vvEPA
United SfcJas
Environments! Ptotocfiou
Agency
Benefits of the Proposed Inter-State Air
Quality Rule
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EPA 452/K-03-001
January 2004
Benefits of the Proposed Inter-State Air Quality
Rule
U.S. Environmental Protection Agency
Office of Air and Radiation
Office of Air Quality Planning and Standards
Air Quality Strategies and Standards Division
Innovative Strategies and Economics Group
Research Triangle Park, North Carolina
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CONTENTS
Section Page
1 Executive Summary 1-1
1.1 Benefit-Cost Comparison 1-3
2 Introduction and Background 2-1
2.1 Background 2-1
2.2 Regulated Entities 2-2
2.3 Control Scenario 2-2
2.4 Cost of Emission Controls 2-2
2.5 Organization of this Report 2-3
3 Emissions and Air Quality Impacts 3-1
3.1 Emissions Inventories and Estimated Emissions Reductions 3-1
3.2 Air Quality Impacts 3-2
3.2.1 PM Air Quality Estimates 3-4
3.2.1.1 Modeling Domain 3-6
3.2.1.2 Simulation Periods 3-7
3.2.1.3 Model Inputs 3-8
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3.2.1.4 Model Performance for Participate Matter (PM) 3-9
3.2.1.5 Converting REMSAD Outputs to Benefits Inputs . . 3-11
3.2.1.6 PM Air Quality Results 3-12
3.2.2 Ozone Air Quality Estimates 3-13
3.2.2.1 Modeling Domain 3-15
3.2.2.2 Simulation Periods 3-15
3.2.2.3 Non-emissions Modeling Inputs 3-16
3.2.2.4 Model Performance for Photochemical Ozone 3-17
3.2.2.5 Converting CAMx Outputs to Full-Season
Profiles for Benefits Analysis 3-19
3.2.2.6 Ozone Air Quality Results 3-19
3.2.3 Visibility Degradation Estimates 3-20
3.2.3.1 Residential Visibility Improvements 3-22
3.2.3.2 Recreational Visibility Improvements 3-22
4 Benefits Analysis and Results 4-1
4.1 Benefit Analysis- Data and Methods 4-10
4.1.1 Valuation Concepts 4-15
4.1.2 Growth in WTP Reflecting National Income Growth
Over Time 4-18
4.1.3 Methods for Describing Uncertainty 4-20
4.1.4 Demographic Projections 4-25
4.1.5 Health Benefits Assessment Methods 4-26
4.1.5.1 Selecting Health Endpoints and Epidemiological
Effect Estimates 4-27
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4.1.5.2 Uncertainties Associated with Health Impact
Functions 4-43
4.1.5.3 Baseline Health Effect Incidence Rates 4-47
4.1.5.4 Accounting for Potential Health Effect Thresholds .4-51
4.1.5.5 Selecting Unit Values for Monetizing Health
Endpoints 4-54
4.1.5.6 Unqualified Health Effects 4-68
4.1.6 Human Welfare Impact Assessment 4-69
4.1.6.1 Visibility Benefits 4-69
4.1.6.2 Agricultural, Forestry and other Vegetation
Related Benefits 4-72
4.1.6.3 Benefits from Reductions in Materials Damage .... 4-75
4.1.6.4 Benefits from Reduced Ecosystem Damage 4-75
4.2 Benefits Analysis—Results 4-76
4.3 Discussion 4-79
5 Qualitative Assessment of Nonmonetized Benefits 5-1
5.1 Introduction 5-1
5.2 Atmospheric Deposition of Sulfur and .Nitrogen—Impacts on Aquatic,
Forest, and Coastal Ecosystems 5-1
5.2.1 Freshwater Acidification 5-2
5.2.1.1 Water/Watershed Modeling 5-4
5.2.1.2 Description of the MAGIC Model and Methods 5-5
5.2.1.3 Model Structure 5-5
5.2.1.4 Model Implementation 5-6
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5.2.1.5 Calibration Procedure 5-7
5.2.1.6 MAGIC Modeling Results 5-9
5.2.2 Forest Ecosystems 5-10
5.2.3 Coastal Ecosystems 5-11
5.3 Benefits of Reducing Mercury Emissions 5-12
Comparison of Benefits and Costs 6-1
References R-l
VI
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LIST OF FIGURES
Number Page
2-1 States Identified as Having Significant Contribution to PM2.5 2-3
2-2 States Identified as Having Significant Contribution to Ozone 2-4
3-1 REMSAD Modeling Domain for Continental United States 3-7
3-2 Example of REMSAD 36 x 36km Grid-cells for Maryland Area 3-8
3-3 CAMx Eastern U.S. Modeling Domain 3-16
3-4 Recreational Visibility Regions for Continental U.S 3-23
4-1 Key Steps in Air Quality Modeling Based Benefits Analysis 4-3
4-2 Visibility Improvements in Southeastern Class I Areas 4-3
5-1 How Emissions of Mercury Can Affect Human Health and Ecosystems .... 5-14
5-2 Mercury Reductions by State after IAQR 5-16
5-3 Concentrations of Mercury in Blood of Women of Chi Id-Bearing Age 5-17
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LIST OF TABLES
Number Page
1-1 Estimated Reductions in Incidence of Health Effects 1-2
1-2 Estimated Monetary Value of Reductions in Incidence of Health and
Welfare Effects (millions of 1999$) 1-4
1-3 Summary of Benefits, Costs, and Net Benefits of the Inter-State Air
Quality Rule 1-5
1-4 Additional Nonmonetized Benefits of the Inter-State Air Quality Rule ....... 1-6
3-1 Emissions Sources and Basis for Current and Future-Year Inventories 3-2
3-2 Summary of Modeled Baseline Emissions for Lower 48 States 3-3
3-3 Summary of Modeled Emissions Changes for the Proposed Transport Rule .. 3-4
3-4 Model Performance Statistics for REMSAD PM25 Species
Predictions: 1996 3-11
3-5 Summary of Base Case PM Air Quality and Changes due to Proposed
Interstate Air Quality Rule: 2010 and 2015 3-13
3-6 Distribution of PM25 Air Quality Improvements over Population due to
Proposed Interstate Air Quality Rule: 2010 and 2015 3-14
3-7 Model Performance Statistics for Hourly Ozone in the Eastern U.S.
CAMx Ozone Simulations: 1995 Base Case 3-18
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3-8 Summary of CAMx Derived Population-Weighted Ozone Air Quality
Metrics Due to Proposed Transport Rule for Health Benefits Endpoints:
Eastern U.S 3-20
3-9 Distribution of Populations Experiencing Visibility Improvements due to
Proposed Interstate Air Quality Rule: 2010 and 2015 3-21
3-10 Summary of Baseline Residential Visibility and Changes by Region:
2010 and 2015 (annual average deciviews) 3-22
3-11 Summary of Baseline Recreational Visibility and Changes by Region:
2010 and 2015 (annual average deciviews) 3-23
4-1 Estimated Monetized Benefits of the Proposed IAQR 4-10
4-2 Human Health and Welfare Effects of Pollutants Affected by the
Proposed IAQR 4-11
4-3 ^Elasticity Values Used to Account for Projected Real Income Growth 4-19
4-4 Adjustment Factors Used to Account for Projected Real Income Growth ... 4-21
4-5 Primary Sources of Uncertainty in the Benefit Analysis 4-22
4-6 Summary of Considerations Used in Selecting C-R Functions 4-28
4-7 Endpoints and Studies Used to Calculate Total Monetized Health Benefits . . 4-32
4-8 Studies Examining Health Impacts in the Asthmatic Population Evaluated
for Use in the Benefits Analysis 4-44
4-9 Baseline Incidence Rates and Population Prevalence Rates for Use in
Impact Functions, General Population 4-49
4-10 Asthma Prevalence Rates Used to Estimate Asthmatic Populations in
Impact Functions 4-52
4-11 Unit Values Used for Economic Valuation of Health Endpoints (1999$) 4-55
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4-12 Expected Impact on Estimated Benefits of Premature Mortality Reductions
of Differences Between Factors Used in Developing Applied VSL and
Theoretically Appropriate VSL 4-60
4-13 Alternative Direct Medical Cost of Illness Estimates forNonfatal
Heart Attacks 4-66
4-14 Estimated Costs Over a 5-Year Period (in 2000$) of a Nonfatal Myocardial
Infarction 4-66
4-15 Women with Children: Number and Percent in the Labor Force, 2000, and
Weighted Average Participation Rate 4-68
4-16 Reductions in Incidence of Adverse Health Effects Associated with
Reductions in Particulate Matter and Ozone Associated with the
Proposed IAQR 4-77
4-17 Results of Human Health and Welfare Benefits Valuation for the
Proposed IAQR (millions of 1999 dollars) 4-78
5-1 Acidification Changes in Water Bodies as a Result of the Inter-State
Air Quality Rule 5-10
6-1 Summary of Benefits, Costs, and Net Benefits of the Inter-State Air
Quality Rule 6-2
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SECTION 1
EXECUTIVE SUMMARY
The Clean Air Act (CAA) contains a number of requirements to address nonattainment of
the fine particulate matter (PM2 5) and the 8-hour ozone national ambient air quality standards
(NAAQS), including requirements that States address interstate transport contributing to such
nonattainment. CAA Section 110(a)(2)(D) requires that the State Implementation Plans
(SIPs) necessary to meet these standards contain adequate provisions to prohibit air pollutant
emissions within those States from "contribut[ing] significantly to nonattainment in, or
interfering] with maintenance by," a downwind State. The EPA is proposing a rule to
reduce interstate transport of fine particulate matter and ozone (Inter-State Air Quality Rule
hereinafter referred to as IAQR) in 29 States and the District of Columbia to ensure that SIPs
provide for necessary regional reductions of emissions of sulfur dioxide (SO2) and/or
nitrogen oxides (N0x), that are important precursors of PM2 5 (NOX and SO2) and ozone
(NOX). The EPA is proposing that emissions reductions be implemented in two phases, with
the first phase hi 2010 and the second phase hi 2015.
This document presents the health and welfare benefits of the IAQR and compares the
benefits of this proposal to the estimated costs of implementing the rule in 2010 and 2015.
Significant health and welfare benefits are likely to occur as a result of this rule. Thousands
of deaths and other serious health effects would be prevented each year. The EPA is able to
monetize annual benefits of approximately $58 billion in 2010 and approximately $84 billion
hi 2015. Table 1-1 presents the primary estimates of reduced incidence of PM- and ozone-
related health effects for the years 2010 and 2015 for the regulatory control strategy, hi
interpreting the results, it is important to keep in mind the limited set of effects we are able to
monetize. Specifically, the table lists the PM- and ozone-related benefits associated with the
reduction of ambient PM and ozone levels. These benefits are substantial both hi incidence
and dollar value. In 2010, we estimate that reduction in exposure to PM2 5 will result in
approximately 9,600 fewer premature deaths annually associated with PM2 5, as well as 5,200
fewer cases of chronic bronchitis, 13,000 fewer nonfatal heart attacks (acute myocardial
infarctions), 8,900 fewer hospitalizations (for respiratory and cardiovascular disease
combined), and significant reductions in days of restricted activity due to respiratory illness
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Table 1-1. Estimated Reductions in Incidence of Health Effects
Endpoint
Premature Mortality-adult
Mortality-infant
Chronic bronchitis
Acute myocardial infarction-total
Hospital admissions - respiratory
Hospital admissions - cardiovascular
Emergency room visits, respiratory
Acute bronchitis
Lower respiratory symptoms
Upper respiratory symptoms
Asthma exacerbation
Acute respiratory symptoms (MRADs)
Work loss days
School loss days
Constituent
PM2.5
PM2.5
PM2.5
PM2.5
PM2.5, 03
PM2,
PM2.5, 03
PM2.5
PM2,
PM2,
PM2.5
PM2.5, 0,
PM2.5
03
2010 Estimated
Reduction
9,600
22
5,200
13,000
5,200
3,700
7,100
12,000
140,000
490,000
190,000
6,400,000
1,000,000
180,000
2015 Estimated
Reduction
13,000
29
6,900
18,000
8,100
5,000
9,400
16,000
190,000
620,000
240,000
8,500,000
1,300,000
390,000
MRADs = minor restricted activity days
(with an estimate of 6.4 million fewer cases). We also estimate substantial health
improvements for children from reductions in upper and lower respiratory illnesses, acute
bronchitis, and asthma attacks. Ozone health-related benefits are expected to occur during
the summer ozone season (usually ranging from May to September in the eastern U.S.).
Based on modeling for 2010, ozone-related health benefits are expected to include 1,000
fewer hospital-admissions for respiratory illnesses, 120 fewer emergency room admissions
for asthma, 280,000 fewer days with restricted activity levels, and 180,000 fewer days where
children are absent from school because of illnesses. In addition, recent reports by Thurston
and Ito (2001) and the World Health Organization (WHO) support an independent ozone
mortality impact, and the EPA Science Advisory Board has recommended that the EPA
reevaluate the ozone mortality literature for possible inclusion in the estimate of total
benefits. Based on these new analyses and recommendations, EPA is sponsoring three
independent meta-analyses of the ozone-mortality epidemiology literature to inform a
determination on inclusion of this important health endpoint. Upon completion and
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peer-review of the meta-analyses, EPA will determine whether benefits of reductions in
ozone-related mortality will be included in the benefits analysis for the final IAQR.
Table 1-2 presents the estimated monetary value of reductions in the incidence of health
and welfare effects. PM-related health benefits and ozone benefits are estimated to be
approximately $56.9 billion and $82.4 billion annually in 2010 and 2015, respectively.
Estimated annual visibility benefits in Southeastern Class I areas brought about by the IAQR
are estimated to be $880 million in 2010 and $1.4 billion in 2015. All monetized estimated
values are stated in 1999$. Table 1-3 presents the total annual monetized benefits for the
years 2010 and 2015. This table also indicates with a "B" those additional health and
environmental effects that we were unable to quantify or monetize. These effects are additive
to the estimate of total benefits, and the EPA believes there is considerable value to the
public of the benefits that could not be monetized. A listing of the benefit categories that
could not be quantified or monetized in our estimate is provided in Table 1-4. Major benefits
not quantified for this proposed rule include the value of increases in yields of agricultural
crops and commercial forests, value of improvements in visibility in places where people live
and work and recreational areas outside of federal Class I areas, and value of reductions in
nitrogen and acid deposition and the resulting changes in ecosystem functions.
In summary, EPA's primary estimate of the annual benefits of the rule is approximately
$58 + B billion in 2010. In 2015, total monetized annual benefits are approximately $84 + B
billion. These estimates account for growth in the willingness to pay for reductions in
environmental health risks with growth in real gross domestic product (GDP) per capita
between the present and the years 2010 and 2015.
1.1 Benefit-Cost Comparison
The estimated annual social benefits of the rule are compared to the annual estimated cost
to implement the proposed rule in Table 1-3. Estimates of the annual costs of implementing
the rule are $3 and $4 billion in 2010 and 2015, respectively (1999$). For further
information concerning the costs of the proposed rule, please see "Preliminary Analysis of
the Costs of the Inter-State Air Quality Rule—January 2004."
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Table 1-2. Estimated Monetary Value of Reductions in Incidence of Health and
Welfare Effects (millions of 1999$)
Endpoint
Preamature Mortality-adult
Chronic bronchitis
Acute myocardial infarction
Acute respiratory symptoms
(MRADs)
Work loss days
Mortality-infant
Hospital admissions, respiratory
Hospital admissions, cardiovascular
School loss days
Worker productivity
Asthma exacerbation
Acute bronchitis
Lower respiratory symptoms
Upper respiratory symptoms
Emergency room visits, respiratory
Visibility, Southeastern Class I areas
TOTAL + B*
Constituent
PM2.5
PM2.5
PM2.5
PM2.5, 03
PM2.5
PM2.5
PM2 5, 03
PM2.5
03
03
PM2.5
PM2.5
PM2.5
PM2.5
PM2.5, 03
Light
extinction
2010 Estimated
Monetary Value of
Reductions
$53,000
$1,900
$1,100
$320
$140
$130
$85
$78
$13
$8.0
$8.0
$4.3
$2.3
$13
$2.0
$880
$58,000
2015 Estimated
Monetary Value of
Reductions
$77,000
$2,700
$1,500
$440
$170
$180
$130
$110
$28
$17
$11
$5.7
. $3.0
$17
$2.6
$1,400
$84,000
MRADs= minor restricted activity days
B= nonmonetized benefits
* Note total dollar benefits are rounded to the nearest billion and column totals may not add due to rounding.
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Table 1-3. Summary of Annual Benefits, Costs, and Net Benefits of the Inter-State Air
Quality Rule
20102015
Description (billions of 1999 dollars) (billions of 1999 dollars)
Social costs8
Social benefits biC
Ozone-related benefits
PM-related health benefits
Visibility benefits
Net benefits (benefits-costs)0'11
$2.9
$0.1
$56.8 + B
$0.9
$55 + B
$3.7
$0.1
$82.3 + B
$1.4
$80 + B
a . Note that costs are the annual total costs of reducing pollutants including NOX and SO2.
b As the table indicates, total benefits are driven primarily by PM-related health benefits. The reduction in
premature fatalities each year accounts for over 90 percent of total benefits. Benefits in this table are
associated with NOX and SO2 reductions.
c Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all
unquantified benefits and disbenefits. Potential benefit categories that have not been quantified and
monetized are listed in Table 1-4.
d Net benefits are rounded to the nearest billion. Columnar totals may not sum due to rounding.
Thus, the annual net benefit (social benefits minus social costs) of the program is
approximately $55 + B billion in 2010 and $80 + B billion in 2015. Therefore,
implementation of the proposed rule is expected to provide society with a net gain hi social
welfare based on economic efficiency criteria. As Table 1-2 shows, although mortality
benefits account for over 90 percent of total monetized benefits, the economic value of
morbidity benefits alone exceed the cost of the proposed rule. As discussed in section IX of
the notice for this rulemaking, we did not complete air quality modeling that precisely
matches the IAQR region. We anticipate that any differences in the estimates presented due
to the modeling region analyzed will be small.
Every benefit-cost analysis examining the potential effects of a change in environmental
protection requirements is limited to some extent by data gaps, limitations in model
capabilities
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Table 1-4. Additional Nonmonetized Benefits of the Inter-State Air Quality Rule
Pollutant
Unqualified Effects
Ozone Health
Ozone Welfare
PM Health
PM Welfare
Nitrogen and Sulfate
Deposition Welfare
Mercury Health
Mercury Deposition
Welfare
Premature mortality
Increased airway responsiveness to stimuli
Inflammation in the lung
Chronic respiratory damage
Premature aging of the lungs
Acute inflammation and respiratory cell damage
Increased susceptibility to respiratory infection
Non-asthma respiratory emergency room visits
Decreased yields for commercial forests
Decreased yields for fruits and vegetables
Decreased yields for commercial and non-commercial crops
Damage to urban ornamental plants
Impacts on recreational demand from damaged forest aesthetics
Damage to ecosystem functions
Low birth weight
Changes in pulmonary function
Chronic respiratory diseases other than chronic bronchitis
Morphological changes
Altered host defense mechanisms
Non-asthma respiratory emergency room visits
Visibility in many Class I areas
Residential and recreational visibility in non-Class I areas
Soiling and materials damage
Damage to ecosystem functions
Impacts of acidic sulfate and nitrate deposition on commercial forests
Impacts of acidic deposition to commercial freshwater fishing
Impacts of acidic deposition to recreation in terrestrial ecosystems
Reduced existence values for currently healthy ecosystems
Impacts of nitrogen deposition on commercial fishing, agriculture, and forests
Impacts of nitrogen deposition on recreation in estuarine ecosystems
Damage to ecosystem functions
Neurological disorders
Learning disabilities
Developmental delays
Potential cardiovascular effects*
Altered blood pressure regulation*
Increased heart rate variability*
Myocardial infarction*
Potential reproductive effects*
Impact on birds and mammals (e.g., reproductive effects)
Impacts to commercial, subsistence, and recreational fishing
Reduced existence values for currently healthy ecosystems
' Premature mortality associated with ozone is not separately included in this analysis.
* These are potential effects as the literature is either contradictory or incomplete.
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(such as geographic coverage), and uncertainties in the underlying scientific and economic
studies used to configure the benefit and cost models. Deficiencies in the scientific literature
often result in the inability to estimate quantitative changes hi health and environmental
effects, such as potential increases in fish populations due to reductions in nitrogen loadings
in sensitive estuaries. Deficiencies in the economics literature often result in the inability to
assign economic values even to those health and environmental outcomes that can be
quantified. Although these general uncertainties in the underlying scientific and economics
literatures (that can cause the valuations to be higher or lower) are discussed in detail in the
economic analyses and its supporting documents and references, the key uncertainties that
have a bearing on the results of the benefit-cost analysis of this proposed rule include the
following:
• the exclusion of potentially significant benefit categories (such as health and
ecological benefits of reductions in mercury emissions),
• errors in measurement and projection for variables such as population growth and
baseline incidence rates,
• uncertainties in the estimation of future-year emissions inventories and air quality,
• variability in the estimated relationships of health and welfare effects to changes
hi pollutant concentrations,
• uncertainties in exposure estimation,
• uncertainties in the size of the effect estimates linking air pollution and health
endpoints,
• uncertainties about relative toxicity of different components within the complex
mixture, and
• uncertainties associated with the effect of potential future actions to limit
emissions.
Despite these uncertainties, we believe the benefit-cost analysis provides a reasonable
indication of the expected economic benefits of the proposed rulemaking in future years
under a set of reasonable assumptions.
In addition, hi valuing reductions in premature fatalities associated with PM, we used a
value of $5.5 million per statistical life. This represents a central value consistent with a
range of values from $1 to $10 million suggested by recent meta-analyses of the wage-risk
value of statistical life (VSL) literature.
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The benefits estimates generated for the Proposed IAQR are subject to a number of
assumptions and uncertainties, which are discussed throughout the document. As Table 1-2
indicates, total benefits are driven primarily by the reduction in premature fatalities each year,
which account for over 90 percent of total benefits. For example, key assumptions
underlying the primary estimate for the mortality category include the following:
(1) Inhalation of fine particles is causally associated with premature death at
concentrations near those experienced by most Americans on a daily basis.
Although biological mechanisms for this effect have not yet been definitively
established, the weight of the available epidemiological evidence supports an
assumption of causality.
(2) All fine particles, regardless of their chemical composition, are equally potent in
causing premature mortality. This is an important assumption, because PM
produced via transported precursors emitted from EGUs may differ significantly
from direct PM released from automotive engines and other industrial sources, but
no clear scientific grounds exist for supporting differential effects estimates by
particle type.
(3) The C-R function for fine particles is approximately linear within the range of
ambient concentrations under consideration. Thus, the estimates include health
benefits from reducing fine particles in areas with varied concentrations of PM,
including both regions that are in attainment with fine particle standard and those
that do not meet the standard.
Although recognizing the difficulties, assumptions, and inherent uncertainties in the overall
enterprise, these analyses are based on peer-reviewed scientific literature and up-to-date
assessment tools, and we believe the results are highly useful in assessing this proposal.
We were unable to quantify or monetize a number of health and environmental effects. A
full appreciation of the overall economic consequences of the proposed rule requires
consideration of all benefits and costs expected to result from the proposed rule, not just
those benefits and costs that could be expressed here in dollar terms. A listing of the benefit
categories that could not be quantified or monetized in our estimate is provided in Table 1-4.
These effects are denoted by "B" in Table 1-3 above and are additive to the estimates of
benefits.
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We are unable to quantify changes in levels of methylmercury contamination in fish
associated with reductions in mercury emissions for this proposal. However, this proposal is
anticipated to decrease annual EGU mercury emissions nationwide by 10.6 tons in 2010 or
approximately 23.5 percent, by 11.8 tons in 2015 or 26.3 percent, and by 14.3 tons or 32
percent in 2020. Emission reduction percentage decreases are based upon expected mercury
emissions changes from fossil-fired EGUs larger than 25 megawatt capacity. In a separate
action, EPA is proposing to regulate mercury and nickel from certain types of electric
generating units using the maximum achievable control technology (MACT) provisions of
section 112 of the CAA or, in the alternative, using the performance standards provisions
under section 111 of the CAA. This proposal will have implications for mercury reductions,
and potential interactions may exist between the rulemakings. Information concerning
potential interactions in the two rulemakings is discussed in the notice for proposed
rulemaking for the CAA Section 112 proposal and in the document Benefit Analysis of the
CAA Section 112 Proposal to Reduce Mercury Emissions included in the docket for the
rulemaking.
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SECTION 2
INTRODUCTION AND BACKGROUND
For this rulemaking, the EPA has assessed the role that transported emissions from
upwind States play in contributing to unhealthy levels of PM2 5 and 8-hour ozone in
downwind States. Based on this assessment, the EPA is proposing emissions reduction
requirements that would apply to upwind States under the Clean Air Act. This report
assesses the health and welfare benefits of the proposed rule. This document presents the
health and welfare benefits of the IAQR and compares the benefits of this proposal to the
estimated costs of implementing the rule in 2010 and 2015. Significant health and welfare
benefits are likely to occur as a result of this rule, and these benefits are enumerated in this
document. This chapter contains background information relative to the rule and an outline
of the chapters of the report.
2.1 Background
Congress recognized that interstate pollution transport from upwind States can contribute
to unhealthy pollution levels in downwind States. Therefore, the CAA contains provisions in
section 110(a)(2)(D) that require upwind States to eliminate emissions that contribute
significantly to nonattainment downwind. Under section 110(a)(2) States are required to
submit plans to the EPA within 3 years of issuance of a revised National Ambient Air Quality
Standard. Among other requirements, these plans are required to prohibit emissions in the
State that contribute significantly to nonattainment downwind.
The EPA's proposal finds that 29 States and the District of Columbia contribute
significantly to nonattainment, or interfere with maintenance, of the NAAQS for PM2 5 and/or
8-hour ozone in downwind States. The EPA is proposing to require these upwind States to
revise then- State Implementation Plans to include control measures to reduce emissions of
SO2 and/or NOX. SO2 is a precursor to PM2 5 formation, and NOX is a precursor to both ozone
and PM2 5 formation. Reducing upwind precursor emissions will assist the downwind PM2 5
and 8-hour ozone nonattainment areas in achieving the NAAQS. Moreover, attainment
would be achieved in a more equitable, cost-effective manner than if each nonattainment area
attempted to achieve attainment by implementing local emissions reductions alone. The
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relevant regions for PM2.5 and ozone significant contribution are depicted in Figures 2-1 and
2-2, respectively.
2.2 Regulated Entities
This action does not propose to directly regulate emissions sources. Instead, it proposes
to require States to revise their SIPs to include control measures to reduce emissions of NOX
and SO2. The proposed emission reduction requirements that would be assigned to the States
are based on controls that are known to be highly cost effective for electric generation units
(EGUs). However, States would have the flexibility to choose what sources to control.
While the EPA is soliciting comments on the potential for pollution control from other
sources, the analysis conducted assumes controls for EGUs only.
2.3 Control Scenario
The analysis conducted assumes that a cap-and-trade program will be used to achieve the
level of emission control requirements desired. The EPA would establish regional emission
budget determinations for SO2 and NOX to address the transport problem. In this proposal,
these requirements would effectively establish emission caps in 2010 for SO2 and NOX of 3.9
million tons and 1.6 million tons, respectively. These budgets would be lowered in 2015 to
provide SO2 and NOX emission caps of 2.7 million tons and 1.3 million tons, respectively in
the proposed control region. These quantities were derived by calculating the amount of
emissions of SO2 and NOX that the EPA believes can be controlled from large EGUs in a
highly cost-effective manner. When fully implemented, this would result in nationwide SO2
emissions of approximately 3.5 million tons. This is significantly lower than the 8.95 million
tons of SO2 emissions allowed under the current Title IV Acid Rain SO2 Trading Program.
The EPA expects that States will elect to join a regional cap-and-trade program for these
pollutants.
2.4 Cost of Emission Controls
The EPA analyzed the costs of IAQR using the Integrated Planning Model (IPM). The
EPA has used the IPM to analyze the impacts of regulations on the power sector. A
description of the methodology used to model the costs and economic impacts to the power
sector may be obtained in "Preliminary Analysis of the Costs of the Inter-State Air Quality
Rule" January 2004. It is estimated that the annual cost of implementing this proposal in
2010 is $2.9 billion and in 2015 is $3.7 billion in the transport region (1999$).
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i.\ J States where NOx control is needed for ozone and PM
States where NOx control is not needed for ozone, only PM
Note SO2 controls are needed in all shaded areas
States where NOX control is not needed for ozone, only PM.
States where NOX control is needed for ozone and PM
Figure 2-1. States Identified as Having Significant Contribution to PM2.5
2.5 Organization of this Report
This document describes the health and welfare benefits of the proposed rule. The
document is organized as follows:
• Chapter 3, Emissions and Air Quality Impacts, describes emission inventories and
air quality modeling that are essential inputs into the benefits assessment.
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West of 100°| East of 100'
Not Significant
Significant SIP Call States
Significant Non-Si P Call States
Deferred States
Figure 2-2. States Identified as Having Significant Contribution to Ozone
Chapter 4, Benefits Analysis and Results, describes the methodology and results
of the benefits analysis.
Chapter 5, Qualitative Assessment of Nonmonetized Benefits, describes benefits
that are not monetized for this rulemaking.
Chapter 6, Comparison of Benefits and Costs, provides a comparison of the
monetized benefits and estimated annual costs of the proposed control scenario.
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SECTION 3
EMISSIONS AND AIR QUALITY IMPACTS
This chapter summarizes the emissions inventories and air quality modeling that serve as
the inputs to the benefits analysis of this proposed rule as detailed in Chapter 4. In summary,
given baseline and post-control emissions inventories for the emission species expected to
impact ambient air quality, we use sophisticated photochemical air quality models to estimate
baseline and post-control ambient concentrations of ozone and PM and deposition of nitrogen
and sulfur for each year. The estimated changes in ambient concentrations are then combined
with monitoring data to estimate population level exposures to changes in ambient
concentrations for use in estimating health effects. Modeled changes in ambient data are also
used to estimate changes in visibility and changes in other air quality statistics that are
necessary to estimate welfare effects.
The initial section of this chapter provides a summary of the baseline emissions
inventories and the emissions reductions that were modeled for this rule. The next section
provides a summary of the methods for and results of estimating air quality for the 2010 and
2015 base cases and control scenarios for the purposes of the benefit analysis. There are
separate sections for PM, ozone, and visibility.
3.1 Emissions Inventories and Estimated Emissions Reductions
The technical support document for emissions inventories discusses the development of
the 2001, 2010 and 2015 baseline emissions inventories for the benefits analysis of this
proposed rule. The emission sources and the basis for current and future-year inventories are
listed in Table 3-1. Tables 3-2 and 3-3 summarize the baseline emissions of NOX and SO2
and the change in the emissions from EGUs that were used in modeling air quality changes.
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Table 3-1. Emissions Sources and Basis for Current and Future-Year Inventories
Emissions Source
2001 Base Year
Future-Year Base Case
Projections
Utilities
Non-Utility Point and Area
sources
Highway vehicles
Nonroad engines (except
locomotives, commercial
marine vessels, and aircraft)
2001 CEM data
Straight-line projections from
1996 NEI
Version 3.12 (point)
Version 3.11 (area)
MOBILESb model with
MOBILE6 adjustment factors
for VOC and NOX;
PARTS model for PM
NONROAD2002 model
Integrated Planning Model
(IPM)
BEA growth projections
VMT projection data
BEA and Nonroad equipment
growth projections
Note: Full description of data, models, and methods applied for emissions inventory development and
modeling are provided in Emissions Inventory TSD (EPA, 2003a).
3.2 Air Quality Impacts
This section summarizes the methods for and results of estimating air quality for the 2010
and 2015 base cases and control scenarios for the purposes of the benefit analysis. EPA has
focused on the health, welfare, and ecological effects that have been linked to air quality
changes. These air quality changes include the following:
1. Ambient particulate matter (PM10 and PM2 5)-as estimated using a national-scale
version of the REgional Modeling System for Aerosols and Deposition
(REMSAD);
2. Ambient ozone-as estimated using regional-scale applications of the
Comprehensive Air Quality Model with Extensions (CAMx); and
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3. Visibility degradation (i.e., regional haze), as developed using empirical estimates
of light extinction coefficients and efficiencies in combination with REMSAD
modeled reductions in pollutant concentrations.
