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 Prospective 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 Prospective Analysis 

February 2011 

ii 

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 Prospective Analysis 

February 2011 

1-1 

CHAPTER 1 | INTRODUCTION 

Section 812 of the Clean Air Act Amendments of 1990 (CAAA) required the U.S. 

Environmental Protection Agency (EPA) to perform periodic, comprehensive analyses of 

the total costs and total benefits of programs 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. The Retrospective was completed in 1997. 

Section 812 also required performance of prospective cost-benefit analyses, the first of 

which was completed in 1999. The prospective analyses address the incremental costs 

and benefits of the CAAA. The First Prospective 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 study. This analytical plan was reviewed by a 

statutorily-mandated outside peer review group, 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 the second prospective section 812 analysis. 

Exhibit 1-1 below outlines the relationship among 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

Time

Emissions

Retrospective First Prospective

Second Prospective

C

With-CAAA

1970 1990 2000 2010 2020

A

B

Pre-CAA

Post-CAA

Without-CAAA

Time

Emissions

Retrospective First Prospective

Second Prospective

C

With-CAAA

1970 1990 2000 2010 2020

A

B

Pre-CAA

Post-CAA

Without-CAAA

Time

Emissions

Retrospective First Prospective

Second Prospective

C

With-CAAA

1970 1990 2000 2010 2020

A

B

Pre-CAA

Post-CAA

Without-CAAA

Time

Emissions

Retrospective First Prospective

Second Prospective

C

With-CAAA
Second Section 812 Prospective Analysis 

February 2011 

1-2 

The scope of this analysis is to estimate the 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 since 1990 to comply with rules promulgated through 

September 20051

; 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, therefore, in emissions attributable to 

economic and population growth. 

Criteria pollutant emissions reductions addressed in this analysis include: volatile 

organic compounds (VOCs), oxides of nitrogen (NOx), sulfur dioxide (SO2), particulate 

matter of 10 microns or less (PM10), and particulate matter with an aerodynamic diameter 

of 2.5 microns or less (PM2.5). Benefits estimates, however, focus not on the emissions 

but on the ambient air concentration outcomes 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 analysis of the future effects of implementation 

of the CAAA’s programs on air emissions from the following emission sectors: 

electricity generating units (EGUs), non-electricity 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 methods 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 Residual Risk rulemaking, promulgated under Title III of the Act in March 2005. We 

omitted this rule because it has a very small impact on criteria pollutant emissions (less than 10 tons per year VOCs) 

relative to the with-CAAA scenario. The primary MACT rule for coke oven emissions, however, involves much larger 

reductions and therefore is included in the with-CAAA scenario. 

2

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

of lead emissions is a major focus of the section 812 retrospective study. Recently finalized revisions to the lead NAAQS 

could have significant effects on emissions for some localities, but those changes were first proposed on May 1, 2008 and 

were therefore not included in the scope of this analysis. 
Second Section 812 Prospective Analysis 

February 2011 

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 Health Benefits: These include avoided premature 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 concentrations 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 pollution 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 data 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 effort. As illustrated in Exhibit 1-2, EPA 

conducted both emissions estimation and air quality modeling analyses to generate data 

that underlies the benefits estimation approaches. 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 cases, some post-processing of air quality data is 

involved to estimate pollutant exposures appropriate to the specific benefits analysis. 

This report focuses on presentation of the primary benefits estimates. The primary 

benefits estimates are based on EPA’s preferred set of analytic assumptions, models, and 

data sources, many of which have been explicitly 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 Office 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 Prospective 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 and 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 addition, 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 Prospective 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 Prospective reflects methodologies, data, or 

benefits categories that are new to Agency analysis. In general, the primary benefits 

results presented here reflect methods, data, and benefits that have been vetted through 

Council review, as well as internal EPA review by OAR economists and benefits 

analysts. 

The general method we apply to quantify and monetize 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 concentrations 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 measure 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 scenarios. 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 values 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 these approaches are provided in the subsequent 

chapters. 

SUMMARY OF RESULTS 

Exhibit 1-4 below provides a summary of the economic benefits results generated for the 

categories of benefits addressed in this report. 
Second Section 812 Prospective 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-based 

concentrationresponse functions 

Forest and 

Agricultural Sector 

Optimization Model 

(FASOM) 

Materials Damage Air Pollution Emissions Experiments 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 Prospective Analysis 

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EXHIBIT 1-4. SUMMARY OF MEAN PRIMARY BENEFITS RESULTS 

BENEFIT CATEGORY 

MONETIZED BENEFITS (MILLION 2006$) 

BY TARGET YEAR 

2000 2010 2020 NOTES 

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 for detailed results summaries. Values presented 

are means from results reported as distributions. Additional, alternative estimates are provided 

in the separate companion report on uncertainty. Estimates presented with two significant 

figures. 

The health effects estimates for the second prospective are much larger 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 and $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 

 

3

 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. 
Second Section 812 Prospective Analysis 

February 2011 

1-8 

included in the comparable first prospective 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 Model (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 regional 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, and achieves improved model performance.

3. Better, more comprehensive exposure estimates: The first prospective relied on first 

generation exposure extrapolation tools to generate monitor-adjusted exposure 

estimates away from monitors. Since then, the monitor network, availability of 

speciated data, and the performance of speciated 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 impact of fine PM has nearly doubled. In 

addition, health effects research has addressed 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 valuation. As noted above, the health benefits 

constitute the majority of the monetized benefits 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. 
Second Section 812 Prospective Analysis 

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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 effects realized as a result 

of the CAAA. The reduced incidence of physical 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 dollar 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 materials 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 significant 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 provide 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 Prospective Analysis 

February 2011 

2-2 

effects and a substantial number of health benefits that we could not quantify or 

monetize. 

Similar to the first section 812 prospective analysis, the study design adopted for this 

analysis uses a sequence of linked analytical models to estimate benefits. The first step is 

an analysis of the likely implementation activities 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) model 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 economic 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 changes in exposure and expected changes in 

specific health effects; and 3) health valuation 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 produces results in terms of avoided health 

effects and monetary valuation of the willingness 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 prospective analysis to use an integrated modeling 

system, the Community Multiscale Air Quality (CMAQ) model, to simulate changes in 

national and regional-scale pollutant concentrations 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 chemical 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 current version of BenMAP can be downloaded from 

http://www.epa.gov/air/benmap/ 
Second Section 812 Prospective Analysis 

February 2011 

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transport, and deposition of gaseous and particulate species in the atmosphere. The latest 

version of CMAQ (Version 4.6) was employed for this analysis. 

The CMAQ model was applied for seven core 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 Scenarios 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 projections, future-year control requirements 

(for attainment of NAAQS), and control efficiencies. Different assumptions were applied 

for the with- and without-CAAA scenarios resulting in a different future-year emissions 

pathway for each scenario. The emissions data 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 studies. In particular, model-ready 

meteorological input files for 2002 were provided by EPA for use in this study. For fine 

particulate matter (PM2.5) 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 Prospective Analysis 

February 2011 

2-4 

Exhibit 2-1). For ozone and related species, the CMAQ model was applied for a fivemonth simulation period that captures the 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 for 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 (comprising 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 description is based on the September 

2008 draft report, Second Prospective 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. Haney, A. Belle Hudischewskyj, 

Thomas C. Myers, and Y. Wei of ICF International. 

CONUS

EUS

WUS
Second Section 812 Prospective Analysis 

February 2011 

2-5 

PM2.5 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 

PM2.5 output is adjusted using the Modeled Attainment Test Software (MATS, Version 

2.1.1, Build 807) procedure and the ozone output is adjusted using the enhanced Voronoi 

Neighbor Averaging (eVNA) routine in BenMAP. 

MATS estimates quarterly mean PM2.5 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 PM2.5 

chemical component concentrations at all grid cells in an Eulerian grid model such as 

CMAQ using a spatial field gradient interpolation procedure. All PM2.5 concentration 

estimates for this analysis were prepared using the spatial and temporal relative 

adjustment method in MATS. PM2.5 concentration estimates in CMAQ grid cells without 

a monitor were interpolated from nearby monitors using the inverse distance squared 

weighting option in the Voronoi Neighbor Averaging (VNA) procedure in MATS. The 

MATS analysis conducted for the PM2.5 used the following input information: 

 Observed PM2.5 data from 1,336 Federal Reference Method (FRM) monitors with 

sufficient data in at least one year from 2002 to 2004 (as provided with the MATS 

Version 2.1.1 installation package); 

 Observed chemically speciated fine particle mass data from both the PM2.5 

Speciated Trends Network (STN) and the 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 PM2.5 species (SO4, NO3, elemental carbon, 

organic carbon, NH4, 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 available in the MATS User Manual (Abt 

Associates, 2009). MATS produced estimated average quarterly concentrations for each 

of the CMAQ 36 km grid cells. These estimates were subsequently rewritten to the 

format required for inputting daily PM2.5 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 software. 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 monitor data was selected because the base case 

CMAQ analysis (“2000 with Clean Air Act”) used a 2002 emission inventory. The 
Second Section 812 Prospective Analysis 

February 2011 

2-6 

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 PM2.5 concentration. There 

are several types of data that can support the development of health impact functions 

relating air pollutant exposure or ambient concentrations 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 concentration-response (C-R) relationships which 

can be used to evaluate population-level impacts of reductions in ambient pollution 

levels. 

However, standard environmental epidemiology 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 sources of uncertainty in the relationships 

between ambient pollution and population level health outcomes, including many sources 

of model uncertainty, such as model specification, 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 investigating how expert elicitation methods can 

be used to integrate across various sources of data in developing health 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, and 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 lead NAAQS (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 PM2.5 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 health 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). 
Second Section 812 Prospective Analysis 

February 2011 

2-7 

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 y y e  , 

where y0 is the baseline incidence (the product of the baseline incidence rate times the 

potentially affected population), β is the effect estimate, and Δx is the estimated change in 

the summary ozone or PM2.5measure. 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: size 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 (Geolytics 2002). BenMAP incorporates 250 

age/gender/race categories to match specific populations potentially affected by ozone 

and PM2.5. The software constructs specific populations 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-level population level. 

Woods & Poole (2007) provides the county-level population forecasts used to calculate 

the scaling ratios. 

HEALTH EFFECT ESTIMATE SOURCES 

The most significant monetized benefits of reducing ambient concentrations of ozone and 

PM are attributable to reductions in human health risks. EPA’s Ozone and PM Criteria 

Documents outline numerous health effects known or suspected to be linked to exposure 

to ambient ozone and PM (EPA, 2006; Anderson et al., 2004). EPA recently evaluated 

the ozone and PM literature for use in the 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 PM2.5. Exhibit 2-3 and 2-4 lists 

the health endpoints associated with ozone and PM2.5, respectively, included in this 

analysis. A number of endpoints that are not health-related may also contribute 

significant monetized benefits. Welfare benefits such as increased recreational and 

residential visibility, increased recreational fishing opportunities, increased commercial 
Second Section 812 Prospective Analysis 

February 2011 

2-8 

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 PM2.5 

POLLUTANT/EFFECT 

QUANTIFIED AND MONETIZED IN BASE 

ESTIMATESa UNQUANTIFIED EFFECTSg,h—CHANGES IN: 

PM/Healthb Premature mortality based on both cohort 

study estimates and on expert elicitationc,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/cerebrovascular disease 

Ozone/Healthf Premature mortality: short-term exposures

Hospital admissions: respiratory 

Emergency room visits for asthma 

Minor restricted-activity days 

School loss days 

Outdoor worker productivity 

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 (+/-)e a Primary quantified and monetized effects are those included when determining the primary estimate of total 

monetized benefits of the alternative standards. 

b

 In addition to primary economic endpoints, there are a number of biological responses that have been associated 

with PM health effects including morphological 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 short-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 number 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 susceptibility to respiratory infection. 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 include both a) those for which there is not consensus on 

causality and b) those for which causality has been determined but empirical data are not available to allow 

calculation of benefits. 
Second Section 812 Prospective 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 yearsa

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 reductions in school absences for all schoolaged children based on the biological similarity between children aged 5 to 17. 
Second Section 812 Prospective 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 Mortality 

Premature mortality—allcauseaPM2.5 (annual 

avg) 

Weibull distribution of 

C-R coefficientsa 

>24 years 

Infant mortality—all-cause PM2.5 (annual 

avg) 

Woodruff et al. (1997) Infant (<1 year) 

Chronic Illness 

Chronic bronchitis PM2.5 (annual 

avg) 

Abbey et al. (1995) >26 years 

Nonfatal myocardial 

infarction 

PM2.5 (24-hour 

avg) 

Peters et al. (2001) Adults (>18 years) 

Hospital Admissions 

Respiratory PM2.5 (24-hour 

avg) 

Pooled estimate: 

Moolgavkar (2003)—ICD 

490–496 (COPD) 

Ito (2003)—ICD 490–496, 

480–487 (COPD, 

pneumonia) 

>64 years 

Respiratory PM2.5 (24-hour 

avg) 

Moolgavkar (2000a)—ICD 

490–492, 494-496 (COPD, 

less asthma) 

20–64 years 

Respiratory PM2.5 (24-hour 

avg) 

Sheppard (2003)—ICD 

493 (asthma) 

<65 years 

Cardiovascular PM2.5 (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 PM2.5 (24-hour 

avg) 

Moolgavkar (2000b)—ICD 

390–429 (all 

cardiovascular) 

20–64 years 

Asthma-related ER visits PM2.5 (24-hour 

avg) 

Norris et al. (1999) <18 years 

Other Health Endpoints 

Acute bronchitis PM2.5 (annual 

avg) 

Dockery et al. (1996) 8–12 years 

Lower respiratory 

symptoms 

PM2.5 (24-hour 

avg) 

Schwartz and Neas 

(2000) 

7–14 years 

Upper respiratory 

symptoms 

PM2.5 (24-hour 

avg) 

Pope et al. (1991) 9–11 years 
Second Section 812 Prospective Analysis 

February 2011 

2-11 

ENDPOINT POLLUTANT STUDY STUDY POPULATION 

Asthma exacerbation PM2.5 (24-hour 

avg) 

Pooled estimate: 

Ostro et al. (2001) 

(cough, wheeze, 

shortness of breath) 

Vedal et al. (1998) 

(cough) 

6–18 yearsb

Minor restricted-activity 

days 

PM2.5 (24-hour 

avg) 

Ostro and Rothschild 

(1989) 

18–64 years 

Work loss days PM2.5 (24-hour 

avg) 

Ostro (1987) 18–64 years 

a This distribution of coefficients for the PM mortality function is based on recommendations 

made by the HES; it features a Weibull distribution 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 basis for the effect in children in the broader age 

group. See: U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the 

Analytical Plan for EPA’s Second Prospective Analysis – Benefits and Costs of the Clean Air Act, 

1990—2020. EPA-SAB-COUNCIL-ADV-04-004. 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 repeated ozone exposure also likely contributes 

to premature death. The 2006 Ozone Criteria Document states, “Consistent with 

observed ozone-related increases in respiratory- and cardiovascular-related morbidity, 

several newer multi-city studies, single-city studies, and several meta-analyses of these 

studies have provided relatively strong epidemiologic evidence for associations between 

short-term ozone exposure and all-cause mortality, even after adjustment for the influence 

of season and PM” (EPA, 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 risk of respiratory or cardiovascular morbidity 

and mortality could be increased by ambient ozone. With respect to short-term exposure, 

the Ozone Criteria Document concludes, “This overall body of evidence is highly 

suggestive that ozone directly or indirectly contributes to non-accidental and 

cardiopulmonary-related mortality, but additional 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 conclusion 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. 
Second Section 812 Prospective Analysis 

February 2011 

2-12 

(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. (2005). 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 (Uncertainty 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 studies (Schwartz (2005) and Huang et al. 

