5-1

Chapter 5: Benefit Analysis and Results 

This chapter reports EPA’s analysis of a subset of the public health and welfare impacts and 

associated monetized benefits to society of illustrative implementation strategies to attain 

alternative NAAQS for fine particulate matter (PM2.5) incremental to attainment of the current 

NAAQS. Accordingly, the analysis presented here attempts to answer two questions: (1) what 

are the estimated nationwide physical health and welfare effects of changes in ambient air quality 

resulting from reductions in precursors to particulate matter (PM) including directly emitted 

carbonaceous particles, NOx, SO2, and NH3 emissions? and (2) what is the estimated monetary 

value of the changes in these effects attributable to the revised standards and a more stringent 

alternative annual standard? This benefit analysis constitutes one part of EPA’s thorough 

examination of the relative merits of this regulation. 

The analysis presented in this chapter uses a methodology generally consistent with benefits 

analyses performed for the recent analysis of the Clean Air Interstate Rule (EPA, 2005). The 

methodology diverges in four areas: 

1. Rather than presenting both a “primary” estimate of the benefits and a separate 

characterization of the uncertainty associated with that estimate, the current analysis 

follows the recommendation of NRC’s 2002 report “Estimating the Public Health 

Benefits of Proposed Air Pollution Regulations” to begin moving the assessment of 

uncertainties from its ancillary analyses into its main benefits presentation through 

the conduct of probabilistic analyses. 

2. Since the publication of CAIR, we have completed a full-scale expert elicitation 

designed to more fully characterize the state of our understanding of the 

concentration-response function for PM-related premature mortality. The elicitation 

results form a major component of the current effort to use probabilistic assessment 

techniques to integrate uncertainty into the main benefits analysis. 

3. We have updated our projections of mortality incidence rates to be consistent with the 

U.S. Census population projections that form the basis of our future population 

estimates. Compared to the methodology used in the CAIR analysis, this change will 

result in a reduction in mortality impacts in future years, as overall mortality rates are 

projected to decline for most age groups. 

4. We are providing additional characterizations of the impacts of assuming alternative 

thresholds in the concentration-response functions derived from the epidemiology 

literature. Unless specifically noted, our base premature mortality benefits estimates 

are based on an assumed cutpoint in the long-term mortality concentration-response 

function at 10 μg/m3

, and an assumed cutpoint in the short-term morbidity 

concentration-response functions at 10 μg/m3

. We also show the results of a 

sensitivity analysis for premature mortality, with 4 alternative cutpoints, at 3 μg/m3, 

7.5 μg/m3, 12 μg/m3, and 14 μg/m3. 

The benefits analysis takes as inputs the results of the CMAQ air quality modeling described in 

Chapter 4. Reductions in certain PM2.5 precursors such as NOx and VOC may also lead to 

changes in ambient concentrations of ozone. These changes in ozone will also have health and 
5-2

welfare effects. However, for this RIA, because the majority of the illustrative strategies 

evaluated do not affect NOx and VOC emissions (with the exception of nonattainment areas in 

parts of the western U.S., where we do not currently have adequate modeling data for ozone), we 

focus on estimating the health and welfare effects associated with changes in ambient PM2.5. 

This adds some uncertainty to the overall results, but given the expected small magnitude of the 

impacts (due to the small amount of NOx controls applied), this uncertainty will likely be small 

relative to other modeling uncertainties. 

A wide range of human health and welfare effects are linked to ambient concentrations of PM2.5. 

Potential human health effects associated with PM2.5 range from premature mortality to 

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]). Welfare effects potentially linked to PM and its 

precursors include materials damage and visibility impacts, as well as the impacts associated 

with deposition of nitrates and sulfates. 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 because of limitations in methods and/or data. Table 5-1 summarizes the annual 

incremental monetized health and welfare benefits associated with the illustrative 

implementation strategies for the revised 15/35 and alternative more stringent14/35 standards in 

2020, when the standards are expected to be fully attained. Table 5-2 lists the full complement 

of human health and welfare effects associated with PM (and its precursors) and identifies those 

effects that are quantified for the primary estimate and those that remain unquantified because of 

current limitations in methods or available data. Note that these two tables summarize the health 

and welfare benefits of fully attaining the revised and alternative more stringent PM2.5 

standards. 

The general benefits analysis framework is as follows: 

1. 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 PM, visibility, and 

deposition of nitrates and sulfates for each year. 

2. The estimated changes in ambient concentrations are then combined with monitoring data 

to estimate population-level potential 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. 
5-3

Table 5-1: Estimated Annual Monetized Benefits in 2020 of Illustrative Implementation 

Strategies for the Selected and Alternative PM2.5 NAAQS, Incremental to Attainment of 

the Current Standards 

Note: Unquantified benefits are not included in these estimates, thus total benefits are likely to be larger than 

indicated in this table.

Total Full Attainment Benefitsa, b (billions 1999$) 

 15/35 (μg/m3) 14/35 (μg/m3) 

Based on Mortality Function from American Cancer Society and Morbidity Functions from Epidemiology Literaturec

$17 $30 

Using a 3% discount rate Confidence Intervals Confidence Intervals 

 ($4.1 – $36) ($7.3 - $63)) 

 

Using a 7% discount rate $15 $26 

Confidence Intervals Confidence Intervals 

($3.5 – $31) ($6.4 - $54) 

Based on Expert Elicitation Derived Mortality Functions and Morbidity Functions from Epidemiology Literature

$9 to $76 $17 to $140 

Confidence Intervals Confidence Intervals 

Lower Bound 

Expert Result 

Upper Bound 

Expert Result 

Lower Bound 

Expert Result 

Upper Bound 

Expert Result 

Using a 3% discount rate 

($0.8 - $42) ($19-$150) ($1.7 - $77) ($36 - $280) 

 

$8 to $64 $15 to $120 

 Confidence Intervals Confidence Intervals 

Lower Bound 

Expert Result

Upper Bound 

Expert Result 

Lower Bound 

Expert Result 

Upper Bound 

Expert Result 

Using a 7% discount rate 

($0.8 - $36) ($16 - $130) ($1.6 - $66) ($31 - $240) 

a

 Results reflect the use of two different discount rates: 3% and 7%, as recommended in EPA’s Guidelines for 

Preparing Economic Analyses (EPA, 2000b) and OMB Circular A-4 (OMB, 2003). Results are rounded to 

two significant digits for ease of presentation and computation. 

b

 Although the overall range across experts is summarized in this table, the full uncertainty in the estimates is 

reflected by the results for the full set of 12 experts. The twelve experts’ judgments as to the likely mean 

effect estimate are not evenly distributed across the range illustrated by arraying the highest and lowest expert 

means. Likewise the 5th and 95th percentiles for these highest and lowest judgments of the effect estimate do 

not imply any particular distribution within those bounds. The distribution of benefits estimates associated 

with each of the twelve expert responses can be found in tables 5-13 through 5-16. c Based on Pope et al 2002, used as primary estimate in recent RIAs. 
5-4

3. Changes in population exposure to ambient air pollution are then input to impact 

functions1

 to generate changes in the incidence of health effects, or changes in other 

exposure metrics are input to dose-response functions to generate changes in welfare 

effects. Because these estimates contain uncertainty, we characterize the benefits 

estimates probabilistically when appropriate information is available. 

4. 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). 

5. Finally, values for individual health and welfare effects are summed to obtain an estimate 

of the total monetary value of the benefits resulting from the changes in emissions. 

The benefits discussed in this chapter represent the estimates based upon illustrative attainment 

strategies for the final PM2.5 standards (and an alternative set of more stringent standards). As 

explained in earlier chapters, we designed illustrative sets of controls in and around areas that 

need additional emission reductions to reach the new standards in 2020. These strategies are 

evaluated after application of existing federal (such as CAIR), state, and local programs. As 

noted in earlier chapters, benefits (and costs) for the final PM2.5 standards are evaluated 

incrementally relative to an illustrative scenario of full attainment with the current PM2.5

standards (15 μg/m3

 annual mean and 65 μg/m3 daily 98th percentile). Based on the nature of the 

air quality problems in different parts of the U.S. (see Chapter 2), we have divided the nation into 

three regions, the Eastern U.S., California, and the Western U.S. excluding California. Benefits 

will be presented separately for each region, as well as for the nation as a whole. 