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Table 3-2. Summary of Modeled Baseline Emissions for Lower 48 States
Pollutant Emissions (tons)
Source
2001 Baseline
EGUs
Non-EGUs
Area
Mobile
Nonroad
Total, All Sources
20 10 Base Case
EGUs
Non-EGUs
Area
Mobile
Nonroad
Total, All Sources
20 15 Base Case
EGUs
Non-EGUs
Area
Mobile
Nonroad
Total, All Sources
NOX
4,824,967
3,180,835
2,220,728
8,694,038
4,059,278
22,979,846
3,943,438
3,228,201
2,225,898
4,931,947
3,404,962
17,734,447
. 4,008,241
3,307,415
2,235,712
3,458,279
2,903,048
15,912,695
S02
10,714,558
3,696,048
1,379,810
261,526
531,203
16,583,145
9,856,926
3,799,163
1,367,643
29,790
236,446
15,289,969
9,222,097
3,893,813
1,369,925
32,551
232,644
14,751,030
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Table 3-3. Summary of Modeled Emissions Changes for the Proposed Interstate Air
Quality Rule: 2010 and 2015
Pollutant
Item NOX SOZ
2010 Emission Changes'
Absolute Tons 1,373,919 3,750,219
Percentage of ECU Emissions 34.8% 38.1%
Percentage of All Manmade Emissions 7.8% 24.5%
2015 Emission Changes"
Absolute Tons 1,704,065 3,820,393
Percentage of ECU Emissions 42.5% 41.4%
Percentage of All Manmade Emissions 10.7% 25.9%
1 Note that the emission changes only occur within the affected transport region; however, the percent
reductions reflect the change as a share of baseline emissions for the lower 48 states as presented in
Table 3-2.
The air quality estimates in this section are based on the emission changes summarized in
the preceding section. These air quality results are in turn associated with human populations
and ecosystems to estimate changes in health and welfare effects. In Section 3.2.1, we
describe the estimation of PM air quality using REMSAD, and in Section 3.2.2, we cover the
estimation of ozone air quality using CAMx. Lastly, in Section 3.2.3, we discuss the
estimation of visibility degradation.
3.2.1 PM Air Quality Estimates
We use the emissions inputs summarized above with a national-scale version of the
REgional Model System for Aerosols and Deposition (REMSAD) to estimate PM air quality
in the contiguous U.S. REMSAD is a three-dimensional grid-based Eulerian air quality
model designed to estimate annual particulate concentrations and deposition over large
spatial scales (e.g., over the contiguous U.S.). Consideration of the different processes that
affect primary (directly emitted) and secondary (formed by atmospheric processes) PM at the
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regional scale in different locations is fundamental to understanding and assessing the effects
of proposed pollution control measures that affect ozone, PM and deposition of pollutants to
the surface.1 Because it accounts for spatial and temporal variations as well as differences in
the reactivity of emissions, REMSAD is useful for evaluating the impacts of the proposed
rule on U.S. PM concentrations.
REMSAD was peer-reviewed in 1999 for EPA as reported in "Scientific Peer-Review of
the Regulatory Modeling System for Aerosols and Deposition" (Seigneur et al., 1999).
Earlier versions of REMSAD have been employed for the EPA's Prospective 812 Report to
Congress, EPA's Heavy Duty (HD) Engine/Diesel Fuel rule, and EPA's air quality
assessment of the Clear Skies Initiative. Version 7.06 of REMSAD was employed for this
analysis and is fully described in the air quality modeling technical support document (EPA,
2003b). This version reflects updates in the following areas to improve performance and
address comments from the 1999 peer-review:
1. Gas phase chemistry updates to "micro-CB4" mechanism including new treatment
for the NO3 and N2O5 species and the addition of several reactions to better
account for the wide ranges in temperature, pressure, and concentrations that are
encountered for regional and national applications.
2. PM chemistry updates to calculate participate nitrate concentrations through use
of the MARS-A equilibrium algorithm and internal calculation of secondary
organic aerosols from both biogenic (terpene) and anthropogenic (estimated
aromatic) VOC emissions.
3. Aqueous phase chemistry updates to incorporate the oxidation of SO2 by O3 and
O2 and to include the cloud and rain liquid water content from MM5
meteorological data directly in sulfate production and deposition calculations.
4. Calculation of the production of secondary organic aerosols (SOA) due to
atmospheric chemistry processes has been added for both anthropogenic and
biogenic organics.
'Given the focus of this rule on secondarily formed particles it is important to employ a Eulerian model such as
REMSAD. The impact of secondarily formed pollutants typically involves primary precursor emissions
from a multitude of widely dispersed sources, and chemical and physical processes of pollutants are best
addressed using an air quality model that employs an Eulerian grid model design.
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As discussed in the Air Quality Modeling TSD, the model tends to underestimate
observed PM2 5 concentrations nationwide.
Our analysis applies the modeling system to the entire U.S. for the six emissions
scenarios: a 1996 baseline year for performance evaluation, a 2001 baseline projection, a
2010 baseline projection and a 2010 projection with controls, a 2015 baseline projection and
a 2015 projection with controls. REMSAD simulates every hour of every day of the year
and, thus, requires a variety of input files that contain information pertaining to the modeling
domain and simulation period. These include gridded, 1-hour average emissions estimates
and meteorological fields, initial and boundary conditions, and land-use information. As
applied to the contiguous U.S., the model segments the area within the region into square
blocks called grids (roughly equal in size to counties), each of which has several layers of air
conditions. Using this data, REMSAD generates predictions of 1-hour average PM
concentrations for every grid. As discussed in the Air Quality Modeling TSD, we use the
relative predictions from the model by combining the 2001 base-year and each future-year
scenario with speciated ambient air quality observations to determine the expected change in
2010 or 2015 concentrations due to the rule. After completing this process, we then
calculated daily and seasonal PM air quality metrics as inputs to the health and welfare C-R
functions of the benefits analysis. The following sections provide a more detailed discussion
of each of the steps in this evaluation and a summary of the results.
3.2.1.1 Modeling Domain
The PM air quality analyses employed the modeling domain used previously in support of
Clear Skies air quality assessment. As shown in Figure 3-1, the modeling domain
encompasses the lower 48 States and extends from 126 degrees to 66 degrees west longitude
and from 24 degrees north latitude to 52 degrees north latitude. The model contains
horizontal grid-cells across the model domain of roughly 36 km by 36 km. There are 12
vertical layers of atmospheric conditions with the top of the modeling domain at 16,200
meters. The 36 by 36 km horizontal grid results in a 120 by 84 grid (or 10,080 grid-cells) for
each vertical layer. Figure 3-2 illustrates the horizontal grid-cells for Maryland and
surrounding areas.
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Figure 3-1. REMSAD Modeling Domain for Continental United States
Note: Gray markings define individual grid-cells in the REMSAD model.
3.2.1.2 Simulation Periods
For use in this benefits analysis, the simulation periods modeled by REMSAD included
separate full-year application for each of the six emissions scenarios, i.e., 1996 and 2001
baseline years and the 2010 and 2015 base cases and control scenarios.
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42
93
104
Figure 3-2. Example of REMSAD 36 x 36km Grid-cells for Maryland Area
3.2.1.3 Model Inputs
REMSAD requires a variety of input files that contain information pertaining to the
modeling domain and simulation period. These include gridded, 1-hour average emissions
estimates and meteorological fields, initial and boundary conditions, and land-use
information. Separate emissions inventories were prepared for the 1996 and 2001 baseline
years and each of the future-year base cases and control scenarios. All other inputs were
specified for the 1996 baseline model application and remained unchanged for each future-
year modeling scenario.
REMSAD requires detailed emissions inventories containing temporally allocated
emissions for each grid-cell in the modeling domain for each species being simulated. The
previously described annual emission inventories were preprocessed into model-ready inputs
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through the SMOKE emissions preprocessing system. Details of the preprocessing of
emissions through SMOKE as provided in the emissions inventory TSD. Meteorological
inputs reflecting 1996 conditions across the contiguous U.S. were derived from Version 5 of
the Mesoscale Model (MM5). These inputs included horizontal wind components (i.e., speed
and direction), temperature, moisture, vertical diffusion rates, and rainfall rates for each grid
cell in each vertical layer. Details of the annual 1996 MM5 modeling are provided in Olerud
(2000).
A postprocessor called MM5REMSAD was developed to convert the MM5 data into the
appropriate REMSAD grid coordinate systems and file formats. This postprocessor was used
to develop the hourly average meteorological input files from the MM5 output.
Documentation of the MM5REMSAD code and further details on the development of the
input files are contained in Mansell (2000). A more detailed description of the development
of the meteorological input data is provided in the Air Quality TSD, which is located in the
docket for this rule.
The modeling specified initial species concentrations and lateral boundary conditions to
approximate background concentrations of the species; for the lateral boundaries the
concentrations varied (decreased parabolically) with height. These initial conditions reflect
relatively clean background concentration values. Terrain elevations and land use
information was obtained from the U.S. Geological Survey database at 10 km resolution and
aggregated to the roughly 36 km horizontal resolution used for this REMSAD application.
The development of model inputs is discussed in greater detail in the Air Quality TSD, which
is available in the docket for this rule.
3.2.1:4 Model Performance for Paniculate Matter (PM)
The purpose of the base year PM air quality modeling was to reproduce the atmospheric
processes resulting in formation and dispersion of fine particulate matter across the U.S. An
operational model performance evaluation for PM2 5 and its related speciated components
(e.g., sulfate, nitrate, elemental carbon etc.) for 1996 was performed in order to estimate the
ability of the modeling system to replicate base year concentrations.
This evaluation is comprised principally of statistical assessments of model versus
observed pairs. The robustness of any evaluation is directly proportional to the amount and
quality of the ambient data available for comparison. Unfortunately, there are few PM25
monitoring networks with available data for evaluation of the PM modeling. Critical
limitations of the 1996 databases are a lack of urban monitoring sites with speciated
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measurements and poor geographic representation of ambient concentration in the Eastern
U.S.
The largest available ambient database for 1996 comes from the Interagency Monitoring
of PRQtected Visual Environments (IMPROVE) network. IMPROVE is a cooperative
visibility monitoring effort between EPA, federal land management agencies, and state air
agencies. Data is collected at Class I areas across the United States mostly at National Parks,
National Wilderness Areas, and other protected pristine areas (IMPROVE 2000). There were
approximately 60 IMPROVE sites that had complete annual PM2 5 mass and/or PM2 5 species
data for 1996. Using the 100th meridian to divide the eastern and western U.S., 42 sites were
located in the West and 18 sites were in the East.
As presented in Table 3-4, the observed IMPROVE data used for the performance
evaluation consisted of PM25 total mass, sulfate ion, nitrate ion, elemental carbon, organic
aerosols, and crustal material (soils). The REMSAD model output species were
postprocessed in order to achieve compatibility with the observation species. The principal
evaluation statistic used to evaluate REMSAD performance is the "ratio of the means." It is
defined as the ratio of the average predicted values over the average observed values. The
annual average ratio of the means was calculated for five individual PM2.5 species as well as
for total PM2.5 mass. The metrics were calculated for all IMPROVE sites across the country
as well as for the East and West individually. The following table shows the ratio of the
annual means. Numbers greater than 1 indicate overpredictions compared to ambient
observations (e.g., 1.23 is a 23 percent overprediction). Numbers less than 1 indicate
underpredictions.
When considering annual average statistics (e.g., predicted versus observed), which are
computed and aggregated over all sites and all days, REMSAD underpredicted fine
particulate mass (PM2 5), by 18 percent. PM2 5 in the Eastern U.S. was underpredicted by 2
percent, while PM2 5 in the West was underpredicted by 33 percent. All PM2 5 component
species were underpredicted in the west. In the East, nitrate and crustal material are
overestimated. Elemental carbon shows neither over or underprediction in the east with a
bias near 0 percent. Eastern sulfate is slightly underpredicted with a bias of 12 percent.
Organic aerosols show little or no bias in the East and West.
Given the state of the science relative to PM modeling, it is inappropriate to judge PM
model performance using criteria derived for other pollutants, like ozone. Still, the
performance of the IAQR PM modeling is very encouraging, especially considering that the
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Table 3-4. Model Performance Statistics for REMSAD PM2.5 Species Predictions: 1996
Ratio of the Means (annual average concentrations)
IMPROVE PM Species
PM2.5, total mass
Sulfate ion
Nitrate ion
Elemental carbon
Organic aerosols
Soil/Other
Nationwide
0.82
0.79
1.55
0.86
1.00
1.33
Eastern U.S.
0.98
0.88
2.66
1.01
1.04
3.08
Western U.S.
. 0.67
0.59
0.69
0.71
0.97
0.81
Note: The dividing line between the West and East was defined as the 100th meridian.
results may be limited by our current knowledge of PM science and chemistry, by the
emissions inventories for primary PM and secondary PM precursor pollutants, by the
relatively sparse ambient data available for comparisons to model output, and by
uncertainties in monitoring techniques. The model performance for sulfate is quite
reasonable, which is key to the analysis due to the importance of SO2 emissions reductions in
the IAQR control strategy. Additional details, including comparisons to other monitoring
networks, can be found in the Air Quality Modeling TSD.
3.2.1.5 Converting REMSAD Outputs to Benefits Inputs
REMSAD generates predictions of hourly PM concentrations for every grid. The
particulate matter species modeled by REMSAD include a primary coarse fraction
(corresponding to PM in the 2.5 to 10 micron size range), a primary fine fraction
(corresponding to PM less than 2.5 microns in diameter), and several secondary particles
(e.g., sulfates, nitrates, and organics). PM25 is calculated as the sum of the primary fine
fraction and all of the secondarily-formed particles. Future-year estimates of PM2 5 were
calculated using relative reduction factors (RRFs) applied to 2000-2002 PM2 5 design values
(EPA, 2003b). The procedures for determining the RRFs are similar to those in EPA's draft
guidance for modeling the PM2 5 standard (EPA, 1999a). The guidance recommends that
model predictions be used in a relative sense to estimate changes expected to occur in each
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major PM2 5 species. These species are sulfate, nitrate, organic carbon, elemental carbon,
crustal and un-attributed mass which is defined as the difference between measured PM2 5 and
the sum of the other five components. The procedure for calculating future year PM2 5 design
values is called the "Speciated Modeled Attainment Test (SMAT)". EPA previously used
this procedure to estimate the ambient impact of the Clear Skies Act emissions controls.
The SMAT procedure was performed using the base year 2001 scenario and each of the
future-year scenarios. The SMAT approach uses temporally scaled speciated PM2.5 monitor
data from 2001-2002, reconstructed into total PM2.5 mass based on 2000-2002 design
values, and kriged to 12 kilometer grids (nested within the standard 36 km REMSAD grid
structure). Temporal scaling is based on ratios of future modeled REMSAD data to 2001
REMSAD model data, using REMSAD modeling conducted at the 36 km grid resolution.
SMAT output files include both quarterly mean and annual mean PM2.5 mass results, which
are then manipulated within SAS to produce a BenMAP input file containing 364 daily
values (created by replicating the quarterly mean values for each day of the appropriate
season).
3.2.1.6 PMAir Quality Results
Table 3-5 provides a summary of the predicted ambient PM25 concentrations for the 2010
and 2015 base cases and changes associated with proposed rule. The REMSAD results
indicate that the predicted change in PM concentrations is composed almost entirely of
reductions in fine particulates (PM2 5) with little or no reduction in coarse particles (PM10 less
PM2 5). Therefore, the observed changes in PM10 are composed primarily of changes in PM2 5.
In addition to the standard frequency statistics (e.g., minimum, maximum, average), we
provide the population-weighted average which better reflects the baseline levels and
predicted changes for more populated areas of the nation. This measure, therefore, better
reflects the potential benefits of these predicted changes through exposure changes to these
populations. As shown, the average annual mean concentrations of PM2 5 across populated
eastern U.S. grid-cells declines by roughly 5.6 percent (or 0.6 jig/m3) and 7.5 percent (or 0.8
ug/m3) in 2010 and 2015, respectively. The population-weighted average mean concentration
declined by 6.1 percent (or 0.74 ^g/m3) in 2010 and 7.9 percent (or 0.94 ng/m3) in 2015,
which is much larger in absolute terms than the spatial average for both years. This indicates
the proposed rule generates greater absolute air quality improvements in more populated,
urban areas.
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Table 3-5. Summary of Base Case PM Air Quality and Changes Due to Proposed
Interstate Air Quality Rule: 2010 and 2015
Statistic
PM2.5 (ug/m3)
Minimum Annual Mean
Maximum Annual Mean
Average Annual Mean
Pop- Weighted Average Annual Mean b
Base Case
5.24
16.88
10.82
12.19
2010
Change*
-0.33
-0.86
-0.61
-0.74
Percent
Change
-6.3%
-5.1%
-5.6%
-6.1%
2015
Base Case
5.13
16.79
10.67
11.99
Change*
-0.33
-1.19
-0.80
-0.94
Percent
Change
-6.4%
-7.1%
-7.5%
-7.9%
* The change is defined as the control case value minus the base case value.
b Calculated by summing the product of the projected REMSAD grid-cell population and the estimated PM concentration,
for that grid-cell and then dividing by the total population.
Table 3-6 provides information on the populations in 2010 and 2015 that will experience
improved PM air quality. There are significant populations that live in areas with meaningful
reductions in annual mean PM2 5 concentrations resulting from the proposed rule. As shown,
in 2015, almost 40 percent of the U.S. population located in the eastern 37 state modeling
domain are predicted, to experience reductions of greater than 1.0 ng/m3. This is an increase
from the 20 percent of the U.S. population that are expected to experience such reductions in
2010. Furthermore, over 7 percent of this population will benefit from reductions in annual
mean PM25 concentrations of greater than 1.5 ug/m3 and almost 2 percent will live in areas
with reductions of greater than 1.75 ug/m3.
3.2.2
Ozone Air Quality Estimates
We use the emissions inputs summarized earlier in this chapter with a regional-scale
version of CAMx to estimate ozone air quality in the Eastern and Western U.S. CAMx is an
Eulerian three-dimensional photochemical grid air quality model designed to calculate the
concentrations of both inert and chemically reactive pollutants by simulating the physical and
chemical processes in the atmosphere that affect ozone formation. Version 3.10 of the
CAMx model was employed for these analyses. Because it accounts for spatial and temporal
variations as well as differences in the reactivity of emissions, CAMx is useful for evaluating
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the impacts of the proposed rule on U.S. ozone concentrations. Although the model tends to
underestimate observed ozone, it exhibits less bias and error than any past regional ozone
Table 3-6. Distribution of PM2.5 Air Quality Improvements Over Population Due to
Proposed Interstate Air Quality Rule: 2010 and 2015
Change in Annual Mean
PM2.5 Concentrations
(ug/m3)
0 > A PM2 5 Cone s 0.25
0.25 > A PM25 Cone <: 0.5
0.5 >APM25Conc <: 0.75
0.75 > APM25Conc s 1.0
1.0>APM25Conc s 1.25
1.25> APM25Conc <. 1.5
1.5>APM25Conc s 1.75
APM25Conc> 1.75
2010 Population
b
Number (millions) Percent (%)
6.1
59.0
57.1
59.3
22.5
11.2
9.0
2.0
2.7%
26.1%
25.3%
26.2%
9.9%
4.9%
4.0%
0.9%
2015 Population
Number (millions)
0.0
29.9
52.9
60.6
34.6
38.0
13.9
4.2
Percent (%)
0.0%
12.8%
22.6%
25.9%
14.8%
16.2%
5.9%
1.8%
" The change is defined as the control case value minus the base case value.
b Population counts and percentages are for the fraction of the national population located in the eastern 37 state modeling
domain considered in modeling health benefits for the rule.
modeling application conducted by EPA (i.e., OTAG, On-highway Tier-2, and HD
Engine/Diesel Fuel).
Our analysis applies the modeling system separately to the Eastern U.S. for six emissions
scenarios: a 1995 baseline projection, a 2001 baseline projection, a 2020 baseline projection
and a 2020 projection with controls, a 2030 baseline projection and a 2030 projection with
controls. The model was applied and evaluated over three episodes that occurred during the
summer of 1995 base year. Subsequently, episodic ozone model runs were made for the
2001 base year scenario and the 2010 and 2015 base and control case scenarios for all
/
episodes. Further discussion of this modeling, including evaluations of model performance
relative to predicted future air quality, is provided in the air quality modeling TSD. As
discussed in chapter 4, we use the relative predictions from the model by combining the 2001
base-year and each future-year scenario with current ambient air quality observations to
determine the expected change in 2010 or 2015 ozone concentrations due to the rule (Abt
Associates, 2003). These results are used solely in the benefits analysis.
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The CAMx modeling system requires a variety of input files that contain information
pertaining to the modeling domain and simulation period. These include gridded, day-
specific emissions estimates and meteorological fields, initial and boundary conditions, and
land-use information. As applied to the Eastern U.S., the model segments the area into
square blocks called grids (roughly equal in size to counties), each of which has several
layers of air conditions that are considered in the analysis. Using this data, the CAMx model
generates predictions of hourly ozone concentrations for every grid. We then calibrate the
results of this process to develop 2010 and 2015 ozone profiles at monitor sites by
normalizing the observations to the observed ozone concentrations at each monitor site. For
areas (grids) without ozone monitoring data, we interpolated ozone values using data from
monitors surrounding the area. After completing this process, we calculated daily and
seasonal ozone metrics to be used as inputs to the health and welfare C-R functions of the
benefits analysis. The following sections provide a more detailed discussion of each of the
steps in this evaluation and a summary of the results.
3.2.2.1 Modeling Domain
The modeling domain representing the Eastern U.S. is the same as that used previously
for OTAG and the On-highway Tier-2 rulemaking. As shown in Figure 3-3, this domain
encompasses most of the Eastern U.S. from the East coast to mid-Texas and consists of two
grids with differing resolutions. The modeling domain extends from 99 degrees to 67
degrees west longitude and from 26 degrees to 47 degrees north latitude. The inner portion
of the modeling domain shown in Figure 3-3 uses a relatively fine grid of 12 km consisting of
nine vertical layers. The outer area has less horizontal resolution, as it uses a 36 km grid with
the same nine vertical layers. The vertical height of the modeling domain is 4,000 meters
above ground level for both areas.
3.2.2.2 Simulation Periods
For use in this benefits analysis, the simulation periods modeled by CAMx included
several multi-day periods when ambient measurements recorded high ozone concentrations.
A simulation period, or episode, consists of meteorological data characterized over a block of
days that are used as inputs to the air quality model. A simulation period is selected to
characterize a variety of ozone conditions including some days with high ozone
concentrations in one or more portions of the U.S. and observed exceedances of the 1-hour
NAAQS for ozone being recorded at monitors. We focused on the summer of 1995 for
selecting the episodes to model because it is a recent time period for which we had model-
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Figure 3-3. CAMx Eastern U.S. Modeling Domain
Note: The inner area represents fine grid modeling at 12 km resolution, while the outer area represents
the coarse grid modeling at 36 km resolution.
ready meteorological inputs and this timeframe contained several periods of elevated ozone
over the Eastern U.S. As detailed in the air quality modeling TSD, this analysis used three
multi-day meteorological scenarios during the summer of 1995 for the model simulations
over the eastern U.S.: June 12-24, July 5-15, and August 7-21. Each of the six emissions
scenarios (1995 base year, 2001 base year, 2010 base and control, 2015 base and control)
were simulated for the selected episodes. These episodes include a three day "ramp-up"
period to initialize the model, but the results for these days are not used in this analysis.
3.2.2.3 Non-emissions Modeling Inputs
The meteorological data required for input into CAMx (wind, temperature, vertical
mixing, etc.) were developed by separate meteorological models. The gridded
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meteorological data for the three historical 1995 episodes were developed using the Regional
Atmospheric Modeling System (RAMS), version 3b. This model provided needed data at
every grid cell on an hourly basis. These meteorological modeling results were evaluated
against observed weather conditions before being input into CAMx and it was concluded that
the model fields were adequate representations of the historical meteorology. A more
detailed description of the settings and assorted input files employed in these applications is
provided in the Air Quality TSD, which is located in the docket for this rule.
The modeling assumed background pollutant levels at the top and along the periphery of
the domain as in Tier 2. Additionally, initial conditions were assumed to be relatively clean
as well. Given the ramp-up days and the expansive'domains, it is expected that these
assumptions will not affect the modeling results, except hi areas near the boundary (e.g.,
Dallas-Fort Worth TX). The other non-emission CAMx inputs (land use, photolysis rates,
etc.) were developed using procedures employed in the Tier 2/OTAG regional modeling. The
development of model inputs is discussed in greater detail in the Air Quality TSD, which is
available in the docket for this rule.
3.2.2.4 Model Performance for Photochemical Ozone
The purpose of the 1995 base year photochemical ozone modeling was to reproduce the
atmospheric processes resulting in the observed ozone concentrations over these domains and
episodes. One of the fundamental assumptions in air quality modeling is that a model which
adequately replicates observed pollutant concentrations in the base year can be used to assess
the effects of future year emissions controls. A series of performance statistics was
calculated for the Eastern U.S. domain as well as the four quadrants and multiple subregions.
The model performance evaluation consisted solely of comparisons against ambient surface
ozone data. There was insufficient data available in terms of ozone precursors or ozone aloft
to allow for a more complete assessment of model performance. Three primary statistical
metrics were used to assess the overall accuracy of the base year modeling simulations.
• Mean normalized bias is defined as the average difference between the hourly
model predictions and observations (paired in space and time) at each monitoring
location, normalized by the magnitude of the observations.
• Mean normalized gross error is defined as the average absolute difference
between the hourly model predictions and observations (paired in space and time)
at each monitoring location, normalized by the magnitude of the observations.
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• Average accuracy of the peak is defined as the average difference between peak
daily model predictions and observations at each monitoring location, normalized
by the magnitude of the observations.
In general, the model tends to underestimate observed ozone. When all hourly observed
ozone values greater than a 60 ppb threshold are compared to their model counterparts for the
30 episode modeling days in the eastern domain, the mean normalized bias is -1.1 percent
and the mean normalized gross error is 20.5 percent. As shown in Table 3-7, the model
generally underestimates observed ozone values for the June and July episodes, but predicts
higher than observed amounts for the August episode.
Table 3-7. Model Performance Statistics for Hourly Ozone in the Eastern U.S. CAMx
Ozone Simulations: 1995 Base Case
Average Accuracy of Mean Normalized Mean Normalized
Episode the Peak Bias Gross Error
June 1995
July 1995
August 1995
-7.3
-3.3
9.6
-8.8
-5.0
8.6
19.6
19.1
23.3
At present, there are no guidance criteria by which one can determine if a regional ozone
modeling exercise is exhibiting adequate model performance. These base case simulations
were determined to be acceptable based on comparisons to previously completed model
rulemaking analyses (e.g., OTAG, Tier-2, and Heavy-Duty Engine). The modeling
completed for this proposal exhibits less bias and error than any past regional ozone
modeling application done by EPA. Thus, the model is considered appropriate for use in
projecting changes in future year ozone concentrations and the resultant health/economic
benefits due to the proposed emissions reductions.
In addition, the CAMx modeling results were also evaluated at a "local" level to ensure
that areas determined to need the emissions reductions based on projected exceedances of the
ozone standard were not unduly influenced by local overestimation of ozone in the model
base year. As detailed in the Air Quality Modeling TSD, performance statistics were
computed for each of 51 local subregjons within the modeling domain. These performance
statistics were compared to the recommended performance ranges for urban attainment
modeling (EPA, 1991). The results indicate that model performance for the June episode was
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within the recommended ranges for 69 percent of the local areas examined. For the July and
August episodes, the percent of local areas with performance within the recommended ranges
was 80 percent and 61 percent, respectively.
3.2.2.5 Converting CAMx Outputs to Full-Season Profiles for Benefits Analysis
This study extracted hourly, surface-layer ozone concentrations for each grid-cell from
the standard CAMx output file containing hourly average ozone values. These model
predictions are used in conjunction with the observed concentrations obtained from the
Aerometric Information Retrieval System (AIRS) to generate ozone concentrations for the
entire ozone season.2'3 The predicted changes in ozone concentrations from the future-year
base case to future-year control scenario serve as inputs to the health and welfare C-R
functions of the benefits analysis, i.e., the Environmental Benefits Mapping and Analysis
Program (BenMAP).
In order to estimate ozone-related health and welfare effects for the contiguous U.S., full-
season ozone data are required for every BenMAP grid-cell. Given available ozone
monitoring data, we generated full-season ozone profiles for each location in the contiguous
48 States in two steps: (1) we combine monitored observations and modeled ozone
predictions to interpolate hourly ozone concentrations to a grid of 8 km by 8 km population
grid-cells, and (2) we converted these full-season hourly ozone profiles to an ozone measure
of interest, such as the daily average.4'5 These methods are described in detail in the benefits
analysis technical support document (Abt Associates, 2003).
3.2.2.6 Ozone Air Quality Results
2The ozone season for this analysis is defined as the 5-month period from May to September; however, to
estimate certain crop yield benefits, the modeling results were extended to include months outside the 5-
month ozone season.
3Based on AIRS, there were 961 ozone monitors with sufficient data, i.e., 50 percent or more days reporting at
least 9 hourly observations per day (8 am to 8 pm) during the ozone season.
*The 8 km grid squares contain the population data used in the health benefits analysis model, BenMAP. See
Chapter 4 for a discussion of this model.
5This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi
Neighbor Averaging (EVNA) spatial interpolation (See Abt Associates (2003) for a more detailed
description).
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This section provides a summary the predicted ambient ozone concentrations from the
CAMx model for the 2010 and 2015 base cases and changes associated with the proposed
rule. Table 3-8 provides those ozone metrics for grid-cells in the Eastern U.S. that enter the
concentration response functions for health benefits endpoints. The population-weighted
average reflects the baseline levels and predicted changes for more populated areas of the
Table 3-8. Summary of CAMx Derived Population-Weighted Ozone Air Quality
Metrics for Health Benefits Endpoints Due to Proposed Interstate Air Quality Rule:
Eastern U.S.
Statistic '
Population-Weighted Average (ppb) *
Daily 1-Hour Maximum Concentration
Daily 5-Hour Average Concentration
Daily 8-Hour Average Concentration
Daily 1 2-Hour Average Concentration
Daily 24-Hour Average Concentration
Base Case
53.32
44.51
43.81
41.28
31.20
2010
Change *
-0.51
-0.42
-0.41
-0.38
-0.28
Percent
Change'
-0.95%
-0.93%
-0.93%
-0.92%
-0.89%
Base Case
52.10
43.65
42.97
40.56
30.83
2015
Change "
-1.05
-0.87
-0.86
-0.80
-0.59
Percent
Change'
-2.02%
-2.00%
-1.99%
-1.98%
-1.91%
" These ozone metrics are calculated at the CAMX grid-cell level for use in health effects estimates based on the results of spatial and
temporal Voronoi Neighbor Averaging. Except for the dairy 24-hour average, these ozone metrics are calculated over relevant time periods
during the daylight hours of the "ozone season," i.e., May through September. For the 5-hour average, the relevant time period is 10 am to
3 pm; fo r the 8 -hr average, it is 9am to 5 pm; and, for the 12-hr average it is 8 am to 8 pm.
* The change is defined as the control case value minus the base case value. The percent change is the "Change" divided by the "Base.
Case," and then multiplied by 100 to convert the value to a percentage.
d Calculated by summing the product of the projected CAMx grid-cell population and the estimated CAMx grid-cell seasonal ozone
concentration, and then dividing by the total population.
nation. This measure, therefore, will better reflect the potential benefits of these predicted
changes through exposure changes to these populations.
3.2.3
Visibility Degradation Estimates
Visibility degradation is often directly proportional to decreases in light transmittal in the
atmosphere. Scattering and absorption by both gases and particles decrease light
transmittance. To quantify changes in visibility, our analysis computes a light-extinction
coefficient, based on the work of Sisler (1996), which shows the total fraction of light that is
decreased per unit distance. This coefficient accounts for the scattering and absorption of
light by both particles and gases, and accounts for the higher extinction efficiency of fine
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particles compared to coarse particles. Fine particles with significant light-extinction
efficiencies include sulfates, nitrates, organic carbon, elemental carbon (soot), and soil
(Sisler, 1996).
Based upon the light-extinction coefficient, we also calculated a unitless visibility index,
called a "deciview," which is used in the valuation of visibility. The deciview metric
provides a scale for perceived visual changes over the entire range of conditions, from clear
to hazy. Under many scenic conditions, the average person can generally perceive a change
of one deciview. The higher the deciview value, the worse the visibility. Thus, an
improvement in visibility is a decrease in deciview value.
