perspectives 104(5):500-505.

Dominici, F., R.D. Peng, M.L. Bell, L. Pham, A. McDermott, S.L. Zeger, J.M. Samet. 2006. “Fine 

Particulate Air Pollution and Hospital Admission for Cardiovascular and Respiratory 

Diseases.” Journal of the American Medical Association 295: 1127-1134.

Eftim SE, Samet JM, Janes H, McDermott A, Dominici F. 2008. “Fine particulate matter and mortality: a comparison of the Six Cities and American Cancer Society cohorts with a 

Medicare cohort.” Epidemiology 19:209–216.
5-93

Eisenstein, E.L., L.K. Shaw, K.J. Anstrom, C.L. Nelson, Z. Hakim, V. Hasselblad and D.B. Mark. 

2001. “Assessing the Clinical and Economic Burden of Coronary Artery Disease: 1986-

1998. “Medical Care 39(8):824-35.

Eisner M, N Anthonisen, D Coultas et al. 2010. “An official American Thoracic Society public 

policy statement: novel risk factors and the global burden of chronic obstructive 

pulmonary disease,” Am J Respir Crit Care Med 182: 693–718.

Fann N, Lamson A, Wesson K, Risley D, Anenberg SC, Hubbell BJ. 2012a. “Estimating the 

National Public Health Burden Associated with Exposure to Ambient PM2.5 and Ozone. 

Risk Analysis,” Risk Analysis. 32(1): 81-95. 

Fann N, Roman HR, Fulcher CF, Gentile M, Wesson K, Hubbell BJ, Levy JI. 2012b. “Maximizing 

Health Benefits and Minimizing Inequality: Incorporating Local Scale Data in the Design 

and Evaluation of Air Quality Policies.” Risk Analysis. 32(6): 908—922.

Freeman (III), AM. 1993. The Measurement of Environmental and Resource Values: Theory and 

Methods. Washington, DC: Resources for the Future.

Gan, W. Q., H. W. Davies, et al. 2012. “Association of Long-term Exposure to Community Noise 

and Traffic-related Air Pollution With Coronary Heart Disease Mortality.” American 

Journal of Epidemiology. In press. doi: 10.1093/aje/kwr424.

GeoLytics Inc. 2002. GeoLytics CensusCD® 2000 Short Form Blocks. CD-ROM Release 1.0. 

GeoLytics, Inc. East Brunswick, NJ. Available: http://www.geolytics.com/ [accessed 29 

September 2004].

Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ, et al. 2001. “The 

effects of ambient air pollution on school absenteeism due to respiratory illnesses.” 

Epidemiology 12(1):43-54.

Glad, J.A., L.L. Brink, E.O. Talbott, P.C. Lee, X. Xu, M. Saul, and J. Rager. 2012. The Relationship 

of Ambient Ozone and PM2.5 Levels and Asthma Emergency Department Visits: Possible 

Influence of Gender and Ethnicity. Archives of Environmental & Occupational Health. 

Vol 62 (2): 103-108.

Hall JV, Brajer V, Lurmann FW. 2003. Economic Valuation of Ozone-related School Absences in 

the South Coast Air Basin of California. Contemporary Economic Policy 21(4):407-417.

Harrington, W., and P.R. Portney. 1987. “Valuing the Benefits of Health and Safety Regulation.” 

Journal of Urban Economics 22:101-112.
5-94

Hollman, F.W., T.J. Mulder, and J.E. Kallan. 2000. Methodology and Assumptions for the 

Population Projections of the United States: 1999 to 2100. Population Division Working 

Paper No. 38, Population Projections Branch, Population Division, U.S. Census Bureau, 

Department of Commerce. January. Available on the Internet at 

<http://www.census.gov/population/www/documentation/twps0038/twps0038.html>.

Hubbell, B., A. Hallberg, D.R. McCubbin, and E. Post. 2005. “Health-Related Benefits of Attaining 

the 8-Hr Ozone Standard.” Environmental Health Perspectives 113:73–82. ICD, 1979

Hubbell BL. 2006. “Implementing QALYs in the analysis of air pollution regulations.” 

Environmental and Resource Economics 34:365–384.

Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the 

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared 

for: Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, 

Research Triangle Park, NC. September. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_tsd_expert_interview_sum

maries.pdf>.

Industrial Economics, Incorporated (IEc). 2012. Updating BenMAP Income Elasticity Estimates—

Literature Review . Prepared for: Office of Air Quality Planning and Standards, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. September. Available on 

the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/IncomeElasticityUpdate_Recommenda

tionswithappendices.pdf>.

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 3.

Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/klecknerandneumann199

9.pdf>.

Kochi, I., B. Hubbell, and R. Kramer. 2006. “An Empirical Bayes Approach to Combining 

Estimates of the Value of Statistical Life for Environmental Policy Analysis.” 

Environmental and Resource Economics. 34: 385-406.

Kloog, I., B.A. Coull, A. Zanobetti, P. Koutrakis, J.D. Schwartz. 2012. Acute and Chronic Effects of 

Particles on Hospital Admissions in New-England. PLoS ONE. Vol 7 (4): 1-8.

Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. “Extended follow-up and 

spatial analysis of the American Cancer Society study linking particulate air pollution and 

mortality.” HEI Research Report, 140, Health Effects Institute, Boston, MA.
5-95

Krewski, D., R.T. Burnett, M.S. Goldbert, K. Hoover, J. Siemiatycki, M. Jerrett, M. Abrahamowicz, 

and W.H. White. 2000. “Reanalysis of the Harvard Six Cities Study and the American 

Cancer Society Study of Particulate Air Pollution and Mortality.” Special Report to the 

Health Effects Institute. Cambridge MA. July.

Krupnick, A.J., and M.L. Cropper. 1992. “The Effect of Information on Health Risk Valuations.” 

Journal of Risk and Uncertainty 5(2):29-48.

Kunzli, N., R. Kaiser, S. Medina, M. Studnicka, O. Chanel, P. Filliger, M. Herry, F. Horak Jr., V. 

Puybonnieux-Texier, P. Quenel, J. Schneider, R. Seethaler, J-C Vergnaud, and H. Sommer. 

2000. “Public-Health Impact of Outdoor and Traffic-Related Air Pollution: A European 

Assessment.” The Lancet 356:795-801.

Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr, and M. Studnicka. 2001. “Assessment of 

Deaths Attributable to Air Pollution: Should We Use Risk Estimates Based on Time Series 

or on Cohort Studies?” American Journal of Epidemiology 153(11):1050-55.

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate Air 

Pollution and Mortality.” American Journal of Respiratory and Critical Care Medicine

173:667-672.

Lave, L.B., and E.P. Seskin. 1977. Air Pollution and Human Health. Baltimore: Johns Hopkins 

University Press for Resources for the Future.

Lepeule J, Laden F, Dockery D, Schwartz J 2012. “Chronic Exposure to Fine Particles and 

Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” 

Environ Health Perspect. Jul;120(7):965-70.

Levy JI, Baxter LK, Schwartz J. 2009. “Uncertainty and variability in health-related damages from 

coal-fired power plants in the United States.” Risk Anal. 29(7) 1000-1014.

Lichtenstein, S. and P. Slovic, eds. 2006. The construction of preference. New York: Cambridge 

University Press. 

Lipfert, F.W., H. Mitchell Perry Jr., J. Philip Miller, Jack D. Baty, Ronald E. Wyzg, and Sharon E. 

Carmody. 2000. “The Washington University-EPRI Veterans’ Cohort Mortality Study: 

Preliminary Results.” Inhalation Toxicology 12:41-74.

Lipfert, F.W., S.C. Morris, and R.E. Wyzga. 1989. “Acid Aerosols—the Next Criteria Air 

Pollutant.” Environmental Science & Technology 23(11):1316-1322.

Lipfert, F.W.; Perry, H.M., Jr.; Miller, J.P.; Baty, J.D.; Wyzga, R.E.; Carmody, S.E. 2003. “Air 

Pollution, Blood Pressure, and Their Long-Term Associations with Mortality.” Inhalation 

Toxicology. 15, 493-512.
5-96

Lipfert, F.W.; Wyzga, R.E.; Baty, J.D.; Miller, J.P. 2006. “Traffic Density as a Surrogate Measure of

Environmental Exposures in Studies of Air Pollution Health Effects: Long-Term Mortality 

in a Cohort of US Veterans.” Atmospheric Environment 40: 154-169.

Lippmann M, Ito K, Nádas A, Burnett RT. 2000. “Association of particulate matter components 

with daily mortality and morbidity in urban populations.” Res Rep Health Eff Inst. 95:5-

72, discussion 73-82.

Lisabeth, L.D. et al. 2008. “Ambient air pollution and risk for ischemic stroke and transient 

ischemic attack.” Annals of Neurology. July (64): 53—59.

Mansfield, Carol; Paramita Sinha; Max Henrion. 2009. Influence Analysis in Support of 

Characterizing Uncertainty in Human Health Benefits Analysis: Final Report. Prepared 

for U.S. EPA, Office of Air Quality Planning and Standards. November. Available on the 

internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/influence_analysis_final_report_psg.p

df>.

Mar, T. F., J. Q. Koenig and J. Primomo. 2010. “Associations between asthma emergency visits 

and particulate matter sources, including diesel emissions from stationary generators in 

Tacoma, Washington.” Inhal Toxicol. Vol. 22 (6): 445-8.Mar, T. F., T. V. Larson, et al. 

2004. “An analysis of the association between respiratory symptoms in subjects with

asthma and daily air pollution in Spokane, Washington.” Inhal Toxicol 16(13): 809-15.

Mathes, R.W., K. Ito, and T. Matte. 2011. Assessing Syndromic Surveillance of Cardiovascular 

Outcomes from Emergency Department Chief Complaint Data in New York City. PloS 

ONE. Vol 6(2): 1-10.

Metzger, K. B., P. E. Tolbert, et al. 2004. “Ambient air pollution and cardiovascular emergency 

department visits.” Epidemiology 15(1): 46-56.

Miller, Kristin A., David S. Siscovick, Lianne Sheppard, Kristen Shepherd, Jeffrey H. Sullivan, 

Garnet L. Anderson, and Joel D. Kaufman. 2007. “Long-Term Exposure to Air Pollution 

and Incidence of Cardiovascular Events in Women.” New England Journal of Medicine. 

356 (5) :447-458.

Moolgavkar, S.H. 2000. “Air Pollution and Hospital Admissions for Diseases of the Circulatory 

System in Three U.S. Metropolitan Areas.” Journal of the Air and Waste Management 

Association 50:1199-1206.

Moolgavkar, S.H. 2003. “Air Pollution and Daily Deaths and Hospital Admissions in Los Angeles 

and Cook Counties.” In Revised Analyses of Time-Series Studies of Air Pollution and 

Health. Special Report. Boston, MA: Health Effects Institute.

Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A Meta-Analysis.” 

Journal of Policy Analysis and Management 21(2):253-270.
5-97

Mustafić H, Jabre P, Caussin C et al. 2012. “Main Air Pollutants and Myocardial Infarction: A 

Systematic Review and Meta-analysis” JAMA. 2012;307(7):713-721.

National Center for Education Statistics (NCHS). 1996. The Condition of Education 1996, 

Indicator 42: Student Absenteeism and Tardiness. U.S. Department of Education. 

Washington, DC.

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air 

Pollution Regulations. Washington, DC: The National Academies Press. Washington, DC.

National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic 

Benefits from Controlling Ozone Air Pollution. National Academies Press. Washington, 

DC.

Neumann, J.E., M.T. Dickie, and R.E. Unsworth. 1994. “Linkage Between Health Effects 

Estimation and Morbidity Valuation in the Section 812 Analysis—Draft Valuation 

Document.” Industrial Economics Incorporated (IEc) Memorandum to Jim DeMocker, 

U.S. Environmental Protection Agency, Office of Air and Radiation, Office of Policy 

Analysis and Review. March 31. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/neumannn1994.pdf>.

Office of Management and Budget (OMB). 2003. Circular A-4: Regulatory Analysis. Washington, 

DC. Available on the Internet at <http://www.whitehouse.gov/omb/circulars/a004/a4.html.>

Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens, and M. White. 2001. “Air Pollution and 

Exacerbation of Asthma in African-American Children in Los Angeles.” Epidemiology

12(2):200–208.

Ostro, B.D. 1987. “Air Pollution and Morbidity Revisited: A Specification Test.” Journal of

Environmental Economics Management 14:87-98.

Ostro, B.D. and S. Rothschild. 1989. “Air Pollution and Acute Respiratory Morbidity: An 

Observational Study of Multiple Pollutants.” Environmental Research 50:238-247.

Ozkaynak, H., and G.D. Thurston. 1987. “Associations between 1980 U.S. Mortality Rates and 

Alternative Measures of Airborne Particle Concentration.” Risk Analysis 7(4):449-461.

Patel, M. M., S. N. Chillrud, J. C. Correa, Y. Hazi, M. Feinberg, D. Kc, S. Prakash, J. M. Ross, D. 

Levy and P. L. Kinney. 2010. “Traffic-Related Particulate Matter and Acute Respiratory 

Symptoms among New York City Area Adolescents.” Environ Health Perspect. 

Sep;118(9):1338-43.

Peel JL; Metzger KB; Klein M; Flanders WD; Mulholland JA; Tolbert PE. 2007. “Ambient air 

pollution and cardiovascular emergency department visits in potentially sensitive 

groups.” Am J Epidemiol 165: 625-633.
5-98

Peng, R. D., M. L. Bell, et al. 2009. “Emergency admissions for cardiovascular and respiratory 

diseases and the chemical composition of fine particle air pollution.” Environ Health 

Perspect 117(6): 957-63.

Peng, R. D., H. H. Chang, et al. 2008. “Coarse particulate matter air pollution and hospital 

admissions for cardiovascular and respiratory diseases among Medicare patients.” JAMA

299(18): 2172-9.

Peters A. 2005. “Particulate matter and heart disease: evidence from epidemiological studies.” 

Toxicol Appl Pharmacol. Sep 1;207(2 Suppl):477-82.

Peters A. 2006. “When a myocardial infarction comes out of the not-so-blue air.” Circulation. 

Dec 5;114(23):2430-1 

Peters, A., D. W. Dockery, J. E. Muller and M. A. Mittleman. 2001. “Increased particulate air 

pollution and the triggering of myocardial infarction.” Circulation. Vol. 103 (23): 2810-5.

Poloniecki, J.D., R.W. Atkinson., A.P. de Leon., and H.R. Anderson. 1997. “Daily Time Series for 

Cardiovascular Hospital Admissions and Previous Day’s Air Pollution in London, UK.” 

Occupational and Environmental Medicine 54(8):535-540

Pope, C.A., III, R.T. Burnett, G.D. Thurston, M.J. Thun, E.E. Calle, D. Krewski, and J.J. Godleski. 

2004. “Cardiovascular Mortality and Long-term Exposure to Particulate Air Pollution.” 

Circulation 109: 71-77.

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. 

“Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate 

Air Pollution.” Journal of the American Medical Association 287:1132-1141. 

Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. 1991. “Respiratory Health and 

PM10 Pollution: A Daily Time Series Analysis.” American Review of Respiratory Diseases

144:668-674.

Pope, CA III, E Majid, D Dockery. 2009. “Fine Particle Air Pollution and Life Expectancy in the 

United States.” New England Journal of Medicine 360: 376–386.

Pope, C. A., III, J. B. Muhlestein, et al. 2006. “Ischemic heart disease events triggered by shortterm exposure to fine particulate air pollution.” Circulation 114(23): 2443-8.

Pope, C.A., III, M.J. Thun, M.M. Namboodiri, D.W. Dockery, J.S. Evans, F.E. Speizer, and C.W. 

Heath, Jr. 1995. “Particulate Air Pollution as a Predictor of Mortality in a Prospective 

Study of U.S. Adults.” American Journal of Respiratory Critical Care Medicine 151:669-

674.
5-99

Puett RC, Hart JE, Suh H, Mittleman M, Laden F. 2011. “Particulate Matter Exposures, Mortality 

and Cardiovascular Disease in the Health Professionals Follow-up Study.” Environ Health 

Perspect. August; 119(8): 1130–1135.

Puett RC, Hart JE, Yanosky JD, Paciorek C, Schwartz J, Suh H, et al. 2009. “Chronic fine and 

coarse particulate exposure, mortality and coronary heart disease in the Nurses’ Health 

Study.” Environ Health Perspect 117:1697–1701.

Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, Bryan 

J. Hubbell, and Patrick L. Kinney. 2008. “Expert Judgment Assessment of the Mortality 

Impact of Changes in Ambient Fine Particulate Matter in the U.S.” Environ. Sci. Technol., 

42(7):2268-2274.

Rosamond, W., G. Broda, E. Kawalec, S. Rywik, A. Pajak, L. Cooper, and L. Chambless. 1999. 

“Comparison of Medical Care and Survival of Hospitalized Patients with Acute 

Myocardial Infarction in Poland and the United States.” American Journal of Cardiology

83:1180-1185

Rowe, R.D., and L.G. Chestnut. 1986. “Oxidants and Asthmatics in Los Angeles: A Benefits 

Analysis—Executive Summary.” Prepared by Energy and Resource Consultants, Inc. 

Report to the U.S. Environmental Protection Agency, Office of Policy Analysis. EPA- 230-

09-86-018. Washington, DC.

Rowlatt, P., Spackman, M., Jones, S., Jones-Lee, M., Loomes, G. 1998. Valuation of deaths from 

air pollution. A Report for the Department of Environment, Transport and the Regions 

and the Department of Trade and Industry. National Economic Research Associates 

(NERA), London.

Russell, M.W., D.M. Huse, S. Drowns, E.C. Hamel, and S.C. Hartz. 1998. “Direct Medical Costs of 

Coronary Artery Disease in the United States.” American Journal of Cardiology

81(9):1110-1115.

Schwartz, J. 1993. “Particulate Air Pollution and Chronic Respiratory Disease.” Environment 

Research 62:7-13.

Schwartz, J., and L.M. Neas. 2000. “Fine Particles are More Strongly Associated than Coarse 

Particles with Acute Respiratory Health Effects in Schoolchildren.” Epidemiology 11:6-

10.

Schwartz J, Dockery DW, Neas LM, Wypij D, Ware JH, Spengler JD, Koutrakis P, Speizer FE, Ferris 

BG Jr. 1994. “Acute effects of summer air pollution on respiratory symptom reporting in 

children.” American Journal of Respiratory and Critical Care Medicine 150(5 Pt 1):1234-

42.
5-100

Schwartz J, Coull B, Laden F. 2008. “The Effect of Dose and Timing of Dose on the Association 

between Airborne Particles and Survival.” Environmental Health Perspectives 116: 64-

69.

Sheppard, L. 2003. “Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in 

Seattle, Washington, 1987-1994.” In Revised Analyses of Time-Series Studies of Air 

Pollution and Health. Special Report. Boston, MA: Health Effects Institute.

Shogren, J., and T. Stamland. 2002. “Skill and the Value of Life.” Journal of Political Economy

110:1168-1197.

Slaughter, James C et al. 2005. “Association between particulate matter and emergency room 

visits, hospital admissions and mortality in Spokane, Washington.” Journal of Exposure 

Analysis and Environmental Epidemiology (2005) 15, 153–159. 

doi:10.1038/sj.jea.7500382

Smith et al. 2004. “Do the Near Elderly Value Mortality Risks Differently?” The Review of 

Economics and Statistics 86(1): 423-429.

Smith, D.H., D.C. Malone, K.A. Lawson, L.J. Okamoto, C. Battista, and W.B. Saunders. 1997. “A 

National Estimate of the Economic Costs of Asthma.” American Journal of Respiratory 

and Critical Care Medicine 156(3 Pt 1):787-793.

Smith, K., Pattanayak SK, Van Houtven G. 2006. “Structural benefit transfer: An example using 

VSL estimates.” Ecological Economics 60: 361-371.

Standard and Poor’s. 2000. “The U.S. Economy: The 25 Year Focus.” Winter. Available on the 

Internet at http://:www.standardandpoors.com

Stanford, R., T. McLaughlin and L. J. Okamoto. 1999. “The cost of asthma in the emergency 

department and hospital.” Am J Respir Crit Care Med.160 (1): 211-5.

Sullivan, J., L. Sheppard, et al. 2005. “Relation between short-term fine-particulate matter 

exposure and onset of myocardial infarction.” Epidemiology 16(1): 41-8.

Tagaris E, Liao KJ, Delucia AJ, et al. 2009. “Potential impact of climate change on air-pollution 

related human health effects.” Environ. Sci. Technol. 43: 4979–4988.

Tolbert, P. E., M. Klein, et al. 2007. “Multipollutant modeling issues in a study of ambient air 

quality and emergency department visits in Atlanta.” J Expo Sci Environ Epidemiol 17 

Suppl 2: S29-35.
5-101

Tolley, G.S. et al. 1986. Valuation of Reductions in Human Health Symptoms and Risks.

University of Chicago. Final Report for the U.S. Environmental Protection Agency, Office 

of Policy Analysis. January. Available at 

<http://yosemite.epa.gov/ee/epa/eerm.nsf/a7a2ee5c6158cedd852563970080ee30/eaa

2842b3d2bc2798525644d0053bf48!OpenDocument#_Section6>.

