## Health and Welfare Benefits Analyses to Support the Second Section 812 Benefit-Cost Analysis of the Clean Air Act

Final Report | February 2011

prepared for:

Office of Air and Radiation

US Environmental Protection Agency

prepared by:

Industrial Economics, Incorporated

2067 Massachusetts Avenue

Cambridge, MA 02140

617/354-0074

### Second Section 812 Pr ospective Analysis February 2011

i 

### TABLE OF CONTENTS

CHAPTER 1 | INTRODUCTION 

### Relationship of this Report to Other Second Prospective Analyses 1-3 

### Overview of Methods 1-5 

### Summary of Results 1-5 

### Organization of this Report 1-8



### CHAPTER 2 | ESTIMATION OF HUMAN HEALTH EFFECTS AND ECONOMIC
BENEFITS 

### Overview of Approach 2-1 

### Quantifying Changes in Air Quality 2-2 

### Health Impact Functions 2-6 

### Literature Sources for Ozone Health Effects Functions 2-11 

### Literature Sources for PM Health Effects Functions 2-15 

### Baseline Incidence Rates 2-22 

### Economic Value for Health Outcomes 2-26 

### Results and Implications 2-35



### CHAPTER 3 | ESTIMATION OF VISIBILITY IMPROVEMENTS AND
ECONOMIC VALUATION 

### Overview 3-1 

### Methodology 3-5 

### Results 3-14



### CHAPTER 4 | AGRICULTURAL AND FOREST PRODUCTIVITY BENEFITS OF
THE CAAA 

### Background 4-1 

### Analytical Framework 4-2 

### Analytical Methods and Results: Relative Yield Loss 4-4 

### Analytical Methods and Results: Agriculture and Timber Markets Welfare Effects 4-14



### CHAPTER 5 | ESTIMATION OF MATERIALS DAMAGE AND ECONOMIC
BENEFITS 

### Introduction 5-1 

### Methodology 5-2 

### Results 5-7

### Second Section 812 Pr ospective Analysis February 2011

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### CHAPTER 6 | SUMMARY OF PRIMARY BENEFITS 

### Summary of Annual Benefits 6-1 

### Summary of Cumulative Benefits 6-1 

### Comparison with Results from the First Prospective 6-6 

### Benefits Uncertainties 6-7



### REFERENCES R-1



### APPENDIX A: PRIMARY ESTIMATES OF VISIBILITY BENEFITS BY STATE APPENDIX B: RELATIVE YIELD LOSS MAPS AND TABLES

### Second Section 812 Pr ospective Analysis February 2011

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### CHAPTER 1 | INTRODUCTION

Section 812 of the Clean Air Act Amendments of 1990 (CAAA) required the U.S. Environmental Protection Agency (EPA) to pe rform periodic, comprehensive analyses of the total costs and total benefits of program s implemented pursuant to the Clean Air Act (CAA). The first analysis conducted was a retrospective analysis, addressing the original CAA and covering the period 1970 to 1990. Th e Retrospective was completed in 1997. Section 812 also required performance of pros pective cost-benefit analyses, the first of which was completed in 1999. The prospec tive analyses address th e incremental costs and benefits of the CAAA. The First Pros pective covered implementation of the CAAA over the period 1990 to 2010. EPA’s Office of Air and Radiation (OAR) began work on the Second Prospective with the drafting of an analytical plan for the stud y. This analytical plan was reviewed by a statutorily-mandated outside peer review gr oup, the Science Advisory Board’s Advisory Council for Clean Air Compliance Analysis (Council), and the Council provided comments, which have been incorporated into the technical analysis planning. This report describes the development of quantified and monetized primary benefits associated with emissions reductions estimated for th e second prospective section 812 analysis. Exhibit 1-1 below outlines the relationship am ong the section 812 Retrospective, the First Prospective, and the Second Prospective.

EXHIBIT 1-1. 812 SCENARIOS: CONCEPTUAL SCHEMATIC



## 1970 1990 2000 2010 2020 A
B Pre-CAA

Post-CAA Without-CAAA

TimeEmissions Retrospective First Prospective Second Prospective



## C

With-CAAA



## 1970 1990 2000 2010 2020 A
B Pre-CAA

Post-CAA Without-CAAA

TimeEmissions Retrospective First Prospective Second Prospective



## C

With-CAAA



## 1970 1990 2000 2010 2020 A
B Pre-CAA

Post-CAA Without-CAAA

TimeEmissions Retrospective First Prospective Second Prospective



## C

With-CAAA



## 1970 1990 2000 2010 2020 A
B Pre-CAA

Post-CAA Without-CAAA

TimeEmissions Retrospective First Prospective Second Prospective



## C

With-CAAA

### Second Section 812 Pr ospective Analysis February 2011

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### The scope of this analysis is to estimate th e benefits of reducing emissions of criteria pollutants under two scenarios, depicted in schematic form in Exhibit 1-1 above:
1. An historical, " with-CAAA " scenario control case that reflects expected or likely future measures implemented si nce 1990 to comply with rules promulgated through September 2005 1 

### ; and

2. A counterfactual “ without CAAA ” scenario baseline case that freezes the scope and stringency of emissions controls at their 1990 levels, while allowing for changes in population and economic activity and, theref ore, in emissions attributable to economic and population growth. Criteria pollutant emissions reductions addr essed in this analysis include: volatile organic compounds (VOCs), oxides of nitrogen (NO x 

### ), sulfur dioxide (SO 2 

### ), particulate matter of 10 microns or less (PM 10 

### ), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM 2.5 

### ). Benefits estimates, however, focus not on the emissions but on the ambient air concentration out comes that result from emissions changes attributed to implementation of the Clean Air Act Amendments. The two major ambient pollutants for which benefits estimates are readily available are fine particulate matter and tropospheric ozone. Air quality changes associated with changes in emissions of lead, the remaining criteria pollutant under the Clean Air Act, are not addressed in this report, and were not addressed in the first prospective analysis, because of the relatively modest impact of CAAA regulations in place by 2005 on lead emissions. 2


### This report presents the results of EPA’s analys is of the future effects of implementation of the CAAA’s programs on air emissions from the following emission sectors: electricity generating units (EGUs), non-elect ricity generating unit point sources, nonroad engines/vehicles, on-road vehicles, and nonpoint sources. The study years for the analysis are 1990, 2000, 2010, and 2020. Because the CAAA was signed into law in 1990, emissions and air quality changes attributed to its implementation were not realized until after that point. As a result, benefits are estimated only for the target years 2000, 2010, and 2020. The purpose of this report is to present the met hods used to generate estimates of physical and economic benefits that result from the CAAA, and to present the results of our analyses for each target year. The scope of the benefits analyses conducted to support the second prospective analysis includes the following:

1 The lone exception is the Coke Ovens Re sidual Risk rulemaking, promulgated under Title III of the Act in March 2005. We

omitted this rule because it has a very small impact on crite ria pollutant emissions (less than 10 tons per year VOCs) relative to the with-CAAA scenario. The pr imary MACT rule for coke oven emissi ons, however, involves much larger reductions and therefore is incl uded in the with-CAAA scenario.

2 Lead emissions were effectively controlled under regulations auth orized by the original Clean Air Act. As a result, analysis

of lead emissions is a major focus of the section 812 retrospe ctive study. Recently finalized revisions to the lead NAAQS could have significant effects on emissions for some localitie s, but those changes were first proposed on May 1, 2008 and were therefore not included in the scope of this analysis.

### Second Section 812 Pr ospective Analysis February 2011

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###  Health Benefits : These include avoided premat ure mortality and avoided

morbidity associated with reduced human exposures to air pollutants.

 Visibility Benefits : Reductions in air pollutants, particularly fine particulate

matter, improve visibility, leading to physical and economic benefits in both recreational and residential settings.

 Agricultural and Forest Productivity Benefits : Tropospheric ozone inhibits plant

growth; as a result, reduction in ozone con centrations yield physical and economic benefits in the form of enhanced agricultural and forest productivity.

 Materials Damage Benefits : Some materials are susceptible to accelerated

deterioration when exposed to air pollution; as a result, reduction in air pollution can extend the life of these materials, yielding physical and economic benefits.

 Ecological Benefits : A wide range of ecological resources are susceptible to

damage when exposed to ambient air po llution or deposition of pollutants to terrestrial or aquatic environments. For a small portion of these effects, it is possible to quantify and estimate the economic value of avoided pollutant exposure. As outlined below, quantified and monetized ecological benefits of the CAAA are included in our summary of the benefits of CAAA programs presented later in this chapter. The methods and da ta used to generate these estimates are not described in this report, but in an accompanying EPA report prepared to support the second prospective analysis.

RELATIONSHIP OF THIS REPORT TO OTHER SECOND PROSPECTIVE ANALYSES 

### The benefits estimates presented in this report rely on results generated in prior analytic components of the overall second prospective effo rt. As illustrated in Exhibit 1-2, EPA conducted both emissions estimation and air quality modeling analyses to generate data that underlies the benefits estimation approach es. EPA plans to make full reports on each of these major analytic steps available to the public online at the project website, www.epa.gov/oar/sect812 . Details on the use of air quality inputs in the health, visibility, agricultural, forestry, and materials damage analyses are provided in the subsequent chapters of this report. In almost all cas es, some post-processing of air quality data is involved to estimate pollutant exposures appropr iate to the specific benefits analysis. This report focuses on presentation of the primary benefits estimates. The primary benefits estimates are based on EPA’s preferre d set of analytic assumptions, models, and data sources, many of which have been e xplicitly reviewed by EPA Science Advisory Board over the course of many years and have been embodied in standard benefits estimation practice as carried out by EPA’s O ffice of Air and Radiation in Regulatory Impact Analyses (RIAs). As an integral part of preparing the primary benefits results, EPA also conducted a series of analyses to estimate uncertainty in the primary results. The methods and results of these uncertainty and sensitivity analyses are described in a separate report, Uncertainty Analyses to Support the Second Section 812 Prospective Benefit-Cost Analysis of the Clean Air Act

.

### Second Section 812 Pr ospective Analysis February 2011

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### In addition, as noted above, estimation of the ecological benefits of the CAAA are described in detail in a separate report, Ecological Benefits Analyses to Support the Second Section 812 Prospective Benefit- Cost Analysis of the Clean Air Act . The ecological benefits report addresses the estimation of quantified ecological benefits, including estimates of the value of reduced lake acidification in the Adirondacks region of New York State, but also characterizes a range of unquantifiable ecological impacts through an exhaustive literature review a nd presentation of maps showing the relation between prevented air pollutant exposure and selected sensitive ecological receptors. Within each of the following chapters, there is a brief discussion of the scope of quantified and monetized benefits. In additio n, we include a brief discussion of other, unquantified benefits of the Clean Air Act. With the completion and review of the benefits analyses, the Agency will prepare an integrated report for the entire project. The integrated report will address each of these major analytic components, and present comparisons of benefits and costs for each of the target years, as well as uncertainty analyses that characterize confidence in these results.

EXHIBIT 1-2. MAY 2003 ANALYTICAL PLAN - SCHEMATIC FLOW CHART

IPM Emissions Projections CAAA Non -utility Controls

Air Quality

CGE IPM

CAAA Utility

Controls Spatial and sectoral non-EGU emissions

Growth factors

New utility emission Factors and spatial & Temporal allocation Of activity growth Base Inventory Files

Non-utility CAAA Direct Control

Costs Air Quality

Health Effects and Valuation

Utility Expenditures IPM CAAA Non -utility Controls

Air Quality

CGE AEO 2005

IPM Sectoral and Spatial Disaggregation of Driver Data

CAAA Utility

Controls Spatial and sectoral non-EGU emissions

Growth factors

New utility emission Factors and spatial & Temporal allocation Of activity growth Base Inventory Files

Non-utility CAAA Direct Control

Costs Air Quality

Utility Expenditures IPM CAAA Non -utility Controls

Air Quality

CGE

CAAA Utility

Controls Spatial and sectoral non-EGU emissions

Growth factors

New utility emission Factors and spatial & Temporal allocation Of activity growth Base Inventory Files

Non-utility CAAA Direct Control

Costs Air Quality

Utility Expenditures IPM CAAA Non -utility Controls

Air Quality

CGE AEO 2005

IPM

CAAA Utility

Controls Spatial and sectoral non-EGU emissions

Growth factors

New utility emission Factors and spatial & Temporal allocation Of activity growth Base Inventory Files

Non-utility CAAA Direct Control

Costs Air Quality

Utility Expenditures IPM Emissions Projections CAAA Non -utility Controls

Air Quality

CGE IPM

CAAA Utility

Controls Spatial and sectoral non-EGU emissions

Growth factors

New utility emission Factors and spatial & Temporal allocation Of activity growth Base Inventory Files

Non-utility CAAA Direct Control

Costs Air Quality

Health Effects and Valuation

Utility Expenditures IPM CAAA Non -utility Controls

Air Quality

CGE AEO 2005

IPM Sectoral and Spatial Disaggregation of Driver Data

CAAA Utility

Controls Spatial and sectoral non-EGU emissions

Growth factors

New utility emission Factors and spatial & Temporal allocation Of activity growth Base Inventory Files

Non-utility CAAA Direct Control

Costs Air Quality

Utility Expenditures IPM CAAA Non -utility Controls

Air Quality

CGE

CAAA Utility

Controls Spatial and sectoral non-EGU emissions

Growth factors

New utility emission Factors and spatial & Temporal allocation Of activity growth Base Inventory Files

Non-utility CAAA Direct Control

Costs Air Quality

Utility Expenditures IPM CAAA Non -utility Controls

Air Quality

CGE AEO 2005

IPM

CAAA Utility

Controls Spatial and sectoral non-EGU emissions

Growth factors

New utility emission Factors and spatial & Temporal allocation Of activity growth Base Inventory Files

Non-utility CAAA Direct Control

Costs Air Quality

Utility Expenditures

### Second Section 812 Pr ospective Analysis February 2011

1-5 OVERVIEW OF METHODS 

### The methods applied in this report generally follow approaches developed by EPA over many years to support Regulatory Impact Analyses for major Office of Air and Radiation rulemakings, prior Section 812 analyses, and other Agency economic analyses. In a few cases, summarized below, this Second Pr ospective reflects methodologies, data, or benefits categories that are new to Agency an alysis. In general, the primary benefits results presented here reflect methods, data, a nd benefits that have been vetted through Council review, as well as internal EPA re view by OAR economists and benefits analysts. The general method we apply to quantify and m onetize benefits involves four basic steps:

1. Access the relevant air quality results from the suite of Second Prospective CMAQ

runs . The Community Multiscale Air Quality (CMAQ) data include estimates of ambient air quality measured as concentrati ons of particulate matter and ozone, estimates of visibility expressed in deciviews, and estimates of deposition measured as a deposition flux per unit area.

2. Estimate exposure for each scenario . Exposure analyses can vary by endpoint – for example, most health endpoints use an 8-hour maximum measure, while the agricultural analyses use a cumulative measu re of ozone exposure over a growing

season.
3. Estimate changes in physical effects. Physical effects are quantified benefits (e.g., cases of chronic bronchitis) attributable to CAAA regulations, and are generated based on differences in exposure between scenar ios. A few effects, such as visibility, are estimated for both scenarios, rather than based on differences in exposure.

4. Value changes in effects . In most cases, this step involves application of a unit economic value. The unit values reflect willingness to pay to avoid a small risk of incidence of a health effect; they are not va lues to avoid a certain health effect. In a few cases, valuation is directly estimated from air quality outcomes, applies avoided cost methods rather than willingness to pay, or is combined with step 3 in an integrated approach or model. Exhibit 1-3 summarizes our approach to steps 2 through 4 above for each major category of benefits. Detailed descriptions of th ese approaches are provided in the subsequent chapters.

SUMMARY OF RESULTS 

### Exhibit 1-4 below provides a summary of the ec onomic benefits results generated for the categories of benefits addressed in this report.

### Second Section 812 Pr ospective Analysis February 2011

1-6 EXHIBIT 1-3. SUMMARY OF ESTIMATION APPROACH FOR MAJOR BENEFITS CATEGORIES

BENEFIT CATEGORY EXPOSURE
ESTIMATION PHYSICAL EFFECTS
ESTIMATION ECONOMIC VALUE
ESTIMATION

Health Effects Model Attainment Test Software (MATS) for PM; Enhanced Voronoi Neighbor Averaging (eVNA) for ozone Benefits Mapping and Analysis Program (BenMAP)

Visibility CMAQ-derived deciview estimates Custom benefits transfer models

Agriculture and Forest Productivity eVNA extrapolation , BenMAP procedure, and offline GIS analysis NCLAN-basedconcentrationresponse functions Forest and Agricultural Sector Optimization Model (FASOM)

Materials Damage Air Pollution Emissions Experime nts and Policy (APEEP) model

Lake Acidification CMAQ deposition outputs Model of Acidification of Groundwater in Catchments (MAGIC) Custom randomutility model for Adirondack lakes

Note: Models and approaches are described in detail in Chapters 2 through 5 of this report.

### Second Section 812 Pr ospective Analysis February 2011

1-7 EXHIBIT 1-4. SUMMARY OF MEAN PRIMARY BENEFITS RESULTS

BENEFIT CATEGORY MONETIZED BENEFI TS (MILLION 2006$)
BY TARGET YEAR

NOTES 2000 2010 2020

Health Effects PM Mortality PM Morbidity Ozone Mortality Ozone Morbidity $710,000 $27,000 $10,000 $420 $1,200,000 $46,000 $33,000

$1,300 $1,700,000 $68,000 $55,000

$2,100 - PM mortality estimates based on Weibull distribution derived from Pope et. al (2002) and Laden et al., 2006. - Ozone mortality estimates based on pooled function Subtotal Health Effects $750,000 $1,300,000 $1,900,000

Visibility Recreational Residential $3,300 $11,000 $8,600 $25,000 $19,000 $48,000 Recreational visibility only includes benefits in the regions analyzed in Chestnut and Rowe, 1990 (i.e., California, the Southwest, and the Southeast). Subtotal Visibility $14,000 $34,000 $67,000 Agricultural and Forest Productivity $1,000 $5,500 $11,000
Materials Damage $58 $93 $110 Ecological $6.9 $7.5 $8.2 Reduced lake acidification benefits to recreational fishing assuming effect threshold of 50 microequivalents per liter. Total: all categories $770,000 $1,300,000 $2,000,000 Note: See Chapters 2 through 5 of this report fo r detailed results summaries. Values presented are means from results reported as distributions. Additional, al ternative estimates are provided in the separate companion report on uncertain ty. Estimates presented with two significant figures.



### The health effects estimates for the second pros pective are much larg er than the estimates EPA developed for the first prospective. The 2020 estimates are new to the second prospective, but the comparable mean estimate of health benefits in 2000 and 2010 for the first prospective were $71 billion in 2000 and $110 billion in 2010, in 1990$ 3 

### - if updated to 2006$, these estimates would be $110 billion in 2000 an d $170 billion in 2010. There are six key reasons we have identified for the increase in benefits:

1. Scenario differences : The with-CAAA scenario, especially for the 2010 target year, includes new rules with substantial additi onal pollutant reductions that were not

3 See The Benefits and Costs of the Clean Air Act 1990 to 2010 , USEPA Office of Air and Radia tion and Office of Policy, EPA410-R-99-001, November 1999.

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### included in the comparable first prospectiv e scenario, such as the Clean Air Interstate Rule (CAIR).

2. Improved air quality models : The first prospective relied on the Regional Acid Deposition Model/Regional Particulate Mode l (RADM/RPM) for PM and deposition estimates in the eastern U.S., the Regulatory Modeling System for Aerosols and Acid Deposition (REMSAD) for PM estimates in the western U.S., and the Urban Airshed Model (versions V and IV) at various re gional and urban scales to generate ozone estimates. The second prospective relies on the integrated CMAQ modeling tool, which reflects substantial improvements in air quality modeling, provides more comprehensive spatial coverage, a nd achieves improved model performance.

3. Better, more comprehen sive exposure estimates : The first prospective relied on first generation exposure extrapolation tools to generate monito r-adjusted exposure estimates away from monitors. Since then , the monitor network, availability of speciated data, and the performance of sp eciated exposure estimation tools have improved substantially.

4. Updated dose-response estimates : Since 1999, some concentration response functions have been updated, most notably the PM-premature mortality C/R function, whose central estimate of the mortality imp act of fine PM has nearly doubled. In addition, health effects research has addr essed endpoints that were not covered in the first prospective, including premature mortality associated with ozone exposure. Although the Agency has not yet conducted a rigorous quantitative analysis to assess the impact of these methodology and data improvements , the impact of most of these factors is to increase the estimates of benefits.

ORGANIZATION OF THIS REPORT 

### The remainder of this report is organized as follows. First, we present methods, data, and results for health effects and their valuati on. As noted above, the health benefits constitute the majority of the monetized bene fits in our analysis. Second, we present benefits associated with changes in visibility in both recreational and residential settings. Third, we present benefits associated with changes in productivity of agricultural crops and commercial forests. Fourth, we present benefits associated with reduced materials damage, including such resources as bridges, architectural coatings, and other materials that can be damaged by air pollution. The report concludes with aggregation and summary of all four of these categories of primary benefits.

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### CHAPTER 2 | ESTIMATION OF HUMAN HEALTH EFFECTS AND ECONOMIC BENEFITS

OVERVIEW OF APPROACH 

### This chapter addresses the economic valuation of human health effect s realized as a result of the CAAA. The reduced incidence of physi cal effects is a valuable measure of health benefits for individual endpoints. To compare or aggregate benefits across endpoints, the benefits must be monetized. Assigning a do llar value to avoided incidences of each effect permits us to sum monetized benefits realized as a result of the CAAA, and compare them with the associated costs. In the second prospective section 812 analysis, we have two broad categories of benefits: health and welfare benefits. Human health effects include mortality and morbidity endpoints, which are presented in this chapter. Welfare effects include visibility, agricultural and ecological benefits, and materia ls damage, which are covered in Chapters 3 through 5. We obtain valuation estimates from the economic literature and report them in “dollars per case reduced” for health effects. Similar to estimates of physical effects provided by health studies, we report each of the monetary values of benefits applied in this analysis in terms of a central estimate and a probability distribution around that value. The statistical form of the probability distribution varies by endpoint. For example, we use a log-normal distribution to describe the estimated dollar value of an avoided premature mortality, while we assume the estimate for the value of a reduced case of acute bronchitis is uniformly distributed between a minimum and maximum value.

Human health benefits of the 1990 Amendments are attributed to reduced emissions of criteria pollutants (Titles I through V) and reduced emission of ozone depleting substances (Title VI). This chapter focuses on the valuation of human health effects attributed to the reduction criteria pollutant emissions. 4 

### Our analysis indicates that the benefit of avoided premature mortality risk reduction dominates the overall net benefit estimate. This is, in part, due to the high monetary value assigned to the avoidance of premature mortality relative to the unit value of other health endpoints. As described in detail in this chapter, there are also significan t reductions in short term and chronic health

4 OAR’s First Prospective analysis of the Costs and Benefits of the Clean Air Act Amendments included a detailed analysis of

the health and welfare benefits of Title VI provisions. That analysis concluded that the benefits of the Title VI stratospheric ozone protection programs were very large compared to the costs. For the Second Prospective analysis, EPA has decided that updating the prior analysis likely would provid e little in the way of additional insights. As a result, the Second Prospective analysis focuses on benefits and costs of criteria pollutant programs.

### Second Section 812 Pr ospective Analysis February 2011

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### effects and a substantial number of health benefits that we could not quantify or monetize. Similar to the first section 812 prospective an alysis, the study design adopted for this analysis uses a sequence of linked analytical mode ls to estimate benefits. The first step is an analysis of the likely implementation ac tivities undertaken in response to the CAAA. These forecasted activities provided a basis for modeling criteria pollutant emissions under the two scenarios considered (the with-CAAA scenario and the without-CAAA scenario), as documented in the Emissions Projections for the Clean Air Act Second Section 812 Prospective Analysis. 5 

### The emissions estimates were input into the Community Multiscale Air Quality (CMAQ) m odel and, in turn, ambient pollutant concentrations estimated by CMAQ were input into the Environmental Benefits Mapping and Analysis Program (BenMAP). BenMAP is a tool developed by the U.S. Environmental Protection Agency (EPA) for estimating the human health effects and econom ic benefits associated with changes in ambient air pollution. 6 

### BenMAP relies on three inputs: 1) forecasted changes in air quality between a baseline and control scenario; 2) health impact functions that quantify the relationship between the forecasted chang es in exposure and expected changes in specific health effects; and 3) health valua tion functions that assign a monetary value to changes in specific health effects. From these inputs, BenMAP compares changes in pollutant exposure between two scenarios and pr oduces results in terms of avoided health effects and monetary valuation of the willingn ess to pay to avoid those effects. This chapter begins by discussing methods used to quantify changes in air quality and how that is interpreted for human exposure to specific pollutants, goes on to describe the health impact functions used, and then details the health valuation functions applied. The chapter concludes with a presentation and discussion of the results.

QUANTIFYING CHANGES IN AIR QUALITY 

### This analysis is the first Section 812 prosp ective analysis to use an integrated modeling system, the Community Multiscale Air Quality (CMAQ) model, to simulate changes in national and regional-scale pollutant con centrations and deposition. CMAQ has previously been deployed in several EPA economic analyses including the 2008 Ozone National Ambient Air Quality Standards ( NAAQS) Regulatory Impact Analysis (RIA) (EPA, 2008) and the 2006 PM NAAQS RIA (EPA, 2006b). The CMAQ model (Byun and Ching, 1999) is a state-of-the-science, regional air quality modeling system that is designed to simulate the physical and chem ical processes that govern the formation,

5 See EH Pechan and Industrial Economics, Emission Projections for the Clean Air Act Second Section 812 Prospective

Analysis: Revised Draft Report , March 2009, available at www.epa.gov/oar/sect 812.

6 This analysis uses BenMAP Version 3.0.16. The cu rrent version of BenMAP can be downloaded from

http://www.epa.gov/air/benmap/

### Second Section 812 Pr ospective Analysis February 2011

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### transport, and deposition of gaseous and partic ulate species in the atmosphere. The latest version of CMAQ (Version 4.6) was employed for this analysis. The CMAQ model was applied for seven co re CAAA scenarios that include four different years that span a 30-year period – 1990, 2000, 2010 and 2020. Scenarios that incorporate the emission reductions associated with the CAA are referred to as withCAAA while those that do not are referred to as without-CAAA . The scenarios include: Retrospective Base-Year Scenario

1990 without-CAAA Base and Projected Year Scenarios without 1990 CAAA Controls

2000 without-CAAA

2010 without-CAAA

2020 without-CAAA Base and Projected Year Scen arios with 1990 CAAA Controls

2000 with-CAAA

2010 with-CAAA

2020 with-CAAA An integral component of the modeling analysis is the estimation of future-year emissions for the seven core scenarios – these are described in detail in companion reports available at EPA’s Section 812 study website. 7 

### Emissions for the historical years (1990 and 2000) were based on the best available emission inventories for these years. Projection to the future years was based on economic growth pr ojections, future-year control requirements (for attainment of NAAQS), and control efficien cies. Different assumptions were applied for the with- and without-CAAA scenarios resulting in a different future-year emissions pathway for each scenario. The emissions da ta were processed for input to the CMAQ modeling using the Sparse-Matrix Operator Kernel Emissions (SMOKE) emissions processing system. The model-ready emission inventories for each scenario and year were then used to obtain base- and future-year estimates of the key criteria pollutants, as well as many other species. The air quality modeling analysis was designed to make use of tools and databases that have recently been developed and evaluated by EPA for other nationaland regional-scale air quality modeling st udies. In particular, model-ready meteorological input files for 2002 were provided by EPA for use in this study. For fine particulate matter (PM 2.5 

### ) and related species, the CMAQ model was applied for an annual simulation period (January through December). A 36-km resolution modeling domain that encompasses the contiguous 48 states was used for the annual modeling (see

7 See www.epa.gov/oar/sect812

### Second Section 812 Pr ospective Analysis February 2011

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### Exhibit 2-1). For ozone and related speci es, the CMAQ model was applied for a fivemonth simulation period that captures th e key ozone-season months of May through September. Two 12-km resolution modeling domains (that when combined cover the contiguous 48 U.S. states) were used fo r the ozone-season modeling (see Exhibit 2-1). Altogether, model-ready emission inventories were prepared and the CMAQ model was applied for a total of 21 simulations (compr ising seven core scenarios and three modeling domains). 8

EXHIBIT 2-1. MAP OF THE CMAQ MODELING DOMAINS USED FOR SECOND SECTION 812
PROSPECTIVE ANALYSIS

Legend:

CONUS: Continental U.S. 36 km grid, PM2.5 and deposition estimates EUS: Eastern U.S. 12 km grid, ozone estimates WUS: Western U.S. 12 km grid, ozone estimates

8 A detailed report on the air quality modeling analyses was prepared for EPA. This descrip tion is based on the September

2008 draft report, Second Prospe ctive Analysis of Air Quality in the U.S .: Air Quality Modeling, prepared for James DeMocker of the EPA Office of Policy Analysis and Review by Sharon G. Douglas, Jay L. Ha ney, A. Belle Hudischewskyj, Thomas C. Myers, and Y. Wei of ICF International.



## CONUS EUS WUS

### Second Section 812 Pr ospective Analysis February 2011

2-5 

### PM 2.5 

### and ozone outputs from CMAQ provide the basis of the air quality inputs needed for BenMAP. The raw CMAQ output is adjusted to take into account monitor data. The PM 2.5 

### output is adjusted using the Modeled A ttainment Test Software (MATS, Version 2.1.1, Build 807) procedure and the ozone out put is adjusted using the enhanced Voronoi Neighbor Averaging (eVNA) routine in BenMAP. MATS estimates quarterly mean PM 2.5 

### chemical component concentrations at monitor locations (point estimates) by conducting a Speciated Modeled Attainment Test (SMAT) analysis. MATS can also estimate quarterly mean concentration estimates for each PM 2.5 

### chemical component concentrations at all grid cells in an Eulerian grid model such as CMAQ using a spatial field gradient interpolation procedure. All PM 2.5 

### concentration estimates for this analysis were prepared using the spatial and temporal relative adjustment method in MATS. PM 2.5 

### concentration estimates in CMAQ grid cells without a monitor were interpolated from nearby m onitors using the inverse distance squared weighting option in the Voronoi Neighbor Averaging (VNA) procedure in MATS. The MATS analysis conducted for the PM 2.5 

### used the following input information:

 Observed PM 2.5 

### data from 1,336 Federal Refere nce Method (FRM) monitors with sufficient data in at least one year fro m 2002 to 2004 (as provided with the MATS Version 2.1.1 installation package);

 Observed chemically speciated fine particle mass data from both the PM 2.5 

### Speciated Trends Network (STN) and th e Interagency Monitoring of Protected Visual Environments (IMPROVE) network, a total of 420 monitors with sufficient data in as least one year from 2002 to 2004 (as provided with the MATS Version 2.1.1 installation package);

 Speciated CMAQ estimates for 6 PM 2.5 

### species (SO 4 

### , NO 3 

### , elemental carbon,

organic carbon, NH 4 

### , and crustal material) at the 36 kilometer PM CMAQ grid cell level for each of the scenarios (from CMAQ speciated output data files provided by ICF/SAI). Additional detail on the MATS procedure is av ailable in the MATS User Manual (Abt Associates, 2009). MATS produced estimated average quarterly concentrations for each of the CMAQ 36 km grid cells. These estimat es were subsequently rewritten to the format required for inputting daily PM 2.5 

### data into EPA’s BenMAP software. The daily ozone concentration estimates used in this analysis were prepared using a monitor and model relative adjustment procedure, combining the hourly CMAQ estimates with observed ozone monitor data. The monitor and model relative adjustment procedure was conducted using the extended VNA procedure (eVNA) with both spatial and temporal scaling in EPA’s BenMAP softwa re. The 1,162 ozone monitors used in the eVNA procedure were the 2002 ozone monitors contained in the BenMAP (ver. 3.0.15) US Setup installation file. The 2002 mon itor data was selected because the base case CMAQ analysis (“2000 with Clean Air Act”) used a 2002 emission inventory. The

### Second Section 812 Pr ospective Analysis February 2011

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### CMAQ ozone estimates were prepared for the two separate eastern and western United States domains shown in Exhibit 2-1, each with a 12 kilometer by 12 kilometer grid.

HEALTH IMPACT FUNCTIONS 9 

### Health impact functions measure the change in a health endpoint of interest, such as hospital admissions, for a given change in ambient ozone or PM 2.5 

### concentration. There are several types of data that can support th e development of health impact functions relating air pollutant exposure or ambient concen trations to incidence of health outcomes. These sources of data include toxicological studies (including animal and cellular studies), human clinical trials, observational epidemiology studies, and meta-analyses of multiple 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 concen tration-response (C-R) relationships which can be used to evaluate population-level impacts of reductions in ambient pollution levels. However, standard environmental epidem iology studies provide only a limited representation of the uncertainty associated with a specific health impact function, measuring only the statistical error in the estimates, and usually relating more to the power of the underlying study (driven largely by population size and the frequency of the outcome measure). There are many other s ources of uncertainty in the relationships between ambient pollution and population level health outcomes, including many sources of model uncertainty, such as model specifi cation, potential confounding between factors that are both correlated with the health outcome and each other, and many other factors. As such, in recent years, EPA has begun inv estigating how expert elicitation methods can be used to integrate across various sources of data in developing h ealth impact functions for regulatory benefits analyses. Expert elicitation is useful in integrating the many sources of information about uncertainty in the health impact function, because it allows experts to synthesize these data sources using their own mental models, a nd provide a probabilistic representation of their synthesis of the data in the form of a probability distribution of the health impact function. EPA has used expert elicitation to inform the regulatory process in the past (see for example the staff paper for the leadNAAQS (EPA, 1990) and the PM NAAQS RIA (EPA, 2006b)). In the current analysis, we have used expert elicitation to characterize one representation of the health impact function for the relationship between PM 2.5 

### and premature mortality. However, similar methods could be used to characterize health impact functions for other health outcomes. A standard health impact function has four components: 1) an effect estimate from a particular study; 2) a baseline incidence rate for the health effect (obtained from either the epidemiology study or a source of public hea lth statistics such as the Centers for Disease

9 Portions of this section were derived from the PM NAAQS RIA (EPA, 2006b) and the Ozone NAAQS RIA (EPA, 2008).

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### Control); 3) the size of the potentially affected population; and 4) the estimated change in the relevant ozone or PM summary measures. A typical health impact function might be of the following generic form: 

#   1 0 

## 

   

 x 

## e y y 



### ,

where y 0 

### is the baseline incidence (the product of the baseline incidence rate times the potentially affected population), β is the effect estimate, and Δ x is the estimated change in the summary ozone or PM 2.5 

### measure. There are other functional forms, but the basic elements remain the same. The ozone and PM air quality inputs to the health impact functions are described in the section above . The following subsections describe the sources for each of the other elements: si ze of potentially affected populations; effect estimates; and baseline incidence rates.

POTENTIALLY AFFECTED POPULATIONS 

### The starting point for estimating the size of potentially affected populations is the 2000 U.S. Census block level dataset (G eolytics 2002). BenMAP incorporates 250 age/gender/race categories to match specific populations potentially affected by ozone and PM 2.5 

### . The software constructs specific popul ations matching the populations in each epidemiological study by accessing the appropriate age-specific populations from the overall population database. To estimate population levels for the years after 2000, BenMAP scales the 2000 Census-based population estimate with the ratio of the countylevel forecast for the future year of interest over the 2000 county-lev el population level. Woods & Poole (2007) provides the county-le vel population forecasts used to calculate the scaling ratios.

HEALTH EFFECT ESTIMATE SOURCES 

### The most significant monetized benefits of re ducing ambient concentr ations of ozone and PM are attributable to reductions in human health risks. EPA’s Ozone and PM Criteria Documents outline numerous health effects know n or suspected to be linked to exposure to ambient ozone and PM (EPA, 2006; Anders on et al., 2004). EPA recently evaluated the ozone and PM literature for use in th e benefits analyses for the Ozone NAAQS RIA (EPA, 2008) and PM NAAQS RIA (EPA, 2006b), respectively. The discussion of individual effect estimates presented in this section relies heavily on the research done for these RIAs.

Exhibit 2-2 lists the human health effects of ozone and PM 2.5 

### . Exhibit 2-3 and 2-4 lists the health endpoints associated with ozone and PM 2.5 

### , respectively, included in this analysis. A number of endpoints that are not health-related may also contribute significant monetized benefits. Welfare be nefits such as increased recreational and residential visibility, increased recreational fishing opportunities, increased commercial

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### forest and agriculture productivity, and decreased building materials damage are discussed in Chapters 3 through 5.