Table 3-9. Distribution of Populations Experiencing Visibility Improvements due to
Proposed Interstate Air Quality Rule: 2010 and 2015
Improvements in Visibility*(annual
average deciviews)
0 > A Deciview f. 0.2
0.2 > A Deciview s 0.4
0.4 > A Deciview s 0.6
0.6 > A Deciview <; 0.8
0.8 > A Deciview <; 1.0
A Deciview > 1.0
2010
Number
(millions)
75.6
24.1
46.5
87.7
56.0
14.3
Population
Percent (%)
24.9%
7.9%
15.3%
28.8%
18.4%
4.7%
2015
Number
(millions)
74.8
15.2
25.3
64.7
57.8
79.1
Population
Percent(%)
23.6%
4.8%
8.0%
20.4%
18.2%
25.0%
Table 3-9 provides the distribution of visibility improvements across 2010 and 2015
populations resulting from this proposed rule. The majority of the 2015 U.S. population live
in areas with predicted improvement in annual average visibility of greater than 0.6 deciviews
resulting from the proposed rule. As shown, almost 72 percent of the 2015 U.S. population
are predicted to experience improved annual average visibility of greater than 0.4 deciviews.
Furthermore, roughly 25 percent of the 2015 U.S. population will benefit from reductions in
annual average visibility of greater than 1 deciviews.
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Because the visibility benefits analysis distinguishes between general regional visibility
degradation and that particular to Federally-designated Class I areas (i.e., national parks,
forests, recreation areas, wilderness areas, etc.), we separated estimates of visibility
degradation into "residential" and "recreational" categories.6 The estimates of visibility
degradation for the "recreational" category apply to Federally-designated Class I areas, while
estimates for the "residential" category apply to non-Class I areas. Deciview estimates are
estimated using outputs from REMSAD for the 2010 and 2015 base cases and control
scenarios.
3.2.3.1 Residential Visibility Improvements
Air quality modeling results predict that the proposed Interstate Air Quality Rule will
create improvements in visibility through the country. In Table 3-10, we summarize
residential visibility improvements across the Eastern U.S. in 2010 and 2015. The baseline
annual average visibility for eastern U.S. counties is 21.61 deciviews in 2010. The mean
improvement across eastern U.S. counties is 0.69 deciviews, or almost 3.2 percent. In urban
areas with a population of 250,000 or more, the mean improvement in annual visibility was
similar at 0.71 deciviews in 2010 and ranged from 0.17 to 1.64 deciviews. In rural areas, the
mean improvement in visibility was 0.68 deciviews in 2010 and ranged from 0.19 to 1.69
deciviews.
6 The visibility calculations presented in this section are changes in the annual average visibility for the purpose
of generating monetized benefits. There improvements in visibility should not be confused with the
requirements under the Regional Haze rule to show "reasonable progress" for the 20% best and 20% worst
days to each Class I area. Example Regional Haze calculations for the 20% best and worst days are
contained in the AQMTSD.
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Table 3-10. Summary of Baseline Residential Visibility and Changes by Region: 2010
and 2015 (annual average deciviews)
Regions*
Eastern U.S.
Urban
Rural
Base
Case
21.61
22.78
21.14
2010
Change1"
0.69
0.71
0.68
Percent
Change
3.17%
3.14%
3.18%
2015
Base
Case
21.31
22.50
20.84
Changeb
0.85
0.88
0.84
Percent
Change
3.95%
3.95%
3.96%
* The dividing line between the Eastern and Western U.S. was defined as the 100th meridian.
b An improvement in visibility is a decrease in deciview value. The change is defined as the control case
deciview level minus the base case deciview level.
3.2.3.2 Recreational Visibility Improvements
In Table 3-11, we summarize recreational visibility improvements in 2010 and 2015 in
Federal Class I areas located in the eastern U.S. These recreational visibility regions are
shown in Figure 3-4. As shown, the improvement in visibility for Federal Class I areas in the
Eastern U.S. increases from 3.8 percent, or 0.77 deciviews, in 2010. The predicted absolute
improvement of 0.94 deciviews in 2015 reflects a 4.6 percent change from 2015 baseline
visibility of 20.38 deciviews.
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Table 3-11. Summary of Baseline Recreational Visibility and Changes by Region: 2010
and 2015 (annual average deciviews)
2010
Class I Visibility Regions"
Eastern U.S.
Southeast
Northeast/Midwest
Base
Case
20.59
22.04
19.28
Changeb
0.77
0.91
0.65
Percent
Change
3.75%
4.11%
3.38%
2015
Base
Case
20.38
21.80
19.11
Change11
0.94
1.17
0.74
Percent
Change
4.61%
5.35%
3.85%
" Regions are pictured in Figure VI-5 and are defined in the technical support document (see Abt Associates,
2003).
b An improvement in visibility is a decrease in deciview value. The change is defined as the control case
deciview level minus the base case deciview level.
F I Stucfy Region
Transfer Region
Figure 3-4. Recreational Visibility Regions for Continental U.S.
Note: Study regions were represented in the Chestnut and Rowe (1990a, 1990b) studies used in evaluating the
benefits of visibility improvements, while transfer regions used extrapolated study results.
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SECTION 4
BENEFITS ANALYSIS AND RESULTS
This chapter reports the EPA's analysis of a subset of the public health and welfare
impacts and associated monetized benefits to society of the proposed IAQR. The EPA is
required by Executive Order 12866 to estimate the benefits and costs of major new pollution
control regulations. Accordingly, the analysis presented here attempts to answer three
questions: 1) what are the physical health and welfare effects of changes in ambient air
quality resulting from reductions in precursors to particulate matter (PM) including (NOx)
and sulfur dioxide (SO2) emissions? 2) how much are the changes in these effects attributable
to the proposed rule worth to U.S. citizens as a whole in monetary terms? and 3) how do the
monetized benefits compare to the costs? It constitutes one part of the EPA's thorough
examination of the relative merits of this proposed regulation.
The analysis presented in this chapter uses a methodology generally consistent with
benefits analyses performed for the recent analysis of Nonroad Diesel Engines Tier 4
Standards and the proposed Clear Skies Act of 2003 (EPA, 2003). The benefits analysis
relies on three major modeling components:
1) Calculation of the impact that a set of preliminary emissions standards for EGUs
based on a state-level cap and trade program would have on the national inventory
of precursors to PM including SO2 and NOx.
2) Air quality modeling for 2010 and 2015 to determine changes in ambient
concentrations of ozone and particulate matter, reflecting baseline and post-
control emissions inventories.
3) A benefits analysis to determine the changes in human health and welfare, both in
terms of physical effects and monetary value, that result from the projected
changes in ambient concentrations of various pollutants for the modeled
standards.
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A wide range of human health and welfare effects are linked to the emissions of NOx and
SOx from EGUs and the resulting impact on ambient concentrations of ozone and PM.
Potential human health effects linked to PM2.5 range from mortality linked to long-term
exposure to PM, to a range of morbidity effects linked to long-term (chronic) and shorter-
term (acute) exposures (e.g., respiratory and cardiovascular symptoms resulting in hospital
admissions, asthma exacerbations, and acute and chronic bronchitis [CB]). Exposure to
ozone has also been linked to a variety of respiratory effects including hospital admissions
and illnesses resulting in school absences.7 Welfare effects potentially linked to PM include
materials damage and visibility impacts, while ozone can adversely affect the agricultural
and forestry sectors by decreasing yields of crops and forests. Although methods exist for
quantifying the benefits associated with many of these human health and welfare categories,
not all can be evaluated at this time due to limitations in methods and/or data. Table 4-1 lists
the full complement of human health and welfare effects associated with PM and ozone and
identifies those effects that are quantified for the primary estimate, are quantified as part of
the sensitivity analysis (to be completed for the supplemental analysis), and remain
unquantified because of to current limitations in methods or available data.
Figure 4-1 illustrates the major steps in the benefits analysis. Given baseline and post-
control emissions inventories for the emission species expected to affect ambient air quality,
we use sophisticated photochemical air quality models to estimate baseline and post-control
ambient concentrations of ozone and PM, and deposition of nitrogen and sulfur for each year.
The estimated changes in ambient concentrations are then combined with monitoring data to
estimate population-level exposures to changes in ambient concentrations for use in
estimating health effects. Modeled changes hi ambient data are also used to estimate changes
in visibility, and changes in other air quality statistics that are necessary to estimate welfare
effects. Changes in population
7Short-term exposure to ambient ozone has also been linked to premature death. The EPA is currently
evaluating the epidemiological literature examining the relationship between ozone and premature mortality,
sponsoring three independent meta-analyses of the literature. Once this evaluation has been completed and
peer-reviewed, the EPA will consider including ozone-related premature mortality in the primary benefits
analysis for the final rule.
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Figure 4-1. Key Steps in Air Quality Modeling Based Benefits Analysis
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exposure to ambient air pollution are then input to impact functions8 to generate changes in
incidence of health effects, or changes in other exposure metrics are input to dose-response
functions to generate changes in welfare effects. The resulting effects changes are then
assigned monetary values, taking into account adjustments to values for growth in real
income out to the year of analysis (values for health and welfare effects are in general
positively related to real income levels). Finally, values for individual health and welfare
effects are summed to obtain an estimate of the total monetary value of the changes in
emissions.
On September 26, 2002, the National Academy of Sciences (NAS) released a report on its
review of the Agency's methodology for analyzing the health benefits of measures taken to
reduce air pollution. The report focused on the EPA's approach for estimating the health
benefits of regulations designed to reduce concentrations of airborne PM.
In its report, the NAS said that the EPA has generally used a reasonable framework for
analyzing the health benefits of PM-control measures. It recommended, however, that the
Agency take a number of steps to improve its benefits analysis. In particular, the NAS stated
that the Agency should
• include benefits estimates for a range of regulatory options;
• estimate benefits for intervals, such as every 5 years, rather than a single year;
• clearly state the projected baseline statistics used in estimating health benefits,
including those for air emissions, air quality, and health outcomes;
• examine whether implementation of proposed regulations might cause unintended
impacts on human health or the environment;
8The term "impact function" as used here refers to the combination of (a) an effect estimate obtained from the
epidemiological literature, (b) the baseline incidence estimate for the health effect of interest in the modeled
population, (c) the size of that modeled population, and (d) the change in the ambient air pollution metric of
interest. These elements are combined in the impact function to generate estimates of changes in incidence
of the health effect. The impact function is distinct from the concentration response (C-R) function, which
strictly refers to the estimated equation from the epidemiological study relating incidence of the health effect
and ambient pollution. We refer to the specific value of the relative risk or estimated coefficients in the
epidemiological study as the "effect estimate." In referencing the functions used to generate changes in
incidence of health effects for this RIA, we use the term impact function rather than C-R function because
"impact function" includes all key input parameters used in the incidence calculation.
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• when appropriate, use data from non-U.S. studies to broaden age ranges to which
current estimates apply and to include more types of relevant health outcomes;
and
• begin to move the assessment of uncertainties from its ancillary analyses into its
base analyses by conducting probabilistic, multiple-source uncertainty analyses.
This assessment should be based on available data and expert judgment.
Although the NAS made a number of recommendations for improvement in the EPA's
approach, it found that the studies selected by the Agency for use in its benefits analysis were
generally reasonable choices. In particular, the NAS agreed with the EPA's decision to use
cohort studies for estimating premature mortality benefits. It also concluded that the
Agency's selection of the American Cancer Society (ACS) study for the evaluation of PM-
related premature mortality was reasonable, although it noted the publication of new cohort
studies that the Agency should evaluate. Since the publication of the NAS report, the EPA
has reviewed new cohort studies, including reanalyses of the ACS study data and has
carefully considered these new study data in developing the analytical approach for the IAQR
(see below).
In addition to the NAS report, the EPA has also received technical guidance and input
regarding its methodology for conducting PM- and ozone-related benefits analysis from two
additional sources, including the Health Effects Subgroup (HES) of the SAB Council
reviewing the 812 blueprint (SAB-HES, 2003) and the Office of Management and Budget
(OMB) through ongoing discussions regarding methods used in conducting regulatory impact
analyses (RIAs). The SAB HES recommendations include the following (SAB-HES, 2003):
• use of the updated ACS Pope et al. (2002) study rather than the ACS Krewski et
al. study to estimate mortality for the primary analysis;
• dropping the alternative estimate used in earlier RIAs and instead including a
primary estimate that incorporates consideration of uncertainty in key effects
categories such as mortality directly into the estimates (e.g., use of the standard
errors from the Pope et al. [2002] study in deriving confidence bounds for the
adult mortality estimates);
• addition of infant mortality (children under the age of one) into the primary
estimate, based on supporting evidence from the World Health Organization
Global Burden of Disease study and other published studies that strengthen the
evidence for a relationship between PM exposure and respiratory inflamation and
infection in children leading to death;
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• inclusion of asthma exacerbations for children in the primary estimate;
• expansion of the age groups evaluated for a range of morbidity effects beyond the
narrow band of the studies to the broader (total) age group (e.g., expanding a
study population for 7 to 11 year olds to cover the entire child age range of 6 to 18
years).
• inclusion of new endpoints (school absences [ozone], nonfatal heart attacks in
adults [PM], hospital admissions for children under two [ozone]), and suggestion
of a new meta-analysis of hospital admissions (PM10) rather than using a few
PM2.5 studies;9 and
• updating of populations and baseline incidences.
Recommendations from OMB regarding RIA methods have focused on the approach used
to characterize uncertainty in the benefits estimates generated for RIAs, as well as the
approach used to value mortality estimates. The EPA is currently in the process of
developing a comprehensive integrated strategy for characterizing the impact of uncertainty
in key elements of the benefits modeling process (e.g., emissions modeling, air quality
modeling, health effects incidence estimation, valuation) on the results that are generated. A
subset of this effort, which is currently underway, involves an expert elicitation designed to
characterize uncertainty in the estimation of PM-related mortality resulting from both short-
term and longer-term exposure. The EPA will be evaluating the results of this elicitation to
determine its usefulness in characterizing uncertainty in our estimates of PM-related
mortality benefits. As elements of this uncertainty analysis strategy are finalized, it may be
possible to integrate them into later iterations of the analysis completed for the IAQR (e.g.,
the supplemental analysis and final rule).
We are also altering the value of a statistical life (VSL) used in the analysis to reflect new
information in the ongoing academic debate over the appropriate characterization of the value
of reducing the risk of premature mortality. In previous analyses, we used a distribution of
VSL based on 26 VSL estimates from the economics literature. For this analysis, we are
characterizing the VSL distribution in a more general fashion, based on two recent meta-
9Note that the S AB-HES comments were made in the context of a review of the methods for the Section 812
analysis of the costs and benefits of the Clean Air Act. This context is pertinent to our interpretation of the
SAB- HES comments on the selection of effect estimates for hospital admissions associated with PM (SAB-
HES, 2003). The Section 812 analysis is focused on a broad set of air quality changes, including both the
coarse and fine fractions of PM10. As such, impact functions that focus on the full impact of PM10 are
appropriate. However, for the IAQR, which is expected to affect primarily the fine fraction (PM2.5) of
PM10, impact functions that focus primarily on PM2.5 are more appropriate.
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analyses of the wage-risk-based VSL literature. The new distribution is assumed to be
normal, with a mean of $5.5 million and a 95 percent confidence interval between $1 and $10
million. The EPA welcomes public comment on the appropriate methodology for valuing
reductions in the risk of premature death.
The EPA has addressed many of the comments received from the NAS, the SAB-HES,
and OMB in developing the analytical approach for the IAQR. We have also reflected
advances in data and methods in air quality modeling, epidemiology, and economics in
developing this analysis. Updates to the assumptions and methods used in estimating PM
2.5-related and ozone-related benefits since completion of the Proposed Nonroad Diesel Rule
include the following:
Air Quality
Use of the Simulated Modeled Attainment Test (SMAT) approach for developing
PM2.5 air modeling results. The nonroad diesel rule used spatially and
temporally scaled total PM2.5 mass based on monitoring data from 1999 to 2001
(averaged by season). For the nonroad diesel rule, spatial scaling was based on
1996 modeled REMSAD data at a 36 km grid resolution, while temporal scaling
was based on the ratios of future modeled REMSAD data to 1996 modeled
REMSAD data. All scaling was conducted internally by BenMAP (see below)
using the monitor and model relative grid creation option. Resulting gridded
outputs were for binned daily PM2.5 averages. For the IAQR, we used the SMAT
approach, which uses temporally scaled speciated PM2.5 monitor data from 2001-
2002, reconstructed into total PM2.5 mass based on 2000-2002 design values and
kriged to 12 kilometer grids (nested within the standard 36 km REMSAD grid
structure). Temporal scaling is based on ratios of future modeled REMSAD data
to 2001 REMSAD model data, using REMSAD modeling conducted at the 36 km
grid resolution, SMAT output files include both quarterly mean and annual mean
PM2.5 mass results, which are then manipulated within SAS to produce a
BenMAP input file containing 364 daily values (created by replicating the
quarterly mean values for each day of the appropriate season). For more details
on the SMAT approach and REMSAD modeling, see the air quality chapter of
this document.
For both PM and ozone, the interstate air quality analysis domain will include
only the eastern United States, focusing on 37 States believed to contribute
significantly to the long-range transport of precursors in the formation of PM2.5.
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Health Endpoints
• Incorporation of updated impact functions to reflect updated time-series studies of
hospital admissions to correct for errors in application of the generalized additive
model (GAM) functions in S-plus. More information on this issue is available at
http://www.healtheffects.org.
• The primary analysis will use an all cause mortality effect estimate based on the
Pope et al. (2002) reanalysis of the ACS study data. In addition, we will provide a
breakout for two major cause of death categories—cardiopulmonary and lung
cancer.
• Infant mortality will be included in the primary analysis.
• Asthma exacerbations are incorporated into the primary analysis. Although the
Nonroad Diesel Rule included asthma exacerbations as a separate endpoint
outside of the base case analysis, for the IAQR, we will include asthma
exacerbations in children 6 to 18 years of age as part of the primary analysis.
Valuation
• In generating the monetized benefits for mortality in the primary analysis, the
VSL will be entered as a mean (best estimate) of 5.5 million. Unlike the Nonroad
Diesel Rule, the IAQR will not include a value of statistical life year (VSLY)
estimate.
In response to comments from the SAB-HES as well as the NAS panel, rather than
including an alternative estimate in the IAQR, the EPA will investigate the impact of key
assumptions on mortality and morbidity estimates through a series of sensitivity analyses (to
be completed for the supplemental analysis).
The benefits estimates generated for the Proposed IAQR are subject to a number of
assumptions and uncertainties, which are discussed throughout the document. For example,
key assumptions underlying the primary estimate for the mortality category include the
following:
(1) Inhalation of fine particles is causally associated with premature death at
concentrations near those experienced by most Americans on a daily basis.
Although biological mechanisms for this effect have not yet been definitively
established, the weight of the available epidemiological evidence supports an
assumption of causality.
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(2) All fine particles, regardless of their chemical composition, are equally potent in
causing premature mortality. This is an important assumption, because PM
produced via transported precursors emitted from EGUs may differ significantly
from direct PM released from automotive engines and other industrial sources, but
no clear scientific grounds exist for supporting differential effects estimates by
particle type.
(3) The C-R function for fine particles is approximately linear within the range of
ambient concentrations under consideration. Thus, the estimates include health
benefits from reducing fine particles in areas with varied concentrations of PM,
including both regions that are in attainment with fine particle standard and those
that do not meet the standard.
(4) The forecasts for future emissions and associated air quality modeling are valid.
Although recognizing the difficulties, assumptions, and inherent uncertainties in
the overall enterprise, these analyses are based on peer-reviewed scientific
literature and up-to-date assessment tools, and we believe the results are highly
useful in assessing this proposal.
In addition to the quantified and monetized benefits summarized above, a number of
additional categories are not currently amenable to quantification or valuation. These include
reduced acid and particulate deposition damage to cultural monuments and other materials,
reduced ozone effects on forested ecosystems, and environmental benefits due to reductions
of impacts of acidification in lakes and streams and eutrophication in coastal areas.
Additionally, we have not quantified a number of known or suspected health effects linked
with PM and ozone for which appropriate health impact functions are not available or which
do not provide easily interpretable outcomes (i.e., changes in forced expiratory volume
[FEV1]). As a result, monetized benefits generated for the primary estimate may
underestimate the total benefits attributable to the proposed regulatory option.
Benefits estimates for the Proposed IAQR were generated using BenMAP, which is a
computer program developed by the EPA that integrates a number of the modeling elements
used in previous RIAs (e.g., interpolation functions, population projections, health impact
functions, valuation functions, analysis and pooling methods) to translate modeled air
concentration estimates into health effects incidence estimates and monetized benefits
estimates. BenMAP provides estimates of both the mean impacts and the distribution of
impacts.
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Table 4-1. Estimated Monetized Benefits of the Proposed IAQR
Using a 3% discount rate
Using a 7% discount rate
Total Benefits'- b
(billions 1999$)
2010
$58+B
$54+B
2015
$84+B
$79+B
For notational purposes, unqualified benefits are indicated with a "B" to represent the sum of additional
monetary benefits and disbenefits. A detailed listing of unquantified health and welfare effects is provided in
Table 4-2.
Results reflect the use of two different discount rates: a 3 percent rate, which is recommended by the EP A's
Guidelines for Preparing Economic Analyses (EPA, 2000c), and 7 percent, which is recommended by OMB
Circular A-94 (OMB, 1992). Results are rounded to two significant digits.
In general, the chapter is organized around the steps illustrated in Figure 4-1. In Section
4.1, we provide an overview of the data and methods that are used to quantify and value
health and welfare endpoints and discuss how we incorporate uncertainty into our analysis.
In Section 4.2, we report the results of the analysis for human health and welfare effects (the
overall benefits estimated for the Proposed IAQR are summarized in Table 4-1). Details on
the emissions inventory and air modeling are presented in Chapter 3.0.
4.1 Benefit Analysis- Data and Methods
Environmental and health economists have a number of methods for estimating the
economic value of improvements in (or deterioration of) environmental quality. The method
used in any given situation depends on the nature of the effect and the kinds of data, time,
and resources that are available for investigation and analysis. This section provides an
overview of the methods we selected to quantify and monetize the benefits included in this
RIA.
Given changes in environmental quality (ambient air quality, visibility, nitrogen, and
sulfate deposition), the next step is to determine the economic value of those changes. We
follow a "damage-function" approach in calculating total benefits of Ihe modeled changes in
environmental quality. This approach estimates changes in individual health and welfare
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endpoints (specific effects that can be associated with changes in air quality) and assigns
values to those changes assuming independence of the individual values. Total benefits are
calculated simply as the sum of the values for all nonoverlapping health and welfare
endpoints. This imposes no overall preference structure and does not account for potential
income or substitution effects (i.e., adding a new endpoint will not reduce the value of
changes in other endpoints). The "damage-function" approach is the standard approach for
most cost-benefit analyses of environmental quality programs and has been used in several
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Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Proposed IAQR
Quantified and Monetized in Base
Quantified and/or Monetized Effects in
Hospital admissions: respiratory
Emergency room visits for asthma
•
Minor restricted activity days
School loss days
Asthma attacks
Acute respiratory symptoms
Increased airway responsiveness to stimuli
Inflammation in the lung
Unqualified Effects
Chronic respiratory damage
Premature aging of the lungs
Acute inflammation and respiratory cell damage
Increased susceptibility to respiratory infection
Nonasthma respiratory emergency room visits
Decreased outdoor worker
productivity
Decreased yields for commercial crops
(selected species)
Decreased eastern commercial forest
productivity (selected species)
Decreased western commercial forest productivity
Decreased eastern commercial forest productivity
(other species)
Decreased yields for fruits and vegetables
Decreased yields for other commercial and
noncommercial crops
Damage to urban ornamental plants
Impacts on recreational demand from damaged forest
aesthetics
Damage to ecosystem functions
-------
Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Proposed IAQR (continued)
Quantified and Monetized in Base
Quantified and/or Monetized Effects in
Premature mortality: long-term
exposures
Bronchitis: chronic and acute
Hospital admissions: respiratory and
cardiovascular
Emergency room visits for asthma
Non-fatal heart attacks (myocardial
infarction)
Lower and upper respiratory illness
Minor restricted activity days
Work loss days
Asthma exacerbations (asthmatic
population)
Respiratory symptoms (asthmatic
population)
Infant mortality
Premature mortality: short-term
mSSfty Analyses
Low birth weight
Changes in pulmonary function
Unqualified Effects
Chronic respiratory diseases other than chronic
bronchitis
Morphological changes
Altered host defense mechanisms
Nonasthma respiratory emergency room visits
Visibility in Southeastern Class I
areas
Visibility in northeastern and
Midwestern Class I areas
Visibility in residential and non-Class I
areas
Household soiling
Visibility in western U.S. Class I areas
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Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Proposed IAQR (continued)
Quantified and Monetized in Base
Quantified and/or Monetized Effects in
Nitrogen and
Sulfate
Deposition/
Welfare
Sensitivity Analyses
Impacts of acidic sulfate and nitrate deposition on
commercial forests
sition on commercial freshwater
fishing
Impacts of acidic deposition on recreation in
terrestrial ecosystems
Impacts of nitrogen deposition on commercial fishing,
agriculture, and forests
Impacts of nitrogen deposition on recreation in
estuarine ecosystems
Reduced existence values for currently healthy
ecosystems
Hospital admissions for respiratory and cardiac
diseases
Respiratory symptoms in asthmatics
Lung irritation
Lowered resistance to respiratory infection
Hospital admissions for respiratory and cardiac
diseases
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Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Proposed IAQR (continued)
Quantified and Monetized in Base
Quantified and/or Monetized Effects in
Mercury
Deposition/
Health
Sensitivity Analyses
Neurological disorders
Learning disabilities
Unquantified Effects
Retarded development
Potential cardiovascular effects *
Altered blood pressure regulation *
Increased heart rate variability *
Myocardial infarctions *
Potential reproductive effects *
Mercury
Deposition/
Welfare
Impacts on birds and mammals (e.g., reproductive
effects)
Impacts to commercial, subsistence, and recreational
fishing
Reduced existence values for currently healthy
ecosystems
a more complete discussion of presentation of benefits estimates.
have a significant effect on daily mortality rates, independent of exposure to PM. The EPA is currently conducting a series of meta-analyses of the ozone
mortality epidemiology literature and will reevaluate inclusion of ozone-related mortality in the primary analysis once the meta-analyses have been completed.
-------
recent published analyses (Banzhaf et al., 2002; Levy et al., 2001; Levy et al., 1999; Ostro
and Chestnut, 1998).
To assess economic value in a damage-function framework, the changes in environmental
quality must He translated into effects on people or on the things that people value. In some
cases, the changes in environmental quality can be directly valued, as is the case for changes
in visibility. In other cases, such as for changes in ozone and PM, a health and welfare
impact analysis must first be conducted to convert air quality changes into effects that can be
assigned dollar values.
For the purposes of this RIA, the health impacts analysis is limited to those health effects
that are directly linked to ambient levels of air pollution and specifically to those linked to
ozone and PM. There may be other, indirect health impacts associated with implementing
controls to meet the preliminary control options, such as occupational health impacts for
equipment operators. These impacts may be positive or negative, but in general, for this set
of control options, they are expected to be small relative to the direct air pollution-related
impacts.
The welfare impacts analysis is limited to changes in the environment that have a direct
impact on human welfare. For this analysis, we are limited by the available data to
examining impacts of changes in visibility. We also provide qualitative discussions of the
impact of changes in other environmental and ecological effects, for example, changes in
deposition of nitrogen and sulfur to terrestrial and aquatic ecosystems, but we are unable to
place an economic value on these changes.
We note at the outset that the EPA rarely has the time or resources to perform extensive
new research to measure either the health outcomes or their values for this analysis. Thus,
similar to Kunzli et al. (2000) and other recent health impact analyses, our estimates are
based on the best available methods of benefits transfer. Benefits transfer is the science and
art of adapting primary research from similar contexts to obtain the most accurate measure of
benefits for the environmental quality change under analysis. Where appropriate,
adjustments are made for the level of environmental quality change, the sociodemographic
and economic characteristics of the affected population, and other factors to improve the
accuracy and robustness of benefits estimates.
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4.1.1 Valuation Concepts
In valuing health impacts, we note that reductions in ambient concentrations of air
pollution generally lower the risk of future adverse health affects by a fairly small amount for
a large population. The appropriate economic measure is therefore willingness to pay (WTP)
for changes in risk prior to the regulation (Freeman, 1993). In general, economists tend to
view an individual's WTP for an improvement in environmental quality as the appropriate
measure of the value of a risk reduction. An individual's willingness to accept (WTA)
compensation for not receiving the improvement is also a valid measure. However, WTP is
generally considered to be a more readily available and conservative measure of benefits.
Adoption of WTP as the measure of value implies that the value of environmental quality
improvements depends on the individual preferences of the affected population and that the
existing distribution of income (ability to pay) is appropriate. For some health effects, such
as hospital admissions, WTP estimates are generally not available. In these cases, we use the
cost of treating or mitigating the effect as a primary estimate. These cost of illness (COI)
estimates generally understate the true value of reductions in risk of a health effect, reflecting
the direct expenditures related to treatment but not the value of avoided pain and suffering
from the health effect (Harrrington and Portnoy, 1987; Berger, 1987).
For many goods, WTP can be observed by examining actual market transactions. For
example, if a gallon of bottled drinking water sells for $1, it can be observed that at least
some people are willing to pay $1 for such water. For goods not exchanged in the market,
such as most environmental "goods," valuation is not as straightforward. Nevertheless, a
value may be inferred from observed behavior, such as sales and prices of products that result
in similar effects or risk reductions (e.g., nontoxic cleaners or bike helmets). Alternatively,
surveys can be used in an attempt to directly elicit WTP for an environmental improvement.
One distinction in environmental benefits estimation is between use values and nonuse
values. Although no general agreement exists among economists on a precise distinction
between the two (see Freeman [1993]), the general nature of the difference is clear. Use
values are those aspects of environmental quality that affect an individual's welfare more or
less directly. These effects include changes in product prices, quality, and availability;
changes in the quality of outdoor recreation and outdoor aesthetics; changes in health or life
expectancy; and the costs of actions taken to avoid negative effects of environmental quality
changes.
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Nonuse values are those for which an individual is willing to pay for reasons that do not
relate to the direct use or enjoyment of any environmental benefit but might relate to
existence values-and bequest values. Nonuse values are not traded, directly or indirectly, in
markets. For this reason, the measurement of nonuse values has proved to be significantly
more difficult than the measurement of use values. The air quality changes produced by the
IAQR cause changes in both use and nonuse values, but the monetary benefit estimates are
almost exclusively for use values.
More frequently than not, the economic benefits from environmental quality changes are
not traded in markets, so direct measurement techniques cannot be used. There are three
main nonmarket valuation methods used to develop values for endpoints considered in this
analysis: stated preference (or contingent valuation.[CV]), indirect market (e.g., hedonic
wage), and avoided cost methods.
The stated preference or CV method values endpoints by using carefully structured
surveys to ask a sample of people what amount of compensation is equivalent to a given
change in environmental quality. There is an extensive scientific literature and body of
practice on both the theory and technique of stated preference-based valuation. The EPA
believes that well-designed and well-executed stated preference studies are valid for
estimating the benefits of air quality regulations.10 Stated preference valuation studies form
the basis for valuing a number of health and welfare endpoints, including the value of
mortality risk reductions, CB risk reductions, minor illness risk reductions, and visibility
improvements.
Indirect market methods can also be used to infer the benefits of pollution reduction. The
most important application of this technique for our analysis is the calculation of the VSL for
use in estimating benefits from mortality risk reductions. No market exists where changes in
the probability of death are directly exchanged. However, people make decisions about
'"Concerns about the reliability of value estimates from CV studies arose because research has shown that bias
can be introduced easily into these studies if they are not carefully conducted. Accurately measuring WTP
for avoided health and welfare losses depends on the reliability and validity of the data collected. There are
several issues to consider when evaluating study quality, including but not limited to 1) whether the sample
estimates of WTP are representative of the population WTP; 2) whether the good to be valued is
comprehended and accepted by the respondent; 3) whether the WTP elicitation format is designed to
minimize strategic responses; 4) whether WTP is sensitive to respondent familiarity with the good, to the
size of the change in the good, and to income; 5) whether the estimates of WTP are broadly consistent with
other estimates of WTP for similar goods; and 6) the extent to which WTP responses are consistent with
established economic principles.
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occupation, precautionary behavior, and other activities associated with changes in the risk of
death. By examining these risk changes and the other characteristics of people's choices, it is
possible to infer information about the monetary values associated with changes in mortality
risk (see Section 4.1.5.5.1).
Avoided cost methods are ways to estimate the costs of pollution by using the
expenditures made necessary by pollution damage. For example, if buildings must be
cleaned or painted more frequently as levels of PM increase, then the appropriately calculated
increment of these costs is a reasonable lower-bound estimate (under most conditions) of true
economic benefits when PM levels are reduced. Avoided costs methods are also used to
estimate some of the health-related benefits related to morbidity, such as hospital admissions
(see Section 4.1.5).