(2005)) have been added based on guidance 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 characterize population-level exposure. Given that the 

majority of the people tend to be outdoors during the daylight hours and concentrations 

are highest during the daylight hours, the 24-hour average metric is not appropriate. 

Moreover, the 1-hour maximum metric uses an exposure window different than that used 

for the current ozone NAAQS. Together, this means that the most biologically relevant 

metric, and the one used in the ozone NAAQS since 1997, is the 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 procedure described in the BenMAP user’s 

manual (see Abt Associates, 2008). A similar method was used for the final Ozone 

NAAQS RIA (2008). 

RESPIRATORY HOSPITAL ADMISSIONS 

Detailed hospital admission and discharge records provide data for an extensive body of 

literature examining the relationship between hospital admissions and air pollution. This 

is especially true for the portion of the population 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 Information. Prepared by the Office of Air and Radiation. Available at 

http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_01_ozone_staff_paper.pdf. pp. 5-36. 
Second Section 812 Prospective Analysis 

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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 concentrations 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., 1997). To estimate total respiratory hospital 

admissions for Minneapolis using the Schwartz 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 possibility 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. Finally, we used random effects pooling to 

combine the results for Minneapolis and Detroit with results from studies in Tacoma and 

New Haven from Schwartz (1995). As noted above, this pooling approach incorporates 

both the 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 emergency 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 then 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, ozone was linked to asthma visits. We use 

the pooled estimate across all three cities as reported 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 daily 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) examined the relationship between ER visits for 

respiratory illnesses and asthma and air pollution for all people residing in Portland, 

 

11 For a complete discussion of the pooling process see Abt Associates, 2008. 
Second Section 812 Prospective Analysis 

February 2011 

2-14 

Maine from 1998–2000 and Manchester, New Hampshire from 1996–2000. For all 

models used in the analysis, the authors restricted 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 effect estimates for hospital admissions, we used 

random effects pooling to combine the results across the individual study estimates for 

ER visits for asthma. The Peel et al. (2005) and Wilson et al. (2005) Manchester 

estimates were not significant at the 95 percent level, and thus, the confidence interval for 

the pooled incidence estimate based on these studies 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 endpoints 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) occur 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 and Rothschild (1989). These researchers 

estimated the impact of ozone and PM2.5 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 the 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 respiratory or other acute diseases caused, or 

aggravated by, exposure to air pollution. Several studies have found a significant 

association between ozone levels and school absence rates. We use two studies (Gilliland 

et al., 2001; Chen et al., 2000) to estimate changes in school absences resulting from 

changes in ozone levels. The Gilliland et al. study estimated the incidence of new periods 

of absence, while the Chen et al. study examined daily absence rates. We converted the 

Gilliland et al. estimate to days of absence by multiplying the absence periods by the 

average duration of an absence. We estimated 1.6 days as the average duration of a 

school absence, the result of dividing the average daily school absence rate from Chen et 

al. (2000) and Ransom and Pope (1992) by the episodic absence duration from Gilliland 
Second Section 812 Prospective Analysis 

February 2011 

2-15 

et al. (2001). Thus, each Gilliland et al. period of absence is converted into 1.6 absence 

days. 

Following advice from the National Research Council (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 both 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 increased outdoor worker productivity resulting 

from improved ozone air quality, we used information reported in Crocker and Horst 

(1981). Crocker and Horst examined the impacts of ozone exposure on the productivity 

of outdoor citrus workers. The study measured productivity impacts. Worker 

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 income associated with a change in ozone 

exposure, given as the elasticity of income with respect to ozone concentration. The 

reported elasticity translates a ten percent reduction 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 reflect regional variations in income using a 

factor based on the ratio of county median household income to national median 

household income. No information was available 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 particulate 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 matter exposure with premature mortality. Our 

knowledge of both the potential biological mechanisms linking PM2.5 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. Short-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 period of elevated PM concentrations. Long-
Second Section 812 Prospective Analysis 

February 2011 

2-16 

term exposure studies examine the potential relationship between longer-term (e.g., 

annual) changes in exposure and annual mortality rates. Although positive, significant 

results have been reported using both of these 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 the 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 the 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 design. Prospective cohort studies follow 

individuals forward in time for a specified 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 pollution. Prospective cohort studies are preferred 

because they are better at controlling a source 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 exposure). 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 smoking 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 studies 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 been 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 coverage and have been 

independently reexamined and reanalyzed. Strengths of the ACS study over the Harvard 

Six Cities study include greater geographic coverage (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, available 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 exposures can result in increased effect estimates (Jerrett et al., 

2005). 
Second Section 812 Prospective Analysis 

February 2011 

2-17 

potentially biased low. The Harvard Six Cities 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 experts 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 PM2.5 concentration 

(IEc, 2006). The results of the EE study showed three important findings: first, that 

advances in the scientific literature led many of the interviewed scientists to espouse 

greater confidence in the linkage between PM2.5 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 distributions of the mortality effect reflected a 

much wider range of possible values, both high 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 μg/m3

 change in annual average PM2.5) for 

use in the PM-mortality C-R function for the second prospective study. This distribution 

is rooted in the epidemiological studies that most inform our understanding of the PMmortality C-R function, but reflects the broader 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 distribution 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 Cities 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 increased confidence in a causal link between PM2.5

exposure and mortality shown in the EE study and the lack of evidence in general to 

support a threshold for mortality effects of PM2.5 in the U.S. population (EPA SAB, 

2010). 
Second Section 812 Prospective Analysis 

February 2011 

2-18 

INFANT MORTALITY 

Recently published studies have strengthened the case for an association between PM 

exposure and respiratory inflammation and infection 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 infants (e.g., maternal educational level, maternal 

ethnicity, parental marital status, and maternal smoking status). Based on these findings, 

the HES recommended that EPA incorporate infant mortality into the primary benefits 

estimate and that infant mortality be evaluated using an impact function developed from 

the Woodruff et al. (1997) study (U.S. EPA-SAB, 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 Association, 2009). A limited number of 

studies have estimated the impact of air pollution on new incidences of CB. Abbey et al. 

(1995) provide evidence that long-term PM2.5 exposure gives rise to the development of 

CB in the United States. 

NONFATAL MYOCARDIAL INFARCTIONS (HEART ATTACKS) 

Nonfatal heart attacks have been linked with short-term exposures to PM2.5 in the United 

States (Peters et al., 2001) and other countries (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 cardiovascular 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 separate estimate for nonfatal heart attacks. The 

estimate used in this analysis is based on the single available U.S. PM2.5 effect estimate 

from Peters et al. (2001). 

RESPIRATORY AND CARDIOVASCULAR HOSPITAL ADMISSIONS 

Because of the availability of detailed hospital admission and discharge records, there is 

an extensive body of literature examining the relationship between hospital admissions 

and air pollution. 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. (2006) continues to find associations between PM2.5 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 support 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 report we have continued to rely on Woodruff et al. (1997). 
Second Section 812 Prospective Analysis 

February 2011 

2-19 

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

hospital admissions. 

To estimate avoided incidences of PM2.5 related cardiovascular hospital admissions in 

populations aged 65 and older, we use effect 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 Ito (2003) for populations over 65 and the 

Moolgavkar (2000a) based impacts for populations aged 20 to 64. Cardiovascular 

hospital admissions include admissions for myocardial infarctions. To avoid doublecounting benefits from reductions in myocardial infarctions when applying the impact 

function for cardiovascular hospital admissions, we first adjusted the baseline 

cardiovascular hospital admissions to remove admissions for myocardial infarctions. 

To estimate total avoided incidences of respiratory 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 functions for this group. Only Moolgavkar (2000a) 

provides a separate effect estimate for populations 20 to 64. Total COPD hospital 

admissions are thus the sum of the pooled estimate 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 asthma 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 relationship 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 emergency 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 admitted to the hospital. The only type of 

emergency room visits that have been consistently linked to PM in the United States are 

asthma-related visits. To estimate the effects of PM air pollution reductions on asthmarelated ER visits, we use the effect estimate from a study of children 18 and under by 

Norris et al. (1999). We selected the Norris et al. (1999) effect estimate because it 

focuses on PM2.5, as opposed to PM10. 

 

15 Note that the Moolgavkar (2000) study has not been updated to reflect the more stringent GAM convergence criteria. 

However, given that no other estimates are available for this age group, we chose to use the existing study. Updates have 

been provided for the 65 and older population, and showed little difference. Given the very small (<5%) difference in the 

effect estimates for people 65 and older with cardiovascular hospital admissions between the original and reanalyzed 

results, we do not expect the difference in the effect estimates for the 20 to 64 population to differ significantly. As such,

the choice to use the earlier, uncorrected analysis will likely not introduce much bias. 
Second Section 812 Prospective 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 effects not requiring hospitalization are associated 

with exposure to ambient levels of PM. The 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 Association, 2002c). Acute bronchitis is 

characterized by coughing, chest discomfort, slight fever, and extreme tiredness, lasting 

for a number of days. According to the MedlinePlus medical encyclopedia, 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 symptoms in asthmatic children aged nine to 

eleven are estimated using an effect estimate 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 PM10 exposure on the incidence of upper respiratory symptoms. The EPA began 

applying the C-R function derived from Pope et al. (1991) for PM10 to PM2.5 air quality estimates in 2005 (EPA, 2005). The 

implicit assumptions of this action are that a) PM2.5 is as toxic as the average of all PM10 and b) if a single rule or policy 

action reduced only precursor pollutants, the change in PM10 would equal the change in PM2.5. 

18 Estimating asthma exacerbations associated with air pollution exposures is difficult, due to concerns about doublecounting of benefits. Concerns over double-counting stem from the fact that studies of the general population also include 

asthmatics, so estimates based solely on the asthmatic population cannot be directly added 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 included in the calculation of total benefits. However, other 

endpoints, such as lower respiratory symptoms and MRADs, are estimated for the total population that includes asthmatics. 

Therefore, to simply add predictions of asthma-related symptoms generated for the population of asthmatics to these total 

population-based estimates could result in double-counting, especially if they evaluate similar endpoints. The HES, in 

commenting on the analytical blueprint for the current 812 study, acknowledged these challenges in evaluating asthmatic 

symptoms and appropriately adding them into the primary analysis (EPA-SAB, 2004b). However, despite these challenges, 

the HES recommended the addition of asthma-related symptoms (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 inclusion of this asthma-related endpoint in the primary estimate. 
Second Section 812 Prospective Analysis 

February 2011 

2-21 

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 would likely double-count incidence for this 

endpoint. However, because the general population 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) could be conducted without concern for doublecounting. 

To characterize asthma exacerbations in children, 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 respiratory symptoms associated with asthma 

exacerbations (e.g., shortness of breath, wheeze, and cough). This study found a 

statistically significant association between PM2.5, measured as a 12-hour average, and 

the daily prevalence of shortness of breath and wheeze endpoints. Although the 

association was not statistically significant for cough, the results were still positive and 

close to significance; consequently, we decided to include this endpoint, along with 

shortness of breath and wheeze, in generating incidence estimates (see below). Vedal et 

al. (1998) followed a group of elementary school children, including 74 asthmatics, 

located on the west coast of Vancouver Island 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 use 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 pooling 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 generated 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 overlap in their predictions. The pooled 

estimate from the Ostro et al. study is then pooled with the cough-related estimate 

generated using the Vedal et al. study. The rationale for this second pooling step is 

similar to the first; both studies are attempting to quantify the same overall endpoint 

(asthma exacerbations). 

Minor Restricted-Activity Days 

Exposure to air pollution can result in restrictions 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 individuals 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 Prospective Analysis 

February 2011 

2-22 

school. The effect of PM2.5 on MRADs was estimated using an effect estimate derived 

from Ostro and Rothschild (1989). 

Work Loss Days 

WLDs due to PM2.5 were estimated using an effect estimate developed from Ostro (1987). 

Ostro (1987) estimated the impacts of PM2.5 on the incidence of WLDs, restricted activity 

days, and respiratory-related restricted activity days in a national sample of the adult 

working population, ages 18 to 64. 

BASELINE INCIDENCE RATES 

Epidemiological studies of the association between pollution levels and adverse health 

effects generally provide a direct estimate of the relationship of air quality changes to the 

relative risk of a health effect, rather than estimating the absolute number of avoided 

cases. For example, a typical result might be that a 10 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 corresponds to baseline pollutant levels in that location. 

To derive the total baseline incidence per year, this rate must be multiplied by the 

corresponding population number. For example, if the baseline incidence rate is the 

number of cases per year per million people, that number must be multiplied by the 

millions of people in the total population. 

Exhibit 2-5 summarizes the sources of baseline incidence rates and provides average 

incidence rates for the endpoints included in the analysis. For both baseline incidence 

and prevalence data, we used age-specific rates where available. We applied C-R 

functions to individual age groups and then summed over the relevant age range to 

provide an estimate of total population benefits. In most cases, we used a single national 

incidence rate, due to a lack of more spatially disaggregated data. Whenever possible, the 

national rates used are national averages, because these data are most applicable to a 

national assessment of benefits. For some studies, however, the only available incidence 

information comes from the studies themselves; in these cases, incidence in the study 

population is assumed to represent typical incidence at the national level. Regional 

incidence rates are available for hospital admissions, 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 

EXHIBIT 2-5. BASELINE INCIDENCE/PREVALENCE RATES 

RATE PER 100 PEOPLE PER YEARD BY AGE GROUP 

ENDPOINT NOTES/SOURCE <18 18–24 25–29 30-34 35-44 45-54 55-64 65-74 75

Mortality All-cause Non-accidental Cardiopulmonary 

CDC Compressed Mortality File, accessed through CDC Wonder (1996–1998) 

0.045 0.025 0.004 

0.093 0.022 0.005 

0.119 0.057 0.013 

0.119 0.057 0.013 

0.211 0.150 0.044 

0.437 0.383 0.143 

1.056 1.006 0.420 

2.518 2.453 1.163 

5.5.3.