Tsuge T, Kishimot A, Takeuchi K. 2005. “A choice experiment approach to the valuation of 

mortality.” Journal of Risk and Uncertainty 31(1):73-95.

U.S. Bureau of Economic Analysis (U.S. BEA). 2004. BEA Economic Areas (EAs). U.S. Department 

of Commerce. Available on the Internet at 

<http://www.bea.gov/regional/docs/econlist.cfm>.

U.S. Bureau of Economic Analysis (U.S. BEA). 1997. Real Gross Domestic Product—Table 2A. U.S. 

Department of Commerce.

U.S. Bureau of Economic Analysis (U.S. BEA). 1997. The Economic and Budget Outlook: An 

Update, Table 4—Economic Projections for Calendar Years 1997 Through 2007. 

September. U.S. Department of Commerce.

U.S. Bureau of the Census. 2001. Statistical Abstract of the United States, 2001, Section 12: 

Labor Force, Employment, and Earnings, Table No. 521. Washington, DC.

U.S. Bureau of the Census. 2002a. Modified Race Data Summary File 2000 Technical 

Documentation. Census of Population and Housing: Washington DC. Available on the 

Internet at <http://www.census.gov/popest/archives/files/MRSF-01-US1.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 1999a. The Benefits and Costs of the Clean Air 

Act, 1990–2010. Prepared for U.S. Congress by U.S. EPA, Office of Air and 

Radiation/Office of Policy Analysis and Review, Washington, DC, November; EPA report 

no. EPA-410-R-99-001. Available on the Internet at 

<http://www.epa.gov/air/sect812/1990-2010/fullrept.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 Innovation. Washington, DC. September. Available on the Internet 

at 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2004. Air Quality Criteria for Particulate 

Matter. National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

EPA/600/P-99/002bF. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=87903>.
5-102

U.S. Environmental Protection Agency (U.S. EPA). 2006a. 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 on the 

Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205—Benefits.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2006b. Air Quality Criteria for Ozone and 

Related Photochemical Oxidants (Final). EPA/600/R-05/004aF-cF. Washington, DC: U.S. 

EPA. February. Available on the Internet at 

http://cfpub.epa.gov/ncea/CFM/recordisplay.cfm?deid=149923.U.S. Environmental 

Protection Agency (U.S. EPA). 2008. Regulatory Impact Analysis, 2008 National Ambient 

Air Quality Standards for Ground-level Ozone, Chapter 6. Office of Air Quality Planning 

and Standards, Research Triangle Park, NC. March. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Regulatory Impact Analysis, 2008 

National Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. March. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2008b. Integrated Science Assessment for 

Oxides of Nitrogen—Health Criteria (Final Report). National Center for Environmental 

Assessment, Research Triangle Park, NC. July. Available on the Internet at 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645

U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessment for 

Sulfur Oxides—Health Criteria (Final Report). National Center for Environmental 

Assessment, Research Triangle Park, NC. September. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843>.

U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: National 

Emission Standards for Hazardous Air Pollutants from the Portland Cement 

Manufacturing Industry. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. April. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2009b. 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). 2010a. Final Regulatory Impact Analysis (RIA) 

for the NO2 National Ambient Air Quality Standards (NAAQS). Office of Air Quality 

Planning and Standards, Research Triangle Park, NC. January. Available on the Internet 

at <http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf>.
5-103

U.S. Environmental Protection Agency (U.S. EPA). 2010b. Quantitative Health Risk Assessment 

for Particulate Matter—Final Report. 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). 2010c. Final Regulatory Impact Analysis (RIA) 

for the SO2 National Ambient Air Quality Standards (NAAQS). Office of Air Quality 

Planning and Standards, Research Triangle Park, NC. June. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/fso2ria100602full.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2010d. Regulatory Impact Analysis (RIA) for 

the Proposed Reconsideration of the Ozone National Ambient Air Quality Standards 

(NAAQS). Office of Air Quality Planning and Standards, Research Triangle Park, NC.

January. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/s1-

supplemental_analysis_full.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2010e. Guidelines for Preparing Economic 

Analyses. EPA 240-R-10-001. National Center for Environmental Economics, Office of 

Policy Economics and Innovation. Washington, DC. December. Available on the Internet 

at <http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0568-50.pdf/$file/EE-0568- 

50.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2010f. Technical Support Document: Summary 

of Expert Opinions on the Existence of a Threshold in the Concentration-Response 

Function for PM2.5-related Mortality. Research Triangle Park, NC. June. Available on the 

Internet at <www.epa.gov/ttn/ecas/regdata/Benefits/thresholdstsd.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2010g. Proposed Regulatory Impact Analysis 

(RIA) for the Transport Rule. Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. January. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/proposaltrria_final.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011a. The Benefits and Costs of the Clean Air 

Act 1990 to 2020: EPA Report to Congress. Office of Air and Radiation, Office of Policy, 

Washington, DC. March. Available on the Internet at 

<http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011b. Policy Assessment for the Review of 

the Particulate Matter National Ambient Air Quality Standards. EPA-452/D-11-003. April. 

Available on the Internet at 

<http://www.epa.gov/ttnnaaqs/standards/pm/s_pm_2007_pa.html>. 
5-104

U.S. Environmental Protection Agency (U.S. EPA). 2011c. Regulatory Impact Analysis for the 

Federal Implementation Plans to Reduce Interstate Transport of Fine Particulate Matter 

and Ozone in 27 States; Correction of SIP Approvals for 22 States. June. Available on the 

Internet at <http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011d. Regulatory Impact Analysis for the 

Final Mercury and Air Toxics Standards. EPA-452/R-11-011. December. Available on the 

Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011e. Integrated Science Assessment for 

Ozone and Related Photochemical Oxidants (Second External Review Draft). EPA/600/R10/076B. U.S. Environmental Protection Agency, National Center for Environmental 

Assessment—RTP Division. September. Available at 

<http://oaspub.epa.gov/eims/eimscomm.getfile?p_download_id=503567>.

U.S. Environmental Protection Agency (U.S. EPA). 2011e. Regulatory Impact Results for the 

Reconsideration Proposal for National Emission Standards for Hazardous Air Pollutants 

for Industrial, Commercial, and Institutional Boilers and Process Heaters at Major 

Sources. December. Available on the Internet at 

<http://www.epa.gov/ttnecas1/regdata/RIAs/boilermactreconria.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2012a. Regulatory Impact Analysis for the 

Proposed Revisions to the National Ambient Air Quality Standards for Particulate Matter.

EPA-452/R-12-003. Office of Air Quality Planning and Standards, Health and 

Environmental Impacts Division. June. Available at 

<http://www.epa.gov/ttnecas1/regdata/RIAs/PMRIACombinedFile_Bookmarked.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2012b. 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 (U.S. EPA). 2012c. Integrated Science Assessment for 

Particulate Ozone and Related Photochemical Oxidants (Third External Review Draft). 

EPA/600/R-10/076C. National Center for Environmental Assessment—RTP Division. 

June. Available on the Internet at 

<http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=242490>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 1999. An SAB 

Advisory on the Clean Air Act Section 812 Prospective Study Health and Ecological Initial 

Studies. Prepared by the Health and Ecological Effects Subcommittee (HEES) of the 

Advisory Council on the Clean Air Compliance Analysis, Science Advisory Board, U.S. 

Environmental Protection Agency. Washington DC. EPA-SAB-COUNCIL-ADV-99-005.

Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/1AFDEBB45C57F6E585257193005

8C1A5/$File/coua9905.pdf>.
5-105

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2000. An SAB 

Report on EPA’s White Paper Valuing the Benefits of Fatal Cancer Risk Reduction. EPASAB-EEAC-00-013. July. Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/41334524148BCCD6852571A7005

16498/$File/eeacf013.pdf>. 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004a. Advisory 

on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective 

Analysis—Benefits and Costs of the Clean Air Act, 1990–2020. EPA-SAB-COUNCIL-ADV04-002. March. Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E54004

33D5D/$File/council_adv_04002.pdf>. 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004b. Review 

of the Draft Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of 

the Clean Air Act, 1990-2020: An Advisory by the Advisory Council for Clean Air 

Compliance Analysis. EPA-SAB-COUNCIL-ADV-04-004. May. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/7CCBBFE15CD4C8B185256F17005E3079/

$File/council_adv_04004.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004c. Advisory 

Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag.

EPA-COUNCIL-LTR-05-001. December. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44B098DB49F3C85257170005293E

0/$File/council_ltr_05_001.pdf>. 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2007. SAB 

Advisory on EPA’s Issues in Valuing Mortality Risk Reduction. EPA-SAB-08-001. October. 

Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/

$File/sab-08-001.pdf>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2008. 

Characterizing Uncertainty in Particulate Matter Benefits Using Expert Elicitation. EPACOUNCIL-08-002. July. Available on the Internet at

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/43B91173651AED9E85257487004EA6C

B/$File/EPA-COUNCIL-08-002-unsigned.pdf>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2009a.

Consultation on EPA’s Particulate Matter National Ambient Air Quality Standards: Scope 

and Methods Plan for Health Risk and Exposure Assessment. EPA-COUNCIL-09-009. May. 

Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86

e/723FE644C5D758DF852575BD00763A32/$File/EPA-CASAC-09-009-unsigned.pdf>.
5-106

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2009b. Review 

of EPA’s Integrated Science Assessment for Particulate Matter (First External Review 

Draft, December 2008). EPA-COUNCIL-09-008. May. Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86

e/73ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2009c. Review 

of Integrated Science Assessment for Particulate Matter (Second External Review Draft, 

July 2009). EPA-CASAC-10-001. November. Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86

e/151B1F83B023145585257678006836B9/$File/EPA-CASAC-10-001-unsigned.pdf>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010a. Review of 

EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air

Act. EPA-COUNCIL-10-001. June. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/

59e06b6c5ca66597852575e7006c5d09!OpenDocument&TableRow=2.3#2.>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010b. Review of 

Risk Assessment to Support the Review of the Particulate Matter (PM) Primary National 

Ambient Air Quality Standards—External Review Draft (September 2009). EPA-CASAC10-003. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/

BC1ECC5D539EF72385257678006D5754/$File/EPA-CASAC-10-003-unsigned.pdf>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010c. CASAC 

Review of Quantitative Health Risk Assessment for Particulate Matter—Second External 

Review Draft (February 2010). EPA-CASAC-10-008. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/

BC4F6E77B6385155852577070002F09F/$File/EPA-CASAC-10-008-unsigned.pdf>. 

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010d. CASAC 

Review of Policy Assessment for the Review of the PM NAAQS—Second External Review 

Draft (June 2010). EPA-CASAC-10-015. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/

CCF9F4C0500C500F8525779D0073C593/$File/EPA-CASAC-10-015-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011. Review of 

Valuing Mortality Risk Reductions for Environmental Policy: A White Paper (December 

10, 2010). EPA-SAB-11-011 July. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/

$File/EPA-SAB-11-011-unsigned.pdf>. 

Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market 

Estimates throughout the World.” Journal of Risk and Uncertainty 27(1):5-76.
5-107

Viscusi, W.K., W.A. Magat, and J. Huber. 1991. “Pricing Environmental Health Risks: Survey 

Assessments of Risk-Risk and Risk-Dollar Trade-Offs for Chronic Bronchitis.” Journal of 

Environmental Economics and Management 21:32-51.

Wellenius, G.A., M.R. Burger, B.A. Coull, J. Schwartz, H.H. Suh, P. Koutrakis, G. Schlaug, D.R. 

Gold, M.A. Mittlemen. 2012.”Ambient air pollution and the risk of acute ischemic 

stroke.” Arch. Intern. Med., 172 (3), pp. 229–234.

Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. 1990. “Medical Costs of Coronary Artery Disease in 

the United States.” American Journal of Cardiology 65(7):432-440.

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 Health Perspectives. Vol. 105 (6): 608-612.

Woodruff TJ, Parker JD, Schoendorf KC. 2006. “Fine particulate matter (PM2.5) air pollution and 

selected causes of postneonatal infant mortality in California.” Environmental Health 

Perspectives 114(5):786-90.

Woods & Poole Economics Inc. 2007. Population by Single Year of Age. CD-ROM. Woods & 

Poole Economics, Inc. Washington, D.C.

World Health Organization (WHO). 1977. International Classification of Diseases, 9th Revision 

(ICD-9). Geneva: WHO.

World Health Organization (WHO). 2008. Part 1: Guidance Document on Characterizing and 

Communicating Uncertainty in Exposure Assessment, Harmonization Project Document 

No. 6. Published under joint sponsorship of the World Health Organization, the 

International Labour Organization and the United Nations Environment Programme. 

WHO Press: Geneva, Switzerland. Available on the Internet at 

<www.who.int/ipcs/methods/harmonization/areas/uncertainty%20.pdf>.

Zanobetti, A., M. Franklin, et al. 2009. “Fine particulate air pollution and its components in 

association with cause-specific emergency admissions.” Environmental Health 8: 58-

60.Zeger S; Dominici F; McDermott A; Samet J. 2008. “Mortality in the Medicare 

population and chronic exposure to fine particulate air pollution in urban centers (2000-

2005).” Environ Health Perspect 116: 1614-1619.

Zanobetti A. and Schwartz, J. 2006. “Air pollution and emergency admissions in Boston, MA.” J 

Epidemiol Community Health 60(10): 890-5.
5.A-1 

APPENDIX 5.A 

ADDITIONAL SENSITIVITY ANALYSES RELATED TO THE HEALTH BENEFITS 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 important 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 limitations 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 

cerebrovascular 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). We conducted these 

sensitivity analyses for the selected annual standard of 12 μg/m3 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 PM2.5-Related Premature 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 reductions 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 that dollar-based representations of health effect incidence changes 

occurring in the future should be discounted to reflect time preferences for consumption in the 
5.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 

PM2.5 will occur over short periods of time in individuals with compromised health status, but 

other effects are likely to occur among individuals who, at baseline, have reasonably good 

health that will deteriorate because of continued exposure. 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-term 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 respiratory 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 cessation 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 combine 
5.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 publication 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 structure 

characterized by 30% of mortality reductions occurring in the first year, 50% occurring evenly 

over years 2 to 5 after the reduction in PM2.5, and 20% occurring evenly over the years 6 to 20 

after the reduction in PM2.5 (EPA-COUNCIL-LTR-05-001, 2004). The distribution of deaths over 

the latency period is intended to reflect the contribution of short-term exposures in the first 

year, cardiopulmonary deaths in the 2- to 5-year period, and long-term lung disease and lung 

cancer in the 6- to 20-year period. Furthermore, in their advisory letter, the SAB-HES 

recommended that EPA include sensitivity analyses on other possible lag structures. In this 

appendix, we investigate the sensitivity of premature mortality-reduction related benefits to 

alternative cessation lag structures, noting that ongoing and future research may result in 

changes to the lag structure used for the 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 PM exposure. In fact, the Pope et al. and 

Dockery et al. analyses do not supporting or refute the existence of a lag. Therefore, any lag 

structure applied to the avoided incidences estimated from either of these studies will be an 

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 the 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 cardiopulmonary 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 PM2.5, and 30% occurring evenly over the years 6 to 20 

after the reduction in PM2.5. The second alternate lag structure (“5-year distributed”) assumes 

the 5-year distributed lag structure used in previous analyses, which is equivalent to a threesegment lag structure with 50% in the first 2-year 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 described 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 PM2.5-related benefits, the SAB-HES continued to support the previous 20-year distributed 

lag as the main estimate. while recommending that EPA 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 PM2.5-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 effects 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. This model assumes the form risk=exp-kt, where k is the time constant 

and t is the time after t0. The relative risk from air pollution (RR) at a given time (t) can be 

calculated from the excess relative risk (ERR) attributable to air pollution from PM cohort 

studies (ERR=RR-R0), 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 Röösli et al. (2004). We have updated this sensitivity analysis in this assessment to reflect the 

published version in Röösli et al. (2005) and generated additional time constants. 
5.A-5 

RR(t) = ERR × exp−kt + R0 (5.A.1)

where R0 is the baseline relative risk in the absence of air pollution (R0=1). After cessation of 

exposure, mortality will start to decline and approach the baseline level. The change in 

mortality (ΔM), in units of percent-years, can be derived from Equation (5.A.1) as follows:

∆M = ERR × t − ∫ ERR × exp−ktdt t

0 (5.A.2)

Integrating Equation (5.A.2) gives:

∆M = ERR × t − ERR

k + ERRk

× exp−kt (5.A.3)

In order to calculate values for the time constant, k, we applied the ΔM 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 PM2.5-related mortality. We applied the 

intervention studies by Clancy 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 Valley. We applied effect

estimates from the American Cancer Society (ACS) cohort by Krewski et al. (2002)3 and the Six 

Cities cohort 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 provides the time 

constants for each of these combinations and averages, and Figure 5.A.2 illustrates the 

exponential decay lag structures. 

 

3 The relative risk coefficient from Krewski et al. (2009) (1.06 per 10 μg/m3 change in average PM2.5 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 difference 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 PM2.5 Cohort Study Intervention Study

0.05 H6C—Laden et al. (2006) Dublin—Clancy et al. (2002)

0.15 ACS—Krewski et al. (2009) 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 estimates 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 illustrates the cumulative distributions of the cessation lags 

applied in this appendix. Because we applied an income adjustment factor specific to the 

analysis year (see section 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, for most reasonable 

distributed lag structures, differences in the specific shape of the lag function have relatively 

small impacts on overall benefits. For example, the overall impact of moving from the previous 

5-year distributed lag to the segmented lag recommended by the 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% relative 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 PM2.5-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/m3

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 PM2.5 Premature Mortality (Cumulative)

Figure 5.A-2. Exponential Lag Structures for PM2.5 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 PM2.5

 

SAB segmented lag (main estimate)

Alternate Segmented Lag

Exponential decay model

5-year distributed lag

0%

25%

50%

75%

100%

1st year

2nd year

3rd year

4th year5th year6th year7th year8th year9th year

10th year11th year12th year13th year14th year15th year16th year17th year18th year19th year20th year

21st year

22nd year

23rd year

24th year25th year26th year27th year28th year29th year30th year

% Premature Mortality Occuring Within Year (Cumulative)

Year Following Reduction in PM2.5

 

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 Growtha

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 

Growtha

Benefit Category Lower Sensitivity Bound 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 Elasticitiesa

Benefit Category

Benefits Incremental to Analytical Baseline (Millions of 2006$)

12 μg/m3

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. 

(2012) 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 Cardiovascular 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 cerebrovascular 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 bronchitis in a sensitivity analysis because of the absence of newer 

studies finding a relationship between long-term PM2.5 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, 95th percentile confidence 

intervals 1.01–1.056 per 10 μg/m3 PM2.5, age 0-99), Tolbert et al. (2007) (RR= 1.005, 95th

percentile confidence intervals 0.993–1.017 per 10 μg/m3 PM2.5, age 0-99), and Mathes et al. 

(2011) (excess risk =0.8%, 95th percentile confidence intervals 0.0%-1.6% per 10 μg/m3 PM2.5, 

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/m3 PM2.5). 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/m3 PM2.5). 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 bronchitis 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 (95th percentile confidence intervals)a

Endpoint 12 μg/m3

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, 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) that were projected from the 2000 Census. We use 
5.A-12

projections based on economic forecasting models developed by Woods and Poole, 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 Total Monetized Benefits for 2000 and 2010 Census projections for 12

μg/m3 in 2020 (2010$)a

Projections from 2000 

Census

Projections from 2010 

Census Percent Change

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 PM2.5 Mortality Estimates Using Cohort Studies in California

In Chapter 5, we described the multi-state cohort studies we used to estimate the PM2.5-

related mortality (i.e., Krewski et al., 2009; Lepeule et al., 2012), 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/m3 are projected to occur in California. 

For this reason, we determined that it was appropriate to consider the sensitivity 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 monetized 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 provides 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 studies (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 PM2.5 in Recent Cohort Studies in California

Authors Cohort

Hazard Ratios per 10 μg/m3 Change in PM2.5

(95th 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/m3 change in PM2.5.

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 applied 
5.A-14

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. 

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

In this RIA, we 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 income 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/m3

, 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 Health Benefits Estimated for 2000 and 2025 for 12 μg/m3 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. 

5.A.7 References

Abbey, David E et al. 1995. “Chronic Respiratory Symptoms Associated with Estimated LongTerm Ambient Concentrations of Fine Particulates less than 2.5 Microns in aerodynamic 

diameter (PM2.5) and Other Air Pollutants.” Journal of Exposure Analysis and 

Environmental Epidemiology 5(2): 137-158.

Agency for Healthcare Research and Quality (AHRQ). 2000. HCUPnet, Healthcare Cost and 

Utilization Project. Rockville, MD. Available on the Internet at 

<http://hcupnet.ahrq.gov>.
5.A-15

Agency for Healthcare Research and Quality (AHRQ). 2007. HCUPnet, Healthcare Cost and 

Utilization Project. Rockville, MD. Available on the Internet at 

<http://hcupnet.ahrq.gov>.

Chen LH, Knutsen SF, Shavlik D, Beeson WL, Petersen F, Ghamsary M, et al. 2005. “The association between fatal coronary heart disease and ambient particulate air pollution: are 

females at greater risk?” Environ Health Perspect 113:1723–1729.