 

1

 The 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 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. 
5-5

Table 5-2: Human Health and Welfare Effects of Pollutants Controlled to Simulate Attainment with 

PM2.5 Standardsa

Pollutant/Effect Quantified and Monetized Effects Unquantified Effects 

PM/Healthb

 Premature mortality based on cohort 

study estimatesc

Bronchitis: chronic and acute 

Hospital admissions: respiratory and 

cardiovascular 

Emergency room visits for asthma 

Nonfatal 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 

Low birth weight 

Pulmonary function 

Chronic respiratory diseases other than chronic 

bronchitis 

Nonasthma respiratory emergency room visits 

UVb exposure (+/-)d

PM/Welfare Visibility in Southeastern, Southwestern, 

and California Class I areas 

Visibility in residential and non-Class I areas 

UVb exposure (+/-)d 

Global climate impacts (+/-)d

Nitrogen and 

Sulfate 

Deposition/ 

Welfare 

 Commercial forests due to acidic sulfate and nitrate 

deposition 

Commercial freshwater fishing due to acidic deposition 

Recreation in terrestrial ecosystems due to acidic 

deposition 

Commercial fishing, agriculture, and forests due to 

nitrogen deposition 

Recreation in estuarine ecosystems due to nitrogen 

deposition 

Ecosystem functions 

Passive fertilization 

SO2/Health Hospital admissions for respiratory and cardiac 

diseases 

Respiratory symptoms in asthmatics 

NOx/Health Lung irritation 

Lowered resistance to respiratory infection 

Hospital admissions for respiratory and cardiac 

diseases a Reductions in certain PM2.5 precursors such as NOx and VOC may also lead to changes in ambient 

concentrations of ozone. These changes in ozone will also have health and welfare effects. However, for this 

RIA, because the majority of the illustrative strategies evaluated do not affect NOx and VOC emissions, we focus 

on estimating the health and welfare effects associated with changes in ambient PM2.5. For a full listing of health 

and welfare effects associated with ozone exposures, see the Ozone Criteria Document (U.S. EPA, 2006), and 

Chapter 4 of the RIA for the Clean Air Interstate Rule (U.S. EPA, 2005).

b

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

with PM health effects including morphological changes and altered host defense mechanisms. The public 

health impact of these biological responses may be partly represented by our quantified endpoints. c

 Cohort estimates are designed to examine the effects of long term exposures to ambient pollution, but relative 

risk estimates may also incorporate some effects due to shorter term exposures (see Kunzli et al, 2001 for a 

discussion of this issue). While some of the effects of short term exposure are likely to be captured by the cohort 

estimates, there may be additional premature mortality from short term PM exposure not captured in the cohort 

estimates included in the primary analysis. 

 d May result in benefits or disbenefits. 
5-6

As noted in previous chapters, we were not able to completely model attainment in several 

locations due to limitations in the data and modeling. In these areas, we extrapolate from 

existing information to develop estimates of the air quality changes that might result from fully 

attaining the alternative standards in residual nonattainment areas. To reflect different levels of 

confidence in the underlying data and models, benefits will be presented as two components, 

representing the fully modeled partial attainment component (referred to from this point forward 

at “modeled partial attainment”, and the extrapolated residual attainment component (referred to 

from this point forward as “residual attainment”). 

EPA is currently 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 benefits estimates. A 

recently completed component of this effort is an expert elicitation designed to characterize more 

fully our understanding of PM-related mortality resulting from both short-term and long-term 

exposure. 2 We include the results of the formal expert elicitation among the sources of 

information used in developing health impact functions for this benefits analysis. The results of 

the ‘pilot’ for this expert elicitation were presented in RIAs for both the Nonroad Diesel and 

Clean Air Interstate Rules (U.S. EPA, 2004a, 2005). The results of these elicitation projects, 

including peer review comments, are available on EPA’s Web site, at 

http://www.epa.gov/ttn/ecas/. In addition, similar to our approach in the Nonroad Diesel and 

CAIR RIAs, we present a distribution of benefits estimates based on a more limited set of 

uncertainties, those characterized by the sampling error and variability in the underlying health 

and economic valuation studies used in the benefits modeling framework. We note that 

incorporating only the uncertainty from random sampling error omits important sources of 

uncertainty (e.g., in the functional form of the model, as discussed below). Use of the expert 

elicitation and incorporation of the standard errors approaches provide insights into the 

likelihood of different outcomes and about the state of knowledge regarding the benefits 

estimates. Both approaches have different strengths and weaknesses that are summarized later in 

this chapter. 

The benefits estimates generated for the final PM2.5 NAAQS rule are subject to a number of 

assumptions and uncertainties, which are discussed throughout this document. For example, key 

assumptions underlying the data-derived concentration-response functions 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 specifically identified, the 

weight of the available epidemiological, toxicological, and experimental evidence 

supports an assumption of causality. The impacts of including a probabilistic 

representation of causality are explored using the results of the expert elicitation. 

2. All fine particles, regardless of their chemical composition, are equally potent in 

causing premature mortality. This is an important assumption, because the 

composition of PM produced via transported precursors emitted from EGUs may 

 

2

 Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, usually of 

multiple experts, are obtained (Ayyub, 2002). 
5-7

differ significantly from direct PM released from automotive engines and other 

industrial sources3

. In accordance with advice from the CASAC, EPA has determined 

that no clear scientific grounds exist for supporting differential effects estimates by 

particle type, based on information in the most recent Criteria Document. We 

provide a decomposition of benefits by PM component species to provide additional 

insights into the makeup of the benefits associated with reductions in overall PM2.5

mass (See Tables 5-32 and 5-33). 

3. The C-R function for fine particles is approximately linear within the range of 

ambient concentrations under consideration (above the assumed threshold of 10 

μg/m3). Thus, we assume that the CR functions are applicable to estimates of health 

benefits associated with reducing fine particles in areas with varied concentrations of 

PM, including both regions that are in attainment with PM2.5 standards and those that 

do not meet the standards. However, we examine the impact of this assumption by 

looking at alternative thresholds in a sensitivity analysis. 

The first and third of these assumptions are directly addressed in the expert elicitation, providing 

probabilistic characterizations of the likelihood of causality and the shape of the concentrationresponse function. The second of these is not directly addressed by the expert elicitation, and 

remains a significant source of uncertainty in the state of knowledge about the health benefits 

associated with various emission reduction strategies. 

In addition, a key assumption underlying the entire analysis is that the forecasts for future 

emissions and associated air quality modeling are valid. Because we are projecting emissions 

and air quality out to 2020, there are inherent uncertainties in all of the factors that underlie the 

future state of emissions and air quality levels. While it is important to keep in mind 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 the impacts of this rule. 

In addition to the quantified and monetized benefits summarized above, a number of additional 

categories associated with PM2.5 and its precursor emissions are not currently amenable to 

quantification or valuation. These include reduced acid and particulate deposition damage to 

cultural monuments and other materials, 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 for which 

appropriate health impact functions are not available or which do not provide easily interpretable 

outcomes (i.e., changes in heart rate variability). As a result, monetized benefits generated for 

the primary estimate may underestimate the total benefits attributable to attainment of alternative 

standards. 

Benefits estimates for attaining alternative standards were generated using BenMAP, a computer 

program developed by 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 

 

3

 Even within certain components such as directly emitted PM, there may be significant differences in toxicity of 

component particles such as trace metals and specific carbonaceous species. 
5-8

health effects incidence estimates and monetized benefits estimates. BenMAP provides 

estimates of both the mean impacts and the distribution of impacts (information on BenMAP, 

including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/ 

benmodels.html). 

In general, this chapter is organized around the benefits framework outlined above. In Section 

5.1, we provide an overview of the data and methods that were used to quantify and value health 

and welfare endpoints and discuss how we incorporate uncertainty into our analysis. In Section 

5.2, we report the results of the analysis for human health and welfare effects (the overall 

benefits estimated for the final PM NAAQS are summarized in Table 5-1). Details on the 

emissions inventory and air modeling are presented in Chapter 3. 

5.1 Benefit Analysis—Data and Methods 

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 the modeled changes in 

environmental quality. This approach estimates changes in individual health and welfare 

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 benefit-cost analyses of 

environmental quality programs and has been used in several recent published analyses 

(Banzhaf, Burtraw, and Palmer, 2002; Hubbell et al., 2004; 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 be translated into effects on people or on the things that people value. In some 

cases, the changes in environmental quality can be directly valued, as is the case for changes in 

visibility. In other cases, such as for changes in PM, a health and welfare impact analysis must 

first be conducted to convert air quality changes into effects that can be assigned dollar values. 

Inherent in each of these steps is a high degree of uncertainty, due both to the randomness of 

environmental factors such as meteorology, and the difficulty in measuring and predicting model 

inputs such as pollutant emissions. As such, where possible, we incorporate probabilistic 

representations of model inputs and outputs. However, in many cases, probabilistic 

representations are not available. In these cases, we use the best available science and models, 

and characterize uncertainty using sensitivity analyses. 

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 PM2.5. There 

may be other, indirect health impacts associated with implementing emissions controls, such as 

occupational health impacts for coal miners. 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. 
5-9

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 examine 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 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. 

5.1.1 Valuation Concepts 

In valuing health impacts, we note that reductions in ambient concentrations of air pollution 

generally lower the risk of future adverse health effects by a fairly small amount for a large 

population. The appropriate economic measure is willingness to pay4

 (WTP) for changes in risk 

prior to the regulation (Freeman, 2003).5 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 the measure of benefits. 

These cost of illness (COI) estimates generally (although not in every case) understate the true 

value of reductions in risk of a health effect, because they do not include the value of avoided 

pain and suffering from the health effect (Harrington and Portney, 1987; Berger et al., 1987). 