Respiratory Hospital Admissions All respiratory Pneumonia Asthma COPD 

1999 NHDSa public 

use data filesb

1.066 0.308 0.281 0.291 

0.271 0.069 0.081 0.089 

0.318 0.103 0.110 0.124 

0.446 0.155 0.099 0.148 

0.763 0.256 0.144 0.301 

1.632 0.561 0.161 0.711 

5.200 2.355 0.205 1.573 

Cardiovascular Hospital Admissions All cardiovascular Ischemic heart disease Dysrhythmia Heart failure 

1999 NHDS public use data filesb

0.030 0.004 0.011 0.003 

0.052 0.008 0.017 0.005 

0.146 0.031 0.027 0.011 

0.534 0.231 0.076 0.011 

1.551 0.902 0.158 0.160 

3.385 2.021 0.392 0.469 

8.541 3.708 1.387 2.167 

Asthma ER Visits 2000 NHAMCS 

public use data filesc; 1999 NHDS 

public use data filesb

1.011 1.087 0.751 0.438 0.352 0.425 0.232 

Chronic Bronchitis Prevalence 

1999 NHIS (American Lung Association, 2002b, Table4) 

0.0367 0.0505 0.0587
Second Section 

RATE PER 100 PEOPLE PER YEARD BY AGE GROUP 

ENDPOINT NOTES/SOURCE <18 18–24 25–29 30-34 35-44 45-54 55-64 65-74 75

Incidence Abbey et al. (1993, Table 3), for ages 27+ 

-- -- 0.378 

Nonfatal Myocardial Infarction (heart attacks) Northeast Midwest South West 

Incidence 1999 NHDS public use data filesb, 

adjusted by 0.93 for probability of surviving after 29 days (Rosamond et al., 1999) 

0.0000 0.0003 0.0006 0.0000 

0.2167 0.1772 0.1620 0.1391 

1.6359 1.4898 1.1797 1.1971 

Minor Restricted Activity Days 

Ostro and Rothschild (1989, p. 243) 

-- 780 -- 

 1996 NIS (Adams et 

al., 1999, Table 41), U.S. Bureau of Census (1997) 

-- 197.1 247.5 179.6 -- 

School Loss Days—allcause National Center for Education Statistics 

(1996) 

990.0 -- -- -- -- -- -- -- -- 
Second Section 

RATE PER 100 PEOPLE PER YEARD BY AGE GROUP 

ENDPOINT NOTES/SOURCE <18 18–24 25–29 30-34 35-44 45-54 55-64 65-74 75

Acute Bronchitis Incidence 

American Lung Association (2002c, Table 11) 

4.3 -- -- -- -- -- -- -- -- 

Lower Respiratory Symptoms 

Incidence Schwartz et al. (1994, Table 2) 

43.8 -- -- -- -- -- -- -- -- 

Upper Respiratory Symptoms 

Incidence among asthmatics Pope et al. (1991, Table 2) 

12479 -- -- -- -- -- -- -- -- 

Asthma Exacerbation Shortness of breath Wheeze Cough 

Incidence (and prevalence) among asthmatic African Americans Ostro et al. (2001) 

1350 (0.074) 2774 (0.173) 2445 (0.145) 

-- -- -- -- -- -- -- -- 

Asthma Exacerbation Cough 

Incidence among asthmatics Vedal et al. (1998) 

3139 

-- -- -- -- -- -- -- -- 

a The following abbreviations are used to describe the national surveys conducted by the National Center for Health Statistics: HIS refers to the National Health Interview Survey; NHDS—National Hospital Discharge Survey; NHAMCS—National Hospital Ambulatory Medical Care Survey. b See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/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 re

percentage of the population. Additional details on the incidence and prevalence rates, as well as the sources for these rates are available upon request.
Second Section 812 Prospective Analysis 

February 2011 

2-26 

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. Exhibit 2-5 lists the baseline incidence rates and their sources for 

asthma symptom endpoints. Exhibit 2-6 lists the prevalence rates used to determine the 

applicable population for asthma symptom endpoints. Note that these reflect current 

asthma prevalence and assume no change in prevalence rates in future years. It should be 

noted that current trends in asthma prevalence 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 individual 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 Association (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 filesa

African American, 5–17 0.0726 American Lung Association (2002a, Table 7)—based on 1999 HIS 

African American, <18 0.0735 American Lung Association (2002a, Table 7)—based on 1999 HIS 

a See ftp://ftp.cdc.gov/pub/Health_Statistics/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 population. Therefore, the appropriate economic 

measure is willingness-to- pay (WTP) for changes in risk of a health effect rather than 

WTP for a health effect that would occur with certainty (Freeman, 1993). 

Epidemiological studies generally provide estimates of the relative risks of a particular 

health effect that is avoided because of a reduction in air pollution. We converted those 

to units of avoided statistical incidence for ease of presentation. We calculated the value 

of avoided statistical incidences by dividing individual WTP for a risk reduction by the 

related observed change in risk. For example, 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 for 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 suffering (Harrington and 
Second Section 812 Prospective Analysis 

February 2011 

2-27 

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 growth in real income out to each of the three 

target years (2000, 2010, and 2020) using the 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 discussion 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) approach, 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 value-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 approximately $7.4 million at 1990 income levels 

(2006$), and $8.8 million (2006$) at 2020 income 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 Reports 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 studies mirrors that of Viscusi (1992) (with the 

addition 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 studies are contingent valuation (CV) studies, 

which directly solicit WTP information from subjects; 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 

EXHIBIT 2-7. UNIT VALUES FOR ECONOMIC VALUATION OF HEALTH ENDPOINTS (2006$) 

HEALTH ENDPOINT 

CENTRAL ESTIMATE OF VALUE 

PER STATISTICAL INCIDENCE 

DERIVATION OF DISTRIBUTIONS OF ESTIMATES 

1990 INCOME 

LEVEL 

2020 INCOME 

LEVEL 

Premature Mortality (Value of a Statistical Life) 

$7,400,000 $8,880,000 Mean Value of Statistical Life (VSL) based on the mean of a distribution fitted 

life” (VSL) estimates that appear in the economics literature and that have b

812 Reports to Congress as “applicable to policy analysis.” The VSL approacstudies mirrors that of Viscusi (1992) (with the addition of two studies), and Viscusi in his review of value-of-life studies. The central estimate of $6.3 miwith Viscusi’s conclusion (updated to 2000$) that “most of the reasonable esare clustered in the $3.8 to $8.9 million range.” Five of the 26 studies are costudies, which directly solicit WTP information from subjects; the rest are wWTP estimates on estimates of the additional compensation demanded in the jobs. The fitted distribution is a Weibull with α=5.32x10-6 and β=1.509588. more details. 

Chronic Bronchitis (CB) 

$399,000 $490,000 

The WTP to avoid a case of pollution-related CB is calculated as the severity of an average CB case, WTP13 is the WTP for a severe case of CB

relating WTP to severity, based on the regression results reported in Krupnicdistribution of WTP for an average severity-level case of CB was generated bdrawing from each of three distributions: (1) WTP to avoid a severe case of probability of being each of the first nine deciles of the distribution of WTP r(1991); (2) the severity of a pollution-related case of CB (relative to the case study) is assumed to have a triangular distribution, with the most likely value endpoints at 1.0 and 12.0; and (3) the constant in the elasticity of WTP with normally distributed with mean = 0.18 and standard deviation = 0.0669 (from (1992)). This process and the rationale for choosing it is described in detail ithe Clean Air Act, 1990 to 2010 (EPA, 1999). 

x WTP W

 
Second Section 

HEALTH ENDPOINT 

CENTRAL ESTIMATE OF VALUE 

PER STATISTICAL INCIDENCE 

DERIVATION OF DISTRIBUTIONS OF ESTIMATES 

1990 INCOME 

LEVEL 

2020 INCOME 

LEVEL 

Nonfatal Myocardial Infarction (heart attack)  7% discount rate  Age 0–24  Age 25–44  Age 45–54  Age 55–65  Age 66 and over 

$84,171 $93,802 $98,366 $166,222 $84,171 

No distributional information available. Age-specific cost-of-illness values re

medical costs over a 5-year period following a nonfatal MI. Lost earnings est

and Krupnick (1990). Direct medical costs are based on simple average of est(1998) and Wittels et al. (1990). Lost earnings: Cropper and Krupnick (1990). Present discounted value of 5 years of lost ear

age of onset: at 7%a 

25–44 $9,631 45–54 $14,195 55–65 $82,051 Direct medical expenses: An average of (2006$): 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 
Second Section 

HEALTH ENDPOINT 

CENTRAL ESTIMATE OF VALUE 

PER STATISTICAL INCIDENCE 

DERIVATION OF DISTRIBUTIONS OF ESTIMATES 

1990 INCOME 

LEVEL 

2020 INCOME 

LEVEL 

Hospital Admissions All respiratory (ages 65+) 

$23,711 $23,711 No distributions available. The COI point estimates (lost earnings plus direct 

on ICD-9 code level information (e.g., average hospital care costs and averag

reported in Agency for Healthcare Research and Quality, 2000 (www.ahrq.go

no adjustments are made to cost of illness values for income growth. All respiratory (ages 0–2) $10,002 $10,002 

Chronic Obstructive Pulmonary Disease (COPD) (ages 65+) 

$17,308 $17,308 

Asthma Admissions (ages <65) 

$10,040 $10,040 

Pneumonia Admissions (ages 65+) 

$23,004 $23,004 

COPD, less asthma (ages 20–64) 

$15,903 $15,903 

All Cardiovascular (ages 65+) 

$27,319 $27,319 

All Cardiovascular (ages 20–64) 

$29,364 $29,364 

Ischemic Heart Disease (ages 65+) 

$33,357 $33,357 

Dysrhythmia (ages 65+) 

$19,643 $19,643 

Congestive Heart Failure (ages 65+) 

$19,619 $19,619 

Emergency Room Visits for Asthma 

$369 $369 No distributional information available. Simple average of two unit COI value

(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 in
Second Section 

HEALTH ENDPOINT 

CENTRAL ESTIMATE OF VALUE 

PER STATISTICAL INCIDENCE 

DERIVATION OF DISTRIBUTIONS OF ESTIMATES 

1990 INCOME 

LEVEL 

2020 INCOME 

LEVEL 

Respiratory Ailments Not Requiring Hospitalization Upper Respiratory Symptoms (URS) 

$28.8 $30.7 Combinations of the three symptoms for which WTP estimates are available 

listed by Pope et al. result in seven different “symptom clusters,” each desc

dollar value was derived for each type of URS, using mid-range estimates of each symptom in the cluster and assuming additivity of WTPs. In the absenc

surrounding the frequency with which each of the seven types of URS occurs 

complex, we assumed a uniform distribution between $10.8 and $50.5 (2006

Lower Respiratory Symptoms (LRS) 

$18 $19 Combinations of the four symptoms for which WTP estimates are available th

listed by Schwartz et al. result in 11 different “symptom clusters,” each descdollar value was derived for each type of LRS, using mid-range estimates of Weach symptom in the cluster and assuming additivity of WTPs. The dollar vaof the dollar values for the 11 different types of LRS. In the absence of infor

frequency with which each of the 11 types of LRS occurs within the LRS sympa uniform distribution between $8.1 and $28.6 (2006$). 

Asthma Exacerbations 

$50 $54 Asthma exacerbations are valued at $45 per incidence, based on the mean o

for the four severity definitions of a “bad asthma day,” described in Rowe anstudy surveyed asthmatics to estimate WTP for avoidance of a “bad asthma dsubjects. For purposes of valuation, an asthma exacerbation is assumed to bwhich asthma is moderate or worse as reported in the Rowe and Chestnut (1assumed 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 

high values based on those recommended for related respiratory symptoms iThe low daily estimate of $20.5 (2006$) is the sum of the mid-range values refor two symptoms believed to be associated with acute bronchitis: coughing 

high daily estimate was taken to be twice the value of a minor respiratory re$118 (2006$). The low and high daily values are multiplied by six to get the 

Work Loss Days (WLDs) 

Variable (U.S. median = $149) 

 No distribution available. Point estimate is based on county-specific median 

50 (assuming 2 weeks of vacation) and then by 5—to get median daily wage. compiled by Geolytics, Inc. 
Second Section 

HEALTH ENDPOINT 

CENTRAL ESTIMATE OF VALUE 

PER STATISTICAL INCIDENCE 

DERIVATION OF DISTRIBUTIONS OF ESTIMATES 

1990 INCOME 

LEVEL 

2020 INCOME 

LEVEL 

Minor Restricted Activity Days (MRADs) 

$61 $64 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). Distribut

triangular with a minimum of $24 and a maximum of $94, with a most likely Range is based on assumption that value should exceed WTP for a single mild 

estimate for a single symptom—for eye irritation—is $24) and be less than thatriangular distribution acknowledges that the actual value is likely to be closthan either extreme. 

School Loss Days $89 $89 No distribution available. Point estimate is based on (1) the probability that

home from school, a parent will have to stay home from work to care for the 

the parent’s lost productivity. Calculated using U.S. Bureau of Census data. 

a Note that a seven percent discount rate is used to discount costs incurred over the 5-year period, while the remainder of this report urate. The use of a seven percent discount rate to estimate costs associated with nonfatal myocardial infarction results in a minor under
Second Section 812 Prospective 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 made 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 estimates 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 level of an average pollution-related case of CB 

(relative to that of the case described by Viscusi et al.); and the elasticity of WTP with 

respect to severity of the illness. Based on assumptions about the distributions of each of 

these three uncertain components, we derive a distribution of WTP to avoid a pollutionrelated case of CB by statistical uncertainty analysis techniques. The expected value (i.e., 

mean) of this distribution, which is about $399,000 (2006$), 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 value with two components: the direct medical 

costs and the opportunity cost (lost earnings) associated with the illness event. Because 

the costs associated with a myocardial infarction extend beyond the initial event itself, we 

consider costs incurred over five years. We used age-specific annual lost earnings 

estimated by Cropper and Krupnick (1990). Cropper and Krupnick (1990) do not provide 

lost earnings estimates for populations under 25 or over 65. As such, we do not include 

lost earnings in the cost estimates for these age groups. 

Three sources were consulted for direct medical costs of myocardial infarction: Wittels 

et al. (1990), Eisenstein et al. (2001), and Russell et al. (1998). Because the wage-related 

opportunity cost estimates from Cropper and Krupnick (1990) cover a 5-year period, we 

used estimates for medical costs that similarly cover a 5-year period (i.e., estimates from 

Wittels et al. (1990) and Russell et al. (1998)). We used a simple average of the two 5-

year estimates.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 percent 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 Prospective Analysis 

February 2011 

2-34 

HOSPITAL ADMISSIONS VALUATION 

In the absence of estimates of societal WTP to avoid hospital visits/admissions for 

specific illnesses, estimates of total 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 (ICD-9, 1979) code-specific COI estimates 

used in this analysis consist of estimated hospital charges and the estimated opportunity 

cost of time spent in the hospital (based on the average length of a hospital stay for the 

illness). We based all estimates of hospital charges and length of stays on statistics 

provided by the Agency for Healthcare Research 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 individual is in the workforce. To estimate 

the lost daily wage, we divided year 2000 median 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-related 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 estimates 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) statistically linked ozone and minor restricted 

activity days, it is likely that most MRADs associated 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 Prospective Analysis 

February 2011 

2-35 

SCHOOL LOSS DAYS 

To value a school absence, we: (1) estimated the probability that if a school child stays 

home from school, a parent will have to stay home from work to care for the child; and 

(2) valued the lost productivity at the parent’s wage. To do this, we estimated the number 

of families with school-age children in which both parents work, and we valued a schoolloss day as the probability that such a day also would result in a work-loss day. We 

calculated this value by multiplying the proportion of households with school-age 

children by a measure of lost wages. 

We used this method in the absence of a preferable WTP method. However, this 

approach suffers from several uncertainties. First, it omits willingness to pay to avoid the 

symptoms/illness that resulted in the school absence; second, it effectively gives zero 

value to school absences that do not result in work-loss days; and third, it uses 

conservative assumptions about the wages of the parent staying home with the child. 