Clancy, L., P. Goodman, H. Sinclair, and D.W. Dockery. 2002. “Effect of Air-pollution Control on 

Death Rates in Dublin, Ireland: An Intervention Study.” Lancet Oct 19;360(9341):1210-4.

Desvousges, W.H., F.R. Johnson, and H.S. Banzhaf. 1998. Environmental Policy Analysis With 

Limited Information: Principles and Applications of the Transfer Method (New Horizons 

in Environmental Economics.) Edward Elgar Pub: London.

Dockery, D.W., C.A. Pope, X.P. Xu, J.D. Spengler, J.H. Ware, M.E. Fay, B.G. Ferris, and F.E. 

Speizer. 1993. “An Association between Air Pollution and Mortality in Six U.S. Cities.” 

New England Journal of Medicine 329(24):1753-1759.

Enstrom JE 2005. “Fine particulate air pollution and total mortality among elderly Californians, 

1973-2002.” Inhal Toxicol 17: 803-816.

Hankey, S., J. D. Marshall, et al. (2011). “Health Impacts of the Built Environment: Within-Urban 

Variability in Physical Inactivity, Air Pollution, and Ischemic Heart Disease Mortality.” 

Environ Health Perspect 120(2).

Jerrett M, Burnett RT, Ma R, Pope CA III, Krewski D, Newbold KB, et al. 2005. “Spatial analysis of 

air pollution and mortality in Los Angeles.” Epidemiology 16(6):727–736.

Kleckner, N., and J. Neumann. 1999. “Recommended Approach to Adjusting WTP Estimates to 

Reflect Changes in Real Income.” Memorandum to Jim Democker, US EPA/OPAR. June 3, 

Available on the Internet at

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/klecknerandneumann199

9.pdf>.

Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. “Extended follow-up and 

spatial analysis of the American Cancer Society study linking particulate air pollution and 

mortality.” HEI Research Report, 140, Health Effects Institute, Boston, MA.

Laden F, Schwartz J, Speizer FE, Dockery DW. 2006. “Reduction in fine particulate air pollution 

and mortality: extended follow-up of the Harvard Six Cities study.” Am J Respir Crit Care 

Med 173:667–672.

Lepeule J, Laden F, Dockery D, Schwartz J. 2012. “Chronic Exposure to Fine Particles and 

Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” 

Environ Health Perspect. Jul;120(7):965-70.
5.A-16

Lipsett MJ, Ostro BD, Reynolds P et al. 2011. “Long-term exposure to air pollution and 

cardiorespiratory disease in the California teachers study cohort.” Am J Respir Crit Care 

Med 184: 828–35. 

Mathes, R.W., K. Ito, and T. Matte. 2011. Assessing Syndromic Surveillance of Cardiovascular 

Outcomes from Emergency Department Chief Complaint Data in New York City. PloS 

ONE. Vol 6(2): 1-10.

McDonnell WF; Nishino-Ishikawa N; Petersen FF; Chen LH; Abbey DE. 2000. “Relationships of 

mortality with the fine and coarse fractions of long-term ambient PM10 concentrations 

in nonsmokers.” J Expo Sci Environ Epidemiol, 10: 427-436.

Metzger, K. B., P. E. Tolbert, et al. (2004). “Ambient air pollution and cardiovascular emergency 

department visits.” Epidemiology 15(1): 46-56.

Miller, Kristin A., David S. Siscovick, Lianne Sheppard, Kristen Shepherd, Jeffrey H. Sullivan, 

Garnet L. Anderson, and Joel D. Kaufman. 2007. “Long-Term Exposure to Air Pollution 

and Incidence of Cardiovascular Events in Women.” New England Journal of Medicine. 

356 (5) :447-458.

Ostro B, Lipsett M, Reynolds P, Goldberg D, Hertz A, Garcia C, et al. 2010. “Long-Term Exposure 

to Constituents of Fine Particulate Air Pollution and Mortality: Results from the 

California Teachers Study.” Environ Health Perspect 118:363-369.

Ostro B, Reynolds P, Goldberg D, Hertz A, et al. 2011. “Assessing Long-Term Exposure in the 

California Teachers Study.” Environ Health Perspect June; 119(6): A242–A243.

Peters, A., D. W. Dockery, J. E. Muller and M. A. Mittleman. 2001. “Increased particulate air 

pollution and the triggering of myocardial infarction.” Circulation. Vol. 103 (23): 2810-5.

Pope, C.A., III, M.J. Thun, M.M. Namboodiri, D.W. Dockery, J.S. Evans, F.E. Speizer, and C.W. 

Heath, Jr. 1995. “Particulate Air Pollution as a Predictor of Mortality in a Prospective 

Study of U.S. Adults.” American Journal of Respiratory Critical Care Medicine 151:669-

674.

Röösli M, Kunzli N, Braun-Fahrlander C. 2004. “Use of Air Pollution ‘Intervention-Type’ Studies 

in Health Risk Assessment.” 16th Conference of the International Society for 

Environmental Epidemiology, New York, August 1-4, 2004.

Röösli M, Kunzli N, Braun-Fahrlander C, Egger M. 2005. “Years of life lost attributable to air 

pollution in Switzerland: dynamic exposure-response model.” International Journal of 

Epidemiology 34(5):1029-35.

Tolbert, P. E., M. Klein, et al. 2007. “Multipollutant modeling issues in a study of ambient air 

quality and emergency department visits in Atlanta.” J Expo Sci Environ Epidemiol 17 

Suppl 2: S29-35.
5.A-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). 2012. 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 Costs and Benefits (1999): 

Advisory by the Health and Ecological Effects Subcommittee on Initial Assessments of 

Health and Ecological Effects. Part 1. EPA-SAB-COUNCIL-ADV-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). 1999b. 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. Available 

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/sab%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. EPASAB-COUNCIL-ADV-04-002. March. Available on the Internet at

<http://yosemite.epa.gov/sab%5CSABPRODUCT.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. 

EPA-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. Review of 

EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air

Act. EPA-COUNCIL-10-001. June. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/

59e06b6c5ca66597852575e7006c5d09!OpenDocument&TableRow=2.3#2.>.
5.B-1 

APPENDIX 5.B

COMPREHENSIVE CHARACTERIZATION 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 analysis 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. We strive to incorporate as many quantitative assessments of uncertainty as possible

(e.g., by using monte carlo assessments, concentration benchmark analyses, alternative 

concentration-response functions, sensitivity analyses, distributional assessments, and 

influence analyses); however, there are some aspects we are only able to characterize 

qualitatively. 

To more comprehensively and systematically address these uncertainties, including 

those we cannot quantify, we adapt the WHO uncertainty framework (WHO, 2008), which 

provides a means for systematically linking the characterization of uncertainty to the 

sophistication of the underlying health impact assessment. EPA has applied similar approaches 

in peer-reviewed analyses of PM2.5-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 

identifying potential bias, potential magnitude, confidence in our approach, and the level of 

quantitative assessment of each uncertainty (U.S. EPA-SAB, 1999, 2001, 2004, 2011a, 2011b). 

The 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 catalogs 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) The direction of the bias that a given uncertainty may introduce into the benefits 

assessment if not taken into account in the analysis approach; 

2) The magnitude of the impact that uncertainty is likely to have on the benefits

estimate if not taken into account in the analysis approach; 
5.B-2 

3) The 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.1.1Direction 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. Where

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 

overestimation of benefits; these cases are identified as “unable to determine.” 

5.B.1.2Magnitude of Impact

The “magnitude of impact” column in Table 5.B-1 is an assessment of how 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 the uncertainty associated with an assumption could influence the total 

monetized benefits by more than 25%. 

 Medium—If the uncertainty associated with an assumption could influence the total 

monetized benefits by 5% to 25%. 

 Low—If the uncertainty associated with an assumption could influence the total 

monetized benefits by less than 5%.

 

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 percent. In the Quantitative Health Risk Assessment for Particulate 

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 uncertainty, we provide as much 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, because PM2.5-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 

uncertainties 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 endpoints that 

are currently not monetized would reduce the fraction of total benefits from mortality. 

Ultimately, the magnitude classification is determined by professional judgment of EPA staff 

based on the results of available information, including other U.S. EPA assessments of 

uncertainty (U.S. EPA, 2010b, 2011) 

Based on this assessment, the uncertainties which we classified as high or medium-high 

impact are: the causal relationship between PM2.5 and mortality, regional differences in PM2.5 

mixtures, shape of the concentration-response function, mortality valuation, and cessation lag. 

The classification of these uncertainties as “high magnitude” is generally consistent with the 

results of EPA’s Influence Analysis (Mansfield et al., 2008), the Quantitative 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.1.3Confidence in Analytic Approach

The “confidence in analytic approach” column of Table 5.B-1 is an assessment of the 

scientific support for the analytic approach chosen (or the inherent assumption made) to 

account for the relationship about which we are uncertain. In other words, based on the 

available evidence, how 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 The three categories used to characterize the 

degree of confidence are: 

 High—the current 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—some evidence exists to support the selected approach, but data gaps are 

present; and

 Low—limited data exists to support the selected approach.

Ultimately, the degree of confidence in the analytic approach is EPA staff’s professional 

judgment based on the volume and consistency of supporting evidence, much of which has 

been evaluated in the PM ISA (U.S. EPA, 2009) and EPA’s independent Science Advisory Board 

(SAB). The PM ISA evaluated 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 projections for morbidity, and omitted morbidity endpoints. However, because these 

uncertainties have been classified as having a low or low-medium impact on the magnitude of 

the benefits, further investment in improving the available evidence would not have a 

substantial impact on the total monetized benefits. 

5.B.1.4Uncertainty 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 the four tiers used in the

WHO uncertainty framework (WHO, 2008). The WHO uncertainty framework is a wellestablished 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 quantitatively. 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 site 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. The uncertainties for which we have 

conducted probabilistic (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 PM2.5 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 PM2.5

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 PM2.5-related 

health 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. Depending on the extent of the 

differences in toxicity 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, we 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 use 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 PM2.5 data does not allow 

differentiation of PM effects in different 

locations (pg. 2–17). Using national risk 

coefficients is supported by SAB (U.S. EPASAB, 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 California studies range 

from -83% to +1300% 

compared to the national 

estimate applied from Krewski 

et al. (2009).

Causal relationship 

between PM2.5 

exposure and 

premature mortality

Overestimate, if PM2.5 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 PM2.5 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 PM2.5 expert 

elicitation, 10 of 12 experts provided 

likelihood of causality of 90% or higher 

(Roman et al., 2008).

Each expert in the PM2.5 expert 

elicitation had the opportunity 

to specify the likelihood of a 

causal relationship into their 

function (Roman et al., 2008). 

Using these expert-derived 

functions 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 PM2.5 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 PM2.5 

exposure and 

premature mortality

Overestimate, if PM2.5 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 PM2.5 

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 PM2.5 expert elicitation, 10 of 12 

experts provided likelihood of causality of 

90% or higher (Roman et al., 2008).

Each expert in the PM2.5 expert 

elicitation had the opportunity 

to specify the likelihood of a 

causal relationship into their 

function(Roman et al., 2008). 

Using these expert-derived 

functions is a probabilistic 

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 model 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 assume a 

log-linear no-threshold model 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 

overall support the use of a no-threshold 

log-linear model for PM2.5-related 

mortality (pg. 2-25). Our approach also 

follows recommendations from the SAB 

(U.S. EPA-SAB, 2010, pg. 13). 

The experts in the PM2.5 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 of 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 PM2.5 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)

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 estimates. These analyses also 

illustrate the complexity and sitespecific 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 quantitative 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 Cities 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 from -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 target 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 

evaluated 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 PM2.5 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)

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 

significant confounding influence on PMrelated 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)

Disentangling the health 

responses of combustion-related 

pollutants (i.e., PM, SOx, NOx, 

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 copollutants (pg. 1–16). Alternately, 

effects attributed to one 

pollutants may be due to another.

Because this uncertainty could affect 

mortality and because mortality 

generally dominates monetized 

benefits, even a small uncertainties 

could have medium impacts on total 

monetized benefits. 

When modeling effects of pollutants jointly, 

we apply multi-pollutant effect 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 

PM2.5-mortality relationship (pg. 2–11). 

EPA’s current approach to modeling has 

been supported during peer-reviews 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 PM2.5 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 shortterm 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 be 

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, pgs. 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 health 

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), we expanded the 

age range for childhood asthma 

exacerbations beyond the original 

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 PM2.5 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

Mortality 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 lead 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, 2011c).

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 PM2.5 exposure, it 

affects the timing of those deaths and 

thus the discounted monetized benefits. 

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 20% 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 evidence 

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 PM2.5 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-topay (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 

adjustment 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 increased 

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 placeholders 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 

distributions specified in the 

underlying literature, where 

available (see Table 5.9). 

(continued)
5.B-13

Table 5.B-1. Summary of Qualitative Uncertainty for Key Modeling Elements in PM2.5 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, 2012). The magnitude 

of impact on total monetized benefits depends 

on the specific location where PM is reduced.

We use population projections for 5-year 

increments for 304 race/ethnicity/gender/age 

groups (Woods and Poole, 2012) 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.

Because mortality 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 continue. 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 PM2.5 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 

PM2.5 exposure that are currently not 

monetized 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 PM2.5 

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 not controlled for this 

potential bias, and the SAB has not 

suggested an approach to adjust for 

this bias. 

(No quantitative method 

available)
5.B-15

5.B.3 References

Alberini, Anna and Alan Krupnick. 2000. “Cost-of-Illness and Willingness-to-Pay Estimates of the 

Benefits of Improved Air Quality: Evidence from Taiwan.” Land Economics

Vol. 76, No. 1 (Feb), pp. 37-53.

Centers for Disease Control and Prevention (CDC). 2008. National Center for Health Statistics. 

National Health Interview Survey, 1999-2008. 

Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the 

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared 

for: Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, 

Research Triangle Park, NC. September. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_tsd_expert_interview_sum

maries.pdf>.

Industrial Economics, Incorporated (IEc). 2012. Updating BenMAP Income Elasticity Estimates—

Literature Review. Memo to Neal Fann. March. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/Income%20Elasticity%20Update_Reco

mmendations_noAppD.pdf>

Jerrett M, Burnett RT, Ma R, et al. 2005. “Spatial analysis of air pollution and mortality in Los 

Angeles.” Epidemiology 16:727-736.

Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. “Extended follow-up and 

spatial analysis of the American Cancer Society study linking particulate air pollution and 

mortality.” HEI Research Report, 140, Health Effects Institute, Boston, MA.

Lepeule J, Laden F, Dockery D, Schwartz .J 2012. “Chronic Exposure to Fine Particles and 

Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” 

Environ Health Perspect. 120: 965-970.

Mansfield, Carol; Paramita Sinha; Max Henrion. 2009. Influence Analysis in Support of 

Characterizing Uncertainty in Human Health Benefits Analysis: Final Report. Prepared 

for U.S. EPA, Office of Air Quality Planning and Standards. November. Available on the 

internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/influence_analysis_final_report_psg.p

df>.

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air 

Pollution Regulations. Washington, DC: The National Academies Press. Washington, DC.

Puett RC, Hart JE, Yanosky JD, Paciorek C, Schwartz J, Suh H, et al. 2009. “Chronic fine and 

coarse particulate exposure, mortality and coronary heart disease in the Nurses’ Health 

Study.” Environ Health Perspect 117:1697–1701. 
5.B-16

Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, Bryan 

J. Hubbell, and Patrick L. Kinney. 2008. “Expert Judgment Assessment of the Mortality 

Impact of Changes in Ambient Fine Particulate Matter in the U.S.” Environ. Sci. Technol., 

42(7):2268-2274.

Schwartz J, Coull B, Laden F. 2008. “The Effect of Dose and Timing of Dose on the Association 

between Airborne Particles and Survival.” Environmental Health Perspectives 116: 64-

69.

U.S. Environmental Protection Agency (U.S. EPA). 2001. Risk assessment guidance for 

Superfund. Vol. III, Part A. Process for conducting probabilistic risk assessment (RAGS 

3A). Washington, DC, United States Environmental Protection Agency (EPA 540-R-02-

002; OSWER 9285.7-45; PB2002 963302. Available on the Internet at

<http://www.epa.gov/oswer/riskassessment/rags3adt/index.htm>. 

U.S. Environmental Protection Agency (U.S. EPA). 2004. EPA’s risk assessment process for air 

toxics: History and overview. In: Air toxics risk assessment reference library. Vol. 1. 

Technical resource manual. Washington, DC, United States Environmental Protection 

Agency, pp. 3-1–3-30 (EPA-453-K-04-001A. Available on the Internet at 

<http://www.epa.gov/ttn/fera/data/risk/vol_1/chapter_03.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for 

Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for 

Environmental Assessment—RTP Division. December. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.

U.S. Environmental Protection Agency (U.S. EPA). 2010a. Valuing Mortality Risk Reductions for

Environmental Policy: A White Paper (SAB Review Draft). December. Available on the 

Internet at <http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0563-1.pdf/$file/EE0563-1.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2010b. Quantitative Health Risk Assessment 

for Particulate Matter—Final Report. 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. The Benefits and Costs of the Clean Air 

Act 1990 to 2020: EPA Report to Congress. Office of Air and Radiation, Office of Policy, 

Washington, DC. March. Available on the Internet at 

<http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>.
5.B-17

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 1999. An SAB 

Advisory: The Clean Air Act Section 812 Prospective Study Health and Ecological Initial 

Studies. EPA-SAB-COUNCIL-ADV-99-005, Available on the Internet at

<http://yosemite.epa.gov/sab/sabproduct.nsf/1AFDEBB45C57F6E5852571930058C1A5/

$File/coua9905.pdf>. 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2000. The Clean 

Air Act Amendments (CAAA) Section 812 Prospective Study of Costs and Benefits (1999): 

Advisory by the Advisory Council on Clean Air Compliance Analysis: Costs and Benefits of 

the CAAA. EPA-SAB-COUNCIL-ADV-00-002. October. Available on the Internet at

<http://yosemite.epa.gov/sab/sabproduct.nsf/E992F918DF32131A852571B9004571F7/

$File/coua0002.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2001. REVIEW 

OF THE DRAFT ANALYTICAL PLAN FOR EPA’S SECOND PROSPECTIVE ANALYSIS - BENEFITS 

AND COSTS OF THE CLEAN AIR ACT 1990- 2020. EPA-SAB-COUNCIL-ADV-01-004, 

Available on the Internet at

<http://yosemite.epa.gov/sab/sabproduct.nsf/572DC1989A3986CC8525718D006BAB8B

/$File/councila01004.pdf>. 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004a. Advisory 

on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective 

Analysis—Benefits and Costs of the Clean Air Act, 1990–2020. EPA-SAB-COUNCIL-ADV04-002. March. Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E54004

33D5D/$File/council_adv_04002.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004b. Review 

of the Draft Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of 

the Clean Air Act, 1990-2020: An Advisory by the Advisory Council for Clean Air 

Compliance Analysis. EPA-SAB-COUNCIL-ADV-04-004. May. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/7CCBBFE15CD4C8B185256F17005E3079/

$File/council_adv_04004.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004c. Advisory 

Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag.

EPA-COUNCIL-LTR-05-001. December. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44B098DB49F3C85257170005293E

0/$File/council_ltr_05_001.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2010. Review of 

EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air

Act. EPA-COUNCIL-10-001. June. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/

72D4EFA39E48CDB28525774500738776/$File/EPA-COUNCIL-10-001-unsigned.pdf>.
5.B-18

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011a. CASAC 

Review of Quantitative Health Risk Assessment for Particulate Matter—Second External 

Review Draft (February 2010). EPA-CASAC-10-008. Available on the Internet at

<http://yosemite.epa.gov/sab/sabproduct.nsf/BC4F6E77B6385155852577070002F09F/

$File/EPA-CASAC-10-008-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011b. Review 

of the Final Integrated Report for the Second Section 812 Prospective Study of the 

Benefits and Costs of the Clean Air Act (August 2010). EPA-COUNCIL-11-001. Available 

on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/1E6218DE3BFF682E852577FB005D46F1/

$File/EPA-COUNCIL-11-001-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011c. Review 

of Valuing Mortality Risk Reductions for Environmental Policy: A White Paper 

(December 10, 2010). EPA-SAB-11-011. July. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/

$File/EPA-SAB-11-011-unsigned.pdf>. 

Woods & Poole Economics, Inc. 2012. Complete Economic and Demographic Data Source 

(CEDDS). CD-ROM. Woods & Poole Economics, Inc. Washington, D.C.

World Health Organization (WHO). 2008. Part 1: Guidance Document on Characterizing and 

Communicating Uncertainty in Exposure Assessment, Harmonization Project Document 

No. 6. Published under joint sponsorship of the World Health Organization, the 

International Labour Organization and the United Nations Environment Programme. 