EXHIBIT 2-2. HUMAN HEALTH EFFECTS OF OZONE AND PM 2.5

POLLUTANT/EFFECT QUANTIFIED AND MONETIZED IN BASE
ESTIMATES a UNQUANTIFIED EFFECTS g,h —CHANGES IN:

PM/Health b Premature mort ality based on both cohort study estimates and on expert elicitation c,d Bronchitis: chronic and acute Hospital admissions : respiratory and cardiovascular Emergency room visits for asthma Nonfatal heart attacks (myocardial infarction) Lower respiratory symptoms Minor restricted-activity days Work loss days Asthma exacerbations (asthmatic population) Upper Respiratory symptoms (asthmatic population) Infant mortality Subchronic bronchitis cases Low birth weight Pulmonary function Chronic respiratory diseases other than chronic bronchitis Morphological changes Altered host defense mechanisms Cancer Non-asthma respiratory emergency room Visits UVb exposure (+/-)e Stroke/cerebrova scular disease

Ozone/Health f Premature mortality: short-term exposures Hospital admissions: respiratory Emergency room visits for asthma Minor restricted-activity days School loss days Outdoor worker productivity Cardiovascular emer gency room visits Asthma attacks Respiratory symptoms Chronic respiratory damage Increased responsiveness to stimuli Inflammation in the lung Premature aging of the lungs Acute inflammation and respiratory cell damage Increased susceptibility to respiratory infection Non-asthma respiratory emergency room Visits UVb exposure (+/-)e

a Primary quantified and monetized effects are those incl uded when determining the primary estimate of total monetized benefits of the alternative standards. b In addition to primary economic endpoints, there are a nu mber of biological responses that have been associated with PM health effects including morphological changes and altered host defense mechanisms. The public health impact of these biological responses may be partly represented by our quantified endpoints.

c Cohort estimates are designed to examine the effects of long-term exposures to ambient pollution, but relative risk estimates may also incorporate some effects due to shorter term exposures (see Kunzli (2001) for a discussion of this issue). d While some of the effects of short-term exposure are likely to be captured by the cohort estimates, there may be additional premature mortality from shor t-term PM exposure not captured in the cohort estimates included in the primary analysis.

e May result in benefits or dis-benefits. f In addition to primary economic endpoints, there are a nu mber of biological responses that have been associated with ozone health including increased airway responsiveness to stimuli, inflammation in the lung, acute inflammation and respiratory cell damage, and increased susc eptibility to respiratory in fection. The public health impact of these biological responses may be partly represented by our quantified endpoints. g The categorization of un-quantified health effects is not exhaustive. h Health endpoints in the un-quantified benefits column in clude both a) those for which there is not consensus on causality and b) those for which causality has been dete rmined but empirical data are not available to allow calculation of benefits.

### Second Section 812 Pr ospective Analysis February 2011

2-9 EXHIBIT 2-3. OZONE RELATED HEALTH ENDPOINTS BASIS FOR THE HEALTH IMPACT FUNCTION ASSOCIATED WITH THAT ENDPOINT, AND SUB-POPULATIONS FOR WHICH THEY
WERE COMPUTED

ENDPOINT POLLUTANT STUDY STUDY
POPULATION
Premature Mortality Premature mortality—all
cause O3 (8-hour max) Equal weight pooling of: Ito et al. (2005) Schwartz (2005) Bell et al. (2004) Bell et al. (2005) Levy et al. (2005) Huang et al. (2005) All ages

Hospital Admissions Respiratory O3 (8-hour max) Pooled estimate: Schwartz (1995)—ICD 460–519 (all respiratory) Schwartz (1994a; 1994b)—ICD 480–486 (pneumonia) Moolgavkar et al. (1997)— ICD 480–487, 490–496 (pneumonia, COPD) Schwartz (1994b)—ICD 491–492, 494–496 (COPD) >64 years

Respiratory O3 (8-hour max) Burnett et al. (2001) <2 years Asthma-related ER visits O3 (8-hour max) Pooled estimate: Jaffe et al (2003) Peel et al (2005) Wilson et al (2005) 5–34 years All ages All ages Other Health Endpoints Minor restricted-activity days O3 (24-hour avg) Ostro and Rothschild (1989) 18–64 years

School loss days O3 (8-hour avg) O3 (1-hour max) Pooled estimate: Gilliland et al. (2001) Chen et al. (2000) 5–17 years a

Outdoor worker productivity O3 (8-hour max) Crocker and Horst (1981) 18–64 years

a Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on recent advice from the National Research Council and the EPA Council’s Health Effects Subcommittee (HES), we have calculated re ductions in school absences for all schoolaged children based on the biological si milarity between children aged 5 to 17.

### Second Section 812 Pr ospective Analysis February 2011

2-10 EXHIBIT 2-4. PM RELATED HEALTH ENDPOINTS BASIS FOR THE HEALTH IMPACT FUNCTION ASSOCIATED WITH THAT ENDPOINT, AND SUB-POPULATIONS FOR WHICH THEY
WERE COMPUTED

ENDPOINT POLLUTANT STUDY STUDY POPULATION
Premature MortalityPremature mortality—allcause a PM 2.5 (annual avg) Weibull distribution of C-R coefficients a >24 years

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

Chronic Illness Chronic bronchitis PM 2.5 (annual avg) Abbey et al. (1995) >26 years

Nonfatal myocardial infarction PM 2.5 (24-hour avg) Peters et al. (2001) Adults (>18 years)

Hospital Admissions Respiratory PM 2.5 (24-hour avg) Pooled estimate: Moolgavkar (2003)—ICD 490–496 (COPD) Ito (2003)—ICD 490–496, 480–487 (COPD, pneumonia) >64 years

Respiratory PM 2.5 (24-hour avg) Moolgavkar (2000a)—ICD 490–492, 494-496 (COPD, less asthma) 20–64 years

Respiratory PM 2.5 (24-hour avg) Sheppard (2003)—ICD 493 (asthma) <65 years

Cardiovascular PM 2.5 (24-hour avg) Pooled estimate: Moolgavkar (2003)—ICD 390–429 (all cardiovascular) Ito (2003)—ICD 411–414, 429, 428 (ischemic heart disease, dysrhythmia, heart failure) >64 years

Cardiovascular PM 2.5 (24-hour avg) Moolgavkar (2000b)—ICD 390–429 (all cardiovascular) 20–64 years

Asthma-related ER visits PM 2.5 (24-hour avg) Norris et al. (1999) <18 years

Other Health Endpoints Acute bronchitis PM 2.5 (annual avg) Dockery et al. (1996) 8–12 years

Lower respiratory symptoms PM 2.5 (24-hour avg) Schwartz and Neas (2000) 7–14 years

Upper respiratory symptoms PM 2.5 (24-hour avg) Pope et al. (1991) 9–11 years

### Second Section 812 Pr ospective Analysis February 2011

2-11 ENDPOINT POLLUTANT STUDY STUDY POPULATION

Asthma exacerbation PM 2.5 (24-hour avg) Pooled estimate: Ostro et al. (2001) (cough, wheeze, shortness of breath) Vedal et al. (1998) (cough) 6–18 years b

Minor restricted-activity days PM 2.5 (24-hour avg) Ostro and Rothschild (1989) 18–64 years

Work loss days PM 2.5 (24-hour avg) Ostro (1987) 18–64 years

a This distribution of coeffi cients for the PM mortality functi on is based on recommendations made by the HES; it features a Weibull dist ribution with a mean value of 1.06 that is approximately the average of coefficients derived from Pope et al. (2002) and Laden et al. (2006). The Pope and Laden coefficients fall roughly at the 25th and 75th percentiles of the Weibull distribution. b The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998) study. Based on advice from the HES, we extended the applied population to 6 to 18, reflecting the common biological basi s for the effect in children in the broader age group. See: U.S. Science Advisory Board. 2004. Ad visory Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Anal ysis – Benefits and Costs of the Clean Air Act, 1990—2020. EPA-SAB-COUNCIL-ADV-04-004. See also National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press.

LITERATURE SOURCES FOR OZONE HEALTH EFFECTS FUNCTIONS

PREMATURE MORTALITY 

### While PM is the criteria pollutant most clearly associated with premature mortality, recent research suggests that short-term re peated ozone exposure also likely contributes to premature death. The 2006 Ozone Criteria Document states, “Consistent with observed ozone-related increases in respirat ory- and cardiovascular-related morbidity, several newer multi-city studies, single-city studies, and several meta-analyses of these studies have provided relatively strong epidem iologic evidence for associations between short-term ozone exposure and all-cause morta lity, even after adjustment for the influence of season and PM” (EPA, 2006a: 8-78). The epidemiologic data are also supported by recent experimental data from both animal and human studies, which provide evidence suggestive of plausible pathways by which ri sk of respiratory or cardiovascular morbidity and mortality could be increased by ambient oz one. With respect to short-term exposure, the Ozone Criteria Document concludes, “This overall body of evidence is highly suggestive that ozone directly or indi rectly contributes to non-accidental and cardiopulmonary-related mortality, but additio nal research is needed to more fully establish underlying mechanisms by which such effects occur” (p. 8-78). With respect to the time-series studies, the c onclusion regarding the relationship between short-term exposure and premature mortality is based, in part, upon recent city-specific time-series studies such as the Schwartz (2005) analysis in Houston and the Huang et al.

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### (2005) analysis in Los Angeles. 10 

### This conclusion is also based on recent meta-analyses by Bell et al. (2005), Ito et al. (2005), and Levy et al. (2005) and on analyses of the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) data set by Bell et al. (2004), Schwartz (2005), and Huang et al. (200 5). Consistent with the methodology used in the Ozone NAAQS RIA (2008), and with more recent advice in NAS (2008), we included ozone mortality in the primary health effects analysis, with the recognition that the exact magnitude of the effects estimate is subject to continuing uncertainty. In this chapter we present estimates derived from an equal-weight pooling of the six studies listed above. The Uncertainty Analysis to Support the Second Section 812 Benefit-Cost analysis of the Clean Air Act (Uncertain ty Analysis) includes estimates from all six studies separately. Use of these six studies represents a slight change from the Ozone NAAQS RIA (2008); two NMMAPS-based studi es (Schwartz (2005) and Huang et al. (2005)) have been added based on guidan ce from the National Academy of Sciences (NAS) (2008). Ozone Exposure Metric. Both the NMMAPS analyses and the individual time series studies upon which the meta-analyses were based use the 24-hour average or 1-hour maximum ozone levels as exposure metrics. The 24-hour average is not the most relevant ozone exposure metric to characteri ze population-level exposure. Given that the majority of the people tend to be outdoors duri ng the daylight hours and concentrations are highest during the daylight hours, the 24-hour average metric is not appropriate. Moreover, the 1-hour maximum metric uses an exposure window different than that used for the current ozone NAAQS. Together, this means that the most biologically relevant metric, and the one used in the ozone NAAQS since 1997, is the 8-hour maximum standard. Thus, for this analysis, we have converted ozone mortality health impact functions that use a 24-hour average or 1- hour maximum ozone metric to maximum 8- hour average ozone concentration using a pr ocedure described in the BenMAP user’s manual (see Abt Associates, 2008). A sim ilar method was used for the final Ozone NAAQS RIA (2008).

RESPIRATORY HOSPITAL ADMISSIONS 

### Detailed hospital admission and discharge reco rds provide data for an extensive body of literature examining the relationship between hospital admissions and air pollution. This is especially true for the portion of the population aged 65 and older, because of the availability of detailed Medicare records. In addition, there is one study (Burnett et al., 2001) providing an effect estimate for respiratory hospital admissions in children less than two years of age.

10 For an exhaustive review of the city-specific time-series studies considered in the ozone staff paper, see: U.S.

Environmental Protection Agency, 2007. Review of the National Ambient Air Quality Standards for Ozone: Policy Assessment of Scientific and Technical Inform ation. Prepared by the Office of Air and Radiation. Available at http://www.epa.gov/ttn/naaqs/standards/ozone/da ta/2007_01_ozone_staff_paper.pdf. pp. 5-36.

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### Because the number of hospital admission studies we considered is so large, we used results from a number of studies to pool some hospital admission endpoints. Pooling is the process by which multiple study results may be combined in order to produce better estimates of the effect estimate, or β

. 11 

### To estimate total respiratory hospital admissions associated with changes in ambient ozone c oncentrations for adults over 65, we first estimated the change in hospital admissions for each of the different effects categories that each study provided for each city. These cities included Minneapolis, Detroit, Tacoma and New Haven. To estimate total respiratory hospital admissions for Detroit, we added the pneumonia and chronic obstructive pulmonary disease (COPD) estimates, based on the effect estimates in the Schwartz study (1994b). Similarly, we summed the estimated hospital admissions based on the effect estimates the Moolgavkar study reported for Minneapolis (Moolgavkar et al., 199 7). To estimate total respiratory hospital admissions for Minneapolis using the Schwar tz study (1994a), we simply estimated pneumonia hospital admissions based on the effect estimate. Making this assumption that pneumonia admissions represent the total impact of ozone on hospital admissions in this city will give some weight to the possibilit y that there is no relationship between ozone and COPD, reflecting the equivocal evidence represented by the different studies. We then used a fixed-effects pooling procedure to combine the two total respiratory hospital admission estimates for Minneapolis. Fina lly, we used random effects pooling to combine the results for Minneapolis and Detr oit with results from studies in Tacoma and New Haven from Schwartz (1995). As noted above, this pooling approach incorporates both the precision of the individual effect estimates and between-study variability characterizing differences across study locations.

ASTHMA-RELATED EMERGENCY ROOM VISITS 

### We used three studies as the source of the C-R functions we used to estimate the effects of ozone exposure on asthma-related emergenc y room (ER) visits: Peel et al. (2005); Wilson et al. (2005); and Jaffe et al. (2003). We estimated the change in ER visits using the effect estimate(s) from each study and th en pooled the results using the random effects pooling technique (see Abt Associates, 2008). The study by Jaffe et al. (2003) examined the relationship between ER visits and air pollution for populations aged five to 34 in the Ohio cities of Cleveland, Columbus and Cincinnati from 1991 through 1996. In single-pollutant Poisson regression models, oz one was linked to asthma visits. We use the pooled estimate across all three cities as repor ted in the study. The Peel et al. study (2005) estimated asthma-related ER visits for all ages in Atlanta, using air quality data from 1993 to 2000. Using Poisson generalized estimating equations, the authors found a marginal association between the maximum da ily 8-hour average ozone level and ER visits for asthma over a 3-day moving average (lags of 0, 1, and 2 days) in a single pollutant model. Wilson et al. (2005) exam ined the relationship between ER visits for respiratory illnesses and asthma and air pollu tion for all people residing in Portland,

11 For a complete discussion of the pooling process see Abt Associates, 2008.

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### Maine from 1998–2000 and Manchester, New Hampshire from 1996–2000. For all models used in the analysis, the authors rest ricted the ozone data incorporated into the model to the months ozone levels are usually measured, the spring-summer months (April through September). Using the generalized additive model, Wilson et al. (2005) found a significant association between the maximum daily 8-hour average ozone level and ER visits for asthma in Portland, but found no significant association for Manchester. Similar to the approach used to generate e ffect estimates for hospital admissions, we used random effects pooling to comb ine the results across the i ndividual study estimates for ER visits for asthma. The Peel et al. (2005) and Wilson et al. (2005) Manchester estimates were not significant at the 95 percent level, and thus, the confidence interval for the pooled incidence estimate based on these studi es includes negative values. This is an artifact of the statistical power of the studies, and the negative values in the tails of the estimated effect distributions do not represent improvements in health as ozone concentrations are increased. Instead, these should be viewed as a measure of uncertainty due to limitations in the statistical power of the study. We included both hospital admissions and ER visits as separate endpoi nts associated with ozone exposure because our estimates of hospital admission costs do not include the costs of ER visits and most asthma ER visits do not result in a hospital admission.

MINOR RESTRICTED-ACTIVITY DAYS 

### Minor restricted-activity days (MRADs) occu r when individuals reduce most usual daily activities and replace them with less-strenuous activities or rest, but do not miss work or school. We estimated the effect of ozone exposure on MRADs using a concentrationresponse function derived from Ostro a nd Rothschild (1989). These researchers estimated the impact of ozone and PM 2.5 

### on MRAD incidence in a national sample of the adult working population (ages 18 to 64) living in metropolitan areas. We developed separate coefficients for each year of th e Ostro and Rothschild analysis (1976–1981), which we then combined for use in EPA’s analysis. The effect estimate used in the impact function is a weighted average of the coefficients in Ostro and Rothschild (1989, Table 4), using the inverse of the variance as the weight.

SCHOOL LOSS DAYS 

### Children may be absent from school due to r espiratory or other acute diseases caused, or aggravated by, exposure to air pollution. Several studies have found a significant association between ozone levels and school absence rates. We use two studies (Gilliland et al., 2001; Chen et al., 2000) to estimate changes in school absences resulting from changes in ozone levels. The Gilliland et al. study estimated the incidence of new periods of absence, while the Chen et al. study exam ined daily absence rates. We converted the Gilliland et al. estimate to days of absence by multiplying the absence periods by the average duration of an absence. We estimated 1.6 days as the average duration of a school absence, the result of dividing the aver age daily school absence rate from Chen et al. (2000) and Ransom and Pope (1992) by the episodic absence duration from Gilliland

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### et al. (2001). Thus, each Gilliland et al. period of absence is converted into 1.6 absence days. Following advice from the National Research C ouncil (2002), we calculated reductions in school absences for the full population of school age children, ages five to 17. We estimated the change in school absences using bo th Chen et al. (2000) and Gilliland et al. (2001) and then, similar to hospital admissions and ER visits, pooled the results using the random effects pooling procedure.

OUTDOOR WORKER PRODUCTIVITY 

### To monetize benefits associated with incr eased outdoor worker pr oductivity resulting from improved ozone air quality, we used information reported in Crocker and Horst (1981). Crocker and Horst examined the im pacts of ozone exposure on the productivity of outdoor citrus workers. The study measured productivity impacts. Worker productivity is measuring the value of the loss in productivity for a worker who is at work on a particular day, but due to ozone, cannot work as hard. It only applies to outdoor workers, like fruit and vegetable pickers, or construction workers. Here, productivity impacts are measured as the change in inco me associated with a change in ozone exposure, given as the elasticity of income w ith respect to ozone concentration. The reported elasticity translates a ten percent re duction in ozone to a 1.4 percent increase in income. Given the national median daily income for outdoor workers engaged in strenuous activity reported by the U.S. Census Bureau (2002), $68 per day (2000$), a ten percent reduction in ozone yields about $0.97 in increased daily wages. We adjust the national median daily income estimate to refl ect regional variations in income using a factor based on the ratio of county median household income to national median household income. No information was av ailable for quantifying the uncertainty associated with the central valuation estimate. Therefore, no uncertainty analysis was conducted for this endpoint.

LITERATURE SOURCES FOR PM HEALTH EFFECTS FUNCTIONS

ADULT PREMATURE MORTALITY 

### The estimated relationship between particulat e matter exposure and premature mortality is one of the most important parameters in the overall quantified and monetized benefit estimate for this study. An extensive base of literature exists to support development of the C-R function linking fine particulate ma tter exposure with premature mortality. Our knowledge of both the potential biological mechanisms linking PM 2.5 

### exposure with mortality and the potential magnitude of this effect has grown since the First Prospective was completed as the result of continued research and follow-up of existing study populations. Both short-term and long-term epidemiological studies have been conducted to examine the PM/mortality relationship. Sh ort-term exposure studies attempt to relate short-term (often day-to-day) changes in PM concentrations and changes in daily mortality rates up to several days after a pe riod of elevated PM concentrations. Long-

### Second Section 812 Pr ospective Analysis February 2011

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### term exposure studies examine the potentia l relationship between longer-term (e.g., annual) changes in exposure and annual morta lity rates. Although positive, significant results have been reported using both of th ese study types, we rely exclusively on longterm studies to quantify PM mortality effects. This is because cohort studies are able to discern changes in mortality rates due to long-term exposure to elevated air pollution concentrations, which more closely matches th e benefits of air pollution control programs under the CAAA, which are themselves focused on reducing long-term exposure. These effect estimates may also include some of th e mortality changes due to short-term peak
exposures. 12 

### Therefore, the use of C-R functions from long-term studies is likely to yield a more complete assessment of the effect of PM on mortality risk. Among long-term PM studies, we prefer those using a prospective cohort design to those using an ecologic or population-level d esign. Prospective cohort studies follow individuals forward in time for a specifi ed period, periodically evaluating each individual’s exposure and health status. Population-level ecological studies assess the relationship between population-wide health information (such as counts of daily mortality) and ambient levels of air polluti on. Prospective cohort studies are preferred because they are better at controlling a s ource of uncertainty known as “confounding.” Confounding is the mis-estimation of an association that results if a study does not control for factors that are correlated with both the outcome of interest (e.g., mortality) and the exposure of interest (e.g., PM expos ure). For example, smoking is associated with mortality. If populations in high PM areas tend to smoke more than populations in low PM areas, and a PM exposure study does not include smoking as a factor in its model, then the mortality effects of smok ing may be erroneously attributed to PM, leading to an overestimate of the risk from PM. Prospective cohort studies are better at controlling for confounding than ecologic st udies because the former follow a group of individuals forward in time and can gather individual-specific information on important risk factors such as smoking. Two major prospective cohort studies have b een conducted in the U.S.: the American Cancer Society (ACS) study and the Harvard Six Cities study. These two cohorts are large, produce consistent results, provide broad geographic covera ge and have been independently reexamined and reanalyzed. Strengths of the ACS study over the Harvard Six Cities study include greater geographic cove rage (50 U.S. cities) and larger sample size. However, a key limitation of this study is a recruitment method that led to a study population with higher income, more education, and greater proportion of whites than the general U.S. population. In addition, availa ble monitoring data was often assigned to all of the individuals within a large metropolitan area, potentially allowing for exposure misclassification. 13 

### Both of these limitations could imply that the ACS results are

12 See Kunzli et al. (2001) for a discussion of this issue.

13 Studies have shown that greater spatial resolution of exposu res can result in increased effect estimates (Jerrett et al.,

2005).

### Second Section 812 Pr ospective Analysis February 2011

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### potentially biased low. The Harvard Six C ities study included a more representative sample of subjects within each community and set up monitors purposefully for the study. It was therefore able to assign exposures at a finer geographic scale. However, this study only included six cities and therefore may not be representative of the entire U.S. population, mix of air pollutants, and other potentially important factors. The extensive epidemiological literature is complemented by EPA’s 2006 expert elicitation (EE) study that asked 12 leading expe rts in PM health effects to integrate this pool of knowledge with the various sources of uncertainty that hinder our ability to precisely identify the true mortality impact of a unit change in annual PM 2.5 

### concentration (IEc, 2006). The results of the EE study showed three important findings: first, that advances in the scientific literature led man y of the interviewed scientists to espouse greater confidence in the linkage between PM 2.5 

### exposure and mortality; second, that many of the experts believed that the central estimate of the mortality effect was considerably higher than the Pope et al., 2002 result used in the first prospective; and third, that most of the experts’ uncertainty di stributions of the mortality effect reflected a much wider range of possible values, both hi gh and low, than were used in the first prospective study. The EE study does not, however, provide an integrated distribution across all 12 experts of possible values for the PM-mortality C-R function.

Based on consultations with the Council’s Health Effects Subcommittee (HES), the 812 Project Team developed a distribution of C- R function coefficients (i.e., the percent change in annual all-cause mortality per one μ g/m 3 

### change in annual average PM 2.5 

### ) for use in the PM-mortality C-R function for the second prospective study. This distributionis rooted in the epidemiological studies that most inform our understanding of the PMmortality C-R function, but reflects the broade r findings of the EE study. We based the primary C-R coefficient estimate of the second prospective study on a Weibull distribution with a mean of 1.06 percent decrease in annual all-cause mortality per one ug/m3. This mean is roughly equidistant between the results of the two most well-studied PM cohorts, the ACS cohort (0.58, as derived from Pope et al., 2002) and the Six Cities cohort (1.5, as derived from Laden et al., 2006), both of whose results have been robust to continued follow-up and extensive re-analysis. Half of the coefficient values in this distribution fall between these two studies, one-quarter are higher than the Laden mean estimate, and one-quarter are lower than the Pope mean estimate; however all coefficient values are greater than zero. This distributi on is consistent with the EE results described above, showing considerable support for higher values based on results from more recent studies (e.g. the Laden et al. 2006 Six Citi es follow-up) and concerns cited by the HES that the ACS cohort results may underestimate the true effect. The use of all positive values is consistent with both the incr eased confidence in a causal link between PM 2.5 

### exposure and mortality shown in the EE study and the lack of evidence in general to support a threshold for mortality effects of PM 2.5 

### in the U.S. population (EPA SAB, 2010).

### Second Section 812 Pr ospective Analysis February 2011

2-18 INFANT MORTALITY 

### Recently published studies have strengthene d the case for an association between PM exposure and respiratory inflammation and inf ection leading to premature mortality in children under 5 years of age. With regard to the cohort study conducted by Woodruff et al. (1997), the HES noted several strengths of the study, including the use of a larger cohort drawn from a large number of metropolitan areas and efforts to control for a variety of individual risk factors in infant s (e.g., maternal educational level, maternal ethnicity, parental marital status, and maternal smoking status). Based on these findings, the HES recommended that EPA incorporate infa nt mortality into the primary benefits estimate and that infant mortality be evalua ted using an impact function developed from the Woodruff et al. (1997) study (U.S. EPA-SAB, 2004b). 14

CHRONIC BRONCHITIS 

### Chronic Bronchitis (CB) is characterized by mucus in the lungs and a persistent wet cough for at least 3 months a year for several years in a row. CB affects an estimated 9.1 million Americans annually (American Lung A ssociation, 2009). A limited number of studies have estimated the impact of air pollu tion on new incidences of CB. Abbey et al. (1995) provide evidence that long-term PM 2.5 

### exposure gives rise to the development of CB in the United States.

NONFATAL MYOCARDIAL INFARCTIONS (HEART ATTACKS) 

### Nonfatal heart attacks have been li nked with short-term exposures to PM 2.5 

### in the United States (Peters et al., 2001) and other countr ies (Poloniecki et al., 1997). Other studies, such as Domenici et al. (2006), Samet et al. (2000), and Moolgavkar (2000b), show a consistent relationship between all cardiovascu lar hospital admissions, including those for nonfatal heart attacks, and PM. Given the lasting impact of a heart attack on long-term health costs and earnings, we provide a separat e estimate for nonfatal heart attacks. The estimate used in this analysis is based on the single available U.S. PM 2.5 

### effect estimate from Peters et al. (2001).

RESPIRATORY AND CARDIOVASCULAR HOSPITAL ADMISSIONS 

### Because of the availability of detailed hospita l admission and discharge records, there is an extensive body of literature examining the relationship between hospital admissions and air pollution. Because of this, many of the hospital admission endpoints use pooled impact functions based on the results of a number of studies. The two main groups of hospital admissions estimated in this analysis are respiratory admissions and

14 A more recent study by Woodruff et al. (200 6) continues to find associations between PM 2.5 and infant mortality. The study

also found the most significant relationships with respiratory- related causes of death. We consulted the Council HES about whether they would recommend continued reliance on Woodruff et al. (1997) or recommend that the relationship be characterized by the more recent Woodruff et al. (2006) study. The Council continued to supp ort EPA’s decision to include infant mortality in its analysis and recommended that EPA do a “reasoned evaluation of relevant studies and synthesize evidence across the studies” (EPA SAB, 2010). For this re port we have continued to re ly on Woodruff et al. (1997).

### Second Section 812 Pr ospective Analysis February 2011

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### cardiovascular admissions. There is not much evidence linking PM with other types of hospital admissions.

To estimate avoided incidences of PM 2.5 

### related cardiovascular hospital admissions in populations aged 65 and older, we use eff ect estimates from studies by Moolgavkar (2003) and Ito (2003). Moolgavkar (2000a) provides the only separate effect estimate for populations 20 to 64. 15 

### Total cardiovascular hospital admissions are thus the sum of the pooled estimates from Moolgavkar (2003) and It o (2003) for populations over 65 and the Moolgavkar (2000a) based impacts for popula tions aged 20 to 64. Cardiovascular hospital admissions include admissions for my ocardial infarctions. To avoid doublecounting benefits from reductions in myocardi al infarctions when applying the impact function for cardiovascular hospital admissi ons, we first adjusted the baseline cardiovascular hospital admissions to remove admissions for myocardial infarctions. To estimate total avoided incidences of resp iratory hospital admissions, we used impact functions for several respiratory causes, including COPD, pneumonia, and asthma. Both Moolgavkar (2003) and Ito (2003) provide effect estimates for COPD in populations over 65, allowing us to pool the impact functi ons for this group. Only Moolgavkar (2000a) provides a separate effect estimate for popula tions 20 to 64. Total COPD hospital admissions are thus the sum of the pooled est imate for populations over 65 and the single study estimate for populations 20 to 64. In addition, Ito (2003) provides an effect estimate for pneumonia hospital admissions in populations 65 and older and Sheppard (2003) provides an effect estimate for as thma hospital admissions in populations under age 65. The total avoided incidence of respiratory-related hospital admissions is the sum of COPD, pneumonia, and asthma admissions.

ASTHMA-RELATED EMERGENCY ROOM VISITS 

### Some studies have examined the relationshi p between air pollution and emergency room visits. Since most emergency room visits do not result in an admission to the hospital (the majority of people going to the emergen cy room are treated and return home), we treat hospital admissions and emergency room visits separately, taking account of the fraction of emergency room visits that are ad mitted to the hospital. The only type of emergency room visits that ha ve been consistently linked to PM in the United States areasthma-related visits. To estimate the effects of PM air pollution reductions on asthmarelated ER visits, we use the effect esti mate from a study of children 18 and under by Norris et al. (1999). We selected the Norri s et al. (1999) effect estimate because it focuses on PM 2.5 

### , as opposed to PM 10 

### .

15 Note that the Moolgavkar (2000) study has not been update d 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. Updates have been provided for the 65 and older population, and showed l ittle difference. Given the very small (<5%) difference in the effect estimates for people 65 and older with cardiovascul ar hospital admissions between the original and reanalyzed results, we do not expect the difference in the effect estimates for the 20 to 64 population to differ significantly. As such, the choice to use the earlier, uncorrected an alysis will likely not introduce much bias.

### Second Section 812 Pr ospective Analysis February 2011

2-20 ACUTE HEALTH EFFECTS 

### As indicated in Exhibit 2- 4, in addition to mortality , chronic illness, and hospital admissions, a number of acute health effect s not requiring hospitalization are associated with exposure to ambient levels of PM. Th e sources for the effect estimates used to quantify these effects are described below. Around 4 percent of U.S. children between the ages of five and 17 experience episodes of acute bronchitis annually (American Lung Asso ciation, 2002c). Acute bronchitis is characterized by coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a number of days. According to the Me dlinePlus medical encyclopedia, symptoms usually go away without treatment. 16 

### Incidence of episodes of acute bronchitis in children between the ages of eight and twelve were estimated using an effect estimate developed from Dockery et al. (1996). Incidences of lower respiratory symptoms (e.g ., wheezing, deep cough) in children aged seven to fourteen were estimated using an effect estimate from Schwartz and Neas (2000). 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. Incidences of upper respiratory sy mptoms in asthmatic children aged nine to eleven are estimated using an effect es timate developed from Pope et al. (1991). 17


### Following recommendations of the HES, to prevent double-counting, we focused on asthma exacerbation occurring in children and excluded adults from the calculation. 18

16 See http://www.nlm.nih.gov/medlineplus/ bronchitis.html, accessed October 2009.

17 Pope et al. (1991) estimates the impact of PM 10 exposure on the incidence of upper respiratory symptoms. The EPA began

applying the C-R function derived from Pope et al. (1991) for PM 10 to PM 2.5 air quality estimates in 2005 (EPA, 2005). The implicit assumptions of this action are that a) PM 2.5 is as toxic as the average of all PM 10 and b) if a single rule or policy action reduced only precursor pollutants, the change in PM 10 would equal the change in PM 2.5 .

18 Estimating asthma exacerbations associated with air pollu tion exposures is difficult, due to concerns about doublecounting of benefits. Concerns over doub le-counting stem from the fact that stud ies of the general population also include asthmatics, so estimates based solely on the asthmatic popu lation cannot be directly a dded to the general population numbers without double-counting. In one specific case (upper respiratory symptoms in children), the only study available is limited to asthmatic children, so this endpoint can be readily in cluded in the calculation of total benefits. However, other endpoints, such as lower respiratory symptoms and MRADs, are estimated for the total populatio n that includes asthmatics. Therefore, to simply add predictions of asthma-related sympto ms generated for the population of asthmatics to these total population-based estimates could result in double-counting, especially if th ey evaluate similar endpoints. The HES, in commenting on the analytical bl ueprint for the current 812 study, acknowledg ed these challenges in evaluating asthmatic symptoms and appropriately adding them into the primary anal ysis (EPA-SAB, 2004b). Howeve r, despite these challenges, the HES recommended the addition of asthma-related sympto ms (i.e., asthma exacerbations) to the primary analysis, provided that the studies use the panel study approach and that they have comparable design and baseline frequencies in both asthma prevalence and exacerbation rates. Note also, that the HES, while supporting the incorporation of asthma exacerbation estimates, did not believe that the association between ambient air pollution, including ozone and PM, and the new onset of asthma is sufficiently strong to support incl usion of this asthma-related endpoint in the primary estimate.

### Second Section 812 Pr ospective Analysis February 2011

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### Asthma exacerbation occurring in adults is assumed to be captured in the general population endpoints such as work loss days and MRADs. Consequently, including an adult-specific asthma exacerbation estimate w ould likely double-count incidence for this endpoint. However, because the general popul ation endpoints do not cover children (with regard to asthmatic effects), an analysis focused specifically on asthma exacerbation for children (six to eighteen years of age) c ould be conducted without concern for doublecounting.

To characterize asthma exacerbations in childre n, we selected two studies (Ostro et al., 2001; Vedal et al., 1998) that followed panels of asthmatic children. Ostro et al. (2001) followed a group of 138 African-American children in Los Angeles for 13 weeks, recording daily occurrences of respirat ory symptoms associated with asthma exacerbations (e.g., shortness of breath, wheeze, and cough). This study found a statistically significant association between PM 2.5 

### , measured as a 12-hour average, and the daily prevalence of 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 deci ded to include this endpoint, along with shortness of breath and wheeze, in generating incidence estimates (see below). Vedal et al. (1998) followed a group of elementary school children, including 74 asthmatics, located on the west coast of Vancouver Isla nd for 18 months including measurements of daily peak expiratory flow (PEF) and the tracking of respiratory symptoms (e.g., cough, phlegm, wheeze, chest tightness) through the u se of daily diaries. Because it is difficult to translate PEF measures into clearly defined health endpoints that can be monetized, we only included the cough-related effect estimate from this study in quantifying asthma exacerbations. We employed the following p ooling approach in combining estimates generated using effect estimates from the two studies to produce a single asthma exacerbation incidence estimate. First, we pooled the separate incidence estimates for shortness of breath, wheeze, and cough genera ted using effect estimates from the Ostro et al. study, because each of these endpoints is aimed at capturing the same overall endpoint (asthma exacerbations) and there could be ove rlap in their predictions. The pooled estimate from the Ostro et al. study is th en pooled with the cough-related estimate generated using the Vedal et al. study. Th e rationale for this second pooling step is similar to the first; both studies are attemp ting to quantify the same overall endpoint (asthma exacerbations).

Minor Restricted-Activity Days 

### Exposure to air pollution can result in restric tions in activity levels. These restrictions range from relatively minor changes in daily activities to serious limitations that can result in missed days of work (either from personal symptoms or from caring for a sick family member). We include two types of restricted activity days, MRADs and work loss days (WLDs). MRADs result when individua ls reduce most usual daily activities and replace them with less strenuous activities or rest, yet not to the point of missing work or

### Second Section 812 Pr ospective Analysis February 2011

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### school. The effect of PM 2.5 

### on MRADs was estimated using an effect estimate derived from Ostro and Rothschild (1989).

Work Loss Days 

### WLDs due to PM 2.5 

### were estimated using an effect es timate developed from Ostro (1987). Ostro (1987) estimated the impacts of PM 2.5 

### on the incidence of WLDs, restricted activity days, and respiratory-related restricted activ ity days in a national sample of the adult working population, ages 18 to 64.

BASELINE INCIDENCE RATES 

### Epidemiological studies of the association be tween 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 ppb decrease in daily ozone levels might, in turn, decrease hospital admissions by 3 percent. The baseline incidence of the health effect is necessary to convert this relative change into a number of cases. A baseline incidence rate is the estimate of the number of cases of the health effect per year in the assessment location, as it co rresponds 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.

Exhibit 2-5 summarizes the sources of baselin e 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 C-R functions to individual age groups and th en 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, b ecause these data are most applicable to a national assessment of benefits. For some st udies, 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. Regional incidence rates are available for hospital admi ssions, and county-level data are available for premature mortality. We have projected mortality rates such that future mortality rates are consistent with our projections of population growth (Abt Associates, 2005).Second Section 812 Prospective AnalysisFebruary 20112-23EXHIBIT 2-5. BASELINE INCIDENCE/PREVALENCE RATESRATE PER 100 PEOPLE PER YEARD BY AGE GROUPENDPOINTNOTES/SOURCE<1818–2425–2930-3435-4445-5455-6465-7475-8485+MortalityAll-causeNon-accidentalCardiopulmonaryCDC CompressedMortality File,accessed throughCDC Wonder (1996–1998)0.0450.0250.0040.0930.0220.0050.1190.0570.0130.1190.0570.0130.2110.1500.0440.4370.3830.1431.0561.0060.4202.5182.4531.1635.7655.6373.17915.16014.8599.846Respiratory HospitalAdmissionsAll respiratoryPneumoniaAsthmaCOPD1999 NHDSapublicuse data filesb1.0660.3080.2810.2910.2710.0690.0810.0890.3180.1030.1100.1240.4460.1550.0990.1480.7630.2560.1440.3011.6320.5610.1610.7115.2002.3550.2051.573CardiovascularHospital AdmissionsAll cardiovascularIschemic heartdiseaseDysrhythmiaHeart failure1999 NHDS publicuse data filesb0.0300.0040.0110.0030.0520.0080.0170.0050.1460.0310.0270.0110.5340.2310.0760.0111.5510.9020.1580.1603.3852.0210.3920.4698.5413.7081.3872.167Asthma ER Visits 2000 NHAMCSpublic use datafilesc; 1999 NHDSpublic use datafilesb1.011 1.087 0.751 0.438 0.352 0.425 0.232Chronic Bronchitis Prevalence1999 NHIS(American LungAssociation, 2002b,Table4)0.0367 0.0505 0.0587Second Section 812 Prospective AnalysisFebruary 20112-24RATE PER 100 PEOPLE PER YEARD BY AGE GROUPENDPOINTNOTES/SOURCE<1818–2425–2930-3435-4445-5455-6465-7475-8485+IncidenceAbbey et al. (1993,Table 3), for ages27+-- -- 0.378Nonfatal MyocardialInfarction (heartattacks)NortheastMidwestSouthWestIncidence1999 NHDS publicuse data filesb,adjusted by 0.93for probability ofsurviving after 29days (Rosamond etal., 1999)0.00000.00030.00060.00000.21670.17720.16200.13911.63591.48981.17971.1971Minor RestrictedActivity DaysOstro andRothschild (1989,p. 243)-- 780 --1996 NIS (Adams etal., 1999, Table41), U.S. Bureau ofCensus (1997)-- 197.1 247.5 179.6 --School Loss Days—allcauseNational Center forEducation Statistics(1996)990.0 -- -- -- -- -- -- -- -- --Second Section 812 Prospective AnalysisFebruary 20112-25RATE PER 100 PEOPLE PER YEARD BY AGE GROUPENDPOINTNOTES/SOURCE<1818–2425–2930-3435-4445-5455-6465-7475-8485+Acute Bronchitis IncidenceAmerican LungAssociation (2002c,Table 11)4.3 -- -- -- -- -- -- -- -- --Lower RespiratorySymptomsIncidenceSchwartz et al.(1994, Table 2)43.8 -- -- -- -- -- -- -- -- --Upper RespiratorySymptomsIncidence amongasthmaticsPope et al. (1991,Table 2)12479 -- -- -- -- -- -- -- -- --Asthma ExacerbationShortness of breathWheezeCoughIncidence (andprevalence) amongasthmatic AfricanAmericansOstro et al. (2001)1350(0.074)2774(0.173)2445(0.145)-- -- -- -- -- -- -- -- --Asthma ExacerbationCoughIncidence amongasthmaticsVedal et al. (1998)3139-- -- -- -- -- -- -- -- --a The following abbreviations are used to describe the national surveys conducted by the National Centerfor Health Statistics: HIS refers to the NationalHealth Interview Survey; NHDS—National HospitalDischarge Survey; NHAMCS—National Hospital Ambulatory Medical Care Survey.b See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/c See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/d All of the rates reported here are population-weighted. Incidence rates are reported per 100 people per year; prevalence rates are reported as apercentage of the population.Additional details on the incidence and prevalence rates, aswell as the sources for these rates are available upon request.