Finally, this method assumes that parents are unable to work from home. If this is not a 

valid assumption, then there would be no lost wages. 

For this valuation approach, we assumed that in a household with two working parents, 

the female parent will stay home with a sick child. From the Statistical Abstract of the 

United States (U.S. Census Bureau, 2001), we obtained: (1) the numbers of single, 

married and “other” (widowed, divorced or separated) working women with children; and 

(2) the rates of participation in the workforce of single, married and “other” women with 

children. From these two sets of statistics, we calculated a weighted average participation 

rate of 72.85 percent. 

Our 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 workforce then multiplied that estimate by the daily 

wage she would lose by missing a workday: 72.85 percent times $103, for a total loss of 

$75 in 2000$, 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 eliminating 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 5th and 95th percentile estimates. 
Second Section 812 Prospective 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 benefits 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 consistent with the most recent recommendation 

by the Council HES (EPA – SAB, 2004a) – but not to premature mortality reduction 

attributed to reduced ozone exposure. Alternative cessation lag structures for PM-related 

mortality risk are explored in the accompanying Second Prospective uncertainty analysis 

report. For the primary results, a five percent 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 roughly 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

60000

Mean Valuation (million 2006$)

Morbidity

Mortality
Second Section 812 Prospective 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 

Cause1 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), Schwartz (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 Prospective 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 

Cause1 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), Schwartz (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 Prospective 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 

Cause1 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), Schwartz (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 Prospective Analysis 

February 2011 

2-40 

EXHIBIT 2-12. NATIONAL OZONE MORBIDITY BENEFITS 

PM BENEFIT ESTIMATES 

PM benefit estimates are calculated at the national 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 year. In addition to the mean incidence and 

valuation estimates, we have included 5th and 95th percentile estimates. 

Benefits of reduced morbidity account for approximately four percent of the total PM 

benefits, depending on the mortality 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

Mean Valuation (million 

2020

2006$)

Morbid

ity

$0

$500

$1,000

$1,500

$2,000

$2,500

2000 2010 2020

Mean 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 Prospective 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 Prospective 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 Prospective 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 69,000 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 Prospective 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 2020

Mean 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 Prospective Analysis 

February 2011 

3-1 

CHAPTER 3 | ESTIMATION OF VISIBILITY IMPROVEMENTS AND 

ECONOMIC VALUATION 

OVERVIEW 

Air pollution impairs visibility in both residential 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 relate to the impact of visibility changes on an 

individual’s daily life (e.g., at home, at work, and while engaged in routine recreational 

activities). Benefits of recreational visibility relate to the impact of visibility changes 

manifested at parks and wilderness areas that are expected to be experienced by 

recreational visitors. 

We calculate household WTP for improvements in both residential and recreational 

visibility. We base our calculations on simulations 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 valuation (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 analyses 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 result, our estimates in this chapter rely on a 

new benefits transfer estimate of WTP derived from other published sources of residential 

visibility WTP. 

A fundamental issue with respect to visibility 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 selection of underlying 

valuation studies and our recommended approach, we attempt to address both of these 

issues. 

VISIBILITY CHANGES 

This analysis is the first Section 812 prospective analysis to use an integrated modeling 

system, CMAQ, to simulate national and regional-scale pollutant concentrations and 
Second Section 812 Prospective 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 was 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, scenarios that incorporate the emission reductions 

associated with the Clean Air Act Amendments (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 include ozone and fine particulate matter (PM2.5), 

where PM2.5 consists of particles less than 2.5 microns in diameter. Visibility is calculated 

using the CMAQ Chemistry-Transport Model (CCTM). The CCTM integrates output 

from emissions and meteorology models to simulate continuous atmospheric chemical 

conditions. Particular to visibility, CCTM’s AERO module integrates Mie scattering (a 

generalized particulate light-scattering mechanism 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 aggregated 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 greater 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 visibility 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 Prospective Analysis 

February 2011 

3-3 

EXHIBIT 3-1. ESTIMATED CHANGE IN VISIBILITY 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 Prospective 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 and 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 level 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 visibility endpoint that can be used for valuation. 

Increases in PM concentrations cause increases in light extinction, a measure of how 

much the components of the atmosphere scatter and absorb light. More light extinction 

means that the clarity of visual images and visual range is reduced, ceteris paribus. Light 

extinction is a variable that can be accurately measured. Pitchford and Malm (1993) 

created a unitless measure of visibility, the deciview, based directly on the amount of 

light extinction. Deciviews are standardized for a reference distance in such a way that 

one deciview corresponds to a change of about 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 circumstances.” Air quality models are used to 
Second Section 812 Prospective Analysis 

February 2011 

3-5 

predict the change in visibility, measured in deciviews, of the areas affected by the 

CAAA.21 

Our analysis considers benefits from two categories of visibility changes: residential 

visibility and recreational visibility. In both cases economic benefits are believed to 

consist of use values and nonuse values. Use values include the aesthetic benefits of 

better visibility, improved road and air safety, and enhanced recreation in activities like 

hunting and 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, particularly national parks and monuments. 

For the purposes of this analysis, recreational visibility improvements are defined as 

those that occur specifically in federal Class I areas, and residential visibility 

improvements are those that occur within 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 all 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 the 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 Analysis, 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 visibility changes is an application of the Constant 

Elasticity of Substitution (CES) utility function approach and is based on the preference 

calibration method developed by Smith, Van Houtven, and Pattanayak (1999). Exhibit 3-

4 outlines the key data sources 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 visibility, but may not be perceptible to the eye. This analysis considers annual average 

changes in visibility, which are likely made up of periods with changes less than one deciview and periods with changes 

exceeding one deciview. 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 approach would enable an analysis of the 

frequency of time when the visibility changes are likely to be perceptible. Our analysis 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 national parks and wilderness areas as Class I areas for visibility protection. 

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

rulemaking (Abt Associates, 2003). 
Second Section 812 Prospective 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, Southwest, 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 and 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 quality 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 visibility 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 quality 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 interpret recreational settings applicable for this 

category of effects to include National Parks and Wilderness Areas. Other recreational 

settings may also be applicable, for example National 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 and/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 
Second Section 812 Prospective Analysis 

February 2011 

3-7 

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

0 1 ( ) WTP Qik  m  m  ik Q ik  Q ik (1) 

where: 

i indexes region, 

k indexes park, 

m = household income, 

ρ = shape parameter, 

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

Q0 = starting visibility, and 

Q1 = visibility after change. 

A similar WTP function is used for out-of-region parks, replaying γik for δik. 

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 Acid 

Precipitation Assessment Program (NAPAP) and, therefore, has been subject to peerreview as part of that program. Moreover, this 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 estimates 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 discussion see Chestnut and Rowe, 1990a. 

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

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

best available” (EPA-SAB-COUNCIL-ADV-00-002, 1999, p. 13). However, the committee did not formally approve use of 

these estimates because of concerns about the peer-reviewed status of the study. EPA believes the study has received 

adequate review and has been cited in numerous peer-reviewed publications (Chestnut and Dennis, 1997). 
Second Section 812 Prospective Analysis 

February 2011 

3-8 

California, the Southwest, and the Southeast. Respondents in five states were asked about 

their WTP to protect national parks or NPS-managed wilderness areas within a particular 

region.27 The survey used photographs reflecting different 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 Rowe study were region-specific, rather 

than park-specific. As visibility improvements are not constant across all parks in a 

region, we must infer park-specific visibility parameters (i.e., γ and δ) in order to 

calculate WTP for projected visibility changes. As the quantity and quality of parks 

differs between regions, we apportion the visibility 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 collective WTP is likely higher) and quantity (more 

people visit parks in a region if the parks are more numerous, so collective WTP is likely 

higher).28 This method has several limitations, including 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 parameters are used to calculate park-specific 

WTP values along with household income and visibility (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 visibility 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 Statistical Abstracts. To estimate recreational benefits in 2010 

and 2020, we use visitation data from 2008, as this is the most current data available. Where the data for a particular park 

was not representative of normal visitation rates at that park (for example due to fire damage that occurred during that 

year), we substitute data from the prior year. We chose to use 2008 data rather than projecting to 2020 based on current 

visitation trends, as atypical years in visitation data pose problems for establishing an overall rate of increase or decrease,

and it is not clear that visitation trends could reliably be projected 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 using 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 trends 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 income adjustments are not appropriate for these types of benefits 

transfers. As a result, household income is adjusted longitudinally across time (i.e. 2000, 2010, and 2020), but not crosssectionally across space (i.e. to reflect income differences across regions). Longitudinal income adjustments were made 

using an income elasticity of 0.9, indicating that a 1 percent increase in income is associated with a 0.9 percent increase in 

WTP for a given change in visibility. 
Second Section 812 Prospective Analysis 

February 2011 

3-9 

only those recreational benefits in the areas that were directly analyzed in the original 

Chestnut and Rowe study. An alternative estimate 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, residential visibility is distinct from recreational 

visibility, which refers specifically to conditions in Class I areas (e.g., certain NPS parks 

and wilderness areas). While improved visibility 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 omitted 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 value for residential visibility. As a result, 

we have revised our methodology 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 summarized 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 provides 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 improvements. To express these value estimates 

in comparable terms, we rely upon a function similar 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 TSD for the Nonroad Diesel rulemaking (Abt Associates, 2003). 

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

incorporate two potentially important adjustments. First, their study does not account for the “warm glow” effect, in 

which respondents may provide higher willingness to pay estimates simply because they favor “good causes” such as 

environmental improvement. Second, while the study accounts for non-response bias, it may not employ the best available 

methods. As a result of these concerns, a prior Council recommended that residential visibility be omitted from the overall 

primary benefits estimate in the First Prospective. 
Second Section 812 Prospective Analysis 

February 2011 

3-10 

  



 



  

0

1 *ln

VR

VR WTP VR b (2) 

where: 

VR0 = mean annual visual range in miles before the improvement, 

VR1 = 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 relates to the above function in the following 

manner: 

    *

0 1 10

DV DV b WTP DV   (3) 

where: 

DV (deciviews)  10 ln243 VR

Five principal residential visibility valuation 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 previous 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 valuation 

method, and therefore do not benefit from more recent methodological advances or bestpractice guidelines established by the NOAA Blue Ribbon Panel on Contingent Valuation 

(Arrow et al, 1993) and other diagnostic research, 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 Dennis, 1997) provide detailed descriptions 

of the three studies. These sources, as well as a review 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 estimates for visual range improvements, we 
Second Section 812 Prospective Analysis 

February 2011 

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 annual 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, Chicago, Denver, Los Angeles, Mobile, San 

Francisco, and Washington D.C. 

EXHIBIT 3-5. SUMMARY OF RESIDENTIAL VISIBILITY PARAMETER ESTIMATES 

CITYA STUDY B ESTIMATEB

IMPLIED WTP FOR 10% 

IMPROVEMENT IN VISUAL RANGEC

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 associated 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 other 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 studies we rely upon were all conducted in 

urban/metropolitan and surrounding areas and generally 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). 
Second Section 812 Prospective Analysis 

February 2011 

3-12 

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 contiguous 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 which 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 considering that the Loehman et al. (1984) value 

is over 30 percent higher than the next highest 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 locations provide dramatically different vistas. 

For example, one would expect that residents 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: values 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 measure to identify where it would seem more 

appropriate to attribute the larger visibility 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 Los Angeles and Riverside MSAs although these MSAs meet the 

elevation range threshold of 1500 meters. The assignment is preserved because Los Angeles is one of the study cities and 

also because Los Angeles has a particular set of location-specific 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 Management District, and therefore is considered by at least some 

measures to be part of the same regulated airshed as Los Angeles. 
Second Section 812 Prospective Analysis 

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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 residential values from the existing literature are 

transferred to all MSAs in the conterminous 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 residential 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 impacts associated with air pollution, such as 

health effects. As health effects are evaluated separately from aesthetic effects, we 

assume that any observed response to visibility is linked to aesthetic concerns rather than 

concerns about health. Otherwise, health benefits would be double counted in the benefits 

analysis. Unfortunately, the visibility valuation 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 value visibility improvements must successfully 

separate respondents’ preferences for visibility from their preferences for health. The 

three studies that we have selected to inform our calculations of the value of visibility 
Second Section 812 Prospective Analysis 

February 2011 

3-14 

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 focus 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 summary 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. Respondents 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 successful in convincing respondents to focus 

solely on visibility is unclear, as none of the three studies includes follow-up questions 

necessary to investigate the issue. Furthermore, no other residential visibility CV studies 

provide evidence regarding the degree to which health effects are embedded in visibility 

values. Although the McClelland et al. (1991) study has a follow-up question designed to 

allocate WTP across several categories, the CV question in the McClelland et al. study 

was focused on air pollution generally rather than visibility. As a result, we do not adjust 

the results from these studies to account for potentially embedded health effects. 

RESULTS 

The primary estimate of benefits to recreational 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 benefits in all MSAs. In general, benefits to 

visibility increase over time as visibility improves 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 Prospective Analysis 

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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 primary 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 total 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 recreational and residential visibility. Totals 

benefits are lowest in Wyoming, North Dakota, Vermont, South Dakota, and Montana. 

Total benefits are highest in California, New 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). These regions are California, the Southwest 

(Arizona, Nevada, Utah, Colorado, and New Mexico), and the Southeast (Delaware, 

Maryland, West Virginia, Virginia, Kentucky, Tennessee, North Carolina, South 

Carolina, Georgia, Alabama, Florida, and Mississippi). Households express WTP for 

visibility improvements in Class I areas located in-region as well as out-of-region. For 

this reason, there may be high recreational benefits in a state that has no Class I areas. 

Although household WTP is higher for in-region parks, 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 Exhibit 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. Residential 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 Prospective Analysis 

February 2011 

3-16 

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 used in the residential visibility study by 

McClelland et al. (1991). The First Prospective 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 higher 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 Prospective Analysis 

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EXHIBIT 3-10. PRIMARY ESTIMATE OF RESIDENTIAL BENEFITS TO VISIBILITY IN 2020 (BILLION 

2006$) 
Second Section 812 Prospective Analysis 

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3-18 

EXHIBIT 3-11. PRIMARY ESTIMATE OF TOTAL BENEFITS TO VISIBILITY IN 2020 (BILLION 2006$) 
Second Section 812 Prospective Analysis 

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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 Wisconsin South Carolina 

22 Colorado Oregon Utah 

23 Wisconsin South Carolina Alabama 

24 Minnesota Louisiana Oregon 

25 Kentucky Connecticut 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 Delaware 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 Prospective Analysis 

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3-20 

RANK 

RECREATIONAL 

BENEFITS RESIDENTIAL BENEFITS TOTAL BENEFITS 

43 Rhode Island New Mexico Nebraska 

44 Montana Vermont District of Columbia 

45 South Dakota South 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 Prospective Analysis 

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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 Prospective Analysis 

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4-1

CHAPTER 4 | AGRICULTURAL AND FOREST PRODUCTIVITY 

BENEFITS OF THE CAAA 

BACKGROUND 

A significant body of literature exists addressing 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 levels of tropospheric ozone have 

been shown to reduce overall plant health and growth by reducing photosynthesis and 

altering carbon allocation. In order to estimate the magnitude of plant growth reductions 

due to elevated tropospheric ozone levels, laboratory studies, such as Lee and Hogsett 

(1996), have developed exposure-response functions describing the functional 

relationship between plant yield and ozone exposure for a variety of plant species.39 

Applying exposure-response functions, this analysis 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 the baseline scenario) 

measure the amount crop and tree yields would 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 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 yield losses to agricultural crops and commercial trees. 