WHO Press: Geneva, Switzerland. Available on the Internet at 

<www.who.int/ipcs/methods/harmonization/areas/uncertainty%20.pdf>. 
6-1 

CHAPTER 6

WELFARE CO-BENEFITS OF THE PRIMARY STANDARD

6.1 Synopsis

The emission reductions to meet the revised primary standard will have “welfare” cobenefits 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 as 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 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 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 simulations 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 defines welfare effects to include any non-health 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), NOX/SOX—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 as 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 effects 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 standard, 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 PM2.5 

precursors (e.g., NOx, SOx, VOCs), other ancillary reductions from the illustrative emission 

reduction strategies (e.g., mercury and CO2), and secondary co-pollutant formation from PM 

precursors (e.g., ozone from NOx 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 emission 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, welfare cobenefits 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 cobenefits, to the aggregate total for the cost-benefit comparison. These unquantified benefits 

and co-benefits may be substantial, although the magnitude is highly uncertain. We 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 in 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 

(SO2) 

Vegetation 

Injury 

(Ozone)

Materials 

Damage Climate

Ecosystem 

Effects—

(Organics 

& Metals)

Acidification 

(freshwater)

Nitrogen 

Enrichment

Mercury 

Methylation

Direct

PM2.5

    

NOx       

SO2      

VOCs     

PM10    

Hg  

CO2  

 = 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 estimate 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 categories 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 is provided in Appendix 6A. The illustrative visibility co-benefits 

results 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 CO2 — — Section 6.5, SCC 

TSDb

Climate impacts from ozone and 

PM

— — Section 6.5, 

Ozone ISA, PM 

ISAc

Other climate impacts (e.g., other 

GHGs, other impacts) 

— — Section 6.5, IPCCc

Reduced effects on materials Household soiling — — Section 6.4, PM 

ISAc

Materials damage (e.g., corrosion, 

increased wear)

— — Section 6.4, PM 

ISAc

Reduced effects from PM 

deposition (metals and 

organics)

Effects on Individual organisms 

and ecosystems

— — Section 6.6.1, PM 

ISAc

Reduced vegetation and 

ecosystem effects from 

exposure to ozone

Visible foliar injury on vegetation — — Section 6.6.4, 

Ozone ISAc

Reduced vegetation growth and 

reproduction

— — Section 6.6.4, 

Ozone ISAb

Yield and quality of commercial 

forest products and crops

— — Section 6.6.4, 

Ozone ISAb,d

Damage to urban ornamental 

plants

— — Section 6.6.4, 

Ozone ISAc

Carbon sequestration in terrestrial 

ecosystems

— — Ozone ISAc

Recreational demand associated 

with forest aesthetics

— — Ozone ISAc

Other non-use effects Ozone ISAc

Ecosystem functions (e.g., water 

cycling, biogeochemical cycles, 

net primary productivity, leaf-gas 

exchange, community 

composition)

— — Ozone ISA2
6-5 

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, 

NOx SOx ISAb

Tree mortality and decline — — Section 6.6.2, 

NOx SOx ISAc

Commercial fishing and forestry 

effects

— — Section 6.6.2, 

NOx SOx ISAc

Recreational demand in terrestrial 

and aquatic ecosystems

— — Section 6.6.2, 

NOx SOx ISAc

Other non-use effects Section 6.6.2, 

NOx SOx ISAc

Ecosystem functions (e.g., 

biogeochemical cycles)

— — Section 6.6.2, 

NOx SOx ISAc

Reduced effects from 

nutrient enrichment

Species composition and 

biodiversity in terrestrial and 

estuarine ecosystems

— — Section 6.6.2, 

NOx SOx ISAc

Coastal eutrophication — — Section 6.6.2, 

NOx SOx ISAc

Recreational demand in terrestrial 

and estuarine ecosystems

— — Section 6.6.2, 

NOx SOx ISAc

Other non-use effects Section 6.6.2, 

NOx SOx ISAc

Ecosystem functions (e.g., 

biogeochemical cycles, fire 

regulation)

— — Section 6.6.2, 

NOx SOx ISAc

Reduced vegetation effects 

from ambient exposure to 

SO2 and NOx

Injury to vegetation from SO2

exposure

— — Section 6.6.2, 

NOx SOx ISAc

Injury to vegetation from NOx

exposure

— — Section 6.6.2, 

NOx SOx ISAc

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 RTCc,d

Commercial, subsistence and 

recreational fishing

— — Section 6.2 and 

6.6.3, Mercury 

Study RTCc

a We quantify these co-benefits in an 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 qualitatively 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 current 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 PM2.5 as well as 

precursor emissions such as NOx and SO2 for an alternative annualstandard at 11 μg/m3. 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 variety 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, 

elemental 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 (βext), 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-benefits 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 �

βext

10 � (6.1)

where VR denotes visual range (in kilometers) and βext 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 humidity 

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 

improvements 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 have improved as emissions 

of SO2 and NOx 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 recent regulations such as the Cross-State Air 

Pollution Rule (U.S. EPA, 2011c) and the Mercury and Air Toxics Standard (U.S. EPA, 2011d) are 

anticipated to reduce SO2 emissions down to 2 million tons nationally, which would lead to 

substantial further improvement 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. Visibility in Selected National Parks and Wilderness Areas in the U.S., 1992–

2008a,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 

alternative 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 function 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 standards is described in detail in Chapter 3 of this 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 result 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, 2010a), 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 estimate 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 potential impact of omitting 

coarse particles from the light extinction estimates for recreational and residential visibility. For 

these sensitivity analyses, 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, the 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/m3 nationwide, 15 μg/m3 in the Southwest with 5 μg/m3 in 

the rest of the country, and 15 μg/m3 in the Southwest with 8 μg/m3 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 CoBenefits Approacha

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/m3

in the Southwest and 8 μg/m3 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 recreational 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 distinguishing 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, the 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 number of 

overlapping areas and their contributions. Specifically, we believe that the potential for doublecounting 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 primary 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 to 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 cobenefits 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 

extinction.

6.3.4 Recreational Visibility

6.3.4.1 Methodology

Our approach for estimating recreational visibility co-benefits is well-established and 

has been used in numerous analyses by the EPA (U.S. EPA, 1999; 2005; 2006; 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 monuments 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 country (U.S. EPA, 2009b).15 The IMPROVE monitoring network 

measures fine particles, coarse particles, and key PM2.5 constituents that affect visibility, such 

as sulfate, nitrate, organic and elemental carbon, soil dust, and several other elements.

Figure 6-4 identifies 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 cobenefits. 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 served 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 

estimating 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 

(Southwest), 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 recreational 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 indicator 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 

Chestnut (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 associated 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 improvements 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 this 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(∆Qik) = m − [mρ + γik ∗ �Q0ik

ρ − Q1ikρ �]

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,

Q0 = starting visibility, and

Q1 = 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 Available 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 substitutability 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% increase 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 visitation 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 that 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 extremely 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 outside 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 data 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 those 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 changes 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 nonstudied 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 one 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 includes 

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 Areasa

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 survey, 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 visibility 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 

ys
6-21

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 of 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-benefits. 

Each of these inputs may contain uncertainty that would affect the recreational visibility cobenefits estimates. Though we are unable to quantify the cumulative effect of all of these 

uncertainties in our approach, we do provide information on uncertainty based on the available 

data, including model evaluation24 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 estimates.

 

24See Chapter 4 for more information on model evaluation.
6-22

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

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 reflect 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 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. 

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 conducted. 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 Chestnut 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 for 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 improvements 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 possible 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 for 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 some 

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 Metropolitan 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 the 
6-24

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 River 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 Associates Inc., 2001). Although 

these studies indicate that some individuals considered the visual air quality associated with 

ambient levels of air pollution in urban areas to be unacceptable, these studies do not provide 

sufficient information on which to develop 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. 

Studies 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, although 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, 

2010a, 2010b).

 

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 Council recommended that residential 

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. (1986) studies.28

Each of the studies provides estimates of household WTP to improve 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 nonzero value for residential visibility improvements. 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 ( VR1

VR0

) (6.3)

where:

VR0 = mean annual visual range in miles before the improvement,

VR1 = 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 the specific 

details need to compute visibility benefits using Tolley et al. (1986) 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 Fabian, 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, 2010a). 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 valuation 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, unlike 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 Visibility 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. (1986) 321 $31 $72

Boston Tolley et al. (1986) 398 $38 $89

Chicago Tolley et al. (1986) 310 $30 $69

Denver Tolley et al. (1986) 696 $66 $155

Los Angeles Brookshire et al. (1979) 94 $9 $21

Mobile Tolley et al. (1986) 313 $30 $70

San Francisco Loehman et al. (1985) 989 $94 $220

Washington, DC Tolley et al. (1986) 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 − [mρ + θ ∗ �VR0

ρ − VR1ρ

�]

1

ρ (6-4)

where:

m = household income,

ρ = shape parameter (0.1),

θ = WTP parameter corresponding to the visibility at MSA,

VR0 = starting visibility, and

VR1 = 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 consistent 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 above 

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 protecting 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 maximum, 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 set of locationspecific 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 to 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 period 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 non-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 attempted 

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-benefitsa

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 people 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 Estimate 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 context 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 developed 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 to estimate the willingness to pay 

(WTP) for air quality changes (see Smith and Huang (1995) and Boyle and Kiel (2001) for 
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literature reviews), but fewer researchers have focused specifically on visibility. Studies that 

have estimated the WTP to improve visibility 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 the 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 surrogate for environmental 

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 hedonic 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 Clean 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 cobenefits (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 improved 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 statistical methods 

(Anselin and Le Gallo, 2006; Anselin and Lozana-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 demographic datasets may 

open possibilities for national-scale hedonics-based benefit analysis in the future. 
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Regardless of whether we use hedonic models or stated preference surveys to estimate 

co-benefits arising from improved visibility, it is important to emphasize 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 unaffected 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 cobenefits is complex and suffers from unavoidable limitations. While 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, 2010a, 2010b). Although the SAB-Council highlighted concerns with the visibility 

approach used in the study, it did not recommend that visibility benefits be excluded. We have 

addressed the SAB-Council’s concern regarding inconsistency between estimation of residential 

and recreational visibility in our approach. However, we do not have the data to address the 

SAB-Council’s concern regarding inclusion of night-time benefits of visibility improvements in 

our annual average, which may lead to an underestimation of visibility benefits. To minimize 

uncertainties related to extrapolation and geographical double counting, our approach only 

includes a subset of monetized visibility co-benefits in the core monetized visibility co-benefits 

estimate to correspond 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 confident about the 

magnitude of those co-benefits. However, it is unclear the degree to which the visibility 

valuation surveys were successful in controlling for potential double counting embedded health 

benefits. 

Consistent with the approach described in the proposal 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-
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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 nonzero 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 literature.

Each of these valuation studies has limitations, which are identified in the sections 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 Congress (U.S. EPA, 2011), they also 

lamented on the need for additional research to improve methods and estimates (U.S. EPASAB, 2010a, 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 valuation 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, we provide the results of an illustrative analysis of the visibility cobenefits 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, 
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temperature fluctuations, sunlight, etc.). Pollution can worsen and accelerate these effects.

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 monuments 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, SO2 is more corrosive than NOx although mixtures of NOx, SO2

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 NOx/SOx 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 soiling 
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of building surfaces by PM, the frequency and duration of 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 cobenefits 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 cobenefits 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 

analysis. 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 damages (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 PM2.5 and ozone, or even visibility cobenefits. 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. 
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In the absence of quantified co-benefits, we provide a qualitative description of the 

avoided damage associated with reducing PM and PM precursor pollutants. Table 6-8 shows 

the effect of various PM2.5 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 state and local 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) such as carbon dioxide (CO2), 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 cocontrolled GHG emission reductions, the energy and associated climate gas implications of 

control technologies assumed in the illustrative regulatory alternatives, and remaining 

uncertainties regarding the impact of long-lived and short-lived climate forcer 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 as 
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CO2 for the regulatory alternatives. Implementation strategies undertaken by state and local 

governments to comply with the standards 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 referred 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 CO2 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-boundary 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, ozone, 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 release of carbon from thawing permafrost and biodiversity loss.” While reducing 

long-lived GHGs such as CO2 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

Black carbon is the most strongly light-absorbing component of PM2.5, and is formed by 

incomplete combustion of fossil fuels, biofuels, and biomass. The short atmospheric lifetime of 

BC lasting from days to weeks and the mechanisms by which BC affects climate distinguish it 

from long-lived GHGs like CO2. This means that actions taken to reduce the BC constituents in 
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direct PM2.5 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-lived, well-mixed 

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 

(“albedo”) 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. Per unit of mass in the 

atmosphere, BC can absorb a million times more energy than CO2 (Bond and Sun 2005). This 

strong absorptive capacity is the property most relevant to its potential to affect the Earth’s 

climate. When 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. 

Regional 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, 

contributing 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 or 

glacier-fed 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 snow) 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 the 
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San Juan Mountains in Colorado indicated a decrease in snow cover duration of 18-35 days as a 

result of dust transported from non-local desert sources.

The 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. Pollutants such as SO2, NOX, and most OC particles 

tend 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 NOx 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 harmful 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 to 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 CO2 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 atmospheric 
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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 nonBC constituents are emitted in greater volume than BC, counteracting the warming influence of 

BC. Qualitatively, it seems likely that BC emission reductions associated with directly emitted 

PM2.5 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 Effects of Long-Lived Greenhouse Gases

The EPA Administrator found in 2009 that elevated concentrations of the six major 

GHGs, including CO2, 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 directly 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.

Ecosystem services can be generally defined as the benefits that individuals and 

organizations obtain from ecosystems. The EPA has defined ecological goods and services as 

the “outputs of 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 ecosystem 
6-41

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 wellbeing 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 regulation 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. Supporting: 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 for 
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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 

responses 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 quantified. 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 Process (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 diphenyl 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 to 
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ecosystem soils or surface waters where the deposited constituents of PM then interacts 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 associated 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 

magnitude 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 effects (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 the 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 accumulate 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). Atmospheric 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. 

(Arzavus, 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., 1995; 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 processes. 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 surfaces 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 other 
6-45

anthropogenic contaminant sources (U.S. EPA, 2009b). Metals associated with PM deposition 

limit phytoplankton growth, affecting aquatic trophic structure. Long-range atmospheric 

transport 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, 

fate, 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 concluded 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 not 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, disease).

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 

composition. 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 Reductions in Nitrogen and Sulfur Emissions

Emissions of the PM precursors, such as nitrogen 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 along the transport path (U.S. EPA, 2008c). Secondary particles are formed from 

NOX and SO2 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, SO2 can affect vegetation at 

ambient levels near pollution sources.
6-47

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

NOX 

Atmospheric 

Fate and 

Transport

Deposition 

Process

Acidification

Aquatic 

Ecosystems

Terrestrial 

Ecosystems

Nutrient 

Enrichment

Aquatic 

Ecosystems

Terrestrial 

Ecosystems

SO2 

Atmospheric 

Fate and 

Transport

Deposition 

Process

Acidification

Aquatic 

Ecosystems

Terrestrial 

Ecosystems

Methylmercury 

Production
6-48

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 gasphase species that lead to the dry deposition of acidity are also precursors of particles. 

Therefore, reductions in NOX and SO2 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 

decrease concentrations of exchangeable base cations in soils (U.S. EPA, 2008c). Major 
6-49

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 levels. 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 NOx/SOx—Ecological Criteria concluded that the evidence is sufficient 

to infer a causal 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 algae.

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 conditions, except where 

chronic acidity conditions are severe. Biological effects of episodes include reduced fish 

condition factor33, changes in species composition and declines in aquatic species richness 

across multiple taxa, ecosystems and regions. These conditions 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 effects 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., sensitive 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 affected.

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 patterns 
6-51

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 ecological 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 water 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. After 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 base 

version and $54 to $154 for the scope version. Using these estimates, the aggregate annual 

benefits of eliminating all anthropogenic sources of NOx and SOx 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 soils, 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 NOx/SOx—Ecological Criteria concluded that the evidence is sufficient to 

infer a causal relationship between acidifying deposition and effects on biogeochemistry 

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 deposition 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 landscapes 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 forest 
6-54

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 timber 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 service 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 endangered 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. Virginia 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 statistics 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 

more 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 wildlife 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 difficult to estimate the portion of these recreational 

services that are specifically attributable 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 

year 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 values 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 associated 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, 2009c).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 NOx/SOx—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

National 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 coastal 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 accumulations of both algae and sediments in estuarine waters. In addition to 

contributing to declines in SAV, high levels of turbidity also degrade the aesthetic qualities of 

the estuarine environment.

Estuaries in the eastern United States are an important source 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 wellpublicized 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 Southeast (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 of 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 

billion 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 enrichment co-benefits associated 

with the revised or alternative annual standards 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 NOx/SOx (U.S. EPA, 2008c) 

supports a causal relationship between atmospheric N deposition and biogeochemical cycling 

and fluxes of N and carbon in terrestrial 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 period; 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 NOx/SOx—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 affected 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 that 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 example, 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 suitability 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 viewing 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. 
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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 associated with those ecosystems. 

Based on analyses in the NOx SOx 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, respectively (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 trail 

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 recreational 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 average, 

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 estimate of the terrestrial nutrient enrichment co-benefits

associated with the revised or alternative annual standards due to data, time, and resource 

limitations. Methods are not yet available to allow estimation of changes in ecosystem services 

due to nitrogen 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. SO2 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 

result of the emission reductions achieved by this rule. 
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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 of 

plant tissues. Another type of visible injury is termed chronic injury and is usually a result of 

variable SO2 exposures over the growing season. Besides foliar injury, chronic exposure to low 

SO2 concentrations can result in reduced photosynthesis, growth, 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.

SO2 is also considered the primary factor causing the death of lichens in many urban and 

industrial areas (Hutchinson et al., 1996). The ISA for NOx/SOx—Ecological Criteria concluded 

that the evidence is sufficient to infer a causal relationship between SO2 injury to vegetation 

(U.S. EPA, 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 (Hg0

), oxidized Hg compounds (Hg+2), and particle-bound Hg (HgP).

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 NOx SOx 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 

between SO4

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 SO4

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 association between sulfur and 

mercury cannot be neglected (U.S. EPA, 2008c).
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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 methylation 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 is based on SO4

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).
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Figure 6-15. Preliminary USGS Map of Mercury 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.

Decreases in SO4

2- deposition have already shown promising reductions in MeHg. 

Observed decreases in MeHg fish tissue concentrations have been linked to decreased 

acidification and declining SO4

2- and mercury deposition in Little Rock Lake, WI (Hrabik and 

Watras, 2002), and to decreased SO4

2- deposition in Isle Royale in Lake Superior, MI (Drevnick 

et al., 2007). Although the possibility exists that reductions in SO4

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 SO4

2-, 

increased mercury methylation downstream may still occur in sulfate-enriched ecosystems with 

increased organic matter and/or downstream transport capabilities.
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Remediation of sediments heavily contaminated with mercury has yielded significant 

reductions of MeHg in biotic tissues. Establishing quantitative 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 SOX 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 terrestrial watersheds. Once mercury deposits, it may be converted to organic 
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methylmercury mediated primarily by sulfate-reducing bacteria. Methylation is enhanced in 

anaerobic and acidic environments, greatly increasing mercury toxicity and potential to 

bioaccumulate 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 deposition 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 

species. 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 at this time. In the sections that follow numerous 

effects have been identified at differing 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 species 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 cannot 

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 could 
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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 addition 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 in 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), physiological (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 

Maine 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). Additionally 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 shown 

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. Ibis territory can range inland 

to Oklahoma, Arkansas and Tennessee. Egret range covers virtually the entire United States 

except the mountain west.
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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 immune 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 

complete 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 viewing. 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 strategies that include NOx 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 ecological systems, leading to changes in the ecological community 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 potential 

for regional-scale forest impacts (U.S. EPA, 2006a). Studies have demonstrated repeatedly that 
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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 (CO2) and other gaseous substances, ozone 

enters plant tissues primarily through the stomata in leaves in a process called “uptake” 

(Winner and Atkinson, 1986). Once sufficient levels of ozone (a highly reactive substance), or its 

reaction products, reaches the interior of plant cells, it can inhibit or damage essential cellular 

components and functions, including enzyme activities, lipids, and cellular membranes, 

disrupting the plant’s osmotic (i.e., water) balance and energy utilization patterns (U.S. EPA, 

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 

environmental stresses, which can all produce a loss in plant vigor in ozone-sensitive species 

that over time may lead to premature plant death. Furthermore, there is evidence that ozone 

can interfere with the formation of mycorrhiza, essential symbiotic fungi 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 (Grulke, 2003). When 

visible injury is present, it is commonly manifested as chlorotic or necrotic spots, and/or 

increased leaf senescence (accelerated leaf aging). Visible foliar injury reduces the aesthetic 

value of ornamental vegetation and trees in urban landscapes and negatively affects scenic 

vistas in protected natural areas.

Ozone can produce both acute and chronic injury in sensitive species depending on the 

concentration level and the duration of the 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 between 
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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 suitability of 

habitats that support threatened or endangered species and below ground organisms living in 

the root zone). Ozone impacts at the community and ecosystem level vary widely depending 

upon numerous factors, including concentration and 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 ecological systems, leading to 

changes in the ecological community 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 commercial and ecologically 

important 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 Health 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 over the course 

of the growing season, examinations are conducted in July and August, when ozone 

concentrations and associated injury are typically highest.
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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 ozonerelated injury. As shown in Figure 6-16, large geographic areas 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 sensitive 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 regions. 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 differences. 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 forests 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.” Typically, 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 

seedlings 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 dominate the forest 

community, the biomass loss from ozone can be significant. 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).
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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 Regionsa,b,c
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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 shortterm 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 service—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 fallcolor 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 educational 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 service in the form of 

reduced ability to sequester carbon and alteration of hydrologic cycles.
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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 Crops 

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 terms 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. EPA, 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 ozone 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 evidence 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 13 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 to 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 

 

45These 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 

rule. 
6-74

ornamental species would require more frequent replacement and/or increased maintenance 

(fertilizer or pesticide application) to maintain 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 

indicate 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 ornamentals 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 standards due to data, time, and resource limitations.