### Second Section 812 Pr ospective Analysis February 2011

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### 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. Exhib it 2-5 lists the baseline incidence rates and their sources for asthma symptom endpoints. Exhibit 2-6 lists the prevalence rates u sed to determine the applicable population for asthma symptom endpoints. Note that these reflect current asthma prevalence and assume no change in prevalen ce rates in future years. It should be noted that current trends in asthma preval ence do not lead us to expect that asthma prevalence rates will be more than 4 percent overall in 2020, or that large changes will occur in asthma prevalence rates for indivi dual age categories (Mansfield et al., 2005).

EXHIBIT 2-6. ASTHMA PREVALENCE RATES USED TO ESTIMATE ASTHMATIC POPULATIONS IN
HEALTH IMPACT FUNCTIONS

POPULATION GROUP VALUE SOURCE
All Ages 0.0386 American Lung Associatio n (2002a, Table 7)—based on 1999 HIS <18 0.0527 American Lung Association (2002a, Table 7)—based on 1999 HIS 5–17 0.0567 American Lung Association (2002a, Table 7)—based on 1999 HIS 18–44 0.0371 American Lung Association (2002a, Table 7)—based on 1999 HIS 45–64 0.0333 American Lung Association (2002a, Table 7)—based on 1999 HIS 65+ 0.0221 American Lung Association (2002a, Table 7)—based on 1999 HIS Male, 27+ 0.021 2000 HIS public use data files a African American, 5–17 0.0726 American Lung A ssociation (2002a, Table 7)—based on 1999 HIS African American, <18 0.0735 American Lung Asso ciation (2002a, Table 7)—based on 1999 HIS a See ftp://ftp.cdc.gov/pub/Health_S tatistics/NCHS/Datasets/NHIS/2000/

ECONOMIC VALUE FOR HEALTH OUTCOMES 

### Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects for a large populati on. Therefore, the appropriate economic measure is willingness-to- pay (WTP) for changes in risk of a health effect rather than WTP for a health effect that would occur with certainty (Freeman, 1993). Epidemiological studies generally provide estimates of the relative risks of a particular health effect that is avoided because of a re duction in air pollution. We converted those to units of avoided statistical incidence fo r ease of presentation. We calculated the value of avoided statistical incide nces by dividing indi vidual WTP for a risk reduction by the related observed change in risk. For examp le, suppose a pollution-reduction regulation is able to reduce the risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then the WTP for an avoided statistical premature death is $1 million ($100/0.0001 change in risk). WTP estimates generally are not available fo r some health effects, such as hospital admissions. In these cases, we used the cost of treating or mitigating the effect as a primary estimate. These cost-of-illness (COI) estimates generally understate the true value of reducing the risk of a health effect , because they reflect the direct expenditures related to treatment, but not the value of avoided pain and su ffering (Harrington and

### Second Section 812 Pr ospective Analysis February 2011

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### Portney, 1987; Berger, 1987). We provide unit values for health endpoints (along with information on the distribution of the unit value) in Exhibit 2-6. All values are in constant year 2006 dollars, adjusted for grow th in real income out to each of the three target years (2000, 2010, and 2020) using th e income growth projections contained in BenMAP. 19 

### Economic theory argues that WTP for most goods, including environmental protection will increase if real income increases. Many of the valuation studies used in this analysis were conducted in the late 1980s and early 1990s. Because real income has grown since the studies were conducted, people’ s willingness to pay for reductions in the risk of premature death and disease likely has grown as well. We did not adjust cost of illness-based values because they are based on current costs, as parameterized in the BenMAP system. Similarly, we did not adjust the value of school absences, because that value is based on current wage rates. Exhibit 2-7 presents the values for individual endpoints adjusted to year 2020 income levels to illustrate the impact of the adjustment for income growth over time. The disc ussion below provides additional details on valuation of specific ozone and PM related endpoints.

MORTALITY VALUATION 

### To estimate the monetary benefit of reducing the risk of premature death, we used the “value of statistical lives” saved (VSL) a pproach, which is a summary measure for the value of small changes in mortality risk for a large number of people. The VSL approach applies information from several published valu e-of-life studies to determine a reasonable monetary value of preventing premature mortality. The mean value of avoiding one statistical death is estimated to be appr oximately $7.4 million at 1990 income levels (2006$), and $8.8 million (2006$) at 2020 inco me levels. This value is the mean of a distribution fitted to 26 VSL estimates that appear in the economics literature and that have been identified in the Section 812 Re ports to Congress as “applicable to policy analysis.” This represents an intermediate value from a variety of estimates, and it is a value EPA has frequently used in RIAs as well as in the Section 812 Retrospective and Prospective Analyses of the Clean Air Act. The VSL approach and the set of selected studi es mirrors that of Viscusi (1992) (with theaddition of two studies), and uses the same criteria as Viscusi in his review of value-oflife studies. The $7.4 million estimate is consistent with Viscusi’s conclusion (updated to 2006$) that “most of the reasonable estimates of the value of life are clustered in the $4.4 to $10.4 million range.” Five of the 26 studi es are contingent valuation (CV) studies, which directly solicit WTP information from s ubjects; the rest are wage-risk studies, which base WTP estimates on estimates of the additional compensation demanded in the labor market for riskier jobs. Because this VSL-based unit value does not distinguish among people based on the age at their death or the quality of their lives, it can be applied to all premature deaths.

19 Projections of income growth in BenMAP are based on data from Standard and Poor’s.Second Section 812 Prospective AnalysisFebruary 20112-28EXHIBIT 2-7. UNIT VALUES FOR ECONOMIC VALUATION OF HEALTH ENDPOINTS (2006$)HEALTH ENDPOINTCENTRAL ESTIMATE OF VALUEPER STATISTICAL INCIDENCEDERIVATION OF DISTRIBUTIONS OF ESTIMATES1990 INCOMELEVEL2020 INCOMELEVELPremature Mortality(Value of a StatisticalLife)$7,400,000 $8,880,000 Mean Value of Statistical Life (VSL) based on the mean of a distribution fitted to 26 “value of statisticallife” (VSL) estimates that appear in the economics literature and that have been identified in the Section812 Reports to Congress as “applicable to policy analysis.” The VSL approach and the set of selectedstudies mirrors that of Viscusi (1992) (with the addition of two studies), and uses the same criteria asViscusi in his review of value-of-life studies. The central estimate of $6.3 million (2000$) is consistentwith Viscusi’s conclusion (updated to 2000$) that “most of the reasonable estimates of the value of lifeare clustered in the $3.8 to $8.9 million range.” Five of the 26 studies are contingent valuation (CV)studies, which directly solicit WTP information from subjects; the rest are wage-risk studies, which baseWTP estimates on estimates of the additional compensation demanded in the labor market for riskierjobs. The fitted distribution is a Weibull withα=5.32x10-6 andβ=1.509588. See Abt Associates, 2008 formore details.Chronic Bronchitis(CB)$399,000 $490,000The WTP to avoid a case of pollution-related CB is calculated as , where x isthe severity of an average CB case, WTP13 is the WTP for a severe case of CB, andβis the parameterrelating WTP to severity, based on the regression results reported in Krupnick and Cropper (1992). Thedistribution of WTP for an average severity-levelcase of CB was generated by Monte Carlo methods,drawing from each of three distributions: (1) WTP to avoid a severe case of CB is assigned a 1/9probability of being each of the first nine deciles ofthe distribution of WTP responses in Viscusi et al.(1991); (2) the severity of a pollution-related case of CB (relative to the case described in the Viscusistudy) is assumed to have a triangular distribution, with the most likely value at severity level 6.5 andendpoints at 1.0 and 12.0; and (3) the constant in the elasticity of WTP with respect to severity isnormally distributed with mean = 0.18 and standard deviation = 0.0669 (from Krupnick and Cropper(1992)). This process and the rationale for choosing it isdescribed in detail in the Costs and Benefits ofthe Clean Air Act, 1990 to 2010 (EPA, 1999).)13(13xxeWTPWTP Second Section 812 Prospective AnalysisFebruary 20112-29HEALTH ENDPOINTCENTRAL ESTIMATE OF VALUEPER STATISTICAL INCIDENCEDERIVATION OF DISTRIBUTIONS OF ESTIMATES1990 INCOMELEVEL2020 INCOMELEVELNonfatal MyocardialInfarction (heartattack)7% discount rateAge 0–24Age 25–44Age 45–54Age 55–65Age 66 and over$84,171$93,802$98,366$166,222$84,171No distributional information available. Age-specificcost-of-illness values reflect lost earnings and directmedical costs over a 5-year periodfollowing a nonfatal MI.Lost earnings estimates are based on Cropperand Krupnick (1990). Direct medical costs are based on simple average of estimates from Russell et al.(1998) and Wittels et al. (1990).Lost earnings:Cropper and Krupnick (1990). Present discountedvalue of 5 years of lost earnings (2006$):age of onset: at 7%a25–44 $9,63145–54 $14,19555–65 $82,051Direct medical expenses: An average of (2006$):1. Wittels et al. (1990) ($141,124—no discounting)2. Russell et al. (1998), 5-year period ($28,787 at 3% discount rate; $21,113 $27,217 at 7% discount rate)Second Section 812 Prospective AnalysisFebruary 20112-30HEALTH ENDPOINTCENTRAL ESTIMATE OF VALUEPER STATISTICAL INCIDENCEDERIVATION OF DISTRIBUTIONS OF ESTIMATES1990 INCOMELEVEL2020 INCOMELEVELHospital AdmissionsAll respiratory (ages65+)$23,711 $23,711 No distributions available. The COI point estimates (lost earnings plus direct medical costs) are basedon ICD-9 code level information (e.g., average hospital carecosts and average length of hospital stay)reported in Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov). As noted in the text,no adjustments are made to cost of illness values for income growth.All respiratory (ages0–2)$10,002 $10,002Chronic ObstructivePulmonary Disease(COPD) (ages 65+)$17,308 $17,308Asthma Admissions(ages <65)$10,040 $10,040PneumoniaAdmissions (ages 65+)$23,004 $23,004COPD, less asthma(ages 20–64)$15,903 $15,903All Cardiovascular(ages 65+)$27,319 $27,319All Cardiovascular(ages 20–64)$29,364 $29,364Ischemic HeartDisease (ages 65+)$33,357 $33,357Dysrhythmia (ages65+)$19,643 $19,643Congestive HeartFailure (ages 65+)$19,619 $19,619Emergency RoomVisits for Asthma$369 $369 No distributional information available. Simple average of two unit COI values (2006$):(1) $401.62, from Smith et al. (1997) and(2) $336.03, from Stanford et al. (1999).As noted in the text, no adjustments are made to cost of illness values for income growth.Second Section 812 Prospective AnalysisFebruary 20112-31HEALTH ENDPOINTCENTRAL ESTIMATE OF VALUEPER STATISTICAL INCIDENCEDERIVATION OF DISTRIBUTIONS OF ESTIMATES1990 INCOMELEVEL2020 INCOMELEVELRespiratory Ailments NotRequiring HospitalizationUpper RespiratorySymptoms (URS)$28.8 $30.7 Combinations of the three symptoms for whichWTP estimates are available that closely match thoselisted by Pope et al. result in seven different “symptom clusters,” each describing a “type” of URS. Adollar value was derived for each type of URS, using mid-range estimates of WTP (IEc, 1994) to avoideach symptom in the cluster and assuming additivity of WTPs. In the absence of informationsurrounding the frequency with which each of the seven types of URS occurs within the URS symptomcomplex, we assumed a uniform distribution between $10.8 and $50.5 (2006$).Lower RespiratorySymptoms (LRS)$18 $19 Combinations of the four symptoms for whichWTP estimates are available that closely match thoselisted by Schwartz et al. result in 11 different “symptom clusters,” each describing a “type” of LRS. Adollar value was derived for each type of LRS, using mid-range estimates of WTP (IEc, 1994) to avoideach symptom in the cluster and assuming additivityof WTPs. The dollar value for LRS is the averageof the dollar values for the 11 different types ofLRS. In the absence ofinformation surrounding thefrequency with which each of the 11 types of LRS occurs within the LRS symptom complex, we assumeda uniform distribution between $8.1 and $28.6 (2006$).AsthmaExacerbations$50 $54 Asthma exacerbations are valued at $45 per incidence, based on the mean of average WTP estimatesfor the four severity definitions of a “bad asthma day,” described in Rowe and Chestnut (1986). Thisstudy surveyed asthmatics to estimate WTP for avoidance of a “bad asthma day,” as defined by thesubjects. For purposes of valuation, an asthma exacerbation is assumed to be equivalent to a day inwhich asthma is moderate or worse as reported in the Rowe and Chestnut (1986) study. The value isassumed have a uniform distribution between $18.3 and $82.9 (2006$).Acute Bronchitis $416 $512 Assumes a 6-day episode, with the distribution of the daily value specified as uniform with the lowandhigh values based on those recommended for related respiratory symptoms in Neumann et al. (1994).The low daily estimate of $20.5 (2006$) 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. Thehigh daily estimate was taken to be twice the valueof a minor respiratory restricted activity day, or$118 (2006$). The low and high daily values are multiplied by six to get the 6-day episode values.Work Loss Days(WLDs)Variable (U.S.median =$149)No distribution available. Point estimate is based on county-specific median annual wages divided by50 (assuming 2 weeks of vacation) and then by 5—to get median daily wage. U.S. Year 2000 Census,compiled by Geolytics, Inc.Second Section 812 Prospective AnalysisFebruary 20112-32HEALTH ENDPOINTCENTRAL ESTIMATE OF VALUEPER STATISTICAL INCIDENCEDERIVATION OF DISTRIBUTIONS OF ESTIMATES1990 INCOMELEVEL2020 INCOMELEVELMinor RestrictedActivity Days (MRADs)$61 $64 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). Distribution is assumed to betriangular with a minimum of $24 and a maximum of $94, with a most likely value of $59 (2006$).Range is based on assumption that value should exceed WTP for a single mild symptom (the highestestimate for a single symptom—for eye irritation—is $24) and be less than that for a WLD. Thetriangular distribution acknowledges that the actualvalue is likely to be closer to the point estimatethan either extreme.School Loss Days $89 $89 No distribution available. Point estimate is based on (1)the probability that, if a school child stayshome from school, a parent will haveto stay home from work to carefor the child, and (2) the value ofthe parent’s lost productivity. Calculated using U.S. Bureau of Census data.a Note that a seven percent discount rate is used to discountcosts incurred over the 5-year period, while the remainder of this report uses a five percent discountrate. The use of a seven percent discount rate to estimate costs associated with nonfatal myocardial infarction results in a minor underestimation of benefits.

### Second Section 812 Pr ospective Analysis February 2011

2-33 CHRONIC BRONCHITIS 

### The best available estimate of WTP to avoid a case of CB comes from Viscusi et al. (1991). The Viscusi et al. study, however, describes a severe case of CB to the survey respondents. We therefore employ an estimate of WTP to avoid a pollution-related case of CB, based on adjusting the Viscusi et al. (1991) estimate of the WTP to avoid a severe case. This is done to account for the likelihood that an average case of pollution-related CB is not as severe. The adjustment is mad e by applying the elasticity of WTP with respect to severity reported in the Krupnick and Cropper (1992) study. Details of this adjustment procedure are provided in the Benefits TSD for the Nonroad Diesel rulemaking (Abt Associates, 2003).

We use the mean of a distribution of WTP est imates as the central tendency estimate of WTP to avoid a pollution-related case of CB in this analysis. The distribution incorporates uncertainty from three sources: the WTP to avoid a case of severe CB, as described by Viscusi et al.; the severity leve l of an average pollution-related case of CB (relative to that of the case d escribed by Viscusi et al.); and the elasticity of WTP with respect to severity of the illness. Based on assumptions about the distributions of each ofthese three uncertain components, we derive a distribution of WTP to avoid a pollutionrelated case of CB by statistical uncertainty anal ysis techniques. The expected value (i.e., mean) of this distribution, which is abou t $399,000 (2006$), is taken as the central tendency estimate of WTP to avoid a PM-related case of CB.

NONFATAL MYOCARDIAL INFARCTION 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 valu e with two components: the direct medical costs and the opportunity cost (lost earnings) associated with the ill ness event. Because the costs associated with a myocardial infarc tion extend beyond the initial event itself, we consider costs incurred over five years. We used age-specific annual lost earnings estimated by Cropper and Krupnick (1990). Cr opper 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.

Three sources were consulted for direct medical costs of myocardial infarction: Wittels et al. (1990), Eisenstein et al. (2001), and Ru ssell et al. (1998). Because the wage-related opportunity cost estimates from Cropper and Krupni ck (1990) cover a 5-year period, we used estimates for medical costs that simila rly 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. 20

20 Note that a seven percent discount rate is used to discount costs incurred over the 5-year period, while the remainder of

this report uses a five percen t discount rate. The use of a seven percent discount rate to estimate costs associated with nonfatal myocardial infarction results in a minor underestimation of benefits.

### Second Section 812 Pr ospective Analysis February 2011

2-34 HOSPITAL ADMISSIONS VALUATION 

### In the absence of estimates of societal W TP to avoid hospital visits/admissions for specific illnesses, estimates of to tal cost of illness (total medical costs plus the value of lost productivity) typically are used as conservative, or lower bound, estimates. These estimates are biased downward, because they do not include the willingness-to-pay value of avoiding pain and suffering. The International Classification of Diseases (I CD-9, 1979) code-specific COI estimates used in this analysis consist of estimated hospital charges and the estimated opportunity cost of time spent in the hospital (based on th e 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 R esearch and Quality (AHRQ, 2000). 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 indi vidual is in the work force. To estimate the lost daily wage, we divided year 2000 me dian annual wage by (52*5) to get median daily wage and inflated the result to year 2006$ using the EPA standard inflator wage index. The resulting estimate is $149. The total cost-of-illness estimate for an ICD codespecific hospital stay lasting n days, then, was the mean hospital charge plus $109 multiplied by n.

ASTHMA-RELATED EMERGENCY ROOM VISITS VALUATION 

### To value asthma emergency room 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-relate d emergency room visits in 1987, at a total cost of $186.5 million (1987$). The average cost per visit that year was $155; in 2006$, that cost was $401.62 (using the EPA standard inflator medical cost index). The second estimate comes from Stanford et al. (1999), who reported the cost of an average asthma-related emergency room visit at $336 .03 (adjusted to 2006$), based on 1996– 1997 data. A simple average of the two est imates yields a (rounded) unit value of $369.

MINOR RESTRICTED ACTIVITY DAYS VALUATION 

### No studies are reported to have estimated WTP to avoid a minor restricted activity day. However, one of EPA’s contractors, IEc (1993) has derived an estimate of willingness to pay to avoid a minor respiratory restricted activity day, using estimates from Tolley et al. (1986) of WTP for avoiding a combination of coughing, throat congestion and sinusitis. The IEc estimate of WTP to avoid a minor respiratory restricted activity day is about $61 ($2006). Although Ostro and Rothschild (1989) statis tically linked ozone and minor restricted activity days, it is likely that most MRADs as sociated with ozone 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.

### Second Section 812 Pr ospective Analysis February 2011

2-35 SCHOOL LOSS DAYS 

### To value a school absence, we: (1) estimated the probability th at if a school child stays home from school, a parent will have to stay home from work to care for the child; and (2) valued the lost productivity at the parent ’s wage. To do this, we estimated the number of families with school-age children in whic h both parents work, and we valued a schoolloss day as the probability that such a day al so would result in a work-loss day. We calculated this value by multiplying the proportion of households with school-age children by a measure of lost wages.

We used this method in the absence of a preferable WTP method. However, this approach suffers from several uncertainties. First, it omits willingness to pay to avoid the symptoms/illness that resulted in the school absence; second, it effectively gives zero value to school absences that do not result in work-loss days; and third, it uses conservative assumptions about the wages of the parent staying home with the child. Finally, this method assumes that parents are una ble to work from home. If this is not a valid assumption, then there would be no lost wages. For this valuation approach, we assumed that in a household with two working parents, the female parent will stay home with a sick child. From the Statistical Abstract of the United States (U.S. Census Bureau, 2001), we obtained: (1) the numbers of single, married and “other” (widowed, divorced or sep arated) working women with children; and (2) the rates of participation in the workfor ce of single, married and “other” women with children. From these two sets of statistics, we calculated a weighted average participation rate of 72.85 percent. Our estimate of daily lost wage (wages lost if a mother must stay at home with a sick child) is based on the year 2000 median weekly wage among women ages 25 and older (U.S. Census Bureau, 2001). This median weekly wage is $551 (2000$). Dividing by five gives an estimated median daily wage of $103. To estimate the expected lost wages on a day when a mother has to stay home with a school-age child, we first estimated the probability that the mother is in the workfor ce then multiplied that estimate by the daily wage she would lose by missing a workday: 72.85 percent times $103, for a total loss of $75 in 2000$, or $89 in 2006$. This valuation approach is similar to that used by Hall et al. (2003).

RESULTS AND IMPLICATIONS

OZONE BENEFIT ESTIMATES 

### Ozone benefit estimates are calculated for a “stitched” National domain, created by merging results from the two original modeling domains, Eastern United States (EUS) and Western United States (WUS), and el iminating double-counting in the areas of overlap (see Exhibit 2-1). Exhibit 2-8 summarizes the mean valuation of ozone benefits for the nation. Exhibits 2-9 through 2-11 give detailed ozone benefit estimates in each target year for the nation. In addition to the mean incidence and valuation estimates, we have included 5 th 

### and 95 th 

### percentile estimates.

### Second Section 812 Pr ospective Analysis February 2011

2-36 

### Based in part on prior Council HES advice, EPA has typically assumed that there is a time lag between changes in pollution exposures and the total realization of changes in health effects. Within the context of benef its analyses, this term is often referred to as “cessation lag”. The existence of such a lag is important for the valuation of premature mortality incidence because economic theory suggests that benefits occurring in the future should be discounted. In this analysis , we apply a twenty-year distributed lag to PM mortality reductions - this method is cons istent with the most recent recommendation by the Council HES (EPA – SAB, 2004a) – but not to premature mortality reduction attributed to reduced ozone exposure. Alte rnative cessation lag structures for PM-related mortality risk are explored in the accompanyi ng Second Prospective uncertainty analysis report. For the primary results, a five per cent discount rate is used to discount future benefits back to the target year of the analysis (i.e., 2000, 2010, or 2020). Benefits of reduced morbidity account for roughl y four percent of the total primary ozone benefits. Exhibit 2-12 presents a more detailed comparison of the primary ozone morbidity estimates. Benefits of reduced mortality make up the remainder of the total ozone benefits.

EXHIBIT 2-8. NATIONAL SUMMARY MEAN OZONE VALUATION RESULTS

2000 2010 2020 0 10000 20000 30000 40000 50000 60000Mean Valuation (million 2006$)

Morbidity Mortality

### Second Section 812 Pr ospective Analysis February 2011

2-37 EXHIBIT 2-9. NATIONAL OZONE BENEFITS OF CAAA IN 2000

ENDPOINT

GROUP INCIDENCE VALUATION (MILLION 2006$)

PERCENTILE
5 MEAN PERCENTILE
95 PERCENTILE
5 MEAN PERCENTILE
95
Mortality Mortality – All Cause 1 210 1,400 2,800 $530 $10,000 $32,000 Morbidity Hospital Admissions, Respiratory (>64) 100 3,000 5,700 $2.5 $70 $140 Hospital Admissions, Respiratory (<2) 1,400 3,000 4,600 $14 $30 $46 Emergency Room Visits, Respiratory 0 2,200 6,200 $0 $0.81 $2.2 Minor Restricted Activity Days 1,300,000 3,100,000 4,800,000 $70 $180 $330 School Loss Days 480,000 1,200,000 1,900,000 $43 $110 $170 Outdoor Worker Productivity $30 $30 $30 Results are rounded to two significant figures.
1 Mortality results from Ito et al. (2005), Schwar tz (2005), Bell et al. (2004), Bell et al. (2005), Levy et al. (2005), and Huang et al. (2005) are pooled using equal weights.

### Second Section 812 Pr ospective Analysis February 2011

2-38 EXHIBIT 2-10. NATIONAL OZONE BENEFITS OF CAAA IN 2010

ENDPOINT

GROUP INCIDENCE VALUATION (MILLION 2006$)

PERCENTILE
5 MEAN PERCENTILE
95 PERCENTILE
5 MEAN PERCENTILE
95

Mortality Mortality – All Cause 1 790 4,300 8,700 $2,000 $33,000 $98,000 Morbidity Hospital Admissions, Respiratory (>64) 740 9,900 18,000 $17 $230 $440 Hospital Admissions, Respiratory (<2) 4,300 9,000 14,000 $43 $90 $140 Emergency Room Visits, Respiratory 0 6,600 18,000 $0 $2.4 $6.4 Minor Restricted Activity Days 4,400,000 9,500,000 15,000,000 $230 $560 $1,000 School Loss Days 1,400,000 3,200,000 5,100,000 $120 $290 $450 Outdoor Worker Productivity $100 $100 $100 Results are rounded to two significant figures.
1 Mortality results from Ito et al. (2005), Schwar tz (2005), Bell et al. (2004), Bell et al. (2005), Levy et al. (2005), and Huang et al. (2005) are pooled using equal weights.

### Second Section 812 Pr ospective Analysis February 2011

2-39 EXHIBIT 2-11. NATIONAL OZONE BENEFITS OF CAAA IN 2020

ENDPOINT

GROUP INCIDENCE VALUATION (MILLION 2006$)

PERCENTILE
5 MEAN PERCENTILE
95 PERCENTILE
5 MEAN PERCENTILE
95

Mortality Mortality – All Cause 1 1,200 7,100 15,000 $3,200 $55,000 $170,000 Morbidity Hospital Admissions, Respiratory (>64) 990 19,000 36,000 $23 $460 $860 Hospital Admissions, Respiratory (<2) 6,600 14,000 22,000 $65 $140 $220 Emergency Room Visits, Respiratory 0 11,000 31,000 $0 $4.1 $11 Minor Restricted Activity Days 6,400,000 15,000,000 23,000,000 $330 $880 $1,600 School Loss Days 2,200,000 5,400,000 8,600,000 $190 $480 $770 Outdoor Worker Productivity $170 $170 $170 Results are rounded to two significant figures.
1 Mortality results from Ito et al. (2005), Schwar tz (2005), Bell et al. (2004), Bell et al. (2005), Levy et al. (2005), and Huang et al. (2005) are pooled using equal weights.

### Second Section 812 Pr ospective Analysis February 2011

2-40 EXHIBIT 2-12. NATIONAL OZONE MORBIDITY BENEFITS

PM BENEFIT ESTIMATES 

### PM benefit estimates are calculated at the na tional level for the contiguous 48 states. Exhibit 2-13 summarizes the valuation of PM benefits. Exhibits 2-14 through 2-16 give detailed PM benefit estimates in each target y ear. In addition to the mean incidence and valuation estimates, we have included 5 th 

### and 95 th 

### percentile estimates. Benefits of reduced morbidity account for a pproximately four percent of the total PM benefits, depending on the mort ality incidence estimate used . Exhibit 2-17 presents a more detailed comparison of the PM morbidity estimates. Benefits of reduced mortality make up the remainder of the total PM benefits.

EXHIBIT 2-13. NATIONAL SUMMARY MEAN PM VALUATION RESULTS



### 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 1,600,000 1,800,000

2000 2010 2020Mean Valuation (million2006$) 

### Morbid ity $0 $500 $1,000 $1,500 $2,000 $2,500

2000 2010 2020Mean Valuation (million 2006$) Outdoor Worker Productivity

School Loss Days

Minor Restricted Activity Days

Emergency Room Visits, Respiratory

Hospital Admissions, Respiratory (<2)

Hospital Admissions, Respiratory (>64)

### Second Section 812 Pr ospective Analysis February 2011

2-41 EXHIBIT 2-14. NATIONAL PM BENEFITS OF CAAA IN 2000

ENDPOINT

GROUP INCIDENCE VALUATION (MILLION 2006$)

PERCENTILE
5 MEAN PERCENTILE
95 PERCENTILE
5 MEAN PERCENTILE
95

Mortality Mortality – Weibull distribution 20,000 110,000 230,000 $66,000 $710,000 $2,200,000 Morbidity Infant Mortality - Woodruff et al., 1997 81 160 250 $190 $1,300 $3,200 Chronic Bronchitis 5,500 34,000 62,000 $1,200 $14,000 $51,000 Nonfatal Myocardial Infarction 31,000 79,000 120,000 $2,300 $8,100 $20,000 Hospital Admissions, Respiratory 6,800 14,000 21,000 $94 $190 $280 Hospital Admissions, Cardiovascular 20,000 26,000 32,000 $550 $760 $990 Emergency Room Visits, Respiratory 34,000 56,000 77,000 $12 $21 $31 Acute Bronchitis -4,000 96,000 180,000 -$2.0 $42 $100 Lower Respiratory Symptoms 600,000 1,200,000 1,800,000 $9.0 $22 $40 Upper Respiratory Symptoms 310,000 980,000 1,700,000 $8.4 $30 $63 Asthma Exacerbation 130,000 1,200,000 3,400,000 $7.1 $61 $190 Minor Restricted Activity Days 39,000,000 46,000,000 53,000,000 $1,600 $2,700 $4,000 Work Loss Days 7,000,000 8,000,000 9,100,000 $1,100 $1,300 $1,400
Notes: Results are rounded to two significant figures.

### Second Section 812 Pr ospective Analysis February 2011

2-42 EXHIBIT 2-15. NATIONAL PM BENEFITS OF CAAA IN 2010

ENDPOINT

GROUP INCIDENCE VALUATION (MILLION 2006$)

PERCENTILE
5 MEAN PERCENTILE
95 PERCENTILE
5 MEAN PERCENTILE
95

Mortality Mortality - Weibull distribution 31,000 160,000 350,000 $110,000 $1,200,000 $3,600,000 Morbidity Infant Mortality - Woodruff et al., 1997 120 230 350 $280 $1,900 $4,900 Chronic Bronchitis 8,800 54,000 96,000 $2,000 $24,000 $84,000 Nonfatal Myocardial Infarction 53,000 130,000 200,000 $4,100 $14,000 $33,000 Hospital Admissions, Respiratory 11,000 22,000 33,000 $150 $310 $460 Hospital Admissions, Cardiovascular 34,000 45,000 54,000 $930 $1,300 $1,700 Emergency Room Visits, Respiratory 49,000 80,000 110,000 $17 $29 $44 Acute Bronchitis -5,000 130,000 250,000 -$2.0 $61 $150 Lower Respiratory Symptoms 840,000 1,700,000 2,400,000 $13 $30 $55 Upper Respiratory Symptoms 440,000 1,400,000 2,300,000 $12 $42 $89 Asthma Exacerbation 190,000 1,700,000 4,900,000 $11 $90 $270 Minor Restricted Activity Days 63,000,000 74,000,000 85,000,000 $2,600 $4,400 $6,300 Work Loss Days 11,000,000 13,000,000 14,000,000 $1,700 $2,000 $2,200
Notes: Results are rounded to two significant figures.

### Second Section 812 Pr ospective Analysis February 2011

2-43 EXHIBIT 2-16. NATIONAL PM BENEFITS OF CAAA IN 2020

ENDPOINT

GROUP INCIDENCE VALUATION (MILLION 2006$)

PERCENTILE
5 MEAN PERCENTILE
95 PERCENTILE
5 MEAN PERCENTILE
95

Mortality Mortality – Weibull distribution 44,000 230,000 480,000 $170,000 $1,700,000 $5,300,000 Morbidity Infant Mortality - Woodruff et al., 1997 140 280 420 $370 $2,500 $6,400 Chronic Bronchitis 12,000 75,000 130,000 $3,100 $36,000 $130,000 Nonfatal Myocardial Infarction 80,000 200,000 300,000 $6,200 $21,000 $48,000 Hospital Admissions, Respiratory 16,000 33,000 49,000 $230 $460 $680 Hospital Admissions, Cardiovascular 52,000 69,0 00 84,000 $1,400 $2,000 $2,600 Emergency Room Visits, Respiratory 66,000 110,000 150,000 $23 $39 $58 Acute Bronchitis -7,000 180,000 340,000 -$4.0 $94 $220 Lower Respiratory Symptoms 1,200,000 2,300,000 3,300,000 $18 $42 $76 Upper Respiratory Symptoms 620,000 2,000,000 3,300,000 $17 $60 $130 Asthma Exacerbation 270,000 2,400,000 6,700,000 $15 $130 $390 Minor Restricted Activity Days 84,000,000 99,000,000 110,000,000 $3,500 $5,900 $8,500 Work Loss Days 15,000,000 17,000,000 19,000,000 $2,300 $2,700 $3,000
Notes: Results are rounded to two significant figures.

### Second Section 812 Pr ospective Analysis February 2011

2-44 EXHIBIT 2-17. NATIONAL PM MORBIDITY BENEFITS

$0 $10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000

2000 2010 2020Mean Valuation (million 2006$) Work Loss Days
Minor Restricted Activity Days

Asthma Exacerbation
Upper Respiratory Symptoms
Lower Respiratory Symptoms

Acute Bronchitis
Emergency Room Visits, Respiratory

### Second Section 812 Pr ospective Analysis February 2011

3-1 

### CHAPTER 3 | ESTIMATION OF VISIBILITY IMPROVEMENTS AND ECONOMIC VALUATION

OVERVIEW 

### Air pollution impairs visibility in both resi dential and recreational settings, and an individual’s willingness to pay (WTP) to avoid reductions in visibility differs in these two settings. Benefits of residential visibility rela te 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 th at are expected to be experienced by recreational visitors. We calculate household WTP for improvement s in both residential and recreational visibility. We base our calculations on simulati ons of future visibility conditions at the 36-km grid-cell level, as estimated by EPA’s Community Multiscale Air Quality (CMAQ) model. The relationship between a household’s WTP and changes in visibility is derived from a number of contingent va luation (CV) studies published in the peerreviewed economics literature. The approach we apply to estimate the benefit of improvements in recreational visibility is consistent with methods EPA has used for Regulatory Impact Analyses (RIAs) conducted since EPA’s First Prospective analysis was completed. In particular, this chapter relies heavily on the research done for the PM NAAQS RIA (U.S. EPA, 2006). Our estimate of the benefit of residential visibility is consistent with methods applied in past anal yses as well, but in the past the Council had expressed concerns about residential visibility estimates based on WTP estimates from the McClelland et al. (1991) study. As a resu lt, our estimates in this chapter rely on a new benefits transfer estimate of WTP deri ved from other published sources of residential visibility WTP. A fundamental issue with respect to visib ility valuation is whether estimated values reflect only visibility conditions and do not include other perceived benefits such as health or ecological improvements. Similarly, it is important to try to distinguish residential from recreational visibility–that is, can these be treated as distinct and additive benefit categories based on the available literature? In our sel ection of underlying valuation studies and our recommended appr oach, we attempt to address both of these issues.