 

38 Industrial Economics, Inc., Effects of Air Pollutants on Ecological 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 Calculating Inputs for Ozone Secondary Standard Benefits Analysis: Part 

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

40 Relative yield losses are estimated instead of relative yield gains because the baseline (with CAAA) scenario in this analysis

defines current conditions, whereas, the counterfactual (no CAAA) scenario defines a change in current 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 Prospective Analysis 

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4-2

Changes in the distribution and yield of crop 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 within the agricultural and timber sectors of 

the economy. This chapter documents our approach and results to estimating the welfare 

effects of changes in agriculture and timber markets resulting from the passage of the 

CAAA; ozone concentration estimates exist 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 implementation of the CAAA. Yield increases are 

greatest in the geographic areas exhibiting the largest reduction in ozone concentration 

with the CAAA; specifically, along the East Coast (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; and 2) the analysis of welfare effects 

stemming from changes in crop and tree yields.

ANALYTICAL FRAMEWORK 

This analysis applies three steps to estimate the welfare benefits of the CAAA with 

respect to commercial agriculture and timber management: 

1. Estimate tropospheric ozone concentrations 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 relative 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 main 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 timber yields are reduced under the counterfactual scenario relative to 

the baseline scenario. 
Second Section 812 Prospective Analysis 

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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 Prospective Analysis 

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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-km2

 grid cells 

Exposure-response 

Functionsb 

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 the 

regional-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 Secondary 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 CAAA scenario. As described above, there 

are two distinct steps necessary to estimate relative yield losses: 1) estimate tropospheric 

ozone concentrations over time under the baseline and counterfactual scenarios; and, 2) 

calculate relative yield losses based on ozone concentration estimates. The section is 

organized by analytic step. For each step, the methods applied to complete the step are 

described, followed by the results of the step. 
Second Section 812 Prospective 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 ozone estimates in accordance with the input 

requirements of exposure-response functions (Exhibit 4-2). Finally, disaggregated and 

aggregated tropospheric ozone estimates are presented in this section. 

Tropospheric ozone concentrations were estimated using Enhanced Voronoi Neighbor 

Averaging (eVNA), which considers both the modeled ozone concentration results and 

monthly ozone monitoring data. Specifically, 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 which 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 concentrations 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 month. 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 Optimization Model (FASOM), the economic model 

employed in this analysis, requires species growth inputs at a subregion-level for crops 

and at a region-level for trees; the subregions 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: 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 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 coterminous U.S. However, some states 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, South Central, Southwest, Rocky 

Mountains, Pacific Southwest, Pacific Northwest (East side), and Pacific Northwest (West side). 
Second Section 812 Prospective Analysis 

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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 





i pm

i am

wC Ci i

8

8

 where: i ci C

e

w 126 1 4403

1   

7-Hour Average 

Average of all tropospheric ozone 

concentration values observed hourly 

between 9 am and 4 pm 





i pm

i am

Ci

4

7 9

1

12-Hour Average 

Average of all tropospheric ozone 

concentration values observed hourly 

between 8 am and 8 pm 





i pm

i am

Ci

8

12 8

1

Note: Ci = 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. Environmental 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. California 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

Cregion subregion wiCi

1

/ 

where: wi = weight of cell i = 



N

i

i

i

a

a

1

 (ai = area of cell i in region/subregion) 

Ci = W126, 7-hour average, or 12-hour average value for cell i 

N = total number of grid cells intersecting 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 Prospective Analysis 

February 2011 

4-7

CAAA).46 CMAQ estimates of tropospheric ozone levels were generated 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 differences 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 differences in ozone concentrations vary by 

region and subregion. Specifically, the Pacific Southwest and Southeast regions exhibit 

the greatest differences in ozone concentration over time followed closely by the South 

Central, Cornbelt, and Northeast regions. 

The subregion map (Exhibit 4-5) provides differences 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 concentration between the two scenarios, on-thewhole, some states and/or portions of states within these regions exhibit greater 

differences than others. Specifically, Virginia, 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 exhibit 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 relative yield losses for crops and trees due to 

elevated tropospheric ozone concentrations under the counterfactual scenario. In order to 

estimate relative yield losses, this analysis 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 values as a measure of ozone concentration. The geographic 

distribution of differences between 7-hour and 12-hour averages with and without the CAAA, in terms 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 are smaller than the differences between 

the W126 data with and without the CAAA because the 7-hour and 12-hour average metrics are not additive metrics, as is 

the W126 metric. 
Second Section 812 Prospective Analysis 

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EXHIBIT 4-4. REDUCTIONS IN OZONE CONCENTRATION WITH THE CAAA BY FASOM REGION 

(PERIOD = MAY – SEPTEMBER; METRIC = W126) 
Second Section 812 Prospective Analysis 

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4-9

EXHIBIT 4-5. REDUCTIONS IN OZONE CONCENTRATION WITH THE CAAA BY FASOM SUBREGION 

(PERIOD = MAY – SEPTEMBER; METRIC = W126) 
Second Section 812 Prospective Analysis 

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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 describing 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 functions 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 harvest 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 planting 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 grown throughout the year. For these crops, 

the growing period within the May through September period which yields the 

greatest difference in ozone concentration between the baseline and counterfactual 

scenarios was applied. The growing period for some crops falls outside of the 

May through September period for which ozone 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 methodology is based on the assumption that 

ozone levels outside of the May through 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 calculating the relevant ozone metric over 

 

47 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 Quality Planning and Standards, Air Quality 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 Technical 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 yield 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 Prospective Analysis 

February 2011 

4-11

the three-month period between May and September that yields the greatest 

difference in ozone concentration between the baseline and counterfactual 

scenarios for each region. This methodology is also based on the assumption that 

ozone levels outside of May 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 FUNCTIONa 

CROP SPECIES 

Barley W126 6,998.50 1.39 95 B

A

O

Y Ce





 









3

Corn W126 97.90 2.97 83 

Cotton W126 96.10 1.48 114 

Orangesb 12-Hour Average 53.70 261.10 214     Y C A B O3   

Potatoes W126 99.50 1.24 66 

B

A

O

Y Ce





 









3 Ricec 7-Hour Average 0.20 2.47 85 

Sorghum W126 205.90 1.96 85 

Soybeans W126 110.20 1.36 93 

Processing 

Tomatoesc 12-Hour Average 9,055.00 32,367.00 66     Y C A B O3   

Wheat (Spring & 

Winter)b W126 53.40 2.37 58 

B

A

O

Y Ce





 









3

TREE SPECIES 

Aspen 

W126 

109.81 1.22 

N/A 

B

A

O

Y Ce





 









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 Prospective Analysis 

February 2011 

4-12

SPECIES OZONE METRIC A (PPM) B 

GROWING 

DAYS FUNCTIONa 

Tulip Poplar 51.38 2.09 

Virginia Pine 1,714.64 1.00 

Notes: Variables defined as follows: 

C = theoretical constant equivalent to the theoretical 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 the expected growth response in 37 percent of the 

theoretical yield at zero ozone exposure; 

B = the shape parameter affecting the change 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 are 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 Protection 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 use of different growing periods for these crops does 

not result in significant changes to relative yield 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 Quality Planning and Standards, Air 

Quality Strategies and Standards Division. 

Relative Yield Loss Estimation: Relative yield losses were calculated based on 

exposure-response functions according 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 hardwood 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 estimated 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. Similarly, relative yield gains are not 

estimated for trees in regions where the given tree species is not potentially present as defined by FASOM. 
Second Section 812 Prospective Analysis 

February 2011 

4-13

Step 2 Results: Relative Yield Losses for Crops and Trees 

Maps presenting crop-subregion- and tree-region-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 sensitivity to ozone, geographic distribution, 

growing period length, and the specific time of year the growing period occurs. Given 

these factors, relative yield losses vary between 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 CAAA 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 2010. Ozone concentrations are lower under 

the baseline, with CAAA scenario in Florida for all other months in 2010. Thus, ozone 

concentrations aggregated across all months of interest, May through September, are 

reduced in Florida in 2010 with the CAAA (Exhibit 4-5). The negative relative yield loss 

for soybeans, however, is minimal given the 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 September) 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 Prospective Analysis 

February 2011 

4-14

Hardwood forests exhibit greater relative yield losses than softwood forests across all 

years in the analysis. The maximum relative 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, respectively). The minimum relative yield loss 

across both forest types is estimated for softwoods in the Pacific Northwest East region in 

2000 (relative yield loss equal to 0.06 percent). 

As presented in Exhibits 4-4 and 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 geographic 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 under the baseline scenario relative to the 

counterfactual scenario. Commercial timber and agriculture operations generally manage 

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 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 yield losses to agricultural crops and commercial 

tree species. 

Changes in the distribution and yield of crop 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 within 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 funders over several decades as a partial 

equilibrium tool to evaluate the welfare and market impacts of public policies affecting 

agriculture and forestry. The model simulates biophysical and economic processes 

affecting land management and land allocation 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 a 
Second Section 

EXHIBIT 4-7. MINIMUM, MAXIMUM, AND AVERAGE RELATIVE YIELD LOSSES ACROSS ALL FASOM SUBREGIONS FOR 

FASOM REGIONS FOR TREES BY YEAR (2000, 2010, 2020) 

CROP/FOREST TYPE 

2000 2010 20

MINIMUM MAXIMUM AVERAGE MINIMUM MAXIMUM AVERAGE MINIMUM MAXI

Barley 0.00% 0.02% 0.01% 0.00% 0.06% 0.02% 0.00% 0Corn 0.00% 1.12% 0.18% 0.00% 3.07% 0.44% 0.00% 3Cotton 0.00% 6.60% 1.15% 0.00% 16.67% 3.00% 0.00% 20Oranges 0.00% 1.95% 0.09% 0.00% 4.68% 0.25% 0.00% 7Potato 0.00% 6.17% 1.76% 0.00% 17.54% 4.99% 0.00% 20Rice -0.08% 0.14% 0.00% 0.00% 1.03% 0.11% 0.00% 1Sorghum 0.00% 0.87% 0.14% 0.00% 2.17% 0.35% 0.00% 2Soybean 0.00% 3.60% 1.24% -0.55% 11.73% 3.07% 0.00% 12Processing Tomatoes 0.00% 1.82% 0.31% 0.00% 5.54% 0.96% 0.00% 8Spring Wheat 0.00% 1.50% 0.06% 0.00% 3.67% 0.15% 0.00% 6Winter Wheat 0.00% 6.53% 1.00% 0.00% 18.23% 2.49% 0.00% 19Hardwood Forests 1.60% 7.16% 5.06% 4.20% 19.12% 13.86% 6.61% 23Softwood Forests 0.06% 3.85% 1.77% 0.25% 10.49% 4.88% 0.42% 12Note: Negative relative yield losses indicate yield reductions with the CAAA. 
Second Section 812 Prospective Analysis 

February 2011 

4-16

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, has 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 foresight 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 FASOM’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 important 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, however, 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 reducing 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 functions are assumed to be zero, and a second where 

impacts on crop/region combinations without specific concentration-response functions were set to the values used in 

adjacent regions and/or proxy crops where possible (for example, 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 overall national results between these two runs was negligible, 

however. As a result, in this chapter we report the results 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 July of 2010 and available at the following web site (accessed November 26, 2010): 

http://www.epa.gov/otaq/fuels/renewablefuels/regulations.htm 

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

perfect foresight rather than a myopic (recursive) approach allows an expanded array of policy simulations and potential 

insights, which is the main purpose of this type of model. 
Second Section 812 Prospective Analysis 

February 2011 

4-17

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, reflecting 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 amount. As a result, FASOM estimates the net 

welfare benefits of the CAAA to be approximately $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 relatively more profitable than in the baseline 

compared with agriculture, resulting in a shift 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 represents 2020 through 2024. Values provided for ozone impacts in 2000, 2010, 

and 2020 were applied to the 2000, 2010, and 2020 model periods in FASOM, respectively. The results presented here do 

not includes losses Canada and the rest of the world; for example, 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 Prospective Analysis 

February 2011 

4-18

decline in pasture as the returns to crop production rise relative to livestock production 

with higher crop prices. As is shown in Exhibit 4-8, the shift towards forestry results in a 

decrease in the damage estimate to this sector 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 impacts reducing productivity. The reason that 

producers often are better off is that most forest and agricultural products have relatively 

inelastic demands, which means that a general 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 commodities, 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-use 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 time 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 pasture) and pasture (lower-quality land that is 

not suitable for growing crops without improvement) 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 Prospective Analysis 

February 2011 

5-1 

CHAPTER 5 | ESTIMATION OF MATERIALS DAMAGE AND 

ECONOMIC BENEFITS 

INTRODUCTION 

Since the mid-19th century air pollution as been suspected of accelerating the degradation 

of natural and man-made materials that are exposed 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, contribute to the increased rate of materials 

damage. The principal components of acid deposition considered injurious to building 

materials are hydrogen ion (H+

), sulfur oxides (SOx), and nitrogen oxides (NOx). In 

addition, volatile organics and oxidizing agents such as ozone (O3) and hydrogen 

peroxide (H2O2) have been shown to play an ancillary role (NAPAP, 1991). Acidic 

deposition has been shown to have an effect on materials including zinc/galvanized steel 

and other metal, carbonate stone (as monuments and building facings), and surface 

coatings (paints) (NAPAP, 1991). 

Metal structures are usually coated by alkaline corrosion product layers and thus are 

subject to increased corrosion by acidic deposition. In addition, research has 

demonstrated that iron, copper, and aluminum based products are subject to increased 

corrosion due to pollution, in particular SO2 (NAPAP, 1991). Research has shown that 

acidic deposition (wet deposition of hydrogen ion and dry deposition of SO2 and nitric 

acid) accelerates the rate of erosion of carbonate stone (marble and limestone) and the 

formation of gypsum crusts on the stone (NAPAP, 1991). Acidic deposition has 

numerous negative effects on painted wood and, in general, increases the weathering rate. 

In addition, acidic pollutants negatively affects painted metals resulting in adsorption, 

weight gain, discoloration, adhesion strength loss and/or failure at the metal/primer 

interface. Acidic deposition may also cause damage to automotive finishes (NAPAP, 

1991). 