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 [2003]), the general nature of the difference is clear. Use values are those 

aspects of environmental quality that affect an individual’s welfare directly. These effects 

include changes in product prices, quality, and availability; changes in the quality of outdoor 

 

4

 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. 

5

 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. In some cases, such as the value of fatal risk reductions, we use 

WTA measures due to the difficulty in obtaining WTP estimates. For cases where the changes in the good are small 

WTP and WTA are approximately equal. 
5-10

recreation and outdoor aesthetics; changes in health or life expectancy; and the costs of actions 

taken to avoid negative effects of environmental quality changes. 

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, 

measuring nonuse values has proven to be significantly more difficult than measuring use values. 

The air quality changes produced by attainment strategies to attain the PM2.5 NAAQS cause 

changes in both use and nonuse values, but the monetary benefits 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 (including contingent valuation [CV]), indirect market (e.g., hedonic wage), 

and avoided cost methods. 

The stated preference method values endpoints by using carefully structured surveys to ask a 

sample of people what amount of compensation is equivalent to an improvement in 

environmental quality. There is an extensive scientific literature and body of practice on both the 

theory and technique of stated preference-based valuation. Well-designed and well-executed 

stated preference studies are valid for estimating the benefits of air quality regulations.6 Stated 

preference valuation studies form the complete or partial 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 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 5.1.5). 

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, although not all, conditions) of true economic 

 

6

 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 understood and accepted by the 

respondent; 3) whether the 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. 
5-11

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 5.1.5). 

In general, avoided cost methods should be used only if there is no information available using 

other valuation methods (OMB Circular A-4 offers some additional caution on the use of 

avoided cost methods). 

5.1.2 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 elasticity7

 of WTP for health 

risk reductions is positive, although there is uncertainty about its exact value. Thus, as real 

income increases, the WTP for environmental improvements also increases. Although many 

analyses assume that the income elasticity of WTP is unit elastic (i.e., a 10% higher real income 

level implies a 10% higher WTP to reduce risk changes), empirical evidence suggests that 

income elasticity is substantially less than one and thus relatively inelastic. As real income rises, 

the WTP value also rises but at a slower rate than real income. 

The effects of real income changes on WTP estimates can influence benefits estimates in two 

different ways: through real income growth between the year a WTP study was conducted and 

the year for which benefits are estimated, and through differences in income between study 

populations and the affected populations at a particular time. Empirical evidence of the effect of 

real income on WTP gathered to date is based on studies examining the former. The 

Environmental Economics Advisory Committee (EEAC) of the Science Advisory Board (SAB) 

advised EPA to adjust WTP for increases in real income over time but not to adjust WTP to 

account for cross-sectional income differences “because of the sensitivity of making such 

distinctions, and because of insufficient evidence available at present” (U.S. EPA-SAB, 2000a). 

A recent advisory by another committee associated with the SAB, the Advisory Council on 

Clean Air Compliance Analysis, has provided conflicting advice. While agreeing with “the 

general principle that the willingness to pay to reduce mortality risks is likely to increase with 

growth in real income (U.S. EPA-SAB, 2004a, p. 52)” and that “The same increase should be 

assumed for the WTP for serious nonfatal health effects (U.S. EPA-SAB, 2004a, p. 52),” they 

note that “given the limitations and uncertainties in the available empirical evidence, the Council 

does not support the use of the proposed adjustments for aggregate income growth as part of the 

primary analysis (U.S. EPA-SAB, 2004a, p. 53).” Until these conflicting advisories have been 

reconciled, EPA will continue to adjust valuation estimates to reflect income growth using the 

methods described below, while providing sensitivity analyses for alternative income growth 

adjustment factors. 

Based on a review of the available income elasticity literature, we adjusted the valuation of 

human health benefits upward to account for projected growth in real U.S. income. Faced with a 

dearth of estimates of income elasticities derived from time-series studies, we applied estimates 

derived from cross-sectional studies in our analysis. Details of the procedure can be found in 

 

7

 Income elasticity is a common economic measure equal to the percentage change in WTP for a 1% change in 

income. 
5-12

Kleckner and Neumann (1999). An abbreviated description of the procedure we used to account 

for WTP for real income growth between 1990 and 2020 is presented below. 

Reported income elasticities suggest that the severity of a health effect is a primary determinant 

of the strength of the relationship between changes in real income and WTP. As such, we use 

different elasticity estimates to adjust the WTP for minor health effects, severe and chronic 

health effects, and premature mortality. Note that because of the variety of empirical sources 

used in deriving the income elasticities, there may appear to be inconsistencies in the magnitudes 

of the income elasticities relative to the severity of the effects (a priori one might expect that 

more severe outcomes would show less income elasticity of WTP). We have not imposed any 

additional restrictions on the empirical estimates of income elasticity. One explanation for the 

seeming inconsistency is the difference in timing of conditions. WTP for minor illnesses is often 

expressed as a short term payment to avoid a single epidsode. WTP for major illnesses and 

mortality risk reductions are based on longer term measures of payment (such as wages or annual 

income). Economic theory suggests that relationships become more elastic as the length of time 

grows, reflecting the ability to adjust spending over a longer time period. Based on this theory, it 

would be expected that WTP for reducing long term risks would be more elastic than WTP for 

reducing short term risks. We also expect that the WTP for improved visibility in Class I areas 

would increase with growth in real income. The relative magnitude of the income elasticity of 

WTP for visibility compared with those for health effects suggests that visibility is not as much 

of a necessity as health, thus, WTP is more elastic with respect to income. The elasticity values 

used to adjust estimates of benefits in 2020 are presented in Table 5-3. 

Table 5-3: Elasticity Values Used to Account for Projected Real Income Growtha

Benefit Category Central Elasticity Estimate 

Minor Health Effect 0.14 

Severe and Chronic Health Effects 0.45 

Premature Mortality 0.40 

Visibility 0.90 

a

 Derivation of estimates can be found in Kleckner and Neumann (1999) and Chestnut (1997). COI estimates 

are assigned an adjustment factor of 1.0. 

In addition to elasticity estimates, projections of real gross domestic product (GDP) and 

populations from 1990 to 2020 are needed to adjust benefits to reflect real per capita income 

growth. For consistency with the emissions and benefits modeling, we used national population 

estimates for the years 1990 to 1999 based on U.S. Census Bureau estimates (Hollman, Mulder, 

and Kallan, 2000). These population estimates are based on application of a cohort-component 

model applied to 1990 U.S. Census data projections (U.S. Bureau of Census, 2000). For the 

years between 2000 and 2020, we applied growth rates based on the U.S. Census Bureau 

projections to the U.S. Census estimate of national population in 2000. We used projections of 

real GDP provided in Kleckner and Neumann (1999) for the years 1990 to 2010.8

 We used 

 

8

 U.S. Bureau of Economic Analysis, Table 2A (1992$) (available at http://www.bea.doc.gov/bea/dn/0897nip2/ 

tab2a.htm.) and U.S. Bureau of Economic Analysis, Economics and Budget Outlook. Note that projections for 2007 

to 2010 are based on average GDP growth rates between 1999 and 2007. 
5-13

projections of real GDP (in chained 1996 dollars) provided by Standard and Poor’s (2000) for 

the years 2010 to 2020.9

Using the method outlined in Kleckner and Neumann (1999) and the population and income data 

described above, we calculated WTP adjustment factors for each of the elasticity estimates listed 

in Table 5-4. Benefits for each of the categories (minor health effects, severe and chronic health 

effects, premature mortality, and visibility) are adjusted by multiplying the unadjusted benefits 

by the appropriate adjustment factor. Table 5-4 lists the estimated adjustment factors. Note that, 

for premature mortality, we applied the income adjustment factor to the present discounted value 

of the stream of avoided mortalities occurring over the lag period. Also note that because of a 

lack of data on the dependence of COI and income, and a lack of data on projected growth in 

average wages, no adjustments are made to benefits based on the COI approach or to work loss 

days and worker productivity. This assumption leads 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). 

Table 5-4: Adjustment Factors Used to Account for Projected Real Income Growtha

Benefit Category 2020 

Minor Health Effect 1.066 

Severe and Chronic Health Effects 1.229 

Premature Mortality 1.201 

Visibility 1.517 

a

 Based on elasticity values reported in Table 5-3, U.S. Census population projections, and projections of real 

GDP per capita. 

5.1.3 Demographic Projections 

Quantified and monetized human health impacts depend on the demographic characteristics of 

the population, including age, location, and income. We use 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 Kallan, 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, 

 

9

 In previous analyses, we used the Standard and Poor’s projections of GDP directly. This led to an apparent 

discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the relative 

growth rates for GDP derived from the Standard and Poor’s projections to the 2010 projected GDP based on the 

Bureau of Economic Analysis projections. 
5-14

and populations 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 exportbased 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 cohortcomponent 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 2020 based on historical 

rates of mortality, fertility, and migration. 