6.7 References

Abt Associates, Inc. 1995. Urban ornamental plants: sensitivity to ozone and potential economic 

losses. U.S. EPA, Office of Air Quality Planning and Standards, Research Triangle Park.

Under contract to RADIAN Corporation, contract no. 68-D3-0033, WA no. 6. pp. 9-10. 

Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/abt1995.pdf>.

Abt Associates, Inc. 2001. Assessing Public Opinions on Visibility Impairment due to Air 

Pollution: Summary Report. Prepared for EPA Office of Air Quality Planning and 

Standards; funded under EPA Contract No. 68-D-98-001. Abt Associates Inc., Bethesda, 

MD. January. Available on the Internet at 

<http://www.epa.gov/ttncaaa1/t1/reports/vis_rpt_final.pdf>.

Adams, Evan M., and Frederick, Peter C. 2008. “Effects of methylmercury and spatial complexity 

on foraging behavior and foraging efficiency in juvenile white ibises (Eudocimus albus).” 

Environmental Toxicology and Chemistry. Vol 27, No. 8.
6-75

Adams, R. M., Glyer, J. D., Johnson, S. L., McCarl, B. A. 1989. “A reassessment of the economic 

effects of ozone on U.S. agriculture.” Journal of the Air Pollution Control Association, 39, 

960-968.

Adams, R. M., Hamilton, S. A., McCarl, B. A. 1986. “The benefits of pollution control: the case of 

ozone and U.S. agriculture.” American Journal of Agricultural Economics, 34, 3-19.

Anselin, L., Le Gallo, J. 2006. “Interpolation of air quality measures in hedonic house price 

models: Spatial aspects.” Spatial Economic Analysis 1: 31–52

Anselin, L., Lozano-Gracia, N. 2008. “Errors in variables and spatial effects in hedonic house 

price models of ambient air quality.” Empirical Economics 34: 5–34.

Arrow, K.J., et al. 1993. Report of the NOAA Panel on Contingent Valuation. Federal Register. 

Volume 58, Number 10. Pages 4601 to 4614. Available on the Internet at 

<http://www.darp.noaa.gov/library/pdf/cvblue.pdf>.

Arzavus K.M., Dickhut R.M., and Canuel E.A. 2001. “Fate of Atmospherically Deposited 

Polycyclic Aromatic Hydrocarbons (PAHs) in Chesapeake Bay.” Environmental Science & 

Technology, 35, 2178-2183.

Banzhaf, S., D. Burtraw, D. Evans, and A. Krupnick. 2006. “Valuation of Natural Resource 

Improvements in the Adirondacks.” Land Economics 82:445-464.

Bayer, P., Keohane, N.O. and Timmins, C. 2009. “Migration and hedonic valuation: the case of 

air quality,” Journal of Environmental Economics and Management, 58 (2009) 1–14.

BBC Research & Consulting. 2003. Phoenix Area Visibility Survey. Draft Report. Available on the 

Internet at <http://www.azdeq.gov/environ/air/download/vis_021903f.pdf>.

Bergeron, CM., Bodinof, CM., Unrine, JM., Hopkins, WA. 2010a. “Mercury accumulation along a 

contamination gradient and nondestructive indices of bioaccumulation in amphibians.” 

Environmental Toxicology and Chemistry 29(4), 980-988.

Bergeron, CM., Bodinof, CM., Unrine, JM., Hopkins, WA. 2010b. “Bioaccumulation and maternal 

transfer of mercury and selenium in amphibians.” Environmental Toxicology and 

Chemistry 29(4), 989-997.

Beron KJ, Murdoch J, Thayer M. 2001. “The benefits of visibility improvement: New evidence 

from Los Angeles metropolitan area.” J Real Estate Finance Econ 22(2–3):319–337.

Beron KJ, Hanson Y, Murdoch JC, Thayer MA. 2004. “Hedonic price functions and spatial 

dependence: implications for the demand for urban air quality.” In: Anselin L, Florax RJ, 

Rey SJ (eds) Advances in spatial econometrics: methodology, tools and applications. 

Springer-Verlag, Berlin, pp 267–281.
6-76

Bond, T.C., and Sun H.L. (2005) Can reducing BC emissions counteract global warming? 

Environmental Science & Technology 39 (16).

Brasso, Rebecka L., and Cristol, Daniel A. 2008. “Effects of mercury exposure in the reproductive 

success of tree swallows (Tachycineta bicolor).” Ecotoxicology. 17:133-141.

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 NonMarket Goods: A Case Study of Alternative Benefit Measures of Air Pollution 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-0271B1.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.

Burgess, Neil M., and Meyer, Michael W. 2008. “Methylmercury exposure associated with 

reduced productivity in common loons.” Ecotoxicology. 17:83-91.

California State Parks. 2003. Public Opinions and Attitudes on Outdoor Recreation in California 

2002. California State Parks, Sacramento, California. December 2003. Available on the 

Internet at <http://parks.ca.gov/pages/795/files/poa2002final.pdf>.

Carson, R.T., R.C. Mitchell, and P.A. Rudd. 1990. “Valuing Air Quality Improvements: Simulating 

a Hedonic Pricing Equation in the Context of a Contingent Valuation Scenario.” In 

Visibility and Fine Particles, Transactions of an AWMA/EPA International Specialty 

Conference, C.V. Mathai, ed. Air and Waste Management Association, Pittsburgh. 

Available on the Internet at 

<http://www.econ.ucsd.edu/~rcarson/papers/AirQuality.pdf>.

Chameides. W.L., Yu, H., Liu, S.C., Bergin, M., Zhou, X., Mearns, L., Wang, G., Kiang, C.S., Saylor, 

R.D., Luo, C. Huang, Y., Steiner, A., and Giorgi, F. 1999. “Case study of the effects of 

atmospheric aerosols and regional haze on agriculture: an opportunity to enhance crop 

yields in China through emission controls?” PNAS, 96: 13626-13633.

Chappelka, A.H., Samuelson, L.J. 1998. “Ambient ozone effects on forest trees of the eastern 

United States: a review.” New Phytologist, 139, 91-108.

Chay KY, Greenstone M. 2005. “Does air quality matter? evidence form the housing market.” J 

Polit Econ 113(2):376–424. 
6-77

Chestnut, L.G. 1997. Methodology for Estimating 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. Dennis. 1997. “Economic Benefits of Improvements in Visibility: Acid Rain 

Provisions of the 1990 Clean Air Act Amendments.” Journal of Air and Waste 

Management Association 47: 395-402.

Chestnut, L.G., and R.D. Rowe. 1990a. “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. and R.D. Rowe. 1990b. Preservation Values for Visibility Protection at the 

National Parks. Prepared for U.S. Environmental Protection Agency, Office of Air Quality 

Planning and Standards, and National Park Service, Air Quality Management Division. 

Available on the Internet at 

<http://yosemite.epa.gov/ee/epa/eerm.nsf/vwGA/EE97D10D71E3FAD08525662400766

534 >.

Chestnut, L.G. and R.D. Rowe. 1990c. “Economic Valuation of Changes in Visibility: A State of 

the Science Assessment for NAPAP.” Section B5 in Irving, Patricia M., e.d., 1991. Acid 

Deposition: State of Science and Technology, Volume IV, Control Technologies, Future 

Emissions, and Effects Valuation, The U.S. National Acid Precipitation Assessment 

Program, Report 27, page 27–153.

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/background/chestnut1986.pdf>.

Cordell, H.K., C.J. Betz, G. Green, and M. Owens. 2005. Off-Highway Vehicle Recreation in the 

United States, Regions and States: A National Report from the National Survey on 

Recreation and the Environment (NSRE). Prepared for the U.S. Department of 

Agriculture Forest Service, Southern Research Station, National OHV Policy and 

Implementation Teams, Athens, GA. Available on the Internet at 

<http://www.fs.fed.us/recreation/programs/ohv/OHV_final_report.pdf>

Cordell, K., B. Leeworthy, G.T. Green, C. Betz, B. Stephens. 2008. The National Survey on 

Recreation & the Environment. Research Work Unit 4953. Pioneering Research on 

Changing Forest Values in the South and Nation, U.S. Department of Agriculture Forest 

Service, Southern Research Station, Athens, GA. Available on the Internet at 

<http://www.srs.fs.fed.us/trends>.
6-78

Cotrufo, M.F., De Santo A.V., Alfani A., Bartoli G., De Cristofaro A. 1995. “Effects of urban heavy 

metal pollution on organic matter decomposition in Quercus ilex L. Woods.” 

Environmental Pollution, 89(1), 81-87.

Coulston, J.W., Riitters, K.H., Smith, G.C. 2004. “A preliminary assessment of the Montreal 

process indicators of air pollution for the United States.” Environmental Monitoring and 

Assessment, 95, 57-74.

Cristol D. A., Brasso R. L., Condon A. M., Fovargue R. E., Friedman S. L., Hallinger K. K., Monroe 

A. P., White A. E. 2008. “The movement of aquatic mercury through terrestrial food 

webs.” Science 320, 335–335.

Cropper, Maureen. 2000. “Has Economic Research Answered the Needs of Environmental 

Policy?” Journal of Environmental Economics and Management 39, 328-350.

Crump, Kate L., and Trudeau, Vance L. 2009. “Mercury-induced reproductive impairment in 

fish.” Environmental Toxicology and Chemistry. Vol. 28, No. 5.

De Steiguer, J., Pye, J., Love, C. 1990. “Air Pollution Damage to U.S. Forests.” Journal of Forestry, 

88(8), 17-22.

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>.

DeHayes, D.H., P.G. Schaberg, G.J. Hawley, and G.R. Strimbeck. 1999. “Acid rain impacts on 

calcium nutrition and forest health.” Bioscience 49(10):789–800.

DeLucchi, M.A., J.J. Murphy, D.R. McCubbin. 2002. “The health and visibility cost of air 

pollution: A comparison of estimation methods,” Journal of Environmental 

Management, 64 (2), pp. 139–152.

Ely, D.W., Leary, J.T., Stewart, T.R., Ross, D.M. 1991. “The Establishment of the Denver Visibility 

Standard.” Presented at Air and Waste Management Association 84th Annual Meeting, 

June 16-21, Paper 91-48.4, 17pp. Available on the Internet at 

<http://www.albany.edu/cpr/stewart/Papers/T0210-ElyDenverVisStandard1991cap.pdf>.

Ericksen, J. A., Gustin, M. S., Schorran, D. E., Johnson, D. W., Lindberg, S. E., & Coleman, J. S. 

(2003). “Accumulation of atmospheric mercury in forest foliage.” Atmospheric 

Environment 37(12), 1613-1622.

Evers, D.C., 2006. Status assessment and conservation plan for the common loon (Gavia immer) 

in North America. U.S. Fish and Wildlife Service, Hadley, MA, USA.
6-79

Evers, David C., Savoy, Lucas J., DeSorbo, Christopher R., Yates, David E., Hanson, William, 

Taylor, Kate M., Siegel, Lori S., Cooley, John H. Jr., Bank, Michael S., Major, Andrew, 

Munney, Kenneth, Mower, Barry F., Vogel, Harry S., Schoch, Nina, Pokras, Mark, 

Goodale, Morgan W., Fair, Jeff. 2008. “Adverse effects from environmental mercury 

loads on breeding common loons.” Ecotoxicology. 17:69-81.

Fox, S., Mickler, R. A. (Eds.). 1996. Impact of Air Pollutants on Southern Pine Forests, Ecological 

Studies. (Vol. 118, 513 pp.) New York: Springer-Verlag.

Frederick, Peter, and Jayasena, Nilmini. 2010. “Altered pairing behavior and reproductive 

success in white ibises exposed to environmentally relevant concentrations of 

methylmercury.” Proceedings of the Royal Society B. doi: 10-1098.

Gorissen, Leen, Snoeijs, Tinne, Van Duyse, Els, and Eens, Marcel. 2005. “Heavy metal pollution 

affects dawn singing behavior in a small passerine bird.” Oecologia. 145: 540-509.

Grantz DA; Garner JHB; Johnson DW. 2003. “Ecological effects of particulate matter.” Environ 

Int, 29: 213-239.

Graves, Phil, James C. Murdoch, Mark A. Thayer, and Don Waldman. 1988. “The Robustness of 

Hedonic Price Estimation: Urban Air Quality.” Land Economics, 64: 220-33. 

Grosclaude P. and N. Soguel. 1994. “Valuing damage to historic buildings using a contingent 

market: a case study of road traffic externalities,” Journal of Environmental Planning and 

Management 37 (1994) (3), pp. 279–287.

Grulke, N.E. 2003. “The physiological basis of ozone injury assessment attributes in Sierran 

conifers.” In A. Bytnerowicz, M.J. Arbaugh, & R. Alonso (Eds.), Ozone air pollution in the 

Sierra Nevada: Distribution and effects on forests. (pp. 55-81). New York, NY: Elsevier 

Science, Ltd.

Hadley O.L., Corrigan C.E., Kirchstetter T.W., Cliff S.S., and Ramanathan V. (2010) Measured 

black carbon deposition on the Sierra Nevada snow pack and implication for snow pack 

retreat. Atmos. Chem. Phys. Discuss. 10, 10463-10485 

(doi:10.5194/acpd-10-10463-2010).

Hageman KJ; Simonich SL; Campbell DH; Wilson GR; Landers DH. 2006. “Atmospheric deposition 

of current-use and historic-use pesticides in snow at national parks in the western 

United States.” Environ Sci Technol, 40: 3174-3180

Hallinger, Kelly K., Cornell, Kerri L., Brasso, Rebecka L., and Cristol, Daniel A. 2010. “Mercury 

exposure and survival in free-living tree swallows (Tachycineta bicolor).” Ecotoxicology. 

Doi: 10.1007/s10646-010-0554-4.
6-80

Hawley, Dana M., Hallinger, Kelly K., Cristol, Daniel A. 2009. “Compromised immune 

competence in free-living tree swallows exposed to mercury.” Ecotoxicology. 18:499-

503.

Heck, W.W. &Cowling E.B. 1997. “The need for a long term cumulative secondary ozone 

standard—an ecological perspective.” Environmental Management, January, 23-33.

Hoffman, David J., Henny, Charles J., Hill, Elwood F., Grover, Robert A., Kaiser, James L., 

Stebbins, Katherine R. 2009. “Mercury and drought along the lower Carson River, 

Nevada: III. Effects on blood and organ biochemistry and histopathology of snowy egrets 

and black-crowned night-herons on Lahontan Reservoir, 2002–2006.” Journal of 

Toxicology and Environmental Health, Part A. 72: 20, 1223-1241.

Hutchison, R., and C.E. Kraft. 1994. “Hmong Fishing Activity and Fish Consumption.” Journal of 

Great Lakes Research 20(2):471–487.

Intergovernmental Panel on Climate Change 4th Assessment Report: Climate Change 2007 

Synthesis Report Summary for Policy Makers Pachauri, R.K. and Reisinger, A. (Eds.). 

Available on the Internet at 

<http://ipcc.ch/publications_and_data/ar4/syr/en/spm.html>.

IMPROVE (Interagency Monitoring of Protected Visual Environments). 2010. 1992–2008 data 

from the IMPROVE network based on the “New IMPROVE algorithm” (updated 

November, 2008). Available on the Internet at 

<http://vista.cira.colostate.edu/improve/Data/IMPROVE/summary_data.htm.>

Irving, P.M., e.d. 1991. Acid Deposition: State of Science and Technology, Volume III, Terrestrial, 

Materials, Health, and Visibility Effects, The U.S. National Acid Precipitation Assessment 

Program, Chapter 24, page 24–76.

Joslin, J.D., Kelly, J.M., and van Miegroet, H. 1992. “Soil chemistry and nutrition of North 

American spruce-fir stands: evidence for recent change. “ Journal of Environmental 

Quality, 21, 12-30.

Kaval, P., and J. Loomis. 2003. Updated Outdoor Recreation Use Values With Emphasis On 

National Park Recreation. Final Report October 2003, under Cooperative Agreement CA 

1200-99-009, Project number IMDE-02-0070. Available on the Internet at 

<http://www.mngt.waikato.ac.nz/departments/staff/pkaval/Kaval%26LoomisNPSRepor

t10-03.pdf>.

Kim, CW, Phipps, T, and Anselin L. 2003. “Measuring the benefits of air quality improvement: a 

spatial hedonic approach.” J Environ Econ Manage 45:24–39.
6-81

Koch, D., Bond, T.C., Streets D., and Unger N. (2007) Linking future aerosol radiative forcing to 

shifts in source activities. Geophys. Res. Lett. 34 (L05821), AGU 

(doi:10.1029/2006GL028360, 2007). Available on the Internet at 

<http://dx.doi.org/10.1029/2006GL028360>. 

Koch D., Bond T.C., Streets D., Unger N., and van der Werf G.R. (2007) Global impacts of 

aerosols from particular source regions and sectors. Journal of Geophysical 

Research-Atmospheres 112 (D02205).

Kopp, R. J., Vaughn, W. J., Hazilla, M., Carson, R. 1985. “Implications of environmental policy for 

U.S. agriculture: the case of ambient ozone standards.” Journal of Environmental 

Management, 20, 321-331.

Kramer, A., T. Holmes, and M. Haefel. 2003. Contingent valuation of forest ecosystem 

protection. Pp. 303–320 in Forests in a Market Economy. Edited by E.O. Sills and K.L. Abt. 

Dordrecht, The Netherlands: Kluwer Academic Publishers.

Landers DH; Simonich SL; Jaffe DA; Geiser LH; Campbell DH; Schwindt AR; Schreck CB; Kent ML; 

Hafner WD; Taylor HE; Hageman KJ; Usenko S; Ackerman LK; Schrlau JE; Rose NL; Blett 

TF; Erway MM. (2008). The Fate, Transport and Ecological Impacts of Airborne 

Contaminants in Western National Parks (USA). EPA/600/R-07/138. U.S. Environmental 

Protection Agency, Office of Research and Development, NHEERL, Western Ecology 

Division. Corvallis, Oregon.

Leeworthy, V.R., and P.C. Wiley. 2001. Current Participation Patterns in Marine Recreation. 

Silver Spring, MD: U.S. Department of Commerce, National Oceanic and Atmospheric 

Administration, National Ocean Service, Special Projects. November. Available on the 

Internet at < http://www.naturetourismplanning.com/pdfs/NSRE_2.pdf>.

Leggett, C.G. and J.E. Neumann. 2004. Responding to SAB Council Comments on the May 2003 

Draft Analytical Plan for the Section 812 Second Prospective—Visibility Benefits.

Prepared for U.S. Environmental Protection Agency OPAR. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/leggettandneumann2004.

pdf>.

Leistritz, F. L., D. A. Bangsund, and N. M. Hodur. 2004. “Assessing the Economic Impact of 

Invasive Weeds: The Case of Leafy Spurge (Euphorbia esula).” Weed Technology

18:1392-1395.

Leitch, J. A, F. L Leistritz, and D. A Bangsund. 1996. “Economic effect of leafy spurge in the 

upper Great Plains: methods, models, and results.” Impact Assessment 14: 419–434.
6-82

Lipton, D. W. 1999. “Pfiesteria’s economic impact on seafood industry sales and recreational 

fishing, p. 35–38.” In B. L. Gardner and L. Koch (ed.), Proceedings of the Conference, 

Economics of Policy Options for Nutrient Management and Pfiesteria. Center for 

Agricultural and Natural Resource Policy, University of Maryland, College Park.

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, Volume 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/e85705387833398f8525644d0053bd

d0!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.

Malm, WC. 1999. Introduction to visibility. Colorado State University, Fort Collins, CO, USA, 

1983, Revised edition, 1999. Available on the Internet at 

<vista.cira.colostate.edu/improve/Education/IntroToVisinstr.htm>.

McBride, J.R., Miller, P.R., Laven, R.D. 1985. “Effects of oxidant air pollutants on forest 

succession in the mixed conifer forest type of southern California.” In: Air Pollutants 

Effects on Forest Ecosystems, Symposium Proceedings, St. P, 1985, p. 157-167.

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 >.

McIntyre, J.W., Barr, J.F. 1997. “Common Loon (Gavia immer)” in: Pool A, Gill F (eds.) The Birds 

of North America. Academy of Natural Sciences, Philadelphia, PA, 313

McIntrye, J.W., and Evers, D.C.,(eds). 2000. “Loons: old history and new finding.” Proceedings of 

a Symposium from the 1997 meeting, American Ornithologists’ Union. North American

Loon Fund, 15 August 1997, Holderness, NH, USA.

Millennium Ecosystem Assessment Board (MEA). 2005. Ecosystems and Human Well-being: 

Synthesis. Washington, DC: World Resources Institute. Available on the Internet at 

<http://www.who.int/globalchange/ecosystems/ecosystems05/en/index.html>.