VISIBILITY CHANGES 

### This analysis is the first Section 812 prosp ective analysis to use an integrated modeling system, CMAQ, to simulate national and re gional-scale pollutant concentrations and

### Second Section 812 Pr ospective Analysis February 2011

3-2 

### deposition. The CMAQ model (Byun and Ching, 1999) is a state-of-the-science, regional air quality modeling system that is designed to simulate the physical and chemical processes that govern the formation, transport, and deposition of gaseous and particulate species in the atmosphere. The CMAQ model w as applied for seven core scenarios that include four different years spanning a 30-year period – 1990, 2000, 2010 and 2020. As outlined in Chapter 1 of this report, scen arios that incorporate the emission reductions associated with the Clean Air Act Ame ndments (CAAA) are referred to as with-CAAA while those that do not are referred to as without-CAAA

. The outputs from the CMAQ model provide the basis for the calculation of health and ecological benefits of the Clean Air Act, as described elsewhere in this document. The airborne criteria pollutants of interest incl ude ozone and fine particulate matter (PM 2.5 

### ), where PM 2.5 

### consists of particles less than 2.5 mi crons in diameter. Visibility is calculated using the CMAQ Chemistry-Transport Model (CCTM). The CCTM integrates output from emissions and meteorology models to si mulate continuous atmospheric chemical conditions. Particular to visibility, CCTM’s AERO module integrates Mie scattering (a generalized particulate light-scattering mechanis m that follows from the laws of electromagnetism applied to particulate matter) over the entire range of particle sizes to obtain a single visibility value for each grid cell (CMAS Center, 2007). The visibility data used in this analysis is annual mean visibility data, by county, measured in deciviews. The data was aggreg ated from the 36-km grid-cell level to the county level using the BenMAP version 3. 0.15 "Air Quality Grid Aggregation" algorithm. The fourth quarter data is corrected for a missing day (the CMAQ runs modeled 364 days, omitting December 31) by reweighting the mean to account for the missing day. Exhibit 3-1 depicts the change in visibility (measured in deciviews) over the 30-year time frame of this analysis (i.e., from 1990 to 2020 with-CAAA ). This map shows that, overall, changes in visibility due to the CAAA are gr eater in the eastern U.S. than the western U.S. Additionally, the largest changes in visibility occur in the Midwestern states. Exhibit 3-2 summarizes trends in visibility at the 13 most-visited U.S. National Parks. Visibility estimates (measured in deciviews) are provided for each of the seven core CAAA scenarios. Exhibit 3-3 presents the data from Exhibit 3-2 graphically. 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). These exhibits show that the CAAA greatly affects visibilit y at National Parks – over the 1990 to 2020 period, visibility markedly improves with the CAAA, and markedly declines without the CAAA. Particularly great differences in visibility with- and without-CAAA are seen at Great Smoky Mountains National Park, which is the most visited park in the U.S.

### Second Section 812 Pr ospective Analysis February 2011

3-3 EXHIBIT 3-1. ESTIMATED CHANGE IN VI SIBILITY WITH CAAA: 1990 TO 2020

EXHIBIT 3-2. VISIBILITY IN DECIVIEWS AT THE 13 MOST-VISITED U.S. NATIONAL PARKS A

WITHOUT CAAA WITH CAAA

RANK NATIONAL PARK 1990 2000 2010 2020 2000 2010 2020

1 Great Smoky Mountains 20.83 21.27 21.71 22.18 19.53 18.21 16.79 2 Grand Canyon 7.91 8.44 8.70 9.07 7.90 7.78 7.78
3 Yosemite 9.48 9.67 10.03 11.11 9.43 9.21 9.21
4 Yellowstone 9.46 9.13 9.53 10.17 8.68 8.63 8.62 5 Sequoia-Kings 11.36 12.03 12.66 13.52 11.88 11.34 11.26 6 Rocky Mountain 10.20 9.68 10.03 10.64 9.27 9.11 8.86
7 Grand Teton 9.46 9.13 9.53 10.17 8.68 8.63 8.62 8 Olympic 12.01 11.21 11.43 11.66 10.81 10.79 10.68 9 Kings Canyon 11.78 11.94 12.64 13.62 11.53 10.98 10.91
10 Zion 6.81 9.23 9.73 10.49 7.56 7.51 7.56
11 Mount Rainier 15.13 14.33 14.65 15.43 13.43 12.90 12.82
12 Glacier 12.11 11.51 11.71 11.94 11.35 11.25 11.19
13 Acadia 17.63 17.55 17.77 17.43 15.21 14.61 13.60 Note: (A) According to the 2000 National Parks Statistical Abstract.

### Second Section 812 Pr ospective Analysis February 2011

3-4 EXHIBIT 3-3. VISIBILITY TRENDS FOR THE 13 MOST-VISITED U.S. NATIONAL PARKS

VISIBILITY BENEFITS 

### Visibility directly affects people’s enjoyment of a variety of daily activities. Individuals value visibility in the places they live an d work, in the places they travel to for recreational purposes, and at sites of unique public value, such as Great Smoky Mountains National Park. Changes in the leve l of ambient PM caused by the reduction in emissions associated with the CAAA will change the level of visibility throughout the United States. This section discusses the measurement of the economic benefits of improved visibility. It is difficult to quantitatively define a visibilit y endpoint that can be used for valuation. Increases in PM concentrations cause increase s in light extinction, a measure of how much the components of the atmosphere scatte r and absorb light. More light extinction means that the clarity of visual images and visu al range is reduced, ceteris paribus. Light extinction is a variable that can be accura tely measured. Pitchford and Malm (1993) created a unitless measure of visibility, the deciview, based directly on the amount of light extinction. Deciviews are standardized fo r a reference distance in such a way that one deciview corresponds to a change of a bout 10% in available light. Pitchford and Malm characterize a change in light extinction of one deciview as “a small but perceptible scenic change under many circum stances.” Air quality models are used to

### Second Section 812 Pr ospective Analysis February 2011

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### predict the change in visibility, measured in deciviews, of the areas affected by the CAAA. 21


### Our analysis considers benefits from two cat egories of visibility changes: residential visibility and recreational vi sibility. In both cases economic benefits are believed to consist of use values and nonuse values. U se values include the aesthetic benefits of better visibility, improved road and air safet y, and enhanced recreation in activities like hunting and bird watching. Nonuse values are based on an individual’s belief that the environment ought to exist free of human-induced haze. Nonuse values may be more important for recreational areas, partic ularly national parks and monuments. For the purposes of this analysis, recreati onal visibility improvements are defined as those that occur specifically in federal Class I areas, and residential visibility improvements are those that occur with in the boundaries of Census Metropolitan Statistical Areas (MSAs). 22 

### A key distinction between recreational and residential benefits is that only those people living in residential areas are assumed to receive benefits from residential visibility, while a ll households in the United States are assumed to derive some benefit from improvements in Class I areas. Values are assumed to be higher if the Class I area is located close to their home. 23

METHODOLOGY

VALUING RECREATIONAL VISIBILITY BENEFITS 

### Benefits of recreational visibility relate to th e impact of visibility changes expected to be experienced by visitors to recreational areas with notable vistas. Our current methodology for valuing recreational visibility differs from the approach used in the First Prospective Analysis. In this Second Prospective Analys is, we follow a methodology used in EPA’s Particulate Matter NAAQS RIA. As discussed in more detail in Appendix 1 of the RIA, this approach to valuing recreational visibilit y changes is an application of the Constant Elasticity of Substitution (CES) utility function approach a nd is based on the preference calibration method developed by Smith, Van H outven, and Pattanayak (1999). Exhibit 3- 4 outlines the key data sour ces and assumptions for this analysis of benefits to recreational visibility.

21 An instantaneous change of less than one deciview (i.e., less than 10% of the light extinction budget) represents a

measurable improvement in visi bility, but may not be percep tible to the eye. This anal ysis considers annual average changes in visibility, which are likely made up of periods with changes less than one deci view and periods with changes exceeding one deciview. One alternative to using annual average changes would be to evaluate changes in visibility during daylight hours for the year displayed as a frequency distribution. Such an appr oach would enable an analysis of the frequency of time when the visibility change s are likely to be perceptible. Our anal ysis instead relies on the simpler annual average changes in visibility because this measure appears to more closely correspond to the WTP literature relied upon in this analysis.

22 The Clean Air Act designates 156 nati onal parks and wilderness areas as Class I areas for visibility protection.

23 For details of the visibility estimates discussed in this chap ter, please refer to the Benefits TSD for the Nonroad Diesel

rulemaking (Abt Associates, 2003).

### Second Section 812 Pr ospective Analysis February 2011

3-6 EXHIBIT 3-4. KEY DATA SOURCES AND ASSUMPTIONS FOR PRIMARY ECONOMIC VISIBILITY
BENEFITS ESTIMATES

DATA SOURCE ASSUMPTION POTENTIAL
EFFECT ON

RESULTS
Recreational Visibility Chestnut and Rowe, 1990a Chestnut and Rowe, 1990b Chestnut, 1997 Chestnut and Rowe study covers parks in three regions: California, Sout hwest, and Southeast. The results from these regions are transferred to other regions in the U.S. Unclear

Chestnut and Rowe study conducted on populations in five states. These results are applied to the entire U.S. population. Unclear

Only includes benefits to National Parks and Wilderness Areas, other recreational settings, such as National Forests an d state parks, are not included in this analysis. Potential Underestimate

Individuals have a greater WTP for visibility changes in parks within their region. Unclear
WTP values reflect only visibility improvements and not overall air qu ality improvements. Potential Overestimate Residential Visibility Tolley et al., 1986 Brookshire et al., 1979 Loehman et al., 1984 Residential and recreational visibility benefits are distinct and separable. Potential Overestimate Estimates residential visibi lity benefits within the boundaries of MSAs. Areas outside of an MSA are not included in this analysis. Potential Underestimate
WTP values reflect only visibility improvements and not overall air qu ality improvements. Potential Overestimate 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. Unclear



### For the purposes of this report, we interpre t recreational settings applicable for this category of effects to include National Park s and Wilderness Areas. Other recreational settings may also be applicable, for example Na tional Forests, state parks, or even hiking trails or roadside areas with scenic vistas. In those cases, a lack of suitable economic valuation literature to identify these other areas a nd/or a lack of visitation data prevents us from generating estimates for those recreational vista areas. Moreover, we develop estimates of recreational visibility changes that account for the tendency of individuals to value visibility changes based on proximity to the National Parks. The underlying assumption is that individuals are more likely to visit parks within their region and would therefore place a higher value on visibility changes within these parks. Recreational

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### visibility benefits may, however, reflect the value an individual places on visibility improvements regardless of whether the person plans to visit the park. 24


### Household WTP for a visibility improvement at an in-region park takes the following form: 

#   

#       

##  1
1 0 

## ) ( ik ik ik ik 

## Q Q m m Q WTP

 

   (1)

where: i indexes region, k indexes park,
m = household income,

ρ = shape parameter,

γ = parameter corresponding to the visibility at in-region parks,
Q 0 

### = starting visibility, and
Q 1 

### = visibility after change.

A similar WTP function is used for out-of-region parks, replaying γ ik 

### for δ ik 

### . Only one existing study provides defensible monetary estimates of the value of recreational visibility (Chestnut and Rowe , 1990b; 1990c). Although the Chestnut and Rowe study is unpublished, it was originally developed as part of the National AcidPrecipitation Assessment Program (NAPAP) and, therefore, has been subject to peerreview as part of that program. Moreover, th is study is frequently cited and recommended for use in published analyses of visibility valuation. 25 

### In EPA’s judgment, the Chestnut and Rowe study contains many of the elements of a valid CV study and is sufficiently reliable to serve as the basis for monetary estimat es of the benefits of visibility changes in recreational areas. 26 

### This study serves as an essential input to our estimates of the benefits of recreational visibility improvements in the primary benefits estimates. The Chestnut and Rowe study measures the demand for visibility in Class I areas managed by the National Park Service (NPS) in three broad regions of the country:

24 This type of valuation is typically labeled “existence value.” For more di scussion see Chestnut and Rowe, 1990a.

25 For example see Desvousges et al. (1998).

26 A Council advisory letter indicates that “many members of th e Council believe that the Chestnut and Rowe study is the

best available” (EPA-SAB-COUNCIL-ADV-0 0-002, 1999, p. 13). However, the comm ittee did not formally approve use of these estimates because of concerns abou t the peer-reviewed status of the study. EPA believes the study has received adequate review and has been cited in numerous peer -reviewed publications (C hestnut and Dennis, 1997).

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### California, the Southwest, and the Southeast. Re spondents in five st ates were asked about their WTP to protect national parks or NPS- managed wilderness areas within a particular region. 27 

### The survey used photographs reflecting di fferent visibility levels in the specified recreational areas. The visibility levels in these photographs were later converted to deciviews for the current analysis. WTP responses reported in the Chestnut and Ro we study were region-specific, rather than park-specific. As visibility improvemen ts are not constant across all parks in a region, we must infer park-specific visibility parameters (i.e., γ and δ ) in order to calculate WTP for projected visibility cha nges. As the quantity and quality of parks differs between regions, we apportion the visi bility parameters based on relative visitation rates at the different parks, as this statistic is likely to get at the issues of both park quality (more people visit better parks, so collectiv e WTP is likely higher) and quantity (more people visit parks in a region if the parks ar e more numerous, so collective WTP is likely higher). 28 

### This method has several limitations, incl uding the fact that visitation rates count both foreign and domestic visitors and that it is not necessarily a good indicator of non-use value. The park-specific visibility para meters are used to calculate park-specific WTP values along with household income and visi bility (measured in deciviews) in the with- and without-CAAA scenarios following Equation 1. 29 

### A more detailed explanation of how park-specific γ and δ are calculated is provided in Appendix I of the PM NAAQS RIA (U.S. EPA, 2006). The Chestnut and Rowe study focused on visi bility improvements in national parks and wilderness areas in California, the Southwest, and the Southeast. These regions cover 86 of the 156 Class I areas in the United States. Given that national parks and wilderness areas exhibit unique characteristics, it is not clear whether the WTP estimate obtained from Chestnut and Rowe can be transferred to other national parks and wilderness areas, without introducing additional uncertainty. As a result, for the primary estimate, we value

27 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 nonsurveyed states. This assumption is applied in this analysis.

28 We use park visitation data from the National Park Service St atistical Abstracts. To estimat e recreational benefits in 2010

and 2020, we use visitation data from 2008, as this is the most current data availabl e. Where the data for a particular park was not representative of normal visitation rates at that park (for example due to fire dama ge that occurred during that year), we substitute data from the prior ye ar. We chose to use 2008 data rather th an projecting to 2020 based on current visitation trends, as atypical years in visitation data pose proble ms for establishing an overall rate of increase or decrease, and it is not clear that visitation trends could reliably be projec ted so far into the future as they depend on many external factors. 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 us ing 1997 data are likely to be small. As we have only one year of visitation data for wilderness areas, and because it is unclear whether visitation tren ds would be comparable across parks and wilderness areas, we chose to use the 1997 data as is rather than projecting it to the years of the analysis.

29 For this analysis EPA has concluded that cross-sectional inco me adjustments are not appropriate for these types of benefits

transfers. As a result, household income is adjusted long itudinally across time (i.e. 2000, 2010, and 2020), but not crosssectionally across space (i.e. to reflect income differences across regions). Longitudinal in come adjustments were made using an income elasticity of 0.9, indicatin g that a 1 percent increase in income is associated with a 0.9 percent increase in WTP for a given change in visibility.

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### only those recreational benefits in the areas that were directly analyzed in the original Chestnut and Rowe study. An alternative est imate is provided that includes all Class I areas. To calculate this alternative estimate region-specific visibility parameters must be inferred for regions not covered by the Chestnut and Rowe study. 30

VALUING RESIDENTIAL VISIBILITY BENEFITS 

### Benefits of residential visibility relate to the impact of visibility changes on an individual’s daily life; at home, at work, and while engaged in routine recreational activities. Residential visibility refers to conditions in large metropolitan areas, cities, towns and associated views and landscapes that individuals interact with on a regular basis. As defined in this analysis, residen tial visibility is distinct from recreational visibility, which refers specifically to cond itions in Class I areas (e.g., certain NPS parks and wilderness areas). While improved visib ility conditions in Class I areas has been recognized in previous policy analyses, most recent benefits analyses do not quantify or monetize residential visibility improvements as part of the primary benefits estimates. In the First Prospective analysis, we omitte d the results of the benefits estimate for residential visibility from the primary benefits estimate due to technical concerns about the methodology of the study upon which our original calculations were based (McClelland et al., 1991). 31 

### There exists a wide range of published, peer-reviewed literature, however, that supports a non-zero valu e for residential visibility. As a result, we have revised our methodolog y for valuing residential visibility, and now include these benefits in our overall primary visibility benefits estimate. For valuing residential visibility improvements, we rely upon a benefits transfer approach detailed in Paterson et al. (2005) and summa rized here, drawing upon information from the published Brookshire (1979), Loehman (1984) and Tolley (1986) studies. Exhibit 3-4 outlines the key data sources and assumptions for this analysis of benefits to residential visibility. Each of the studies used pr ovides estimates of household WTP to improve visibility conditions from a status quo visual range to an improved visual range. While uncertainty exists regarding the precision of these older, stated-preference residential valuation studies, we believe their results support the argument that individuals have a non-zero value for residential visibility im provements. To express these value estimates in comparable terms, we rely upon a function si milar to that used in the First Prospective analysis to express household WTP for a change in visual range:

30 A more detailed description of the benefits transfer method used to infer values for visibility changes in Class I areas

outside the study regions is provided in the Benefits TS D for the Nonroad Diesel rulemaking (Abt Associates, 2003).

31 Council review of early drafts of the First Prospective analysis noted that th e McClelland et al. (1991) study may not

incorporate two potentially important adju stments. First, their study does not account for the “warm glow” effect, in which respondents may provide higher wi llingness 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, a prior Council reco mmended that residential visibility be omitted from the overall primary benefits estimate in the First Prospective.

### Second Section 812 Pr ospective Analysis February 2011

3-10 

#        

 

0 1 

## ln * VR VR b VR WTP (2)

where:

VR 0 

### = mean annual visual range in miles before the improvement,

VR 1 

### = mean annual visual range in miles after the improvement, and b = parameter.

As originally described by Chestnut and Rowe (1990c), this function implies a constant WTP for a given percentage change in visual range. This is consistent with the EPA’s current use of the deciview scale, which rela tes to the above function in the following

manner: 

#   

#   1 0 

## * 10 DV DV b DV WTP

   (3)

where: 

#   

#   VR deciviews DV 243 ln 10 ) (  

Five principal residential visibility valuat ion studies were identified and reviewed for quality and applicability: Brookshire et al. ( 1979), Loehman et al. (1984), McClelland et al. (1991), Rae (1983) and Tolley et al. (1986) . Of these, we exclude McClelland (1991) due to various concerns articulated by a prev ious Council, as noted above. In addition, we exclude Rae (1983) because it represents a novel application of a choice method for which there existed no established practices for design, implementation and data analysis. While the remaining three studies represent early applications of the contingent valuationmethod, and therefore do not benefit from more recent methodological advances or bestpractice guidelines established by the NOAA Blue Ribbon Panel on C ontingent Valuation (Arrow et al, 1993) and other diagnostic resea rch, they nonetheless build upon previous literature and incorporate varying degrees of tests for internal consistency. Of these remaining three studies, Loehman et al. (1984) 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. (1986) work was not published, but was subject to peer review during study development. Previous visibility literature summaries (e.g., Chestnut and Rowe, 1990c and Chestnut and De nnis, 1997) provide detailed descriptions of the three studies. These sources, as well as a re view of the Tolley et al. study (Chestnut and Rowe, 1986) and a Project Team memorandum (Leggett et al., 2004) discuss criticisms associated with each study. Following Chestnut and Rowe (1990c), we utilize value estimates and the associated change in visual range from each study to estimate the b parameter for the eight study areas. Where studies provide multiple estima tes for visual range improvements, we

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3-11 

### estimate b by regressing the natural log of the ratio of visual range following and prior to improvement on WTP (see Equation 2). Exhibit 3-5 below provides a summary of these estimates, as well as an illustrative implied WTP value for a 10-percent improvement in visual range. All estimates are expressed in 2006$ using the Consumer Price Index. 32


### As shown, the implied annua l per-household WTP estimates for a hypothetical 10-percent improvement ranges from $14 to $145, with a mean of $69 and median of $53. It is not surprising that such a range of values exists , as these areas all feature different landscapes and vistas, populations and prevailing visibility conditions. Fortunately, the three recommended studies provide primary visibility values for a variety of cities throughout the United States: Atlanta, Boston, Chic ago, Denver, Los Angeles, Mobile, San Francisco, and Washington D.C.

EXHIBIT 3-5. SUMMARY OF RESIDENTIAL VISIBILITY PARAMETER ESTIMATES

CITY A STUDY B ESTIMATE B IMPLIED WTP FOR 10%
IMPROVEMENT IN VISUAL RANGE C
Atlanta Tolley et al. (1986) 321 $47 Boston Tolley et al. (1986) 398 $59 Chicago Tolley et al. (1986) 310 $46 Denver Tolley et al. (1986) 696 $102 Los Angeles Brookshire et al. (1979) 94 $14 Mobile Tolley et al. (1986) 313 $46 San Francisco Loehman et al. (1984) 989 $145 Washington, DC Tolley et al. (1986) 614 $90 a Recognizing potential fundamental issues asso ciated with data collected in Cincinnati and Miami (e.g., see Chestnut and Rowe, 1986 and 1990c), we do not include values for these cities in our analysis. b b/10 = WTP for a one deciview improvement c Annual household willingness to pay, 2006$ at 1990 income levels. Income adjustments across time are applied after total benefits have been calculated.



### To estimate visibility benefits in locations othe r than those considered in the three studies, we transfer the b parameters from the eight study areas to other areas of the country based primarily on geographic proximity. The st udies we rely upon were all conducted in urban/metropolitan and surrounding areas and ge nerally do not provide information on values for residential visibility improvements in rural areas. Thus, we restrict transfer of values to Metropolitan Statistical Areas (MSAs). 33 

### While MSAs account for roughly 20

32 As we are considering only MSAs, we have chosen to use the CPI-U as the most representative measure of CPI.

33 MSA boundaries are as most recent defined (2003).

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### percent of total U.S. land area, over 80 percent of the population resides within them (Census, 2000). We assign each of the 359 MSAs in the c ontiguous U.S. a value based on geographic proximity to one of the eight study cities, with two exceptions. We apply the Loehman et al. (1984) value only to the six San Francisco Bay area MSAs. The Loehman et al. study is unique among the three in the manner in wh ich visibility changes were described to respondents (i.e., a distribution of days versus average conditions). In addition, the study area is unique in the landscape and vistas it offers, as well as prevailing weather conditions. In light of these factors, and cons idering that the Loehman et al. (1984) value is over 30 percent higher than the next hi ghest value in the range, we believe is conservative and appropriate to restrict this value to the San Francisco study region.

In addition to different baseline levels of visibility, different weather conditions, and different resident characteristics, different lo cations provide dramatically different vistas. For example, one would expect that resident s of Denver, with a dramatic view of the Rocky Mountains that is rarely obstructed by trees, would have a greater interest in protecting visibility than a city without a dramatic skyline or nearby mountains. We therefore add an additional constraint: va lues associated with Denver are not assigned on the basis of proximity but are instead assigned only to MSAs which meet an elevation range threshold of 1500 meters within the MSA. 34 

### While not a perfect way to identify areas with superb mountain vistas, the range of elevation within the MSA is nonetheless a reasonable, objective, and feasibly applied mea sure to identify where it would seem more appropriate to attribute the larger visib ility values derived from the Denver study instead of the values from studies of the next closest city in our grouping. Exhibit 3-6 depicts assignment of the study cities to MSAs.

34 The geographic proximity assignment is preserved for the Lo s Angeles and Riverside MSAs although these MSAs meet the

elevation range threshold of 1500 meters. The assignment is preser ved because Los Angeles is one of the study cities and also because Los Angeles has a particular set of location-spec ific characteristics that set it apart from Denver. As a conservative measure, Riverside MSA is also assigned to the Los Angeles study area because a significant portion of Riverside County itself is located in the South Coast Air Quality Manageme nt District, and therefore is considered by at least some measures to be part of the same regulated airshed as Los Angeles.

### Second Section 812 Pr ospective Analysis February 2011

3-13 EXHIBIT 3-6. RESIDENTIAL VISIBILITY STUDY CITY ASSIGNMENT



### In this analysis, we assume that residential and recreational visibility benefits are distinct and separable. Under this approach reside ntial values from the existing literature are transferred to all MSAs in the contermin ous U.S.; however, it is conceivable that respondents to Chestnut and Rowe’s (1990b) recreational visibility survey may have partially included values for their own reside ntial visibility when evaluating changes at national parks and wilderness areas in their region. We also must assume that individuals care about visibility for aesthetic reasons rather than viewing visibility as a proxy for other im pacts associated with air pollution, such as health effects. As health effects are ev aluated separately from aesthetic effects, we assume that any observed response to visibility is linked to aesthetic concerns rather than concerns about health. Otherwise, health bene fits would be double counted in the benefits analysis. Unfortunately, the visibility valuati on literature indicates that individuals have trouble separating visibility from other impacts of air pollution (e.g., McClelland et al., 1991; Chestnut and Rowe, 1990c; Carson , Mitchell, and Ruud, 1990). Contingent valuation studies designed to va lue visibility improvements must successfully separate respondents’ preferences for visibilit y from their preferences for health. The three studies that we have selected to inform our calculations of the value of visibility

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### accomplish this objective in somewhat different ways. 35 

### Tolley et al. (1986) specify a hypothetical pollution control program that will only affect visibility: “Suppose a program could be set up to prevent the decline in visibility, realizing that there would be no health effects.” In contrast, Brookshire et al. (1979) specify a more general pollution control program, but they ask respondents to fo cus only on their preferences for visibility improvements: “I am only interested in how you value being able to see long distances.” Finally, Loehman et al. (1986) present summar y tables to respondents that describe the expected number of days per year at various health and visibility levels for both the baseline and the improved situations. Res pondents are asked to provide WTP for air quality improvements with an increased number of good visibility days but with health levels held constant. The degree to which the three studies were su ccessful in convincing respondents to focus solely on visibility is unclear, as none of the three studies includes follow-up questions necessary to investigate the issu e. Furthermore, no other residential visibility CV studies provide evidence regarding the degree to whic h 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 fo r potentially embedded health effects.

RESULTS 

### The primary estimate of benefits to recreati onal and residential visibility is provided in Exhibit 3-7. The primary estimate for recreational visibility only includes benefits in the original study regions (i.e., California, the Southwest, and the Southeast). The primary estimate for residential visibility includes benef its in all MSAs. In general, benefits to visibility increase over time as visibility impr oves due to the CAAA. Benefits to residential visibility are approximately three times as large as benefits to recreational visibility. Exhibit 3-8 provides an alternative estimate of benefits to recreational visibility. This alternative estimate includes all Class I areas, not just those that were directly analyzed in the original Chestnut and Rowe study. The alternative recreational visibility benefits estimate is approximately 40 percent greater than the primary estimate.
EXHIBIT 3-7. PRIMARY ESTIMATE OF BENEFITS TO VISIBILITY (BILLION 2006$)

2000 BENEFITS 2010 BENEFITS 2020 BENEFITS
Recreational Benefits $3.3 $8.6 $19 Residential Benefits $11 $25 $48 Total Benefits $14 $34 $67

35 See Leggett et al. (2004) for a more detailed discussion of this issue.

### Second Section 812 Pr ospective Analysis February 2011

3-15 EXHIBIT 3-8. ALTERNATIVE ESTIMATE OF BENEFITS TO VISIBILITY (BILLION 2006$)

2000 BENEFITS 2010 BENEFITS 2020 BENEFITS
Recreational Benefits $4.9 $12 $26 Residential Benefits $11 $25 $48 Total Benefits $16 $37 $74



### Exhibit Exhibits 3-9 through 3-11 map the pr imary estimate of benefits to recreational, residential, and total visibility by state in 2020. Exhibit 3-12 ranks states by their level of benefits to recreational, residential and tota l visibility. Exhibit 3-13 provides a visual comparison of the primary benefits estimate visibility across all years (i.e., 2000, 2010, and 2020). The full set of primary results by State is given in Appendix A. Overall, the spatial pattern of benefits is similar for r ecreational and residential visibility. Totals benefits are lowest in Wyoming, North Da kota, Vermont, South Dakota, and Montana. Total benefits are highest in California, Ne w York, Texas, Pennsylvania, and Florida. Benefits appear to be largely driven by population as these are some of the least and most populous states. Recreational visibility benefits are driven by population and park location. The primary benefits estimate includes only those Class I areas located within the original study regions of Chestnut and Rowe (1990a). Th ese regions are California, the Southwest (Arizona, Nevada, Utah, Colorado, and New Mexico), and the Southeast (Delaware, Maryland, West Virginia, Virginia, Ke ntucky, Tennessee, North Carolina, South Carolina, Georgia, Alabama, Florida, and Mississippi). Households express WTP for visibility improvements in Class I areas locat ed in-region as well as out-of-region. For this reason, there may be high recreational be nefits in a state that has no Class I areas. Although household WTP is higher for in-region pa rks, this effect seems to be dominated by the effect of population. For example, less populated states such as New Mexico and Utah with Class I areas have low benefits to recreational visibility, while more populated states such as New York without Class I areas have high benefits to recreational visibility (see Exhibit 3-12). In some cases, the effect of being an in-region state is evident, for example Florida is ranked second in recreational benefits, but fifth in residential benefits (see Exhibit 3-12). Residential visibility benefits are driven by population and visibility improvements. Overall, benefits are greater in the East. This is due in part to greater population levels as well as greater visibility improvements (see Exhib it 3-1). Benefits are also very high in California due to the state’s large population and visibility improvements, especially in and around Los Angeles and San Francisco. R esidential visibility is also dependent upon the WTP value applied. Much of the West uses the WTP value for Denver (see Exhibit 3-6), which is highest WTP value being widely applied. Yet, the West still has lower

### Second Section 812 Pr ospective Analysis February 2011

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### overall benefits to residential visibility. 36 

### This impact shows that the effect of population and visibility improvement dominates the effect of WTP value applied. The First Prospective Analysis (U.S. EPA, 1999) only considers benefits to recreational visibility due to concerns about the methods u sed in the residential visibility study by McClelland et al. (1991). The First Prospec tive Analysis finds benefits to recreational visibility of $3.1 billion in 2000 and $4.5 billion in 2010 (2006$). 37 

### These results are smaller than those found in this analysis ( $3.3 billion in 2000 and $8.6 billion in 2010). The difference in benefits is largely due to differences in the air quality estimates between the First and Second Prospective Analyses. This analysis attributes greater visibility improvements to the CAAA, and thus has to higher benefits estimates.

EXHIBIT 3-9. PRIMARY ESTIMATE OF RECREATIONAL BENEFITS TO VISIBILITY IN 2020 (BILLION 2006$)

36 The WTP value for San Francisco is high er than Denver, but the San Francisco value is not applied to other MSA’s.

37 Adjusted from 1990$ to 2006$ using the CPI-U

### Second Section 812 Pr ospective Analysis February 2011

3-17 EXHIBIT 3-10. PRIMARY ESTIMATE OF RESIDENTIA L BENEFITS TO VISIBILITY IN 2020 (BILLION 2006$)

### Second Section 812 Pr ospective Analysis February 2011

3-18 EXHIBIT 3-11. PRIMARY ESTIMATE OF TOTAL BENEFITS TO VISIBILITY IN 2020 (BILLION 2006$)

### Second Section 812 Pr ospective Analysis February 2011

3-19 EXHIBIT 3-12. PRIMARY ESTIMATE OF BENEFITS TO VISIBILITY IN 2020, STATE RANK

RANK RECREATIONAL
BENEFITS RESIDENTIAL BENEFITS TOTAL BENEFITS

1 California California California 2 Florida New York New York 3 Texas Pennsylvania Pennsylvania 4 New York Texas Texas 5 Illinois New Jersey Florida 6 Georgia Ohio Illinois 7 Pennsylvania Illinois Ohio 8 North Carolina Florida New Jersey 9 Ohio Maryland North Carolina 10 Virginia North Carolina Virginia 11 Michigan Virginia Maryland 12 New Jersey Georgia Georgia 13 Tennessee Michigan Michigan 14 Arizona Massachusetts Massachusetts 15 Maryland Indiana Indiana 16 Washington Tennessee Tennessee 17 Indiana Washington Washington 18 Massachusetts Utah Colorado 19 Missouri Colorado Missouri 20 Alabama Missouri Wisconsin 21 South Carolina Wisc onsin South Carolina 22 Colorado Oregon Utah 23 Wisconsin South Carolina Alabama 24 Minnesota Louisiana Oregon 25 Kentucky Conne cticut Louisiana 26 Louisiana Alabama Minnesota 27 Oregon Minnesota Connecticut 28 Mississippi Kentucky Kentucky 29 Oklahoma Arkansas Oklahoma 30 Nevada Oklahoma Nevada 31 Connecticut Nevada Arkansas 32 Utah West Virginia Mississippi 33 Arkansas Delawa re West Virginia 34 Iowa Mississippi Arizona 35 Kansas Iowa Iowa 36 New Mexico Kansas Kansas 37 West Virginia Maine Delaware 38 Nebraska Rhode Island Maine 39 Idaho District of Columbia New Hampshire 40 New Hampshire New Hampshire Rhode Island 41 Maine Idaho Idaho 42 Delaware Nebraska New Mexico

### Second Section 812 Pr ospective Analysis February 2011

3-20 RANK RECREATIONAL
BENEFITS RESIDENTIAL BENEFITS TOTAL BENEFITS

43 Rhode Island New Mexico Nebraska 44 Montana Vermont District of Columbia 45 South Dakota So uth Dakota Montana 46 Vermont Montana South Dakota 47 North Dakota North Dakota Vermont 48 District of Columbia Arizona North Dakota 49 Wyoming Wyoming Wyoming

### Second Section 812 Pr ospective Analysis February 2011

3-21 EXHIBIT 3-13. PRIMARY ESTIMATE OF BENEFITS TO VISIBILITY IN 2000, 2010, AND 2020 (BILLION 2006$, SAME SCALE AS PREVIOUS MAPS)

### Second Section 812 Pr ospective Analysis February 2011

4-1 

### CHAPTER 4 | AGRICULTURAL AND FOREST PRODUCTIVITY BENEFITS OF THE CAAA

BACKGROUND 

### A significant body of literature exists a ddressing the effects of tropospheric ozone on plants, including commercial tree species and agricultural crops. In a companion report prepared to support the Second Prospective study, we summarize peer-reviewed research that characterizes these effects. 38 

### In general, elevated leve ls of tropospheric ozone have been shown to reduce overall plant health and growth by reducing photosynthesis and altering carbon allocation. In order to estimat e the magnitude of plant growth reductions due to elevated tropospheric ozone levels, laboratory studies, such as Lee and Hogsett (1996), have developed ex posure-response functions describing the functional relationship between plant yield and ozone exposure for a variety of plant species. 39 

### Applying exposure-response functions, this an alysis estimates yield losses in agricultural crops and commercial tree species under the counterfactual, without-CAAA scenario relative to the baseline, with-CAAA scenario. Relative yield losses (i.e., reductions in crop and tree yield under the counterfactual scenario relative to th e baseline scenario) measure the amount crop and tree yields w ould be reduced in the absence of CAAA regulations, and therefore, indicate a benefit of the CAAA. 40


### Commercial timber and agriculture operations generally manage their land to maximize profits. As such, changes in crop yields between the baseline and counterfactual scenarios may affect the distribution of commercial species planted; for example, landowners may shift production towards plants that are less sensitive to elevated ozone concentrations under the counterfactual scenario . This may occur at the individual plant level, replacing one crop or tree species for a nother with a higher growth rate; or, it may occur at the community level, converting agri cultural lands to timberlands, or vice versa, to adjust for combined yiel d losses to agricultural crops and commercial trees.

38 Industrial Economics, Inc., Effects of Air Pollutants on Ecol ogical Resources: Literature Review and Case Studies, Draft

Report to USEPA Office of Air and Radiation, February 2010.

39 Lee, E.H. and W.E. Hogsett. 1996. Methodology for Calculatin g Inputs for Ozone Secondary Stan dard Benefits Analysis: Part

II. Prepared for the U.S. EPA, Office of Air Quality Planning and Standards, Air Qu ality Strategies and Standards Division.

40 Relative yield losses are estimat ed instead of relative yield ga ins because the baseline (with CAAA) scenario in this analysis

defines current conditions, whereas, the counterfactual (no CAAA) scenario define s a change in curre nt conditions. The models applied in this analysis forecast changes in yield relative to current conditions (i.e., relative to the baseline scenario).

### Second Section 812 Pr ospective Analysis February 2011

4-2 

### Changes in the distribution and yield of cr op and tree species may in turn affect the supply of and demand for agricultural crops and commercial tree species, resulting in changes in producer and consumer surplus w ithin the agricultural and timber sectors of the economy. This chapter documents our appr oach and results to estimating the welfare effects of changes in agriculture and timb er markets resulting from the passage of the CAAA; ozone concentration estimates exis t for years 2000 through 2020, however, changes in ozone concentration during this period with and without the CAAA may result in welfare effects that extend beyond 2020. This analysis finds that crop and timber yields increase over time with reductions in ozone concentration associated with implem entation of the CAAA. Yield increases are greatest in the geographic areas exhibiting th e largest reduction in ozone concentration with the CAAA; specifically, along the East Co ast (the Southeast, in particular), in the Midwest (within the Ohio River Valley), and in California (Exhibits 4-4 and 4-5). The remainder of this chapter consists of three sections. The first section presents the analytical framework for the overall analysis , from forecasting ozone concentrations to estimating welfare effects. The second and third sections describe, respectively, the analytical methods and results of: 1) the analysis of relative yield losses in crops and trees under the counterfactual, no CAAA scenario; a nd 2) the analysis of welfare effects stemming from changes in crop and tree yields.

ANALYTICAL FRAMEWORK 

### This analysis applies three steps to esti mate the welfare benefits of the CAAA with respect to commercial agriculture and timber management:

1. Estimate tropospheric ozone concentrat ions between 2000 and 2020 across the conterminous U.S. under two scenarios: 1) the current state of regulation, including the CAAA (“baseline scenario”); and 2) a counterfactual scenario assuming a hypothetical rollback of the CAAA (“counterfactual scenario”);

2. Estimate relative yield losses for various commercial tree and agricultural crop species due to increased ozone concentrations under the counterfactual scenario (as opposed to the baseline scenario); 41 

### and,

3. Estimate the economic welfare effects (i.e., changes in both producer and consumer surplus) of increased yield in agricultural crops and commercial tree species under the baseline scenario relati ve to the counterfactual scenario. Exhibit 4-1 describes the conceptual framework for this analysis. Additional detail on the specific models used to complete the three mai n steps applied in this analysis is provided in Exhibit 4-2. The following section details the first two analytic steps described above and in Exhibit 4-1, while the final section of this chapter describes the third analytical step described above and in Exhibit 4-1.