4.1.2 Growth in WTP Reflecting National Income Growth Over Time
Our analysis accounts for expected growth in real income over time. Economic theory
argues that WTP for most goods (such as environmental protection) will increase if real
incomes increase. There is substantial empirical evidence that the income elasticity11 of WTP
for health risk reductions is positive, although there is uncertainty about its exact value.
Thus, as real income increases, the WTP for environmental improvements also increases.
Although many analyses assume that the income elasticity of WTP is unit elastic (i.e., 10
percent higher real income level implies a 10 percent higher WTP to reduce risk changes),
empirical evidence suggests that income elasticity is substantially less than one and thus
relatively inelastic. As real income rises, the WTP value also rises but at a slower rate than
real income.
The effects of real income changes on WTP estimates can influence benefit estimates in
two different ways: through real income growth between the year a WTP study was
conducted and the year for which benefits are estimated, and through differences in income
between study populations and the affected populations at a particular time. Empirical
evidence of the effect of real income on WTP gathered to date is based on studies examining
the former. The Environmental Economics Advisory Committee (EEAC) of the SAB
advised the EPA to adjust WTP for increases in real income over time but not to adjust WTP
to account for cross-sectional income differences "because of the sensitivity of making such
"income elasticity is a common economic measure equal to the percentage change in WTP for a 1 percent
change in income.
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distinctions, and because of insufficient evidence available at present" (EPA-SAB-EEAC-00-
013).
Based on a review of the available income elasticity literature, we adjust the valuation of
human health benefits upward to account for projected growth in real U.S. income. Faced
with a dearth of estimates of income elasticities derived from time-series studies, we applied
estimates derived from cross-sectional studies in our analysis. Details of the procedure can
be found in Kleckner and Neumann (1999). An abbreviated description of the procedure we
used to account for WTP for real income growth between 1990 and 2010 and 2015 is
presented below.
Reported income elasticities suggest that the severity of a health effect is a primary
determinant of the strength of the relationship between changes in real income and WTP. As
such, we use different elasticity estimates to adjust the WTP for minor health effects, severe
and chronic health effects, and premature mortality. We also expect that the WTP for
improved visibility in Class I areas would increase with growth in real income. The elasticity
values used to adjust estimates of benefits in 2010 and 2015 are presented in Table 4-3.
Table 4-3. Elasticity Values Used to Account for Projected Real Income Growth8
Benefit Category
Minor Health Effect
Severe and Chronic Health Effects
Premature mortality
Visibility"
Central Elasticity Estimate
0.14
0.45
0.40
0.90
0 Derivation of estimates can be found in Kleckner and Neumann (1999) and Chestnut (1997). COI estimates
are assigned an adjustment factor of 1.0.
b No range was applied for visibility because no ranges were available in the current published literature.
In addition to elasticity estimates, projections of real gross domestic product (GDP) and
populations from 1990 to 2010 and 2015 are needed to adjust benefits to reflect real per
capita income growth. For consistency with the emissions and benefits modeling, we use
national population estimates for the years 1990 to 1999 based on U.S. Census Bureau
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estimates (Hollman, Mulder and Kalian, 2000). These population estimates are based on
application of a cohort-component model applied to 1990 U.S. Census data projections (U.S.
Bureau of Census, 2000).12 For the years between 2000 and 2015, we applied growth rates
based on the U.S. Census Bureau projections to the U.S. Census estimate of national
population in 2000. We use projections of real GDP provided in Kleckner and Neumann
(1999) for the years 1990 to 2010.13 We use projections of real GDP (in chained 1996
dollars) provided by Standard and Poor's14 (2000) for the years 2010 to 2015.15
Using the method outlined in Kleckner and Neumann (1999) and the population and
income data described above, we calculate WTP adjustment factors for each of the elasticity
estimates listed in Table 4-4. Benefits for each of the categories (minor health effects, severe
and chronic health effects, premature mortality, and visibility) will be adjusted by multiplying
the unadjusted benefits by the appropriate adjustment factor. Table 4-4 lists the estimated
adjustment factors. Note that, for premature mortality, we apply the income adjustment
factor ex post io the present discounted value of the stream of avoided mortalities occurring
over the lag period. Also note that no adjustments will be made to benefits based on the COI
approach or to work loss days and worker productivity. This assumption will also lead us to
underpredict benefits in future years because it is likely that increases in real U.S. income
would also result in increased COI (due, for example, to increases in wages paid to medical
workers) and increased cost of work loss days and lost worker productivity (reflecting that if
worker incomes are higher, the losses resulting from reduced worker production would also
be higher).
12U.S. Bureau of Census. Annual Projections of the Total Resident Population, Middle Series, 1999-2100.
(Available on the internet at http://www.census.gov/population/www/projections/natsum-Tl .html)
"U.S. Bureau of Economic Analysis, Table 2 A (1992$). (Available on the internet at
http://www.bea.doc.gov/bea/dn/0897nip2/tab2a.htm) and U.S. Bureau of Economic Analysis, Economics
and Budget Outlook. Note that projections for 2007 to 2010 are based on average GDP growth rates
between 1999 and 2007.
'"Standard and Poor's. 2000. "The U.S. Economy: The 25 Year Focus." Winter.
15In previous analyses, we used the Standard and Poor's projections of GDP directly. This led to an apparent
discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the
relative growth rates for GDP derived from the Standard and Poor's projections to the' 2010 projected GDP
based on the Bureau of Economic Analysis projections.
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Table 4-4. Adjustment Factors Used to Account for Projected Real Income Growth8
Benefit Category
Minor Health Effect
Severe and Chronic Health Effects
Premature Mortality
Visibility
2010
1.034
1.113
1.100
1.239
L_ 2015
1.073
1.254
1.222
1.581
" Based on elasticity values reported in Table 4-3, U.S. Census population projections, and projections of real
gross domestic product per capita
4.1.3 Methods for Describing Uncertainty
In any complex analysis using estimated parameters and inputs from numerous models,
there are likely to be many sources of uncertainty. This analysis is no exception. As outlined
both in this and preceding chapters, many inputs are used to derive the final estimate of
benefits, including emission inventories, air quality models (with their associated parameters
and inputs), epidemiological health effect estimates, estimates of values (both from WTP and
COI studies), population estimates, income estimates, and estimates of the future state of the
world (i.e., regulations, technology, and human behavior). Each of these inputs may be
uncertain and, depending on their location in the benefits analysis, may have a
disproportionately large impact on final estimates of total benefits. For example, emissions
estimates are used in the first stage of the analysis. As such, any uncertainty in emissions
estimates will be propagated through the entire analysis. When compounded with uncertainty
in later stages, small uncertainties in emission levels can lead to much larger impacts on total
benefits.
Some key sources of uncertainty in each stage of the benefits analysis are the following:
• gaps in scientific data and inquiry;
• variability in estimated relationships, such as epidemiological effect estimates,
introduced through differences in study design and statistical modeling;
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• errors in measurement and projection for variables such as population growth
rates;
• errors due to misspecification of model structures, including the use of surrogate
variables, such as using PM10 when PM2 5 is not available, excluded variables, and
simplification of complex functions; and
• biases due to omissions or other research limitations.
Some of the key uncertainties in the benefits analysis are presented in Table 4-5. Given
the wide variety of sources for uncertainty and the potentially large degree of uncertainty
about any primary estimate, it is necessary for us to address this issue in several ways, based
on the following types of uncertainty:
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Table 4-5. Primary Sources of Uncertainty in the Benefit Analysis
/. Uncertainties Associated With Impact Functions
- The value of the ozone orPM effect estimate in each impact function.
— Application of a single impact function to pollutant changes and populations in all locations.
- Similarity of future year impact functions to current impact functions.
- Correct functional form of each impact function.
— Extrapolation of effect estimates beyond the range of ozone or PM concentrations observed in the source epidemiological study.
— Application of impact functions only to those subpopulations matching the original study population.
2. Uncertainties Associated With Ozone and PM Concentrations
Responsiveness of the models to changes in precursor emissions resulting from the control policy.
Projections of future levels of precursor emissions, especially ammonia and crustal materials.
Model chemistry for the formation of ambient nitrate concentrations.
Lack of ozone monitors in rural areas requires extrapolation of observed ozone data from urban to rural areas.
Use of separate air quality models for ozone and PM does not allow for a fully integrated analysis of pollutants and their interactions.
Full ozone season air quality distributions are extrapolated from a limited number of simulation days.
- Comparison of model predictions of paniculate nitrate with observed rural monitored nitrate levels indicates that REMSAD overpredicts
nitrate in some parts of the Eastern US
3. Uncertainties Associated with PM Mortality Risk
Limited scientific literature supporting a direct biological mechanism for observed epidemiological evidence.
— Direct causal agents within the complex mixture of PM have not been identified.
- The extent to which adverse health effects are associated with low level exposures that occur many times in the year versus peak exposures.
- The extent to which effects reported in the long-term exposure studies are associated with historically higher levels of PM rather than the levels
occurring during the period of study.
— Reliability of the limited ambient PMI5 monitoring data in reflecting actual PM2 3 exposures.
4. Uncertainties Associated With Possible Lagged Effects
te portion of the PM-related long-term exposure mortality effects associated with changes in annual PM levels would occur in a single year is
- Th
uncertain as well as the portion that might occur in subsequent years.
5. Uncertainties Associated With Baseline Incidence Rates
- Some baseline incidence rates are not location-specific (e.g., those taken from studies) and may therefore not accurately represent the actual
location-specific rates.
— Current baseline incidence rates may not approximate well baseline incidence rates in 2015.
— Projected population and demographics may not represent well future-year population and demographics.
6. Uncertainties Associated With Economic Valuation
— Unit dollar values associated with health and welfare endpoints are only estimates of mean WTP and therefore have uncertainty surrounding them.
— Mean WTP (in constant dollars) for each type of risk reduction may differ from current estimates due to differences in income or other factors.
7. Uncertainties Associated With Aggregation of Monetized Benefits
- Health and welfare benefits estimates are limited to the available impact functions. Thus, unquantified or unmonetizcd benefits are not included.
a. Quantifiable uncertainty in benefits estimates. For some parameters or inputs it
may be possible to provide a statistical representation of the underlying
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uncertainty distribution. Quantitative uncertainty may include measurement
uncertainty or variation in estimates across or within studies. For example, the
variation in VSL results across available meta-analyses provides a source of
uncertainty that can be characterized in calculating monetized benefits. Methods
typically used to evaluate the impact of these quantifiable sources of uncertainty
on benefits and incidence estimates center on Monte Carlo-based probabilistic
simulation. This technique allows uncertainty in key inputs to be propagated
through the model to generate a single distribution of results reflecting the
combined impact of multiple sources of uncertainty. Variability can also be
considered along with uncertainty using nested two-stage Monte Carlo simulation.
b. Uncertainty in the basis for quantified estimates. Often it is possible to identify a
source of uncertainty (e.g., an ongoing debate over the proper method to estimate
premature mortality) that is not readily addressed through traditional uncertainty
analysis. In these cases, it is possible to characterize the potential impact of this
uncertainty on the overall benefits estimates through sensitivity analyses.
c. Nonquantifiable uncertainty. Uncertainties may also result from omissions of
known effects from the benefits calculation, perhaps owing to a lack of data or
modeling capability. For example, in this analysis we were unable to quantify the
benefits of avoided airborne nitrogen deposition on aquatic and terrestrial
ecosystems.
It should be noted that, even for individual endpoints, there is usually more than one source
of uncertainty. This makes it difficult to provide an overall quantified uncertainty estimate
for individual endpoints or for total benefits, without conducting a comprehensive uncertainty
analysis that considers the aggregate impact of multiple sources of uncertainty on benefits
estimates.
The NAS report on the EPA's benefits analysis methodology highlighted the need for the
EPA to conduct rigorous quantitative analysis of uncertainty in its benefits estimates, hi
response to these comments, the EPA has initiated the development of a comprehensive
methodology for characterizing the aggregate impact of uncertainty in key modeling elements
on both health incidence and benefits estimates. This methodology will begin by identifying
those modeling elements that have a significant impact on benefits due to either the
magnitude of their uncertainty or other factors such as nonlinearity within the modeling
framework. A combination of influence analysis and sensitivity analysis methods may be
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used to focus the analysis of uncertainty on these key sources of uncertainly. A probabilistic
simulation approach based on Monte Carlo methods will be developed for propagating the
impact of these sources of uncertainty through the modeling framework. Issues such as
correlation between input parameters and the identification of reasonable upper and lower
bounds for input distributions characterizing uncertainty will be addressed in developing the
approach.
One component of the EPA's uncertainty analysis methodology that is currently
underway is an expert elicitation intended to characterize uncertainty in the effect estimates
used to estimate mortality resulting from both short-term (timer series studies) and longer-
term (cohort studies) exposure to PM. This expert elicitation is aimed at evaluating
uncertainty in both the form of the mortality impact function (e.g., threshold versus linear
models) and the fit of a specific model to the data (e.g., confidence bounds for specific
percentiles of the mortality effect estimates). Additional issues such as the ability of longer-
term cohort studies to capture mortality resulting from short-term peak PM exposures is also
being addressed in the expert elicitation.
EPA will consider incorporating elements of this uncertainty analysis methodology,
including information from the expert elicitation addressing the mortality estimate, into
subsequent analysis conducted for the IAQR (e.g., the Supplemental Analysis and Final Rule)
as they become available. For the Proposed IAQR, EPA has addressed key sources of
uncertainty through a series of sensitivity analyses (to be completed for the supplemental
analysis) examining the impact of alternate assumptions on the benefits estimates that are
generated.
Our estimate of total benefits should be viewed as an approximate result because of the
sources of uncertainty discussed above (see Table 4-5). Uncertainty about specific aspects of
the health and welfare estimation models are discussed in greater detail in the following
sections. The total benefits estimate may understate or overstate actual benefits of the rule.
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In considering the monetized benefits estimates, the reader should remain aware of the
many limitations of conducting these analyses mentioned throughout this RIA. One
significant limitation of both the health and welfare benefits analyses is the inability to
quantify many of the serious effects listed in Table 4-1. For many health and welfare effects,
such as changes in ecosystem functions and PM-related materials damage, reliable impact
functions and/or valuation functions are not currently available. In general, if it were possible
to monetize these benefits categories, the benefits estimates presented in this analysis would
increase. Unqualified benefits are qualitatively discussed in the health and welfare effects
sections. In addition to unqualified benefits, there may also be environmental costs that we
are unable to quantify. These endpoints are qualitatively discussed in the health and welfare
effects sections as well. The net effect of excluding benefit and disbenefit categories from
the estimate of total benefits depends on the relative magnitude of the effects.
4.1.4 Demographic Projections
Quantified and monetized human health impacts depend critically on the demographic
characteristics of the population, including age, location, and income. In previous analyses,
we have used simple projections of total population that did not take into account changes in
demographic composition over time, hi the current analysis, we use more sophisticated
projections based on economic forecasting models developed by Woods and Poole, Inc. The
Woods and Poole (WP) database contains county-level projections of population by age, sex,
and race out to 2025. Projections in each county are determined simultaneously with every
other county in the United States to take into account patterns of economic growth and
migration. The sum of growth in county-level populations is constrained to equal a
previously determined national population growth, based on Bureau of Census estimates
(Hollman, Mulder and Kalian, 2000). According to WP, linking county-level growth
projections together and constraining to a national-level total growth avoids potential errors
introduced by forecasting each county independently. County projections are developed in a
four-stage process. First, national-level variables such as income, employment, populations,
etc. are forecasted. Second, employment projections are made for 172 economic areas
defined by the Bureau of Economic Analysis, using an "export-base" approach, which relies
on linking industrial sector production of nonlocally consumed production items, such as
outputs from mining, agriculture, and manufacturing with the national economy. The export-
base approach requires estimation of demand equations or calculation of historical growth
rates for output and employment by sector. Third, population is projected for each economic
area based on net migration rates derived from employment opportunities and following a
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cohort-component method based on fertility and mortality in each area. Fourth, employment
and population projections are repeated for counties, using the economic region totals as
bounds. The age, sex, and race distributions for each region or county are determined by
aging the population by single year of age by sex and race for each year through 2015 based
on historical rates of mortality, fertility, and migration.
The WP projections of county-level population are based on historical population data
from 1969-1999 and do not include the 2000 Census results. Given the availability of
detailed 2000 Census data, we constructed adjusted county-level population projections for
each future year using a two-stage process. First, we constructed ratios of the projected WP
populations in a future year to the projected WP population in 2000 for each future year by
age, sex, and race. Second, we multiplied the block level 2000 Census population data by the
appropriate age-, sex-, and race-specific WP ratio for the county containing the census block,
for each future year. This results in a set of future population projections that is consistent
with the most recent detailed census data.
As noted above, values for environmental quality improvements are expected to increase
with growth in real per capita income. Accounting for real income growth over time requires
projections of both real GDP and total U.S. populations. For consistency with the emissions
and benefits modeling, we use national population estimates based on the U.S. Census
Bureau projections.
4.1.5 Health Benefits Assessment Methods
The most significant monetized benefits of reducing ambient concentrations of PM and
ozone are attributable to reductions in health risks associated with air pollution. The EPA's
Criteria Documents for ozone and PM list numerous health effects known to be linked to
ambient concentrations of these pollutants (EPA, 1996a and 1996b). As illustrated in Figure
4-1, quantification of health impacts requires several inputs, including epidemic logical effect
estimates, baseline incidence and prevalence rates, potentially affected populations, and
estimates of changes in ambient concentrations of air pollution. Previous sections have
described the population and air quality inputs. This section describes the effect estimates
and baseline incidence and prevalence inputs and the methods used to quantify and monetize
changes in the expected number of incidences of various health effects.
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4.. 1.5.1 Selecting Health Endpoints and Epidemiological Effect Estimates
Quantifiable health benefits of the proposal may be related to ozone only, PM only, or
both pollutants. Decreased worker productivity, respiratory hospital admissions for children
under two, and school absences are related to ozone but not PM. PM-only health effects
include premature mortality, nonfatal heart attacks, CB, acute bronchitis, upper and lower
respiratory symptoms, asthma exacerbations, and work loss days.16 Health effects related to
both PM and ozone include hospital admissions, emergency room visits for asthma, and
minor restricted activity days.
We relied on the available published scientific literature to ascertain the relationship
between PM and ozone exposure and adverse human health effects. We evaluated studies
using the selection criteria summarized in Table 4-6. These criteria include consideration of
whether the study was peer reviewed, the match between the pollutant studied and the
pollutant of interest, the study design and location, and characteristics of the study
population, among other considerations. The selection of C-R functions for the benefits
analysis is guided by the goal of achieving a balance between comprehensiveness and
scientific defensibility.
Recently, the Health Effects Institute (HEI) reported findings by health researchers at
Johns Hopkins University and others that have raised concerns about aspects of the statistical
methods used in a number of recent time-series studies of short-term exposures to air
pollution and health effects (Greenbaum, 2002). The estimates derived from the long-term
exposure studies, which account for a major share of the economic benefits described in
"Evidence has been found linking ozone exposures with premature mortality independent of PM exposures. A
recent analysis by Thurston and Ito (2001) reviewed previously published time-series studies of the effect of
daily ozone levels on daily mortality and found that previous EPA estimates of the short-term mortality
benefits of the ozone NAAQS (EPA, 1997) may have been underestimated by up to a factor of two, even
when PM is controlled for in the models. In its September 2001 advisory on the draft analytical blueprint for
the second Section 812 prospective analysis, the SAB cited the Thurston and Ito study as a significant
advance in understanding the effects of ozone on daily mortality and recommended re-evaluation of the
ozone mortality endpoint for inclusion in the next prospective study (EPA-SAB-COUNCIL-ADV-01-004,
2001). In addition, a recent World Health Organization (WHO) report found that "recent epidemiological
studies have strengthened the evidence that there are short-term O3 effects on mortality and respiratory
morbidity and provided further information on exposure-response relationships and effect modification."
(WHO, 2002). Based on these new analyses and recommendations, the EPA is currently reevaluating ozone-
related mortality for inclusion in the primary benefits analysis. The EPA is sponsoring three independent
meta-analyses of the ozone-mortality epidemiology literature to inform a determination on inclusion of this
important health endpoint. Upon completion and peer review of the meta-analyses, the EPA will make its
determination on whether benefits of reductions in ozone-related mortality will be included in the benefits
analysis for the final IAQR.
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this chapter, are not affected. Similarly, the time-series studies employing generalized linear
models (GLMs) or other parametric methods, as well as case-crossover studies, are not
affected. As discussed in HEI materials provided to the EPA and to CASAC (Greenbaum,
2002), researchers working on the National Morbidity, Mortality, and Air Pollution Study
(NMMAPS) found problems in the default "convergence criteria" used in Generalized
Additive Models (GAM) and a separate issue first identified by Canadian investigators about
the potential to underestimate standard errors in the same statistical package. Following
identification of the GAM issue, a number of time-series studies were reanalyzed using
alternative methods, typically GAM with more stringent convergence criteria and an
alternative model such as generalized linear models (GLM) with natural smoothing splines,
and the results of the reanalyses have been compiled and reviewed in a recent HEI
publication (HEI, 2003a). In most, but not all, of the reanalyzed studies, it was found that
risk estimates were reduced and confidence intervals increased with the use of GAM with
more stringent convergence criteria or GLM analyses; however, the reanalyses generally did
not substantially change the findings of the original studies, and the changes in risk estimates
with alternative analysis methods were much smaller than the variation in effects across
studies. The HEI review committee concluded the following:
a. Although the number of studies showing an association of PM with mortality was
slightly smaller, the PM association persisted in the majority of studies.
b. In some of the large number of studies in which the PM association persisted, the
estimates of PM effect were substantially smaller.
c. In the few studies in which investigators performed further sensitivity analyses, some
showed marked sensitivity of the PM effect estimate to the degree of smoothing
and/or the specification of weather (HEI, 2003b, p. 269)
Examination of the original studies used in our benefits analysis found that the health
endpoints that are potentially affected by the GAM issues include reduced hospital
admissions and reduced lower respiratory symptoms. For the IAQR, we have incorporated a
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Table 4-6. Summary of Considerations Used in Selecting C-R Functions
Consideration
Peer reviewed
research
Study type
Study period
Population
attributes
Study size
Study location
Pollutants
included in model
Measure of PM
Economically
valuable health
effects
Non-overlapping
endpoints
Comments
Peer reviewed research is preferred to research that has not undergone the peer review process.
Among studies that consider chronic exposure (e.g., over a year or longer) prospective cohort
studies are preferred over cross-sectional studies because they control for important individual-
level confounding variables that cannot be controlled for in cross-sectional studies.
Studies examining a relatively longer period of time (and therefore having more data) are
preferred, because they have greater statistical power to detect effects. More recent studies are
also preferred because of possible changes in pollution mixes, medical care, and life style over
time. However, when there are only a few studies available, studies from all years will be
included.
The most technically appropriate measures of benefits would be based on impact functions that
cover the entire sensitive population, but allow for heterogeneity across age or other relevant
demographic factors. In the absence of effect estimates specific to age, sex, preexisting condition
status, or other relevant factors, it may be appropriate to select effect estimates that cover the
broadest population, to match with the desired outcome of the analysis, which is total national-
level health impacts.
Studies examining a relatively large sample are preferred because they generally have more power
to detect small magnitude effects. A large sample can be obtained in several ways, either through
a large population, or through repeated observations on a smaller population, i.e. through a
symptom diary recorded for a panel of asthmatic children.
U.S. studies are more desirable than non-U.S. studies because of potential differences in pollution
characteristics, exposure patterns, medical care system, population behavior and life style.
When modeling the effects of ozone and PM (or other pollutant combinations) jointly, it is
important to use properly specified impact functions that include both pollutants. Use of single
pollutant models in cases where both pollutants are expected to affect a health outcome can lead to
double-counting when pollutants are correlated.
For this analysis, impact functions based on PM2 5 are preferred to PM10 because the IAQR will
regulate emissions of PM2.5 precursors and air quality modeling was conducted for this size
fraction of PM. Where PM2 5 functions are not available, PM10 functions are used as surrogates,
recognizing that there will be potential downward (upward) biases if the fine fraction of PM10 is
more (less) toxic than the coarse fraction.
Some health effects, such as forced expiratory volume and other technical measurements of lung
function, are difficult to value in monetary terms. These health effects are not quantified in this
analysis.
Although the benefits associated with each individual health endpoint may be analyzed separately,
care must be exercised in selecting health endpoints to include in the overall benefits analysis
because of the possibility of double counting of benefits.
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number of studies that have been updated to correct for the GAM issue, including Ito et al.
(2003) for respiratory-related hospital admissions (COPD and pneumonia), Shepard et al.
(2003) for respiratory-related hospital admissions (asthma), Moolgavkar (2003) for
cardiovascular-related hospital admissions (ICD codes 390-429), and Ito et al. (2003) for
cardiovascular-related hospital admissions (ischemic heart disease, dysrhythmia, and heart
failure). Several additional hospital admissions-related studies have not yet been formally
updated to correct for the GAM issue. These include the lower respiratory symptoms study
and hospital admissions for respiratory and cardiovascular causes in populations aged 20 to
64. However, as discussed above, available evidence suggests that the errors introduced into
effect estimates due to the GAM issue should not significantly affect incidence results.
It is important to reiterate that the estimates derived from the long-term exposure studies,
which account for a major share of the economic benefits described in this chapter, are not
affected by the GAM issue. Similarly, the time-series studies employing GLMs or other
parametric methods, as well as case-crossover studies, are not affected.
Although a broad range of serious health effects has been associated with exposure to
elevated ozone and PM levels (as noted for example in Table 4-1 and described more fully in
the ozone and PM Criteria Documents (EPA, 1996a, 1996b)), we include only a subset of
health effects in this quantified benefit analysis. Health effects are excluded from this
analysis for three reasons: the possibility of double counting (such as hospital admissions for
specific respiratory diseases); uncertainties in applying effect relationships based on clinical
studies to the affected population; or a lack of an established relationship between the health
effect and pollutant in the published epidemiological literature.
In general, the use of results from more than a single study can provide a more robust
' estimate of the relationship between a pollutant and a given health effect. However, there are
often differences between studies examining the same endpoint, making it difficult to pool
the results in a consistent manner. For example, studies may examine different pollutants or
different age groups. For this reason, we consider very carefully the set of studies available
examining each endpoint and select a consistent subset that provides a good balance of
population coverage and match with the pollutant of interest. In many cases, either because
of a lack of multiple studies, consistency problems, or clear superiority in the quality or
comprehensiveness of one study over others, a single published study is selected as the basis
of the effect estimate.
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When several effect estimates for a pollutant and a given health endpoint have been
selected, they are quantitatively combined or pooled to derive a more robust estimate of the
relationship. The benefits Technical Support Document (TSD) completed for the nonroad
diesel rulemaking provides details of the procedures used to combine multiple impact
functions (Abt Associates, 2003). In general, we use fixed or random effects models to pool
estimates from different studies of the same endpoint. Fixed effects pooling simply weights
each study's estimate by the inverse variance, giving more weight to studies with greater
statistical power (lower variance). Random effects pooling accounts for both within-study
variance and between-study variability, due, for example, to differences in population
susceptibility. We use the fixed effects model as our null hypothesis and then determine
whether the data suggest that we should reject this null hypothesis, in which case we would
use the random effects model.17 Pooled impact functions are used to estimate hospital
admissions (PM), school absence days (ozone), lower respiratory symptoms (PM), asthma
exacerbations (PM), and asthma-related emergency room visits (ozone). For more details on
methods used to pool incidence estimates, see the benefits TSD for the nonroad diesel
rulemaking (Abt Associates, 2003).
Effect estimates for a pollutant and a given health endpoint are applied consistently across
all locations nationwide. This applies to both impact functions defined by a single effect
estimate and those defined by a pooling of multiple effect estimates. Although the effect
estimate may, in fact, vary from one location to another (e.g., due to differences in population
susceptibilities or differences in the composition of PM), location-specific effect estimates
are generally not available.
The specific studies from which effect estimates for the primary analysis are drawn are
included in Table 4-7.
Premature Mortality. Both long- and short-term exposures to ambient levels of air
pollution have been associated with increased risk of premature mortality. The size of the
mortality risk estimates from these epidemiological studies, the serious nature of the effect
itself, and the high monetary value ascribed to prolonging life make mortality risk reduction
the most important health endpoint quantified in this analysis.
17The fixed effects model assumes that there is only one pollutant coefficient for the entire modeled area. The
random effects model assumes that different studies are estimating different parameters; therefore, there may
be a number of different underlying pollutant coefficients.
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Epidemiological analyses have consistently linked air pollution, especially PM, with
excess mortality. Although a number of uncertainties remain to be addressed by continued
research (NRC, 1998), a substantial body of published scientific literature documents the
correlation between elevated PM concentrations and increased mortality rates. Community
epidemiological studies that have used both short-term and long-term exposures and response
have been used to estimate PM/ mortality relationships. Short-term studies use a time-series
approach 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-term studies examine the potential relationship between community-level PM
exposures over multiple years and community-level annual mortality rates.
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Table 4-7. Endpoints and Studies Used to Calculate Total Monetized Health Benefits
Endpoint
Pollutant
Study
Study Population
Premature Mortality
Premature Mortality — Long-
term exposure, all-cause
Premature Mortality — Long-
term exposure, all-cause
PM2.,
PM;.,
Pope et al. (2002)
Woodruff etal., 1997
>29 years
Infant (<1 yr)
Chronic Illness
Chronic Bronchitis
Non-fatal Heart Attacks
PM2,
PM2J
Abbey, etal. (1995)
Peters etal. (2001)
> 26 years
Adults
Hospital Admissions
Respiratory
Cardiovascular
Asthma-Related ER Visits
Ozone
Ozone
PM2J
PM2.5
PM2.,
PM2,
PM2J
PM,.,
Ozone
PMZJ
Pooled estimate:
Schwartz (1 995) - ICD 460-5 1 9 (all resp)
Schwartz (1994a, 1994b) - ICD 480-486 (pneumonia)
Moolgavkar etal. (1997) - ICD 480-487 (pneumonia)
Schwartz (1 994b) - ICD 491-492, 494^96 (COPD)
Moolgavkar etal (1997) -ICD 490-496 (COPD)
Burnett et al. (2001)
Pooled estimate:
Moolgavkar (2003) - ICD 490-496 (COPD)
Ito (2003) -ICD 490-496 (COPD)
Moolgavkar (2000) - ICD 490-496 (COPD)
Ito (2003) - ICD 480-486 (pneumonia)
Sheppard, et al. (2003) - ICD 493 (asthma)
Pooled estimate:
Moolgavkar (2003) - ICD 390-429 (all cardiovascular)
Ito (2003) - ICD 410-414, 427-428 (ischemic heart disease,
dysrhythmia, heart failure)
Moolgavkar (2000) - ICD 390-429 (all cardiovascular)
Pooled estimate: Weisel et al. (1995), Cody et al. (1992), Stieb et
al. (1996)
Morris etal (1999)
> 64 years
< 2 years
> 64 years
20-64 years
> 64 years
< 65 years
> 64 years
20-64 years
All ages
0-18 years
(continued)
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Table 4-7. Endpoints and Studies Used to Calculate Total Monetized Health Benefits
(continued)
Endpoint
Pollutant
Study
Study Population
Other Health Endpoints
Acute Bronchitis
Upper Respiratory
Symptoms
Lower Respiratory
Symptoms
Asthma Exacerbations
Work Loss Days
School Absence Days
Worker Productivity
Minor Restricted Activity
Days
PM2.5
PM10
PM2.5
PM2.5
PM2.5
Ozone
Ozone
PM2.5>
Ozone
Dockeryetal. (1996)
Pope etal. (1991)
Schwartz and Neas (2000)
Pooled estimate:
Ostro et al. (2001) (cough, wheeze and shortness of
breath)
Vedal etal. (1998) Cough
Ostro (1987)
Pooled estimate:
Gilliland etal (2001)
Chen et al (2000)
Crocker and Horst (1981)
Ostro and Rothschild (1989)
8-12 years
Asthmatics, 9-11
years
7-14 years
6-18 years*
1 8-65 years
9- 10 years
6- 1 1 years
Outdoor workers,
18-65
18-65 years
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 SAB-HES, we have extended the applied population to 6 to 18,
reflecting the common biological basis for the effect in children in the broader age group.
Researchers have found statistically significant associations between PM and premature
mortality using both types of studies. In general, the risk estimates based on the long-term
exposure studies are larger than those derived from short-term studies. Cohort analyses are
better able to capture the full public health impact of exposure to air pollution over time
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(Kunzli, 2001; NRC, 2002). This section discusses some of the issues surrounding the
estimation of premature mortality.