The WP projections of county-level population are based on historical population data from 1969 

through 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 used national population estimates based on the U.S. Census Bureau 

projections. 

5.1.4 Methods for Describing Uncertainty 

The NRC (2002) highlighted the need for EPA to conduct rigorous quantitative analysis of 

uncertainty in its benefits estimates as well as the need for presenting these estimates to decision 

makers in ways that foster an appropriate appreciation of their inherent uncertainty. In response 

to these comments, 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 

In the current analysis EPA continues to move forward on one of the key recommendations of 

the NRC – moving the assessment of uncertainties from its ancillary analyses into its main 

benefits presentation through the conduct of probabilistic analyses. In this final rule, EPA 

addressed key sources of uncertainty by Monte Carlo propagation of uncertainty in the C-R 

functions and economic valuation functions through its base estimates as well as by continuing 

its practice of conducting a series of ancillary sensitivity analyses examining the impact of 

alternate assumptions on the benefits estimates. It should be noted that the Monte Carlogenerated distributions of benefits reflect only some of the uncertainties in the input parameters. 

Uncertainties associated with emissions, air quality modeling, populations, and baseline health 
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effect incidence rates are not represented in the distributions of benefits of attaining alternative 

standards. Issues such as correlation between input parameters and the identification of 

reasonable upper and lower bounds for input distributions characterizing uncertainty in 

additional model elements will be addressed in future versions of the uncertainty framework. 

In benefit analyses of air pollution regulations conducted to date, the estimated impact of 

reductions in premature mortality has accounted for 85% to 95% of total benefits. Therefore, in 

characterizing the uncertainty related to the estimates of total benefits it is particularly important 

to attempt to characterize the uncertainties associated with this endpoint. As such the analysis for 

this rule incorporates the results of our recent expert elicitation to characterize uncertainty in the 

effect estimates used to estimate premature mortality resulting from exposures to PM into the 

main analysis. In collaboration with OMB, EPA completed a pilot expert elicitation in 2004, 

which was used to characterize uncertainty in the PM mortality C R function in the Nonroad 

Diesel and CAIR RIAs. EPA has recently completed a full-scale expert elicitation that 

incorporated peer-review comments on the pilot application, and that provides a more robust 

characterization of the uncertainty in the premature mortality function. This expert elicitation 

was designed to evaluate uncertainty in the underlying causal relationship, 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 long-term cohort studies to capture premature mortality 

resulting from short-term peak PM exposures, were also addressed in the expert elicitation. 

For this final rule, EPA addressed key sources of uncertainty through Monte Carlo propagation 

of uncertainty in the C-R functions and economic valuation functions and through a series of 

sensitivity analyses examining the impact of alternate assumptions on the benefits estimates that 

are generated. It should be noted that the Monte Carlo-generated distributions of benefits reflect 

only some of the uncertainties in the input parameters. Uncertainties associated with emissions, 

air quality modeling, populations, and baseline health effect incidence rates are not represented 

in the distributions of benefits of attaining alternative standards. 

Our distributions of total benefits do not completely represent full uncertainty because of the 

uncertainty in model elements discussed above (see Table 5-5). Uncertainty about specific 

aspects of the health and welfare estimation models is discussed in greater detail in the following 

sections. The estimated distributions of total benefits may not completely capture the shape and 

location of the actual distribution of total benefits. 

5.1.4.1 Sources of 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 were 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 its role 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 
5-16

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 large 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; 

• 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 5-5. 

More specifically, there are key uncertainties in many aspects of the health impact functions used 

in our analyses. These are discussed in detail in the following section. 
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Table 5-5: Primary Sources of Uncertainty in the Benefits Analysis 

1. Uncertainties Associated with Impact Functions 

● The value of the PM 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 PM concentrations observed in the 

source epidemiological study. 

● Application of some impact functions only to those subpopulations matching the original 

study population. 

2. Uncertainties Associated with PM Concentrations 

● Responsiveness of the models to changes in precursor emissions resulting from the control 

policy. 

● Projections of future levels of precursor emissions, especially organic carbonaceous particle 

emissions. 

● Model chemistry for the formation of ambient nitrate concentrations. 

● Lack of speciation monitors in some areas requires extrapolation of observed speciation 

data. 

● CMAQ model performance in the Western U.S., especially California indicates significant 

underprediction of PM2.5. 

3. Uncertainties Associated with PM Mortality Risk 

● Differential toxicity of specific component species within the complex mixture of PM has not 

been determined. 

● 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 PM2.5 monitoring data in reflecting actual PM2.5 exposures. 

4. Uncertainties Associated with Possible Lagged Effects 

● The portion of the PM-related long-term exposure mortality effects associated with 

changes in annual PM levels that would occur in a single year is 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 

therefore may not accurately represent the actual location-specific rates. 

● Current baseline incidence rates may not approximate well baseline incidence rates in 2020. 

● 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 because of 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 unmonetized benefits are not included. 
5-18

5.1.4.2 Uncertainties Associated with Health Impact Functions based on Reported Effect 

Estimates from the Epidemiological Literature 

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 

studies provide both a “best estimate” of this relationship plus a measure of the statistical 

uncertainty of the relationship. The 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 well designed and executed studies and may result in health effect estimates that vary 

considerably. Across-study variation can result from a variety of possible causes. Such 

differences might simply be associated with different measurement techniques. Sources of 

variation can be introduced by the air quality monitoring technique, measurement averaging 

times, health endpoint data sources (differences in the way medical records are kept at different 

institutions or questionnaire wording). One possibility is that estimates of the single true 

relationship between a given pollutant and a health effect differ across studies because of 

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, 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 differences in the relative sensitivity of these two populations to PM pollution and 

differences in the composition of PM in these two locations, as well as other factors. 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 of C-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 applying 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 
5-19

literature does not allow for a region-specific estimation of health benefits for most health 

outcomes.10 

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 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 6- to 18-year child age group), we have done so and used those expanded 

incidence estimates in the primary analysis. 

Uncertainties in Concentration-Response Functions. The following uncertainties exist in 

almost all concentration-response functions for PM related health effects. For expository 

purposes, and because of the importance of mortality, we focus the discussion on how these 

uncertainties affect the PM mortality concentration-response functions. 

Causality: Epidemiological studies are not designed to definitively prove causation. For the 

analysis of the PM NAAQS, 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 (CASAC, 2005). As 

with all health effects included in our analysis, a weight of evidence process is used to evaluate 

endpoints before including them in the analysis. 

Other Pollutants: PM concentrations are correlated with the concentrations of other criteria 

pollutants, such as ozone and CO. To the extent that there is correlation, this analysis may be 

assigning mortality effects to PM exposure that are actually the result of exposure to other 

pollutants. Recent studies (see Thurston and Ito [2001] and Bell et al. [2004]) have explored 

whether ozone may have mortality effects independent of PM. EPA is currently evaluating the 

epidemiological literature on the relationship between ozone and mortality. 

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 has a non-threshold log-linear form throughout the 

relevant range of exposures. If this is not the correct form of the C-R function, or if certain 

 

10 Although 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. 
5-20

scenarios predict concentrations well above the range of values for which the C-R function was 

fitted, avoided mortality may be misestimated. 

In addition there is ongoing debate as to whether there exists a threshold below which there 

would be no benefit to further reductions in PM2.5. Some researchers have hypothesized the 

presence of a threshold relationship. The nature of the hypothesized relationship is the 

possibility that there exists a PM concentration level below which further reductions no longer 

yield premature mortality reduction benefits. EPA’s most recent PM2.5 Criteria Document 

concludes that “the available evidence does not either support or refute the existence of 

thresholds for the effects of PM on mortality across the range of concentrations in the studies” 

(U.S. EPA, 2004b, p. 9-44). EPA’s Science Advisory Board (SAB) that provides advice on 

benefits analysis methods11 has been to model premature mortality associated with PM exposure 

as a non-threshold effect, that is, with harmful effects to exposed populations regardless of the 

absolute level of ambient PM concentrations. 

Regional Differences: As discussed above, significant variability exists in the results of different 

PM/mortality studies. This variability may reflect regionally specific C-R 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 misestimation of effects in these regions. 

Relative Toxicity of PM Component Species: In this analysis, all fine particles, regardless of 

their chemical composition, are assumed to be equally potent in causing premature mortality. 

This is an important assumption, because there may be significant differences between PM 

produced via transported precursors, direct PM released from automotive engines, and direct PM 

from other industrial sources. The analysis also assumes that all components of fine particles 

have equal toxicity (because the available epidemiological effect estimates are based on total 

PM2.5 mass rather than the mass of individual component species). While it is reasonable to 

expect that the potency of components may vary across the numerous effect categories 

associated with particulate matter, EPA’s interpretation of scientific information considered to 

date is that such information does not yet provide a basis for quantification beyond using fine 

particle mass. However, to provide information that may be useful as additional studies become 

available, we are providing estimates of the proportions of benefits that are attributable to 

specific components of PM2.5, e.g., ammonium sulfate, ammonium nitrate, elemental carbon, 

organic carbon, and crustal material (which includes metals). This apportionment does not make 

any assumptions about the relative toxicity of the different species; rather, it divides total 

benefits based on the contribution of reductions in individual component species to the overall 

reduction in PM2.5 mass. 