41 Relative yield losses indicate percentage crop and timbe r yields are reduced under the counterfactual scenario relative to

the baseline scenario.

### Second Section 812 Pr ospective Analysis February 2011

4-3 EXHIBIT 4-1. DIAGRAM OF THE ANALYTICAL STEPS APPLIED TO ESTIMATE BENEFITS OF THE
CAAA WITH RESPECT TO AGRICULTURE AND COMMERCIAL TIMBER PRODUCTION

### Second Section 812 Pr ospective Analysis February 2011

4-4 EXHIBIT 4-2. DETAILS ON THE FORMAT AND CONTENT OF DATA INPUT AND OUTPUT FOR THE
DIFFERENT MODELS APPLIED

MODEL INPUT REQUIREMENTS OUTPUT OUTPUT FORMAT

Community Multiscale Air Quality (CMAQ) Modeling System /Enhanced Voronoi Neighbor Averaging (eVNA) a Climate and contaminant parameters for CMAQ; hourly ozone monitoring data combined with CMAQ results for eVNA Tropospheric ozone concentrations under both CAAA scenarios for 2000, 2010, and 2020 12-km 2 grid cells

Exposure-response Functions b Crop-subregion-specific and region-specific ozone metrics (W126, 7-hour average, 12- hour average) Relative yield losses for select agricultural crops and commercial tree species under no CAAA scenario for 2000, 2010, 2020 Crop-subregionspecific and treeregion-specific relative yield losses

Forest and Agricultural Sector Optimization Model (FASOM) c Relative yield losses at the subregional-level for crops and at theregional-hardwoodand regional-softwoodlevel for trees Changes in consumer/producer surpluses for the agricultural and timber sectors Changes in agricultural sector surpluses at the subregional-level and changes in the agricultural and timber surpluses at the regional- and national-levels.

Notes: a) CMAQ model results provided by ICF International on October 8, 2008; eVNA results provided by Stratus Consulting on July 20, 2009 and September 28, 2009. b) Exposure-response functions used in analysis from: Lee, E.H. and W.E. Hogsett. 1996. Methodology for Calculating Inputs for Ozone Se condary Standard Benefits Analysis: Part II. Prepared for the U.S. EPA, Office of Air Quality Planning and Standards, Air Quality Strategies and Standards Division. c) FASOM results provided by RTI International on July 30, 2010.

ANALYTICAL METHODS AND RESULTS: RELATIVE YIELD LOSS 

### This section describes the methods and results of the analysis of relative yield losses in crops and trees under the counterfactual, no C AAA scenario. As described above, there are two distinct steps necessary to estimat e relative yield losses: 1) estimate tropospheric ozone concentrations over time under the base line and counterfactual scenarios; and, 2) calculate relative yield losses based on ozone concentration estimates. The section is organized by analytic step. For each step, th e methods applied to complete the step are described, followed by th e results of the step.

### Second Section 812 Pr ospective Analysis February 2011

4-5 Step 1: Estimating Tropospheric Ozone Concentrations With and Without the CAAA 

### This section describes the methodology used to estimate tropospheric ozone levels over time (2000-2020) both with and without the CAAA. 42 

### Further, this section describes the steps taken to aggregate tropospheric ozon e estimates in accordan ce with the input requirements of exposure-response functions (E xhibit 4-2). Finally, disaggregated and aggregated tropospheric ozone estimates are presented in this section. Tropospheric ozone concentrations were est imated using Enhanced Voronoi Neighbor Averaging (eVNA), which considers both the modeled ozone concen tration results and monthly ozone monitoring data. Specifica lly, the Community Multiscale Air Quality (CMAQ) Modeling System version 4.6 was used to estimate tropospheric ozone concentrations at a 12 square-kilometer grid level for both the eastern and western U.S. These estimates were then adjusted according to EPA hourly ozone monitoring data (EPA Air Quality System Data for 2002) using eVNA, a modified inverse distance weighted interpolation technique in whic h the ozone concentration at a given point is adjusted by weighting the concentrations at surrounding points by the distance from the point of interest. The eVNA analysis is based on the assumption that the distance between points and the variation in ozone concentr ations between points are correlated. 43


### This analysis considered three different ozone concentration metrics: W126, 7-hour average, and 12-hour average. These metrics are described in Exhibit 4-3. Each metric was calculated on a monthly basis for the May through September period. For the W126 metric (a cumulative exposure metric), monthly values were estimated by summing the daily W126 values for each day in the mont h. For the 7-hour and 12-hour averages, monthly values were estimated by taking the average 7- or 12-hour average estimated for each day in a given month. The same methodologies used to estimate monthly values were used to estimate combined W126 values and 7- and 12-hour averages for the entire May through September period.

The Forest and Agricultural Sector Optimi zation Model (FASOM), the economic model employed in this analysis, requires species gr owth inputs at a subregion-level for crops and at a region-level for trees; the subregi ons and regions defined by the model are highlighted in Exhibits 4-4 and 4-5. Subregions define state or sub-state areas. There are a total of 63 subregions defined in FASOM. 44 

### Regions define sets of multiple states or sub-state areas. There are a total of 11 regions defined in FASOM. 45

42 Welfare effects associated with changes in crop and commercial timber yield may be experienced beyond 2020.

43 The eVNA methodology is described in greater detail in: EP A. 2007. Technical Report on Oz one Exposure, Risk, and Impact

Assessments for Vegetation. EPA 452/R-07- 002. Prepared by Abt Associates Inc. for U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Health and Environmental Impacts Division.

44 FASOM subregions refer to each of the 48 states of the cote rminous U.S. However, some stat es including Texas, California,

Indiana, Illinois, Ohio, and Iowa are subdivided into multiple subregions.

45 FASOM regions include: Northeast, Lake States, Corn Belt , Great Plains, Southeast, Sout h Central, Southwest, Rocky

Mountains, Pacific Southwest, Pa cific Northwest (East side), and Pacific Northwest (West side).

### Second Section 812 Pr ospective Analysis February 2011

4-6 EXHIBIT 4-3. DETAILS ON OZONE METRICS APPLIED IN EXPOSURE-RESPONSE FUNCTIONS

METRIC DESCRIPTION FORMULA

W126 Weighted sum of all tropospheric ozone concentration values observed hourly between 8 am and 8 pm 

#  

 pm i

am i i C 

## C w i 8

8 where:

i i C c 

## e w 126 

## 4403 1 1





##  

7-Hour Average Average of all tropospheric ozone concentration values observed hourly between 9 am and 4 pm 

#  

 pm i

am i i 

## C 4

9 

## 7 1

12-Hour Average Average of all tropospheric ozone concentration values observed hourly between 8 am and 8 pm 

#  

 pm i

am i i 

## C 8

8 

## 12 1

Note: C i = hourly ozone concentration at hour i in parts per million (ppm)
Sources: 1. EPA. 2007. Technical Report on Ozone Exposure, Risk, and Impact Assessments for Vegetation. EPA 452/R-07- 002. Prepared by Abt Associates Inc. for U.S. Environm ental Protection Agency, Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. 2. Olszyk, D.M., H. Cabrera and C.R. Thompson. 1988. Cali fornia statewide assessment of the effects of ozone on crop productivity. APCA Notebook. 38(7):928-931.



### Given the requirements for FASOM inputs, differences in ozone concentration were estimated at the subregion level and at the region level. Ozone metrics were aggregated by region and subregion by calculating weighted averages for the CMAQ 12 squarekilometer grid cells intersecting each region and subregion. Grid cell weights were derived by calculating the area of a grid cell intersecting a given region or subregion divided by the total area of the region or subregion. Specifically, the following equation was used to aggregate ozone metrics by region or subregion. 

# 

 

##  N

i i i subregion region 

## C w C

1 /



### where: w i 

### = weight of cell i = 

# 

 N

i i i



## a a

1 

### (a i 

### = area of cell i in region/subregion)

C

i 

### = W126, 7-hour average, or 12-hour average value for cell i N = total number of grid cells int ersecting a given region or subregion

Step 1 Results: Tropospheric Ozone Estimates With and Without the CAAA 

### Exhibits 4-4 and 4-5 present differences in W126 values with and without the CAAA by region and subregion, respectively, for each year in the analysis (differences are calculated by deducting W126 values with the CAAA from W126 values without the

### Second Section 812 Pr ospective Analysis February 2011

4-7 

### CAAA). 46 

### CMAQ estimates of tropospheric ozone levels were genera ted for 2000, 2010, and 2020. Thus, the results of the eVNA analysis are limited to these years.

Exhibits 4-4 and 4-5 indicate that the differe nces in ozone concentration between the two CAAA scenarios increase over time. That is, ozone concentrations without the CAAA increase over time while concentrations with the CAAA decrease, leading to increased differences between the two scenarios. The di fferences in ozone concentrations vary by region and subregion. Specifically, the Paci fic Southwest and Southeast regions exhibit the greatest differences in ozone concentra tion over time followed closely by the South Central, Cornbelt, and Northeast regions. The subregion map (Exhibit 4-5) provides diffe rences in ozone concentration at a finer spatial resolution than the region map. It appears that while the regions listed above exhibit the greatest differences in ozone con centration between the two scenarios, on-thewhole, some states and/or portions of states within these regions exhibit greater differences than others. Specifically, Vi rginia, North Carolina, South Carolina, Tennessee, and southern California exhibit the greatest differences in ozone concentration between the two scenarios over time. Secondarily, Pennsylvania, West Virginia, Kentucky, Indiana, Illinois, and Ohio exhib it large differences in ozone concentration.

Step 2: Estimating Effects of Changes in Tropospheric Ozone Concentrations on Crop and Tree Growth 

### This section describes the calculation of rela tive yield losses for crops and trees due to elevated tropospheric ozone concentrations unde r the counterfactual scenario. In order to estimate relative yield losses, this analys is relies on species-specific exposure-response functions that estimate plant yield as a function of W126, 7-hour average, or 12-hour average ozone metrics. This section presents the exposure-response functions applied in this analysis; describes the methodology used to derive the appropriate ozone metric inputs for each crop-subregion combination and each region; and, describes the methodology used to estimated relative yield losses based on exposure-response functions. Finally, relative yield losses are presented for select crops and forest types by FASOM region and subregion for each year in the analysis (2000, 2010, and 2020).

46 Differences in 7-hour and 12-hour average ozone concentrations are not displayed because the majority of exposureresponse functions used in this analysis require W126 valu es as a measure of ozone conc entration. The geographic distribution of differences between 7-hour and 12-hour averages with and without the CAAA, in te rms of the areas with the greatest or smallest differences, is similar to the differences presented in Exhibits 4 and 5. However, the magnitude of the differences between the 7-hour and 12-hour averages with and without the CAAA ar e smaller than the differences between the W126 data with and without the CAAA because the 7-hour an d 12-hour average metrics are not additive metrics, as is the W126 metric.

### Second Section 812 Pr ospective Analysis February 2011

4-8 EXHIBIT 4-4. REDUCTIONS IN OZONE CONCENTRATION WITH THE CAAA BY FASOM REGION (PERIOD = MAY – SEPTEMBER; METRIC = W126)

### Second Section 812 Pr ospective Analysis February 2011

4-9 EXHIBIT 4-5. REDUCTIONS IN OZONE CONCENTRATION WITH THE CAAA BY FASOM SUBREGION (PERIOD = MAY – SEPTEMBER; METRIC = W126)

### Second Section 812 Pr ospective Analysis February 2011

4-10 

###  Exposure-Response Functions: Exposure-response functions are derived through

laboratory studies measuring the growth effects of various ambient ozone concentrations on plants. The functions used in this analysis are either exponential or linear regression equations d escribing plant growth as a function of ozone concentration. While the use of laboratory-derived functions may not be ideal (i.e., field studies may be preferable), for many species the laboratory studies provide the best available information regarding responses to ozone exposure. This analysis applies exposure-response functions from a Lee and Hogsett study (1996), which was developed for EPA and has been applied in other recent EPA analyses. 47 

### The specific exposure-response func tions used in this analysis are each based on one of the three different ozone concentration metrics: W126, 7- hour average, and 12-hour average, as defined in Exhibit 4-3. Exhibit 4-6 presents the exposure-response functions applied in this analysis for different crops and
trees. 48



###  Ozone Metric Inputs for Crops: Crop-subregion-specific ozone metrics were

derived by determining each crop’s harv est date using, “Usual Planting and Harvesting Dates for U.S. Field Crops” released by the U.S. Department of Agriculture, and then rolling back by the number of growing days to determine the crop planting date (the period between the pl anting date and the harvest date is the growing period; crops are only exposed to ozone during their growing period). 49 

### Ozone metrics were then calculated for the growing period. Some crops, including tomatoes and potatoes, are grow n throughout the year. For these crops, the growing period within the May thro ugh September period which yields the greatest difference in ozone concentrati on between the baseline and counterfactual scenarios was applied. The growing peri od for some crops falls outside of the May through September period for which oz one estimates exist (i.e., the planting date is before May 1 or the harvest date is after September 30). For these crops, the ozone metric was calculated utilizing only those growing days within the May through September period. This method ology is based on the assumption that ozone levels outside of the May throug h September period are not elevated to levels that would affect plant growth. 50



###  Ozone Metric Inputs for Trees: The harvest rotation for trees spans multiple

years. Therefore, tree species do not have a specific growing period. Regionspecific ozone metrics were derived by cal culating the relevant ozone metric over

47 Lee, E.H. and W.E. Hogsett. 1996. Methodology for Calculatin g Inputs for Ozone Secondary Stan dard Benefits Analysis: Part

II. Prepared for the U.S. EPA, Office of Air Quality Planning and Standards, Air Qu ality Strategies and Standards Division. For example, EPA applied functions from Lee and Hogsett (1996) in: USEPA. July 2007. Review of the National Ambient Air Quality Standards for Ozone: Policy Assessment of Scientific and Technica l Information. EPA-452/R-07-007.

48 The crop and tree species included in Exhibit 4-6 are selected for inclusion in this analysis because: a) the functional

relationship between ozone exposure and yi eld is established for each species (i.e ., an exposure-response function has been estimated); and, b) each species is explicitly considered in FASOM.

49 U.S. Department of Agriculture. 1997. Usual Planting and Harvesting Dates for U.S. Field Crops. USDA, National Agricultural

Statistics Service. Agricultural Handbook No. 628.

50 Given that ozone concentrations and crop growing periods vary by subregion, ozone concentration inputs are specific to

each crop-subregion combination.

### Second Section 812 Pr ospective Analysis February 2011

4-11 

### the three-month period between May and September that yields the greatest difference in ozone concentration be tween the baseline and counterfactual scenarios for each region. This methodology is also based on the assumption that ozone levels outside of Ma y through September are not elevated to levels that would affect plant growth.

EXHIBIT 4-6. EXPOSURE-RESPONSE FUNCTIONS AND FUNCTION PARAMETERS FOR CROPS AND TREES

SPECIES OZONE METRIC A (PPM) B GROWING DAYS FUNCTION a

CROP SPECIES Barley W126 6,998.50 1.39 95
B

A O 

## Ce Y      





##  3 Corn W126 97.90 2.97 83
Cotton W126 96.10 1.48 114

Oranges b 12-Hour Average 53.70 261.10 214 

#   

#   3 

## O B A C Y 

 

Potatoes W126 99.50 1.24 66

B

A O 

## Ce Y      





##  3 Rice c 7-Hour Average 0.20 2.47 85

Sorghum W126 205.90 1.96 85 Soybeans W126 110.20 1.36 93

Processing Tomatoes c 12-Hour Average 9,055.00 32,367.00 66 

#   

#   3 

## O B A C Y 

 

Wheat (Spring & Winter) b W126 53.40 2.37 58 B

A O 

## Ce Y      





##  3

TREE SPECIES Aspen

W126 109.81 1.22

N/A B

A O 

## Ce Y      





##  3 Black Cherry 38.92 0.99

Douglas Fir 106.83 5.96

Eastern White Pine 63.23 1.66

Ponderosa Pine 159.63 1.19

Red Maple 318.12 1.38

Sugar Maple 36.55 5.78

### Second Section 812 Pr ospective Analysis February 2011

4-12 SPECIES OZONE METRIC A (PPM) B GROWING DAYS FUNCTION a

Tulip Poplar 51.38 2.09

Virginia Pine 1,714.64 1.00

Notes: Variables defined as follows: C = theoretical constant equivalent to the theoretica l yield at zero ozone exposure in the exponential functions and 2.70 in the linear functions making C*A equal to the theoretical yield at zero ozone exposure; A = scale parameter for ozone exposure at which th e expected growth response in 37 percent of the theoretical yield at zero ozone exposure; B

= the shape parameter affecting the ch ange in the predicted rate of loss. (a) Exposure-response functions do not exist for different types of oranges and spring wheat, both of which are included in FASOM, therefore the same function parameters ar e used for all orange types, and winter and spring wheat, based on the assumption that the growth of these crops is similar. (b) The number of growing days for rice and processing tomatoes applied in the: EPA. 2007. Technical Report on Ozone Exposure, Risk, and Impact Assessments for Vegetation. EPA 452/R-07-002. Prepared by Abt Associates Inc. for U.S. Environmental Protecti on Agency, Office of Air Quality Planning and Standards, Health and Environmental Impacts Division, differs from the growing days applied for these crops in this analysis. The 2007 report applied a growing period of 69 days for rice and 76 days for processing tomatoes. The us e of different growing periods for these crops does not result in significant changes to relative yiel d loss estimates (maximum differences < +/- 0.2%).
Source: Lee, E.H. and W.E. Hogsett. 1996. Methodology for Calculating Inputs for Ozone Secondary Standard Benefits Analysis: Part II. Prepared for the U.S. EPA, Office of Air Qu ality Planning and Standards, Air Quality Strategies and Standards Division.



### Relative Yield Loss Estimation: Relative yield losses were calculated based on exposure-response functions accordi ng to the following formula:

WithCAAA NoCAAA 

## Y Y RYL

  1 where Y = plant yield

Crop-subregion- and tree-region-specific relative yield losses are calculated for each year in the analysis (2000, 2010, and 2020). 51


### Because FASOM models tree growth by hard wood and softwood forest types, relative yield losses for individual tree species were aggregated by hardwoods and softwoods. This was accomplished through averaging the relative yield losses of each hardwood and softwood species potentially present in a given region. If no hardwood or softwood species for which relative yield losses were es timated is potentially present in a region, the national average of hardwood or softwood relative yield losses was applied.

51 Not all crop and tree species are present in every subregion or region. Relative yield gains are not estimated for crops in

subregions where the given crop is not potentially present as defined by FASOM. Similarl y, relative yield gains are not estimated for trees in regions where the given tree spec ies is not potentially present as defined by FASOM.

### Second Section 812 Pr ospective Analysis February 2011

4-13 Step 2 Results: Relative Yield Losses for Crops and Trees 

### Maps presenting crop-subregion- and tree-re gion-specific relative yield losses for the different crops and forest types included in this analysis are provided in Appendix B (Exhibits B-1 through B-13). Exhibit 4-7 provides a summary of relative yield losses by crop/forest type and year. Relative yield losses indicate a benefit of the CAAA; the larger the relative yield loss without the CAAA, the greater the crop or tree yield with the CAAA.

Outside of reductions in ozone concentration with the CAAA, a number of factors affect yield changes in crops and trees including sen sitivity to ozone, geographic distribution, growing period length, and the specific time of year the growing period occurs. Given these factors, relative yield losses vary betw een the different crops and forest types included in this analysis, with some crops and forest types exhibiting limited changes in growth (e.g., barley, rice, and sorghum) and others exhibiting relatively great changes in growth (e.g., cotton, potato, winter wheat, hardwoods, and softwoods). In general, relative yield losses range from 0 to 23 percent across all years, crops, and forest types. Relative yield losses tend to increase over time, with the smallest yield losses occurring in 2000 and the largest occurring in 2020. The maximum relative yield loss for crops is estimated for potatoes growing in Maryland in 2020 (relative yield loss without the C AAA equals 20.80 percent). The minimum relative yield loss is estimated for soybeans growing in Florida in 2010 (relative yield loss equals -0.55 percent). The negative relative yield loss for soybeans in Florida in 2010 indicates that soybean growth is improved without the CAAA. The growing period for soybeans in Florida is roughly mid-July through September. The negative relative yield loss is due to reductions in W126 ozone metric values under the counterfactual, no CAAA scenario in Florida in September of 201 0. Ozone concentrations are lower under the baseline, with CAAA scenario in Florida for all other m onths in 2010. Thus, ozone concentrations aggregated across all months of interest, May through September, are reduced in Florida in 2010 with the CAAA (E xhibit 4-5). The negative relative yield loss for soybeans, however, is minimal given th e relatively minor differences in forecast ozone concentrations between the scenarios (a relative yield loss of -0.55 percent indicates that yield with the CAAA is 99.5 percent of yield without the CAAA). No other crops exhibit negative yield losses in Florida in 2010. Negative yield losses are also estimated for rice in the California-North and CaliforniaSouth subregions in 2000 (relative yield losses of -0.02 and -0.08 percent, respectively). The relative yield loss function for rice is a function of the 7-hour ozone metric (Exhibit 4-6). Although W126 ozone metric values are lower under the baseline, with CAAA scenario for all months (May through Septem ber) in these subregions, the 7-hour average values for these subregions are lower under the counterfactual, no CAAA scenario in 2000, leading to negative yield losses for rice. Similar to soybeans, the effects of the negative relative yield losses for rice are minimal given the relatively minor differences in forecast ozone concentrations between the scenarios.

### Second Section 812 Pr ospective Analysis February 2011

4-14 

### Hardwood forests exhibit greater relative yi eld losses than softwood forests across all years in the analysis. The maximum relativ e yield losses in hardwoods and softwoods are estimated for the Southeast region in 2020 (relative yield losses equal 23.04 and 12.27 percent for hardwoods and softwoods, respec tively). The minimum relative yield loss across both forest types is estimated for softw oods in the Pacific Northwest East region in 2000 (relative yield loss equal to 0.06 percent). As presented in Exhibits 4-4 a nd 4-5, reductions in tropospheric ozone concentrations are greatest along the East Coast, particularly the Southeast, in the Midwest (within the Ohio River Valley), and in California. Relative yield losses in crops and trees, therefore, are expected to be greatest in these geogra phic areas because of large reductions in tropospheric ozone concentrations attributable to the CAAA. Overall, relative yield losses appear to be greatest in the geographic areas with the greatest reduction in ozone concentration (see maps in Appendix B). In particular, the greatest relative yield losses for both crops and trees occur in the Southeast , frequently in Virginia, North Carolina, South Carolina, and Tennessee.

ANALYTICAL METHODS AND RESULTS: AGRICULTURE AND TIMBER MARKETS
WELFARE EFFECTS 

### This section describes the methods and results of the analysis of welfare effects stemming from changes in crop and tree yields unde r the baseline scenario relative to the counterfactual scenario. Commercial timber a nd agriculture operations generally manage land to maximize profits. As such, chang es in crop yields between the baseline and counterfactual scenarios may affect the distri bution of commercial species planted; for example, landowners may shift production towards plants that are less sensitive to elevated ozone concentrations under the count erfactual scenario. This may occur at the individual plant level, replacing one crop or tree species for another with a higher growth rate; or, it may occur at the community level, converting agricultural lands to timberlands, or vice versa, to adjust for combined yi eld losses to agricultural crops and commercial tree species. Changes in the distribution and yield of cr op and tree species may in turn affect the supply of and demand for agricultural crops and commercial tree species, resulting in changes in the welfare of consumers and w ithin agricultural and timber sectors of the economy. To quantify this economic benefit of cleaner air, we used FASOM. FASOM development was funded by EPA’s Climate Economics Branch (CEB) and other EPA, U.S. government, and non-governmental funde rs over several decades as a partial equilibrium tool to evaluate the welfare a nd market impacts of public policies affecting agriculture and forestry. The model simulates biophysical and economic processes affecting land management and land alloca tion decisions over time to potentially competing agriculture and forest activities. Although the latest version of FASOM was developed to evaluate climate and biofuels policies, the model is capable of assessing aSecond Section 812 Prospective AnalysisFebruary 20114-15EXHIBIT 4-7. MINIMUM, MAXIMUM, AND AVERAGE RELATIVE YIELD LOSSES ACROSS ALL FASOM SUBREGIONS FOR CROPS AND ALLFASOM REGIONS FOR TREES BY YEAR (2000, 2010, 2020)CROP/FOREST TYPE200020102020MINIMUM MAXIMUM AVERAGEMINIMUM MAXIMUM AVERAGEMINIMUM MAXIMUM AVERAGEBarley 0.00% 0.02% 0.01% 0.00%0.06% 0.02% 0.00% 0.07% 0.02%Corn 0.00% 1.12% 0.18% 0.00%3.07% 0.44% 0.00% 3.45% 0.56%Cotton 0.00% 6.60% 1.15% 0.00%16.67% 3.00% 0.00% 20.31% 3.81%Oranges 0.00% 1.95% 0.09% 0.00%4.68% 0.25% 0.00% 7.87% 0.43%Potato 0.00% 6.17% 1.76% 0.00%17.54% 4.99% 0.00% 20.80% 6.50%Rice -0.08% 0.14% 0.00% 0.00% 1.03% 0.11% 0.00% 1.66% 0.18%Sorghum 0.00% 0.87% 0.14% 0.00%2.17% 0.35% 0.00% 2.65% 0.47%Soybean 0.00% 3.60% 1.24% -0.55%11.73% 3.07% 0.00% 12.74% 4.26%Processing Tomatoes 0.00% 1.82% 0.31%0.00% 5.54% 0.96% 0.00% 8.21% 1.47%Spring Wheat 0.00% 1.50% 0.06% 0.00% 3.67% 0.15% 0.00% 6.98% 0.28%Winter Wheat 0.00% 6.53% 1.00% 0.00% 18.23% 2.49% 0.00% 19.23% 3.29%Hardwood Forests 1.60% 7.16% 5.06% 4.20% 19.12% 13.86% 6.61% 23.04% 16.68%Softwood Forests 0.06% 3.85% 1.77% 0.25% 10.49% 4.88% 0.42% 12.27% 6.11%Note: Negative relative yield losses indicate yield reductions with the CAAA.

### Second Section 812 Pr ospective Analysis February 2011

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### broad range of factors that might affect plant growth; for this project, we worked with the model’s developers to develop input files to characterize the impact of ozone on plant and tree growth at a regional and crop-specific level, using the exposure-response results described above. 52


### Although FASOM has been widely applied to agricultural sector analysis and has been peer reviewed in many contexts, it has not to date been subject to a validation exercise comparing the model results for an historical period to historical data for that period. 53 

### As a result, the performance of the model in forecasting future agricultural sector effects, such as those estimated for this study, h as not yet been assessed. Two other potential limitations may pertain in EPA’s application of FASOM for this study. First, FASOM adopts a model simulation approach which assumes perfect foresight by economic actors in the agricultural sector. A perfect for esight assumption may be of concern for some long-term analyses, but is likely to be less problematic for this study because our time horizon extends only to 2020. Furthermore, USDA projections of commodity prices and outputs also extend nearly to 2020, and FA SOM’s projections for their base case agree well with the USDA projections. As a result, the effect of perfect foresight on model outcomes in the present study is reduced. 54 

### A second potential limitation of FASOM is its approach to estimating the sensitivity of imports to changes in domestic prices. Although FASOM is not a full international model, it does incorporate an import elasticity estimate for the largest and most im portant commodity crops. This allows the model to capture, for example, increases in agricultural imports to the U.S. under a scenario in which domestic crop prices are projected to rise. For a number of minor crops, traded in very small quantities, howeve r, FASOM holds imports fixed. The effect of this factor on our results is not clear, but we estimate that a more flexible import sector for these much less important crops would have only a minor effect on our estimates of the net benefits of reducing ozone exposure for U.S. crops. We expect the directional bias of holding minor crop imports fixed, while small, would be to slightly reduce our estimates of the net welfare benefit of reduc ing ozone exposure, and thereby improving productivity, of domestic agricultural crops.

52 Note that we performed two runs of the FASOM model, one where the response to ozone for those crop/region

combinations without specific individual concentration-response f unctions are assumed to be zero, and a second where impacts on crop/region combinations without specific concentr ation-response functions were set to the values used in adjacent regions and/or proxy crops where possible (for exam ple, soft white wheat was used for barley and sugarbeets; tomatoes for processing were used for potatoes; soybeans for fresh tomatoes; corn for fresh tomatoes if there is not a value for soybeans; etc.). We found that the difference in the ov erall national results between th ese two runs was negligible, however. As a result, in this chapter we report the resu lts from the run that applies proxy crop/region concentrationresponse functions. Note further that the version of FASOM used for this analysis is the version current as of July 21, 2010.

53 See, for example, a review commissioned by USEPA for its application of FASOM to support regulatory analysis of

renewable fuels standards, concluded in Ju ly of 2010 and available at the following web site (accessed November 26, 2010): http://www.epa.gov/otaq/fuels/r enewablefuels/regulations.htm

54 Perfect foresight is a basic assumption of the modeling approa ch on which FASOM is based. Structuring the model based on

perfect foresight rather than a myopic (recursive) approach a llows an expanded array of policy simulations and potential insights, which is the main purpose of this type of model.

### Second Section 812 Pr ospective Analysis February 2011

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### The economic welfare results of the FASOM modeling are presented in Exhibit 4-8. FASOM generates total welfare estimates for the agricultural and forest sectors for each of our scenarios, for each target year, reflec ting the sum of total consumer and producer surplus derived from agriculture and forest production. In general, higher ozone concentrations in the without-CAAA scenario lead to reduced agricultural and forest productivity, raising prices for these products , which in turn increases producer surplus but reduces consumer surplus by a larger am ount. As a result, FASOM estimates the net welfare benefits of the CAAA to be approxi mately $1 billion in 2000, $5.5 billion in 2010, and $10.7 billion in 2020, increasing over time as the differences in ozone concentrations grows. 55

EXHIBIT 4-8. SUMMARY OF FASOM RESULTS: TOTAL CONSUMER AND PRODUCER SURPLUS VALUES
FOR THE AGRICULTURAL AND FOREST SECTORS

VARIABLE MODEL RUN 2000 2010 2020

Annual Welfare, US Forest Sector With Clean Air Act ($ billion) $637 $877 $1426 Without Clean Air Act ($ billion) $636 $875 $1426 Damage Estimate ($ billion) $1.5 $1.7 $0
Percent change 0.24% 0.20% 0%
Annual Welfare, US Agriculture Sector With Clean Air Act ($ billion) $1706 $1831 $1916 Without Clean Air Act ($ billion) $1706 $1828 $1905 Damage Estimate ($ billion)

− $0.5 $3.8 $10.6
Percent change -0.03% 0.21% 0.55%
Annual Welfare, Forest and Agriculture Sector Combined With Clean Air Act ($ billion) $2343 $2708 $3341 Without Clean Air Act ($ billion) $2242 $2703 $3331 Damage Estimate ($ billion) $1.0 $5.5 $10.7
Percent change 0.05% 0.20% 0.32%

Notes: 1. Results are expressed in year 2006 dollars.



### In general, FASOM forecasts a relative shift towards forestry and away from agriculture under the without-CAAA scenario, indicating that the net impacts of the ozone effects on forests and agriculture would make forestry re latively more profitable than in the baseline compared with agriculture, resulting in a shif t in land use. The model forecasts a sizable increase in cropland in the without-CAAA scenario, however there is an even greater

55 Note that the year 2000 in FASOM represents average annual activity over the 5-year period from 2000 to 2004; 2010

represents 2010 through 2014; and 2020 repr esents 2020 through 2024. Values prov ided for ozone impacts in 2000, 2010, and 2020 were applied to the 2000, 2010, and 2020 model periods in FASOM, respectively. Th e results presented here do not includes losses Canada and th e rest of the world; for exampl e, in 2020, higher US prices in the without-CAAA scenario result in additional consumer surplus losses to non-US consumers of $1.7 billion in the forest sector and $3.3 billion in the agricultural sector.

### Second Section 812 Pr ospective Analysis February 2011

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### decline in pasture as the returns to crop production rise relative to livestock production with higher crop prices. As is shown in Exhib it 4-8, the shift towards forestry results in a decrease in the damage estimate to this sect or over time; by 2020 the damage estimate is $0. As noted above, the model suggests that the damages attributed to higher ozone concentrations indicate that producers gain in many cases, while consumers are always substantially worse off with the ozone imp acts reducing productivity. The reason that producers often are better off is that most fo rest and agricultural products have relatively inelastic demands, which means that a genera l decline in productivity will tend to increase prices by more than the reduction in quantity, increasing revenue and often profits as well. In general, FASOM attributes large price increases in response to the reductions in productivity for these inelastic products, and production declines in the without-CAAA scenario for most agricultural commoditi es, with larger declines in general for those products experiencing larger ozone impacts, and also sizable reductions in exports. FASOM also is capable of modeling land-u se changes in response to the higher ozone concentrations in the without-CAAA scenario. The model indicates changes in major land use categories at the national level over ti me under the ozone impacts scenario, which leads to a net increase in forest of about 6.1 million acres by the 2020 model period and a increase in cropland of 7.6 million acres by 2020 in response to the productivity declines. At the same time, the model indicates that cropland pasture (high-quality land that is suitable for cropland but is being used as pastur e) and pasture (lower-quality land that is not suitable for growing crops without improve ment) decline by a total of 12.7 million acres and Conservation Reserve Program (CRP) land decreases by about 1 million acres. The crop experiencing the largest reduction in acreage is soybeans, while there is an increase in wheat acreage and a number of smaller shifts between alternative crops.

### Second Section 812 Pr ospective Analysis February 2011

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### CHAPTER 5 | ESTIMATION OF MATERIALS DAMAGE AND ECONOMIC BENEFITS

INTRODUCTION 

### Since the mid-19 th 

### century air pollution as been suspected of accelerating the degradation of natural and man-made materials that are e xposed to the outdoor environment. Concern over the effect of pollutants on materials has mainly been directed towards the economic consequences of damage to materials used in construction, but aesthetic damage to historic buildings and monuments is also a concern. Wet and dry acidic deposition, alone or combined with other air pollutants, c ontribute to the increased rate of materials damage. The principal components of acid deposition considered injurious to building materials are hydrogen ion (H + 

### ), sulfur oxides (SO x 

### ), and nitrogen oxides (NO x 

### ). In addition, volatile organics and oxi dizing agents such as ozone (O 3 

### ) and hydrogen peroxide (H 2 

### O 2 

### ) have been shown to play an an cillary role (NAPAP, 1991). Acidic deposition has been shown to have an effect on materials including zinc/galvanized steel and other metal, carbonate stone (as monu ments and building facings), and surface coatings (paints) (NAPAP, 1991). Metal structures are usually coated by alka line 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, in particular SO 2 

### (NAPAP, 1991). Research has shown that acidic deposition (wet deposition of h ydrogen ion and dry deposition of SO 2 

### and nitric acid) accelerates the rate of erosion of carbonate stone (marble and limestone) and the formation of gypsum crusts on the st one (NAPAP, 1991). Acidic deposition has numerous negative effects on painted wood and, in general, increases the weathering rate. In addition, acidic pollutants negatively aff ects painted metals resulting in adsorption, weight gain, discoloration, adhesion strengt h loss and/or failure at the metal/primer interface. Acidic deposition may also cau se damage to automotive finishes (NAPAP, 1991). This analysis will focus on quantifying the impa ct of sulfur dioxide deposition on exterior building and infrastructural materials including carbonate stone, galvanized steel, carbon steel, and painted wood. Exhibit 1 lists the materials damage effects that are quantified and unquantified in this analysis. The econom ic impact of materials damage will be calculated for six scenarios: with- and without-CAAA in 2000, 2010, and 2020. The difference between the with- and without-CAAA scenarios in each year represents the benefits of reduced materials damage due to CAAA-related programs.

### Second Section 812 Pr ospective Analysis February 2011

5-2 EXHIBIT 5-1. MATERIALS DAMAGE EFFECTS

POLLUTANT QUANTIFIED EFFECTS—DAMAGE TO: UNQUANTIFIED EFFECTS A —DAMAGE
TO:

Sulfur oxides Infrastructu ral 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) a The categorization of unquant ified effects is not exhaustive.