This analysis will focus on quantifying the impact 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 economic 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 Prospective Analysis 

February 2011 

5-2 

EXHIBIT 5-1. MATERIALS DAMAGE EFFECTS 

POLLUTANT QUANTIFIED EFFECTS—DAMAGE TO:

UNQUANTIFIED EFFECTSA—DAMAGE 

TO: 

Sulfur oxides Infrastructural materials – galvanized 

and painted carbon steel 

Commercial buildings – carbonate 

stone, metal, and painted wood 

surfaces 

Residential buildings – carbonate 

stone, metal, and painted wood 

surfaces 

Monuments – carbonate stone and 

metal 

Structural aesthetics 

Automotive finishes – painted metal 

Hydrogen ion and 

nitrogen oxides 

 Infrastructural materials – galvanized 

and painted carbon steel 

Zinc-based metal products, such as 

galvanized steel 

Commercial and residential buildings 

– carbonate stone, metal, and wood 

surfaces 

Monuments – carbonate stone and 

metal 

Structural aesthetics 

Automotive finishes – painted metal 

Carbon dioxide Zinc-based metal products, such as 

galvanized steel 

Formaldehyde Zinc-based metal products, such as 

galvanized steel 

Particulate 

matter 

 Household cleanliness (i.e., 

household soiling) 

Ozone Rubber products (e.g., tires) 

a The categorization of unquantified 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 inputs, the air quality model in APEEP predicts 

seasonal and annual average county concentrations 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 Prospective 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 infrastructure, commercial buildings and residential 

buildings separately. The commercial inventory 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 Residential 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 surveys provide structure size in terms of floor 

space (ft2

). Thus, a simplifying assumption regarding 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. This 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 extrapolates 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 residential inventory, the survey provides 

number of buildings, by size, by region which 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 being 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 manner 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: 

SAmc = ∑t(Nct)(Sct)(Pmc)
Second Section 812 Prospective Analysis 

February 2011 

5-4 

where: SAmc = exposed surface area of building material (m) in receptor county (c), 

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

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

 Pmc = 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 (focusing 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 states 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 SO2 on galvanized steel is from Atteraas and Haagenrud (1982). 

The function is based on analysis of mass loss data 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 

SO2 concentration. The dose-response function for galvanized steel assumes the 

following form: 

ΔMc = (β0SO2c + β1)Mc

where: ΔMc = mass loss of material by county (c), 

β0, β1 = statistically estimated parameters from the literature, 

 SO2c = ambient concentration of SO2 by county (c), and 

 Mc = 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 SO2 and moisture. The model predicts the increase in erosion over the 

estimated erosion at a pH of 5.2 and an SO2 concentration of zero (representative of a 

clean environment). The pH data used in this 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 rainfall do not vary across scenarios. The doseresponse function for painted surfaces takes the following form: 
Second Section 812 Prospective Analysis 

February 2011 

5-5 

ΔMc = Rcβ0(10-pH – 10-5.2) + β1SO2cFc (3) 

where: ΔMc = mass loss of material by county (c), 

β0, β1 = statistically estimated parameters from the literature, 

 SO2c = ambient concentration of SO2 by county (c), 

 pH = average pH by region, 

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

 Rc = annual rainfall. 

The dose-response function representing the effect of ambient SO2 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 data on materials corrosion at 39 exposure 

sites in 12 European countries, the U.S., and Canada and measured gaseous pollutants, 

precipitation, and climate parameters at or nearby the exposure sites. Regression 

techniques were then used to relate the materials corrosion data to the environmental 

parameters. It should be noted that ambient 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: 

ΔSc = (β0SO2cκ

)exp(γTc) + β1Rc)H+

where: ΔSc = surface recession of material by county (c), 

β0, β1,γ,κ = statistically estimated parameters from the literature, 

 SO2c = ambient concentration of SO2 by county (c), 

 Mc = existing material by county (c), 

 Tc = ambient temperature by county (c), 

 Rc = 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 activities. Under baseline emission conditions 

we assume a five-year maintenance schedule. The present value of materials 

maintenance costs occurring on a five-year schedule is calculated using the following 

formula: 
Second Section 812 Prospective Analysis 

February 2011 

5-6 

Mrb = δ x (RCrb(e-rt)/(1 - e-rt) 

where: Mrb = annual maintenance costs in county (r), baseline SO2, 

δ = market interest rate (4%),56

RCrb = replacement costs in receptor county (r), baseline SO2, and 

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

A change in the frequency of maintenance activities due to a change in emissions is 

calculated as the ratio of the materials inventory after the emission change (Ip) to the 

materials inventory before the change (Ib). 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 Ip<Ib, 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 (Ip/Ib). 

The materials maintenance cost equation (Equation 5) is adjusted to account for the 

amended maintenance schedule as follows: 

Mrp = ∑t [δ x (RCrp(1+r)-t*)]

where: Mrp = annual maintenance costs in county (r), change emission SO2, 

δ= market interest rate (4%), 

RCrp = Replacement costs in receptor county (r), change emission SO2, and 

t

* = new schedule of maintenance. 

The change in the present value of the maintenance schedules extending into the future 

constitutes the monetary impact of an emission 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: 

ΔMs = ∑r (Mrp – Mrb).

 

56 The APEEP model used for this analysis incorporates a four percent 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 Prospective Analysis 

February 2011 

5-7 

RESULTS 

Exhibit 2 summarizes the benefits of reduced 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 over time. The spatial distribution of the 

benefits is primarily owing to the distribution of the materials inventory and SO2

exposure. The effect of SO2 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 SO2 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 significant figures. Totals may not sum due to 

rounding. 
Second Section 812 Prospective Analysis 

February 2011 

6-1 

CHAPTER 6 | SUMMARY OF PRIMARY BENEFITS 

This chapter presents an integrated summary of the quantified and monetized primary 

benefits estimates described in this report 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 demonstrate that implementation of the CAAA’s 

programs on air emissions yields substantial human 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 cardiovascular 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. Exhibit 6-1 presents a summary of the mean primary 

annual economic benefits results from the Second Prospective analysis for 2000, 2010, 

and 2020. Total annual benefits range from $770 billion 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 from 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, including 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 greater benefit values and would alter the relative 

contribution of the different categories of effects. 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 annual 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 cumulative benefits by interpolating between the 

target years, using information on the expected trend and trajectory of benefits throughout 

the period, and aggregating the resulting values to produce a discounted present value 

estimate of the cumulative benefits of Titles I through V of the CAAA. 
Second Section 812 Prospective Analysis 

February 2011 

6-2 

EXHIBIT 6-1. SUMMARY OF MEAN PRIMARY BENEFITS RESULTS 

BENEFIT CATEGORY 

MONETIZED BENEFITS (MILLION 2006$) 

BY TARGET YEAR 

2000 2010 2020 NOTES 

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 for detailed results summaries. Values presented 

are means from results reported as distributions. Additional, alternative estimates are provided 

in the separate companion report on uncertainty. Estimates presented with two significant 

figures. 
Second Section 812 Prospective 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 related to ozone (e.g., sulfur deposition effects on timber).d

Materials Damage  Monuments – carbonate stone and metal (sulfur oxides, Hydrogen ion and 

nitrogen oxides) 

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

 Automotive finishes – painted metal (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 pollutant 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 ecological benefit not quantified include: 

forest productivity benefits due to decreased acidic deposition; commercial freshwater fishing; 

preservation of biodiversity; increased carbon sequestration in forests; and decreased 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 Prospective Analysis 

February 2011 

6-4 

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 resulting from the annual changes in air quality. 

They do not, however, characterize the uncertainty associated with the yearly estimates 

for intervening years. In an effort to generate improved estimates of the trajectory of 

benefits in these years, the 812 Project Team generated emissions reduction trajectories 

across the study period for seven pollutants in the with- and without-CAAA scenarios. 

Appendix O of the Second Section 812 Prospective Emissions Analysis describes the 

methods used to derive trajectories for each major emitting sector and presents emissions 

trajectories for VOC, NOx, CO, SO2, PM10, PM2.5, and NH3, 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 relatively 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 target 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, NOX , AND SO2 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

2004

2006

2008

2010

2012

2014

2016

2018

2020

Calendar Years

 VOC 

 NOx 

 SO2 
Second Section 812 Prospective Analysis 

February 2011 

6-5 

EXHIBIT 6-3B. TRAJECTORY OF CAAA-RELATED REDUCTIONS IN PM1 0 AND PM2.5 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
Second Section 812 Prospective Analysis 

February 2011 

6-6 

In an attempt to represent uncertainty associated with these estimates, we relied on the 

ratios of the 5th percentile to the mean and the 95th percentile to the 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 each intervening year by the central estimate 

generated for that year provided estimates of the 5th and 95th 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., 5th percentile 

of the distribution) and Primary High (i.e., 95th percentile of the distribution) estimates, 

for all provisions of Titles I through V. Aggregating 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$, DISCOUNTED 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 prospective are much larger 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 and $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 prospective 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. 
Second Section 812 Prospective Analysis 

February 2011 

6-7 

2. Improved air quality models: The first prospective relied on the Regional Acid 

Deposition Model/Regional Particulate Model (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 regional 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, and achieves improved model performance.

3. Better, more comprehensive exposure estimates: The first prospective relied on first 

generation exposure extrapolation tools to generate monitor-adjusted exposure 

estimates away from monitors. Since then, the monitor network, availability of 

speciated data, and the performance of speciated 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 impact of fine PM has nearly doubled. In 

addition, health effects research has addressed 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. 

BENEFITS UNCERTAINTIES 

The benefits values presented in this report are subject to a number of uncertainties 

related to data limitations, analytical choices 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 tools 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 economic value. The unquantified and unmonetized 

benefits thus represent an underestimation bias in the summary benefit results. 

The uncertainties in our quantified and monetized estimates that are most likely to 

significantly influence the primary benefit results are those affecting the largest benefit 

category: the estimation and valuation of reductions in premature mortality due to 

decreases in PM2.5. 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 estimate. These are influential assumptions in 

our analysis and those for which plausible alternative 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 benefits results to these and other factors. It 
Second Section 812 Prospective Analysis 

February 2011 

6-8 

also presents tables describing in a qualitative manner additional uncertainties that are not 

currently amenable to quantitative analysis, indicating the potential direction and 

significance of any potential bias introduced by each uncertain factor. 
Second Section 812 Prospective Analysis 

February 2011 

R-1 

REFERENCES 

Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. Mills, 1995. “Estimated 

Long-Term Ambient Concentrations of PM(10) and Development of Respiratory 

Symptoms in a Nonsmoking Population.” Archives of Environmental Health 50(2): 

139-152. 

Abt Associates, Inc. April 2003. Proposed Nonroad Land-based Diesel Engine Rule: Air 

Quality Estimation, Selected Health and Welfare Benefits Methods, and Benefit 

Analysis Results. Prepared for Office of Air Quality Planning and Standards, U.S. 

EPA. 

Abt Associates, Inc., 2005. Methodology for County-level Mortality Rate Projections. 

Memorandum to Bryan Hubbell and Zachary Pekar, U.S. EPA. 

Abt Associates, Inc., 2008. Environmental Benefits Mapping and Analysis Program 

(BenMAP) User’s Manual Appendices. Prepared for EPA/OAR/OAQPS, 

September 2008. 

Abt Associates, Inc., 2009. Modeled Attainment Test Software User’s Manual. Report 

prepared for Brian Timmon, US EPA Office of Air Quality Planning and Standards, 

Research Triangle Park, NC. March, 2009. 

Adams, P. F., G. E. Hendershot and M. A. Marano, 1999. Current Estimates from the 

National Health Interview Survey, 1996. Vital Health Stat. Vol. 10 (200): 1-212. 

Agency for Healthcare Research and Quality. 2000. HCUPnet, Healthcare Cost and 

Utilization Project. Rockville, MD. 

American Lung Association. 2002a. Trends in Asthma Morbidity and Mortality. 

American Lung Association, Best Practices and Program Services, Epidemiology 

and Statistics Unit. 

American Lung Association. 2002b. Trends in Chronic Bronchitis and Emphysema: 

Morbidity and Mortality. American Lung Association, Best Practices and Program 

Services, Epidemiology and Statistics Unit. 

http://www.lungusa.org/data/copd/COPD1.pdf. 

American Lung Association. 2002c. Trends in Morbidity and Mortality: Pneumonia, 

Influenza, and Acute Respiratory Conditions. American Lung Association, Best 

Practices and Program Services, Epidemiology and Statistics Unit. 

American Lung Association, 2009. “Chronic Bronchitis.” Available at 

http://www.lungusa.org/site/apps/nlnet/content3.aspx?c=dvLUK9O0E&b=4294229

&ct=3052285
Second Section 812 Prospective Analysis 

February 2011 

R-2 

Anderson HR, Atkinson RW, Peacock JL, Marston L, Konstantinou K., 2004. Metaanalysis of time-series studies and panel studies of Particulate Matter (PM) and 

Ozone (O3): Report of a WHO task group. Copenhagen, Denmark: World Health 

Organization. 

Arrow, K., R. Solow, P. R. Portney, E. E. Leamer, R. Radner, and H. Schuman. 1993. 

"Report of the NOAA Panel on Contingent Valuation," Federal Register, January 

15, vol. 58, no. 10, pp. 4601-4614. 

Atteraas, Haagenrud. 1982. "Atmospheric Corrosion Testing in Norway. Pp. 873-892 in: 

W.H. Ailor, ed., "Atmospheric Corrosion" Wiley-Interscience, New York 

Berger MC, Blomquist GC, Kenkel D, Tolley GS. 1987. Valuing Changes in Health 

Risks: A Comparison of Alternative Measures. The Southern Economic Journal 

53:977-984. 

Bell, M.L., et al., 2004. Ozone and short-term mortality in 95 US urban communities, 

1987-2000. Jama, 2004. 292(19): p. 2372-8. 

Bell, M. L., F. Dominici and J. M. Samet, 2005. A meta-analysis of time-series studies of 

ozone and mortality with comparison to the national morbidity, mortality, and air 

pollution study. Epidemiology. Vol. 16 (4): 436-45. 

Brookshire, D.S., Thayer, M.A., Schulze, W.D. & D'Arge, R.C. 1982. “Valuing Public 

Goods: A Comparison of Survey and Hedonic Approaches.” The American 

Economic Review. 72(1): 165-177. 

Brookshire, D.S., R.C. d’Arge, W.D. Schulze and M.A. Thayer. 1979. Methods 

Development for Assessing Tradeoffs in Environmental Management, Vol. II: 

Experiments in Valuing Non-Market Goods: A Case Study of Alternative Benefit 

Measures of Air Pollution Control in the South Coast Air Basin of Southern 

California. Prepared for the U.S. Environmental Protection Agency, Office of 

Research and Development. 

Burnett RT, Smith-Doiron M, Stieb D, Raizenne ME, Brook JR, Dales RE, et al.,2001. 

Association between ozone and hospitalization for acute respiratory diseases in 

children less than 2 years of age. Am J Epidemiol 153(5):444-452. 

Byun, D. W. and J. K. S. Ching, 1999. “Science Algorithms of the EPA Models-3 

Community Multiscale Air Quality (CMAQ) Modeling System.” U.S. EPA Office of 

Research and Development, Washington, D.C. (EPA/600/R-99/030). 

Carson, R.T., R.C. Mitchell, and P.A. Rudd. 1990. “Valuing Air Quality Improvements: 

Simulating a Hedonic Pricing Equation in the Context of a Contingent Valuation 

Scenario.” In Visibility and Fine Particles, Transactions of an AWMA/EPA 

International Specialty Conference, C.V. Mathai, ed. Air and Waste Management 

Association, Pittsburgh. 
Second Section 812 Prospective Analysis 

February 2011 

R-3 

Chen L, Jennison BL, Yang W, Omaye ST., 2000. Elementary school absenteeism and air 

pollution. Inhal Toxicol 12(11):997-1016. 

Chestnut, L.G., R.D. Rowe and J. Murdoch. 1986. Review of ‘Establishing and Valuing 

the Effects of Improved Visibility in Eastern United States.’ Prepared for the U.S. 

Environmental Protection Agency. October. 