Over a dozen studies have found significant associations between various measures of
long-term exposure to PM and elevated rates of annual mortality, beginning with Lave and
Seskin (1977). Most of the published studies found positive (but not always statistically
significant) associations with available PM indices such as total suspended particles (TSP),
however, exploration of alternative model specifications sometimes raised questions about
causal relationships (e.g., Lipfert, [1989]). These early "cross-sectional" studies (e.g., Lave
and Seskin [1977]; Ozkaynak and Thurston [1987]) were criticized for a number of
methodological limitations, particularly for inadequate control at the individual level for
variables that are potentially important in causing mortality, such as wealth, smoking, and
diet. More recently, several long-term studies have been published that use improved
approaches and appear to be consistent with the earlier body of literature. These new
"prospective cohort" studies reflect a significant improvement over the earlier work because
they include individual-level information with respect to health status and residence. The
most extensive study and analyses has been based on data from two prospective cohort
groups, often referred to as the Harvard "Six-City Study" (Dockery et al., 1993) and the
"American Cancer Society or ACS study" ( Pope et al., 1995); these studies have found
consistent relationships between fine particle indicators and premature mortality across
multiple locations in the United States. A third major data set comes from the California
based 7th Day Adventist Study (e.g., Abbey et al, 1999), which reported associations between
long-term PM exposure and mortality in men. Results from this cohort, however, have been
inconsistent and the air quality results are not geographically representative of most of the
United States. More recently, a cohort of adult male veterans diagnosed with hypertension
has been examined (Lipfert et al., 2000). The characteristics of this group differ from the
cohorts in the ACS, Six-Cities, and 7th Day Adventist studies with respect to income, race,
health status, and smoking status. Unlike previous long-term analyses, this study found some
associations between mortality and ozone but found inconsistent results for PM indicators.
Because of the selective nature of the population in the veteran's cohort, which may have
resulted in estimates of relative risk that are biased relative to a relative risk for the general
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population, we have chosen not to include any effect estimates from the Lipfert et al. (2000)
study in our benefits assessment.18
Given their consistent results and broad geographic coverage, the Six-City and ACS data
have been particularly important in benefits analyses. The credibility of these two studies is
further enhanced by the fact that they were subject to extensive reexamination and reanalysis
by an independent team of scientific experts commissioned by HEI (Krewski et al., 2000).
The final results of the reanalysis were then independently peer reviewed by a Special Panel
of the HEI Health Review Committee. The results of these reanalyses confirmed and
expanded those of the original investigators. This intensive independent reanalysis effort was
occasioned both by the importance of the original findings as well as concerns that the
underlying individual health effects information has never been made publicly available.
The HEI re-examination lends credibility to the original studies and highlights
sensitivities concerning the relative impact of various pollutants, the potential role of
education in mediating the association between pollution and mortality, and the influence of
spatial correlation modeling. Further confirmation and extension of the overall findings
using more recent air quality and a longer follow-up period for the ACS cohort was recently
published in the Journal of the American Medical Association (Pope et al., 2002).
In developing and improving the methods for estimating and valuing the potential
reductions in mortality risk over the years, the EPA has consulted with the SAB-HES. That
panel recommended use of long-term prospective cohort studies in estimating mortality risk
reduction (EPA-SAB-COUNCIL-ADV-99-005, 1999). This recommendation has been
l8The EPA recognizes that the ACS cohort also is not completely representative of the demographic mix in the
general population. The ACS cohort is almost entirely white, and has higher income and education levels
relative to the general population. The EPA's approach to this problem is to match populations based on the
potential for demographic characteristics to modify the effect of air pollution on mortality risk. Thus, for the
various ACS-based models, we are careful to apply the effect estimate only to ages matching those in the
original studies, because age has a potentially large modifying impact on the effect estimate, especially when
younger individuals are excluded from the study population. For the Lipfert analysis, the applied population
should be limited to that matching the sample used in the analysis. This sample was all male, veterans, and
diagnosed hypertensive. There are also a number of differences between the composition of the sample and
the general population, including a higher percentage of African Americans (35 percent), and a much higher
percentage of smokers (81 percent former smokers, 57 percent current smokers) than the general population
(12 percent African American, 24 percent current smokers). These composition differences cannot be
controlled for, but should be recognized as adding to the potential extrapolation bias. The EPA recognizes
the difficulty in controlling for composition of income and education levels. However, in or out criterion
such as age, veteran status, hypertension, race and sex are all controllable by applying filters to the
population data. The EPA has traditionally only controlled for age, because the ACS study used only age as
a screen.
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confirmed by a recent report from the National Research Council, which stated that "it is
essential to use the cohort studies in benefits analysis to capture all important effects from air
pollution exposure" (NAS, 2002, p. 108). More specifically, the SAB recommended
emphasis on the ACS study because it includes a much larger sample size and longer
exposure interval and covers more locations (e.g., 50 cities compared to the Six Cities Study)
than other studies of its kind. As explained in the regulatory impact analysis for the
Heavy-Duty Engine/Diesel Fuel rule (EPA, 2000a), more recent EPA benefits analyses have
relied on an improved specification of the ACS cohort data that was developed hi the HEI
reanalysis (Krewski et al., 2000). The latest reanalysis of the ACS cohort data (Pope et al.,
2002), provides additional refinements to the analysis of PM-related mortality by
.(a) extending the follow-up period for the ACS study subjects to 16 years, which triples the
size of the mortality data set; (b) substantially increasing exposure data, including
consideration for cohort exposure to PM2.5 following implementation of PM2.5 standard in
1999; (c) controlling for a variety of personal risk factors including occupational exposure
and diet; and (d) using advanced statistical methods to evaluate specific issues that can
adversely affect risk estimates including the possibility of spatial autocorrelation of survival
times in communities located near each other. Because of these refinements, the SAB- HES
recommends using the Pope et al. (2002) study as the basis for the primary mortality estimate
for adults and suggests that alternate estimates of mortality generated using other cohort and
time series studies could be included as part of the sensitivity analysis (SAB-HES, 2003).
The SAB-HES also recommended using the estimated relative risks from the Pope et al.
(2002) study based on the average exposure to PM2.5, measured by the average of two
PM2.5 measurements, over the periods 1979-1983, and 1999-2000. In addition to relative
risks for all-cause mortality, the Pope et al. (2002) study provides relative risks for
cardiopulmonary, lung cancer, and all other cause mortality. Because of concerns regarding
the statistical reliability of the all-other cause mortality relative risk estimates, we calculate
mortality impacts for the primary analysis based on the all-cause relative risk. However, we
provide separate estimates of cardiopulmonary and lung cancer deaths to show how these
important causes of death are affected by reductions in PM2.5.
In previous RIAs, infant mortality has not been evaluated as part of the primary analysis.
Instead, benefits estimates related to reduced infant mortality have been included as part of
the sensitivity analysis for RIAs. However, recently published studies have strengthened the
case for an association between PM exposure and respiratory infiamation and infection
leading to premature mortality in children under 5 years of age. Specifically, the SAB- HES
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noted the release of the World Health Organization Global Burden of Disease Study focusing
on ambient air, which cites several recently published time-series studies relating daily PM
exposure to mortality in children (SAB-HES, 2003). The SAB-HES also cites the study by
Belanger et al. (2003) as corroborating findings linking PM exposure to increased respiratory
inflamation and infections in children. Recently, a study by Chay and Greenstone (2003)
found that reductions in TSP caused by the recession of 1981-1982 were related to reductions
in infant mortality at the county level. With regard to the cohort study conducted by
Woodruff et al. (1997), the SAB- HES notes several strengths of the study, including the use
of a larger cohort drawn from a large number of metropolitan areas and efforts to control for
a variety of individual risk factors in infants (e.g., maternal educational level, maternal
ethnicity, parental marital status, and maternal smoking status). Based on these findings, the
SAB-HES recommends that the EPA incorporate infant mortality into the primary benefits
estimate and that infant mortality be evaluated using a impact function developed from the
Woodruff et al. (1997) study (SAB-HES, 2003).
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 5
percent of the U.S. population (American Lung Association, 1999). A limited number of
studies have estimated the impact of air pollution on new incidences of CB. Schwartz
(1993) and Abbey et al.(1995) provide evidence that long-term PM exposure gives rise to the
development of CB in the United States. Because the Inter-State Air Quality regulations are
expected to reduce primarily PM2 5, this analysis uses only the Abbey et al (1995) study,
because it is the only study focusing on the relationship between PM2 5 and new incidences of
CB.
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). We use a recent study by Peters et al. (2001) as the basis
for the impact function estimating the relationship between PM2.5 and nonfatal heart attacks.
Peters et al. is the only available U.S. study to provide a specific estimate for heart attacks.
Other studies, such as Samet et al. (2000) and Moolgavkar et al. (2000), show a consistent
relationship between all cardiovascular hospital admissions, including for nonfatal heart
attacks, and PM. Given the lasting impact of a heart attack on longer-term health costs and
earnings, we choose to provide a separate estimate for nonfatal heart attacks based on the
single available U.S. effect estimate. The finding of a specific impact on heart attacks is
consistent with hospital admission and other studies showing relationships between fine
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particles and cardiovascular effects both within and outside the United States. These studies
provide a weight of evidence for this type of effect. Several epidemiologic studies (Liao et
al., 1999; Gold et al., 2000; Magari et al., 2001) have shown that heart rate variability (an
indicator of how much the heart is able to speed up or slow down in response to momentary
stresses) is negatively related to PM levels. Heart rate variability is a risk factor for heart
attacks and other coronary heart diseases (Carthenon et a.l, 2002; Dekker et al., 2000; Liao et
al., 1997, Tsuji et al., 1996). As such, significant impacts of PM on heart rate variability are
consistent with an increased risk of heart attacks.
Hospital and Emergency Room 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. In addition, some studies have examined the relationship between air pollution and
emergency room (ER) visits. Because most ER visits do not result in an admission to the
hospital (the majority of people going to the ER are treated and return home), we treat
hospital admissions and ER visits separately, taking account of the fraction of ER visits that
are admitted to the hospital.
Hospital admissions require the patient to be examined by a physician and, on average,
may represent more serious incidents than ER visits. The two main groups of hospital
admissions estimated in this analysis are respiratory admissions and cardiovascular
admissions. There is not much evidence linking ozone or PM with other types of hospital
admissions. The only type of ER visits that have been consistently linked to ozone and PM
in the United States are asthma-related visits.
To estimate avoided incidences of cardiovascular hospital admissions associated with
PM2.5, we use studies by Moolgavkar (2003) and Ito et al. (2003). There are additional
published studies showing a statistically significant relationship between PM10 and
cardiovascular hospital admissions. However, given that the preliminary control options we
are analyzing are expected to reduce primarily PM2.5, we have chosen to focus on the two
studies focusing on PM2.5. Both of these studies provide an effect estimate for populations
over 65, allowing us to pool the impact functions for this age group. Only Moolgavkar
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(2000) provided a separate effect estimate for populations 20 to 64.19 Total cardiovascular
hospital admissions are thus the sum of the pooled estimate for populations over 65 and the
single study estimate for populations 20 to 64. Cardiovascular hospital admissions include
admissions for myocardial infarctions. To avoid double counting benefits from reductions in
myocardial infarctions when applying the impact function for cardiovascular hospital
admissions, we first adjusted the baseline cardiovascular hospital admissions to remove
admissions for myocardial infarctions.
To estimate total avoided incidences of respiratory hospital admissions, we use impact
functions for several respiratory causes, including chronic obstructive pulmonary disease
(COPD), pneumonia, and asthma. As with cardiovascular admissions, there are additional
published studies showing a statistically significant relationship between PM10 and
respiratory hospital admissions. We use only those focusing on PM2.5. Both Moolgavkar
(2000) and Ito et al. (2003) provide effect estimates for COPD in populations over 65,
allowing us to pool the impact functions for this group. Only Moolgavkar (2000) provided a
separate effect estimate for populations 20 to 6420. 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. Only Ito et al (2003) estimated pneumonia, and only for the population
65 and older. In addition, Sheppard et al. (2003) provided an effect estimate for asthma
hospital admissions for populations under age 65. Total avoided incidences of PM-related
respiratory-related hospital admissions is the sum of COPD, pneumonia, and asthma
admissions.
To estimate the effects of PM air pollution reductions on asthma-related ER visits, we use
the effect estimate from a study of children 18 and under by Norris et al. (1999). As noted
earlier, there is another study by Schwartz examining a broader age group (less than 65), but
the Schwartz study focused on PM10 rather than PM2.5. We selected the Norris et al. (1999)
19Note 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 have chosen to use the
existing study. Given the very small (<5 percent) difference in the effect estimates for 65 and older
cardiovascular hospital admissions between the original and reanalyzed results, we do not expect there to be
much bias introduced by this choice.
20Note 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 have chosen to use the
existing study. Given the very small (<10 percent) difference in the effect estimates for 65 and older COPD
hospital admissions between the original and reanalyzed results, we do not expect there to be much bias
introduced by this choice.
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effect estimate because it better matched the pollutant of interest. Because children tend to
have higher rates of hospitalization for asthma relative to adults under 65, we will likely
capture the majority of the impact of PM2.5 on asthma ER visits in populations under 65,
although there may still be significant impacts in the adult population under 65.
To estimate avoided incidences of respiratory hospital admissions associated with ozone,
we use a number of studies examining hospital admissions for a range of respiratory illnesses,
including pneumonia and COPD. Two age groups, adults over 65 and children under 2, are
examined. For adults over 65, Schwartz (1995) provides effect estimates for two different
cities relating ozone and hospital admissions for all respiratory causes (defined as ICD codes
460-519). Impact functions based on these studies are pooled first before being pooled with
other studies. Two studies (Moolgavkar et al., 1997; Schwartz, 1994a) examined ozone and
pneumonia hospital admissions in Minneapolis. One additional study (Schwartz, 1994b)
examined ozone and pneumonia hospital admissions in Detroit. The impact functions for
Minneapolis are pooled together first, and the resulting impact function is then pooled with
the impact function for Detroit. This avoids assigning too much weight to the information
coming from one city. For COPD hospital admissions, there are two available studies,
Moolgavkar et al. (1997), conducted in Minneapolis, and Schwartz (1994b), conducted in
Detroit. These two studies are pooled together. To estimate total respiratory hospital
admissions for adults over 65, COPD admissions are added to pneumonia .admissions, and
the result is pooled with the Schwartz (1995) estimate of total respiratory admissions.
Burnett et al. (2001) is the only study providing an effect estimate for respiratory hospital
admissions in children under 2.
Acute Health Events and School/Work Loss Days. As indicated in Table 4-1, 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 ozone and PM.
The sources for the effect estimates used to quantify these effects are described below.
Around 4 percent of U.S. children between ages 5 and 17 experience episodes of acute
bronchitis annually (American Lung Association, 2002). Acute bronchitis is characterized by
coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a number of days.
According to the MedlinePlus medical encyclopedia,21 with the exception of cough, most
acute bronchitis symptoms abate within 7 to 10 days. Incidence of episodes of acute
2lSee http://www.nlm.nih.gov/medlineplus/ency/article/000124.htm, accessed January 2002.
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bronchitis in children between the ages of 5 and 17 are estimated using an effect estimate
developed from Dockery et al. (1996).
Incidences of lower respiratory symptoms (e.g., wheezing, deep cough) in children aged 7
to 14 are 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 9 to 11 are estimated
using an effect estimate developed from Pope et al. (1991).
Health effects from air pollution can also result in missed days of work (either from
personal symptoms or from caring for a sick family member). Work loss days due to PM2.5
are estimated using an effect estimate developed from Ostro (1987). Children may also be
absent from school due to respiratory or other diseases caused by exposure to air pollution.
Most studies examining school absence rates have found little or no association with PM2.5,
but several studies have found a significant association between ozone levels and school
absence rates. We use two recent studies, Gilliland et al. (2001) and Chen et al. (2000), to
estimate changes in absences (school loss days) due to changes in ozone levels. The
Gilliland et al. study estimated the incidence of new periods of absence, while the Chen et al.
study examined absence on a given day. We convert the Gilliland estimate to days of
absence by multiplying the absence periods by the average duration of an absence. We
estimate an average duration of school absence of 1.6 days by dividing the average daily
school absence rate from Chen et al. (2000) and Ransom and Pope (1992) by the episodic
absence rate from Gilliland et al. (2001). This provides estimates from Chen et al. (2000)
and Gilliland et al. (2000), which can be pooled to provide an overall estimate.
Minor restricted activity days (MRAD) result when individuals reduce most usual daily
activities and replace them with less strenuous activities or rest, yet not to the point of
missing work or school. For example, a mechanic who would usually be doing physical
work most of the day will instead spend the day at a desk doing paper and phone work due to
difficulty breathing or chest pain. The effect of PM2.5 and ozone on MRAD is estimated
using an effect estimate derived from Ostro and Rothschild (1989).
In previous RIAs, we have not included estimates of asthma exacerbations hi the
asthmatic population in the primary analysis because of concerns over double counting of
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benefits and difficulties in differentiating asthma symptoms for purposes of first developing
impact functions that cover distinct endpoints and then establishing the baseline incidence
estimates required for predicting incidence reductions. 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 was 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 SAB-HES, in commenting on the
analytical blueprint for 812 acknowledged these challenges in evaluating asthmatic symptoms
and appropriately adding them into the primary analysis (SAB-HES, 2003). However,
despite these challenges, the SAB-HES recommends 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 SAB-HES, while
supporting the incorporation of asthma exacerbation estimates, does 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. For the IAQR, we have followed the SAB-HES recommendations
regarding asthma exacerbations in developing the primary estimate. To prevent double
counting, we are focusing the estimation on asthma exacerbations occurring in children and
are excluding adults from the calculation. Asthma exacerbations occurring in adults are
assumed to be captured in the general population endpoints such as work loss days and
MRADs. Consequently, if we had included an adult-specific asthma exacerbation estimate,
we 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 exacerbations for children (6 to 18 years of age) could be
conducted without concern for double counting.
To characterize asthma exacerbations in children, we selected two studies (Ostro et al.,
2001 and 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.,
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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. Association between PM10 and respiratory symptoms for the asthmatic population
was only reported for two endpoints: cough and PEF. 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 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).
Additional epidemiological studies are available for characterizing asthma-related health
endpoints (the full list of epidemiological studies considered for modeling asthma-related
incidence are presented in Table 4-8). However, based on recommendations from the SAB-
HES, we decided not to use these additional studies in generating the primary estimate. In
particular, the Yu et al. (2000) estimates show a much higher baseline incidence rate than
other studies, which may lead to an overstatement of the expected impacts in the overall
asthmatic population. The Whittemore and Korn (1980) study did not use a well-defined
endpoint, instead focusing on a respondent-defined "asthma attack." Other studies looked at
respiratory symptoms in asthmatics but did not focus on specific exacerbations of asthma.
4.1.5.2 Uncertainties Associated with Health Impact Functions
Within-Study Variation. Within-study variation refers to the precision with which a given
study estimates the relationship between air quality changes and health effects. Health effects
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studies provide both a "best estimate" of this relationship plus a measure of the statistical
uncertainty of the relationship. This size of this uncertainty depends on factors such as the
number of subjects studied and the size of the effect being measured. The results of even the
most well-designed epidemiological studies are characterized by this type of uncertainty,
though well-designed studies typically report narrower uncertainty bounds around the best
estimate than do studies of lesser quality. In selecting health endpoints, we generally focus
on endpoints where a statistically significant relationship has been observed in at least some
studies, although we may pool together results from studies with both statistically significant
and insignificant estimates to avoid selection bias.
Across-Study Variation. Across-study variation refers to the fact that different published
studies of the same pollutant/health effect relationship typically do not report identical
findings; in some instances the differences are substantial. These differences can exist even
between equally reputable studies and may result in health effect estimates that vary
considerably. Across-study variation can result from two possible causes. One possibility is
that studies report different estimates of the single true relationship between a given pollutant
and a health effect due to differences in study design, random chance, or other factors. For
example, a hypothetical study conducted in New York and one conducted in Seattle may
report different C-R functions for the relationship between PM and mortality, in part because
of differences between these two study populations (e.g., demographics,
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Table 4-8. Studies Examining Health Impacts in the Asthmatic Population Evaluated
for Use in the Benefits Analysis
Endpoint
Definition
Pollutant | Study j Study Population
Asthma Attack Indicators'
Shortness of
breath
Cough
Wheeze
Asthma exacerbation
Cough
Prevalence of shortness
of breath; incidence of
shortness of breath
Prevalence of cough; incidence
of cough
Prevalence of wheeze;
incidence of wheeze
2 1 mild asthma symptom:
wheeze, cough, chest tightness,
shortness of breath)
Prevalence of cough
PM2.5
PM2.5
PM2.5
PM10, PMLO
PM10
Ostro et al. (2001)
Ostroetal. (2001)
Ostro etal. (2001)
Yu et al. (2000)
Vedal etal. (1998)
African- American
asthmatics, 8-13
African-American
asthmatics, 8-13
African-American
asthmatics, 8-13
Asthmatics, 5-13
Asthmatics, 6-13
Other symptoms/illness endpoints
Upper respiratory
symptoms
Moderate or worse
asthma
Acute bronchitis
Phlegm
Asthma attacks
* 1 of the following: runny or
stuffy nose; wet cough;
burning, aching, or red eyes
Probability of moderate (or
worse) rating of overall asthma
status
s 1 episodes of bronchitis in Ihe
past 12 months
"Other than with colds, does
this child usually seem
congested in the chest or bring
up phlegm?"
Respondent-defined asthma
attack
PM10
PM2.5
PM2.5
PM2.5
PM2.5,
ozone
Pope etal. (1991)
Ostroetal. (1991)
McConnell et al.
(1999)
McConnell et al.
(1999)
Whittemore and Korn
(1980)
Asthmatics 9- 11
Asthmatics, all ages
Asthmatics, 9-15*
Asthmatics, 9-15*
Asthmatics, all ages
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activity patterns). Alternatively, study results may differ because these two studies are in fact
estimating different relationships; that is, the same reduction in PM in New York and Seattle
may result in different reductions in premature mortality. This may result from a number of
factors, such as differences in the relative sensitivity of these two populations to PM
pollution and differences in the composition of PM in these two locations. In either case,
where we identified multiple studies that are appropriate for estimating a given health effect,
we generated a pooled estimate of results from each of those studies.
Application ofC-R Relationship Nationwide. Regardless of the use of impact functions
based on effect estimates from a single epidemiological study or multiple studies, each
impact function was applied uniformly throughout the United States to generate health
benefit estimates. However, to the extent that pollutant/health effect relationships are
region-specific, applying a location-specific impact function at all locations in the United
States may result in overestimates of health effect changes in some locations and
underestimates of health effect changes in other locations. It is not possible, however, to
know the extent or direction of the overall effect on health benefit estimates introduced by
application of a single impact function to the entire United States. This may be a significant
uncertainty in the analysis, but the current state of the scientific literature does not allow for a
region-specific estimation of health benefits.22
Extrapolation of Impact Functions Across Populations. Epidemiological studies often
focus on specific age ranges, either due to data availability limitations (e.g., most hospital
admission data come from Medicare records, which are limited to populations 65 and older),
or to simplify data collection (e.g., some asthma symptom studies focus on children at
summer camps, which usually have a limited age range). We have assumed for the primary
analysis that most impact functions should be applied only to those populations with ages that
strictly match the populations in the underlying epidemiological studies. However, in many
cases, there is no biological reason why the observed health effect would not also occur in
other populations within a reasonable range of the studied population. For example, Dockery
et al. (1996) examined acute bronchitis in children aged 8 to 12. There is no biological
reason to expect a very different response in children aged 6 or 14. By excluding populations
outside the range in the studies, we may be underestimating the health impact in the overall
population. In response to recommendations from the SAB-HES, where there appears to be a
22Although we are not able to use region-specific effect estimates, we use region-specific baseline incidence
rates where available. This allows us to take into account regional differences in health status, which can
have a significant impact on estimated health benefits.
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reasonable physiological basis for expanding the age group associated with a specific effect
estimate beyond the study population to cover the full age group (e.g., expanding from a
study population of 7 to 11 year olds to the full 6to 18 year child age group), we have done so
and used those expanded incidence estimates in the primary analysis.
Uncertainties in the PMMortality Relationship. Health researchers have consistently
linked air pollution, especially PM, with excess mortality. A substantial body of published
scientific literature recognizes a correlation between elevated PM concentrations and
increased mortality rates. However, much about this relationship is still uncertain. These
uncertainties include the following:
• Causality: A substantial number of published epidemiological studies find an
association between elevated PM concentrations and increased mortality rates;
however, these epidemiological studies are not designed to definitively prove
causation. For the analysis of the IAQ rulemaking, we assumed a causal relationship
between exposure to elevated PM and premature mortality, based on the consistent
evidence of a correlation between PM and mortality reported in the substantial body
of published scientific literature.
• Other Pollutants: PM concentrations are correlated with the concentrations of other
criteria pollutants, such as ozone and CO, and it is unclear how much each of these
pollutants may influence mortality rates. Recent studies (see Thurston and Ito [2001])
have explored whether ozone may have mortality effects independent of PM, but we
do not view the evidence as conclusive at this time. The EPA is currently evaluating
the epidemiological literature on the relationship between ozone and mortality and
will determine whether to include ozone mortality as a separate impact in the analysis
of the final IAQR based on the results of our evaluation. To the extent that the C-R
functions we use to evaluate the preliminary control options in fact capture mortality
effects of other criteria pollutants besides PM, we may be overestimating the benefits
of reductions in PM. However, we are not providing separate estimates of the
mortality benefits from the ozone and CO reductions likely to occur due to the
preliminary control options.
• Shape of the C-R Function: The shape of the true PM mortality C-R function is
uncertain, but this analysis assumes the C-R function to have a log-linear form (as
derived from the literature) throughout the relevant range of exposures. If this is not
the correct form of the C-R function, or if certain scenarios predict concentrations
well above the range of values for which the C-R function was fitted, avoided
mortality may be mis-estimated.
• Regional Differences: As discussed above, significant variability exists in the results
of different PM/mortality studies. This variability may reflect regionally specific C-R
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functions resulting from regional differences in factors such as the physical and
chemical composition of PM. If true regional differences exist, applying the
PM/mortality C-R function to regions outside the study location could result in
mis-estimation of effects in these regions.
• Exposure/Mortality Lags: There is a potential time lag between changes in PM
exposures and changes in mortality rates. For the chronic PM/mortality relationship,
the length of the lag is unknown and may be dependent on the kind of exposure. 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. There is no specific scientific evidence of the existence or structure of a
PM effects lag. However, current scientific literature on adverse health effects similar
to those associated with PM (e.g., smoking-related disease) and the difference in the
effect size between chronic exposure studies and daily mortality studies suggests that
all incidences of premature mortality reduction associated with a given incremental
change in PM exposure probably would not occur in the same year as the exposure
reduction. The smoking-related literature also implies that lags of up to a few years or
longer are plausible. Adopting the lag structure used in the Tier 2/Gasoline Sulfur
and Heavy-Duty Engine/Diesel Fuel RIAs and endorsed by the SAB
(EPA-SAB-COUNCIL-ADV-00-001, 1999), we assume a 5-year lag structure.23 This
approach assumes that 25 percent of PM-related premature deaths occur in each of the
first 2 years after the exposure and the rest occur in equal parts (approximately 17
percent) in each of the ensuing 3 years.
• Cumulative Effects: As a general point, we attribute the PM/mortality relationship in
the underlying epidemiological studies to cumulative exposure to PM. However, the
relative roles of PM exposure duration and PM exposure level in inducing premature
mortality remain unknown at this time.
4.1.5.3 Baseline Health Effect Incidence Rates
The 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 an estimate of the absolute number of avoided
cases. For example, a typical result might be that a 10 M,g/m3 decrease in daily PM2 5 levels
might 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. The baseline incidence
23 The SAB-HES has recently recommended that EPA rethink the use of a 5-year lag. They recommend that a
more complex lag structure be considered incorporation components dealing with short-term (0-6 months),
intermediate (1-2 years) and long-term (15-25 years) exposures. EPA is evaluating techniques for
characterizing lag structures and will incorporate new methods as they become available.
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rate provides an estimate of the incidence rate (number of cases of the health effect per year,
usually per 10,000 or 100,000 general population) in the assessment location corresponding
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 (e.g., if the baseline
incidence rate is number of cases per year per 100,000 population, it must be multiplied by
the number of 100,000s in the population).
Some epidemiological studies examine the association between pollution levels and
adverse health effects in a specific subpopulation, such as asthmatics or diabetics. In these
cases, it is necessary to develop not only baseline incidence rates, but also prevalence rates
for the defining condition (e.g., asthma). For both baseline incidence and prevalence data, we
use age-specific rates where available. Impact functions are applied to individual age groups
and then summed over the relevant age range to provide an estimate of total population
benefits.
In most cases, because of a lack of data or methods, we have not attempted to project
incidence rates to future years, instead assuming that the most recent data on incidence rates
is the best prediction of future incidence rates. In recent years, better data on trends in
incidence and prevalence rates for some endpoints, such as asthma, have become available.
We are working to develop methods to use these data to project future incidence rates.
However, for our primary benefits analysis of the proposed IAQR, we will continue to use
current incidence rates. We will examine the impact of using projected mortality rates and
asthma prevalence in sensitivity analyses.
Table 4-9 summarizes the baseline incidence data and sources used in the benefits
analysis. In most cases, a single national incidence rate is used, due to a lack of more
spatially disaggregated data. We used national incidence rates whenever possible, because
these data are most applicable to a national assessment of benefits. However, for some
studies, 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. However, for hospital admissions, regional rates are available, and for
premature mortality, county-level data are available.
Age, cause, and county-specific mortality rates were obtained from the U.S. Centers
for Disease Control (CDC) for the years 1996 through 1998. CDC maintains an online data
repository of health statistics, CDC Wonder, accessible at http://wonder.cdc.gov/. The
mortality
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Table 4-9. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact
Functions, General Population
Endpoint
Mortality
Hospitalizations
Asthma ER visits
Chronic Bronchitis
Nonfatal MI (heart
attacks)
Asthma
Exacerbations
Acute Bronchitis
Parameter
Daily or annual mortality rate
Daily hospitalization rate
Daily asthma ER visit rate
Annual prevalence rate per
person
Age 18-44
Age 45-64
Age 65 and older
Annual incidence rate per
person
Daily nonfatal myocardial
infarction incidence rate per
person, 18+
Northeast
Midwest
South
West
Incidence (and prevalence)
among asthmatic African
American children
- daily wheeze
- daily cough
- daily dyspnea
Prevalence among asthmatic
children
- daily wheeze
- daily cough
- daily dyspnea
Annual bronchitis incidence
rate, children
Rates
Value
Age, cause, and county-specific
rate
Age, region, cause-specific rate
Age, Region specific visit rate
0.0367
0.0505
0.0587
0.00378
0.0000159
0.0000135
0.0000111
0.0000100
0.076(0.173)
0.067 (0.145)
0.037 (0.074)
0.038
0.086
0.045
0.043
Source*
CDC Wonder (1996- 1998)
1999 NHDS public use data files"
2000 NHAMCS public use data
files'; 1999 NHDS public use data
files"
1999 HIS (American Lung
Association, 2002b, Table 4)
Abbey et al. (1993, Table 3)
1999 NHDS public use data files';
adjusted by 0.93 for prob. of
surviving after 28 days (Rosamond et
al., 1999)
Ostroetal. (2001)
Vedaletal. (1998)
American Lung Association (2002a,
Table 11)
(continued)
4-53
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Table 4-9. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact
Functions, General Population (continued)
Endpoint
Lower Respiratory
Symptoms
Upper Respiratory
Symptoms
Work Loss Days
Minor Restricted
Activity Days
School Loss Days'
Parameter
Daily lower respiratory
symptom incidence among
children11
Daily upper respiratory
symptom incidence among
asthmatic children
Daily WLD incidence rate
per person (18-65)
Age 18-24
Age 25^4
Age 45-64
Daily MRAD incidence rate
per person
Daily school absence rate
per person
Daily illness-related school
absence rate per person0
Northeast
Midwest
South
Southwest
Daily respiratory illness-
related school absence rate
per person
Northeast
Midwest
South
West
Rates
Value
0.0012
0.3419
0.00540
0.00678
0.00492
0.02137
0.055
0.0136
0.0146
0.0142
0.0206
0.0073
0.0092
0.0061
0.0124
Source*
Schwartz (1994, Table 2)
Pope etal. (1991, Table 2)
1996 HIS (Adams etal., 1999,
Table 41); U.S. Bureau of the
Census (2000)
Ostro and Rothschild (1989, p.