 

11 The advice from the 2004 SAB-HES (U.S. EPA-SAB, 2004b) is characterized by the following: “For the studies 

of long-term exposure, the HES notes that Krewski et al. (2000) have conducted the most careful work on this issue. 

They report that the associations between PM2.5 and both all-cause and cardiopulmonary mortality were near linear 

within the relevant ranges, with no apparent threshold. Graphical analyses of these studies (Dockery et al., 1993, 

Figure 3, and Krewski et al., 2000, page 162) also suggest a continuum of effects down to lower levels. Therefore, it 

is reasonable for EPA to assume a no threshold model down to, at least, the low end of the concentrations reported 

in the studies.” 
5-21

Lag Time Between Change in Exposure and Health Impact: There is a time lag between changes 

in PM exposures and the total realization of changes in health effects. Within the context of 

benefits analyses, this term is often referred to as “cessation lag”. For the chronic PM/mortality 

relationship, the length of the cessation lag is unknown. 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 health effects cessation lag for reductions in exposures to fine PM. 

Information about latency (the amount of time between exposure and onset of a health effect) 

may inform our understanding of cessation lags. 

Scientific literature on adverse health effects similar to those associated with PM (e.g., smokingrelated 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, although it is worth noting here that in the case of ambient air pollution we 

are predicting the effects of reduced exposure rather than complete cessation. The SAB-HES 

suggests that appropriate lag structures may be developed based on the distribution of causespecific deaths within the overall all-cause estimate. Diseases with longer progressions should 

be characterized by long-term lag structures, while impacts occurring in populations with 

existing disease may be characterized by short-term lags. 

A key question is the distribution of causes of death within the relatively broad categories 

analyzed in the cohort studies used. While we may be more certain about the appropriate length 

of cessation lag for lung cancer deaths, it is not clear what the appropriate lag structure should be 

for different types of cardiopulmonary deaths, which include both respiratory and cardiovascular 

causes. Some respiratory diseases may have a long period of progression, while others, such as 

pneumonia, have a very short duration. In the case of cardiovascular disease, there is an 

important question of whether air pollution is causing the disease, which would imply a 

relatively long cessation lag, or whether air pollution is causing premature death in individuals 

with preexisting heart disease, which would imply very short cessation lags. 

The SAB-HES provides several recommendations for future research that could support the 

development of defensible lag structures, including the use of disease-specific lag models, and 

the construction of a segmented lag distribution to combine differential lags across causes of 

death. The SAB-HES recommended that until additional research has been completed, EPA 

should assume a segmented lag structure characterized by 30% of mortality reductions occurring 

in the first year, 50% occurring evenly over years 2 to 5 after the reduction in PM2.5, and 20% 

occurring evenly over the years 6 to 20 after the reduction in PM2.5 (EPA-COUNCIL-LTR-05-

001, 2004). The distribution of deaths over the latency period is intended to reflect the 

contribution of short-term exposures in the first year, cardiopulmonary deaths in the 2- to 5-year 

period, and long-term lung disease and lung cancer in the 6- to 20-year period. For future 

analyses, the specific distribution of deaths over time will need to be determined through 

research on causes of death and progression of diseases associated with air pollution. It is 

important to keep in mind that changes in the lag assumptions do not change the total number of 

estimated deaths but rather the timing of those deaths. 
5-22

Cumulative Effects: 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 are still uncertain at 

this time. 

5.1.5 Health Benefits Assessment Methods 

The largest monetized benefits of reducing ambient concentrations of PM are attributable to 

reductions in health risks associated with air pollution. EPA’s Criteria Documents for ozone and 

PM list numerous health effects known to be linked to ambient concentrations of these pollutants 

(EPA, 2004; 2006). As discussed above, quantification of health impacts requires several inputs, 

including epidemiological effect estimates (concentration-response functions), 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. These include premature mortality, nonfatal heart attacks, chronic 

bronchitis, acute bronchitis, hospital admissions, emergency room visits for asthma, upper and 

lower respiratory symptoms, asthma exacerbations, minor restricted activity days and days of 

work lost. 

Some health effects are excluded from this analysis for three reasons: the possibility of doublecounting, 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. Unquantifed effects are listed in Table 5-2. An 

improvement in ambient PM2.5 air quality may reduce the number of incidences within each 

unquantified effect category that the U.S. population would experience. Although these health 

effects are believed to be PM 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. 
5-23

5.1.5.1 Selection of Health Endpoints 

We base our selection of health endpoints on consistency with EPA Criteria Documents and 

Staff Papers, with input and advice from the EPA Science Advisory Board Health Effects 

Subcommittee, a scientific review panel specifically established to provide advice on the use of 

the scientific literature in developing benefits analyses for air pollution regulations 

(http://www.epa.gov/sab/). In general, we follow a weight of evidence approach, based on the 

biological plausibility of effects, availability of concentration-response functions from wellconducted peer-reviewed epidemiological studies, cohesiveness of results across studies, and a 

focus on endpoints reflecting public health impacts (like hospital admissions) rather than 

physiological responses (such as changes in clinical measures like Forced Expiratory Volume 

(FEV1)). 

5.1.5.2 Sources of Information for Effect Estimates 

There are several types of data that can support the determination of types and magnitude of 

health effects associated with air pollution exposures. These sources of data include 

toxicological studies (including animal and cellular studies), human clinical trials, and 

observational epidemiology studies. All of these data sources provide important contributions to 

the weight of evidence surrounding a particular health impact, however, only epidemiology 

studies provide direct concentration-response relationships which can be used to evaluate 

population-level impacts of reductions in ambient pollution levels. 

However, standard environmental epidemiology studies provide only a limited representation of 

the uncertainty associated with a specific C-R function, measuring only the statistical error in the 

estimates, and usually relating more to the power of the underlying study (driven largely by 

population size and the frequency of the outcome measure). There are many other sources of 

uncertainty in the relationships between ambient pollution and population level health outcomes, 

including many sources of model uncertainty, such as model specification, potential confounding 

between factors that are both correlated with the health outcome and each other, and many other 

factors. As such, in recent years, EPA has begun investigating how expert elicitation methods 

can be used to integrate across various sources of data in developing C-R functions for 

regulatory benefits analyses. 

Expert elicitation is useful in integrating the many sources of information about uncertainty in 

the C-R function, because it allows experts to synthesize these data sources using their own 

mental models, and provide a probabilistic representation of their synthesis of the data in the 

form of a probability distribution of the C-R function. Figure 5-1 shows how expert elicitation 

builds on both the direct empirical data on C-R relationships and other less direct evidence to 

develop probabilistic distributions of C-R functions. EPA has used expert elicitation to inform 

the regulatory process in the past (see for example the previous staff paper for the lead NAAQS, 

U.S. EPA, 1990). In the current analysis, we have only used expert elicitation to characterize the 

C-R function for the relationship between fine PM and premature mortality. However, similar 

methods could be used to characterize C-R functions for other health outcomes. 
5-24

Epidemiology Toxicology Laboratory/

Clinical

Direct 

C-R functions + 

Standard Errors

Expert 

Elicitation

Probabalistic C-R functions

Sources of Scientific Data

Epidemiology Toxicology Laboratory/

Clinical

Direct 

C-R functions + 

Standard Errors

Expert 

Elicitation

Probabalistic C-R functions

Epidemiology Toxicology Laboratory/

Clinical

Direct 

C-R functions + 

Standard Errors

Expert 

Elicitation

Probabalistic C-R functions

Sources of Scientific Data

Figure 5-1. Sources and Integration of Scientific Data in Informing Development of Health 

Impact Functions 

5.1.5.3 Information Used in Quantifying C-R Functions 

For the data-derived estimates, we relied on the published scientific literature to ascertain the 

relationship between PM and adverse human health effects. We evaluated epidemiological 

studies using the selection criteria summarized in Table 5-6. 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. 

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 
5-25

groups. For this reason, we consider very carefully the set of studies available examining each 

endpoint and select a consistent subset that provides a good balance of population coverage and 

match with the pollutant of interest. In many cases, either because of a lack of multiple studies, 

consistency problems, or clear superiority in the quality or comprehensiveness of one study over 

others, a single published study is selected as the basis of the effect estimate. 

When several effect estimates for a pollutant and a given health endpoint have been selected, 

they are quantitatively combined or pooled to derive a more robust estimate of the relationship. 

The BenMAP Technical Appendices provides details of the procedures used to combine multiple 

impact functions (Abt Associates, 2005). In general, we used 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 used the fixed effects model as our null hypothesis and then determined 

whether the data suggest that we should reject this null hypothesis, in which case we would use 

the random effects model.12 Pooled impact functions are used to estimate hospital admissions 

and asthma exacerbations. For more details on methods used to pool incidence estimates, see the 

BenMAP Technical Appendices (Abt Associates, 2005), which are available with the BenMAP 

software at http://www.epa.gov/ttn/ecas/benmodels.html. 