Miller, P.R., O.C. Taylor, R.G. Wilhour. 1982. Oxidant air pollution effects on a western 

coniferous forest ecosystem. Corvallis, OR: U.S. Environmental Protection Agency, 

Environmental Research Laboratory (EPA600-D-82-276).
6-83

Mitro, Matthew G., Evers, David C., Meyer, Michael W., and Piper, Walter H. 2008. “Common 

loon survival rates and mercury in New England and Wisconsin.” Journal of Wildlife 

Management 72(3): 665-673.

Munthe, J., Bodaly, R. A., Branfireun, B. A., Driscoll, C. T., Gilmour, C. C., Harris, R., et al. (2007). 

“Recovery of Mercury-Contaminated Fisheries.” Environmental Science & Technology

36(1), 33-44.

Murdoch, J. C. and M. A. Thayer. 1988. “Hedonic price estimation of variable urban air quality,” 

J. Environ. Econ. Management 15, 143-146. 

National Agricultural Statistics Service (NASS). 2008. Maple Syrup—June 12, 2008: Maple Syrup 

Production Up 30 Percent Nationwide. U.S. Department of Agriculture, National 

Agricultural Statistics Service, New England Agricultural Statistics, Concord, NH.

National Association of State Foresters (NASF). 2009. Quadrennial Fire Review 

2009.Washington, DC: NASF. Quadrennial Fire and Fuel Review Final Report 2009. 

National Wildfire Coordinating Group Executive Board. January.

National Oceanic and Atmospheric Administration (NOAA). 2007. Annual Commercial Landing 

Statistics. August. Available on the Internet at <http://www.st.nmfs.noaa.gov/ 

st1/commercial/landings/annual_landings.html>.

National Park Service (NPS). 1997. Statistical Abstract: 1997. 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/abst1997.pdf>.

National Park 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>.

Neill, Helen R., David M. Hassenzahl and Djeto D. Assane. 2007. “Estimating the Effect of Air 

Quality: Spatial versus Traditional Hedonic Price Models,” Southern Economic Journal

73, No. 4, pp. 1088-1111.

Neumann, J.E. and R.W. Patterson. 2009. Alternative Approach to Estimating Monetized 

Benefits of Residential Visibility for the Section 812 Second Prospective. Prepared for U.S. 

Environmental Protection Agency OAR/OPAR. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/neumannpaterson2009.p

df>.

Niklinska M., Laskowski R., Maryanski M. (1998). “Effect of heavy metals and storage time on 

two types of forest litter: basal respiration rate and exchangeable metals” 

Ecotoxicological Environmental Safety, 41, 8-18.
6-84

Office of Management and Budget (OMB). 2003. Circular A-4: Regulatory Analysis. Washington, 

DC. Available on the internet at <http://www.whitehouse.gov/omb/circulars/a004/a4.html>. 

Painter, et al. 2007. “Impact of disturbed desert soils on duration of mountain snow cover.” 

Geophys. Res. Lett. 34, L12502. 

Parrish ZD; White JC; Isleyen M; Gent MPN; Iannucci-Berger W; Eitzer BD; Kelsey JW; Mattina 

MI. 2006. “Accumulation of weathered polycyclic aromatic hydrocarbons (PAHs) by 

plant and earthworm species.” Chemosphere, 64: 609-618.

Patterson, R.W., J.E. Neumann, and C.G. Leggett. 2005. Recommended Residential Visibility 

Values for the Section 812 Second Prospective Analysis. Prepared for U.S. Environmental 

Protection Agency OPAR. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/patersonneumanleggett2

005.pdf>.

Peterson, D.E., M.S. Kanarek, M.A. Kuykendall, J.M. Diedrich, H.A. Anderson, P.L. Remington, 

and T.B. Sheffy. 1994. “Fish Consumption Patterns and Blood Mercury Levels in 

Wisconsin Chippewa Indians.” Archives of Environmental Health 49(1):53–58.

Pitchford, M and W. Malm. 1994. “Development and Application of a Standard Visual Index.” 

Atmospheric Environment 28(5): 1049-1054.

Prasad, A.M. and Iverson, L.R. 2003. “Little’s range and FIA importance value database for 135 

eastern U.S. tree species.” Northeastern Research Station, USDA Forest Service.

Available on the Internet at 

<http://www.fs.fed.us/ne/delaware/4153/global/littlefia/index.html>.

Pryor, S.C. 1996. “Assessing Public Perception of Visibility for Standard Setting Exercises.” 

Atmospheric Environment 30, no. 15, pp. 2705-2716.

Pye, J.M. 1988. “Impact of ozone on the growth and yield of trees: A review.” Journal of 

Environmental Quality, 17, 347-360.

Qian Y., Gustafson W.I., Leung L.R., and Ghan S.J. 2009. “Effects of soot-induced snow albedo 

change on snowpack and hydrological cycle in western United States based on Weather 

Research Forecasting Chemistry and Regional Climate Simulations.” Journal of 

Geophysical Research 114 (DO2108). 

Rae, D.A. 1983. Benefits of Visual Air Quality in Cincinnati Results of a Contingent Ranking 

Survey. Prepared for Electric Power Research Institute. May.

Rea, A. W., Lindberg, S. E., Scherbatskoy, T., & Keeler, G. J. 2002. “Mercury Accumulation in 

Foliage over Time in Two Northern Mixed-Hardwood Forests.” Water, Air, & Soil 

Pollution 133(1), 49-67.
6-85

Rimmer, C. C., McFarland, K. P., Evers, D. C., Miller, E. K., Aubry, Y., Busby, D., et al. 2005. 

“Mercury Concentrations in Bicknell’s Thrush and Other Insectivorous Passerines in 

Montane Forests of Northeastern North America.” Ecotoxicology 14(1), 223-240.

Rosen, S. 1974. “Hedonic prices and implicit markets: product differentiation in pure 

competition.” Journal of Political Economy 82(1): 34-55.

Scheuhammer, Anton M., Meyer Michael W., Sandheinrich, Mark B., and Murray, Michael W. 

2007. “Effects of environmental methylmercury on the health of wild birds, mammals, 

and fish,” Ambio Vol.36, No.1.

Sepulveda, Maria S., Frederick, Peter C., Spalding, Marilyn G., and Williams, Gary E. Jr. 1999. 

“Mercury contamination in free-ranging great egret nestlings (Ardea albus) from 

southern Florida, USA,” Environmental Toxicology and Chemistry Vol. 18, No.5.

Simcik M.F., Eisenreich S.J., Lioy P.J. 1999. “Source apportionment and source/sink relationship 

of PAHs in the coastal atmosphere of Chicago and Lake Michigan.” Atmospheric 

Environment, 33, 5071-5079.

Simcik M.F., Eisenreich, S.J., Golden K.A., et al. 1996. “Atmospheric Loading of Polycyclic 

Aromatic Hydrocarbons to Lake Michigan as Recorded in the Sediments.” Environmental 

Science and Technology, 30, 3039-3046.

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. CIRA 

Report, ISSN 0737-5352-32, Colorado State University.

Smith, G., Coulston, J., Jepsen, E., Prichard, T. 2003. “A national ozone biomonitoring program—

results from field surveys of ozone sensitive plants in Northeastern forests (1994–

2000).” Environmental Monitoring and Assessment, 87, 271-291.

Smith, A.E.; Kemp, M.A.; Savage, T.H.; Taylor, C.L. 2005. “Methods and Results from a New 

Survey of Values for Eastern Regional Haze Improvements,” J. Air & Waste Manage 

Assoc 55, 1767-1779.

Smith, V.K., G. Van Houtven, and S.K. Pattanayak. 2002. “Benefit Transfer via Preference 

Calibration.” Land Economics 78:132-152.

Smith, V.K., and J.C. Huang. 1995. “Can markets value air quality? A meta-analysis of hedonic 

property value models,” J. Polit. Econ, 103, pp. 209–227.

Spencer, D.M., and D.F. Holecek. 2007. “Basic characteristics of the fall tourism market.” 

Tourism Management 28:491–504.

Taylor R. 1994. “Deterministic versus stochastic evaluation of the aggregate economic effects of

price support programs.” Agricultural Systems 44: 461-473.
6-86

Tingey, D.T., and Taylor, G.E. 1982. “Variation in plant response to ozone: a conceptual model 

of physiological events.” In M.H. Unsworth & D.P. Omrod (Eds.), Effects of Gaseous Air 

Pollution in Agriculture and Horticulture. (pp.113-138). London, UK: Butterworth 

Scientific.

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. 1986. Establishing and Valuing the 

Effects of Improved Visibility in Eastern United States. Prepared 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/vwGA/e673ccafbda23639852565f00007412

f!OpenDocument&ExpandSection=3>.

Tolley, George and Robert Fabian (eds). 1988. The Economic Value of Visibility. Series : Studies 

in urban and resource economics. Mount Pleasant, MO: Blackstone.

Trijonis, J.C. et al. 1987. “Preliminary extinction budget results from the RESOLVE program,” pp. 

872-883. In: P.J. Bhardwaja, et al. Visibility Protection Research and Policy Aspects. Air 

Pollution Control Assoc., Pittsburgh, PA.

Trijonis, J.C. et al. 1988. RESOLVE Project Final Report: Visibility conditions and Causes of 

Visibility Degradation in the Mojave Desert of California. NWC TP #6869. Naval Weapons 

Center, China Lake, CA. Available on the Internet at 

<www.dtic.mil/dtic/tr/fulltext/u2/a206322.pdf>.

United Nations Environmental Programme, World Meteorological Organization and Stockholm 

Environment Institute (UNEP). 2011. Integrated Assessment of Black Carbon and 

Tropospheric Ozone Summary for Decision Makers. Available on the Internet at 

<http://www.unep.org/dewa/Portals/67/pdf/Black_Carbon.pdf>.

U.S. Census Bureau. 2010. “Sector 56: EC075612: Administrative Support and Waste 

Management and Remediation Services: Industry Series: Preliminary Comparative 

Statistics for the United States (2002 NAICS Basis): 2007 and 2002.” American FactFinder 

Available on the Internet at http://factfinder.census.gov (accessed August 25, 2010)

U.S. Department of the Interior (DOI). 2007. 2006 National Survey of Fishing, Hunting, and 

Wildlife-Associated Recreation. U.S. Department of the Interior, Fish and Wildlife 

Service, and U.S. Department of Commerce, U.S. Census Bureau. Washington, DC. 

Available on the Internet at <http://www.census.gov/prod/2008pubs/fhw06-nat.pdf>.
6-87

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 1999a. The 

Clean Air Act Amendments (CAAA) Section 812 Prospective Study of Costs and Benefits 

(1999): Advisory by the Advisory Council on Clean Air Compliance Analysis: Costs and 

Benefits of the CAAA. EPA-SAB-COUNCIL-ADV-00-002. October. Available on the Internet 

at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/E992F918DF32131A852571B9004571F7/

$File/coua0002.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 1999b. Final 

Advisory by the Advisory Council on Clean Air Compliance Analysis on the 1999 

Prospective Study of Costs and Benefits (1999) of Implementation of the Clean Air Act 

Amendments (CAAA). EPA-SAB-COUNCIL-ADV-00-003. November. Available on the 

Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/93BE842F5CBA7BEF852571B900461197/

$File/coua0003.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004. Review of 

the Draft Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of 

the Clean Air Act, 1990-2020: An Advisory by the Advisory Council for Clean Air 

Compliance Analysis. EPA-SAB-COUNCIL-ADV-04-004. May. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/7CCBBFE15.BD4C8B185256F17005E3079

/$File/council_adv_04004.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2007. Clean Air 

Scientific Advisory Committee’s (CASAC) Review of the Agency’s Final Ozone Staff Paper.

EPA-CASAC-07-002. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/FE915E916333D776852572AC007397B5/

$File/casac-07-002.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2010a. Advisory 

on a Preliminary Draft of the Second Section 812 Prospective Study of the Benefits and 

Costs of the Clean Air Act (April 2010). EPA-COUNCIL-10-004. July. Available on the 

Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/553D59A280CDCF388525776D005EE83A

/$File/EPA-COUNCIL-10-004-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2010b. Review 

of the Final Integrated Report for the Second Section 812 Prospective Study of the 

Benefits and Costs of the Clean Air Act (August 2010). EPA-COUNCIL-11-001. December. 

Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/1E6218DE3BFF682E852577FB005

D46F1/$File/EPA-COUNCIL-11-001-unsigned.pdf>.
6-88

U.S. Environmental Protection Agency (U.S. EPA). 1997. Mercury Study Report to Congress. 

Volume V: Health Effects of Mercury and Mercury Compounds. EPA-452/R-97-007. U.S. 

EPA Office of Air Quality Planning and Standards, and Office of Research and 

Development. Available on the Internet at 

<http://www.epa.gov/ttncaaa1/t3/reports/volume5.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 1998. Advisory Council on Clean Air 

Compliance Analysis Advisory on the Clean Air Act Amendments (CAAA) of 1990 Section 

812 Prospective Study: Overview of Air Quality and Emissions Estimates: Modeling, 

Health and Ecological Valuation Issues Initial Studies. EPA-SAB-COUNCIL-ADV-98-003.

Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/D20F5AE06F98883F852571AF00474927/

$File/coad9803.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 1999a. Regional Haze Regulations, July 1, 

1999. (64 FR 35714, July 1, 1999).

U.S. Environmental Protection Agency (U.S. EPA). 1999b. The Benefits and Costs of the Clean Air 

Act, 1990–2010. Prepared for U.S. Congress by U.S. EPA, Office of Air and Radiation, 

Office of Policy Analysis and Review, Washington, DC, November; EPA report no.

EPA410-R-99-001. Available on the Internet at <http://www.epa.gov/oar/sect812/1990-

2010/chap1130.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2004. Air Quality Criteria for Particulate 

Matter Volume II of II. National Center for Environmental Assessment, Office of 

Research and Development, U.S. Environmental Protection Agency, Research Triangle 

Park, NC EPA/600/P-99/002bF. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=87903>.

U.S. Environmental Protection Agency (U.S. EPA). 2005. Regulatory Impact Analysis of the Final 

Clean Air Mercury Rule. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC., March; EPA report no. EPA-452/R-05-003. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/mercury_ria_final.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2006b. Regulatory Impact Analysis, 2006 

National Ambient Air Quality Standards for Particulate Matter. Office of Air Quality

Planning and Standards, Research Triangle Park, NC. October. Available on the Internet 

at < http://www.epa.gov/ttn/ecas/regdata/RIAs/Appendix%20I--

Visibility%20Benefits%20Methodology.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2006c. Ecological Benefits Assessment 

Strategic Plan. EPA-240-R-06-001. Office of the Administrator. Washington, DC. October.

Available on the Internet at 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/EcologBenefitsPlan.html>.
6-89

U.S. Environmental Protection Agency (U.S. EPA). 2007a. Guidance on the Use of Models and 

Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and 

Regional Haze. Office of Air Quality Planning and Standards. EPA-454/B-07-002. April.

<Available on the Internet at http://www.epa.gov/scram001/guidance/guide/final-03-

pm-rh-guidance.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2007b. Review of the National Ambient Air 

Quality Standards for Ozone: Policy assessment of scientific and technical information. 

Staff Paper. Office of Air Quality Planning and Standards. EPA-452/R-07-007a. July.

Available on the Internet at 

<http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_07_ozone_staff_paper.p

df>.

U.S. Environmental Protection Agency (U.S. EPA). 2007c. Technical Report on Ozone Exposure, 

Risk, and Impact Assessments for Vegetation. Office of Air Quality Planning and 

Standards. EPA-452/R-07-002. January. Available on the Internet at 

http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_pr.html

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Regulatory Impact Analysis, 2008 

National Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. March. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2008b. U.S. EPA’s 2008 Report on the 

Environment (Final Report). Washington, D.C., EPA/600/R-07/045F (NTIS PB2008-

112484). May. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=190806>. Updated in 

December 2010, available on the Internet at 

<http://cfpub.epa.gov/eroe/index.cfm?fuseaction=detail.viewInd&showQues=Air&ch=4

7&lShowInd=0,287&subtop=341&lv=list.listByQues&r=188207>.

U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessment for 

Oxides of Nitrogen and Sulfur—Ecological Criteria National (Final Report). National 

Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/139.

December. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>.

U.S. Environmental Protection Agency (U.S. EPA). 2009a. The NOx Budget Trading Program: 

2008 Environmental Results. Clean Air Markets Division. September. Available on the 

Internet at <http://www.epa.gov/airmarkt/progress/NBP_3/

NBP_2008_Environmental_Results.pdf>.
6-90

U.S. Environmental Protection Agency (U.S. EPA). 2009b. 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). 2009c. Risk and Exposure Assessment for 

Review of the Secondary National Ambient Air Quality Standards for Oxides of Nitrogen 

and Oxides of Sulfur (Final Report). EPA-452/R-09-008a. Office of Air Quality Planning 

and Standards, Research Triangle Park, NC. September. Available on the Internet at 

<http://www.epa.gov/ttn/naaqs/standards/no2so2sec/data/NOxSOxREASep2009MainC

ontent.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2009d. Endangerment and Cause or 

Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air Act. 

December 15, 2009. (74 Fed. Reg. 66,496, December 15, 2009). 

U.S. Environmental Protection Agency (U.S. EPA). 2010a. Updated Regulatory Impact Analysis 

(RIA) for the Reconsideration of the 2008 Ozone National Ambient Air Quality Standard 

(NAAQS). Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

January. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/s1-

supplemental_analysis_full.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2010b. Particulate Matter Urban-Focused 

Visibility Assessment—Final Document (Corrected). Office of Air Quality Planning and 

Standards, Research Triangle Park, NC. July. Available on the Internet at <Particulate

Matter Urban-Focused Visibility Assessment—Final Document>.

U.S. Environmental Protection Agency (U.S. EPA). 2011a. Policy Assessment for the Review of 

the PM NAAQS—Final Document. Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. April. Available on the Internet at 

<http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pa.html>.

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). 2011c. Regulatory Impact Analysis for the 

Federal Implementation Plans to Reduce Interstate Transport of Fine Particulate Matter 

and Ozone in 27 States; Correction of SIP Approvals for 22 States. June. Available on the 

Internet at: <http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf>

U.S. Environmental Protection Agency (U.S. EPA). 2011d. Regulatory Impact Analysis for the 

Final Mercury and Air Toxics Standards. EPA-452/R-11-011. December. Available on the 

Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf>. 
6-91

U.S. Environmental Protection Agency (U.S. EPA). 2012a. Integrated Science Assessment for 

Particulate Ozone and Related Photochemical Oxidants (Third External Review Draft). 

EPA/600/R-10/076C. National Center for Environmental Assessment—RTP Division. 

June. Available on the Internet at 

<http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=242490>.

U.S. Environmental Protection Agency (U.S. EPA). 2012b. Black Carbon Report to Congress. EPA450/R-12-001. March. Available on the Internet at 

<http://www.epa.gov/blackcarbon/2012report/fullreport.pdf>. 

U.S. Fish and Wildlife Service and U.S. Census Bureau (FWS and Census). 2007. 2006 National 

Survey of Fishing, Hunting, and Wildlife-Associated Recreation (FHWAR). FHW/06-NAT. 

U.S. Department of the Interior, U.S. Fish and Wildlife Service, Washington, DC, and U.S. 

Department of Commerce, U.S. Census Bureau, Washington, DC.

U.S. Forest Service (USFS). 2006. Forest Inventory and Analysis National Program: Forest 

Inventory Data Online. Online database. U.S. Department of Agriculture Forest Service, 

Forest Inventory and Analysis, Arlington, VA. Available on the Internet at 

<http://fia.fs.fed.us/tools-data>.

Valigura, R.A., R.B. Alexander, M.S. Castro, T.P. Meyers, H.W. Paerl, P.E. Stacy, and R.E. Turner. 

2001. Nitrogen Loading in Coastal Water Bodies: An Atmospheric Perspective. 

Washington, DC: American Geophysical Union.

Van Sickle, J., Baker, J.P., Simonin, H.A., Baldigo, B.P., Kretser, W.A., Sharpe, W.E. 1996.

“Episodic acidification of small streams in the northeastern United States: Fish mortality 

in field bioassays.” Ecological Applications, 6, 408-421.

Wada, H. and Cristol, D.A. and McNabb, F.M.A. and Hopkins, W.A. 2009. “Suppressed 

adrenocortical responses and thyroid hormone levels in birds near a mercurycontaminated river.” Environmental Science & Technology 43(15), 6031-6038.

Wada, H., Yates, DE., Evers, DC., Taylor, RJ., Hopkins, WA. 2010. “Tissue mercury concentrations 

and adrenocortical responses of female big brown bats (Eptesicus fuscus) near a 

contaminated river.” Ecotoxicology 19(7), 1277-1284.

Winner, W.E. 1994. “Mechanistic analysis of plant responses to air pollution.” Ecological 

Applications, 4(4), 651-661.

Winner, W.E., and C.J. Atkinson. 1986. “Absorption of air pollution by plants, and consequences 

for growth.” Trends in Ecology and Evolution 1:15-18.