243)
National Center for Education
Statistics (1996)
1996 HIS (Adams etal., 1999,
Table 47); estimate of 180 school
days per year
1996 HIS (Adams etal., 1999,
Table 47); estimate of 180 school
days per year
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.
See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/
Seeftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/
Lower Respiratory Symptoms are defined as 2 2 of the following: cough, chest pain, phlegm, wheeze
The estimate of daily illness-related school absences excludes school loss days associated with injuries to match the
4-54
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rates provided are derived from U.S. death records and U.S. Census Bureau postcensal
population estimates. Mortality rates were averaged across 3 years (1996 through 1998) to
provide more stable estimates. When estimating rates for age groups that differed from the
CDC Wonder groupings, we assumed that rates were uniform across all ages in the reported
age group. For example, to estimate mortality rates for individuals ages 30 and up, we scaled
the 25- to 34-year old death count and population by one-half and then generated a
population-weighted mortality rate using data for the older age groups. Note that we have not
projected any changes in mortality rates over time. We are aware that the U.S. Census
projections of total and age-specific mortality rates used in our population projections are
based on projections of declines in mortality rates for younger populations and increases in
mortality rates for older populations over time. We are evaluating the most appropriate way
to incorporate these projections of changes in overall national mortality rates into our
database of county-level cause-specific mortality rates. In the interim, we have not attempted
to adjust future mortality rates. This will lead to an overestimate of mortality benefits in
future years, with the overestimation bias increasing the further benefits are projected into the
future. We do not at this time have a quantified estimate of the magnitude of the potential
bias in the years analyzed for this rule (2010 and 2015).
For the set of endpoints affecting the asthmatic population, in addition to baseline
incidence rates, prevalence rates of asthma in the population are needed to define the
applicable population. Table 4-9 lists the baseline incidence rates and their sources for
asthma symptom endpoints. Table 4-10 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. As noted above, we are
investigating methods for projecting asthma prevalence rates in future years.
4.1.5.4 Accounting for Potential Health Effect Thresholds
When conducting clinical (chamber) and epidemiological studies, functions may be
estimated with or without explicit thresholds. Air pollution levels below the threshold are
assumed to have no associated adverse health effects. When a threshold is not assumed, as is
often the case in epidemiological studies, any exposure level is assumed to pose a nonzero
risk of response to at least one segment of the population.
The possible existence of an effect threshold is a very important scientific question
and issue for policy analyses such as this one. The EPA SAB Advisory Council for Clean
Air Compliance, which provides advice and review of the EPA's methods for assessing the
4-55
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benefits and costs of the Clean Air Act under Section 812 of the Clean Air Act, has advised
the EPA that there is currently no scientific basis for selecting a threshold of 15 ng/m3 or any
other specific threshold for the PM-related health effects considered in typical benefits
analyses (EPA-SAB-Council-ADV-99-012, 1999). This is supported by the recent literature
on health effects of PM exposure (Daniels et al., 2000; Pope, 2000; Rossi et al., 1999;
Schwartz, 2000) that finds in most cases no evidence of a nonlinear relationship between PM
4-56
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Table 4-10. Asthma Prevalence Rates Used to Estimate Asthmatic Populations in
Impact Functions
Population Group
All Ages
<18
5-17
18-44
45-64
65+
Male, 27+
African-American, 5 to 17
African-American, <18
Asthma Prevalence Rates
Value
0.0386
0.0527
0.0567
0.0371
0.0333
0.0221
0.021
0.0726
0.0735
Source
American Lung Association (2002c, Table 7) — based on 1999 HIS
American Lung Association (2002c, Table 7) — based on 1999 HIS
American Lung Association (2002c, Table 7) — based on 1999 HIS
American Lung Association (2002c, Table 7) — based on 1999 HIS
American Lung Association (2002c, Table 7) — based on 1999 HIS
American Lung Association (2002c, Table 7) — based on 1999 HIS
2000 HIS public use data files"
American Lung Association (2002c, Table 9)— based on 1999 HIS
American Lung Association (2002c, Table 9) — based on 1999 HIS v
" See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/
and health effects and certainly does not find a distinct threshold. The most recent draft of
the EPA Air Quality Criteria for Particulate Matter (EPA, 2002) reports only one study,
analyzing data from Phoenix, AZ, that reported even limited evidence suggestive of a
possible threshold for PM2.5 (Smith et al., 2000).
Recent cohort analyses by HEI (Krewski et al., 2000) and Pope et al. (2002) provide
additional evidence of a quasi-linear relationship between long-term exposures to PM25 and
mortality. According to the latest draft PM criteria document, Krewski et al. (2000) found a
"visually near-linear relationship between all-cause and cardiopulmonary mortality residuals
and mean sulfate concentrations, near-linear between cardiopulmonary mortality and mean
PM2 5, but a somewhat nonlinear relationship between all-cause mortality residuals and mean
PM2 5 concentrations that flattens above about 20 ng/m3. The confidence bands around the
fitted curves are very wide, however, neither requiring a linear relationship nor precluding a
nonlinear relationship if suggested by reanalyses."
4-57
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The Pope et al. (2002) analysis, which represented an extension to the Krewski et al.
analysis, found that the functions relating PM2.5 and mortality "were not significantly
different from linear associations."
Daniels et al. (2000) examined the presence of thresholds in PM10 C-R relationships
for daily mortality using the largest 20 U.S. cities for 1987-1994. The results of their models
suggest that the linear model was preferred over spline and threshold models. Thus, these
results suggest that linear models without a threshold may well be appropriate for estimating
the effects of PM10 on the types of mortality of main interest. Schwartz and Zanobetti (2000)
investigated the presence of threshold by simulation and actual data analysis of 10 U.S. cities.
In the analysis of real data from 10 cities, the combined C-R curve did not show evidence of
a threshold in the PM10-mortality associations. Schwartz, Laden, and Zanobetti (2002)
investigated thresholds by combining data on the PM2.5-mortality relationships for six cities
and found an essentially linear relationship down to 2 ng/m3, which is at or below
anthropogenic background in most areas. They also examined just traffic-related particles
and again found no evidence of a threshold. The Smith et al. (2000) study of associations
between daily total mortality and PM25 and PM10.2 5 in Phoenix, AZ, (during 1995-1997) also
investigated the possibility of a threshold using a piecewise linear model and a cubic spline
model. For both the piecewise linear and cubic spline models, the analysis suggested a
threshold of around 20 to 25 ng/m3. However, the C-R curve for PM2 5 presented in this
publication suggests more of a U- or V-shaped relationship than the usual "hockey stick"
threshold relationship.
Based on the recent literature and advice from the SAB, we assume there are no
thresholds for modeling health effects. Although not included in the primary analysis, the
potential impact of a health effects threshold on avoided incidences of PM-related premature
mortality is explored as a key sensitivity analysis and is presented in Appendix 9-B (to be
completed for the supplemental analysis).
Our assumptions regarding thresholds are supported by the National Research
Council hi its recent review of methods for estimating the public health benefits of air
pollution regulations. In their review, the National Research Council concluded that there is
no evidence for any departure from linearity in the observed range of exposure to PMIO or
PM25, nor any indication of a threshold. They cite the weight of evidence available from
both short- and long-term exposure models and the similar effects found in cities with low
and high ambient concentrations of PM.
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4.1.5.5 Selecting Unit Values for Monetizing Health Endpoints
The appropriate economic value of a change in a health effect depends on whether the
health effect is viewed ex ante (before the effect has occurred) or ex post (after the effect has
occurred). Reductions in ambient concentrations of air pollution generally lower the risk of
future adverse health affects by a fairly small amount for a large population. The appropriate
economic measure is therefore ex ante WTP for changes in risk. However, epidemiological
studies generally provide estimates of the relative risks of a particular health effect avoided
due to a reduction in air pollution. A convenient way to use this data in a consistent
framework is to convert probabilities to units of avoided statistical incidences. This measure
is calculated by dividing individual WTP for a risk reduction by the related observed change
in risk. For example, suppose a measure is able to reduce the risk of premature mortality
from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk
reduction is $100, then the WTP for an avoided statistical premature mortality amounts to $1
million ($100/0.0001 change hi risk). Using this approach, the size of the affected population
is automatically taken into account by the number of incidences predicted by epidemiological
studies applied to the relevant population. The same type of calculation can produce values
for statistical incidences of other health endpoints.
For some health effects, such as hospital admissions, WTP estimates are generally not
available. In these cases, we use the cost of treating or mitigating the effect as a primary
estimate. For example, for the valuation of hospital admissions we use the avoided medical
costs as an estimate of the value of avoiding the health effects causing the admission. These
COI estimates generally understate the true value of reductions in risk of a health effect.
They tend to reflect the direct expenditures related to treatment but not the value of avoided
pain and suffering from the health effect. Table 4-11 summarizes the value estimates per
health effect that we used in this analysis. Values are presented both for a 1990 base income
level and adjusted for income growth in the two future analysis years, 2010 and 2015. Note
that the unit values for hospital admissions are the weighted averages of the ICD-9 code-
specific values for the group of ICD-9 codes included in the hospital admission categories. A
discussion of the valuation methods for premature mortality and CB is provided here because
of the relative importance of these effects. Discussions of the methods used to value nonfatal
myocardial infarctions (heart attacks) and school absence days are provided because these
endpoints have only recently been added to the analysis and the valuation methods are still
under development. We welcome comment on these valuation methods. In the following
4-59
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discussions, unit values are presented at 1990 levels of income for consistency with previous
analyses. Equivalent future year values can be obtained from Table 4-11.
4-60
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Table 4-11. Unit Values Used for Economic Valuation of Health Endpoints (1999$)
Premature Mortality (Value of a
Statistical Life)
Health '
Endpoint
Nonfatal Myocardial Infarction (heart
attack)
Central Estimate of Value Per Statistical Incidence
1990 Income
Level
$66,902
$74,676
$78,834
$140,649
$66,902
$65,293
$73,149
$76,871
$132,214
2010 Income
Level
$66,902
$74,676
$78,834
$140,649
$66,902
$65,293
$73,149
$76,871
$132,214
J£C TOT
2015 Income
Level
$66,902
$74,676
$78,834
$140,649
$66,902
$65,293
$73,149
$76,871
$132,214
?65 l-193
Point estimate is the mean of a normal distribution with a 95 percent
confidence interval between $1 and $10 million. Confidence interval is based
on two meta-analyses of the wage-risk VSL literature. $1 million represents the
lower end of the interquartile range from the Mrozek and Taylor (2000) meta-
analysis. $10 million represents the upper end of the interquartile range from
the Viscusi and Aldy (2003) meta-analysis. The VSL represents the value of a
small change in mortality risk aggregated over the affected population.
Point estimate is the mean of a generated distribution of WTP to avoid a case
B/eM^ktttfff'tff fe&ttlififltesWI'P to avoid a case of pollution-related CB is
derived by adjusting WTP (as described in Viscusi et al., 1991) to avoid a
severe case of CB for the difference in severity and taking into account the
elasticity of WTP with respect to severity of CB.
Age specific cost-of-illness values reflecting lost earnings and direct medical
costs over a 5 year period following a non-fatal MI. Lost earnings estimates
based on Cropper and Krupnick (1990). Direct medical costs based on simple
average of estimates from Russell et al. (1998) and Wittels et al. (1990).
Cropper and Krupnick (1990). Present discounted value of 5 yrs of lost
earnings:
at 7%
25-44 $8,774 $7,855
45-54 $12,932 $11,578
55-65 $74,746 $66,920
: An average of:
1. Wittels etal., 1990($102,658-no discounting)
2. Russell etal., 1998, 5-yr period. ($22,331 at 3% discount rate; $21,113 at
7% discount rate)
-------
Table 4-11. Unit Values Used for Economic Valuation of Health Endpoints (1999$) (continued)
Central Estimate of Value Per Statistical Incidence
1990 Income
2010 Income
2015 Income
Hospital Admissions Level Level Level
Chronic Obstructive Pulmonary
a&fflfCCOPD)
pefreiaks 490-492, 494-496)
Pneumonia
(ICD codes 480-487)
All Cardiovascular
(ICD codes 390-429)
The COI estimates (lost earnings plus direct medical costs) are based on ICD-9
code level information (e.g., average hospital care costs, average length of
hospital stay, and weighted share of total COPD category illnesses) reported in
Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov).
The COI estimates (lost earnings plus direct medical costs) are based on ICD-9
code level information (e.g., average hospital care costs, average length of
toft[UlatenX>PBdtft1SlgbSed share of total pneumonia category illnesses)
reported in Agency for Healthcare Research and Quality, 2000
(www.ahrq.gov).
The COI estimates (lost earnings plus direct medical costs) are based on ICD-9
code level information (e.g., average hospital care costs, average length of
hospital stay, and weighted share of total asthma category illnesses) reported in
Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov).
The COI estimates (lost earnings plus direct medical costs) are based on ICD-9
code level information (e.g., average hospital care costs, average length of
hospital stay, and weighted share of total cardiovascular category illnesses)
reported in Agency for Healthcare Research and Quality, 2000
(www.ahrq.gov).
Simple average of two unit COI values:
(1) $31 1.55, from Smith et al., 1997, and
(2) $260.67, from Stanford et al., 1999.
*>.
KJ
-------
Table 4-11. Unit Values Used for Economic Valuation of Health Endpoints (1999$) (continued)
Central Estimate of Value Per Statistical Incidence
1990 Income
2010 Income
2015 Income
Respiratory Ailments Not Requiring Hospitij^ajjon Level Level
Health
Endpoint
Combinations of the 3 symptoms for which WTP estimates are available that
closely match those listed by Pope, et al. result in 7 different "symptom
clusters," each describing a "type" of URS. A dollar value was derived for
each type of URS, using mid-range estimates of WTP (lEc, 1994) to avoid
each symptom in the cluster and assuming additivity of WTPs. The dollar
DyHVJKtoWe? EsftftaY#age of the dollar values for the 7 different types of
URS.
Combinations of the 4 symptoms for which WTP estimates are available that
closely match those listed by Schwartz, et al. result in 1 1 different "symptom
clusters," each describing a "type" of LRS. A dollar value was derived for
each type of LRS, using mid-range estimates of WTP (lEc, 1994) to avoid each
symptom in the cluster and assuming additivity of WTPs. The dollar value for
LRS is the average of the dollar values for the 1 1 different types of LRS.
Asthma exacerbations are valued at $42 per incidence, based on the mean of
average WTP estimates for the four severity definitions of a "bad asthma day,"
described in Rowe and Chestnut (1986). This study surveyed asthmatics to
estimate WTP for avoidance of a "bad asthma day," as defined by the subjects.
For purposes of valuation, an asthma attack is assumed to be equivalent to a
day in which asthma is moderate or worse as reported in the Rowe and
Chestnut (1986) study.
Assumes a 6 day episode, with daily value equal to the average of low and
high values for related respiratory symptoms recommended in Neumann, et al.
1994.
OS
-------
Table 4-11. Unit Values Used for Economic Valuation of Health Endpoints (1999$) (continued)
Central Estimate of Value Per Statistical Incidence
1990 Income
2010 Income
2015 Income
Restricted Activity and Work/School Loss 5aj(%l Level Level
Health
Endpoint
Minor Restricted Activity Days
(MRADs)
Variable
(national
mculuii — )
$0.95 per
rd^EhBigein
ozone per day
$0.95 per
workerper .
\(fK change in
ozone per day
$0.95 per
worker per .
10% change in
ozone per day
County-specific median annual wages divided by 50 (assuming 2 weeks of
vacation) and then by 5 - to get median daily wage. U.S. Year 2000 Census,
compiled by Geolytics, Inc.
Based on expected lost wages from parent staying home with child. Estimated
daily lost wage (if a mother must stay at home with a sick child) is based on
^ffia!J8fl#tl89iw^<5 among women age 25 and older in 2000 (U.S. Census
Bureau, Statistical Abstract of the United States: 2001 .Section 12: Labor
Force, Employment, and Earnings, Table No. 621). This median wage is $551.
Dividing by 5 gives an estimated median daily wage of $103.
The expected loss in wages due to a day of school absence in which the mother
would have to stay home with her child is estimated as the probability that the
mother is in the workforce times the daily wage she would lose if she missed a
day = 72.85% of $103, or $75.
Based on $68 - median daily earnings of workers in farming, forestry and
fishing - from Table 62 1 , Statistical Abstract of the United States ("Full-Time
Wage and Salary Workers - Number and Earnings: 1985 to 2000") (Source of
data in table: U.S. Bureau of Labor Statistics, Bulletin 2307 and Employment
and Earnings, monthly).
Median WTP estimate to avoid one MRAD from Tolley, et al. (1986) .
-------
4.1.5.5.1 Valuing Reductions in Premature Mortality Risk. We estimate the
monetary benefit of reducing premature mortality risk using the "value of statistical lives
saved" (VSL) approach, which is a summary measure for the value of small changes in
mortality risk experienced by a large number of people. The VSL approach applies
information from several published value-of-life studies to determine a reasonable benefit of
preventing premature mortality. The mean value of avoiding one statistical death is assumed
to be $5.5 million in 1999 dollars. This represents a central value consistent with the range
of values suggested by recent meta-analyses of the wage-risk VSL literature. The distribution
of VSL is characterized by a confidence interval from $1 to $10 million, based on two
meta-analyses of the wage-risk VSL literature. The $1 million lower confidence limit
represents the lower end of the interquartile range from the Mrozek and Taylor (2000)
meta-analysis. The $10 million upper confidence limit represents the upper end of the
interquartile range from the Viscusi and Aldy (2003) meta-analysis.
In previous analyses, we used an estimate of mean VSL equal to $6.3 million, based
on a distribution fitted to the estimates from 26 value-of-life studies identified in the Section
812 reports as "applicable to policy analysis." The EPA welcomes comments on the
departure from this approach for the current analysis.
As indicated in the previous section on quantification of premature mortality benefits,
we assume for this analysis that some of the incidences of premature mortality related to PM
exposures occur in a distributed fashion over the 5 years following exposure. To take this
into account in the valuation of reductions in premature mortality, we apply an annual 3
percent discount rate to the value of premature mortality occurring in future years.24
The economics literature concerning the appropriate method for valuing reductions in
premature mortality risk is still developing. The adoption of a value for the projected
reduction in the risk of premature mortality is the subject of continuing discussion within the
economics and public policy analysis community. Regardless of the theoretical economic
considerations, the EPA prefers not to draw distinctions in the monetary value assigned to the
24The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the
federal government. The EPA adopted a 3 percent discount rate for its base estimate in this case to reflect
reliance on a "social rate of time preference" discounting concept. We have also calculated benefits and
costs using a 7 percent rate consistent with an "opportunity cost of capital" concept to reflect the time value
of resources directed to meet regulatory requirements. In this case, the benefit and cost estimates were not
significantly affected by the choice of discount rate. Further discussion of this topic appears in the EPA's
Guidelines for Preparing Economic Analyses (in press).
4-65
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lives saved even if they differ in age, health status, socioeconomic status, gender, or other
characteristic of the adult population.
Following the advice of the EEAC of the SAB, the EPA currently uses the VSL
approach in calculating the primary estimate of mortality benefits, because we believe this
calculation provides the most reasonable single estimate of an individual's willingness to
trade off money for reductions in mortality risk (EPA-SAB-EEAC-00-013). Although there
are several differences between the labor market studies the EPA uses to derive a VSL
estimate and the PM air pollution context addressed here, those differences in the affected
populations and the nature of the risks imply both upward and downward adjustments.
Table 4-12 lists some of these differences and the expected effect on the VSL estimate for air
pollution-related mortality. In the absence of a comprehensive and balanced set of
adjustment factors, the EPA believes it is reasonable to continue to use the $5.5 million value
while acknowledging the significant limitations and uncertainties in the available literature.
Table 4-12. Expected Impact on Estimated Benefits of Premature Mortality Reductions
of Differences Between Factors Used in Developing Applied VSL and Theoretically
Appropriate VSL
Attribute
Age
Life expectancy/health status
Attitudes toward risk
Income
Voluntary vs. Involuntary
Catastrophic vs. protracted death
Expected Direction of Bias
Uncertain, perhaps overestimate
Uncertain, perhaps overestimate
'Underestimate
Uncertain
Uncertain, perhaps underestimate
Uncertain, perhaps underestimate
Some economists emphasize that the VSL is not a single number relevant for all
situations. Indeed, the VSL estimate of $5.5 million (1999 dollars) is itself the central
tendency of a number of estimates of the VSL for some rather narrowly defined populations.
When there are significant differences between the population affected by a particular health
4-66
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risk and the populations used in the labor market studies, as is the case here, some
economists prefer to adjust the VSL estimate to reflect those differences.
The SAB-EEAC advised the EPA "continue to use a wage-risk-based VSL as its
primary estimate, including appropriate sensitivity analyses to reflect the uncertainty of these
estimates," and that "the only risk characteristic for which adjustments to the VSL can be
made is the timing of the risk" (EPA-SAB-EEAC-00-013, EPA, 2000b). In developing our
primary estimate of the benefits of premature mortality reductions, we have followed this
advice and discounted over the lag period between exposure and premature mortality.
Uncertainties Specific to Premature Mortality Valuation. The economic benefits
associated with premature mortality are the largest category of monetized benefits of the
proposed LAQR. In addition, in prior analyses, the EPA has identified valuation of mortality
benefits as the largest contributor to the range of uncertainty in monetized benefits (see EPA
[1999]). Because of the uncertainty in estimates of the value of premature mortality
avoidance, it is important to adequately characterize and understand the various types of
economic approaches available for mortality valuation. Such an assessment also requires an
understanding of how alternative valuation approaches reflect that some individuals may be
more susceptible to air pollution-induced mortality or reflect differences in the nature of the
risk presented by air pollution relative to the risks studied in the relevant economics
literature.
The health science literature on air pollution indicates that several human
characteristics affect the degree to which mortality risk affects an individual. For example,
some age groups appear to be more susceptible to air pollution than others (e.g., the elderly
and children). Health status prior to exposure also affects susceptibility. An ideal benefits
estimate of mortality risk reduction would reflect these human characteristics, in addition to
an individual's WTP to improve one's own chances of survival plus WTP to improve other
individuals' survival rates. The ideal measure would also take into account the specific
nature of the risk reduction commodity that is provided to individuals, as well as the context
in which risk is reduced. To measure this value, it is important to assess how reductions in
air pollution reduce the risk of dying from the time that reductions take effect onward, and
how individuals value these changes. Each individual's survival curve, or the probability of
surviving beyond a given age, should shift as a result of an environmental quality
improvement. For example, changing the current probability of survival for an individual
also shifts future probabilities of that individual's survival. This probability shift will differ
4-67
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across individuals because survival curves depend on such characteristics as age, health state,
and the current age to which the individual is likely to survive.
Although a survival curve approach provides a theoretically preferred method for
valuing the benefits of reduced risk of premature mortality associated with reducing air
pollution, the approach requires a great deal of data to implement. The economic valuation
literature does not yet include good estimates of the value of this risk reduction commodity.
As a result, in this study we value avoided premature mortality risk using the VSL approach.
Other uncertainties specific to premature mortality valuation include the following:
• Across-study variation: There is considerable uncertainty as to whether the
available literature on VSL provides adequate estimates of the VSL saved by air
pollution reduction. Although there is considerable variation in the analytical
designs and data used in the existing literature, the majority of the studies involve
the value of risks to a middle-aged working population. Most of the studies
examine differences in wages of risky occupations, using a wage-hedonic
approach. Certain characteristics of both the population affected and the mortality
risk facing that population are believed to affect the average WTP to reduce the
risk. The appropriateness of a distribution of WTP based on the current VSL
literature for valuing the mortality-related benefits of reductions in air pollution
concentrations therefore depends not only on the quality of the studies (i.e., how
well they measure what they are trying to measure), but also on the extent to
which the risks being valued are similar and the extent to which the subjects in the
studies are similar to the population affected by changes hi pollution
concentrations.
Level of risk reduction: The transferability of estimates of the VSL from the
wage-risk studies to the context of the Interstate Air Quality Rulemaking analysis
rests on the assumption that, within a reasonable range, WTP for reductions in
mortality risk is linear in risk reduction. For example, suppose a study estimates
that the average WTP for a reduction in mortality risk of 1/100,000 is $50, but
that the actual mortality risk reduction resulting from a given pollutant reduction
is 1/10,000. If WTP for reductions hi mortality risk is linear in risk reduction,
then a WTP of $50 for a reduction of 1/100,000 implies a WTP of $500 for a risk
reduction of 1/10,000 (which is 10 times the risk reduction valued in the study).
Under the assumption of linearity, the estimate of the VSL does not depend on the
particular amount of risk reduction being valued. This assumption has been
shown to be reasonable provided the change in the risk being valued is within the
range of risks evaluated in the underlying studies (Rowlatt et al., 1998).
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• Voluntariness of risks evaluated: Although job-related mortality risks may differ
in several ways from air pollution-related mortality risks, the most important
difference maybe that job-related risks are incurred voluntarily, or generally
assumed to be, whereas air pollution-related risks are incurred involuntarily.
Some evidence suggests that people will pay more to reduce involuntarily
incurred risks than risks incurred voluntarily. If this is the case, WTP estimates
based on wage-risk studies may understate WTP to reduce involuntarily incurred
air pollution-related mortality risks.
• Sudden versus protracted death: A final important difference related to the nature
of the risk may be that some workplace mortality risks tend to involve sudden,
catastrophic events, whereas air pollution-related risks tend to involve longer
periods of disease and suffering prior to death. Some evidence suggests that WTP
to avoid a risk of a protracted death involving prolonged suffering and loss of
dignity and personal control is greater than the WTP to avoid a risk (of identical
magnitude) of sudden death. To the extent that the mortality risks addressed in
this assessment are associated with longer periods of illness or greater pain and
suffering than are the risks addressed in the valuation literature, the WTP
measurements employed in the present analysis would reflect a downward bias.
• Self-selection and skill in avoiding risk. Recent research (Shogren et al., 2002)
suggests that VSL estimates based on hedonic wage studies may overstate the
average value of a risk reduction. This is based on the fact that the risk-wage
tradeoff revealed in hedonic studies reflects the preferences of the marginal
worker (i.e., that worker who demands the highest compensation for his risk
reduction). This worker must have either higher risk, lower risk tolerance, or
both. However, the risk estimate used in hedonic studies is generally based on
average risk, so the VSL may be upwardly biased because the wage differential
and risk measures do not match.
4.1.5.5.2 Valuing Reductions in the Risk of 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).
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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 pollution-related case of CB by
statistical uncertainty analysis techniques. The expected value (i.e., mean) of this
distribution, which is about $331,000 (2000$), is taken as the central tendency estimate of
WTP to avoid a PM-related case of CB.
4.1.5.5.3 Valuing Reductions in Non-Fatal Myocardial Infarctions (Heart Attacks).
The Agency has recently incorporated into its analyses the impact of air pollution on the
expected number of nonfatal heart attacks, although it has examined the impact of reductions
in other related cardiovascular endpoints. We were not able to identify a suitable WTP value
for reductions in the risk of nonfatal heart attacks. Instead, we propose a cCOI 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 an myocardial infarction
extend beyond the initial event itself, we consider costs incurred over several years. Using
age-specific annual lost earnings estimated by Cropper and Krupnick (1990) and a 3 percent
discount rate, we estimated a present discounted value in lost earnings (in 2000$) over 5
years due to an myocardial infarction of $8,774 for someone between the ages of 25 and 44,
$12,932 for someone between the ages of 45 and 54, and $74,746 for someone between the
ages of 55 and 65. The corresponding age-specific estimates of lost earnings (in 2000$)
using a 7 percent discount rate are $7,855, $11,578, and $66,920, respectively. Cropper and
Krupnick (1990) do not provide lost earnings estimates for populations under 25 or over 65.
As such, we do not include lost earnings in the cost estimates for these age groups.
We found three possible sources in the literature of estimates of the direct medical
costs of myocardial infarction:
• Wittels et al. (1990) estimated expected total medical costs of myocardial
infarction over 5 years to be $51,211 (in 1986$) for people who were admitted to
the hospital and survived hospitalization. (There does not appear to be any
discounting used.) Wittels et al. was used to value coronary heart disease in the
812 Retrospective Analysis of the Clean Air Act. Using the CPI-U for medical
care, the Wittels estimate is $109,474 in year 2000$. This estimated cost is based
on a medical cost model, which incorporated therapeutic options, projected
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outcomes, and prices (using "knowledgeable cardiologists" as consultants). The
model used medical data and medical decision algorithms to estimate the
probabilities of certain events and/or medical procedures being used. The authors
note that the average length of hospitalization for acute myocardial infarction has
decreased over time (from an average of 12.9 days in 1980 to an average of 11
days in 1983). Wittels et al. used 10 days as the average in their study. It is
unclear how much further the length of stay for myocardial infarction may have
decreased from 1983 to the present. The average length of stay for ICD code 410
(myocardial infarction) in the year-2000 AHQR HCUP database is 5.5 days.
However, this may include patients who died in the hospital (not included among
our nonfatal myocardial infarction cases), whose length of stay was therefore
substantially shorter than it would be if they had not died.
• Eisenstein et al. (2001) estimated 10-year costs of $44,663 in 1997$, or $49,651
in 2000$ for myocardial infarction patients, using statistical prediction
(regression) models to estimate inpatient costs. Only inpatient costs (physician
fees and hospital costs) were included.
• Russell et al. (1998) estimated first-year direct medical costs of treating nonfatal
myocardial infarction of $15,540 (in 1995$) and $1,051 annually thereafter.
Converting to year 2000$, that would be $23,353 for a 5-year period (without
discounting) or $29,568 for a 10-year period.
In summary, the three different studies provided significantly different values (see
Table 4-13).
As noted above, the estimates from these three studies are substantially different, and
we have not adequately resolved the sources of differences in the estimates. Because the
wage-related opportunity cost estimates from Cropper and Krupnick (1990) cover a 5-year
period, we use 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 use a simple average of the two 5-
year estimates, or $65,902, and add it to the 5-year opportunity cost estimate. The resulting
estimates are given in Table 4-14.
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Table 4-13. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart
Attacks
Study
Wittels et al. (1990)
Russell et al. (1998)
Eisenstein et al. (2001)
Russell et al. (1998)
Direct Medical Costs (2000$!
$109,474"
$22,331"
$49,651"
$27,242"
Over an x-Year Period, for x =
5
5
10
10
Wittels et al. did not appear to discount costs incurred in future years.
Using a 3 percent discount rate.
Table 4-14. Estimated Costs Over a 5-Year Period (in 2000$) of a Nonfatal Myocardial
Infarction
Age Group
0-24
25-44
45-54
55-65
>65
Opportunity Cost
$0
$8,774"
$12,253"
$70,619"
$0
Medical Cost"
$65,902
$65,902
$65,902
$65,902
$65,902
Total Cost
$65,902
$74,676
$78,834
$140,649
$65,902
1 An average of the 5-year costs estimated by Wittels et al., 1990, and Russell et al., 1998.
" From Cropper and Krupnick, 1990, using a 3 percent discount rate.
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4.1.5.5.4 Valuing Reductions in School Absence Days. School absences associated
with exposure to ozone are likely to be due to respiratory-related symptoms and illnesses.
Because the respiratory symptom and illness endpoints we are including are all PM-related
rather than ozone-related, we do not have to be concerned about double counting of benefits
if we aggregate the benefits of avoiding ozone-related school absences with the benefits of
avoiding PM-related respiratory symptoms and illnesses.
One possible approach to valuing a school absence is using a parental opportunity
cost approach. This method requires two steps: estimate 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 value the lost productivity at the person's wage. Using this method, we would estimate
the proportion of families with school-age children in which both parents work, and value a
school loss day as the probability of a work loss day resulting from a school loss day (i.e., the
proportion of households with school-age children in which both parents work) times some
measure of lost wages (whatever measure we use to value work loss days). There are two
significant problems with this method, however. First, it omits WTP to avoid the
symptoms/illness that resulted in the school absence. Second, it effectively gives zero value
to school absences which do not result in a work loss day (unless we derive an alternative
estimate of the value of the parent's time for those cases in which the parent is not in the
labor force). We are investigating approaches using WTP for avoid the symptoms/illnesses
causing the absence. In the interim, we will use the parental opportunity cost approach.
For the parental opportunity cost approach, we make an explicit, conservative
assumption that in married households with two working parents, the female parent will stay
home with a sick child. From the U.S. Census Bureau, Statistical Abstract of the United
States: 2001, we obtained (1) the numbers of single, married, and "other" (i.e., widowed,
divorced, or separated) women with children in the workforce, and (2) the rates of
•participation in the workforce of single, married, and "other" women with children. From
these two sets of statistics, we inferred the numbers of single, married, and "other" women
with children, and the corresponding percentages. These percentages were used to calculate a
weighted average participation rate, as shown hi Table 4-15.