METHODOLOGY 

### This analysis applies the Air Pollution Emissions Experiments and Policy (APEEP) analysis model, described in Muller and Mendelsohn (2007, 2009), to link SO2 emissions to ambient SO2 levels. Using emission input s, the air quality model in APEEP predicts seasonal and annual average county concentrati ons for SO2, amongst other pollutants. As reported in Muller and Mendelsohn (2007), APEEP’s air quality modeling has been statistically tested against the predictions generated by the Community Multi-scale Air Quality Model (CMAQ). Materials damage estimates are derived using dose-response functions that relate material mass loss to ambient SO2. A key piece of information needed in the dose-response functions is the existing materials inventories. Categorization of materials inventories has been a challenge in the past . This analysis presents a method for estimating materials inventories by county. The materials inventory characterizes the quantity of four exterior

### Second Section 812 Pr ospective Analysis February 2011

5-3 

### building and infrastructural materials in each county in the lower 48 states. These include inventories of carbonate stone, galvanized steel, carbon steel, and painted wood surfaces. The inventory is developed for infrastruct ure, commercial buildings and residential buildings separately. The commercial inve ntory uses empirical results from the U.S. Department of Energy (U.S.DOE) Information Administration’s Commercial Buildings Energy Consumption Survey (U.S. DOE, 2006). The residential materials inventory employs data from the U.S. DOE’s Residen tial Energy Consumption Survey and the Annual Housing Survey conducted by the U. S. Census Bureau (USDOE, 2005; Census Bureau, 2007). The inventory differentiates buildings based on their reported size, given how the primary survey data are reported. That is, the surv eys provide structure size in terms of floor space (ft 2 

### ). Thus, a simplifying assumption regardin g the shape of the structure (it is posited that each structure is cubic with two stories of living space) permits the conversion from floor space to wall space. Th is implies that the total area of living space is equivalent to twice the area of one story. Thus, given the cubic shape assumption, the area of the four walls is equivalent to four times the area of one story, or two times the total reported area. This inventory also estimates the number of buildings based on the share of regional and state population in each county. Since the U. S. DOE reports total commercial buildings (by size) by region, the inventory extrapolat es to state-level inventories by assuming that the number of commercial buildings in each state is proportional to the share of regional population in each state. The same approach is used to extrapolate from the state to the county level. For both the commercial and r esidential inventory, the survey provides number of buildings, by size, by region whic h permits an assessment of the number of buildings by size, for each state and county. Having estimated the number and size of buildings by county, the next step involves calculating the probability of each material bei ng used in each region of the country, for each building type. Materials use for commercial buildings is computed directly from the U.S. DOE’s commercial survey since the number of buildings using each exterior building material is estimated in the survey. Proportions of total buildings using each material type are computed from the survey data directly. Materials used for residential buildings is computed in an analogous ma nner from the U.S. DOE’s residential survey; the proportion of total residential buildings using particular building materials is computed directly from the reported materials use probabilities by region. The building surface area calculations assume the following form:

SA mc 

### = ∑ t 

### (N ct 

### )(S ct 

### )(P mc 

### )

### Second Section 812 Pr ospective Analysis February 2011

5-4 

### where: SA mc 

### = exposed surface area of building material (m) in receptor county (c),

N ct 

### = number of structures type (t) in county (c),

S ct 

### = area of exterior wall space per structure type (t) and county (c), and

P mc 

### = probability that material (m) is used on exterior wall space in county (c). For infrastructural materials (galvanized and painted carbon steel), the materials inventory relies on methods developed in the National Acid Precipitation Assessment Program (NAPAP) (NAPAP, 1991). In particular, NAPAP reported surface area estimates for galvanized and carbon steel (fo cusing on bridges, transmission towers, railroads, and guardrail) for particular areas of the country. The ratios of exposed surface area to land area are then extrapolated to st ates and regions not covered by the original NAPAP surveys. Dose-response functions for man-made materials damages are obtained from two sources; the NAPAP studies (Atteraas, Haagenrud, 1982; Haynie, 1986) and from the International Cooperative Programme on Effects on Materials (ICP, 1998). The materials corrosion dose-response functions assume three slightly different forms. The function representing the effect of ambient SO 2 

### on galvanized steel is from Atteraas and Haagenrud (1982). The function is based on analysis of mass loss da ta of standard material test panels using regression techniques. Field data from 22 sites in Norway were used to obtain this doseresponse function. The function predicts that mass loss is a linear function of ambient SO 2 

### concentration. The dose-response f unction for galvanized steel assumes the following form:

Δ M c 

### = ( β 0 

### SO 2c 

### + β 1 

### )M c 

### where: Δ M c 

### = mass loss of material by county (c), β 0 

### , β 1 

### = statistically estimated parameters from the literature,

SO 2c 

### = ambient concentration of SO 2 

### by county (c), and

M c 

### = existing material by county (c). For painted surfaces, the dose-response relationship is from Haynie (1986). Haynie developed this equation on the basis of the erosion data obtained from painted specimens exposed to SO 2 

### and moisture. The model predicts the increase in erosion over the estimated erosion at a pH of 5.2 and an SO 2 

### concentration of zero (representative of a clean environment). The pH data used in th is function is from the National Atmospheric Deposition Program (NADP) and varies by region . It should be noted that pH, frequency exposed surface area is wet, and annual rain fall do not vary across scenarios. The doseresponse function for painted surfaces takes the following form:

### Second Section 812 Pr ospective Analysis February 2011

5-5 

### Δ M c 

### = R c 

### β 0 

### (10 -pH 

### – 10 -5.2 

### ) + β 1 

### SO 2c 

### F c 

### (3)

where: Δ M c 

### = mass loss of material by county (c), β 0 

### , β 1 

### = statistically estimated parameters from the literature,

SO 2c 

### = ambient concentration of SO 2 

### by county (c), pH = average pH by region,

F c 

### = frequency exposed surface area is wet by county (c), and

R c 

### = annual rainfall. The dose-response function representing the effect of ambient SO 2 

### on carbonate stone surfaces comes from the International Cooperative Programme on Effects on Materials Report No 30. This report summarizes the results obtained from an extensive field exposure program. The program gathered da ta on materials corrosion at 39 exposure sites in 12 European countries, the U.S., a nd Canada and measured gaseous pollutants, precipitation, and climate parameters at or nearby the exposure sites. Regression techniques were then used to relate the mat erials corrosion data to the environmental parameters. It should be noted that ambien t temperature, annual rainfall, and hydrogen concentration of precipitation do not vary across scenarios. The resulting dose-response function for carbonate stone surfaces takes the following form:

Δ S c 

### = ( β 0 

### SO 2c κ 

### )exp( γ T c 

### ) + β 1 

### R c 

### )H +



### where: Δ S c 

### = surface recession of material by county (c), β 0 

### , β 1 

### , γ , κ = statistically estimated parameters from the literature,

SO 2c 

### = ambient concentration of SO 2 

### by county (c),

M c 

### = existing material by county (c),

T c 

### = ambient temperature by county (c),

R c 

### = annual rainfall by county (c), and

H + 

### = hydrogen concentration of precipitation. Materials damage is valued as the cost of future materials maintenance activities. The accelerated rate of materials decay due to pollution exposure increases the frequency of regularly scheduled future maintenance activ ities. Under baseline emission conditions we assume a five-year maintenance schedule. The present value of materials maintenance costs occurring on a five-year sch edule is calculated using the following formula:

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### M rb 

### = δ x (RC rb 

### (e -rt 

### )/(1 - e -rt 

### )

where: M rb 

### = annual maintenance costs in county (r), baseline SO 2 

### , δ = market interest rate (4%), 56



### RC rb 

### = replacement costs in receptor county (r), baseline SO 2 

### , and t= time of repairs (5,10,15,...,T).

A change in the frequency of maintenance ac tivities due to a change in emissions is calculated as the ratio of the material s inventory after the emission change (I p 

### ) to the materials inventory before the change (I b 

### ). This ratio characterizes the extent to which a change in emissions has enhanced or mitigated materials decay rates. If the emission change increases pollution, then I p 

### <I b 

### , and the optimal maintenance schedule will occur earlier than every five years. This ratio is then multiplied by the five-year maintenance schedule, as shown in Equation 6, to yield the timing of the amended maintenance schedule due to the change in pollution (t * 

### ):

t * 

### = 5 x (I p 

### /I b 

### ). The materials maintenance cost equation (Equa tion 5) is adjusted to account for the amended maintenance schedule as follows:

M rp 

### = 

# ∑ t 

### [ δ x (RC rp 

### (1+r) -t* 

### )]

where: M rp 

### = annual maintenance costs in county (r), change emission SO 2 

### , δ = market interest rate (4%),

RC rp 

### = Replacement costs in receptor county (r), change emission SO 2 

### , and

t * 

### = new schedule of maintenance. The change in the present value of the mai ntenance schedules extending into the future constitutes the monetary impact of an emissi on change on materials damage. The effect of an emission change from source (s) is the sum of the change in all affected pollution receptor counties:

Δ M s 

### = ∑ r 

### (M rp 

### – M rb 

### ).

56 The APEEP model used for this analysis incorporates a four pe rcent discount rate. A five percent discount rate has been

used in other portions of the Second Prospective Analysis. Use of a five percent discount rate would lead to somewhat lower present value benefits.

### Second Section 812 Pr ospective Analysis February 2011

5-7 RESULTS 

### Exhibit 2 summarizes the benefits of redu ced materials damage due to CAAA programs in 2000, 2010, and 2020. Benefits are given by EPA region. As expected, benefits of CAAA programs to materials damage increase ove r time. The spatial distribution of the benefits is primarily owing to the dist ribution of the materials inventory and SO 2 

### exposure. The effect of SO 2 

### exposure seems to be driving the results. For example, the benefits in Region 5 are approximately twice as large as those in any other EPA region. This is due to the significant decrease in SO 2 

### exposure associated with the CAAA in this region.

EXHIBIT 5-2. BENEFITS OF REDUCED MATERIALS DAMAGE DUE TO CAAA PROGRAMS

EPA REGION VALUATION (THOUSAND 2006$)

2000 2010 2020

1: CT, ME, MA, NH, RI, VT $720 $2,100 $2,100 2: NY, NY $9,000 $10,000 $12,000 3: DE, DC, MD, PA, VA, WV $9,400 $19,000 $23,000 4: AL, FL, GA, KY, MS, NC, SC, TN $8,400 $16,000 $21,000 5: IL, IN, MI, MN, OH, WI $26,000 $38,000 $38,000 6: AR, LA, NM, OK, TX $2,200 $4,000 $7,300 7: IA, KS, MO, NE $2,000 $1,600 $1,600 8: CO, MT, ND, SD, UT, WY $400 $570 $730 9: AZ, CA, NV -$100 $490 $640 10: ID, OR, WA $340 $510 $560
Total $58,000 $93,000 $110,000 Notes: Results are rounded to two signific ant figures. Totals may not sum due to rounding.

### Second Section 812 Pr ospective Analysis February 2011

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### CHAPTER 6 | SUMMARY OF PRIMARY BENEFITS

This chapter presents an integrated summar y of the quantified and monetized primary benefits estimates described in this repor t and in the companion Second Prospective Section 812 study report, Ecological Benefits Analyses to Support the Second Section 812 Prospective Benefit-Cost Analysis of the Clean Air Act.

SUMMARY OF ANNUAL BENEFITS 

### The results of this benefits analysis d emonstrate that implementation of the CAAA’s programs on air emissions yields substantial hum an health and welfare benefits across the U.S. over the period from 1990 to 2020. These benefits include reductions in mortality risk, reductions in respiratory and cardi ovascular morbidity, improved visibility, improved productivity of agricultural crops and commercial forests, and reduced materials damage to resources as bridges, architectural coatings, and other materials that can be damaged by air pollution. Exhibi t 6-1 presents a summary of the mean primary annual economic benefits results from the S econd Prospective analysis for 2000, 2010, and 2020. Total annual be nefits range from $770 billi on in 2000 to $2,000 billion 2020 across all monetized benefit categories, with increasing benefits for each target year.

The bulk of the economic benefits result fro m improvements in human health; primarily from the reduction in premature mortality, which constitutes 88 percent of the total monetized benefits value in 2020. As we acknowledge throughout this report, there are numerous effects of improved air quality, includ ing most of the ecological benefits that we currently are unable to quantify and/or monetize. A proper economic accounting of these benefits would likely lead to even great er benefit values and would alter the relative contribution of the different categories of e ffects. Exhibit 6-2 presents a list of the unquantified and/or un-monetized benefit associated with CAAA improvements in air quality.

SUMMARY OF CUMULATIVE MONETIZED BENEFITS 

### Although this analysis focused on estimating an nual benefits for each of three target years, benefits of improved air quality due to the CAAA are expected to accrue through the study period. We estimate these cumulativ e benefits by interpolating between the target years, using information on the expect ed trend and trajectory of benefits throughout the period, and aggregating the resulting va lues to produce a discounted present value estimate of the cumulative benefits of Titles I through V of the CAAA.

### Second Section 812 Pr ospective Analysis February 2011

6-2 EXHIBIT 6-1. SUMMARY OF MEAN PRIMARY BENEFITS RESULTS

BENEFIT CATEGORY MONETIZED BENEFI TS (MILLION 2006$)
BY TARGET YEAR

NOTES 2000 2010 2020

Health Effects PM Mortality PM Morbidity Ozone Mortality Ozone Morbidity $710,000 $27,000 $10,000 $420 $1,200,000 $46,000 $33,000

$1,300 $1,700,000 $68,000 $55,000

$2,100 - PM mortality estimates based on Weibull distribution derived from Pope et. al (2002) and Laden et al., 2006. - Ozone mortality estimates based on pooled function Subtotal Health Effects $750,000 $1,300,000 $1,900,000

Visibility Recreational Residential $3,300 $11,000 $8,600 $25,000 $19,000 $48,000 Recreational visibility only includes benefits in the regions analyzed in Chestnut and Rowe, 1990 (i.e., California, the Southwest, and the Southeast). Subtotal Visibility $14,000 $34,000 $67,000 Agricultural and Forest Productivity $1,000 $5,500 $11,000
Materials Damage $58 $93 $110 Ecological $6.9 $7.5 $8.2 Reduced lake acidification benefits to recreational fishing assuming effect threshold of 50 microequivalents per liter. Total: all categories $770,000 $1,300,000 $2,000,000 Note: See Chapters 2 through 5 of this report fo r detailed results summaries. Values presented are means from results reported as distributions. Additional, al ternative estimates are provided in the separate companion report on uncertain ty. Estimates presented with two significant figures.

### Second Section 812 Pr ospective Analysis February 2011

6-3 EXHIBIT 6-2. SUMMARY OF UNQUANTIFIED BENEFITS

BENEFIT CATEGORY UNQUANTIFIED BENEFITS IN PRIMARY ESTIMATE a

Health Effects - PM  Subchronic bronchitis cases

 Low birth weight

 Pulmonary function

 Chronic respiratory diseases other than

 chronic bronchitis

 Morphological changes

 Altered host defense mechanisms

 Cancer

 Non-asthma respiratory emergency room

 Visits

 UVb exposure (+/-) b

 Stroke/cerebrovascular disease Health Effects - Ozone  Cardiovascular emergency room visits

 Asthma attacks

 Respiratory symptoms

 Chronic respiratory damage

 Increased responsiveness to stimuli

 Inflammation in the lung

 Premature aging of the lungs

 Acute inflammation and respiratory cell damage

 Increased susceptibility to respiratory infection

 Non-asthma respiratory emergency room

 Visits

 UVb exposure (+/-) b Visibility Recreational benefits for Class I areas outside of California, Southwest, and Southeast. Agricultural and Forest Productivity Productivity benefits not rela ted to ozone (e.g., sulfur de position effects on timber). d
Materials Damage  Monuments – carbonate stone and metal (sul fur oxides, Hydrogen ion and nitrogen oxides)

 Structural aesthetics (sulfur oxides , Hydrogen ion and nitrogen oxides)

 Automotive finishes – painted meta l (sulfur oxides, Hydrogen ion and nitrogen oxides)

 Infrastructural materials – galvanized and painted carbon steel (Hydrogen ion and nitrogen oxides)

 Zinc-based metal products, such as galvanized steel (Hydrogen ion and nitrogen oxides, Carbon dioxide, formaldehyde)

 Commercial and residential buildings – carbonate stone, metal, and wood surfaces(Hydrogen ion and nitrogen oxides)

 Household cleanliness (i.e., household soiling) (PM)

 Rubber products (e.g., tires) (Ozone) Ecological The majority of ecological benefits are qualitative. c Notes:

a The categorization of unquantified health effects is not exhaustive.
b May result in benefits or disbenefits.

c Chapter 2 of the ecological report (Effects of Air Pollutants on Ecological Resources: Literature Review and

Case Studies) provides a qualitative characterization of ecological effects of the CAAA. Specifically, Exhibits 2-2, 2-4, 2-6, and 2-8 summarize by pollut ant class and level of biological organization the potential effects of pollutants regulated by the CAAA on ecosystem structures and functions. Based on the availability of both ecological and economic data and models, we identified a subset of ecological endpoints amenable to monetization: a case study of the effects of acidic deposition effects on recreational fishing, and a national level analysis focused on the effects of tropospheric ozone exposure on commercial agriculture and silviculture . Categories of potential ecologic al benefit not quantified include: forest productivity benefits du e to decreased acidic deposition ; commercial freshwater fishing; preservation of biodiversity; increased carbon seques tration in forests; and de creased eutrophication of estuaries.
d Chapter 4 only focuses on the effects of tropospheric ozone. Effects of other pollution on agricultural and forest productivity are not quantified.

### Second Section 812 Pr ospective Analysis February 2011

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### Air quality modeling was carried out only for the three target years (2000, 2010, and 2020). The resulting annual benefit estimates indicate an increasing temporal trend of monetized benefits across the period resulti ng from the annual changes in air quality. They do not, however, characterize the uncertain ty associated with the yearly estimates for intervening years. In an effort to ge nerate improved estimates of the trajectory of benefits in these years, the 812 Project Team generated emissions reduction trajectories across the study period fo r seven pollutants in the with- and without-CAAA scenarios. Appendix O of the Second Section 812 Pros pective Emissions Analysis describes the methods used to derive trajectories for each major emitting sector and presents emissions trajectories for VOC, NO x 

### , CO, SO 2 

### , PM10, PM 2.5 

### , and NH 3 

### , which we reproduce here as Exhibits 6-3a and 6-3b. In general, these trajectories show flat to slightly increasing reductions in the early 1990s followed by re latively rapid increases in reductions between the mid-1990s and 2000. From 2000 through the end of the study period, the seven pollutants show a steady linear increase in reductions. To estimate benefits between each of the targ et years, the 812 Project Team developed a benefits index by multiplying the emission trajectories for each pollutant/source category by dollar per ton values taken from the Ozone NAAQS RIA. The 812 Project Team used this index to interpolate benefits between each of the target years. Our interpolation approach is illustrated in Exhibit 6-4.

EXHIBIT 6-3A. TRAJECTORY OF CAAA-RELATED REDUCTIONS IN VOC, NO X , AND SO 2 EMISSIONS: 1990 THROUGH 2020 (TONS OF POLLUTANT REDUCED)

0 5,000,000 10,000,000 15,000,000 20,000,000 25,000,000

1990 1992 1994 1996 1998 2000 2002 2 0 0 4 2006 20 0 8 2 0 10 20 1 2 2 0 14 2016 2 0 18 2020

Calendar Years VOC
NOx
SO2

### Second Section 812 Pr ospective Analysis February 2011

6-5 EXHIBIT 6-3B. TRAJECTORY OF CAAA-RELATED REDUCTIONS IN PM 1 0 AND PM 2.5 EMISSIONS: 1990 THROUGH 2020 (TONS OF POLLUTANT REDUCED)

EXHIBIT 6-4. INTERPOLATION STRATEGY FOR CUMULATIVE BENEFITS 

### 0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 7,000,000 8,000,000 9,000,000

Calendar Years PM1 0



### $0 $200,000 $400,000 $600,000 $800,000 $1,000,000 $1,200,000 $1,400,000 $1,600,000 $1,800,000 $2,000,000

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### In an attempt to represent uncertainty associ ated with these estimates, we relied on the ratios of the 5th percentile to the mean and the 95th percentile to th e mean in the target years. In general, these ratios were fairly constant across the target years, for a given endpoint. The ratios were interpolated between the target years, yielding ratios for the intervening years. Multiplying the ratios for eachintervening year by the central estimate generated for that year provided estimates of the 5th and 95 th 

### percentiles, which we use to characterize uncertainty about the Primary Central estimate. In Exhibit 6-5 we present the cumulative monetized benefits aggregated from 1990 to 2020. We present the mean estimate from the aggregation procedure, along with the Primary Low (i.e., 5 th 

### percentile of the distribution) and Primary High (i.e., 95th percentile of the distribution) estimates, for all provisions of Titles I through V. Aggr egating the stream of monetized benefits across years involved discounting the stream of monetized benefits estimated for each year to the 1990 present value (using a five percent discount rate).

EXHIBIT 6-5. CUMULATIVE MONETIZED BENEFITS OF CAAA TITLES I THROUGH V IN THE U.S.

PRESENT VALUE (MILLIONS 2006$, DISC OUNTED TO 1990 AT 5 PERCENT)

PRIMARY LOW PRIMARY CENTRAL PRIMARY HIGH $1,400,000 $12,000,000 $35,000,000

Note: Values presented in this table are in millions of 2006$, discounted to 1990 using a 5 percent discount rate.

COMPARISON WITH RESULTS FROM THE FIRST PROSPECTIVE 

### The health effects estimates for the second pros pective are much larg er than the estimates EPA developed for the first prospective. The 2020 estimates are new to the second prospective, but the comparable mean estimate of health benefits in 2000 and 2010 for the first prospective were $71 billion in 2000 and $110 billion in 2010, in 1990$ 57 

### - if updated to 2006$, these estimates would be $110 billion in 2000 an d $170 billion in 2010. There are six key reasons we have identified for the increase in benefits:

1. Scenario differences : The with-CAAA scenario, especially for the 2010 target year, includes new rules with substantial additional pollutant reductions that were not included in the comparable first pr ospective scenario, such as the Clean Air Interstate Rule (CAIR).

57 See The Benefits and Costs of the Clean Air Act 1990 to 2010, USEPA Office of Air and Radiation and Office of Policy, EPA410-R-99-001, November 1999.

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### 2. Improved air quality models : The first prospective relied on the Regional Acid Deposition Model/Regional Particulate Mode l (RADM/RPM) for PM and deposition estimates in the eastern U.S., the Regulatory Modeling System for Aerosols and Acid Deposition (REMSAD) for PM estimates in the western U.S., and the Urban Airshed Model (versions V and IV) at various re gional and urban scales to generate ozone estimates. The second prospective relies on the integrated CMAQ modeling tool, which reflects substantial improvements in air quality modeling, provides more comprehensive spatial coverage, a nd achieves improved model performance.

3. Better, more comprehen sive exposure estimates : The first prospective relied on first generation exposure extrapolation tools to generate monito r-adjusted exposure estimates away from monitors. Since then , the monitor network, availability of speciated data, and the performance of sp eciated exposure estimation tools have improved substantially.

4. Updated dose-response estimates : Since 1999, some concentration response functions have been updated, most notably the PM-premature mortality C/R function, whose central estimate of the mortality imp act of fine PM has nearly doubled. In addition, health effects research has addr essed endpoints that were not covered in the first prospective, including premature mo rtality associated with ozone exposure. Although the Agency has not yet conducted a rigorous quantitative analysis to assess the impact of these methodology and data improvements , the impact of most of these factors is to increase the estimates of benefits.

BENEFITS UNCERTAINTIES 

### The benefits values presented in this report are subject to a number of uncertainties related to data limitations, analytical choi ces related to models and input parameters, difficulties predicting future scenarios, and other factors. Among the most significant uncertainties is the extensive list of benefits categories, mostly in the ecological area, for which we currently lack the data and/or t ools to quantify and monetize benefits. These categories are implicitly treated as having zero value though in reality they may include physical benefits that have a positive econom ic value. The unquantified and unmonetized benefits thus represent an underestimation bias in the summary benefit results. The uncertainties in our quantified and mone tized estimates that are most likely to significantly influence the primary benefit resu lts are those affecting the largest benefit category: the estimation and valuation of reductions in premature mortality due to decreases in PM 2.5 

### . Three key uncertainties affecting economic estimates of avoided PM mortality include: (1) the C-R function estimate; (2) the PM/mortality cessation lag structure; and (3) the mortality valuation est imate. These are influential assumptions in our analysis and those for which plausible alte rnative quantitative estimates are available. The companion Second Prospective Section 812 report, Uncertainty Analyses to Support the Second Section 812 Benefit-Cost Analysis of the Clean Air Act , presents detailed quantitative analyses of the sensitivity of benef its results to these and other factors. It

### Second Section 812 Pr ospective Analysis February 2011

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### also presents tables describing in a qualitativ e manner additional uncertainties that are not currently amenable to quantitative analys is, indicating the potential direction and significance of any potential bias introduced by each uncertain factor.

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Poloniecki, J.D., R.W. Atkinson., A.P. de Le on., and H.R. Anderson, 1997. “Daily Time Series for Cardiovascular Hospital Admissions and Previous Day’s Air Pollution in London, UK.” Occupational and Enviro nmental Medicine 54(8):535-540. Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne, 1991. “Respiratory Health and PM10 Pollution: A Daily Time Se ries Analysis.” American Review of Respiratory Diseases 144:668-674. 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.” Ameri can Journal of Respiratory Critical Care Medicine 151:669-674. Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston, 2002. “Lung Cancer, Cardi opulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141.

### Second Section 812 Pr ospective Analysis February 2011

R-9 

### Rae, D.A. 1983. Benefits of Visual Air Quality in Cincinnati Results of a Contingent Ranking Survey . Prepared for Electric Powe r Research Institute. May. Ransom, M. R. and C. A. Pope, 1992. El ementary school absences and PM10 pollution in Utah Valley. Environ Res. Vol. 58 (2): 204-19. Rosamond, W., G. Broda, E. Kawalec, S. Rywi k, A. Pajak, L. Cooper and L. Chambless, 1999. Comparison of medical care and surviv al of hospitalized patients with acute myocardial infarction in Poland and the Unite d States. Am J Cardiol. Vol. 83 (8): 180-5. Rowe, R. D. and L. G. Chestnust. 1986. Oxidants and Asthmatics in Los Angeles: A Benefits Analysis -- Executive Summary. Prepared for U.S. Environmental Protection Agency, Office of Policy Analysis. Prepared by Energy and Resource Consultants, Inc. Washington, DC. EPA-230-09-86-018. March.
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. Am J Cardiol. Vol. 81 (9): 1110-5. Samet, J.M., S.L. Zeger, F. Dominici, F. Curriero, I. Coursac, D.W. Dockery, J. Schwartz, and A. Zanobetti, June 2000. Th e National Morbidity, Mortality and Air Pollution Study: Part II: Morbidity, Morta lity and Air Pollution in the United States. Research Report No. 94, Part II. Health Effects Institute, Cambridge MA. Schwartz J, 1994a. PM(10) Ozone, and Hospital Admissions For the Elderly in Minneapolis St Paul, Minnesota. Ar ch Environ Health 49(5):366-374. Schwartz J, 1994b. Air Pollution and Hospital Admissions For the Elderly in Detroit, Michigan. Am J Respir Crit Care Med 150(3):648-655. 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.
Schwartz J, 1995. Short term fluctuations in air pollution and hospital admissions of the elderly for respiratory disease. Thorax 50(5):531-538.
Schwartz, J., and L.M. Neas, 2000. “Fine Par ticles are More Strong ly Associated than Coarse Particles with Acute Respirator y Health Effects in Schoolchildren.” Epidemiology 11:6- 10.
Schwartz, J., 2005. How sensitive is the associ ation between ozone and daily deaths to control for temperature? Am J Respir Crit Care Med. Vol. 171 (6): 627-31. 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.

### Second Section 812 Pr ospective Analysis February 2011

R-10 

### Smith, V.K., G. Van Houtven, and S. Pattanaya k. 1999. Benefits Transfer as Preference Calibration. Resources for the Future Working Paper (Unnumbered). 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. U.S.Environmental Protection Agency Grant #807768-01-0.
U.S. Bureau of the Census, 1997. Statistical Abstract of the United States: 1997. Washington, DC.

U.S. Census Bureau. 2007. Amer ican Housing Survey. Accessed: http://www.census.gov/hhes/ww w/housing/ahs/ahs.html U.S. DOE. 2005. Energy Information Administration’s 2005 Residential Energy Consumption Survey. Accessed: http://www.eia.doe.gov/emeu/recs/ U.S. DOE. 2006. Energy Information Administration's 2003 Commercial Buildings Energy Consumption Survey. Accessed: http://www.eia.doe.gov/emeu/cbecs/
Vedal, S., J. Petkau, R. White, and J. Blair, 1998. “Acute Effects of Ambient Inhalable Particles in Asthmatic and Nonasthma tic Children.” American Journal of Respiratory and Critical Care Medicine 157(4):1034-1043. 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 Ec onomics and Management. Vol. 21 (1): 32- 51. Viscusi, W. Kip. 1992. Fatal Tradeoffs . (Oxford University Press: New York). Viscusi, W. K. and J. E. Aldy. 2003. The Value of a Statistical Life: A Critical Review of Market Estimates throughout the World. AEI-Brookings Joint Center for Regulatory Studies. Washington, DC. January. Wilson, A. M., C. P. Wake, T. Kelly, et al., 2005. Air pollution, weather, and respiratory emergency room visits in two northern Ne w England cities: an ecological time-series study. Environ Res. Vol. 97 (3): 312-21. Wittels, E. H., J. W. Hay and A. M. Gotto, Jr. 1990. Medical costs of coronary artery disease in the United States. Am J Cardiol. Vol. 65 (7): 432-40.
Woodruff, T.J., J. Grillo, and K.C. Scho endorf. 1997. “The Relationship Between Selected Causes of Postneonatal Infant Mort ality and Particulate Air Pollution in the United States.” Environmental H ealth Perspectives 105(6):608-612.
Woodruff TJ, Parker JD, Schoendorf KC. 2006 . Fine particulate matter (PM2.5) air pollution and selected causes of postneona tal infant mortality in California. Environmental Health Perspectives 114(5):786-90.

### Second Section 812 Pr ospective Analysis February 2011

R-11 

### Woods & Poole Economics Inc., 2007. Comp lete Demographic Database. Washington, DC. http://woodsandpoole.com/index.php.

### Second Section 812 Pr ospective Analysis February 2011

A-1 

### APPENDIX A | PRIMARY ESTIMATE S OF VISIBILITY BENEFITS BY STATE

This appendix gives the primary estimate of benefits to visibility from the CAAA by State in 2000, 2010, and 2020.

EXHIBIT A-1. PRIMARY ESTIMATE OF BENEFITS TO VISIBILITY BY STATE - 2000 (BILLION 2006$)

STATE RECREATIONAL
BENEFITS RESIDENTIAL
BENEFITS TOTAL
BENEFITS
Alabama $0.06 $0.14 $0.20 Arizona $0.06 -$0.24 -$0.18 Arkansas $0.03 $0.08 $0.11 California $0.39 $1.1 $1.4 Colorado $0.05 $0.12 $0.17 Connecticut $0.04 $0.14 $0.18 Delaware $0.01 $0.06 $0.07 District of Columbia $0.01 $0.05 $0.05 Florida $0.22 $0.42 $0.64 Georgia $0.11 $0.26 $0.37 Idaho $0.01 $0.03 $0.04 Illinois $0.13 $0.86 $0.99 Indiana $0.07 $0.27 $0.33 Iowa $0.03 $0.06 $0.09 Kansas $0.03 $0.04 $0.07 Kentucky $0.06 $0.11 $0.16 Louisiana $0.05 $0.19 $0.24 Maine $0.01 $0.08 $0.09 Maryland $0.07 $0.37 $0.45 Massachusetts $0.07 $0.25 $0.31 Michigan $0.11 $0.37 $0.48 Minnesota $0.05 $0.11 $0.16 Mississippi $0.04 $0.06 $0.10 Missouri $0.06 $0.20 $0.26 Montana $0.01 $0.01 $0.02 Nebraska $0.02 $0.02 $0.04 Nevada $0.02 $0.03 $0.06 New Hampshire $0.01 $0.04 $0.05 New Jersey $0.09 $0.55 $0.64 New Mexico $0.02 $0.01 $0.03 New York $0.21 $1.6 $1.8

### Second Section 812 Pr ospective Analysis February 2011

A-2 STATE RECREATIONAL
BENEFITS RESIDENTIAL
BENEFITS TOTAL
BENEFITS
North Carolina $0.11 $0.30 $0.41 North Dakota $0.01 $0.00 $0.01 Ohio $0.12 $0.54 $0.66 Oklahoma $0.04 $0.07 $0.11 Oregon $0.04 $0.15 $0.18 Pennsylvania $0.13 $0.91 $1.0 Rhode Island $0.01 $0.04 $0.05 South Carolina $0.06 $0.10 $0.15 South Dakota $0.01 $0.01 $0.01 Tennessee $0.08 $0.18 $0.26 Texas $0.23 $0.57 $0.79 Utah $0.03 $0.15 $0.18 Vermont $0.01 $0.01 $0.02 Virginia $0.10 $0.28 $0.38 Washington $0.06 $0.12 $0.18 West Virginia $0.02 $0.08 $0.11 Wisconsin $0.06 $0.20 $0.26 Wyoming $0.01 $0.00 $0.01 TOTAL $3.3 $11 $14

### Second Section 812 Pr ospective Analysis February 2011

A-3 EXHIBIT A-2. PRIMARY ESTIMATE OF BENEFITS TO VISIBILITY BY STATE - 2010 (BILLION 2006$)

STATE RECREATIONAL
BENEFITS RESIDENTIAL
BENEFITS TOTAL
BENEFITS
Alabama $0.16 $0.29 $0.44 Arizona $0.18 -$0.16 $0.03 Arkansas $0.08 $0.17 $0.25 California $1.1 $2.7 $3.8 Colorado $0.14 $0.25 $0.39 Connecticut $0.09 $0.29 $0.38 Delaware $0.03 $0.14 $0.17 District of Columbia $0.02 $0.10 $0.12 Florida $0.63 $1.1 $1.8 Georgia $0.31 $0.68 $1.00 Idaho $0.04 $0.06 $0.10 Illinois $0.34 $1.4 $1.8 Indiana $0.17 $0.57 $0.73 Iowa $0.08 $0.12 $0.20 Kansas $0.07 $0.11 $0.18 Kentucky $0.14 $0.26 $0.40 Louisiana $0.12 $0.32 $0.43 Maine $0.04 $0.12 $0.15 Maryland $0.19 $0.97 $1.2 Massachusetts $0.17 $0.54 $0.71 Michigan $0.27 $0.71 $0.98 Minnesota $0.14 $0.23 $0.37 Mississippi $0.10 $0.13 $0.23 Missouri $0.15 $0.39 $0.55 Montana $0.03 $0.01 $0.04 Nebraska $0.05 $0.06 $0.11 Nevada $0.08 $0.12 $0.20 New Hampshire $0.04 $0.08 $0.12 New Jersey $0.23 $1.2 $1.5 New Mexico $0.06 $0.05 $0.10 New York $0.50 $2.9 $3.4 North Carolina $0.30 $0.95 $1.2 North Dakota $0.02 $0.01 $0.03 Ohio $0.30 $1.3 $1.6 Oklahoma $0.09 $0.17 $0.26 Oregon $0.10 $0.24 $0.33 Pennsylvania $0.33 $2.0 $2.3 Rhode Island $0.03 $0.09 $0.12 South Carolina $0.15 $0.29 $0.44 South Dakota $0.02 $0.01 $0.04 Tennessee $0.20 $0.44 $0.64 Texas $0.63 $1.38 $2.01 Utah $0.08 $0.33 $0.40

### Second Section 812 Pr ospective Analysis February 2011

A-4 STATE RECREATIONAL
BENEFITS RESIDENTIAL
BENEFITS TOTAL
BENEFITS
Vermont $0.02 $0.02 $0.04 Virginia $0.26 $0.92 $1.2 Washington $0.17 $0.30 $0.47 West Virginia $0.06 $0.19 $0.25 Wisconsin $0.15 $0.36 $0.51 Wyoming $0.01 $0.00 $0.01 TOTAL $8.6 $25 $34

### Second Section 812 Pr ospective Analysis February 2011

A-5 EXHIBIT A-3. PRIMARY ESTIMATE OF BENEFITS TO VISIBILITY BY STATE - 2020 (BILLION 2006$)

STATE RECREATIONAL
BENEFITS RESIDENTIAL
BENEFITS TOTAL
BENEFITS
Alabama $0.33 $0.55 $0.87 Arizona $0.43 $0.01 $0.44 Arkansas $0.16 $0.35 $0.51 California $2.4 $5.9 $8.3 Colorado $0.32 $0.71 $1.0 Connecticut $0.19 $0.57 $0.77 Delaware $0.06 $0.26 $0.32 District of Columbia $0.03 $0.17 $0.20 Florida $1.4 $2.2 $3.6 Georgia $0.69 $1.4 $2.1 Idaho $0.09 $0.14 $0.24 Illinois $0.71 $2.3 $3.0 Indiana $0.35 $0.95 $1.3 Iowa $0.16 $0.23 $0.39 Kansas $0.15 $0.22 $0.37 Kentucky $0.30 $0.44 $0.74 Louisiana $0.25 $0.61 $0.86 Maine $0.07 $0.18 $0.26 Maryland $0.42 $1.9 $2.4 Massachusetts $0.35 $1.1 $1.4 Michigan $0.55 $1.2 $1.8 Minnesota $0.31 $0.47 $0.78 Mississippi $0.21 $0.25 $0.46 Missouri $0.33 $0.69 $1.0 Montana $0.06 $0.03 $0.08 Nebraska $0.10 $0.13 $0.22 Nevada $0.20 $0.33 $0.53 New Hampshire $0.08 $0.16 $0.24 New Jersey $0.50 $2.3 $2.8 New Mexico $0.13 $0.10 $0.23 New York $1.03 $4.8 $5.8 North Carolina $0.65 $1.9 $2.6 North Dakota $0.04 $0.02 $0.05 Ohio $0.61 $2.3 $2.9 Oklahoma $0.20 $0.33 $0.53 Oregon $0.22 $0.64 $0.86 Pennsylvania $0.67 $3.5 $4.2 Rhode Island $0.06 $0.18 $0.24 South Carolina $0.32 $0.61 $0.93 South Dakota $0.04 $0.03 $0.08 Tennessee $0.44 $0.84 $1.3 Texas $1.4 $2.7 $4.1 Utah $0.18 $0.72 $0.90

### Second Section 812 Pr ospective Analysis February 2011

A-6 STATE RECREATIONAL
BENEFITS RESIDENTIAL
BENEFITS TOTAL
BENEFITS
Vermont $0.04 $0.03 $0.07 Virginia $0.58 $1.9 $2.5 Washington $0.38 $0.81 $1.2 West Virginia $0.12 $0.32 $0.44 Wisconsin $0.32 $0.67 $0.99 Wyoming $0.03 $0.00 $0.03 TOTAL $19 $48 $67

### Second Section 812 Pr ospective Analysis February 2011

B-1 

### APPENDIX B | RELATIVE YIELD LOSS MAPS AND TABLES

This appendix provides relative yield loss maps for the crops and forest types included in the analysis. Relative yield losses are expr essed as the percent reduction in the overall yield of a crop or forest type under the counterfactual (no CAAA) scenario. 58 

### Changes in crop yield are presented by FASOM subregion; while, changes in forest yield are presented by FASOM region. Relative yield losses are only presented for subregions and regions where the specific crop or forest type being considered is present as defined byFASOM. Exhibits B-1 through B-11 present relative yield losses for crops; Exhibits B12 and B-13 present relative yield losses fo r hardwood and softwood forest types, respectively. In addition to relative yield loss maps, th is appendix provides tables presenting relative yield losses for each crop by subregion (Exhibits B-14 through B-24) and for hardwood and softwood forest types by region (Exhibits B-25 through B-32). 59 

### Exhibits B-14 through B-32 also present intermediate values used to calculate relative yield losses for crops and trees. Relative yield loss tables for hardwood and softwood forest types (Exhibit B-25 through B-32) present relative yield losses for individual hardwood and softwood tree species found in each region, as well as, the average relative yield loss for all hardwood and softwood species found in each region (only average relative yield losses for hardwood and softwood forest types are used to estimate welfare effects). None of the hardwood species, for which e xposure-response functions exist, are present (as defined in FASOM) in the Great Plains, Pacific Northwest-Westside, Pacific Southwest, and Rocky Mountains regions. The average relative yield loss in hardwood forest types across all regions, for which ha rdwood relative yield losses are estimated, is applied as the best-estimate of hardwood relative yield losses in these regions (5.06 percent in 2000; 13.86 percent in 2010; and, 16.68 percent in 2020). None of the softwood species, for which exposure-response f unctions exist, are present (as defined in FASOM) in the Great Plains region. As w ith hardwoods, the average relative yield loss in softwood forest types across all regions, for which softwood relative yield losses are estimated, is applied as the best-estimate of softwood relative yield losses in the Great Plains region (1.77 percent in 2000; 4.88 per cent in 2010; and, 6.11 percent in 2020).