Wiscombe W. and Warren S. 1980. “A model for the spectral albedo of snow. Snow containing 

atmospheric aerosols.” J. Atmos. Sci. 37, 2734-2274.
6-92

Yates, David E., Mayack, David T., Munney, Kenneth, Evers David C., Major, Andrew, Kaur, 

Taranjit, and Taylor, Robert J. 2005. “Mercury levels in mink (Mustela vison) and river 

otter (Lonra canadensis) from northeastern North America,” Ecotoxicology 14, 263-274.
6.A-1 

APPENDIX 6.A

ADDITIONAL DETAILS REGARDING THE VISIBILITY BENEFITS METHODOLOGY

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 residential 

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 methodology 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 and 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. Section 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 extinction 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 to 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 degree 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 �

βext

10 �

where VR denotes visual range (in kilometers) and βext 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 

instead of converting directly. Therefore, the full formula incorporating the two-step 

conversion is

VR = 391 ∗ e

−0.1∗(10∗ln�

βext

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 visibility 
6.A-3 

improvements at parks and wilderness areas that are “in-region” than at those that are “out-ofregion.” 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 use an approach that is consistent with 

economic theory. Below we discuss an application of the Constant Elasticity of Substitution 

(CES) utility function approach3 to value both residential visibility improvements and visibility 

improvements at Class I areas 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. visibility 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 household’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 nth

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 calculated 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 1
6.A-4 

where

Zn = the level of visibility in the nth residential area;

Qik = the level of visibility at the kth in-region park (i.e., the kth park in the ith region);

Qjk = the level of visibility at the k

th park in the jth region (for which the household is 

out of region), j≠i;

Ni = the number of Class I areas in the ith region;

Nj = the number of Class I areas in the jth region (for which the household is out of 

region), j≠i; and

θ, the γ’s and δ’s are parameters 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 γik’s are the parameters corresponding to visibility at in-region Class I 

areas; the δ1’s are the parameters corresponding to visibility at Class I areas in region 1 

(California), if i≠1; the δ2’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 assumes 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 

variable is X. The household maximizes its utility by choosing X=m. The indirect utility function 

for a household in the nth residential area and the ith region is therefore

where Q denotes the vector of vectors, Q1, Q2, Q3, Q4, Q5, and Q6, and the unsubscripted γ and 

δ denote vectors as well.

m p X − ⋅ ≤ 0 ,

V m Z Q ni n m Z Q n ik ik Q

k

N

jk jk

k

N

j i

i j

( , , ; , , , ) ( ) , / θ γ δ ρ θ γ δ ρ ρ ρ ρ ρ = + + + = =≠

∑ ∑∑

1 1

1
6.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 

Qi1 = Q0i1 to Qi1 = Q1i1, 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 derivative is negative. WTP for a change from Q0 to a higher (improved) 

level of visibility, Q1, is therefore a concave function of Q1, 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:

WTPni Z Q m m Z Z Q Q n n ik ik ik Q Q

k

N

jk jk jk

k

N

j i

i j

( , ) [ ( ) ( ) ( )] . / ∆ ∆ = − + − + − + − = =≠

∑ ∑∑ ρ ρ ρ ρ ρ ρ ρ ρ θ 0 1 γ 0 1 δ

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 same 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 other 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 deciviews (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), αwill 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 Q1 = Q01 to Q1 = Q11 is:

if this is the first (or only) visibility change the household values.

Alternatively, if we have estimates of m as well as WTP1

in and WTP1out of in-region and 

out-of-region households, respectively, for a given change in visibility from Q01 to Q11 in 

Southeast parks, we can solve for γ1 and δ1 as a function of our estimates of m, WTP1

in and 

WTP1

out, for any given value of ρ. Generalizing, we can derive the values of γ and δ for the jth

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

j ρ ρ

j

in

j j

m WTP m

Q Q = − −

−

( )

( ) 0 1

δ

ρ ρ

j ρ ρ

j

out

j j

m WTP m

Q Q = − −

−

( )

( ) . 0 1
6.A-8 

of-region. The Chestnut 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 sources of benefits transfer-based estimates that we 

employ in the absence of estimates, 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 Regiona Out of Regionb

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 Southeast 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 parameters 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 γ1

a δ2 δ3 δ4 δ5 δ6

Region 2 δ1 γ2 δ3 δ4 δ5 δ6

Region 3 δ1 δ2 γ3 δ4 δ5 δ6

Region 4 δ1 δ2 δ3 γ4 δ5 δ6

Region 5 δ1 δ2 δ3 δ4 γ5 δ6

Region 6 δ1 δ2 δ3 δ4 δ5 γ6

a The parameters arrayed in this table are region-specific rather than park-specific or wilderness area-specific. For 

example, δ1 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 Class I area-specific parameters (e.g., δ1k, for the kth 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 estimates 

from the study to estimate the parameters in the utility functions corresponding to visibility at 

in-region parks (γ1); similarly, we can directly use the out-of-region WTP estimates from the 

study to estimate the parameters for out-of-region parks (δ1). 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 subjects. Because 
6.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 addition, 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 preference 

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 course, 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)—γ1, γ2, and γ3 and δ1, δ2, and δ3. 

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.)—γ4, γ5, and γ6 and δ4, δ5, and δ6.

3. We derive park- and wilderness area-specific parameters within each region (γ1k and 

δ1k, for k=1, ..., N1; γ2k and δ2k, for k=1, ..., N2; 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 visibility 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 regionspecific recreational visibility parameters for regions not covered in the Chestnut 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 wilderness 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 Q0 to Q1 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 single δ. 

Denoting as our estimate of γ for the jth 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 jth 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 WTPij and WTPij() are the observed and the predicted WTPs for a change in visibility in 

the jth region from Q0 = Q0i to Q1= Q1i, i=1, ..., 3. An analogous procedure was used to select an 

optimal δ, for each of the three regions in the Chestnut 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) (γ4, γ5, and γ6 and δ4, δ5, and δ6) 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 δ4 in the utility function of a California household, 

corresponding to visibility at national parks 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 (income held constant). Suppose, for example, that we have an estimated mean WTP 

of out-of-region households for a visibility change from Q01 to Q11 at national parks in California

(region 1), denoted WTP1

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

3
6.A-12

study was m. We might assume that, for the same change in visibility at national parks in the 

Northwest United States, WTP4

out = WTP1out among out-of-region individuals with income m.

We could then derive the value of δ4, given a value of ρ as follows:

where Q04 = Q01 and Q14 = Q11, (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 transfer method assumes that (1) all households have the same 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 to 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 that 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 (“visibility at national parks”) are not the same.

A preferable way to estimate δ4, then, might be to assume the following relationship:

(income held constant), where n1 = the average annual number of visitor-days to California 

parks and n4 = 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

4 ρ ρ

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, n1 = 2n4, WTP4

out would be half of WTP1out. 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 same in all regions.

The parameter δ4 is estimated as shown above, using this adjusted WTP4

out. The same 

procedure is used to estimate δ5 and δ6. We estimate γ4, γ, and γ6 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 noted 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 parameters 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 wilderness 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 Derivation 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 

wilderness 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 possible 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 Region-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 WTP1

in and WTP1out 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, n1k denotes the annual average number of visitordays to the kth Class I area in California, and n1 denotes the annual average number of visitordays to all Class I areas in California (that are included in the benefits analysis), then we assume 

that

and

WTP

n

n k WTP in k in

1

1

1

1 = * ,
6.A-15

Using WTPj

in and WTPjout, 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 area-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 (WTPj

in) or out of that region (but in the United 

States) (WTPj

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 

Wilderness-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. 

Recall 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-ofregion 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:

WTPj

in

 = the in-region WTP for the change in visibility from Q0 to Q1 in the jth region;

WTPjk

in

= the in-region WTP for the same visibility change (from Q0 to Q1) in the kth

Class I area in the jth region (= sjk*WTPj

in, where sjk is the kth area’s share of 

visitor-days in the jth region);

m = income;

γj* = the optimal value of γ for the jth region; and

γjk = the value of γjk calibrated to WTPjk

in and the change from Q0 to Q1; 

then11: 

and

which implies that:

where:

 

11 γj

* is only approximately equal to the right-hand side because, although it is the optimal value designed to 

reproduce as closely as possible all three of the WTPs corresponding to the three visibility changes in the 

Chestnut and Rowe study, γj* will not exactly reproduce any of these WTPs. 

γ

ρ ρ

j ρ ρ

j

in

m WTP m

Q Q

* ( )

( )

≈ − −

− 0 1

γ

ρ ρ

jk ρ ρ

jk

in

m WTP m

Q Q = − −

−

( )

( ) 0 1

γ γ jk jk a j ≈ * , *

a

m WTP m

m WTP m jk

jk

in

j

in = − −

− −

( )

( ) .

ρ ρ

ρ ρ
6.A-17

We use the adjustment factor, ajk, to derive γjk from γj*, for the kth Class I area in the jth

region. We use an analogous procedure to derive δjk from δj* for the kth Class I area in the jth

region (where, in this case, we use WTPj

out and WTPjkout instead of WTPjin and WTPjkin).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 Costs 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 visibility 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 region. Although the choice of WTP 

will affect the resulting adjustment factors (the ajk’s) and therefore the resulting γjk’s and δjk’s, the effect is 

negligible. We confirmed this by using each of the three in-region WTPs in California and comparing the 

resulting three sets of γjk’s and δjk’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 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 Council recommended that 

residential visibility be omitted from the overall primary benefits estimate. (U.S. EPA-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 mZ Z 0 1
6.A-18

where:

m = household income,

ρ = shape parameter (0.1),

θ = WTP parameter corresponding to the visibility at MSA,

Z0 = starting visibility, and

Z1 = 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 household 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 ith region and the nth 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 calculated 

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 (that 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. Estimating 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 Class I areas 

in the California) is assumed to be the first, and they cannot all be the first. The upward bias 

WTPni Z Q m m Z Z n n ik ik ik Q Q Q Q

k

N

jk jk jk

k

N

j i

i j

( , ) [ ( ) ( ) ( ) ] . / ∆ ∆ = − + − + − + − = =≠

∑ ∑∑ ρ ρ ρ ρ ρ ρ ρ ρ θ 0 1 γ 0 1 δ

1

0 1

1

1
6.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-calibrated 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 

elasticity16 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 benefits 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 Income 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, 2004).” 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 factors.

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 visibility 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 projections 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 projections.
6.A-21

Table 6.A-3. Mean Annual Household WTP for Changes in Visual Range 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

$80.19 $53.88 90 150 $48,759

$71.42 $51.37 45 90

Southwest $50.12 $45.11 155 200

$72.67 $55.13 155 250 $48,759

$61.40 $48.87 115 155

Southeast $66.41 $35.08 25 50

$82.70 $53.88 25 75 $48,759

$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 Benefitsa

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.4
6.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 $19,900a 1982 0.033446

0.021316

$281 12 32 $19,900a 1982 0.031661

$82 12 22 $27,600d 1984 0.010738

$119 12 32 $27,600d 1984 0.009417

Boston

(Tolley et al., 1984)

$139 18 28 $25,000a 1982 0.026636

0.022843

$171 18 38 $25,000a 1982 0.019049

Chicago

(Tolley et al., 1984)

$202 9 18 $30,000b 1981 0.022313

0.015480

$269 9 30 $30,000b 1981 0.016696

$121 10 20 $29,400d 1984 0.013180

$144 10 30 $29,400d 1984 0.009732

Denver

(Tolley et al., 1984)

$115 50 60 $32,000c 1984 0.038558

0.033181

$154 50 70 $32,000c 1984 0.027803

Los Angeles

(Brookshire et al., 

1979)

$43 2 12 $15,200d 1978 0.003866

$116 2 28 $15,200 0.007428 d 1978 0.006716

$71 12 28 $15,200d 1978 0.011702

Mobile

(Tolley et al., 1984)

$168 10 20 $20,200a 1982 0.026078

0.022480

$197 10 30 $20,200a 1982 0.018882

San Francisco

(Loehman et al., 1985)

$71 16.3 18.6 $26,100c 1980 0.045307 0.045307

Washington, DC

(Tolley et al., 1984)

$238 15 25 $27,500a 1982 0.036866

0.032335

$303 15 35 $27,500a 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 Visibility Benefits

Adjustment Step Parameter Estimate

Central Estimate of Elasticity a 0.90

Adjustment Factor Used to Account for Projected Real Income Growth in 2020b 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 NonMarket Goods: A Case Study of Alternative Benefit Measures of Air Pollution 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-0271B1.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 Estimating 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/background/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/regdata/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, Volume 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 Collins, 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. Prepared 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 

<http://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/E992F918DF32131A852571B9004571F7/

$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 Innovation. 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

ILLUSTRATIVE SCENARIO OF RECREATIONAL AND RESIDENTIAL 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 refer 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 standards, we 

cannot 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 important because light extinction can 

vary substantially depending on the specific combination of SO2, NOX, 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 

specific 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 the 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 benefits. Table 6.B-3 provides the results of this sensitivity analysis. Figure 6.B-1 

 

1 See Chapter 4 of this RIA for 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 benefits 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 Debell 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 2020a,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 NPc 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 county 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 Regions for the Illustrative Scenario in 

2020 (in millions of 2010$)

a 

Studied Park Region Illustrative Scenario Benefits

Southeast $4.3

Southwest - 

California $350 

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 extinction 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/m3 coarse $330

+ 5 μg/m3 coarse with 15 μg/m3 in Southwest $300

+8 μg/m3 coarse with 15 μg/m3 in Southwest $300

Sensitivity 

Analysis 

(Other Park 

Regions)

No coarse particles -

+ 5μg/m3 coarse -

+ 5 μg/m3 coarse with 15 μg/m3 in Southwest -

+8 μg/m3 coarse with 15 μg/m3 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 whether 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 benefits 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 analysis 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 $190 

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 Areas 

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/m3 coarse $180

+ 5 μg/m3 coarse with 15 μg/m3 in Southwest $150

+8 μg/m3 coarse with 15 μg/m3 in Southwest $130

Sensitivity Analysis No coarse particles $330

+ 5 μg/m3 coarse $310

+ 5 μg/m3 coarse with 15 μg/m3 in Southwest $250

+8 μg/m3 coarse with 15 μg/m3 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 Park 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 ANALYSIS

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 PM2.5 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/m3 in conjunction with retaining the 24-hour standard 

of 35 μg/m3, as well as control strategies designed to demonstrate attainment of the alternative 

annual standards of 13 and 11 μg/m3 in conjunction with retaining the 24-hour standard of 35

μg/m3 (referred to as 12, 13, and 11). These illustrative 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 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, discussed in Section 7.2.3, are 

referred to as “extrapolated” costs. By definition, no cost data currently exists for the additional 

emissions reductions needed beyond known controls. We employ two methodologies for 

estimating the costs of unidentified future controls, and 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.

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 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 the revised 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 technologies. Also, EPA recognizes 

the extrapolated portion of the engineering cost estimates are uncertain because extrapolated 
7-2 

costs do not contain information about which sectors may be affected or which control 

measures may be employed in the future. 

The engineering cost estimates are limited in their scope. This analysis focuses on the 

emissions reductions needed for attainment of the revised and alternative standards that are 

described earlier in this RIA. EPA understands that some states will incur costs designing 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 present 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 issuance 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 Control Strategy Tool1 (CoST) to estimate engineering control costs for 

mobile, non-EGU point and area 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 was 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. 

The cost equations located in CoST require either unit capacity or stack flow to 

determine annualized, capital and/or operating and maintenance (O&M) costs. Capital costs 

 

1 The Control Strategy 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 costs represent an equal stream of yearly costs over the period the control technology is expected to 

operate.
7-3 

are converted to annualized 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 annual 

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 includes annualized capital and O&M costs for those 

controls included in our identified control strategy 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 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 

 

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 2010 versus otheryear dollars, if the other-year dollars are properly adjusted. For this analysis, all costs are reported in real 2010 

dollars. 6 http://epa.gov/ttn/catc/products.html#cccinfo
7-4 

and/or alternative standards. We included all known controls at an 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 controls that had an annual cost of more than $20,000 per ton because either (i) the 

remaining emissions sources were relatively small sources, and we believe they are already 

controlled, or (ii) the equations in CoST were 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 A4, 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 analysis, for the revised annual standard of 12 μg/m3 and the 

alternative standard of 13 μg/m3 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. See 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 fixedcost 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 choices of these 

parameters in this 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 2010$)a, b

Revised and 

Alternative Standard Emissions Sector Emissions Reductionsc

7% 

Discount Rate

13 μg/m3 Non-EGU Point Sources -- — 

Area Sources 53 $0.63

Total 53 $0.63

12 μg/m3 Non-EGU Point Sources 380 $0.87 

Area Sources 430 $4.3

Total 800 $5.1

11 μg/m3 Non-EGU Point Sources 23,000 $88

Area Sources 2,300 $13

Total 25,400 $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 PM2.5 and SO2 emissions reductions.

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 costs to allow for comparison 

across regions and between costs and benefits. These numbers reflect the engineering costs 

annualized at a discount rate of 7%. Engineering 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 2020 

for Revised and Alternative Standards Analyzed (millions of 2010$)a,b

Revised & Alternative Standard Region Known Controls

13 μg/m3 East — 

West — 

California $0.63

Total $0.63

12 μg/m3 East — 

West — 

California $5.1
7-6 

Total $5.1

11 μg/m3 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 alternative annual 

standard of 13 μg/m3, and $100 million for a more stringent alternative annual standard of 11

μg/m3. 

7.2.3 Extrapolated Costs

This section presents the methodology and results of the extrapolated engineering cost 

calculations for attainment of the revised annual PM2.5 standard of 12 μg/m3, as well as 

alternative annual standards of 13 μg/m3and 11 μg/m3. All costs presented for the illustrative 

control strategies are calculated incrementally from the current PM2.5 standard of 15/35 μg/m3, 

therefore, any additional emission reductions needed to attain the current 24-hour standard of 

35 μg/m3 are part of the baseline analysis and not presented here. 

As mentioned earlier in this chapter, and as described in more detail in Chapter 4, the 

application of the known control strategy was not successful in reaching attainment for all 

areas for these alternative PM2.5 standards. Because some areas remained in nonattainment, 

the engineering costs detailed in Section 7.2.2 represent the costs of partial attainment for the 

revised annual standard of 12 μg/m3 and alternative annual PM2.5 standards of 13 μg/m3 and 11

μg/m3. For the revised standard and each alternative standard and geographic area that cannot 

reach attainment with known controls, we estimated the additional emissions reductions 

needed for PM2.5 to attain the standard. See Chapter 3, Section 3.3.2 and Tables 3-7, 3-8, and 3-

9 for a detailed discussion of how the additional, needed emissions reductions were estimated

and for a summary of 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 alternative 

standards, in addition to the application of known controls EPA assumes the application of 

unidentified future controls that make possible the additional emission reductions needed for 

attainment in 2020. By definition, no cost data currently exists for unidentified future 

technologies or innovative strategies. EPA used two methodologies for estimating the costs of 

unidentified 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 PM2.5 reduced; 

the hybrid methodology 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-costbased 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; any such 

adjustment would be considered well within 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 specific 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 The final 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. Controls 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, which 
7-8 

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 different than the 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 any 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/m3 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 generates a total annual cost curve for PM2.5 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.

14 For PM2.5 reductions needed in each area, the cost begins with a national constant 

cost-per-ton for PM2.5 and increases as emissions reduction for PM2.5 are needed, reflecting 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/m3, 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 ton. For other geographical areas, the average cost 

 

attempts to identify viable alternatives for any control requirements 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 fixedcost 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 choices of these 

parameters in this 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 uncontrolled 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 PM2.5

emissions. The simplest form of ax2 + bx + c was used where x is the tons of a particular 

pollutant to be 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 PM2.5, 

and c is set to zero because there is no cost to imposing no control. The hybrid methodology 

has a different a for PM2.5 for each 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 alternative standard) prior to estimating needed emission 

 reductions from unknown controls to achieve the alternative standard. 

U = unknown emissions reductions by geographic area and standard. 

T = cost by geographic area and standard, or 

N

E

U2 + NU (i.e., ax2 + bx). 

An example of the hybrid methodology is provided below. In this example, in Area B the 

percentage of total PM2.5 reductions needed from unknown controls relative to total emissions 

reductions needed (e.g., 100/150, or 67%) is larger than the percentage of total PM2.5

reductions needed from unknown controls relative to total emissions reductions needed in 

Area A (e.g., 100/200, or 50%). Because Area B needs a higher portion of emissions reductions 

from unknown controls, total cost using the hybrid methodology is higher in Area B. This

illustration shows that the 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 needs 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 

methodologies described above. The extrapolated cost estimates range from $48 million

(2010$) to $340 million (2010$) for the revised standard of 12 μg/m3

. We included sensitivity 

analyses using both the alternative fixed cost-per-ton and the hybrid methodologies in 

Appendix 7.A. 

Table 7-3. Extrapolated Costs by Revised and Alternative Standard Analyzeda,b (millions of 

2010$)

Revised and Alternative 

Standard Region

Extrapolated Cost

Fixed-Cost Methodology Hybrid Methodologyc

7% 7%

13 μg/m3 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, 500, 000

Example of Applying Hybrid Methodology

PM2.5 Emissions

Reductions Achieved

With Known Controls

PM2.5 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 = $45,000
7-11

12 μg/m3 East — — 

West — — 

California $48 $340

Total $48 $340

11 μg/m3 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 to estimate prior emissions reductions for these two counties for use 

in the hybrid methodology cost calculations: for the remaining 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 base case PM emissions. We selected the overall minimum 

percentage and for each of the two counties without any known PM controls, we multiplied that overall 

minimum percentage by the specific county’s base case PM emissions.