Chestnut, L.G., and R.D. Rowe. 1990a. “A New National Park Visibility Value 

Estimates.” In Visibility and Fine Particles, Transactions of an AWMA/EPA 

International Specialty Conference, C.V. Mathai, ed. Air and Waste Management 

Association, Pittsburgh. 

Chestnut, L.G. and R.D. Rowe. 1990b. Preservation Values for Visibility Protection at 

the National Parks. Prepared for U.S. Environmental Protection Agency, Office of 

Air Quality Planning and Standards, and National Park Service, Air Quality 

Management Division. 

Chestnut, L.G. and R.D. Rowe. 1990c. Economic Valuation of Changes in Visibility: A 

State of the Science Assessment for NAPAP. Section B5 in NAPAP State of Science 

and Technology Report 27. 

Chestnut, L.G. April 15, 1997. Draft Memorandum: Methodology for Estimating Values 

for Changes in Visibility in National Parks. 

Chestnut, L.G. and R. Dennis. 1997. “Economic Benefits of Improvements in Visibility: 

Acid Rain Provisions of the 1990 Clean Air Act Amendments.” Journal of Air and 

Waste Management Association 47: 395-402. 

Community Modeling and Analysis System (CMAS) Center, 2007. CMAQ v4.6 

Operational Guidance Document. Available at http://www.cmaqmodel.org/op_guidance_4.6/manual.pdf. 

Crocker, T.D. and R.L. Horst, Jr., 1981. Hours of Work, Labor Productivity, and 

Environmental Conditions: A Case Study. The Review of Economics and Statistics, 

1981. 63: p. 361-368. 

Cropper, M. L. and A. J. Krupnick. 1990. The Social Costs of Chronic Heart and Lung 

Disease. Resources for the Future. Washington, DC. Discussion Paper QE 89-16-

REV. 

Desvouges, William H. et al. 1998. Economic Policy Analysis with Limited Information, 

Principles and Applications of the Transfer Method. Edward Elgar Publishing 

Limited: Northampton. 

Dockery, D.W., C.A. Pope, X.P. Xu, J.D. Spengler, J.H. Ware, M.E. Fay, B.G. Ferris, 

and F.E. Speizer, 1993. “An Association between Air Pollution and Mortality in Six 

U.S. Cities.” New England Journal of Medicine 329(24):1753-1759. 
Second Section 812 Prospective Analysis 

February 2011 

R-4 

Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler, P. Koutrakis, 

J.H. Ware, M. Raizenne, and F.E. Speizer, 1996. “Health Effects of Acid Aerosols 

On North American Children-Respiratory Symptoms.” Environmental Health 

Perspectives 104(5):500-505. 

Domenici, F., R.D. Peng, M.L. Bell, L. Pham, A. McDermott, S.L. Zeger, J.M. Samet, 

2006. “Fine Particulate Air Pollution and Hospital Admission for Cardiovascular and 

Respiratory Diseases. Journal of the American Medical Association 295: 1127-1134. 

Douglas, S.G, J.L. Haney, A.B. Hudischewskyj, T.C. Myers, and Y. Wei. 2008. Second 

Prospective Analysis of Air Quality in the U.S.: Air Quality Modeling: Draft Report. 

Prepared for the U.S. Environmental Protection Agency OPAR. 

EPA, 1990. U.S. Environmental Protection Agency, “Review of the National Ambient 

Air Quality Standards for Lead: Assessment of Scientific and Technical 

Information,” OAQPS Staff Paper, EPA-450/2-89-022, Research Triangle Park, NC, 

December 1990. 

EPA. 1999. The Benefits and Costs of the Clean Air Act: 1990 to 2010: EPA Report to 

Congress. U.S. EPA, Office of Air and Radiation, Office of Policy. Washington, 

DC. EPA 410-R-99-001. November. 

http://www.epa.gov/airprogm/oar/sect812/index.html. 

EPA, 2004. Air Quality Criteria for Particulate Matter Volume II of II. National Center 

for Environmental Assessment, Office of Research and Development, U.S. 

Environmental Protection Agency, Research Triangle Park, NC EPA/600/P99/002bF 

EPA, 2005. U.S. Environmental Protection Agency, “Regulatory Impact Analysis for the 

Final Clean Air Interstate Rule,” EPA 452/R-05-002, Office of Air and Radiation, 

U.S. Environmental Protection Agency, March 2005. 

EPA, 2006a. U.S. Environmental Protection Agency, “Air Quality Criteria for Ozone 

and Related Photochemical Oxidants,” EPA 600/R-05/004aF, Office of Research 

and Development, Research Triangle Park, NC, February 2006. 

EPA, 2006b. U.S. Environmental Protection Agency, “Final PM NAAQS Regulatory 

Impact Analysis,” October 2006. 

EPA, 2008. U.S. Environmental Protection Agency, “Final Ozone NAAQS Regulatory 

Impact Analysis,” EPA-452/R-08-003, Office of Air Quality Planning and 

Standards, Research Triangle Park, NC, March 2008. 

EPA – Science Advisory Board, 1999. "The Clean Air Act Amendments (CAAA) 

Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the 

Advisory Council on Clean Air Compliance Analysis: Costs and Benefits of the 

CAAA." Letter from Advisory Council on Clean Air Compliance Analysis to EPA 

Administrator Carol M. Browner, EPA-SAB-COUNCIL-ADV-00-002, October 29, 

1999. 
Second Section 812 Prospective Analysis 

February 2011 

R-5 

EPA – Science Advisory Board, 2004a. Advisory Council on Clean Air Compliance 

Analysis Response to Agency Request on Cessation Lag. Letter from the Health 

Effects Subcommittee to the U.S. Environmental Protection Agency Administrator, 

December. 

EPA – Science Advisory Board, 2004b. Advisory on Plans for Health Effects Analysis in 

the Analytical Plan for EPA’s Second Prospective Analysis – Benefits and Costs of 

the Clean Air Act, 1990-2020. EPA-SAB-COUNCIL-ADV-04- 002 March. 

EPA – Science Advisory Board, 2010. Advisory Council on Clean Air Act Compliance 

Analysis, Health Effects Subcommittee, Review of EPA’s Draft Health Benefits of 

the Second Section 812 Prospective Study of the Clean Air Act. EPA-COUNCIL10-001, June 16, 2010, available at 

http://yosemite.epa.gov/sab/sabpeople.nsf/WebCommittees/COUNCIL. 

Eisenstein, E.L., L.K. Shaw, K.J. Anstrom, C.L. Nelson, Z. Hakim, V. Hasselblad and 

D.B. Mark. 2001. “Assessing the Clinical and Economic Burden of Coronary Artery 

Disease: 1986-1998.” Medical Care 39(8):824-35. 

Freeman(III), AM. 1993. The Measurement of Environmental and Resource Values: 

Theory and Methods. Washington, DC: Resources for the Future. 

Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ, et al., 

2001. The effects of ambient air pollution on school absenteeism due to respiratory 

illnesses. Epidemiology 12(1):43-54. 

Hall JV, Brajer V, Lurmann FW. 2003. Economic Valuation of Ozone-related School 

Absences in the South Coast Air Basin of California. Contemporary Economic 

Policy 21(4):407-417. 

Harrington W, Portney PR. 1987. Valuing the Benefits of Health and Safety Regulation. 

Journal of Urban Economics 22:101-112. 

Haynie, 1986. “Atmospheric Acid Deposition Damage to Paints.” Environmental 

Research Brief EPA 600/M-85/019. Atmospheric Science Research Laboratory, 

Research Triangle Park, NC. 

Huang, Y., F. Dominici and M. L. Bell, 2005. Bayesian hierarchical distributed lag 

models for summer ozone exposure and cardio-respiratory mortality. 

Environmetrics. Vol. 16: 547–562. 

Ito, K, 2003. “Associations of Particulate Matter Components with Daily Mortality and 

Morbidity in Detroit, Michigan.” In Revised Analyses of Time-Series Studies of Air 

Pollution and Health. Special Report. Health Effects Institute, Boston, MA. 

Industrial Economics Incorporated (IEc). 1993. Memorandum to Jim DeMocker, U.S. 

Environmental Protection Agency, Office of Air and Radiation, Office of Policy 

Analysis and Review. September 30. Washington, D.C. 
Second Section 812 Prospective Analysis 

February 2011 

R-6 

International Cooperative Programme on Effects on Materials, Including Historic and 

Cultural Monuments. 1998. “Report No 30: Statistical Analysis of 8 Year Materials 

Exposure and Acceptable Deterioration and Pollution Levels.” Compiled by the 

Swedish Corrosion Institute, Stockholm, Sweden 

Ito, K., S. F. De Leon and M. Lippmann, 2005. Associations between ozone and daily 

mortality: analysis and meta-analysis. Epidemiology. Vol. 16 (4): 446-57. 

Jaffe DH, Singer ME, Rimm AA., 2003. Air pollution and emergency department visits 

for asthma among Ohio Medicaid recipients, 1991-1996. Environ Res 91(1):21-28. 

Krewski D., R.T. Burnett, M.S. Goldbert, K. Hoover, J. Siemiatycki, M. Jerrett, M. 

Abrahamowicz, and W.H. White, July 2000. Reanalysis of the Harvard Six Cities 

Study and the American Cancer Society Study of Particulate Air Pollution and 

Mortality. Special Report to the Health Effects Institute, Cambridge MA. 

Krupnick, A. J. and M. L. Cropper. 1992. The Effect of Information On Health Risk 

Valuations. Journal of Risk and Uncertainty. Vol. 5 (1): 29-48. 

Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr, and M. Studnicka, 2001. 

“Assessment of Deaths Attributable to Air Pollution: Should We Use Risk Estimates 

Based on Time Series or on Cohort Studies?” American Journal of Epidemiology 

153(11):1050-55. 

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery, 2006. Reduction in Fine 

Particulate Air Pollution and Mortality. American Journal of Respiratory and Critical 

Care Medicine. 173: 667-672. 

Leggett, C.G. and J.E. Neumann. 2004. Responding to SAB Council Comments on the 

May 2003 Draft Analytical Plan for the Section 812 Second Prospective – Visibility 

Benefits. Prepared for U.S. Environmental Protection Agency OPAR. 

Levy, J. I., S. M. Chemerynski and J. A. Sarnat, 2005. Ozone exposure and mortality: an 

empiric bayes metaregression analysis. Epidemiology. Vol. 16 (4): 458-68. 

Loehman, E.T., D. Boldt and K. Chaikin. 1984. Measuring the Benefits of Air Quality 

Changes in the San Francisco Bay Area. Prepared for U.S. Environmental 

Protection Agency, Office of Policy, Planning and Evaluation. October. 

Loehman, E.T., S. Park, and D. Boldt. 1994. “Willingness to Pay for Gains and Losses in 

Visibility and Health.” Land Economics 70(4): 478-498. 

Mansfield, C., K. Thomas, M. Farrelly, L. Clayton, T. Wilcosky, 2005. Development of 

Projections of Asthma Prevalence, Chronic Bronchitis Prevalence, and ConditionRelated Hospitalizations, 2004–2030, Final Report. Research Triangle Park, NC. 

RTI International. Prepared for U.S. EPA Office of Air and Radiation. 

 
Second Section 812 Prospective Analysis 

February 2011 

R-7 

McClelland, G., W. Schulze, D. Waldman, J. Irwin, D. Schenk, T. Stewart, L. Deck and 

M. Thayer. 1991. Valuing Eastern Visibility: A Field Test of the Contingent 

Valuation Method. Prepared for U.S. Environmental Protection Agency, Office of 

Policy, Planning and Evaluation. 

Moolgavkar SH, Luebeck EG, Anderson EL, 1997. Air pollution and hospital admissions 

for respiratory causes in Minneapolis St. Paul and Birmingham. Epidemiology 

8(4):364-370. 

Moolgavkar, S. H., 2000a. Air Pollution and Hospital Admissions for Chronic 

Obstructive Pulmonary Disease in Three Metropolitan Areas in the United States. 

Inhalation Toxicology. Vol. 12 (Supplement 4): 75-90. 

Moolgavkar, S.H., 2000b. “Air Pollution and Hospital Admissions for Diseases of the 

Circulatory System in Three U.S. Metropolitan Areas.” Journal of the Air and Waste 

Management Association 50:1199-1206. 

Moolgavkar, S.H., 2003. “Air Pollution and Daily Deaths and Hospital Admissions in 

Los Angeles and Cook Counties.” In Revised Analyses of Time-Series Studies of 

Air Pollution and Health. Special Report. Boston, MA: Health Effects Institute. 

Muller, N. Z., R.O. Mendelsohn. 2007. "Measuring the Damages from Air Pollution in 

the United States." Journal of Environmental Economics and Management, 54(1):1-

14. 

Muller, N.Z., R.O. Mendelsohn. 2009. “Efficient Pollution Control: Getting the Prices 

Right.” Forthcoming. American Economic Review. 

NAS, 2008. Committee on Estimating Mortality Risk Reduction Benefits from 

Decreasing Tropospheric Ozone Exposure, National Research Council. Estimating 

Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air 

Pollution (National Academies Press: Washington, DC). April 2008. 

National Acid Precipitation Assessment Program (NAPAP). 1991. Acidic Deposition: 

State of Science and Technology. Volume III. Patricia M. Irving, PhD, ed., National 

Acid Precipitation Assessment Program, 722 Jackson Place NW, Washington D.C., 

20503 

National Atmospheric Deposition Program (NADP). 

<http://nadp.sws.uiuc.edu/sites/ntnmap.asp?>. 

National Research Council (NRC), 2002. Estimating the Public Health Benefits of 

Proposed Air Pollution Regulations. The National Academies Press: Washington, 

D.C. 

Neumann, J.E., M.T. Dickie, and R.E. Unsworth. March 31, 1994. “Linkage Between 

Health Effects Estimation and Morbidity Valuation in the Section 812 Analysis—

Draft Valuation Document.” Industrial Economics Incorporated (IEc) Memorandum 

to Jim DeMocker, U.S. Environmental Protection Agency, Office of Air and 

Radiation, Office of Policy Analysis and Review. 
Second Section 812 Prospective Analysis 

February 2011 

R-8 

Neumann, J.E. and R.W. Patterson. 2009. Alternative Approach to Estimating Monetized 

Benefits of Residential Visibility for the Section 812 Second Prospective. Prepared 

for U.S. Environmental Protection Agency OAR/OPAR. 

Norris, G., S.N. YoungPong, J.Q. Koenig, T.V. Larson, L. Sheppard, and J.W. Stout, 

1999. “An Association between Fine Particles and Asthma Emergency Department 

Visits for Children in Seattle.” Environmental Health Perspectives 107(6):489-493. 

Ostro, B.D., 1987. “Air Pollution and Morbidity Revisited: A Specification Test.” Journal 

of Environmental Economics Management 14:87-98. 

Ostro BD, Rothschild S., 1989. Air Pollution and Acute Respiratory Morbidity—an 

Observational Study of Multiple Pollutants. Environ Res 50(2):238-247. 

Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens, and M. White, 2001. “Air Pollution 

and Exacerbation of Asthma in African-American Children in Los Angeles.” 

Epidemiology 12(2):200-208. 

Patterson, R.W., J.E. Neumann, and C.G. Leggett. 2005. Recommended Residential 

Visibility Values for the Section 812 Second Prospective Analysis. Prepared for U.S. 