58 Note that relative yield losses are based on ozone concentratio ns during the growing period for each crop and forest type,

not ozone concentrations for the entire May through September period. Growing periods are specific to individual crops and subregions, and to indivi dual regions for hardwood and softwood forest types.

59 Relative yield loss tables for crop are split by crop; while, relative yield loss tables for hardwood/softwood forest types ar e

split by region.

### Second Section 812 Pr ospective Analysis February 2011

B-2 

### There is no table for the Great Plains region, given that no hardwood or softwood species, for which relative yield losses are estimated, are present in this region. Further, timber management is not defined by FASOM in eith er the Southwest or the Pacific NorthwestEastside, therefore, relative yield loss tables are not presented for these regions.

EXHIBIT B-1. RELATIVE YIELD LOSSES IN BARLEY UNDER THE COUNTERFACTUAL (NO CAAA)
SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONAL-SPECIFIC
OZONE CONCENTRATIONS AND GROWING PERIODS

### Second Section 812 Pr ospective Analysis February 2011

B-3 EXHIBIT B-2. RELATIVE YIELD LOSSES IN CORN UNDER THE COUNTERFACTUAL (NO CAAA)
SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONAL-SPECIFIC
OZONE CONCENTRATIONS AND GROWING PERIODS

### Second Section 812 Pr ospective Analysis February 2011

B-4 EXHIBIT B-3. RELATIVE YIELD LOSSES IN COTTON UNDER THE COUNTERFACTUAL (NO CAAA)
SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONAL-SPECIFIC
OZONE CONCENTRATIONS AND GROWING PERIODS

### Second Section 812 Pr ospective Analysis February 2011

B-5 EXHIBIT B-4. RELATIVE YIELD LOSSES IN ORANGES UNDER THE COUNTERFACTUAL (NO CAAA)
SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONAL-SPECIFIC
OZONE CONCENTRATIONS AND GROWING PERIODS

### Second Section 812 Pr ospective Analysis February 2011

B-6 EXHIBIT B-5. RELATIVE YIELD LOSSES IN POTATOES UNDER THE COUNTERFACTUAL (NO CAAA)
SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONAL-SPECIFIC
OZONE CONCENTRATIONS AND GROWING PERIODS

### Second Section 812 Pr ospective Analysis February 2011

B-7 EXHIBIT B-6. RELATIVE YIELD LOSSES IN RI CE UNDER THE COUNTERFACTUAL (NO CAAA)
SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONAL-SPECIFIC
OZONE CONCENTRATIONS AND GROWING PERIODS

### Second Section 812 Pr ospective Analysis February 2011

B-8 EXHIBIT B-7. RELATIVE YIELD LOSSES IN SORGHUM UNDER THE COUNTERFACTUAL (NO CAAA)
SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONAL-SPECIFIC
OZONE CONCENTRATIONS AND GROWING PERIODS

### Second Section 812 Pr ospective Analysis February 2011

B-9 EXHIBIT B-8. RELATIVE YIELD LOSSES IN SOYB EANS UNDER THE COUNTERFACTUAL (NO CAAA)
SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONAL-SPECIFIC
OZONE CONCENTRATIONS AND GROWING PERIODS

### Second Section 812 Pr ospective Analysis February 2011

B-10 EXHIBIT B-9. RELATIVE YIELD LOSSES IN PROCESSING TOMATOES UNDER THE COUNTERFACTUAL(NO CAAA) SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONALSPECIFIC OZONE CONCENTRATIONS AND GROWING PERIODS

### Second Section 812 Pr ospective Analysis February 2011

B-11 EXHIBIT B-10. RELATIVE YIELD LOSSES IN SPRING WHEAT UNDER THE COUNTERFACTUAL (NOCAAA) SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONALSPECIFIC OZONE CONCENTRATIONS AND GROWING PERIODS

### Second Section 812 Pr ospective Analysis February 2011

B-12 EXHIBIT B-11. RELATIVE YIELD LOSSES IN WINTER WHEAT UNDER THE COUNTERFACTUAL (NOCAAA) SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONALSPECIFIC OZONE CONCENTRATIONS AND GROWING PERIODS

### Second Section 812 Pr ospective Analysis February 2011

B-13 EXHIBIT B-12. RELATIVE YIELD LOSSES IN HARDWOOD FOREST TYPES UNDER THE COUNTERFACTUAL (NO CAAA) SCENARIO BY FASOM REGION AND YEAR BASED ON
REGIONAL-SPECIFIC OZONE CONCENTRATIONS AND GROWING PERIODS

### Second Section 812 Pr ospective Analysis February 2011

B-14 EXHIBIT B-13. RELATIVE YIELD LOSSES IN SOFTWOOD FOREST TYPES UNDER THE COUNTERFACTUAL (NO CAAA) SCENARIO BY FASOM REGION AND YEAR BASED ON
REGIONAL-SPECIFIC OZONE CONCENTRATIONS AND GROWING PERIODS

### Second Section 812 Pr ospective Analysis February 2011

B-15 EXHIBIT B-14. DERIVATION OF RELATIVE YIELD LOSSES FOR BARLEY BY FASOM SUBREGION AND YEAR

SUBREGION X: B

A NoCAAA O



## e      

 3 Y: B

A WithCAAA O



## e      

 3 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020

Arizona 0.9997 0.9996 0.9995 0.9998 0.9998 0.9999 0.01% 0.02% 0.04%
Arkansas 0.9998 0.9997 0.9997 0.9999 1.0000 1.0000 0.01% 0.02% 0.02%
California North 0.9996 0.9995 0.9994 0.9997 0.9998 0.9998 0.01% 0.03% 0.05%
California South 0.9994 0.9993 0.9991 0.9996 0.9997 0.9998 0.02% 0.04% 0.07%
Colorado 0.9998 0.9998 0.9998 0.9999 0.9999 0.9999 0.00% 0.01% 0.02%
Delaware 0.9994 0.9993 0.9992 0.9996 0.9998 0.9999 0.02% 0.06% 0.07% Georgia 0.9998 0.9997 0.9997 0.9999 0.9999 1.0000 0.01% 0.02% 0.03%
Idaho 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.00% 0.00% 0.01%
Illinois North 0.9997 0.9996 0.9996 0.9998 0.9999 0.9999 0.01% 0.03% 0.03%
Illinois South 0.9996 0.9995 0.9996 0.9998 0.9999 1.0000 0.01% 0.04% 0.04%
Indiana North 0.9995 0.9994 0.9994 0.9997 0.9999 0.9999 0.02% 0.04% 0.05%
Indiana South 0.9996 0.9994 0.9995 0.9997 0.9999 0.9999 0.01% 0.05% 0.05%
Iowa West 0.9999 0.9999 0.9998 0.9999 0.9999 1.0000 0.00% 0.01% 0.01%
Iowa Central 0.9999 0.9999 0.9999 0.9999 1.0000 1.0000 0.00% 0.01% 0.01%
Iowa Northeast 0.9999 0.9999 0.9998 0.9999 1.0000 1.0000 0.00% 0.01% 0.01%
Iowa South 0.9999 0.9998 0.9998 0.9999 1.0000 1.0000 0.00% 0.01% 0.01%
Kansas 1.0000 0.9999 0.9999 1.0000 1.0000 1.0000 0.00% 0.00% 0.00% Kentucky 0.9997 0.9995 0.9996 0.9998 0.9999 1.0000 0.01% 0.04% 0.04%
Maine 0.9999 0.9999 0.9999 1.0000 1.0000 1.0000 0.00% 0.00% 0.01% Maryland 0.9993 0.9992 0.9992 0.9995 0.9998 0.9999 0.02% 0.06% 0.07% Michigan 0.9998 0.9998 0.9998 0.9999 0.9999 1.0000 0.00% 0.01% 0.02%
Minnesota 0.9999 0.9999 0.9999 1.0000 1.0000 1.0000 0.00% 0.00% 0.00%
Missouri 0.9998 0.9996 0.9997 0.9998 0.9999 1.0000 0.01% 0.03% 0.03%
Montana 0.9999 0.9999 0.9999 0.9999 1.0000 1.0000 0.00% 0.00% 0.00%
Nebraska 0.9999 0.9999 0.9999 1.0000 1.0000 1.0000 0.00% 0.00% 0.00%
Nevada 0.9998 0.9997 0.9997 0.9998 0.9999 0.9999 0.00% 0.01% 0.02% New Jersey 0.9994 0.9993 0.9993 0.9995 0.9998 0.9999 0.02% 0.05% 0.07%
New Mexico 0.9998 0.9998 0.9998 0.9999 0.9999 0.9999 0.00% 0.01% 0.02%
New York 0.9997 0.9997 0.9997 0.9998 0.9999 1.0000 0.01% 0.02% 0.03%
North Carolina 0.9995 0.9993 0.9993 0.9997 0.9999 1.0000 0.02% 0.06% 0.07%
North Dakota 1.0000 1.0000 0.9999 1.0000 1.0000 1.0000 0.00% 0.00% 0.00%
Ohio Northwest 0.9995 0.9994 0.9994 0.9996 0.9998 0.9999 0.01% 0.05% 0.06%
Ohio South 0.9995 0.9994 0.9994 0.9997 0.9999 0.9999 0.01% 0.05% 0.05%
Ohio Northeast 0.9995 0.9994 0.9994 0.9996 0.9998 0.9999 0.01% 0.04% 0.06%
Oklahoma 0.9998 0.9998 0.9998 0.9999 0.9999 1.0000 0.00% 0.01% 0.02% Oregon 0.9999 0.9999 0.9999 1.0000 1.0000 1.0000 0.00% 0.00% 0.00% Pennsylvania 0.9995 0.9995 0.9994 0.9997 0.9999 0.9999 0.02% 0.04% 0.05%
South Carolina 0.9996 0.9994 0.9994 0.9997 0.9999 1.0000 0.02% 0.05% 0.06%
South Dakota 0.9999 0.9999 0.9999 0.9999 1.0000 1.0000 0.00% 0.00% 0.00%
Tennessee 0.9996 0.9995 0.9994 0.9997 0.9999 1.0000 0.02% 0.04% 0.05% Texas High Plains 0.9999 0.9999 0.9999 0.9999 0.9999 1.0000 0.00% 0.01% 0.01%

### Second Section 812 Pr ospective Analysis February 2011

B-16 SUBREGION X: B

A NoCAAA O



## e      

 3 Y: B

A WithCAAA O



## e      

 3 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020
Texas Rolling Plains 0.9999 0.9998 0.9998 0.9999 0.9999 1.0000 0.00% 0.01% 0.02%
Texas Central Blacklands 0.9998 0.9998 0.9998 0.9999 0.9999 1.0000 0.01% 0.01% 0.02%
Texas East 0.9998 0.9998 0.9997 0.9999 1.0000 1.0000 0.01% 0.02% 0.02%
Texas Edwards Plateau 0.9999 0.9999 0.9999 0.9999 1.0000 1.0000 0.00% 0.01% 0.01%
Texas South 0.9999 0.9999 0.9998 0.9999 1.0000 1.0000 0.00% 0.01% 0.01%
Texas Trans Pecos 0.9999 0.9999 0.9999 0.9999 1.0000 1.0000 0.00% 0.01% 0.01%
Utah 0.9997 0.9996 0.9995 0.9998 0.9998 0.9999 0.01% 0.02% 0.03% Virginia 0.9995 0.9993 0.9993 0.9997 0.9999 0.9999 0.02% 0.05% 0.06% Washington 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.00% 0.00% 0.00% West Virginia 0.9996 0.9995 0.9995 0.9998 0.9999 1.0000 0.01% 0.04% 0.04%
Wisconsin 0.9999 0.9999 0.9998 0.9999 0.9999 1.0000 0.00% 0.01% 0.01% Wyoming 0.9998 0.9998 0.9997 0.9999 0.9999 0.9999 0.01% 0.01% 0.02% Notes: 1. Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by cro p in Exhibit X-6. 2. Relative yield losses are only deri ved for subregions where the crop is present as defined in FASOM. 3. Relative yield losses are based on ozone concentrations during th e growing period for each crop and forest type, not ozone conc entrations for the entire May through September period. Growing periods are spec ific to individual crops and subregions, and to individual re gions for hardwood and softwood forest types.

### Second Section 812 Pr ospective Analysis February 2011

B-17 EXHIBIT B-15. DERIVATION OF RELATIVE YIELD LOSSES FOR CORN BY FASOM SUBREGION AND YEAR

SUBREGION X: B

A NoCAAA O



## e      

 3 Y: B

A WithCAAA O



## e      

 3 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020

Alabama 0.9974 0.9960 0.9946 0.9991 0.9999 1.0000 0.17% 0.40% 0.54%
Arizona 0.9994 0.9989 0.9984 0.9997 0.9999 0.9999 0.04% 0.10% 0.15%
Arkansas 0.9958 0.9914 0.9909 0.9986 0.9999 1.0000 0.27% 0.85% 0.90%
California North 0.9959 0.9936 0.9927 0.9970 0.9985 0.9986 0.11% 0.49% 0.59%
California South 0.9947 0.9925 0.9910 0.9973 0.9988 0.9992 0.26% 0.63% 0.82%
Colorado 0.9998 0.9998 0.9997 0.9999 1.0000 1.0000 0.01% 0.02% 0.02%
Connecticut 0.9972 0.9967 0.9960 0.9986 0.9996 1.0000 0.14% 0.29% 0.40%
Delaware 0.9849 0.9779 0.9744 0.9937 0.9983 0.9999 0.88% 2.04% 2.55%
Florida 0.9998 0.9997 0.9995 0.9999 1.0000 1.0000 0.01% 0.03% 0.05% Georgia 0.9958 0.9927 0.9905 0.9983 0.9998 1.0000 0.25% 0.71% 0.95%
Idaho 0.9999 0.9999 0.9999 1.0000 1.0000 1.0000 0.00% 0.00% 0.01%
Illinois North 0.9988 0.9985 0.9979 0.9995 0.9999 1.0000 0.07% 0.14% 0.21%
Illinois South 0.9961 0.9954 0.9939 0.9984 0.9998 1.0000 0.22% 0.44% 0.60%
Indiana North 0.9980 0.9976 0.9968 0.9991 0.9998 1.0000 0.11% 0.22% 0.32%
Indiana South 0.9972 0.9968 0.9959 0.9986 0.9997 1.0000 0.14% 0.30% 0.41%
Iowa West 0.9999 0.9999 0.9998 1.0000 1.0000 1.0000 0.00% 0.01% 0.01%
Iowa Central 0.9999 0.9998 0.9997 0.9999 1.0000 1.0000 0.01% 0.02% 0.03%
Iowa Northeast 0.9998 0.9998 0.9996 0.9999 1.0000 1.0000 0.01% 0.02% 0.04%
Iowa South 0.9995 0.9993 0.9990 0.9998 1.0000 1.0000 0.03% 0.07% 0.10%
Kansas 0.9990 0.9987 0.9984 0.9995 0.9999 0.9999 0.05% 0.12% 0.15% Kentucky 0.9895 0.9833 0.9828 0.9959 0.9995 0.9999 0.64% 1.62% 1.71%
Louisiana 0.9990 0.9981 0.9976 0.9997 0.9999 1.0000 0.06% 0.18% 0.24%
Maine 0.9998 0.9997 0.9997 0.9999 0.9999 1.0000 0.01% 0.02% 0.03% Maryland 0.9837 0.9775 0.9735 0.9926 0.9981 0.9999 0.90% 2.06% 2.63%
Massachusetts 0.9974 0.9970 0.9964 0.9988 0.9996 1.0000 0.14% 0.26% 0.36% Michigan 0.9997 0.9996 0.9995 0.9998 1.0000 1.0000 0.01% 0.03% 0.05%
Minnesota 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.00% 0.00% 0.00% Mississippi 0.9987 0.9976 0.9970 0.9996 0.9999 1.0000 0.09% 0.23% 0.29%
Missouri 0.9961 0.9931 0.9927 0.9985 0.9998 1.0000 0.24% 0.68% 0.73%
Montana 0.9999 0.9999 0.9998 0.9999 1.0000 1.0000 0.00% 0.01% 0.01%
Nebraska 0.9999 0.9999 0.9999 1.0000 1.0000 1.0000 0.00% 0.01% 0.01%
Nevada 0.9994 0.9991 0.9988 0.9996 0.9998 0.9998 0.02% 0.07% 0.10% New Hampshire 0.9993 0.9992 0.9989 0.9997 0.9999 1.0000 0.04% 0.07% 0.11% New Jersey 0.9984 0.9981 0.9977 0.9991 0.9996 1.0000 0.07% 0.15% 0.23%
New Mexico 0.9998 0.9997 0.9995 0.9999 1.0000 1.0000 0.01% 0.03% 0.04%
New York 0.9990 0.9989 0.9986 0.9996 0.9999 1.0000 0.06% 0.10% 0.14%
North Carolina 0.9845 0.9680 0.9654 0.9942 0.9987 1.0000 0.97% 3.07% 3.45%
North Dakota 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.00% 0.00% 0.00%
Ohio Northwest 0.9969 0.9964 0.9953 0.9985 0.9997 1.0000 0.16% 0.33% 0.46%
Ohio South 0.9966 0.9962 0.9952 0.9983 0.9997 1.0000 0.16% 0.35% 0.48%
Ohio Northeast 0.9973 0.9968 0.9960 0.9986 0.9998 0.9999 0.13% 0.30% 0.39%
Oklahoma 0.9968 0.9955 0.9943 0.9985 0.9997 0.9998 0.17% 0.42% 0.54% Oregon 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.00% 0.00% 0.00%

### Second Section 812 Pr ospective Analysis February 2011

B-18 SUBREGION X: B

A NoCAAA O



## e      

 3 Y: B

A WithCAAA O



## e      

 3 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020
Pennsylvania 0.9978 0.9974 0.9966 0.9991 0.9998 1.0000 0.13% 0.24% 0.34%
Rhode Island 0.9973 0.9968 0.9961 0.9987 0.9996 1.0000 0.14% 0.28% 0.39%
South Carolina 0.9852 0.9709 0.9657 0.9947 0.9994 1.0000 0.96% 2.85% 3.43%
South Dakota 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.00% 0.00% 0.00%
Tennessee 0.9825 0.9737 0.9671 0.9936 0.9992 1.0000 1.12% 2.55% 3.29% Texas High Plains 0.9993 0.9992 0.9986 0.9997 0.9999 1.0000 0.03% 0.07% 0.13% Texas Rolling Plains 0.9985 0.9980 0.9969 0.9993 0.9999 1.0000 0.08% 0.19% 0.31%
Texas Central Blacklands 0.9978 0.9972 0.9953 0.9991 0.9998 1.0000 0.13% 0.26% 0.46%
Texas East 0.9976 0.9965 0.9947 0.9992 0.9999 1.0000 0.16% 0.34% 0.53%
Texas Edwards Plateau 0.9994 0.9993 0.9987 0.9997 0.9999 1.0000 0.03% 0.07% 0.13%
Texas Coastal Bend 0.9979 0.9977 0.9959 0.9991 0.9999 1.0000 0.12% 0.22% 0.40%
Texas South 0.9994 0.9994 0.9988 0.9998 1.0000 1.0000 0.03% 0.06% 0.11%
Texas Trans Pecos 0.9995 0.9993 0.9988 0.9997 0.9999 1.0000 0.02% 0.06% 0.12%
Utah 0.9993 0.9989 0.9984 0.9996 0.9998 0.9999 0.03% 0.09% 0.14%
Vermont 0.9993 0.9991 0.9988 0.9997 0.9999 1.0000 0.05% 0.08% 0.12% Virginia 0.9840 0.9722 0.9691 0.9941 0.9984 0.9999 1.02% 2.62% 3.08% Washington 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.00% 0.00% 0.00% West Virginia 0.9927 0.9899 0.9875 0.9972 0.9995 1.0000 0.45% 0.96% 1.24%
Wisconsin 0.9998 0.9998 0.9997 0.9999 1.0000 1.0000 0.01% 0.02% 0.03% Wyoming 0.9998 0.9997 0.9996 0.9999 0.9999 0.9999 0.01% 0.02% 0.03% Notes: 1. Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by cro p in Exhibit X-6. 2. Relative yield losses are only deri ved for subregions where the crop is present as defined in FASOM. 3. Relative yield losses are based on ozone concentrations during th e growing period for each crop and forest type, not ozone conc entrations for the entire May through September period. Growing periods are spec ific to individual crops and subregions, and to individual re gions for hardwood and softwood forest types.

### Second Section 812 Pr ospective Analysis February 2011

B-19 EXHIBIT B-16. DERIVATION OF RELATIVE YIELD LOSSES FOR COTTON BY FASOM SUBREGION AND YEAR

SUBREGION X: B

A NoCAAA O



## e      

 3 Y: B

A WithCAAA O



## e      

 3 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020

Alabama 0.9249 0.9086 0.8944 0.9544 0.9785 0.9955 3.08% 7.15% 10.16%
Arizona 0.9538 0.9404 0.9238 0.9701 0.9823 0.9874 1.68% 4.26% 6.44%
Arkansas 0.9157 0.8846 0.8783 0.9498 0.9825 0.9932 3.59% 9.96% 11.57%
California North 0.8839 0.8550 0.8334 0.9022 0.9332 0.9408 2.04% 8.38% 11.42%
California South 0.8554 0.8290 0.8029 0.8974 0.9335 0.9533 4.67% 11.19% 15.78%
Florida 0.9894 0.9856 0.9818 0.9932 0.9885 0.9990 0.38% 0.30% 1.72% Georgia 0.9438 0.9261 0.9154 0.9643 0.9795 0.9965 2.12% 5.45% 8.13%
Illinois South 0.8680 0.8130 0.8366 0.9128 0.9712 0.9869 4.91% 16.29% 15.23%
Kansas 0.9605 0.9553 0.9505 0.9719 0.9857 0.9875 1.18% 3.08% 3.75% Kentucky 0.8629 0.8183 0.8276 0.9125 0.9695 0.9888 5.44% 15.59% 16.31%
Louisiana 0.9522 0.9375 0.9281 0.9719 0.9767 0.9944 2.03% 4.02% 6.67% Mississippi 0.9551 0.9416 0.9344 0.9734 0.9856 0.9972 1.88% 4.46% 6.30%
Missouri 0.9051 0.8590 0.8731 0.9390 0.9783 0.9890 3.61% 12.19% 11.71%
Nevada 0.9657 0.9573 0.9487 0.9719 0.9796 0.9836 0.64% 2.27% 3.54%
New Mexico 0.9827 0.9787 0.9749 0.9886 0.9929 0.9931 0.59% 1.43% 1.84%
North Carolina 0.8783 0.8314 0.8231 0.9236 0.9581 0.9932 4.91% 13.23% 17.13%
Oklahoma 0.9503 0.9428 0.9371 0.9664 0.9859 0.9837 1.66% 4.37% 4.73%
South Carolina 0.9078 0.8744 0.8614 0.9442 0.9682 0.9948 3.86% 9.69% 13.41%
Tennessee 0.8425 0.8050 0.7900 0.9021 0.9660 0.9914 6.60% 16.67% 20.31% Texas High Plains 0.9726 0.9686 0.9656 0.9808 0.9902 0.9887 0.84% 2.18% 2.33% Texas Rolling Plains 0.9507 0.9434 0.9397 0.9672 0.9860 0.9821 1.71% 4.32% 4.32%
Texas Central Blacklands 0.9336 0.9255 0.9131 0.9578 0.9800 0.9844 2.53% 5.56% 7.24%
Texas East 0.9400 0.9290 0.9142 0.9656 0.9805 0.9929 2.64% 5.25% 7.93%
Texas Edwards Plateau 0.9640 0.9588 0.9499 0.9773 0.9876 0.9920 1.36% 2.92% 4.24%
Texas Coastal Bend 0.9381 0.9330 0.9156 0.9589 0.9800 0.9912 2.17% 4.79% 7.63%
Texas South 0.9611 0.9564 0.9433 0.9757 0.9847 0.9951 1.49% 2.88% 5.20%
Texas Trans Pecos 0.9726 0.9678 0.9641 0.9805 0.9898 0.9884 0.81% 2.22% 2.46% Virginia 0.9025 0.8786 0.8665 0.9398 0.9655 0.9928 3.97% 9.00% 12.72% Notes: 1. Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by cro p in Exhibit X-6. 2. Relative yield losses are only deri ved for subregions where the crop is present as defined in FASOM. 3. Relative yield losses are based on ozone concentrations during th e growing period for each crop and forest type, not ozone conc entrations for the entire May through September period. Growing periods are spec ific to individual crops and subregions, and to individual re gions for hardwood and softwood forest types.

### Second Section 812 Pr ospective Analysis February 2011

B-20 EXHIBIT B-17. DERIVATION OF RELATIVE YIELD LOSSES FOR ORANGES BY FASOM SUBREGION AND YEAR

SUBREGION X: 

#   NoCAAA O B A 3 

## 

 Y: 

#   WithCAAA O B A 3 

## 

 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020

Arizona 39.55 39.24 38.76 40.06 40.61 41.24 1.28% 3.37% 6.02%
California North 40.12 39.76 39.33 40.39 40.85 41.25 0.68% 2.69% 4.67%
California South 38.23 37.81 37.19 38.99 39.67 40.37 1.95% 4.68% 7.87%
Florida 44.06 43.88 43.60 44.36 45.10 45.63 0.67% 2.71% 4.44%
Texas South 42.56 42.40 42.13 42.92 43.54 43.93 0.85% 2.60% 4.10% Notes: 1. Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by cro p in Exhibit X-6. 2. Relative yield losses are only deri ved for subregions where the crop is present as defined in FASOM. 3. Relative yield losses are based on ozone concentrations during th e growing period for each crop and forest type, not ozone conc entrations for the entire May through September period. Growing periods are spec ific to individual crops and subregions, and to individual re gions for hardwood and softwood forest types.

### Second Section 812 Pr ospective Analysis February 2011

B-21 EXHIBIT B-18. DERIVATION OF RELATIVE YIELD LOSSES FOR POTATOES BY FASOM SUBREGION AND YEAR

SUBREGION X: B

A NoCAAA O



## e      

 3 Y: B

A WithCAAA O



## e      

 3 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020

Alabama 0.9396 0.9308 0.9187 0.9606 0.9881 0.9947 2.18% 5.79% 7.65%
Arizona 0.9106 0.8945 0.8673 0.9325 0.9513 0.9684 2.35% 5.98% 10.44%
California North 0.8840 0.8594 0.8330 0.9002 0.9287 0.9488 1.80% 7.46% 12.21%
California South 0.8267 0.8028 0.7669 0.8686 0.9067 0.9425 4.82% 11.46% 18.63%
Colorado 0.9385 0.9324 0.9192 0.9538 0.9630 0.9715 1.61% 3.18% 5.39%
Connecticut 0.8738 0.8665 0.8564 0.9050 0.9607 0.9877 3.44% 9.80% 13.30%
Delaware 0.8136 0.7811 0.7739 0.8672 0.9472 0.9770 6.17% 17.54% 20.79%
Florida 0.9749 0.9694 0.9611 0.9815 0.9915 0.9937 0.68% 2.22% 3.28%
Idaho 0.9656 0.9609 0.9528 0.9702 0.9753 0.9796 0.48% 1.48% 2.74%
Illinois North 0.8933 0.8645 0.8706 0.9207 0.9624 0.9784 2.99% 10.17% 11.01%
Indiana North 0.8392 0.8097 0.8081 0.8825 0.9469 0.9728 4.91% 14.49% 16.93%
Iowa Northeast 0.9564 0.9520 0.9392 0.9684 0.9838 0.9906 1.24% 3.23% 5.19%
Kansas 0.9554 0.9496 0.9415 0.9665 0.9804 0.9874 1.16% 3.14% 4.64%
Louisiana 0.9563 0.9456 0.9383 0.9724 0.9867 0.9934 1.66% 4.17% 5.55%
Maine 0.9681 0.9660 0.9628 0.9785 0.9897 0.9960 1.06% 2.39% 3.33% Maryland 0.8061 0.7753 0.7687 0.8569 0.9379 0.9706 5.93% 17.33% 20.80%
Massachusetts 0.8865 0.8718 0.8711 0.9158 0.9625 0.9844 3.20% 9.42% 11.51% Michigan 0.9311 0.9314 0.9167 0.9458 0.9700 0.9819 1.55% 3.98% 6.64%
Minnesota 0.9785 0.9759 0.9731 0.9828 0.9888 0.9926 0.43% 1.31% 1.97%
Missouri 0.9156 0.8706 0.8921 0.9421 0.9754 0.9869 2.81% 10.74% 9.61%
Montana 0.9749 0.9734 0.9700 0.9788 0.9816 0.9844 0.39% 0.83% 1.46%
Nebraska 0.9697 0.9673 0.9620 0.9768 0.9852 0.9897 0.73% 1.81% 2.79%
Nevada 0.9319 0.9195 0.9008 0.9422 0.9557 0.9675 1.09% 3.78% 6.90% New Jersey 0.8067 0.7785 0.7781 0.8509 0.9328 0.9725 5.19% 16.54% 19.99%
New Mexico 0.9482 0.9398 0.9250 0.9611 0.9710 0.9787 1.34% 3.21% 5.48%
New York 0.9016 0.8890 0.8862 0.9286 0.9667 0.9841 2.90% 8.04% 9.95%
North Carolina 0.8485 0.7968 0.7962 0.8955 0.9642 0.9850 5.25% 17.36% 19.17%
North Dakota 0.9867 0.9858 0.9844 0.9894 0.9922 0.9943 0.27% 0.64% 1.00%
Ohio Northwest 0.8321 0.8088 0.8026 0.8733 0.9429 0.9715 4.71% 14.22% 17.38% Oregon 0.9810 0.9775 0.9728 0.9829 0.9860 0.9881 0.20% 0.86% 1.55% Pennsylvania 0.8488 0.8367 0.8207 0.8937 0.9588 0.9801 5.03% 12.73% 16.26%
Rhode Island 0.8670 0.8506 0.8483 0.8988 0.9583 0.9861 3.54% 11.25% 13.97%
South Dakota 0.9717 0.9726 0.9658 0.9772 0.9858 0.9878 0.56% 1.34% 2.23%
Tennessee 0.8613 0.8286 0.8220 0.9083 0.9711 0.9891 5.17% 14.68% 16.89% Texas High Plains 0.9653 0.9611 0.9531 0.9743 0.9853 0.9872 0.92% 2.45% 3.45% Texas Rolling Plains 0.9433 0.9363 0.9421 0.9596 0.9803 0.9902 1.70% 4.49% 4.86%
Texas East 0.9324 0.9221 0.9083 0.9577 0.9762 0.9893 2.64% 5.54% 8.19%
Texas Coastal Bend 0.9323 0.9277 0.9119 0.9522 0.9751 0.9871 2.09% 4.87% 7.61%
Texas South 0.9570 0.9439 0.9410 0.9710 0.9760 0.9923 1.44% 3.28% 5.17%
Utah 0.9000 0.8853 0.8595 0.9213 0.9390 0.9568 2.31% 5.72% 10.17% Virginia 0.8482 0.8038 0.8051 0.8976 0.9628 0.9832 5.50% 16.51% 18.11% Washington 0.9911 0.9897 0.9877 0.9919 0.9936 0.9950 0.09% 0.40% 0.73% West Virginia 0.8826 0.8585 0.8548 0.9212 0.9756 0.9880 4.19% 12.00% 13.47%

### Second Section 812 Pr ospective Analysis February 2011

B-22 SUBREGION X: B

A NoCAAA O



## e      

 3 Y: B

A WithCAAA O



## e      

 3 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020

Wisconsin 0.9472 0.9446 0.9318 0.9598 0.9778 0.9866 1.31% 3.39% 5.56% Wyoming 0.9326 0.9261 0.9131 0.9489 0.9578 0.9670 1.72% 3.31% 5.58% Notes: 1. Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by cro p in Exhibit X-6. 2. Relative yield losses are only deri ved for subregions where the crop is present as defined in FASOM. 3. Relative yield losses are based on ozone concentrations during th e growing period for each crop and forest type, not ozone conc entrations for the entire May through September period. Growing periods are spec ific to individual crops and subregions, and to individual re gions for hardwood and softwood forest types.

### Second Section 812 Pr ospective Analysis February 2011

B-23 EXHIBIT B-19. DERIVATION OF RELATIVE YIELD LOSSES FOR RICE BY FASOM SUBREGION AND YEAR

SUBREGION X: B

A NoCAAA O



## e      

 3 Y: B

A WithCAAA O



## e      

 3 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020

Arkansas 0.9696 0.9681 0.9653 0.9700 0.9775 0.9814 0.04% 0.97% 1.64%
California North 0.9625 0.9589 0.9567 0.9623 0.9655 0.9681 -0.02% 0.68% 1.18%
California South 0.9597 0.9538 0.9524 0.9589 0.9637 0.9684 -0.08% 1.03% 1.66%
Louisiana 0.9772 0.9763 0.9744 0.9779 0.9819 0.9844 0.07% 0.57% 1.02% Mississippi 0.9761 0.9749 0.9727 0.9762 0.9823 0.9858 0.00% 0.75% 1.33%
Missouri 0.9671 0.9659 0.9636 0.9674 0.9745 0.9787 0.03% 0.89% 1.54%
Texas Central Blacklands 0.9787 0.9773 0.9761 0.9787 0.9824 0.9846 0.01% 0.53% 0.86%
Texas East 0.9752 0.9740 0.9721 0.9763 0.9813 0.9839 0.11% 0.74% 1.20%
Texas Coastal Bend 0.9801 0.9796 0.9784 0.9815 0.9841 0.9856 0.14% 0.46% 0.73% Notes: 1. Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by cro p in Exhibit X-6. 2. Relative yield losses are only deri ved for subregions where the crop is present as defined in FASOM. 3. Relative yield losses are based on ozone concentrations during th e growing period for each crop and forest type, not ozone conc entrations for the entire May through September period. Growing periods are spec ific to individual crops and subregions, and to individual re gions for hardwood and softwood forest types.