Of note is the geographic distribution of extrapolated costs. For the revised and 

alternative standards, the above costs indicate that control measures applied in California 

represent a significant portion of the extrapolated costs. Using the fixed-cost methodology, for 

the revised annual standard of 12 μg/m3 and the alternative annual standards of 13 μg/m3 and 

11 μg/m3, 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/m3

and the alternative annual standards 

of 13 μg/m3 and 11 μg/m3, 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 costper-ton values.

Estimating 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 technological 
7-12

advances and development of innovative strategies can reduce emissions and reduce the costs 

of emerging technologies over time. Technological change may provide new possibilities for 

controlling emissions as well as reducing 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 additional 

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 

Particulate Matter 2.5 Ambient Air Monitoring, 40 CFR Part 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 air monitoring 

regulations will revise the network design requirements for PM2.5 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 

Tables 7-4 and 7-5 present a summary of the total national costs of attaining the revised 

annual standard of 12 μg/m3 and the alternative annual standards of 13 μg/m3 and 11 μg/m3 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 increasing the size of the national PM2.5 monitoring network; the Agency anticipates that states would 

be able to relocate existing monitors to meet the near-roadway 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 extrapolated costs were calculated at a 7% discount rate only. The total cost 

estimates are $53 million (2010$) and $350 million (2010$) for the revised annual standard of 

12 μg/m3

; $11 million and $100 million for the alternative annual standard of 13 μg/m3; and 

$320 million and $1,700 million for the alternative annual standard of 11 μg/m3. To further 

evaluate potential costs ranges, we 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 μg/m3. 

For the revised annual standard of 12 μg/m3, 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 emissions reductions from unknown to known controls, it could be 

more representative of total costs. In an effort to consider the potential fitness of the

extrapolated 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) transactions in both the SCAQMD and 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. This list of control measures includes further PM2.5 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-ton cost format we 

obtained the California Air Resources Board’s 2009 and 2010 Emission Reduction Offset 

Transaction Costs, Summary Report and reviewed the PM10 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 PM10 ERC prices in SJV

APCD ranged from $40,000 per ton per year (tpy) to $70,000/tpy, and PM10 ERC prices in the 

SCAQMD ranged from $575,000/tpy to more than $1.9 million/tpy. These prices reflect both 

 

16 Note that the extrapolated cost estimates do not represent lower and upper bound estimates, but simply 

represent estimates generated 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 Alternative Standard Analyzed (millions of 2010$)a

, 

Fixed-Cost Methodologyb,c

Revised and Alternative 

Standard Region

Known Control 

Costs

Unknown Control 

Costs—Fixed-Cost 

Methodology Total Costs

13 μg/m3 East — — — 

West — — — 

California $0.63 $10 $11

Total $0.63 $10 $11

12 μg/m3 East — — — 

West — — — 

California $5.1 $48 $53

Total $5.1 $48 $53

11 μg/m3 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. As 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

Table 7-5 Total Costs by Revised and Alternative Standard Analyzed (millions of 2010$)a, 

Hybrid Methodologyb,c 

Revised and Alternative 

Standard Region

Known Control

Costs

Unknown Control 

Costs—Hybrid 

Methodology Total Costs

13 μg/m3 East — — — 

West — — — 

California $0.63 $100 $100

Total $0.63 $100 $100

12 μg/m3 East — — — 

West — — — 

California $5.1 $340 $350

Total $5.1 $340 $350

11 μg/m3 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. As 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 technological 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 emissions control in relation to original estimates. Studies have 

concluded 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. Technical 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.

Increasing marginal abatement costs could possibly induce the type of innovation that 

would result in lower costs than estimated in this chapter. By 2020, breakthrough 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 number of examples of low-emissions 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 NOx burners for NOx emissions

 Scrubbers, which achieve 95% and potentially greater SO2 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 possible that the shape of the marginal abatement 

cost curve is non-linear.

MC0 MC1

SO2 Reductions

Cost/

Ton

Induced Technology Shift

Slope =?0

Slope =?1

MCLONG
7-17

 Low- or zero-VOC paints, consumer 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 electrification 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 some 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.

 “Learning by doing” or “learning curve impacts,” a distinct concept from technological 

innovation, has also made it possible to achieve greater emissions reductions than had been 

feasible earlier or has reduced the costs of emissions 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 themselves as a lower expected cost to operate 

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 Second Section 812 Prospective Analysis.20 In that report, learning curve 

adjustments were included for those sectors and technologies for which learning curve data 

were 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 Prospective Analysis: Final Report, prepared for U.S. EPA, Office of Air and Radiation, 

February 2011. 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 Equation (Gumerman and Marnay, 2004).21 This equation, 

shown below, has been shown to be widely applicable in manufacturing:

Wright’s Equation: CN = Co * Nb, (7.2)

where:

N = cumulative production

CN = cost to produce Nth unit of capacity

Co = cost to produce the first unit

b = learning parameter = ln (1-LR)/ln(2), where

LR = learning by doing rate, or cost reduction per doubling of capacity or output.

The percentage adjustments 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 Report.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 virtually every sector considered in the Draft Direct Cost Report is a 10% 

reduction in O&M costs for two doublings of cumulative output, with proxies such as 

cumulative fuel sales or cumulative emissions reductions being used when output data was 

unavailable.

 

21 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. 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. EPA, Office of Air and Radiation, 

August 13, 2007. 
7-19

For 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 including 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 fell 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 Agency 

overestimated per-unit costs for five regulations, underestimated them for four regulations 

(three of these were relatively small pesticide rules), and accurately estimated them for four. 

Based on an examination of eight rules, “for those rules that employed economic incentive 

mechanisms, overestimation of per-unit costs 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 emissions 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 SO2 Trading Program

Recent cost estimates of the Acid Rain SO2 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, the fuel switching from high-sulfur to low-sulfur coal was spurred by a 

reduction in rail transportation costs due to deregulation of rail rates during the 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) than expected, and that unanticipated 

opportunities arose to blend low- and high-sulfur coal in older boilers up to a 40/60 mixture, 

compared with the 5/95 mixture originally estimated.

Table 7-6. Phase 2 Cost Estimates

Phase 2 Cost Estimates

Ex ante estimates $2.7 to $6.2 billiona

Ex post estimates $1.0 to $1.4 billion

a 2010 Phase II cost estimate in 1995$.

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. The 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 

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. 25 Holmstead, Jeffrey, 2002. “Testimony of Jeffrey Holmstead, Assistant Administrator, Office of Air and Radiation, 

U.S. Environmental Protection Agency, Before the Subcommittee on Energy and air Quality of the committee 

on Energy and Commerce, U.S. House of Representatives, May 1, 2002, p. 10.
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 hydrofluorocarbon HFC-134a could be 

substituted for CFC-12 in refrigeration.” However, as Hammit26 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). Hammit cites a similar 

story for HCFRC-141b and 142b, which are currently substituting for CFC-11 in important foamblowing 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 technologyforcing 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). They demonstrate 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 regulatory 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 evidence 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 based its estimates of emissions 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, emissions 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 overestimated? 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 Elements of PM Engineering 

Costs

Potential Source of Uncertainty

Direction 

of 

Potential 

Bias

Magnitude of 

Impact on 

Monetized 

Costsa 

Degree of 

Confidence 

in Our 

Analytical 

Approachb 

Ability to 

Assess 

Uncertaintyc 

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 PM10

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 characterization

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-andTrade 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 Jeffrey Holmstead, Assistant Administrator, Office of 

Air and Radiation, U.S. Environmental Protection Agency, Before the Subcommittee on 

Energy and air Quality of the committee on Energy and Commerce, U.S. House of 

Representatives, May 1, 2002, p. 10.

ICF Consulting. 2005. The Clean Air Act Amendment: Spurring Innovation and Growth While 

Cleaning the Air. Washington, DC. Available at 

http://www.icfi.com/Markets/Environment/doc_files/caaa-success.pdf. 

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.

Keohane, 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, Research 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 Protection 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 Radiation. Also available at 

http://www.epa.gov/air/sect812/feb11/fullreport.pdf
7.A-1 

APPENDIX 7.A 

DATA TO CALCULATE COSTS TO MEET 12 μG/M3 AND SENSITIVITY ANALYSES OF 

EXTRAPOLATED COST ESTIMATES

7.A.1 PM2.5 Emission Reductions and Costs to Meet 12 μg/m3 

Table7.A.1 below includes costs and information needed to calculate those costs, 

by county, to meet 12 μg/m3. The Table includes the PM2.5 emissions reductions needed to 

reach the 15/35 μg/m3level because the hybrid methodology includes a parameter that uses

the quantity of prior emissions reductions.1

Table 7.A.1 PM2.5 Emission Reductions and Costs to Meet 12 μg/m3

FIPS 

Code County

12 μg/m3

Known 

Controls

Unknown

Controls

PM2.5

Emissions

Reductions

to Reach 

15/35 μg/m3

Emissions 

Reductions Costs

Emissions 

Reductions

Hybrid 

Methodology

Costs

06037 Los Angeles -- 743 $4.5 -- --

06065 Riverside -- 53 $0.63 980 $290

06025 Imperial 404 -- -- 294 $7.6

06029 Kern 1,769 -- -- 418 $7.8

06107 Tulare 726 -- -- 635 $18

06047 Merced 76 -- -- 19 $0.36

06071 San 

Bernardino 988 -- -- 844 $23

 

1 EPA developed a model of increasing total annualized costs for controlling PM2.5 emissions -- ax2 + 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 Sensitivity Analyses of Extrapolated Cost Estimates 

Because of the uncertainties associated with estimating costs for the PM2.5 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 to estimate 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 analysis, we selected analyses below, 

keeping emissions estimates constant, to 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 alternative standard, the primary cost estimate of 

$15,000/ton. The Table also includes, by region and revised and alternative 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 ranges from $32 million to $64 million, 

depending on the fixed-cost-per-ton assumed.

Table 7.A.2 Sensitivity Analysis of Fixed-Cost Methodology for Unknown Controls by Revised 

and Alternative Standard Analyzed (millions of 2010$)a 

Revised & 

Alternative 

Standard Region

Extrapolated Costs

$10,000/ton $15,000/ton $20,000/ton

7% 7% 7%

13 μg/m3 East — — — 

West — — — 

California $6.7 $10 $13

Total $6.7 $10 $13

12 μg/m3 East — — — 

West — — —
7.A-3 

California $32 $48 $64

Total $32 $48 $64

11 μg/m3 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, the 

Table also includes, by region and revised and alternative 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 

Alternative Standard Analyzed (millions of 2010$)a 

Revised & 

Alternative 

Standard Region

Extrapolated Costs

$10,000/ton $15,000/ton $20,000/ton

7% 7% 7%

13 μg/m3 East — — — 

West — — — 

California $69 $100 $140

Total $69 $100 $140

12 μg/m3 East — — — 

West — — — 

California $230 $340 $460

Total $230 $340 $460

11 μg/m3 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 BENEFITS 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/m3 and the alternative annual standards of 13

μg/m3 and 11 μg/m3 relative to the analytical baseline that includes recently promulgated 

national regulations and additional emissions reductions needed to attain the existing 15/35

μg/m3 standards, as well as adjustments to NOx 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 Table 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 PM2.5 standard of 12 μg/m3 and alternative annual standards of 

13 μg/m3 and 11 μg/m3, incremental to the 2020 analytical baseline. For the revised annual 

standard of 12 μg/m3, 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 (2010 dollars). For an 

alternative annual standard of 13 μg/m3, net benefits are estimated to be $1.2 billion to $2.9 

billion at the 3% discount rate and $1.1 billion to $2.6 billion at the 7% discount rate. Net 

benefits of an alternative annual PM2.5 standard of 11 μg/m3are estimated to be $11 billion to 

$29 billion at a 3% discount rate and $10 billion to $26 billion at a 7% discount rate in 2020. 

For the revised annual standard of 12 μg/m3, the EPA estimates that the benefits of full 

attainment exceed the costs of full attainment by 11 to 154 times at a 7% discount rate. For the 

alternative annual standard of 13 μg/m3

, the EPA estimates that the benefits of full attainment 

exceed the costs of full attainment by 11 to 246 times at a 7% discount rate. For the alternative 

annual standards of 11 μg/m3, 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 Attainmenta

Revised 

Annual

Standard

Total Costsb Monetized Benefits d Net Benefits 

3% 

Discount 

Ratec

7% Discount 

Rate

3% 

Discount 

Rate

7% Discount 

Rate

3% Discount 

Rateb

7% Discount 

Rate

12 $53 to 

$350

$53 to 350 $4,000 to 

$9,100

$3,600 to 

$8,200

$3,700 to 

$9,000

$3,300 to 

$8,100

Alternative 

Standards

13 $11 to $100 $11 to $100 $1,300 to 

$2,900

$1,200 to 

$2,600

$1,200 to 

$2,900

$1,100 to 

$2,600

11 $320 to 

$1,700

$320 to 

$1,700

$13,000 to 

$29,000

$12,000 to 

$26,000

$11,000 to 

$29,000

$10,000 to 

$26,000

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 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 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 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 those 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/m3 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/m3 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/m3 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 PM2.5, 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/m3 consisted of only directly emitted PM2.5, these 

tables are limited to only those categories associated with emission reductions of directly 

emitted PM2.5.

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 NOx and SO2, 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 functions

 
8-5 

Table 8-2. Human Health Effects of Ambient PM2.5

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 PM2.5

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 ISAb

Other respiratory effects (e.g., pulmonary 

function, non-asthma ER visits, nonbronchitis chronic diseases, other ages and 

populations)

— — PM ISAb

Reproductive and developmental effects 

(e.g., low birth weight, pre-term births, etc.) 

— — PM ISAb,c

Cancer, mutagenicity, and genotoxicity 

effects

— — PM ISAb,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 the strength of the association.
8-6 

Table 8-3. Welfare Co-Benefits of PM2.5 

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 

ISAb

Reduced effects on materials Household soiling — — Section 6.4, PM 

ISAb

Materials damage (e.g., corrosion, 

increased wear)

— — Section 6.4, PM 

ISAc

Reduced effects from PM 

deposition (metals and 

organics)

Effects on Individual organisms 

and ecosystems

— — Section 6.6.1, 

PM ISAb

a We quantify these co-benefits in an 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 PM2.5 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 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 PM2.5 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 PM2.5 

monitors in the United States would meet an annual standard of 12 μg/m3without additional 

Federal, State, 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 and Air Toxics Standards (MATS) and other 

Federal programs such as diesel standards will provide substantial improvements in regional 

concentrations of PM2.5. 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 array of emission control strategies for different sources. The 

costs and benefits identified in this RIA will not be realized until specific controls are mandated 

by SIPs or other Federal 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. Emission 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 assumed the application of a significant number of

unidentified future controls that would make possible the additional emissions reductions 

needed for attainment in 2020. EPA used two methodologies—the fixed-cost and hybrid 

methodologies—for estimating the costs of unidentified future controls, and 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. 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 cost 
8-8 

varies by geographic area and by degree of difficulty associated with obtaining the needed 

emissions reductions. 

For the revised annual standard of 12 μg/m3

, 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 emissions reductions from unknown to known controls, it could be 

more representative of total costs. In an effort to consider the potential fitness of the

extrapolated 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) transactions 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/m3, including further PM2.5 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. In addition, the California Air Resources Board’s 

2009 and 2010 Emission Reduction Offset Transaction Costs, Summary Report included PM10

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 PM10 ERC prices in SJV APCD ranged from $40,000 per ton per year (tpy) to 

$70,000/tpy, and PM10 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 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/m3

. In comparison, 

modeling by Fann et al. (2012) estimated that 2005 levels of air pollution were responsible for 

between 130,000 and 320,000 PM2.5-related deaths, or between 6.1% and 15% of total deaths 
8-9 

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 PM2.5 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 PM2.5 will have improved substantially by 2020, due to major emissions 

reductions resulting from implementation of Federal regulations. For example, we estimate 

that SO2 emissions (an important PM2.5 precursor) in the United 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 PM2.5 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, 2010c). 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. Although there are greater 

uncertainties at lower PM2.5 concentrations, there is no evidence of a population-level 

threshold in PM2.5-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 

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. 

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 PM2.5 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. 

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. 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). 2009. Integrated Science Assessment for 

Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for 

Environmental Assessment—RTP Division. December. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546. 

U.S. Environmental Protection Agency (U.S. EPA). 2010. Quantitative Health Risk Assessment for 

Particulate Matter—Final Report. 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 Approvals for 22 States. June. Available at: 

http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2011b. Regulatory Impact Analysis for the 

Final Mercury and Air Toxics Standards. EPA-452/R-11-011. December. Available at: 

http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2011c. 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. 
8-11

U.S. Environmental Protection Agency (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 EXECUTIVE 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, October 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 

entities 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 organization 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 economic 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 section 109 of the CAA. See also American Trucking Associations v. EPA. 175 F.3d at 1044-45 

(NAAQS do not have significant impacts upon small entities because NAAQS themselves impose 

no regulations upon small entities). 

9.5 Unfunded Mandates Reform Act

This action 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, local 

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 

considered to a degree in the development of state plans to implement the standards. See also 

American Trucking Associations v. EPA, 175 F. 3d at 1043 (noting that because the EPA is 

precluded from considering costs of implementation in establishing NAAQS, preparation of a 

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, as 
9-3 

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 

quality 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 state, 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 development 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 limited 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 

information 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 Southern 

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 economically 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 environmental 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 action” 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 action 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) directs 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 applicable voluntary consensus standards.
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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 particles 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 Reference 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 specifications 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 performancebased 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 

Populations 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 health 
9-6 

or environmental effects of their programs, policies, and activities on minority populations and 

low-income populations 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 eliminating 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 Integrated 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 preamble and copies of all documents have been placed in the public 

docket for this action.
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CHAPTER 10

QUALITATIVE DISCUSSION OF EMPLOYMENT IMPACTS 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, “our regulatory system must 

protect public health, welfare, safety, and our environment while promoting economic growth, 

innovation, competitiveness, and job creation. It must be based on the best available science” 

(Executive Order 13563, 2011). Although 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 reflecting relatively high levels of 

unemployment. This chapter provides a context for considering the potential influence of 

environmental regulation on growth and job shifts in the U.S. economy. Section 10.2 addresses 

the particular influence of this proposed rule on employment. Section 10.3 presents a 

descriptive overview of the peer-reviewed literature relevant to evaluating the effect of air 

quality regulation on employment. Finally, in Section 10.4, we offer several conclusions.

10.2 Influence of NAAQS Controls 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 effect 

environmental regulations have been shown to have on net employment in the regulated 

sectors, we do not expect them to have a significant impact on the overall economy.

Estimating specific employment impacts from a new NAAQS standard is particularly 

challenging for two reasons. First, the NAAQS targets 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 these 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 makes it challenging to predict how specific 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 except to the extent that labor costs are part of total costs in a cost-benefit analysis.
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measures applied across all sectors and locations, specific control strategies adopted by the 

states, 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 

applicable 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 NAAQS 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 largely due to the sharp increase in unemployment 

that 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 effect 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, Section 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 regulated 
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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 firms 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 another (e.g., from producing electricity or steel to 

producing pollution abatement equipment). Theory supports the argument that, in the case of 

full employment, the net national employment effects from environmental regulation are likely 

to be small and transitory (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 

suggest 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 economy.

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 regulation. 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

Environmental 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 Diperna, 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 their 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 require 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 workforce 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 insight 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 

quality 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 and 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 NOX) 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 show 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 employment in regulated industries. The first mechanism is the 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 the 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 labor 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 estimate effects on net employment; therefore, their empirical 

results are not directly applicable to the full range of manufacturing and nonmanufacturing 

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 

sectors. More specifically, their results suggest that, on average across the industries studied, 

each additional $1 million ($1987) spent on pollution abatement resulted 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 NOX emissions as a precursor to ozone and PM10 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 only examined 

impacts in Southern California and impacts may differ in other nonattainment 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 related 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 nonattainment 

counties (counties not in compliance with one or more NAAQSs) are likely to face more 

stringent air pollution regulations 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 

limited evidence that employment grows more slowly, investment is lower, or fewer new 

polluting plants open in nonattainment areas relative to attainment areas. However, this 

evidence does not mean that there is less aggregate economic activity as a result of

environmental 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 nonattainment 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 technology (ET) firms generating 

approximately $300 billion in revenues domestically (2% of national gross domestic product ), 

producing $43.8 billion in exports (2% of total 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 revenue. The remaining 1% of companies are large companies supplying 49% of ET 

revenue (OEEI, 2010).

5

As described above, deriving estimates of how regulations will impact economy-wide 

net employment is a difficult task, especially in the case of setting a new NAAQS, given that 

economic theory predicts that the net effect of an environmental regulation on regulated 

sectors and the overall economy is indeterminate (not necessarily positive or negative). Peerreviewed econometric studies that use a structural approach, 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 national 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 and 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 Environmental Regulations on Industrial Activity: 

Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of 

Manufactures.” Journal of Political Economy 110(6): 1175-1219.

Henderson, V. (1996). “Effects of Air Quality Regulation.” The American Economic 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, and W. W. McHone (2003). “Effects of Environmental 

Regulations on Manufacturing Plant Births: Evidence from a Propensity Score Matching 

Estimator.” The Review of Economics and Statistics 85(4): 944-952.

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.gov/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-12-005

December 2012