Our estimated daily lost wage (if a mother must stay at home with a sick child) is
based on the median weekly wage among women age 25 and older in 2000 (U.S. Census
Bureau, Statistical Abstract of the United States: 2001, Section 12: Labor Force,
Employment, and Earnings, Table No. 621). This median wage is $551. Dividing by 5 gives
an estimated median daily wage of $103.
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Table 4-15. Women with Children: Number and Percent in the Labor Force, 2000, and
Weighted Average Participation Rate9
Single
Married
Other"
Total:
Number (in
millions) in
Labor Force
(1)
3.1
18.2
4.5
Participation
Rate
(2)
73.9%
70.6%
82.7%
Implied Total
Number in
Population (in
millions)
(3) = (l)/(2)
4.19
25.78
5.44
35.42
Implied
Percent in
Population
(4)
11.84%
72.79%
15.36%
Weighted
Average
Participation
Rate [=sum
(2)*(4) over
rows]
72.85%
Data in columns (1) and (2) are from U.S. Census Bureau, Statistical Abstract of the United States: 2001,
Section 12: Labor Force, Employment, and Earnings, Table No. 577.
Widowed, divorced, or separated.
The expected loss in wages due to a day of school absence in which the mother would
have to stay home with her child is estimated as the probability that the mother is in the
workforce times the daily wage she would lose if she missed a day = 72.85% of $103, or
$75.25
25In a very recent article, Hall, Brajer, and Lurmann (2003) use a similar methodology to derive a mid-estimate
value per school absence day for California of between $70 and $81, depending on differences in incomes
between three counties in California. Our national average estimate of $75 per absence is consistent with
these published values.
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4.1.5.6 Unqualified Health Effects
In addition to the health effects discussed above, there is emerging evidence that
human exposure to ozone may be associated with premature mortality (Ito and Thurston,
1996; Samet, et al. 1997, Ito and Thurston, 2001), PM and ozone with increased emergency
room visits for non-asthma respiratory causes (US EPA, 1996a; 1996b), ozone with impaired
airway responsiveness (US EPA, I996a), ozone with increased susceptibility to respiratory
infection (US EPA, 1996a), ozone with acute inflammation and respiratory cell damage (US
EPA, 1996a), ozone and PM with premature aging of the lungs and chronic respiratory
damage (US EPA, 1996a; 1996b), ozone with onset of asthma in exercising children
(McConnell et al. 2002), and PM with reduced heart rate variability and other changes in
cardiac function. An improvement in ambient PM and ozone air quality may reduce the
number of incidences within each effect category that the U.S. population would experience.
Although these health effects are believed to be PM or ozone-induced, effect estimates are
not available for quantifying the benefits associated with reducing these effects. The inability
to quantify these effects lends a downward bias to the monetized benefits presented in this
analysis.
4.1.6 Human Welfare Impact Assessment
PM and ozone have numerous documented effects on environmental quality that
affect human welfare. These welfare effects include direct damages to property, either
through impacts on material structures or by soiling of surfaces, direct economic damages in
the form of lost productivity of crops and trees, indirect damages through alteration of
ecosystem functions, and indirect economic damages through the loss in value of recreational
experiences or the existence value of important resources. EPA's Criteria Documents for PM
and ozone list numerous physical and ecological effects known to be linked to ambient
concentrations of these pollutants (US EPA, 1996a; 1996b). This section describes
individual effects and how we quantify and monetize them. These effects include changes in
commercial crop and forest yields, visibility, and nitrogen deposition to estuaries.
4.1.6.1 Visibility Benefits
Changes in the level of ambient particulate matter caused by the reduction in
emissions from the IAQR will change the level of visibility in much of the Eastern U.S.
Visibility directly affects people's enjoyment of a variety of daily activities. Individuals
value visibility both 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 the Great Smokey
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Mountains National Park. This section discusses the measurement of the economic benefits
of 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. Light
extinction is a measure of how much the components of the atmosphere absorb light. More
light absorption means that the clarity of visual images and visual range is reduced, ceteris
paribus. Light absorption is a variable that can be accurately measured. Sisler (1996) created
a unitless measure of visibility based directly on the degree of measured light absorption
called the deciview. Deciviews are standardized for a reference distance in such a way that
one deciview corresponds to a change of about 10 percent in available light. Sisler
characterized a change in light extinction of one deciview as "a small but perceptible scenic
change under many circumstances." Air quality models were used to predict the change in
visibility, measured in deciviews, of the areas affected by the preliminary control options.26
EPA considers benefits from two categories of visibility changes: residential
visibility and recreational visibility. In both cases economic benefits are believed to consist
of both use values and non-use values. Use values include the aesthetic benefits of better
visibility, improved road and air safety, and enhanced recreation in activities like hunting and
birdwatching. Non-use values are based on people's beliefs that the environment ought to
exist free of human-induced haze. Non-use values may be a more important component of
value for recreational areas, particularly national parks and monuments.
Residential visibility benefits are those that occur from visibility changes in urban,
suburban, and rural areas, and also in recreational areas not listed as federal Class I areas.27
For the purposes of this analysis, recreational visibility improvements are defined as those
that occur specifically in federal Class I areas. 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 U.S. are assumed to derive
26A change of less than 10 percent in the light extinction budget represents a measurable improvement in
visibility, but may not be perceptible to the eye in many cases. Some of the average regional changes in
visibility are less than one deciview (i.e. less than 10 percent of the light extinction budget), and thus less
than perceptible. However, this does not mean that these changes are not real or significant. Our assumption
is then that individuals can place values on changes in visibility that may not be perceptible. This is quite
plausible if individuals are aware that many regulations lead to small improvements in visibility which when
considered together amount to perceptible changes in visibility.
27The Clean Air Act designates 156 national parks and wilderness areas as Class I areas for visibility protection.
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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.28
Only two existing studies provide defensible monetary estimates of the value of
visibility changes. One is a study on residential visibility conducted in 1990 (McClelland, et.
al., 1993) and the other is a 1988 survey on recreational visibility value (Chestnut and Rowe,
1990a; 1990b). While there are a number of other studies in the literature, they were
conducted in the early 1980s and did not use methods that are considered defensible by
current standards. Both the Chestnut and Rowe and McClelland et al studies utilize the
contingent valuation method. There has been a great deal of controversy and significant
development of both theoretical and empirical knowledge about how to conduct CV surveys
in the past decade. 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.29 This study serves as an
essential input to our estimates of the benefits of recreational visibility improvements in the
primary benefits estimates. Consistent with SAB advice, EPA has designated the
McClelland, et al. study as significantly less reliable for regulatory benefit-cost analysis,
although it does provide useful estimates on the order of magnitude of residential visibility
benefits (EPA-SAB-COUNCIL-ADV-00-002, 1999). Residential visibility benefits are
therefore only included as a sensitivity estimate in Appendix 9-B (to be completed for the
Supplemental Analysis).
The Chestnut and Rowe study measured the demand for visibility in Class I areas
managed by the National Park Service (NPS) in three broad regions of the country:
California, the Southwest, and the Southeast. Respondents in five states were asked about
their willingness to pay to protect national parks or NPS-managed wilderness areas within a
particular region. The survey used photographs reflecting different visibility levels in the
specified recreational areas. The visibility levels in these photographs were later converted to
deciviews for the current analysis. The survey data collected were used to estimate a WTP
28For details of the visibility estimates discussed in this chapter, please refer to the benefits technical support
document for the Nonroad Diesel rulemaking (Abt Associates 2003).
29 An SAB advisory letter indicates thaf'many members of the Council believe that the Chestnut and. Rowe
study is the best available." (EPA-SAB-COUNCIL-ADV-00-002, 1999) 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).
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equation for improved visibility. In addition to the visibility change variable, the estimating
equation also included household income as an explanatory variable.
The Chestnut and Rowe study did not measure values for visibility improvement in
Class I areas outside the three regions. Their study covered 86 of the 156 Class I areas in the
U.S. We can infer the value of visibility changes in the other Class I areas by transferring
values of visibility changes at Class I areas in the study regions. However, these values are
not as defensible and are thus presented only as a sensitivity analysis (to be completed for the
Supplemental Analysis). A complete 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).
The estimated relationship from the Chestnut and Rowe study is only directly
applicable to the populations represented by survey respondents. EPA used benefits transfer
methodology to extrapolate these results to the population affected by the proposed IAQR. A
general willingness to pay equation for improved visibility (measured in deciviews) was
developed as a function of the baseline level of visibility, the magnitude of the visibility
improvement, and household income. The behavioral parameters of this equation were taken
from analysis of the Chestnut and Rowe data. These parameters were used to calibrate WTP
for the visibility changes resulting from the IAQR. The method for developing calibrated
WTP functions is based on the approach developed by Smith, et al. (2002). Available
evidence indicates that households are willing to pay more for a given visibility improvement
as their income increases (Chestnut, 1997). The benefits estimates here incorporate
Chestnut's estimate that a 1 percent increase in income is associated with a 0.9 percent
increase in WTP for a given change in visibility.
Using the methodology outlined above, EPA estimates that the total WTP for the
visibility improvements in Southeastern Class I areas brought about by the IAQR is $880
million in 2010 and $1,400 million in 2015. This value includes the value to households
living in the same state as the Class I area as well as values for all households in the U.S.
living outside the state containing the Class I area, and the value accounts for growth in real
income. We examine the impact of expanding the visibility benefits analysis to other areas of
the country in a sensitivity analysis to be completed for the Supplemental Analysis.
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The benefits resulting from visibility improvements in Southeastern Class I areas
under the Proposed IAQR are presented in Figure 4-2. This figure presents these benefits
both in terms of the total benefits modeled for each of the Class I areas (i.e., the "Park
Benefits" map) and the benefits realized by the populations in each of the 48 contiguous
states (i.e., the "State Benefits" map). The latter results reflect the willingness to pay of state
residents for visibility improvements occuring in Class I areas in the Southeastern United
States.
One major source of uncertainty for the visibility benefit estimate is the benefits
transfer process used. Judgments used to choose the functional form and key parameters of
the estimating equation for willingness to pay for the affected population could have
significant effects on the size of the estimates. Assumptions about how individuals respond
to changes in visibility that are either very small, or outside the range covered in the Chestnut
and Rowe study, could also affect the results.
4.1.6.2 Agricultural, Forestry and other Vegetation Related Benefits
The Ozone Criteria Document notes that "ozone affects vegetation throughout the
United States, impairing crops, native vegetation, and ecosystems more than any other air
pollutant" (US EPA, 1996). Changes in ground level ozone resulting from the preliminary
control options are expected to impact crop and forest yields throughout the affected area
Well-developed techniques exist to provide monetary estimates of these benefits to
agricultural producers and to consumers. These techniques use models of planting decisions,
yield response functions, and agricultural products supply and demand. The resulting welfare
measures are based on predicted changes in market prices and production costs. Models also
exist to measure benefits to silvicultural producers and consumers. However, these models
have not been adapted for use in analyzing ozone related forest impacts. As such, our
analysis (to be completed for the Supplemental Analysis) provides monetized estimates of
agricultural benefits, and a discussion of the impact of ozone changes on forest productivity,
but does not monetize commercial forest related benefits.
4.1.6.2.1 Agricultural Benefits. Laboratory and field experiments have shown
reductions in yields for agronomic crops exposed to ozone, including vegetables (e.g.,
lettuce) and field crops (e.g., cotton and wheat). The most extensive field experiments,
conducted under the National Crop Loss Assessment Network (NCLAN) examined 15
species and numerous cultivars. The
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St»tt Benefits
P»rk Benefits
Figure 4.2.
2015 Visibility B«n«fits
In Millions of Dollars
0-005
itnandoah
linviiie uorg*
Shining Rock
krooh
0.1
0.25-0.4
•
»
•
*
0-15
15-40
40-55
55-130
Park
130-800
Changt in Dfolview
•• 0-0.20
•i 0.2-0.5
I O.S-0.8
O 0.8-1 JO
C3 1.0-1.82
State level dollar values reflect
the willingness to pay of state
residents for visibility
improvements occurring in Class I
areas in the southeastern United
States.
There are 16 Class I areas in the
southeast which account for
approximately 17% of total park
visits in the United States.
State dollar benefits are driven by
population and proximity to parks.
Park dollar benefits are driven by
degree of visibility improvement
and park visitation.
Visib
ility Improvements in Southeastern Class I Areas
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NCLAN results show that "several economically important crop species are sensitive to
ozone levels typical of those found in the U.S." (US EPA, 1996). In addition, economic
studies have shown a relationship between observed ozone levels and crop yields (Garcia, et
al., 1986). Due to data limitations, we were unable to assess ozone-related agricultural
benefits associated with the proposed IAQR. However, we will be assessing these benefits
for the Supplemental Analysis and for the analysis of the final IAQR.
4.1.6.2.2 Forestry Benefits. Ozone also has been shown conclusively to cause
discernible injury to forest trees (US EPA, 1996; Fox and Mickler, 1996). In our previous
analysis of the HD Engine/Diesel Fuel rule, we were able to quantify the effects of changes in
ozone concentrations on tree growth for a limited set of species. Due to data limitations, we
were not able to quantify such impacts for this analysis. We plan to assess both physical
impacts on tree growth and the economic value of those physical impacts in our analysis of
the final rule. We will use econometric models of forest product supply and demand to
estimate changes in prices, producer profits and consumer surplus. These benefits will be
estimated for the final IAQR.
4.1.6.2.3 Other Vegetation Effects. An additional welfare benefit expected to accrue
as a result of reductions in ambient ozone concentrations in the U.S. is the economic value
the public receives from reduced aesthetic injury to forests. There is sufficient scientific
information available to reliably establish that ambient ozone levels cause visible injury to
foliage and impair the growth of some sensitive plant species (US EPA, 1996c, p. 5-521).
However, present analytic tools and resources preclude EPA from quantifying the benefits of
improved forest aesthetics.
Urban ornamentals represent an additional vegetation category likely to experience
some degree of negative effects associated with exposure to ambient ozone levels and likely
to impact large economic sectors. In the absence of adequate exposure-response functions
and economic damage functions for the potential range of effects relevant to these types of
vegetation, no direct quantitative economic benefits analysis has been conducted. It is
estimated that more than $20 billion (1990 dollars) are spent annually on landscaping using
ornamentals (Abt Associates, 1995), both by private property owners/tenants and by
governmental units responsible for public areas. This is therefore a potentially important
welfare effects category. However, information and valuation methods are not available to
allow for plausible estimates of the percentage of these expenditures that maybe related to
impacts associated with ozone exposure.
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The EGU standards, by reducing NOX emissions, will also reduce nitrogen deposition
on agricultural land and forests. There is some evidence that nitrogen deposition may have
positive effects on agricultural output through passive fertilization. Holding all other factors
constant, farmers' use of purchased fertilizers or manure may increase as deposited nitrogen
is reduced. Estimates of the potential value of this possible increase in the use of purchased
fertilizers are not available, but it is likely that the overall value is very small relative to other
health and welfare effects. The share of nitrogen requirements provided by this deposition is
small, and the marginal cost of providing this nitrogen from alternative sources is quite low.
In some areas, agricultural lands suffer from nitrogen over-saturation due to an abundance of
on-farm nitrogen production, primarily from animal manure, hi these areas, reductions in
atmospheric deposition of nitrogen represent additional agricultural benefits.
Information on the effects of changes in passive nitrogen deposition on forests and
other terrestrial ecosystems is very limited. The multiplicity of factors affecting forests,
including other potential stressors such as ozone, and limiting factors such as moisture and
other nutrients, confound assessments of marginal changes in any one stressor or nutrient in
forest ecosystems. However, reductions in deposition of nitrogen could have negative effects
on forest and vegetation growth in ecosystems where nitrogen is a limiting factor (US EPA,
1993).
On the other hand, there is evidence that forest ecosystems in some areas of the
United States are nitrogen saturated (US EPA, 1993). Once saturation is reached, adverse
effects of additional nitrogen begin to occur such as soil acidification which can lead to
leaching of nutrients needed for plant growth and mobilization of harmful elements such as
aluminum. Increased soil acidification is also linked to higher amounts of acidic runoff to
streams and lakes and leaching of harmful elements into aquatic ecosystems.
4.1.6.3 Benefits from Reductions in Materials Damage
The preliminary control options that we modeled are expected to produce economic
benefits in the form of reduced materials damage. There are two important categories of
these benefits. Household soiling refers to the accumulation of dirt, dust, and ash on exposed
surfaces. Criteria pollutants also have corrosive effects on commercial/industrial buildings
and structures of cultural and historical significance. The effects on historic buildings and
outdoor works of art are of particular concern because of the uniqueness and irreplaceability
of many of these objects.
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Previous EPA benefit analyses have been able to provide quantitative estimates of
household soiling damage. Consistent with SAB advice, we determined that the existing data
(based on consumer expenditures from the early 1970's) are too out of date to provide a
reliable enough estimate of current household soiling damages (EPA-SAB-Council-ADV-
003, 1998) to include in our base estimate. We calculate household soiling damages in a
sensitivity estimate that will be completed as part of the Supplemental Analysis.
EPA is unable to estimate any benefits to commercial and industrial entities from
reduced materials damage. Nor is EPA able to estimate the benefits of reductions in PM-
related damage to historic buildings and outdoor works of art. Existing studies of damage to
this latter category in Sweden (Grosclaude and Soguel, 1994) indicate that these benefits
could be an order of magnitude larger than household soiling benefits.
4.1.6.4 Benefits from Reduced Ecosystem Damage
The effects of air pollution on the health and stability of ecosystems are potentially
very important, but are at present poorly understood and difficult to measure. The reductions
in NOX caused by the final rule could produce significant benefits. Excess nutrient loads,
especially of nitrogen, cause a variety of adverse consequences to the health of estuarine and
coastal waters. These effects include toxic and/or noxious algal blooms such as brown and
red tides, low (hypoxic) or zero (anoxic) concentrations of dissolved oxygen hi bottom
waters, the loss of submerged aquatic vegetation due to the light-filtering effect of thick algal
mats, and fundamental shifts in phytoplankton community structure (Bricker et al., 1999).
Direct functions relating changes in nitrogen loadings to changes in estuarine benefits
are not available. The preferred WTP based measure of benefits depends on the availability
of these functions and on estimates of the value of environmental responses. Because neither
appropriate functions nor sufficient information to estimate the marginal value of changes in
water quality exist at present, calculation of a WTP measure is not possible.
If better models of ecological effects can be defined, EPA believes that progress can
be made in estimating WTP measures for ecosystem functions. These estimates would be
superior to avoided cost estimates in placing economic values on the welfare changes
associated with air pollution damage to ecosystem health. For example, if nitrogen or sulfate
loadings can be linked to measurable and definable changes in fish populations or definable
indexes of biodiversity, then CV studies can be designed to elicit individuals' WTP for
changes in these effects. This is an important area for further research and analysis, and will
require close collaboration among air quality modelers, natural scientists, and economists.
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4.2 Benefits Analysis—Results
Applying the impact and valuation functions described in Section C to the estimated
changes in ozone and PM described in Section B yields estimates of the changes in physical
damages (i.e. premature mortalities, cases, admissions, change in light extinction, etc.) and
the associated monetary values for those changes. Estimates of physical health impacts are
presented in Table 4-16. Monetized values for both health and welfare endpoints are
presented in Table 4-17, along with total aggregate monetized benefits. All of the monetary
benefits are in constant year 1999 dollars.
Not all known PM- and ozone-related health and welfare effects could be quantified
or monetized. The monetized value of these unqualified effects is represented by adding an
unknown "B" to the aggregate total. The estimate of total monetized health benefits is thus
equal to the subset of monetized PM- and ozone-related health and welfare benefits plus B,
the sum of the nonmonetized health and welfare benefits.
Total monetized benefits are dominated by benefits of mortality risk reductions. The
primary analysis estimate projects that the proposed rule will result in 9,600 avoided
premature deaths in 2010 and 13,000 avoided premature deaths in 2015. The increase in
benefits from 2010 to 2015 reflects additional emission reductions from the standards, as
well as increases in total population and the average age (and thus baseline mortality risk) of
the population. Note that unaccounted for changes in baseline mortality rates over time may
lead to reductions in the estimated number of avoided premature mortalities.
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Table 4-16. Reductions in Incidence of Adverse Health Effects Associated with Reductions in Participate Matter and
Ozone Associated with the.Proposed IAQR*
Endpoint
PM-related Endpoints
Premature mortality1
Long-term exposure (adults, 30 and over)
Long-term exposure (infant, <1 yr)
Chronic bronchitis (adults, 26 and over)
Non-fatal myocardial infarctions (adults, 18 and older)
Hospital admissions — Respiratory (all ages)F
Hospital admissions — Cardiovascular (adults, >1&Y
Emergency Room Visits for Asthma (18 and younger)
Acute bronchitis (children, 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthmatic children, 9-18)
Asthma Exacerbations (asthmatic children, 6-18)
Work loss days (adults, 18-65)
Minor restricted activity days (adults, age 18-65)
Ozone-related Endpoints
Hospital Admissions — Respiratory Causes (adults, 65 and older)1
Hospital Admissions — Respiratory Causes (children, under 2 years)
Emergency Room Visits for Asthma (all ages)
Minor restricted activity days (adults, age 18-65)
School absence days (children, age 6-18)
2010
9,600
22
5,200
13,000
4,200
3,700
7,000
12,000
140,000
490,000
190,000
1,000,000
6,100,000
630
380
120
280,000
180,000
2015
13,000
29
6,900
18,000
5,800
5,000
9,200
16,000
190,000
620,000
240,000
1,300,000
7,900,000
1,500
840
250
610,000
390,000
Incidences are rounded to two significant digits.
Premature mortality associated with ozone is not separately included in this analysis. It is assumed that the Impact
function for premature mortality captures both PM mortality benefits and any mortality benefits associated with other air
pollutants.
Respiratory hospital admissions for PMincludes admissions for COPD, pneumonia, and asthma.
Cardiovascular hospital admissions for PM includes total cardiovascular and subcategories for ischemic heart disease,
dysrhythmias, and heart failure.
Respiratory hospital admissions for ozone includes admissions for all respiratory causes and subcategories for COPD
and pneumonia.
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Table 4-17. Results of Human Health and Welfare Benefits Valuation for the Proposed
IAQR (millions of 1999 dollars)"'"
Endpnint
Premature mortality"
Long-term exposure, (adults, >30yrs)
3% discount rate
7% discount rate
Long-term exposure (child
-------
Our estimate of total monetized benefits in 2010 for the proposed rule is $58 billion
using a 3 percent discount rate and $54 billion using a 7 percent discount rate. In 2015, the
monetized benefits are estimated at $84 billion using a 3 percent discount rate and $79 billion
using a 7 percent discount rate. Health benefits account for 98 percent of total benefits,
mainly because we are unable to quantify most of the non-health benefits. The monetized
benefit associated with reductions in the risk of premature mortality, which accounts for $53
billion in 2010 and $77 billion in 2015, is over 90 percent of total monetized health benefits.
The next largest benefit is for reductions in chronic illness (CB and non-fatal heart attacks),
although this value is more than an order of magnitude lower than for premature mortality.
Hospital admissions for respiratory and cardiovascular causes, visibility, minor restricted
activity days, work loss days, school absence days, and worker productivity account for the
majority of the remaining benefits. The remaining categories account for less than $10
million each, however, they represent a large number of avoided incidences affecting many
individuals.
A comparison of the incidence table to the monetary benefits table reveals that there
is not always a close correspondence between the number of incidences avoided for a given
endpoint and the monetary value associated with that endpoint. For example, there are 100
times more work loss days than premature mortalities, yet work loss days account for only a
very small fraction of total monetized benefits. This reflects the fact that many of the less
severe health effects, while more common, are valued at a lower level than the more severe
health effects. Also, some effects, such as hospital admissions, are valued using a proxy
measure of WTP. As such the true value of these effects may be higher than that reported in
Table 4-16.
Ozone benefits are in aggregate positive for the nation. However, due to ozone
increases occurring during certain hours of the day in some urban areas, there is a dampening
of overall ozone benefits in both 2010 and 2015, although the net incidence and benefits
estimates for all health effects categories are net positive. Overall, ozone benefits are low
relative to PM benefits for similar endpoint categories because of the increases in ozone
concentrations during some hours of some days in certain urban areas.
4.3 Discussion
This analysis has estimated the health and welfare benefits of reductions in ambient
concentrations of particulate matter and ozone resulting from reduced emissions of NOx and
SO2 from affected EGUs. The result suggests there will be significant health and welfare
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benefits arising from the regulation of emissions from EGUs in the U.S. Our estimate that
13,000 premature mortalities would be avoided in 2015, when emission reductions from the
regulation are fully realized, provides additional evidence of the important role that pollution
from the EGU sector plays in the public health impacts of air pollution.
To examine the importance of specific assumptions and analytical choices we made
for this analysis, we will be providing a number of sensitivity analyses in an appendix to be
completed for the upcoming Supplemental Analysis of the proposed rule. In addition, there
are other uncertainties that we could not quantify, such as the importance of unqualified
effects and uncertainties in the modeling of ambient air quality. Inherent in any analysis of
future regulatory programs are uncertainties in projecting atmospheric conditions, and source-
level emissions, as well as population, health baselines, incomes, technology, and other
factors. The assumptions used to capture these elements are reasonable based on the
available evidence. However, data limitations prevent an overall quantitative estimate of the
uncertainty associated with estimates of total economic benefits. If one is mindful of these
limitations, the magnitude of the benefit estimates presented here can be useful information
in expanding the understanding of the public health impacts of reducing air pollution from
EGUs.
The U.S. EPA will continue to evaluate new methods and models and select those
most appropriate for the estimation the health benefits of reductions in air pollution. It is
important to continue improving benefits transfer methods in terms of transferring economic
values and transferring estimated Impact functions. The development of both better models
of current health outcomes and new models for additional health effects such as asthma and
high blood pressure will be essential to future improvements in the accuracy and reliability of
benefits analyses (Guo et al., 1999; Ibald-Mulli et al., 2001). Enhanced collaboration
between air quality modelers, epidemiologists, and economists should result in a more tightly
integrated analytical framework for measuring health benefits of air pollution policies. The
Agency welcomes comments on how we can improve the quantification and monetization of
health and welfare effects and on methods for characterizing uncertainty in our estimates.
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SECTION 5
QUALITATIVE ASSESSMENT OF NONMONETIZED BENEFITS
5.1 Introduction
This proposal will result in benefits in addition to the enumerated human health and
welfare benefits resulting from reductions in ambient levels of PM and ozone. This rule will
also result in benefits that we were unable to monetize. This chapter discusses welfare
benefits associated with reduced acid deposition, reduced eutrophication in water bodies, and
the reduced health and welfare effects due to the deposition of mercury. Welfare benefits
including visibility benefits, agricultural, forestry and other benefits due to reductions in
ozone levels, and benefits from reductions in materials damage are discussed in chapter 4 of
this report. In contrast to the benefits discussed, it is also possible that this proposal will
lessen the benefits of passive fertilization for forest and terrestrial ecosystems where nutrients
are a limiting factor and for some croplands.
5.2 Atmospheric Deposition of Sulfur and Nitrogen—Impacts on Aquatic, Forest,
and Coastal Ecosystems
Atmospheric deposition of sulfur and nitrogen, more commonly known as acid rain,
occurs when emissions of SO2 and NOX react in the atmosphere (with water, oxygen, and
oxidants) to form various acidic compounds. These acidic compounds fall to earth in either a
wet form (rain, snow, and fog) or a dry form (gases and particles). Prevailing winds transport
the acidic compounds hundreds of miles, often across state and national borders. Acidic
compounds (including small particles such as sulfates and nitrates) cause many negative
environmental effects. These pollutants
• acidify lakes and streams,
• harm sensitive forests, and
• harm sensitive coastal ecosystems.
The effect of atmospheric deposition of acids on freshwater and forest ecosystems depends
largely on the ecosystem's ability to neutralize the acid (Driscoll et al., 2001). This is
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referred to as an ecosystem's acid neutralizing capacity (ANC). Acid neutralization occurs
when positively charged ions such as calcium, potassium, sodium, and magnesium,
collectively known as base cations, are released. As water moves through a watershed, two
important chemical processes act to neutralize acids. The first involves cation exchange in
soils, a process by which hydrogen ions from the acid deposition displace other cations from
the surface of soil particles, releasing these cations to soil and surface water. The second
process is mineral weathering, where base cations bound in the mineral structure of rocks are
released as the minerals gradually break down over long time periods. As the base cations
are released by weathering, they neutralize acidity and increase the pH level in soil water and
surface waters. Acid deposition, because it consists of acid anions (e.g., sulfate, nitrate),
leaches some of the accumulated base cation reserves from the soils into drainage waters.
The leaching rate of these base cations may accelerate to the point where it significantly
exceeds the resupply via weathering (Driscoll et al., 2001).
Soils, forests, surface waters and aquatic biota (fish, algae, and the rest), and coastal
ecosystems share water, nutrients, and other essential ecosystem components and are
inextricably linked by the chemical processes described above. For example, the same base
cations that help to neutralize acidity in lakes and streams are also essential nutrients in forest
soils, meaning that cation depletion both increases freshwater acidification and decreases
forest productivity. Similarly, the same nitrogen atom that contributes to stream acidification
can ultimately contribute to coastal eutrophication as it travels downstream to an estuarine
environment. Therefore, to understand the full effects of atmospheric deposition, it is
necessary to recognize the interactions between all of these systems.
5.2.1 Freshwater Acidification
Acid deposition causes acidification of surface waters. In the 1980s, acid rain was
found to be the dominant cause of acidification in 75 percent of acidic lakes and 50 percent of
acidic streams. Areas especially sensitive to acidification include portions of the Northeast
(particularly the Adirondack and Catskill Mountains, portions of New England, and streams
in the mid-Appalachian highlands) and Southeastern streams. Some high elevation Western
lakes, particularly in the Rocky Mountains, have become acidic, especially during snowmelt.
However, although many Western lakes and streams are sensitive to acidification, they are
not subject to continuously high levels of acid deposition and so have not become chronically
acidified (NAPAP, 1990).
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ANC, a key indicator of the ability of the water and watershed soil to neutralize the
acid deposition it receives, depends largely on the watershed's physical characteristics:
geology, soils, and size. Waters that are sensitive to acidification tend to be located in small
watersheds that have few alkaline minerals and shallow soils. Conversely, watersheds that
contain alkaline minerals, such as limestone, tend to have waters with a high ANC.
As acidity increases, aluminum leached from the soil flows into lakes and streams and
can be toxic to aquatic species. The lower pH levels and higher aluminum levels that result
from acidification make it difficult for some fish and other aquatic species to survive, grow,
and reproduce. In some waters, the number of species offish able to survive has been
directly correlated to water acidity. Acidification can also decrease fish population density
and individual fish size (U.S. Department of the Interior 2003).
Recent watershed mass balance studies in the Northeast reveal that loss of sulfate
from the watershed exceeds atmospheric sulfur deposition (Driscoll et al., 2001). This
suggests that these soils have become saturated with sulfur, meaning that the supply of sulfur
from deposition exceeds the sulfur demands of the ecosystem. As a result, sulfur is gradually
being released or leached from the watershed into the surface waters as sulfate. Scientists
now expect that the release of sulfate that previously accumulated in watersheds will delay
the recovery of surface waters in the Northeast that is anticipated in response to the recent
SO2 emission controls (Driscoll et al., 2001).
A recent study at a stream in the Catskill Mountains found that stream nitrate
concentrations were positively correlated to mean annual air temperature but not to annual
nitrogen deposition (Murdoch et al., 1998). This research suggests that, in nitrogen-saturated
soils, microbial processes (nitrogen mineralization and nitrification), which are sensitive to
changes in temperature and moisture, are the primary factors controlling nitrate leaching,
rather than atmospheric deposition or vegetation uptake of nitrogen. Therefore, declines in
nitrogen deposition in nitrogen-saturated soils may not immediately lead to improvements in
stream water chemistry (Murdoch et al., 1998).
A major study of the ecological response to acidification is taking place in the Bear
Brook Watershed in Maine. Established in 1986 as part of the EPA's Watershed
Manipulation Project, the project has found that experimental additions of sulfur and nitrogen
to the watershed increased the concentrations of both sulfate and nitrate in the West Bear
Brook stream. Stream water concentrations of several other ions, including base cations,
aluminum, and ANC, changed substantially as well (Norton et al., 1999). During the first
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year of treatment, 94 percent of the nitrogen added experimentally to the Bear Brook
watershed was retained, while the remainder leached into streams as nitrate. Nitrogen
retention decreased to about 82 percent in subsequent years (Kahl et al., 1993, 1999).