Effect estimates selected for a given health endpoint were applied consistently across all 

locations nationwide. This applies to both impact functions defined by a single effect estimate 

and those defined by a pooling of multiple effect estimates. Although the effect estimate may, in 

fact, vary from one location to another (e.g., because of differences in population susceptibilities 

or differences in the composition of PM), location-specific effect estimates are generally not 

available. 

The specific studies from which effect estimates for the primary analysis are drawn are included 

in Table 5-7. In all cases where effect estimates are drawn directly from epidemiological studies, 

standard errors are used as a partial representation of the uncertainty in the size of the effect 

estimate. Below we provide the basis for selecting these studies. 

 

12 In this analysis, the fixed effects model assumes that there is only one pollutant coefficient for the entire modeled 

area. The random effects model assumes that studies conducted in different locations are estimating different 

parameters; therefore, there may be a number of different underlying pollutant coefficients. 
5-26

Table 5-6: Summary of Considerations Used in Selecting C-R Functions 

Consideration Comments 

Peer-Reviewed 

Research 

Peer-reviewed research is preferred to research that has not undergone the 

peer-review process. 

Study Type Among studies that consider chronic exposure (e.g., over a year or longer), 

prospective cohort studies are preferred over ecological studies because they 

control for important individual-level confounding variables that cannot be 

controlled for in ecological studies. 

Study Period Studies examining a relatively longer period of time (and therefore having more 

data) are preferred, because they have greater statistical power to detect effects. 

More recent studies are also preferred because of possible changes in pollution 

mixes, medical care, and lifestyle over time. However, when there are only a 

few studies available, studies from all years will be included. 

Population 

Attributes 

The most technically appropriate measures of benefits would be based on 

impact functions that cover the entire sensitive population but allow for 

heterogeneity across age or other relevant demographic factors. In the absence 

of effect estimates specific to age, sex, preexisting condition status, or other 

relevant factors, it may be appropriate to select effect estimates that cover the 

broadest population to match with the desired outcome of the analysis, which is 

total national-level health impacts. When available, multi-city studies are 

preferred to single city studies because they provide a more generalizable 

representation of the C-R function. 

Study Size Studies examining a relatively large sample are preferred because they 

generally have more power to detect small magnitude effects. A large sample 

can be obtained in several ways, either through a large population or through 

repeated observations on a smaller population (e.g., through a symptom diary 

recorded for a panel of asthmatic children). 

Study Location U.S. studies are more desirable than non-U.S. studies because of potential 

differences in pollution characteristics, exposure patterns, medical care system, 

population behavior, and lifestyle. 

Pollutants 

Included in Model 

When modeling the effects of ozone and PM (or other pollutant combinations) 

jointly, it is important to use properly specified impact functions that include both 

pollutants. Using single-pollutant models in cases where both pollutants are 

expected to affect a health outcome can lead to double-counting when pollutants 

are correlated. 

Measure of PM For this analysis, impact functions based on PM2.5 are preferred to PM10

because of the focus on reducing emissions of PM2.5 precursors, and because 

air quality modeling was conducted for this size fraction of PM. Where PM2.5

functions are not available, PM10 functions are used as surrogates, recognizing 

that there will be potential downward (upward) biases if the fine fraction of PM10

is more (less) toxic than the coarse fraction. 

Economically 

Valuable Health 

Effects 

Some health effects, such as forced expiratory volume and other technical 

measurements of lung function, are difficult to value in monetary terms. These 

health effects are not quantified in this analysis. 

Nonoverlapping 

Endpoints 

Although the benefits associated with each individual health endpoint may be 

analyzed separately, care must be exercised in selecting health endpoints to 

include in the overall benefits analysis because of the possibility of doublecounting of benefits. 
5-27

Adult Premature Mortality – Epidemiological Basis. 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 epidemiological studies, the serious nature of the 

effect itself, and the high monetary value ascribed to prolonging life make mortality risk 

reduction the most significant health endpoint quantified in this analysis. 

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 (US EPA, 2004). Time-series methods have 

been used 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. Cohort 

methods have been used to examine the potential relationship between community-level PM 

exposures over multiple years (i.e., long-term exposures) and community-level annual mortality 

rates. Researchers have found statistically significant associations between PM and premature 

mortality using both types of studies. In general, the risk estimates based on the cohort studies 

are larger than those derived from time-series studies. Cohort analyses are thought to better 

capture the full public health impact of exposure to air pollution over time, because they capture 

the effects of long-term exposures and possibly some component of short-term exposures 

(Kunzli et al., 2001; NRC, 2002). This section discusses some of the issues surrounding the 

estimation of premature mortality. To demonstrate the sensitivity of the benefits estimates to the 

specific sources of information regarding the impact of PM2.5 exposures on the risk of 

premature death, we are providing estimates in our results tables based on studies derived from 

the epidemiological literature and from the recent EPA sponsored expert elicitation. The 

epidemiological studies from which these estimates are drawn are described below. The expert 

elicitation project and the derivation of effect estimates from the expert elicitation results are 

described in the next section. 

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, Morris, and Wyzga [1989]). These early “ecological 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. 

Over the last 10 years, several studies using “prospective cohort” designs have been published 

that 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 individuallevel information with respect to health status and residence. The most extensive analyses have 

been based on data from two prospective cohort groups, often referred to as the Harvard “SixCities Study” (Dockery et al., 1993; Laden et al, 2006) and the “American Cancer Society or 

ACS study” (Pope et al., 1995; Pope et al, 2002; Pope et al, 2004); 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 
5-28

the air quality results are not geographically representative of most of the United States, and the 

lifestyle of the population is not reflective of much of the U.S. population. Analysis is also 

available for a cohort of adult male veterans diagnosed with hypertension has been examined 

(Lipfert et al., 2000; Lipfert et al, 2003, 2006). The characteristics of this group differ from the 

cohorts in the Six-Cities, ACS, 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, we have chosen not to 

include any effect estimates from the Lipfert et al. (2000) study in our benefits assessment.13

 

13 EPA recognizes that the ACS cohort also is not 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. 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%) and a much higher percentage of smokers (81% former smokers, 57% current smokers) 

than the general population (12% African American, 24% current smokers). 
5-29

Table 5-7: Endpoints and Studies Used to Calculate Total Monetized Health Benefits 

Endpoint Pollutant Study 

Study 

Population 

Premature Mortality 

Premature mortality 

—cohort study, allcause 

PM2.5 (annual) Pope et al. (2002) 

Laden et al. (2006) 

>29 years 

>25 years 

Premature mortality, 

total exposures 

PM2.5 (annual) Expert Elicitation (IEc, 2006) >24 years 

Premature mortality 

— all-cause 

PM2.5 (annual) Woodruff et al. (1997) Infant (<1 year) 

Chronic Illness 

Chronic bronchitis PM2.5 (annual) Abbey et al. (1995) >26 years 

Nonfatal heart attacks PM2.5 (daily) Peters et al. (2001) Adults 

Hospital Admissions 

PM2.5 (daily) Pooled estimate: 

Moolgavkar (2003)—ICD 490-496 

(COPD) 

Ito (2003)—ICD 490-496 (COPD) 

>64 years 

PM2.5 (daily) Moolgavkar (2000)—ICD 490-496 

(COPD) 

20–64 years 

PM2.5 (daily) Ito (2003)—ICD 480-486 (pneumonia) >64 years 

Respiratory 

PM2.5 (daily) Sheppard (2003)—ICD 493 (asthma) <65 years 

PM2.5 (daily) Pooled estimate: 

Moolgavkar (2003)—ICD 390-429 (all 

cardiovascular) 

Ito (2003)—ICD 410-414, 427-428 

(ischemic heart disease, dysrhythmia, 

heart failure) 

Cardiovascular >64 years 

PM2.5 (daily) Moolgavkar (2000)—ICD 390-429 (all 

cardiovascular) 

20–64 years 

Asthma-related ER 

visits 

PM2.5 Norris et al. (1999) 0–18 years 

Other Health Endpoints 

Acute bronchitis PM2.5 Dockery et al. (1996) 8–12 years 

Upper respiratory 

symptoms 

PM10 Pope et al. (1991) Asthmatics, 9–

11 years 

Lower respiratory 

symptoms 

PM2.5 Schwartz and Neas (2000) 7–14 years 

Asthma 

exacerbations 

PM2.5 Pooled estimate: 

Ostro et al. (2001) (cough, wheeze and 

shortness of breath) 

Vedal et al. (1998) (cough) 

6–18 yearsa

Work loss days PM2.5 Ostro (1987) 18–65 years 

MRADs PM2.5 Ostro and Rothschild (1989) 18–65 years a

 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 extended the applied population to 6 to 18, reflecting the 

common biological basis for the effect in children in the broader age group. 
5-30

Given their consistent results and broad geographic coverage, and importance in informing the 

NAAQS development process, the Six-Cities and ACS data have been particularly important in 

benefits analyses. The credibility of these two studies is further enhanced by the fact that the 

initial published studies (Pope et al, 1995 and Dockery et al 1993) 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. While the HEI reexamination lends 

credibility to the original studies, it also highlights sensitivities concerning the relative impact of 

various pollutants, such as SO2, 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 findings of the 1993 Six City Study and the 1995 ACS 

study were recently completed using more recent air quality and a longer follow-up period for 

the ACS cohort was recently published (Pope et al, 2002, 2004; Laden et al, 2006). The follow 

up to the Harvard Six City Study both confirmed the effect size from the first analysis and 

provided additional confirmation that reductions in PM2.5 are likely to result in reductions in the 

risk of premature death. This additional evidence stems from the observed reductions in PM2.5 in 

each city during the extended follow-up period. Laden et al. (2006) found that mortality rates 

consistently went down at a rate proportionate to the observed reductions in PM2.5. 