### Second Section 812 Pr ospective Analysis February 2011

B-24 EXHIBIT B-20. DERIVATION OF RELATIVE YIELD LOSSES FOR SORGHUM BY FASOM SUBREGION AND YEAR

SUBREGION X: B

A NoCAAA O



## e      

 3 Y: B

A WithCAAA O



## e      

 3 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020

Alabama 0.9966 0.9957 0.9945 0.9983 0.9992 0.9999 0.17% 0.35% 0.54%
Arizona 0.9990 0.9985 0.9982 0.9994 0.9997 0.9997 0.05% 0.12% 0.15%
Arkansas 0.9932 0.9892 0.9887 0.9967 0.9993 0.9998 0.35% 1.01% 1.11%
California North 0.9900 0.9863 0.9839 0.9920 0.9952 0.9957 0.21% 0.89% 1.18%
California South 0.9870 0.9835 0.9803 0.9918 0.9954 0.9969 0.49% 1.20% 1.67%
Colorado 0.9992 0.9990 0.9989 0.9995 0.9997 0.9997 0.03% 0.06% 0.08%
Delaware 0.9876 0.9843 0.9824 0.9930 0.9969 0.9996 0.54% 1.26% 1.72% Georgia 0.9952 0.9930 0.9915 0.9974 0.9988 0.9999 0.22% 0.58% 0.83%
Illinois North 0.9973 0.9970 0.9962 0.9985 0.9996 0.9998 0.11% 0.26% 0.36%
Illinois South 0.9943 0.9936 0.9923 0.9968 0.9993 0.9997 0.25% 0.57% 0.74%
Indiana North 0.9929 0.9917 0.9903 0.9959 0.9987 0.9996 0.30% 0.70% 0.93%
Indiana South 0.9912 0.9894 0.9886 0.9944 0.9983 0.9995 0.33% 0.89% 1.09%
Iowa West 0.9994 0.9993 0.9992 0.9996 0.9998 0.9999 0.02% 0.04% 0.07%
Iowa Central 0.9993 0.9991 0.9988 0.9996 0.9998 0.9999 0.03% 0.06% 0.11%
Iowa Northeast 0.9992 0.9990 0.9986 0.9996 0.9997 0.9999 0.04% 0.07% 0.13%
Iowa South 0.9983 0.9978 0.9974 0.9991 0.9997 0.9999 0.08% 0.19% 0.25%
Kansas 0.9984 0.9982 0.9979 0.9990 0.9996 0.9996 0.06% 0.15% 0.17% Kentucky 0.9933 0.9924 0.9907 0.9963 0.9990 0.9998 0.31% 0.66% 0.91%
Louisiana 0.9974 0.9961 0.9954 0.9987 0.9995 0.9998 0.13% 0.34% 0.45% Maryland 0.9848 0.9812 0.9790 0.9910 0.9963 0.9993 0.63% 1.52% 2.03% Mississippi 0.9971 0.9957 0.9950 0.9986 0.9996 0.9999 0.15% 0.39% 0.49%
Missouri 0.9942 0.9917 0.9913 0.9969 0.9993 0.9997 0.27% 0.75% 0.84%
Nebraska 0.9994 0.9993 0.9992 0.9996 0.9998 0.9998 0.02% 0.05% 0.06%
New Mexico 0.9995 0.9993 0.9993 0.9997 0.9999 0.9998 0.02% 0.05% 0.06%
North Carolina 0.9908 0.9865 0.9842 0.9952 0.9974 0.9998 0.44% 1.08% 1.56%
Oklahoma 0.9974 0.9968 0.9966 0.9985 0.9995 0.9993 0.11% 0.27% 0.27% Pennsylvania 0.9883 0.9868 0.9844 0.9935 0.9980 0.9996 0.53% 1.13% 1.52%
South Carolina 0.9912 0.9866 0.9844 0.9956 0.9978 0.9998 0.44% 1.12% 1.55%
South Dakota 0.9996 0.9996 0.9995 0.9997 0.9999 0.9999 0.01% 0.03% 0.04%
Tennessee 0.9824 0.9762 0.9732 0.9910 0.9979 0.9997 0.87% 2.17% 2.65% Texas High Plains 0.9981 0.9977 0.9968 0.9987 0.9994 0.9997 0.07% 0.17% 0.28% Texas Rolling Plains 0.9966 0.9960 0.9945 0.9980 0.9993 0.9997 0.13% 0.33% 0.51%
Texas Central Blacklands 0.9956 0.9949 0.9929 0.9976 0.9992 0.9997 0.20% 0.43% 0.68%
Texas East 0.9956 0.9942 0.9924 0.9979 0.9995 0.9998 0.24% 0.52% 0.74%
Texas Edwards Plateau 0.9982 0.9979 0.9968 0.9989 0.9996 0.9998 0.08% 0.17% 0.30%
Texas Coastal Bend 0.9959 0.9955 0.9936 0.9976 0.9993 0.9997 0.18% 0.38% 0.61%
Texas South 0.9982 0.9980 0.9971 0.9990 0.9997 0.9998 0.08% 0.17% 0.28%
Texas Trans Pecos 0.9984 0.9980 0.9972 0.9989 0.9995 0.9997 0.05% 0.14% 0.25% Virginia 0.9863 0.9805 0.9788 0.9929 0.9969 0.9996 0.67% 1.65% 2.08% Notes: 1. Parameter values for A and B in the equations for X and Y are pres ented along with the appropriate ozone metric to apply by cro p in Exhibit X-6. 2. Relative yield losses are only deri ved for subregions where the crop is present as defined in FASOM. 3. Relative yield losses are based on ozone co ncentrations during the growing period fo r each crop and forest type, not ozone conc entrations for the entire May through September period. Growing periods are specif ic to individual crops and subregions, an d to individual regions for hardwood and softwood forest types.

### Second Section 812 Pr ospective Analysis February 2011

B-25 EXHIBIT B-21. DERIVATION OF RELATIVE YIELD LOSSES FOR SOYBEANS BY FASOM SUBREGION AND YEAR

SUBREGION X: B

A NoCAAA O



## e      

 3 Y: B

A WithCAAA O



## e      

 3 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020

Alabama 0.9710 0.9670 0.9605 0.9814 0.9864 0.9978 1.06% 1.96% 3.74%
Arkansas 0.9418 0.9309 0.9176 0.9644 0.9863 0.9946 2.34% 5.62% 7.75%
Delaware 0.9397 0.9329 0.9227 0.9589 0.9707 0.9939 2.01% 3.89% 7.16%
Florida 0.9958 0.9948 0.9931 0.9972 0.9894 0.9995 0.13% -0.55% 0.64% Georgia 0.9763 0.9720 0.9656 0.9842 0.9858 0.9979 0.80% 1.40% 3.24%
Illinois North 0.9306 0.9125 0.9124 0.9511 0.9799 0.9886 2.16% 6.88% 7.71%
Illinois South 0.8951 0.8601 0.8721 0.9285 0.9743 0.9875 3.60% 11.73% 11.68%
Indiana North 0.8940 0.8765 0.8705 0.9259 0.9665 0.9853 3.45% 9.31% 11.65%
Indiana South 0.8865 0.8569 0.8658 0.9166 0.9633 0.9842 3.29% 11.05% 12.02%
Iowa West 0.9766 0.9749 0.9708 0.9827 0.9888 0.9928 0.62% 1.41% 2.21%
Iowa Central 0.9762 0.9733 0.9670 0.9837 0.9901 0.9958 0.75% 1.69% 2.89%
Iowa Northeast 0.9753 0.9722 0.9634 0.9834 0.9887 0.9957 0.82% 1.67% 3.25%
Iowa South 0.9602 0.9457 0.9460 0.9737 0.9884 0.9938 1.38% 4.32% 4.81%
Kansas 0.9640 0.9599 0.9560 0.9738 0.9860 0.9871 1.00% 2.65% 3.15% Kentucky 0.9225 0.9140 0.9033 0.9485 0.9792 0.9923 2.74% 6.66% 8.96%
Louisiana 0.9652 0.9575 0.9497 0.9789 0.9796 0.9956 1.39% 2.26% 4.61% Maryland 0.9219 0.9138 0.9024 0.9451 0.9665 0.9905 2.46% 5.45% 8.90% Michigan 0.9635 0.9618 0.9542 0.9727 0.9862 0.9921 0.95% 2.47% 3.82%
Minnesota 0.9910 0.9898 0.9883 0.9932 0.9941 0.9973 0.23% 0.43% 0.90% Mississippi 0.9543 0.9420 0.9353 0.9718 0.9836 0.9965 1.80% 4.23% 6.14%
Missouri 0.9348 0.9163 0.9142 0.9570 0.9838 0.9912 2.32% 6.87% 7.77%
Nebraska 0.9784 0.9765 0.9735 0.9840 0.9901 0.9918 0.57% 1.38% 1.85% New Jersey 0.9212 0.9140 0.9069 0.9404 0.9647 0.9900 2.04% 5.25% 8.39%
New York 0.9461 0.9418 0.9356 0.9633 0.9807 0.9927 1.78% 3.96% 5.75%
North Carolina 0.9752 0.9708 0.9643 0.9841 0.9776 0.9980 0.91% 0.69% 3.38%
North Dakota 0.9908 0.9902 0.9893 0.9928 0.9947 0.9958 0.20% 0.46% 0.65%
Ohio Northwest 0.8814 0.8660 0.8575 0.9139 0.9612 0.9827 3.55% 9.90% 12.74%
Ohio South 0.8819 0.8628 0.8610 0.9129 0.9636 0.9843 3.39% 10.45% 12.53%
Ohio Northeast 0.8813 0.8670 0.8611 0.9106 0.9618 0.9791 3.22% 9.85% 12.05%
Oklahoma 0.9499 0.9432 0.9380 0.9650 0.9843 0.9816 1.57% 4.17% 4.44% Pennsylvania 0.9271 0.9209 0.9116 0.9512 0.9768 0.9924 2.54% 5.73% 8.14%
South Carolina 0.9822 0.9787 0.9738 0.9888 0.9832 0.9985 0.67% 0.46% 2.47%
South Dakota 0.9846 0.9833 0.9814 0.9882 0.9920 0.9933 0.37% 0.87% 1.20%
Tennessee 0.9085 0.8976 0.8790 0.9410 0.9749 0.9938 3.45% 7.93% 11.55% Texas High Plains 0.9675 0.9632 0.9598 0.9765 0.9872 0.9857 0.92% 2.43% 2.63% Texas Rolling Plains 0.9458 0.9386 0.9348 0.9626 0.9828 0.9789 1.74% 4.50% 4.51%
Texas Central Blacklands 0.9293 0.9216 0.9094 0.9533 0.9764 0.9813 2.51% 5.61% 7.33%
Texas East 0.9346 0.9233 0.9091 0.9607 0.9769 0.9907 2.71% 5.49% 8.24%
Texas Edwards Plateau 0.9600 0.9550 0.9458 0.9738 0.9850 0.9899 1.41% 3.05% 4.46%
Texas Coastal Bend 0.9335 0.9291 0.9117 0.9543 0.9764 0.9888 2.18% 4.85% 7.80%
Texas South 0.9574 0.9530 0.9398 0.9723 0.9818 0.9935 1.53% 2.94% 5.40%
Texas Trans Pecos 0.9676 0.9625 0.9582 0.9763 0.9868 0.9855 0.89% 2.47% 2.77% Virginia 0.9466 0.9392 0.9279 0.9660 0.9731 0.9950 2.01% 3.48% 6.74%

### Second Section 812 Pr ospective Analysis February 2011

B-26 SUBREGION X: B

A NoCAAA O



## e      

 3 Y: B

A WithCAAA O



## e      

 3 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020
West Virginia 0.9373 0.9313 0.9200 0.9588 0.9785 0.9941 2.24% 4.82% 7.45%
Wisconsin 0.9766 0.9745 0.9687 0.9833 0.9889 0.9953 0.68% 1.46% 2.67% Notes: 1. Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by cro p in Exhibit X-6. 2. Relative yield losses are only deri ved for subregions where the crop is present as defined in FASOM. 3. Relative yield losses are based on ozone concentrations during th e growing period for each crop and forest type, not ozone conc entrations for the entire May through September period. Growing periods are spec ific to individual crops and subregions, and to individual re gions for hardwood and softwood forest types.

### Second Section 812 Pr ospective Analysis February 2011

B-27 EXHIBIT B-22. DERIVATION OF RELATIVE YIELD LOSSES FOR PROCESSING TOMATOES BY FASOM SUBREGION AND
YEAR

SUBREGION X: 

#   NoCAAA O B A 3 

## 

 Y: 

#   WithCAAA O B A 3 

## 

 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020
California North 7,290.13 7,232.09 7,164.21 7,332.20 7,406.13 7,469.18 0.57% 2.35% 4.08% California South 7,096.73 7,032.19 6,938.08 7,209.71 7,313.33 7,431.04 1.57% 3.84% 6.63% Colorado 7,415.28 7,390.86 7,344.74 7,478.11 7,532.51 7,591.02 0.84% 1.88% 3.24% Delaware 7,054.53 7,034.34 6,984.21 7,177.10 7,423.04 7,584.43 1.71% 5.24% 7.91% Indiana North 7,167.08 7,138.69 7,094.80 7,269.14 7,456.14 7,585.62 1.40% 4.26% 6.47% Indiana South 7,188.07 7,161.13 7,114.87 7,294.13 7,506.79 7,644.14 1.45% 4.60% 6.92% Maryland 7,022.28 6,982.46 6,933.75 7,152.79 7,392.19 7,553.57 1.82% 5.54% 8.21% Michigan 7,522.37 7,546.02 7,528.65 7,560.12 7,690.92 7,769.18 0.50% 1.88% 3.10% New Jersey 7,067.04 7,029.99 7,003.71 7,160.55 7,356.54 7,533.69 1.31% 4.44% 7.03% New York 7,398.89 7,382.07 7,385.19 7,465.67 7,594.68 7,718.73 0.89% 2.80% 4.32% Ohio Northwest 7,110.99 7,083.27 7,045.09 7,211.10 7,408.55 7,548.21 1.39% 4.39% 6.67% Ohio South 7,159.61 7,134.65 7,091.84 7,256.56 7,489.78 7,622.76 1.34% 4.74% 6.97% Ohio Northeast 7,097.34 7,073.26 7,045.48 7,190.71 7,390.46 7,531.13 1.30% 4.29% 6.45% Pennsylvania 7,201.80 7,176.27 7,150.19 7,308.58 7,518.60 7,656.11 1.46% 4.55% 6.61% Virginia 7,143.70 7,102.29 7,047.07 7,267.16 7,510.62 7,653.34 1.70% 5.44% 7.92% Notes: 1. Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by cro p in Exhibit X-6. 2. Relative yield losses are only deri ved for subregions where the crop is present as defined in FASOM. 3. Relative yield losses are based on ozone concentrations during th e growing period for each crop and forest type, not ozone conc entrations for the entire May through September period. Growing periods are spec ific to individual crops and subregions, and to individual re gions for hardwood and softwood forest types.

### Second Section 812 Pr ospective Analysis February 2011

B-28 EXHIBIT B-23. DERIVATION OF RELATIVE YIELD LOSSES FOR SPRING WHEAT BY FASOM SUBREGION AND YEAR

SUBREGION X: B

A NoCAAA O



## e      

 3 Y: B

A WithCAAA O



## e      

 3 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020

Colorado 0.9893 0.9868 0.9802 0.9944 0.9967 0.9982 0.51% 0.99% 1.80%
Idaho 0.9962 0.9950 0.9925 0.9972 0.9982 0.9988 0.09% 0.31% 0.63%
Minnesota 0.9992 0.9990 0.9988 0.9995 0.9998 0.9999 0.03% 0.08% 0.12%
Montana 0.9980 0.9977 0.9970 0.9986 0.9990 0.9993 0.06% 0.13% 0.23%
Nevada 0.9833 0.9762 0.9631 0.9882 0.9933 0.9965 0.50% 1.71% 3.35%
North Dakota 0.9990 0.9989 0.9986 0.9993 0.9996 0.9998 0.03% 0.07% 0.12% Oregon 0.9982 0.9975 0.9965 0.9985 0.9990 0.9993 0.03% 0.15% 0.28%
South Dakota 0.9958 0.9953 0.9940 0.9972 0.9984 0.9991 0.14% 0.31% 0.50%
Utah 0.9639 0.9514 0.9246 0.9785 0.9876 0.9940 1.50% 3.67% 6.98% Washington 0.9996 0.9995 0.9993 0.9997 0.9998 0.9999 0.01% 0.03% 0.06%
Wisconsin 0.9906 0.9899 0.9840 0.9945 0.9983 0.9994 0.39% 0.85% 1.54% Wyoming 0.9857 0.9824 0.9747 0.9922 0.9950 0.9971 0.66% 1.26% 2.25% Notes: 1. Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by cro p in Exhibit X-6. 2. Relative yield losses are only deri ved for subregions where the crop is present as defined in FASOM. 3. Relative yield losses are based on ozone concentrations during th e growing period for each crop and forest type, not ozone conc entrations for the entire May through September period. Growing periods are spec ific to individual crops and subregions, and to individual re gions for hardwood and softwood forest types.

### Second Section 812 Pr ospective Analysis February 2011

B-29 EXHIBIT B-24. DERIVATION OF RELATIVE YIELD LOSSES FOR WINTER WHEAT BY FASOM SUBREGION AND YEAR

SUBREGION X: B

A NoCAAA O



## e      

 3 Y: B

A WithCAAA O



## e      

 3 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020

Alabama 0.9960 0.9951 0.9934 0.9979 0.9995 0.9998 0.19% 0.44% 0.64%
Arizona 0.9816 0.9772 0.9690 0.9873 0.9916 0.9952 0.58% 1.45% 2.63%
Arkansas 0.9948 0.9935 0.9903 0.9973 0.9992 0.9997 0.25% 0.57% 0.94%
California North 0.9716 0.9612 0.9442 0.9783 0.9874 0.9918 0.68% 2.64% 4.80%
California South 0.8857 0.8583 0.8109 0.9284 0.9586 0.9797 4.60% 10.47% 17.24%
Colorado 0.9839 0.9810 0.9741 0.9900 0.9931 0.9955 0.62% 1.22% 2.16%
Delaware 0.9425 0.9313 0.9166 0.9681 0.9923 0.9977 2.64% 6.14% 8.13%
Florida 0.9994 0.9992 0.9989 0.9996 0.9999 0.9999 0.02% 0.06% 0.10% Georgia 0.9963 0.9954 0.9937 0.9979 0.9994 0.9998 0.15% 0.40% 0.61%
Illinois North 0.9758 0.9703 0.9661 0.9858 0.9959 0.9983 1.02% 2.57% 3.23%
Illinois South 0.9746 0.9660 0.9628 0.9865 0.9968 0.9989 1.21% 3.09% 3.61%
Indiana North 0.9326 0.9155 0.9062 0.9626 0.9905 0.9970 3.12% 7.58% 9.10%
Indiana South 0.9520 0.9294 0.9327 0.9734 0.9939 0.9982 2.20% 6.49% 6.57%
Iowa West 0.9908 0.9896 0.9863 0.9943 0.9976 0.9990 0.34% 0.80% 1.27%
Iowa Central 0.9919 0.9903 0.9868 0.9953 0.9985 0.9994 0.34% 0.82% 1.26%
Iowa Northeast 0.9918 0.9900 0.9856 0.9953 0.9985 0.9994 0.35% 0.85% 1.38%
Iowa South 0.9922 0.9870 0.9869 0.9960 0.9989 0.9996 0.38% 1.19% 1.27%
Kansas 0.9953 0.9946 0.9924 0.9972 0.9988 0.9994 0.19% 0.42% 0.70% Kentucky 0.9824 0.9790 0.9725 0.9911 0.9980 0.9993 0.87% 1.91% 2.68%
Louisiana 0.9976 0.9973 0.9963 0.9986 0.9994 0.9997 0.10% 0.21% 0.33% Maryland 0.9266 0.9113 0.8967 0.9570 0.9887 0.9964 3.18% 7.82% 10.00% Michigan 0.9804 0.9799 0.9722 0.9875 0.9958 0.9983 0.72% 1.59% 2.62%
Minnesota 0.9983 0.9979 0.9974 0.9989 0.9996 0.9998 0.06% 0.16% 0.24% Mississippi 0.9966 0.9959 0.9942 0.9981 0.9994 0.9998 0.15% 0.36% 0.55%
Missouri 0.9911 0.9893 0.9857 0.9951 0.9985 0.9994 0.40% 0.91% 1.37%
Montana 0.9976 0.9972 0.9964 0.9983 0.9987 0.9991 0.07% 0.15% 0.26%
Nebraska 0.9971 0.9967 0.9957 0.9982 0.9991 0.9995 0.11% 0.24% 0.38%
Nevada 0.9812 0.9737 0.9599 0.9866 0.9921 0.9958 0.54% 1.86% 3.61% New Jersey 0.8505 0.8020 0.8048 0.9100 0.9808 0.9964 6.53% 18.23% 19.23%
New Mexico 0.9823 0.9779 0.9695 0.9884 0.9923 0.9952 0.61% 1.44% 2.58%
New York 0.9643 0.9549 0.9534 0.9797 0.9947 0.9985 1.58% 3.99% 4.51%
North Carolina 0.9695 0.9617 0.9500 0.9827 0.9957 0.9986 1.35% 3.42% 4.87%
North Dakota 0.9986 0.9985 0.9984 0.9989 0.9992 0.9995 0.03% 0.07% 0.11%
Ohio Northwest 0.9087 0.8864 0.8751 0.9468 0.9877 0.9965 4.03% 10.25% 12.18%
Ohio South 0.9327 0.9083 0.9080 0.9614 0.9931 0.9979 2.99% 8.54% 9.01%
Ohio Northeast 0.9033 0.8827 0.8755 0.9403 0.9854 0.9952 3.94% 10.42% 12.02%
Oklahoma 0.9940 0.9929 0.9899 0.9964 0.9985 0.9993 0.24% 0.57% 0.95% Pennsylvania 0.9199 0.9090 0.8908 0.9585 0.9931 0.9982 4.03% 8.47% 10.75%
South Carolina 0.9845 0.9800 0.9733 0.9919 0.9979 0.9993 0.74% 1.79% 2.61%
South Dakota 0.9961 0.9957 0.9947 0.9973 0.9984 0.9990 0.12% 0.27% 0.43%
Tennessee 0.9753 0.9699 0.9596 0.9872 0.9974 0.9992 1.20% 2.75% 3.96% Texas High Plains 0.9951 0.9943 0.9923 0.9966 0.9981 0.9988 0.15% 0.37% 0.65% Texas Rolling Plains 0.9930 0.9917 0.9885 0.9957 0.9981 0.9990 0.27% 0.65% 1.06%

### Second Section 812 Pr ospective Analysis February 2011

B-30 SUBREGION X: B

A NoCAAA O



## e      

 3 Y: B

A WithCAAA O



## e      

 3 RYL: (X/Y) * (100%)

2000 2010 2020 2000 2010 2020 2000 2010 2020

Texas Central Blacklands 0.9868 0.9843 0.9782 0.9928 0.9973 0.9987 0.60% 1.31% 2.05%
Texas East 0.9895 0.9875 0.9816 0.9950 0.9984 0.9992 0.55% 1.09% 1.76%
Texas Edwards Plateau 0.9934 0.9919 0.9887 0.9958 0.9982 0.9991 0.24% 0.62% 1.04%
Texas Coastal Bend 0.9896 0.9879 0.9839 0.9939 0.9977 0.9988 0.43% 0.98% 1.50%
Texas South 0.9906 0.9887 0.9845 0.9944 0.9976 0.9987 0.38% 0.90% 1.42%
Texas Trans Pecos 0.9979 0.9975 0.9966 0.9984 0.9989 0.9993 0.05% 0.14% 0.27%
Utah 0.9562 0.9426 0.9140 0.9730 0.9837 0.9917 1.72% 4.18% 7.84% Virginia 0.9539 0.9407 0.9274 0.9756 0.9947 0.9981 2.23% 5.43% 7.09% West Virginia 0.9525 0.9355 0.9300 0.9764 0.9970 0.9991 2.45% 6.17% 6.92%
Wisconsin 0.9882 0.9873 0.9804 0.9931 0.9978 0.9992 0.49% 1.06% 1.88% Wyoming 0.9801 0.9762 0.9671 0.9886 0.9922 0.9952 0.86% 1.61% 2.82% Notes: 1. Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by cro p in Exhibit X-6. 2. Relative yield losses are only deri ved for subregions where the crop is present as defined in FASOM. 3. Relative yield losses are based on ozone concentrations during th e growing period for each crop and forest type, not ozone conc entrations for the entire May through September period. Growing periods are spec ific to individual crops and subregions, and to individual re gions for hardwood and softwood forest types.Second Section 812 Prospective AnalysisFebruary 2011B-31EXHIBIT B-25. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FORHARDWOOD AND SOFTWOOD FOREST TYPES IN THE NORTHEAST FASOMREGIONFORESTTYPESPECIESX:BANoCAAAOe3Y:BAWithCAAAOe3RYL: (X/Y) * (100%)AVERAGE RYL2000 2010 2020 2000 2010 2020 2000 2010 2020 2000 2010 2020HardwoodsBlack Cherry 0.5345 0.5059 0.4873 0.6227 0.7968 0.8837 14.17% 36.51% 44.86%7.16% 17.13% 21.24%Tulip Poplar 0.8113 0.7791 0.7562 0.8904 0.9756 0.9932 8.88% 20.14% 23.86%Sugar Maple 0.9100 0.8574 0.8103 0.9817 0.9997 1.0000 7.30% 14.24% 18.97%Red Maple 0.9714 0.9679 0.9654 0.9805 0.9929 0.9969 0.93% 2.52% 3.16%Aspen 0.8532 0.8386 0.8286 0.8935 0.9554 0.9786 4.51% 12.23% 15.34%SoftwoodsEastern White Pine 0.8149 0.7902 0.77290.8796 0.9631 0.9865 7.36% 17.96% 21.66%3.85% 9.49% 11.50%Virginia Pine 0.9859 0.9847 0.9839 0.9894 0.9949 0.9972 0.35% 1.03% 1.34%Notes:1.Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by tree species in Exhibit X-6.2.Relative yield losses are only derived for tree species present in the region as defined in FASOM.3.Relative yield losses are based on ozone concentrations during the growing period for each crop and forest type, not ozone concentrations for the entire May through September period.Growing periods are specific to individual crops and subregions,and to individual regions for hardwood and softwood forest types.4.Average relative yield losses for hardwood and softwood forest types are estimated by taking the arithmetic average of relativeyield losses for all hardwood orsoftwood species present in theregion.5.If no hardwood or softwood species, for which relative yield losses are estimated, are present in the region, the average relative yield loss of hardwoods or softwoods from all other regions isapplied as a proxy estimate of average relative yield losses.Second Section 812 Prospective AnalysisFebruary 2011B-32EXHIBIT B-26. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FORHARDWOOD AND SOFTWOOD FOREST TYPES IN THE SOUTHEAST FASOMREGIONFORESTTYPESPECIESX:BANoCAAAOe3Y:BAWithCAAAOe3RYL: (X/Y) * (100%)AVERAGE RYL2000 2010 2020 2000 2010 2020 2000 2010 2020 2000 2010 2020HardwoodsBlack Cherry 0.5648 0.4961 0.4765 0.6601 0.8326 0.9097 14.44% 40.41% 47.62%6.49% 19.12% 23.04%Tulip Poplar 0.8418 0.7673 0.7423 0.9157 0.9844 0.9961 8.08% 22.06% 25.48%Sugar Maple 0.9463 0.8340 0.7777 0.9914 0.9999 1.0000 4.55% 16.59% 22.23%Red Maple 0.9747 0.9666 0.9640 0.9837 0.9947 0.9979 0.91% 2.83% 3.40%Aspen 0.8678 0.8334 0.8226 0.9086 0.9656 0.9846 4.49% 13.69% 16.45%SoftwoodsEastern White Pine 0.8391 0.7812 0.76250.9021 0.9741 0.9913 6.99% 19.81% 23.08%3.67% 10.49% 12.27%Virginia Pine 0.9872 0.9843 0.9834 0.9907 0.9959 0.9979 0.35% 1.17% 1.46%Notes:1.Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by tree species in Exhibit X-6.2.Relative yield losses are only derived for tree species present in the region as defined in FASOM.3.Relative yield losses are based on ozone concentrations during the growing period for each crop and forest type, not ozone concentrations for the entire May through September period.Growing periods are specific to individual crops and subregions,and to individual regions for hardwood and softwood forest types.4.Average relative yield losses for hardwood and softwood forest types are estimated by taking the arithmetic average of relativeyield losses for all hardwood orsoftwood species present in theregion.5.If no hardwood or softwood species, for which relative yield losses are estimated, are present in the region, the average relative yield loss of hardwoods or softwoods from all other regions isapplied as a proxy estimate of average relative yield losses.Second Section 812 Prospective AnalysisFebruary 2011B-33EXHIBIT B-27. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FOR HARDWOOD AND SOFTWOOD FOREST TYPES IN THE LAKE STATES FASOMREGIONFORESTTYPESPECIESX:BANoCAAAOe3Y:BAWithCAAAOe3RYL: (X/Y) * (100%)AVERAGE RYL2000 2010 2020 2000 2010 2020 2000 2010 2020 2000 2010 2020HardwoodsBlack Cherry 0.7571 0.7493 0.7164 0.7975 0.8677 0.9094 5.06% 13.64% 21.23%1.60% 4.20% 6.61%Tulip Poplar 0.9628 0.9599 0.9460 0.9758 0.9909 0.9961 1.33% 3.12% 5.02%Sugar Maple 0.9992 0.9990 0.9976 0.9997 1.0000 1.0000 0.06% 0.10% 0.24%Red Maple 0.9906 0.9901 0.9879 0.9929 0.9963 0.9979 0.23% 0.62% 1.00%Aspen 0.9431 0.9406 0.9295 0.9556 0.9748 0.9845 1.31% 3.50% 5.59%SoftwoodsEastern White Pine 0.9487 0.9455 0.93110.9634 0.9830 0.9913 1.53% 3.82% 6.07%0.82% 2.07% 3.30%Virginia Pine 0.9938 0.9935 0.9925 0.9949 0.9968 0.9979 0.12% 0.33% 0.54%Notes:1.Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by tree species in Exhibit X-6.2.Relative yield losses are only derived for tree species present in the region as defined in FASOM.3.Relative yield losses are based on ozone concentrations during the growing period for each crop and forest type, not ozone concentrations for the entire May through September period.Growing periods are specific to individual crops and subregions,and to individual regions for hardwood and softwood forest types.4.Average relative yield losses for hardwood and softwood forest types are estimated by taking the arithmetic average of relativeyield losses for all hardwood orsoftwood species present in theregion.5.If no hardwood or softwood species, for which relative yield losses are estimated, are present in the region, the average relative yield loss of hardwoods or softwoods from all other regions isapplied as a proxy estimate of average relative yield losses.Second Section 812 Prospective AnalysisFebruary 2011B-34EXHIBIT B-28. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FORHARDWOOD AND SOFTWOOD FOREST TYPES IN THE CORN BELT FASOMREGIONFORESTTYPESPECIESX:BANoCAAAOe3Y:BAWithCAAAOe3RYL: (X/Y) * (100%)AVERAGE RYL2000 2010 2020 2000 2010 2020 2000 2010 2020 2000 2010 2020HardwoodsBlack Cherry 0.5734 0.5111 0.5165 0.6540 0.8035 0.8773 12.33% 36.39% 41.13%5.48% 16.76% 17.96%Tulip Poplar 0.8498 0.7853 0.7914 0.9119 0.9774 0.9923 6.82% 19.66% 20.24%Sugar Maple 0.9539 0.8686 0.8793 0.9903 0.9998 1.0000 3.67% 13.12% 12.07%Red Maple 0.9757 0.9685 0.9692 0.9832 0.9933 0.9967 0.77% 2.49% 2.76%Aspen 0.8718 0.8413 0.8441 0.9063 0.9574 0.9771 3.80% 12.12% 13.62%SoftwoodsEastern White Pine 0.8455 0.7948 0.79950.8987 0.9653 0.9852 5.91% 17.66% 18.84%3.11% 9.34% 10.02%Virginia Pine 0.9875 0.9849 0.9852 0.9905 0.9951 0.9971 0.30% 1.02% 1.20%Notes:1.Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by tree species in Exhibit X-6.2.Relative yield losses are only derived for tree species present in the region as defined in FASOM.3.Relative yield losses are based on ozone concentrations during the growing period for each crop and forest type, not ozone concentrations for the entire May through September period.Growing periods are specific to individual crops and subregions,and to individual regions for hardwood and softwood forest types.4.Average relative yield losses for hardwood and softwood forest types are estimated by taking the arithmetic average of relativeyield losses for all hardwood orsoftwood species present in theregion.5.If no hardwood or softwood species, for which relative yield losses are estimated, are present in the region, the average relative yield loss of hardwoods or softwoods from all other regions isapplied as a proxy estimate of average relative yield losses.Second Section 812 Prospective AnalysisFebruary 2011B-35EXHIBIT B-29. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FOR HARDWOOD AND SOFTWOOD FOREST TYPES IN THE SOUTH CENTRALFASOM REGIONFORESTTYPESPECIESX:BANoCAAAOe3Y:BAWithCAAAOe3RYL: (X/Y) * (100%)AVERAGE RYL2000 2010 2020 2000 2010 2020 2000 2010 2020 2000 2010 2020HardwoodsBlack Cherry 0.6403 0.5900 0.5698 0.7302 0.8722 0.9265 12.32% 32.36% 38.50%4.58% 12.08% 14.56%Tulip Poplar 0.9029 0.8644 0.8465 0.9522 0.9916 0.9975 5.18% 12.82% 15.14%Sugar Maple 0.9871 0.9659 0.9509 0.9983 1.0000 1.0000 1.13% 3.41% 4.91%Red Maple 0.9820 0.9774 0.9753 0.9889 0.9965 0.9984 0.69% 1.92% 2.32%Aspen 0.9008 0.8793 0.8701 0.9342 0.9759 0.9881 3.58% 9.89% 11.94%SoftwoodsEastern White Pine 0.8905 0.8576 0.84290.9374 0.9841 0.9940 5.00% 12.85% 15.20%2.65% 6.87% 8.15%Virginia Pine 0.9900 0.9882 0.9874 0.9930 0.9969 0.9983 0.30% 0.88% 1.10%Notes:1.Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by tree species in Exhibit X-6.2.Relative yield losses are only derived for tree species present in the region as defined in FASOM.3.Relative yield losses are based on ozone concentrations during the growing period for each crop and forest type, not ozone concentrations for the entire May through September period.Growing periods are specific to individual crops and subregions,and to individual regions for hardwood and softwood forest types.4.Average relative yield losses for hardwood and softwood forest types are estimated by taking the arithmetic average of relativeyield losses for all hardwood orsoftwood species present in theregion.5.If no hardwood or softwood species, for which relative yield losses are estimated, are present in the region, the average relative yield loss of hardwoods or softwoods from all other regions isapplied as a proxy estimate of average relative yield losses.Second Section 812 Prospective AnalysisFebruary 2011B-36EXHIBIT B-30. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FORHARDWOOD AND SOFTWOOD FOREST TYPES IN THE ROCKY MOUNTAINSFASOM REGIONFORESTTYPESPECIESX:BANoCAAAOe3Y:BAWithCAAAOe3RYL: (X/Y) * (100%)AVERAGE RYL2000 2010 2020 2000 2010 2020 2000 2010 2020 2000 2010 2020SoftwoodsPonderosa Pine 0.9488 0.9415 0.9288 0.9595 0.9683 0.9764 1.11% 2.77% 4.88%0.56% 1.38% 2.44%Douglas Fir 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.00% 0.00% 0.00%Notes:1.Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by tree species in Exhibit X-6.2.Relative yield losses are only derived for tree species present in the region as defined in FASOM.3.Relative yield losses are based on ozone concentrations during the growing period for each crop and forest type, not ozone concentrations for the entire May through September period.Growing periods are specific to individual crops and subregions,and to individual regions for hardwood and softwood forest types.4.Average relative yield losses for hardwood and softwood forest types are estimated by taking the arithmetic average of relativeyield losses for all hardwood orsoftwood species present in theregion.5.If no hardwood or softwood species, for which relative yield losses are estimated, are present in the region, the average relative yield loss of hardwoods or softwoods from all other regions isapplied as a proxy estimate of average relative yield losses.Second Section 812 Prospective AnalysisFebruary 2011B-37EXHIBIT B-31. DERIVATION OF AVERAGE RELATIVE YIELD LOSSESFOR HARDWOOD AND SOFTWOOD FOREST TYPES IN THE PACIFICNORTHWEST-WESTSIDE FASOM REGIONFORESTTYPESPECIESX:BANoCAAAOe3Y:BAWithCAAAOe3RYL: (X/Y) * (100%)AVERAGE RYL2000 2010 2020 2000 2010 2020 2000 2010 2020 2000 2010 2020SoftwoodsPonderosa Pine 0.9892 0.9873 0.9852 0.9907 0.9922 0.9936 0.15% 0.49% 0.85%0.07% 0.25% 0.42%Douglas Fir 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.00% 0.00% 0.00%Notes:1.Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by tree species in Exhibit X-6.2.Relative yield losses are only derived for tree species present in the region as defined in FASOM.3.Relative yield losses are based on ozone concentrations during the growing period for each crop and forest type, not ozone concentrations for the entire May through September period.Growing periods are specific to individual crops and subregions,and to individual regions for hardwood and softwood forest types.4.Average relative yield losses for hardwood and softwood forest types are estimated by taking the arithmetic average of relativeyield losses for all hardwood orsoftwood species present in theregion.5.If no hardwood or softwood species, for which relative yield losses are estimated, are present in the region, the average relative yield loss of hardwoods or softwoods from all other regions isapplied as a proxy estimate of average relative yield losses.Second Section 812 Prospective AnalysisFebruary 2011B-38EXHIBIT B-32. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FORHARDWOOD AND SOFTWOOD FOREST TYPES IN THE PACIFIC SOUTHWESTFASOM REGIONFORESTTYPESPECIESX:BANoCAAAOe3Y:BAWithCAAAOe3RYL: (X/Y) * (100%)AVERAGE RYL2000 2010 2020 2000 2010 2020 2000 2010 2020 2000 2010 2020SoftwoodsPonderosa Pine 0.8803 0.8595 0.8312 0.9006 0.9280 0.9485 2.25% 7.38% 12.37%1.14% 3.73% 6.30%Douglas Fir 0.9996 0.9991 0.9977 0.9999 1.0000 1.0000 0.02% 0.08% 0.23%Notes:1.Parameter values for A and B in the equations for X and Y are presented along with the appropriate ozone metric to apply by tree species in Exhibit X-6.2.Relative yield losses are only derived for tree species present in the region as defined in FASOM.3.Relative yield losses are based on ozone concentrations during the growing period for each crop and forest type, not ozone concentrations for the entire May through September period.Growing periods are specific to individual crops and subregions,and to individual regions for hardwood and softwood forest types.4.Average relative yield losses for hardwood and softwood forest types are estimated by taking the arithmetic average of relativeyield losses for all hardwood orsoftwood species present in theregion.5.If no hardwood or softwood species, for which relative yield losses are estimated, are present in the region, the average relative yield loss of hardwoods or softwoods from all other regions isapplied as a proxy estimate of average relative yield losses.