Environmental Protection Agency OPAR. 

Peel, J. L., P. E. Tolbert, M. Klein, et al., 2005. Ambient air pollution and respiratory 

emergency department visits. Epidemiology. Vol. 16 (2): 164-74. 

Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mittleman, 2001. “Increased Particulate 

Air Pollution and the Triggering of Myocardial Infarction.” Circulation 103:2810-

2815. 

Pitchford, M and W. Malm. 1993. Development and Application of a Standard Visual 

Index. Atmospheric Environment 28(5): 1049-1054. 

Poloniecki, J.D., R.W. Atkinson., A.P. de Leon., and H.R. Anderson, 1997. “Daily Time 

Series for Cardiovascular Hospital Admissions and Previous Day’s Air Pollution in 

London, UK.” Occupational and Environmental Medicine 54(8):535-540. 

Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne, 1991. “Respiratory 

Health and PM10 Pollution: A Daily Time Series Analysis.” American Review of 

Respiratory Diseases 144:668-674. 

Pope, C.A., III, M.J. Thun, M.M. Namboodiri, D.W. Dockery, J.S. Evans, F.E. Speizer, 

and C.W. Heath, Jr., 1995. “Particulate Air Pollution as a Predictor of Mortality in a 

Prospective Study of U.S. Adults.” American Journal of Respiratory Critical Care 

Medicine 151:669-674. 

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. 

Thurston, 2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term 

Exposure to Fine Particulate Air Pollution.” Journal of the American Medical 

Association 287:1132-1141. 
Second Section 812 Prospective 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 Power Research Institute. May. 

Ransom, M. R. and C. A. Pope, 1992. Elementary school absences and PM10 pollution in 

Utah Valley. Environ Res. Vol. 58 (2): 204-19. 

Rosamond, W., G. Broda, E. Kawalec, S. Rywik, A. Pajak, L. Cooper and L. Chambless, 

1999. Comparison of medical care and survival of hospitalized patients with acute 

myocardial infarction in Poland and the United States. 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. The National Morbidity, Mortality and Air 

Pollution Study: Part II: Morbidity, Mortality 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. Arch 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 Particles are More Strongly Associated than 

Coarse Particles with Acute Respiratory Health Effects in Schoolchildren.” 

Epidemiology 11:6- 10. 

Schwartz, J., 2005. How sensitive is the association 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 Prospective Analysis 

February 2011 

R-10 

Smith, V.K., G. Van Houtven, and S. Pattanayak. 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. American Housing Survey. Accessed: 

http://www.census.gov/hhes/www/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 Nonasthmatic 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 Economics 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 New 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. Schoendorf. 1997. “The Relationship Between 

Selected Causes of Postneonatal Infant Mortality and Particulate Air Pollution in the 

United States.” Environmental Health Perspectives 105(6):608-612. 

Woodruff TJ, Parker JD, Schoendorf KC. 2006. Fine particulate matter (PM2.5) air 

pollution and selected causes of postneonatal infant mortality in California. 

Environmental Health Perspectives 114(5):786-90. 
Second Section 812 Prospective Analysis 

February 2011 

R-11 

Woods & Poole Economics Inc., 2007. Complete Demographic Database. Washington, 

DC. http://woodsandpoole.com/index.php. 
Second Section 812 Prospective Analysis 

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A-1 

APPENDIX A | PRIMARY ESTIMATES 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 Prospective 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 Prospective 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 Prospective 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 Prospective 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 Prospective 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 Prospective 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 expressed 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 by 

FASOM. Exhibits B-1 through B-11 present relative yield losses for crops; Exhibits B12 and B-13 present relative yield losses for hardwood and softwood forest types, 

respectively. 

In addition to relative yield loss maps, this 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 exposure-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 hardwood 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 functions exist, are present (as defined in 

FASOM) in the Great Plains region. As with 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 percent in 2010; and, 6.11 percent in 2020). 

 

58 Note that 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. 

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

split by region. 
Second Section 812 Prospective 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 either 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 Prospective 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 Prospective 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 Prospective 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 Prospective 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 Prospective Analysis 

February 2011 

B-7

EXHIBIT B-6. RELATIVE YIELD LOSSES IN RICE UNDER THE COUNTERFACTUAL (NO CAAA) 

SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONAL-SPECIFIC 

OZONE CONCENTRATIONS AND GROWING PERIODS 
Second Section 812 Prospective 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 Prospective Analysis 

February 2011 

B-9

EXHIBIT B-8. RELATIVE YIELD LOSSES IN SOYBEANS UNDER THE COUNTERFACTUAL (NO CAAA) 

SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONAL-SPECIFIC 

OZONE CONCENTRATIONS AND GROWING PERIODS 
Second Section 812 Prospective 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 Prospective Analysis 

February 2011 

B-11

EXHIBIT B-10. RELATIVE YIELD LOSSES IN SPRING WHEAT UNDER THE COUNTERFACTUAL (NO 

CAAA) SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONALSPECIFIC OZONE CONCENTRATIONS AND GROWING PERIODS 
Second Section 812 Prospective Analysis 

February 2011 

B-12

EXHIBIT B-11. RELATIVE YIELD LOSSES IN WINTER WHEAT UNDER THE COUNTERFACTUAL (NO 

CAAA) SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONALSPECIFIC OZONE CONCENTRATIONS AND GROWING PERIODS 
Second Section 812 Prospective 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 Prospective 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 Prospective Analysis 

February 2011 

B-15

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

SUBREGION X: 

B

A

O NoCAAA

e





 





 3

Y: 

B

A

O WithCAAA

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 Prospective Analysis 

February 2011 

B-16

SUBREGION X: 

B

A

O NoCAAA

e





 





 3

Y: 

B

A

O WithCAAA

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 crop in Exhibit X-6. 

2. Relative yield losses are only derived for subregions where the crop is present 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. 
Second Section 812 Prospective Analysis 

February 2011 

B-17

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

SUBREGION X: 

B

A

O NoCAAA

e





 





 3

Y: 

B

A

O WithCAAA

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 Prospective Analysis 

February 2011 

B-18

SUBREGION X: 

B

A

O NoCAAA

e





 





 3

Y: 

B

A

O WithCAAA

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 crop in Exhibit X-6. 

2. Relative yield losses are only derived for subregions where the crop is present 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. 
Second Section 812 Prospective Analysis 

February 2011 

B-19

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

SUBREGION X: 

B

A

O NoCAAA

e





 





 3

Y: 

B

A

O WithCAAA

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 crop in Exhibit X-6. 

2. Relative yield losses are only derived for subregions where the crop is present 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. 
Second Section 812 Prospective Analysis 

February 2011 

B-20

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

SUBREGION 

X: A    B O3NoCAAA Y: A B O WithCAAA 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 crop in Exhibit X-6. 

2. Relative yield losses are only derived for subregions where the crop is present 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. 
Second Section 812 Prospective Analysis 

February 2011 

B-21

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

SUBREGION X: 

B

A

O NoCAAA

e





 





 3

Y: 

B

A

O WithCAAA

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 Prospective Analysis 

February 2011 

B-22

SUBREGION X: 

B

A

O NoCAAA

e





 





 3

Y: 

B

A

O WithCAAA

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 crop in Exhibit X-6. 

2. Relative yield losses are only derived for subregions where the crop is present 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. 
Second Section 812 Prospective Analysis 

February 2011 

B-23

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

SUBREGION X: 

B

A

O NoCAAA

e





 





 3

Y: 

B

A

O WithCAAA

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 crop in Exhibit X-6. 

2. Relative yield losses are only derived for subregions where the crop is present 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. 
Second Section 812 Prospective Analysis 

February 2011 

B-24

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

SUBREGION X: 

B

A

O NoCAAA

e





 





 3

Y: 

B

A

O WithCAAA

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 presented along with the appropriate ozone metric to apply by crop in Exhibit X-6. 

2. Relative yield losses are only derived for subregions where the crop is present 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.
Second Section 812 Prospective Analysis 

February 2011 

B-25

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

SUBREGION X: 

B

A

O NoCAAA

e





 





 3

Y: 

B

A

O WithCAAA

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 Prospective Analysis 

February 2011 

B-26

SUBREGION X: 

B

A

O NoCAAA

e





 





 3

Y: 

B

A

O WithCAAA

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 crop in Exhibit X-6. 

2. Relative yield losses are only derived for subregions where the crop is present 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. 
Second Section 812 Prospective Analysis 

February 2011 

B-27

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

YEAR 

SUBREGION 

X: A    B O3NoCAAA Y: A B O WithCAAA 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 crop in Exhibit X-6. 

2. Relative yield losses are only derived for subregions where the crop is present 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. 
Second Section 812 Prospective 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

O NoCAAA

e





 





 3

Y: 

B

A

O WithCAAA

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 crop in Exhibit X-6. 

2. Relative yield losses are only derived for subregions where the crop is present 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. 
Second Section 812 Prospective 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

O NoCAAA

e





 





 3

Y: 

B

A

O WithCAAA

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 Prospective Analysis 

February 2011 

B-30

SUBREGION X: 

B

A

O NoCAAA

e





 





 3

Y: 

B

A

O WithCAAA

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 crop in Exhibit X-6. 

2. Relative yield losses are only derived for subregions where the crop is present 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. 
Second Section 

EXHIBIT B-25. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FOR HARDWOOD AND SOFTWOOD FOREST TYPES IN THE REGION 

FOREST 

TYPE SPECIES X: 

B

A

O NoCAAA

e

   3 Y: B A O WithCAAA e    3 RYL: (X/Y) * (100%) 

2000 2010 2020 2000 2010 2020 2000 2010 2020 

Hardwoods 

Black Cherry 0.5345 0.5059 0.4873 0.6227 0.7968 0.8837 14.17% 36.51% 44.86% 

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% 

Softwoods 

Eastern White Pine 0.8149 0.7902 0.7729 0.8796 0.9631 0.9865 7.36% 17.96% 21.66% 

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 M

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 relative yield losses for all hardwoo

region.

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 

applied as a proxy estimate of average relative yield losses.
Second Section 

EXHIBIT B-26. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FOR HARDWOOD AND SOFTWOOD FOREST TYPES IN THE REGION 

FOREST 

TYPE SPECIES X: 

B

A

O NoCAAA

e

   3 Y: B A O WithCAAA e    3 RYL: (X/Y) * (100%) 

2000 2010 2020 2000 2010 2020 2000 2010 2020 

Hardwoods 

Black Cherry 0.5648 0.4961 0.4765 0.6601 0.8326 0.9097 14.44% 40.41% 47.62% 

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% 

Softwoods 

Eastern White Pine 0.8391 0.7812 0.7625 0.9021 0.9741 0.9913 6.99% 19.81% 23.08% 

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 M

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 relative yield losses for all hardwoo

region.

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 

applied as a proxy estimate of average relative yield losses.
Second Section 

EXHIBIT B-27. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FOR HARDWOOD AND SOFTWOOD FOREST TYPES IN THE REGION 

FOREST 

TYPE SPECIES X: 

B

A

O NoCAAA

e

   3 Y: B A O WithCAAA e    3 RYL: (X/Y) * (100%) 

2000 2010 2020 2000 2010 2020 2000 2010 2020 

Hardwoods 

Black Cherry 0.7571 0.7493 0.7164 0.7975 0.8677 0.9094 5.06% 13.64% 21.23% 

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% 

Softwoods 

Eastern White Pine 0.9487 0.9455 0.9311 0.9634 0.9830 0.9913 1.53% 3.82% 6.07% 

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 M

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 relative yield losses for all hardwoo

region.

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 

applied as a proxy estimate of average relative yield losses.
Second Section 

EXHIBIT B-28. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FOR HARDWOOD AND SOFTWOOD FOREST TYPES IN THE REGION 

FOREST 

TYPE SPECIES X: 

B

A

O NoCAAA

e

   3 Y: B A O WithCAAA e    3 RYL: (X/Y) * (100%) 

2000 2010 2020 2000 2010 2020 2000 2010 2020 

Hardwoods 

Black Cherry 0.5734 0.5111 0.5165 0.6540 0.8035 0.8773 12.33% 36.39% 41.13% 

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% 

Softwoods 

Eastern White Pine 0.8455 0.7948 0.7995 0.8987 0.9653 0.9852 5.91% 17.66% 18.84% 

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 M

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 relative yield losses for all hardwoo

region.

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 

applied as a proxy estimate of average relative yield losses.
Second Section 

EXHIBIT B-29. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FOR HARDWOOD AND SOFTWOOD FOREST TYPES IN THE 

FASOM REGION 

FOREST 

TYPE SPECIES X: 

B

A

O NoCAAA

e

   3 Y: B A O WithCAAA e    3 RYL: (X/Y) * (100%) 

2000 2010 2020 2000 2010 2020 2000 2010 2020 

Hardwoods 

Black Cherry 0.6403 0.5900 0.5698 0.7302 0.8722 0.9265 12.32% 32.36% 38.50% 

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% 

Softwoods 

Eastern White Pine 0.8905 0.8576 0.8429 0.9374 0.9841 0.9940 5.00% 12.85% 15.20% 

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 M

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 relative yield losses for all hardwoo

region.

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 

applied as a proxy estimate of average relative yield losses.
Second Section 

EXHIBIT B-30. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FOR HARDWOOD AND SOFTWOOD FOREST TYPES IN THE 

FASOM REGION 

FOREST 

TYPE SPECIES X: 

B

A

O NoCAAA

e

   3 Y: B A O WithCAAA e    3 RYL: (X/Y) * (100%) 

2000 2010 2020 2000 2010 2020 2000 2010 2020 

Softwoods 

Ponderosa Pine 0.9488 0.9415 0.9288 0.9595 0.9683 0.9764 1.11% 2.77% 4.88% 

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 M

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 relative yield losses for all hardwoo

region.

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 

applied as a proxy estimate of average relative yield losses.
Second Section 

EXHIBIT B-31. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FOR HARDWOOD AND SOFTWOOD FOREST TYPES IN THE 

NORTHWEST-WESTSIDE FASOM REGION 

FOREST 

TYPE SPECIES X: 

B

A

O NoCAAA

e

   3 Y: B A O WithCAAA e    3 RYL: (X/Y) * (100%) 

2000 2010 2020 2000 2010 2020 2000 2010 2020 

Softwoods 

Ponderosa Pine 0.9892 0.9873 0.9852 0.9907 0.9922 0.9936 0.15% 0.49% 0.85% 

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 M

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 relative yield losses for all hardwoo

region.

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 

applied as a proxy estimate of average relative yield losses.
Second Section 

EXHIBIT B-32. DERIVATION OF AVERAGE RELATIVE YIELD LOSSES FOR HARDWOOD AND SOFTWOOD FOREST TYPES IN THE 

FASOM REGION 

FOREST 

TYPE SPECIES X: 

B

A

O NoCAAA

e

   3 Y: B A O WithCAAA e    3 RYL: (X/Y) * (100%) 

2000 2010 2020 2000 2010 2020 2000 2010 2020 

Softwoods 

Ponderosa Pine 0.8803 0.8595 0.8312 0.9006 0.9280 0.9485 2.25% 7.38% 12.37% 

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 M

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 relative yield losses for all hardwoo

region.

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 

applied as a proxy estimate of average relative yield losses.