The extended analyses of the ACS cohort data (Pope et al., 2002, 2004) 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 additional measurement of cohort exposure to PM2.5

following implementation of the 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. 

The NRC (2002) also recommended that EPA review the database of cohort studies and consider 

developing a weighted mean estimate based on selected studies. Because of the differences in 

the study designs and populations considered in the ACS and Harvard Six-cities studies, we have 

elected to not pool the results of the studies, instead presenting a range of estimates reflecting the 

different sources of impact estimates. 

In developing and improving the methods for estimating and valuing the potential reductions in 

mortality risk over the years, EPA consulted with the SAB-HES. That panel recommended using 

long-term prospective cohort studies in estimating mortality risk reduction (U.S. EPA,1999b). 

This recommendation has been 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” (NRC, 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. Because of the refinements in the extended follow-up 

analysis, 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 
5-31

using other cohort and time-series studies could be included as part of the sensitivity analysis 

(U.S. EPA-SAB, 2004b). 

The SAB-HES also recommended using the specific 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 allcause 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 calculated mortality impacts for the 

primary analysis based on the all-cause relative risk. Based on our most recently available SAB 

guidance, we provide mortality impacts based on the ACS study as the best estimate for 

comparing across the current and previous RIAs. This provides historical continuity with past 

analyses and serves as one point of reference in interpreting the results of the expert elicitation 

(see discussion below). 

In recent RIAs (see for example the CAIR and Nonroad Diesel RIAs), we have included an 

estimate of mortality impacts based on application of the C-R function derived from the Harvard 

Six-cities study. In those analyses, the Six-cities estimate was included as a sensitivity analysis 

in an appendix to the RIA. Following the NAS advice to begin moving sensitivity and 

uncertainty analyses into the main body of the RIA, we are including a separate estimate based 

on the Six-cities study to complement the estimate based on the ACS study. This also reflects 

the weight that was placed on both the ACS and Harvard Six-city studies by experts participating 

in the PM2.5 mortality expert elicitation. 

As noted above, since the most recent SAB review, an extended followup of the Harvard Sixcities study has been published (Laden et al., 2006). We use this specific estimate to represent 

the Six-cities study because it reflects the most up-to-date science and because it was cited by 

many of the experts in their elicitation responses. We note that because of the recent publication 

date of the Laden et al (2006) study, it has not undergone the CASAC and SAB-HES review 

received by the Pope et al (2002) and earlier Six-cities publications (see Dockery et al, 1993). 

However, it is clear from the expert elicitation that the results published in Laden et al (2006) are 

potentially influential, and in fact, the expert elicitation results encompass within their range the 

estimates from both the Pope et al (2002) and Laden et al (2006) studies. As part of the NAAQS 

review process, EPA conducted a provisional assessment of “new” science published since the 

closing date for the PM Criteria Document. The provisional assessment found that “new” 

studies generally strengthen the evidence that acute and chronic exposures to fine particles are 

associated with health effects. The provisional assessment found that the results reported in the 

studies do not dramatically diverge from previous findings, and, taken in context with the 

findings of the Criteria Document, the new information and findings do not materially change 

any of the broad scientific conclusions regarding the health effects of PM exposure made in the 

Criteria Document. The Laden et al (2006) study was included in this provisional assessment 

and therefore can be considered to be covered under the broad findings of the provisional 

assessment. 

A number of additional analyses have been conducted on the ACS cohort data (Jarrett et al., 

2005; Krewski et al., 2005; Pope et al., 2004). These studies have continued to find a strong 

significant relationship between PM2.5 and mortality outcomes. Specifically, much of the recent 
5-32

research has suggested a stronger relationship between cardiovascular mortality and lung cancer 

mortality with PM2.5, and a less significant relationship between respiratory-related mortality and 

PM2.5. 

EPA’s is committed to seeking the advice of its Science Advisory Board to review how EPA has 

incorporated expert elicitation results into the benefits analysis, and the extent to which they find 

the presentation in this RIA responsive to the NRC (2002) guidance to incorporate uncertainty 

into the main analysis and further, whether the agency should move toward presenting a central 

estimate with uncertainty bounds or continue to provide separate estimates for each of the 12 

experts as well as from the ACS and Six Cities studies, and if so, the appropriateness of using 

Laden et al 2006, the most recently published update, as the estimate for the Six Cities based 

model. 

Adult Premature Mortality – Expert Elicitation Study 

Among the recommendations made by the National Research Council (NRC) in its 2002 review 

of EPA's method for assessing health benefits of air pollution regulations was a recommendation 

for EPA to consider the use of formally elicited expert judgments as a means of characterizing 

uncertainty in inputs to health benefits analyses. As part of its efforts to improve the 

characterization of uncertainties in its benefits estimates, EPA has conducted a study of the 

concentration-response (C-R) relationship between changes in PM2.5 exposures and mortality 

using formally elicited expert judgments. The goal of the study was to elicit from a sample of 

health experts probabilistic distributions describing uncertainty in estimates of the reduction in 

mortality among the adult U.S. population resulting from reductions in ambient annual average 

PM2.5 levels. These distributions were obtained through a formal interview protocol using 

methods designed to elicit subjective expert judgments. 

In 2003 and 2004, EPA conducted a pilot-scale elicitation study with five experts to explore the 

effectiveness of expert judgment techniques for characterizing uncertainty and to explore the use 

of the expert judgment results in the context of economic benefits analysis (Industrial 

Economics, 2004). EPA previously applied the results of the pilot-scale study as part of its 

uncertainty analysis in the regulatory analysis accompanying the Clean Air Interstate Rule 

(CAIR) (U.S. EPA, 2005). EPA has recently completed a full-scale expert elicitation analysis of 

the PM2.5-mortality relationship that included numerous refinements based on insights from 

conducting the pilot study and on comments from peer reviewers of the pilot (Industrial 

Economics, 2006). This analysis applies the results of the full-scale study. 

The full-scale study involved personal interviews with twelve health experts who have conducted 

research on the relationship between PM2.5 exposures and mortality. These experts were 

selected through a peer-nomination process and included experts in epidemiology, toxicology, 

and medicine. The elicitation interview consisted of a protocol of carefully structured questions, 

both qualitative and quantitative, about the nature of the PM2.5-mortality relationship.14 The 

 

14 In addition to the elicitation interviews, the twelve experts participated in pre- and post-elicitation 

workshops. The pre-elicitation workshop was designed to prepare the experts by familiarizing them with the 

protocol, providing them information about probabilistic judgments, and allowing them to discuss key issues and 

relevant evidence. At this workshop, the experts were also provided with “briefing book” materials, including a CD 

containing relevant studies and background information pages with data on air quality in the US, population 
5-33

questions requiring qualitative responses probed experts' beliefs concerning key evidence and 

critical sources of uncertainty and enabled them to establish a conceptual basis supporting their 

quantitative judgments. Questions covered topics such as potential biological mechanisms 

linking PM2.5 exposures with mortality; the role of study design in capturing PM/mortality 

effects; key scientific evidence on the magnitude of the PM/mortality relationship; sources of 

potential error or bias in epidemiological results; the likelihood of a causal relationship between 

PM2.5 and mortality, and the shape of the C-R function. The main quantitative question in the 

protocol asked experts to provide the 5th, 25th, 50th, 75th, and 95th percentiles of a probabilistic 

distribution for the percent change in U.S. annual, adult all-cause mortality resulting from a 1 

μg/m3

 change in annual average PM2.5 exposure, assuming a range of baseline PM2.5 levels 

between 4 and 30 μg/m3. This quantitative question was designed to yield results appropriate for 

application in EPA's quantitative health benefit analyses. 

The results of the full-scale study consist of twelve individual distributions for the coefficient or 

slope of the C-R function relating changes in annual average PM2.5 exposures to annual, adult 

all-cause mortality. The results have not been combined in order to preserve the breadth and 

diversity of opinion on the expert panel. In applying these results in a benefits analysis context, 

EPA incorporates information about each expert's judgments concerning the shape of the C-R 

function (including the potential for a population threshold PM2.5 concentration below which 

there is no effect on mortality), the distribution of the slope of the C-R function, and the 

likelihood that the PM2.5-mortality relationship is or is not causal (unless the expert incorporated 

this last element directly in his slope distribution - see Industrial Economics, 2006). 