Although the forest ecosystem continued to accumulate nitrogen, nitrate leaching into the
stream continued at elevated levels throughout the length of the experiment. This nitrate
contributed to both episodic and chronic acidification of the stream. This and other similar
studies have allowed scientists to quantify acidification and recovery relationships in eastern
watersheds in much more detail than was possible in 1990.
The Appalachian Mountain region receives some of the highest rates of acid
deposition in the United States (Herlihy et al.j 1993). The acid-base status of stream waters
in forested upland watersheds in the Appalachian Mountains was extensively investigated in
the early 1990s (e.g., Church et al. [1992], Herlihy et al. [1993], Webb et al. [1994], van
Sickle and Church [1995]). A more recent assessment of the southern Appalachian region
from West Virginia to Alabama identified watersheds that are sensitive to acid deposition
using geologic bedrock and the associated buffering capacity of soils to neutralize acid. The
assessment found that approximately 59 percent of all trout stream length in the region is in
areas that are highly vulnerable to acidification, and that 27 percent is in areas that are
moderately vulnerable (SAMAB, 1996). Another study estimated that 18 percent of potential
brook trout streams in the mid-Appalachian Mountains are too acidic for brook trout survival
(Herlihy et al., 1996). Perhaps the most important study of acid-base chemistry of streams in
the Appalachian region in recent years has been the Virginia Trout Stream Sensitivity Study
(Webb et al., 1994). Trend analyses of these streams indicate that few long-term sampling
sites are recovering from acidification, most are continuing to acidify, and the continuing
acidification is at levels that are biologically significant for brook trout populations (Webb et
al., 2000).
5.2.1.1 Water/Watershed Modeling
Researchers have used models to help them understand and predict atmospheric,
environmental, and human health responses to acid deposition for well over 20 years. Since
1990, watershed modeling capabilities have also improved as researchers are continuing to
refine and expand models that project acidification of waterbodies. Unlike the response of
air quality and deposition to changes in emissions, lakes and streams take years to decades to
fully reflect reductions in acid deposition. In some cases, soil chemistry has been
significantly altered and ions must either build up or be leached out before the chemistry can
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return to its pre-acidification status. Therefore, lake and stream conditions are presented for
2030.
5.2.1.2 Description of the MA GIC Model and Methods
A number of mathematical models of soil and surface water acidification in response
to atmospheric deposition were developed in the early 1980s (e.g., Christopherson and
Wright [1981]; Christopherson et al. [1982]; Schnoor et al. [1984]; Booty and Kramer
[1984]; Goldstein et al. [1984]; Cosby et al. [1985a,b,c]). These models were based on
process-level information about the acidification process and were built for a variety of
purposes ranging from estimating transient water quality responses for individual storm
events to estimating chronic acidification of soils and base flow surface water. One of these
models (MAGIC—the Model of Acidification of Groundwater In Catchments; Cosby et al.
[1985a,b,c]) has been in use now for more than 15 years. MAGIC has been applied
extensively in North America and Europe to both individual sites and regional networks of
sites and has also been used in Asia, Africa, and South America. The utility of MAGIC for
simulating a variety of water and soil acidification responses at the laboratory, plot, hillslope,
and catchment scales has been tested using long-term monitoring data and experimental
manipulation data. MAGIC has been widely used in policy and assessment activities in the
United States and in several countries in Europe.
5.2.1.3 Model Structure
MAGIC is a lumped-parameter model of intermediate complexity, developed to
predict the long-term effects of acidic deposition on surface water chemistry. The model
simulates soil solution chemistry and surface water chemistry to predict the monthly and
annual average concentrations of the major ions in these waters. MAGIC consists of the
following: 1) a section in which the concentrations of major ions are assumed to be governed
by simultaneous reactions involving sulfate adsorption, cation exchange, dissolution-
precipitation-speciation of aluminum, and dissolution-speciation of inorganic carbon; and
2) a mass balance section in which the flux of major ions to and from the soil is assumed to
be controlled by atmospheric inputs, chemical weathering, net uptake, and loss in biomass
and losses to runoff. At the heart of MAGIC is the size of the pool of exchangeable base
cations in the soil. As the fluxes to and from this pool change over time owing to changes in
atmospheric deposition, the chemical equilibria between soil and soil solution shift to give
changes in surface water chemistry. The degree and rate of change of surface water acidity
thus depend both on flux factors and the inherent characteristics of the affected soils.
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Cation exchange is modeled using equilibrium (Gaines-Thomas) equations with
selectivity coefficients for each base cation and aluminum. Sulfate adsorption is represented
by a Langmuir isotherm. Aluminum dissolution and precipitation are assumed to be
controlled by equilibrium with a solid phase of aluminum trihydroxide. Aluminum
speciation is calculated by considering hydrolysis reactions as well as complexation with
sulfate, fluoride, and dissolved organic compounds. Effects of carbon dioxide on pH and on
the speciation of inorganic carbon are computed from equilibrium equations. Organic acids
are represented in the model as tri-protic analogues. Weathering rates are assumed to be
constant. Two alternate mechanisms are offered for simulation of nitrate and ammonium in
soils: either 1) first order equations representing net uptake and retention or 2) a set of
equations and compartments describing process-based nitrogen dynamics in soils controlled
by soil nitrogen pools. Input-output mass balance equations are provided for base cations and
strong acid anions, and charge balance is required for all ions in each compartment. Given a
description of the historical, current, and expected future deposition at a site, the model
equations are solved numerically to give long-term reconstructions of surface water chemistry
(for complete details of the model see Cosby et al. [1985 a,b,c], [2001]).
MAGIC has been used to reconstruct the history of acidification, to examine current
patterns of recovery, and to simulate the future trends in stream water acidity in both
individual catchment and regional applications at a large number of sites across North
America and Europe (e.g., Beier et al. [1995]; Cosby et al. [1985b,1990, 1995, 1996, 1998];
Ferrier, et al. [2001]; Hornberger et al. [1989]; Jenkins et al. [1990]; Moldan et al. [1998];
Norton et al. [1992]; Whitehead et al. [1988, 1997]; Wright et al. [1990, 1994, 1998]).
5.2.1.4 Model Implementation
Atmospheric deposition and net uptake-release fluxes for the base cations and strong
acid anions are required as inputs to the model. These inputs are generally assumed to be
uniform over the catchment. Atmospheric fluxes are calculated from concentrations of the
ions in precipitation and the rainfall volume into the catchment. The atmospheric fluxes of
the ions must be corrected for dry deposition of gas, particulates, and aerosols and for inputs
in cloud/fog water. The volume discharge for the catchment must also be provided to the
model. In general, the model is implemented using average hydrologic conditions and
meteorological conditions in annual or seasonal simulations (i.e., mean annual or mean
monthly deposition); precipitation and lake discharge are used to drive the model. Values for
soil and surface water temperature, partial pressure of carbon dioxide, and organic acid
concentrations must also be provided at the appropriate temporal resolution.
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As implemented in this project, the model is a two-compartment representation of a
catchment. Atmospheric deposition enters the soil compartment, and the equilibrium
equations are used to calculate soil water chemistry. The water is then routed to the" stream
compartment, and the appropriate equilibrium equations are reapplied to calculate runoff
chemistry.
Once initial conditions (initial values of variables in the equilibrium equations) have
been established, the equilibrium equations are solved for soil water and surface water
concentrations of the remaining variables. These concentrations are used to calculate the lake
discharge output fluxes of the model for the first time step. The mass balance equations are
(numerically) integrated over the time step, providing new values for the total amounts of
base cations and strong acid anions in the system. These in turn are used to calculate new
values of the remaining variables, new lake discharge fluxes, and so forth. The output from
MAGIC is thus a time trace for all major chemical constituents for the period of time chosen
for the integration.
5.2.1.5 Calibration Procedure
The aggregated nature of the model requires that it be calibrated to observed data
from a system before it can be used to examine potential system response. Calibration is
achieved by setting the values of certain parameters within the model that can be directly
measured or observed in the system of interest (called "fixed" parameters). The model is
then run (using observed atmospheric and hydrologic inputs) and the simulated values of
surface water and soil chemical variables (called "criterion" variables) are compared to
observed values of these variables. If the observed and simulated values differ, the values of
another set of parameters in the model (called "optimized" parameters) are adjusted to
improve the fit. After a number of iterations, the simulated-minus-observed values of the
criterion variables usually converge to zero (within some specified tolerance). The model is
then considered calibrated. If new assumptions (or values) for any of the fixed variables or
inputs to the model are subsequently adopted, the model must be recalibrated by readjusting
the optimized parameters until the simulated-minus-observed values of the criterion variables
again fall within the specified tolerance.
Calibrations are based on volume weighted mean annual or seasonal fluxes for a
given period of observation. The length of the period of observation used for calibration is
not arbitrary. Model output will be more reliable if the annual flux estimates used in
calibration are based on a number of years rather than just 1 year. There is a lot of year-to-
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year variability in atmospheric deposition and catchment runoff. Averaging over a number of
years reduces the likelihood that an "outlier" year (very dry, etc.) is the primary data on which
model forecasts are based. On the other hand, averaging over too long a period may remove
important trends in the data that the model needs to simulate.
The calibration procedure requires that stream water quality, soil chemical and
physical characteristics, and atmospheric deposition data be available for each catchment.
The water quality data needed for calibration are the concentrations of the individual base
cations (Ca, Mg, Na, and K) and acid anions (Cl, SO4, and NO3) and the pH. The soil data
used in the model include soil depth and bulk density, soil pH, soil cation-exchange capacity,
and exchangeable bases on the soil (Ca, Mg, Na, and K). The atmospheric deposition inputs
to the model must be estimates of total deposition, not just wet deposition. In some
instances, direct measurements of either atmospheric deposition or soil properties may not be
available for a given site with stream water data. In these cases, the required data can often
be estimated by assigning soil properties based on some landscape classification of the
catchment and assigning deposition using model extrapolations from some national or
regional atmospheric deposition monitoring network.
Soil Physical and Chemical Properties. Soil data for model calibration are usually
derived as a really averaged values of soil parameters within a catchment. If soils data for a
given location are vertically stratified, the soils data for the individual soil horizons at that
sampling site can be aggregated based on horizon, depth, and bulk density to obtain single
vertically aggregated values for the site, or the stratified data can be used directly in the
model.
Total Atmospheric Deposition. Total atmospheric deposition consists of three
components: wet deposition, the flux of ions occurring in precipitation; dry deposition,
resulting from gaseous and particulate fluxes; and cloud/fog deposition (which can be
particularly important in mountainous inland areas or moderate highlands hi areas adjacent to
oceans or seas). Estimates of precipitation volume and ionic concentrations in precipitation
can be used to calculate wet deposition for a site. Observations of dry deposition or
cloud/fog deposition are very infrequent. The approach usually used to quantify these
components relies on some estimate of the ratio of estimated total deposition to the observed
wet deposition for important ions (e.g., sulphate, nitrate, and ammonium ions). These ratios
(called dry deposition factors) are then used to calculate total deposition from the observed
wet deposition data.
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Historical Loading. Calibration of the model (and estimation of the historical
changes at the sites) requires a temporal sequence of historical anthropogenic deposition.
Our current understanding of ecosystem responses to acidic deposition suggests that future
ecosystem responses can be strongly conditioned by historical acidic loadings. Thus, as part
of the model calibration process, the model should be constrained by some measure of
historical deposition to the site. However, such long-term, continuous historical deposition
data do not exist. The usual approach is to use historical emissions data as a surrogate for
deposition. The emissions for each year in the historical period can be normalized to
emissions in a reference year (a year for which observed deposition data are available).
Using this scaled sequence of emissions, historical deposition can be estimated by
multiplying the total deposition estimated for each site in reference year by the emissions
scale factor for any year in the past to obtain deposition for that year.
5.2.1.6 MA GIC Modeling Results
Watershed modeling undertaken for IAQR projects that, under IAQR, 1 percent of
northeastern lakes would be chronically acidic in 2030. In contrast, the same model used to
analyze existing control programs projects 6 percent of northeastern lakes would be
chronically acidic in 2030. The modeling projects that, under IAQR, 28 percent of
northeastern lakes would be episodically acidic in 2030, compared to 25 percent in 2030
under existing control programs. For Adirondack lakes, a subset of northeastern lakes, the
signals of surface water chemical recovery are much stronger. Under IAR, no Adirondack
lakes would be chronically acidic, and 64 percent would be episodically acidic in 2030, as
opposed to 12 percent chronically acidic and 52 percent episodically acidic in 2030 under
current control programs.
Because of the age and types of soils in many high elevation areas of the southeast,
streams in that region are more frequently characterized by a delayed response to changes in
deposition. For the ecosystems modeled in this region, 17 percent of streams are currently
chronically acidic, and this level stays the same under IAQR 2030; the proportion of
episodically acidic streams increases from 19 percent under current conditions to 23 percent
under IQAR, which reflects a decrease in the proportion of nonacidic streams from 64
percent under current conditions to 60 percent under IQAR in 2030. It is important to note
that, under the Base Case, the proportion of nonacidic streams decreases even further,
dropping from 64 percent under current conditions to 58 percent in 2030. Thus, in the
southeast, IQAR would slow the deterioration of stream health (episodically acidic) expected
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under the Base Case and would prevent additional streams from becoming chronically acidic.
Results of the MAGIC modeling are summarized in Table 5-1.
5.2.2 Forest Ecosystems
Our current understanding of the effects of acid deposition on forest ecosystems has
come to focus increasingly on the effects of biogeochemical processes that affect plant
uptake, retention, and cycling of nutrients within forested ecosystems. Research results from
the 1990s indicate that documented decreases in base cations (calcium, magnesium,
potassium, and others) from soils in the northeastern and southeastern United States are at
least partially attributable to acid deposition (Lawrence et al., 1997; Huntington et al., 2000).
Base cation depletion is a cause for concern because of the role these ions play in acid
neutralization and, in the case of calcium, magnesium, and potassium, their importance as
essential nutrients for tree growth. It has been known for some time that depletion of base
cations from the soil interferes with the uptake of calcium by roots in forest soils (Shortle and
Smith 1988). Recent research indicates it also leads to aluminum mobilization (Lawrence et
al., 1995), which can have harmful effects on fish (US Dept. of Interior 2003).
The plant physiological processes affected by reduced calcium availability include
cell wall structure and growth, carbohydrate metabolism, stomatal regulation, resistance to
plant pathogens, and tolerance of low temperatures (DeHayes et al., 1999). Soil structure,
macro and micro fauna, decomposition rates, and nitrogen metabolism are also important
processes that are significantly influenced by calcium levels in soils. The importance of
calcium as an indicator of forest ecosystem function is due to its diverse physiological roles,
coupled with the fact that calcium mobility in plants is very limited and can be further
reduced by tree age, competition, and reduced soil water supply (McLaughlin and Wimmer
1999).
A clear link has now been established in red spruce stands between acid deposition,
calcium supply, and sensitivity to abiotic stress. Red spruce uptake and retention of calcium
is affected by acid deposition in two main ways: leaching of important stores of calcium
from needles (DeHayes et al., 1999) and decreased root uptake of calcium due to calcium
depletion from the soil and aluminum mobilization (Smith and Shortle, 2001; Shortle et al.,
1997; Lawrence et al., 1997). Acid deposition leaches calcium from mesophyll cells of
5-10
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1-year old red spruce needles (Schaberg et al., 2000), which in turn reduces freezing tolerance
(DeHayes et al., 1999). These changes increase the sensitivity of red spruce to winter injuries
under normal winter conditions in the Northeast, result in the loss of needles, and impair the
overall health of forest ecosystems (DeHayes et al., 1999). Red spruce must also expend
more metabolic energy to acquire calcium from soils in areas with low calcium/aluminum
ratios, resulting in slower tree growth (Smith and Shortle, 2001).
Losses of calcium from forest soils and forested watersheds have now been
documented as a sensitive early indicator of the soil response to acid deposition for a wide
range of forest soils in the United States (Lawrence et al., 1999; Huntington et al., 2000).
There is a strong relationship between acid deposition and leaching of base cations from
hardwood forest (e.g., maple, oak) soils, as indicated by long-term data on watershed mass
balances (Likens et al., 1996; Mitchell et al., 1996), plot- and watershed-scale acidification
experiments in the Adirondacks (Mitchell et al., 1994) and in Maine (Norton et al., 1994;
Rustad et al., 1996), and studies of soil solution chemistry along an acid deposition gradient
from Minnesota to Ohio (MacDonald et al., 1992).
Although sulfate is the primary cause of base cation leaching, nitrate is a significant
contributor in watersheds that are nearly nitrogen saturated (Adams et al., 1997). Recent
studies of the decline of sugar maples in the Northeast demonstrate a link between low base
cation availability, high levels of aluminum and manganese in the soil, and increased levels
of tree mortality due to native defoliating insects (Horsley et al., 2000). The chemical
composition of leaves and needles may also be altered by acid deposition, resulting in
changes in organic matter turnover and nutrient cycling.
5.2.3 Coastal Ecosystems
Since 1990, a large amount of research has been conducted on the impact of nitrogen
deposition to coastal waters. It is now known that nitrogen deposition is a significant source
of nitrogen to many estuaries (Valigura et al., 2001; Howarth 1998). The amount of nitrogen
entering estuaries due to atmospheric deposition varies widely, depending on the size and
location of the estuarine watershed and other sources of nitrogen in the watershed. For a
handful of estuaries, atmospheric deposition of nitrogen contributes well over 40 percent of
the total nitrogen load; however, in most estuaries for which estimates exist, the contribution
from atmospheric deposition ranges from 15 to 30 percent. The area with the highest
deposition rates stretches from Massachusetts to the Chesapeake Bay and along the central
Gulf of Mexico coast.
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Nitrogen is often the limiting nutrient in coastal ecosystems. Increasing the levels of
nitrogen in coastal waters can cause significant changes to those ecosystems. Approximately
60 percent of estuaries in the United States (65 percent of the estuarine surface area) suffer
from overenrichment of nitrogen, a condition known as eutrophication (Bricker et al., 1999).
Symptoms of eutrophication include changes in the dominant species of plankton (the
primary food source for many kinds of marine life) that can cause algal blooms, low levels of
oxygen in the water column, fish and shellfish kills, and cascading population changes up the
food chain. Many of the most highly eutrophic estuaries are along the Gulf and mid-Atlantic
coasts, overlapping many of the areas with the highest nitrogen deposition, but there are
eutrophic estuaries in every region of the coterminous U.S. coastline.
5.3 Benefits of Reducing Mercury Emissions
According to baseline emission estimates, the sources affected by this proposal would
emit approximately 45.1 tons of mercury per year in 2010. This estimate is specific to fossil-
fired electric generating units in excess of 25 megawatt capacity. The proposed regulation
would reduce approximately 10.6 tons of mercury (or 23.5 percent) from the 2010 baseline,
11.8 tons of mercury (or 26.3 percent) from the 2015 baseline, and 14.3 tons (or 32 percent)
from the 2020 baseline at affected electric generating units.
Mercury emitted from utilities and other natural and man-made sources is carried by
winds through the air and eventually is deposited to water and land. Recent estimates (which
are highly uncertain) of annual total global mercury emissions from all sources (natural and
anthropogenic) are about 5,000 to 5,500 tons per year (tpy). Of this total, about 1,000 tpy are
estimated to be natural emissions and about 2,000 tpy are estimated to be contributions
through the natural global cycle of re-emissions of mercury associated with past
anthropogenic activity. Current anthropogenic emissions account for the remaining 2,000
tpy. Point sources such as fuel combustion; waste incineration; industrial processes; and
metal ore roasting, refining, and processing are the largest point source categories on a world-
wide basis. Given the global estimates noted above, U.S. anthropogenic mercury emissions
are estimated to account for roughly 3 percent of the global total, and U.S. utilities are
estimated to account for about 1 percent of total global emissions. Mercury exists in three
forms: elemental mercury, inorganic mercury compounds (primarily mercuric chloride), and
organic mercury compounds (primarily methylmercury). Mercury is usually released in an
elemental form and later converted into methylmercury by bacteria. Methylmercury is more
toxic to humans than other forms of mercury, in part because it is more easily absorbed in the
body (EPA, 1996).
5-1-2
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If the deposition is directly to a water body, then the processes of aqueous fate,
transport, and transformation begin. If deposition is to land, then terrestrial fate and transport
processes occur first and then aqueous fate and transport processes occur once the mercury
has cycled into a water body. In both cases, mercury may be returned to the atmosphere
through resuspension. In water, mercury is transformed to methylmercury through biological
processes and for exposures affected by this rulemaking, methylmercury is considered to be
the form of greatest concern. Once mercury has been transformed into methylmercury, it can
be ingested by the lower trophic level organisms where it can bioaccumulate in fish tissue
(i.e., concentrations of mercury remain in the fish's system for a long period of time and
accumulates in the fish tissue as predatory fish consume other species in the food chain).
Fish and wildlife at the top of the food chain can, therefore, have mercury concentrations that
are higher than the lower species, and they can have concentrations of mercury that are higher
than the concentration found in the water body itself. In addition, when humans consume
fish contaminated with methylmercury, the ingested methymercury is almost completely
absorbed into the blood and distributed to all tissues (including the brain); it also readily
passes through the placenta to the fetus and fetal brain (EPA, 200la).
Based on the findings of the National Research Council, EPA has concluded that
benefits of Hg reductions would be most apparent at the human consumption stage, as
consumption offish is the major source of exposure to methylmercury. At lower levels,
documented Hg exposure effects may include more, subtle, yet potentially important,
neurodevelopmental effects. Figure 5-1 shows how emissions of mercury can transport from
the air to water and impact human health and ecosystems.
5-13
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Lake
Ocean
Power Plant
Emissions
Emissions*
Deductions
volatilization
Wet and Dry
Deposit on
M ercury trans ferns Into methykneroury In soils
and water, then can bioaccumulate In ten
Reduce Atmospheric
> Transport and
Deposition
educe Ecosystem
, Transport
and Methyl ation
Fishing
•commerdai
•recreational
•subsistence
Humans and
»ldHe affected
primartyby
eating
contaminated
Ml
Reduce Human and
Wildlife Exposure
Largest in pacts on
young children
Impacts include:
• Impaired motor and
cognitive ski Is
• Potential
cardiovascular,
immune, and
reproductive system
problem s in adults
Reduce
Health
Impacts
Figure 5-1. How
Emissions of Mercury
Can Impact Human
Health and Ecosystems30
30 Cardiovascular, immune, and reproductive system problems in adults are potential effects as the literature is either contradictory or incomplete.
5-14
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Some subpopulations in the U.S., such as: Native Americans, Southeast Asian
Americans, and lower income subsistence fishers, may rely on fish as a primary source of
nutrition and/or for cultural practices. Therefore, they consume larger amounts of fish than
the general population and may be at a greater risk to the adverse health effects from Hg due
to increased exposure. In pregnant women, methylmercury can be passed on to the
developing fetus, and at sufficient exposure may lead to a number of neurological disorders
in children. Thus, children who are exposed to low concentrations of methylmercury
prenatally maybe at increased risk of poor performance on neurobehavioral tests, such as
those measuring attention, fine motor function, language skills, visual-spatial abilities (like
drawing), and verbal memory. The effects from prenatal exposure can occur even at doses
that do not result in effects in the mother. Mercury may also affect young children who
consume fish contaminated with Hg. Consumption by children may lead to neurological
disorders and developmental problems, which may lead to later economic consequences.
Monitoring the concentrations of mercury in the blood of women of child-bearing age
can help identify the proportion of children who may be at risk. EPA's reference dose (RfD)
for methylmercury is 0.1 micrograms per kilogram body weight per day, which is
approximately equivalent to a concentration of 5.8 parts per billion mercury in blood.
Although the prenatal period is the most sensitive period of exposure, exposure to mercury
during childhood also could pose a potential health risk (NAS, 2000).
Figure 5-2 shows reported concentrations of mercury in blood of women of
childbearing age from the National Health and Nutrition Examination Survey (NHANES)
(EPA, 2003b). The data presented are for total mercury, which includes methylmercury and
other forms of mercury. Total blood mercury is a reasonable indicator of methylmercury
exposure in people who consume fish and have no significant exposure to inorganic or
elemental mercury (JAMA, April 2003). Thus the measured concentrations are a good
indication of methylmercury concentrations. From this survey, about 8 percent of women of
child-bearing age had at least 5.8 parts per billion of mercury in their blood in 1999-2000.
5-15
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Measure B4
Distribution of concentrations of mercury in Wood of women of
chilclbearing age, 199§-2000
CcncenttB&ofj of mercury m Moot! (parte per billion)
SOURCE: Centers tot Disease Control and Prevention, NaKoMf Center tot Health Statistics. National Health
ami Nutrition Examination Survey
Note' EPA's lefeience cto$e (RfD) fat methyfmwcuty h 0 1 mlaogiams pw kilogram body wetj^tt pw day
ThHs is «pf»o*lma)eiy etjiitvaiMit to a eonc«ntration o? 5 8 parts p«f billion mwciify In blood
Figure 5-2. Concentrations of Mercury in Blood of Women of Childbearing Age
5-16
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Figure 5-3 shows relative values of the BMD, BMDL and the RID. The data show a
Benchmark Dose (BMD) BMD at 85 ppb. The BMD is the dose or concentration that
produced a doubling of the number of children with a response at the 5th percentile of the
population. In this case, the changes evaluated were changes on neuropsychological testing
batteries (i.e. the Boston Naming Test). In determining the RfD, EPA started with the BMD
(85 ppb) and then used the 95% lower confidence limit to arrive at the 58 ppb BMDL. EPA
then applied a composite uncertainty factor of 10 to calculate a final RfD of 5.8 ppb. The
uncertainty factor adjustment was used to account for pharmacokmetic and
pharmacodynamic uncertainty and variability.
5.8
RfD
50 58
BMDL
85
BMD
Figure 5-3. Relative Values of BMD, BMDL, and the RfD
(Values in ppb)
In response to potential risks of mercury-contaminated fish consumption, EPA and
FDA have issued fish consumption advisories which provide recommended limits on
consumption of certain fish species for different populations. EPA and FDA are currently
developing a joint advisory that has been released in draft form. This newest draft FDA-EPA
fish advisory recommends that women and young children reduce the risks of Hg
consumption in their diet by moderating their fish consumption, diversifying the types of fish
they consume, and by checking any local advisories that may exist for local rivers and
streams. This collaborative FDA-EPA effort will greatly assist in educating the most
susceptible populations. Additionally, the reductions of Hg from this regulation may
potentially lead to fewer fish consumption advisories (both from federal or state agencies),
which will benefit the fishing community. As Figure 5-4 shows, currently 44 states have
issued fish consumption advisories for non-commercial fish for some or all of their waters
due to contamination of mercury. The scope of FCA issued by states varies considerably,
with some warnings applying to all water bodies in a state and others applying only to
5-17
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individual lakes and streams. Note that the absence of a state advisory does not necessarily
indicate that there is no risk of exposure to unsafe levels of mercury in recreationally caught
fish. Likewise, the presence of a state advisory does not indicate that there is a risk of
exposure to unsafe levels of mercury in recreationally caught fish, unless people consume
these fish at levels greater than those recommended by the fish advisory.
Reductions in methylmercury concentrations in fish should reduce exposure,
subsequently reducing the risks of mercury-related health effects in the general population, to
children, and to certain subpopulations. Fish consumption advisories (FCA) issued by the
States may also help to reduce exposures to potential harmful levels of methylmercury in fish
(although some studies have shown limited knowledge of and compliance with advisories by
at risk populations (May and Burger, 1996; Burger, 2000)). To the extent that reductions in
mercury emissions reduces the probability that a water body will have a FCA issued, there are
a number of benefits that will result from fewer advisories, including increased fish
consumption, increased fishing choices for recreational fishers, increased producer and
consumer surplus for the commercial fish market, and increased welfare for subsistence
fishing populations.
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Figure 5-4. Mercury Reductions By State In 2015
Legend
Change in Hg In Tons for the year 2015
| ~] 4.0230-0.0964
0.0965 -0.2846
0.2847-0.6518
0.6519-2,1568
2.1569-3.6200
L= Statewide lake advisory
R = Statewide river advisory
L, R = Statewide lake and river advisory
S = Advisories for specific waterbodies only
None= No advisories for chemical contaminants
* There may be
some slight increases
in Hg in some states.
Source: Fish advisory information from
http ://www.e pa .g ov/wate rscfe nce/p resen tations/fish/
maps_gra ph ics_files/frame. htm
There is a great deal of variability among individuals in fish consumption rates;
however, critical elements in estimating methylmercury exposure and risk from fish
consumption include the species offish consumed, the concentrations of methylmercury in
the fish, the quantity offish consumed, and how frequently the fish is consumed. The typical
U.S. consumer eating a wide variety offish from restaurants and grocery stores is not in
danger of consuming harmful levels of methylmercury from fish and is not advised to limit
fish consumption. Those who regularly and frequently consume large amounts of fish, either
marine or freshwater, are more exposed. Because the developing fetus may be the most
sensitive to the effects from methylmercury, women of child-bearing age are regarded as the
population of greatest interest. The EPA, Food and Drug Administration, and many States
have issued fish consumption advisories to inform this population of protective consumption
levels.
The EPA's 1997 Mercury Study RTC supports a plausible link between
anthropogenic releases of Hg from industrial and combustion sources in the U.S. and
methylmercury in fish. However, these fish methylmercury concentrations also result from
existing background concentrations of Hg (which may consist of Hg from natural sources, as
well as Hg which has been re-emitted from the oceans or soils) and deposition from the
5-19
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global reservoir (which includes Hg emitted by other countries). Given the current scientific
understanding of the environmental fate and transport of this element, it is not possible to
quantify how much of the methylmercury in locally-caught fish consumed by the U.S.
population is contributed by U.S. emissions relative to other sources of Hg (such as natural
sources and re-emissions from the global pool). As a result, the relationship between Hg
emission reductions from Utility Units and methylmercury concentrations in fish cannot be
calculated in a quantitative manner with confidence. In addition, there is uncertainty
regarding over what time period these changes would occur. This is an area of ongoing
study.
Given the present understanding of the Hg cycle, the flux of Hg from the atmosphere
to land or water at one location is comprised of contributions from: the natural global cycle;
the cycle perturbed by human activities; regional sources; and local sources. Recent
advances allow for a general understanding of the global Hg cycle and the impact of the
anthropogenic sources. It is more difficult to make accurate generalizations of the fluxes on a
regional or local scale due to the site-specific nature of emission and deposition processes.
Similarly, it is difficult to quantify how the water deposition of Hg leads to an increase in fish
tissue levels. This will vary
based on the specific characteristics of the individual lake, stream, or ocean.
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SECTION 6
COMPARISON OF BENEFITS AND COSTS
The estimated social costs to implement the proposed IAQR, as described in the cost
analysis document, are approximately $2.9 billion annually and $3.7 billion annually for 2010
and 2015, respectively (1999$). Thus, the net benefits (social benefits minus social costs) of
the program in 2010 are approximately $55 + B billion annually in 2010 and $80 + B billion
annually in 2015 (1999$). (B represents the sum of all unqualified benefits and
disbenefits.) Therefore, implementation of the proposed rule is expected, based purely on
economic efficiency criteria, to provide society with a significant net gain in social welfare,
even given the limited set of health and environmental effects we were able to quantify.
Addition of ozone-, directly emitted PM2 5-, mercury-, acidification-, and eutrophication-
related impacts would increase the net benefits of the proposed rule. As discussed in section
IX of the notice for this rulemaking, we did not complete air quality modeling that precisely
matches the IAQR region. We anticipate that any differences in estimates presented due to
the modeling region analyzed will be small. Table 6-1 presents a summary of the benefits,
costs, and net benefits of the proposed rule.
6-1
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Table 6-1. Summary of Annual Benefits, Costs, and Net Benefits of the Inter-State Air
Quality Rule
Description
Social costs"
Social benefits biC
Ozone-related benefits
PM-related health benefits
Visibility benefits
Net benefits (benefits-costs)"'"'"
2010
(billions of 1999 dollars)
$2.9
$0.1
$56.8 +B
$0.9
$55 + B
2015
(billions of 1999 dollars)
$3.7
$0.1
$82.3 + B
$1.4
$80 + B
" Note that costs are the annual total costs of reducing pollutants including NOX and SO2.
b As the table indicates, total benefits are driven primarily by PM-related health benefits. The reduction in
premature fatalities each year accounts for over 90 percent of total benefits. Benefits in this table are
associated with NO, and SO2 reductions.
c Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all
unquantified benefits and disbenefits. Potential benefit categories that have not been quantified and
monetized are listed in Table 1-4.
d Net benefits are rounded to the nearest billion. Columnar totals may not sum due to rounding.
6-2
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