Based on the responses of the 12 experts (designated A through L), we constructed a 

corresponding set of 12 health impact functions for premature mortality. For those experts 

providing log-linear non-threshold functions, construction of a health impact function was 

straightforward, and directly matched the construction of health impact functions based on the 

epidemiology literature.15 In these cases, the expert’s function can be translated into a health 

impact function of the form: 

( 1) ∆ = 0 ⋅ − ⋅∆PM

y y eβ , 

Where y0 is the baseline incidence, equal to the baseline incidence rate time the potentially 

affected population, β is the effect estimate provided by the expert, and ∆PM is the change in 

PM2.5. 

Some experts specified a piecewise log-linear function, in which case we developed health 

impact functions that incorporate ambient concentration levels. For example, Expert B specified 

 demographics, health status, summaries of published effect estimates, and data on other factors potentially useful to 

experts in developing their judgments (air conditioning use, housing stock, PM composition, educational 

attainment). The post-elicitation workshop was designed to anonymously share and discuss results of the expert 

interviews; discuss key areas where expert opinion varied; and clarify any questions that may have arisen during the 

interviews. Experts were given the opportunity to revise their judgments in response to discussions at this 

workshop; however, experts were not encouraged to reach a consensus opinion. 

15 Note that in the expert elicitation protocol, we specified the relevant range of exposure as between 4 and 30 

μg/m3. As such, when applying the expert elicitation based functions, benefits are only estimated for starting 

concentrations greater than 4 μg/m3

. 
5-34

a piecewise function with two segments, representing the concentration-response function for 

ambient concentrations between 4 and 10 μg/m3

 and between 10 and 30 μg/m3. In this case, the 

expert’s function can be translated into a health impact function of the form: 

( )

⎪ ( ) ⎩

⎪

⎨

⎧

⋅ − ≥

⋅ − <

∆ = ⋅∆

⋅∆

1 10

1 10

02 0

01 0

2

1

y e if Q

y e if Q

y PM

PM

β

β

, 

Where Q0 is the baseline concentration of PM2.5, y01 is the baseline incidence for populations 

living in areas with baseline concentrations of PM2.5 less than 10 μg/m3

, y02 is the baseline 

incidence for populations living in areas with baseline concentrations of PM2.5 greater than or 

equal to10 μg/m3

, and β1 and β2 are the effect estimates corresponding to the segments of the 

C-R function relating to ambient concentrations between 4 and 10 μg/m3

 and 10 and 30 μg/m3, 

respectively. 

A third form specified by one expert (Expert K) included both a piecewise log-linear function 

and a probabilistic threshold. Expert K did not provide a full set of information about the shape 

of the distribution of the threshold, providing only the probability that a threshold existed 

between 0 and 5 μg/m3

 (equal to 0.4) and the probability that a threshold existed between 5 and 

10 μg/m3

 (equal to 0.1). The probability that a threshold above 10 existed was set to zero, and 

the probability that there was no threshold was specified as 0.50. We assumed that the 

probability distribution across the range 0 to 5 was uniform, such that the probability of a 

threshold between 0 and 1, 1 and 2, etc. was equal. Likewise, we assumed that the probability 

distribution across the range 5 to 10 was uniform. Expert K also provided a two segment 

piecewise log-linear function, with the segments defined over the ranges 4 to 16 μg/m3

, and 16 to 

30 μg/m3

. Using this information, we translated Expert K’s responses into the following three 

conditional health impact functions: 

(K1) ( )

⎪ ( ) ⎩

⎪

⎨

⎧

⋅ − ≥

⋅ − <

∆ = ⋅∆

⋅∆

1 16

1 16

02 0

01 0

2

1

y e if Q

y e if Q

y PM

PM

β

β

(K2) 

( )

( )

( )

( )

( )

( )

⎪ ( ) ⎪

⎪

⎪

⎪

⎩

⎪

⎪

⎪

⎪

⎪

⎨

⎧

⋅ − ≥

⋅ − ≤ <

⋅ − × ≤ <

⋅ − × ≤ <

⋅ − × ≤ <

⋅ − × ≤ <

⋅ − × ≤ <

∆ =

⋅∆

⋅∆

⋅∆

⋅∆

⋅∆

⋅∆

⋅∆

1 16

1 5 16

1 0.8 4 5

1 0.6 3 4

1 0.4 2 3

1 0.2 1 2

1 0.0 0 1

02 0

01 0

01 0

01 0

01 0

01 0

01 0

2

1

1

1

1

1

1

y e if Q

y e if Q

y e if Q

y e if Q

y e if Q

y e if Q

y e if Q

y

PM

PM

PM

PM

PM

PM

PM

β

β

β

β

β

β

β
5-35

(K3) 

( )

( )

( )

( )

( )

( )

⎪ ( ) ⎪

⎪

⎪

⎪

⎩

⎪

⎪

⎪

⎪

⎪

⎨

⎧

⋅ − ≥

⋅ − × ≤ <

⋅ − × ≤ <

⋅ − × ≤ <

⋅ − × ≤ <

⋅ − × ≤ <

⋅ − × ≤ <

∆ =

⋅∆

⋅∆

⋅∆

⋅∆

⋅∆

⋅∆

⋅∆

1 16

1 1.0 10 16

1 0.8 9 10

1 0.6 8 9

1 0.4 7 8

1 0.2 6 7

1 0.0 0 6

02 0

01 0

01 0

01 0

01 0

01 0

01 0

2

1

1

1

1

1

1

y e if Q

y e if Q

y e if Q

y e if Q

y e if Q

y e if Q

y e if Q

y

PM

PM

PM

PM

PM

PM

PM

β

β

β

β

β

β

β

Function K1 is associated with a no threshold segmented log-linear specification with a knot at 

16 μg/m3

. Function K2 represents the segmented log-linear function with a threshold between 0 

and 5 μg/m3

, with the cumulative probability of a threshold at or below the initial concentration 

Q0 increasing as Q0 decreases (this will result in a declining expected value of the impact at 

lower initial concentrations). Likewise, function K3 represented the segmented log-linear 

function with a threshold between 5 and 10 μg/m3

. The results of applying the three conditional 

functions are then combined using Monte Carlo analysis with weights of 0.5, 0.4, and 0.1 

assigned to conditional functions K1, K2, and K3, respectively. 

In addition to specifying a function form, each expert provided a representation of the 

distribution (or distributions for those who specified piecewise functions) of the effect size (in 

terms of the percent change in premature mortality associated with a one microgram change in 

annual mean PM2.5). Six of the experts simply chose a normal distribution, which is completely 

specified with two parameters, the mean and standard deviation (see Figure 5-2 for example). In 

one case, the expert specified a triangular distribution, which is represented by a minimum, 

maximum, and most likely value (see Figure 5-3). In another case, the expert specified a 

Weibull distribution, which has three parameters representing scale, location, and shape (see 

Figure 5-4). Four of the experts did not choose a parametric distribution, preferring instead to 

provide only effect estimates at particular percentiles of their distributions. In these cases, we 

constructed custom distributions to represent their percentiles. For these custom distributions, 

we assume a continuous and smooth transition of the distribution between the reported 

percentiles (see Figure 5-5 for example). 
5-36

Figure 5-2. Example Normal Distribution for Expert A 

Figure 5-3. Example Triangular Distribution for Expert D 
5-37

Figure 5-4. Example Weibull Distribution for Expert J 

Figure 5-5. Example Custom Distribution for Expert B 

In one special case, Expert E provided a normal distribution that implied a negative tail at the 

2.5th percentile (the lower bound of a typical 95 percent confidence interval), but also specified a 

minimum value at zero. In this case, we treated the distribution as a truncated normal. In the 

case, the mean of the resulting incidence distribution will be shifted upwards relative to a full 

normal, to adjust for the mass of the distribution that would have been below zero (see 

Figure 5-6). Note that in the figure, the mean of the normal distribution specified by Expert C is 

1.2, while the mean of the implied truncated normal will be 1.34. 
5-38

Figure 5-6. Truncated Normal Distribution for Expert C 

In some cases, experts included in their reported distributions the likelihood that the relationship 

between PM2.5 and mortality was not causal, e.g., that reducing PM2.5 would not actually reduce 

the risk of premature death. In these cases, the distributions are unconditional, and included zero 

with some probability to reflect views on less than certain causality. In most cases, the experts 

chose to specify a conditional distribution, such that the distribution of the effect estimate is 

conditional on there being a causal relationship. In these cases, the final estimated distribution of 

avoided incidence of premature mortality will be the expected value of the unconditional 

distribution. In practice, we implement this by estimating each expert’s conditional distribution 

and then, using Monte Carlo sampling, construct an unconditional distribution using the expert’s 

reported probability of a causal relationship. To illustrate how these various components of an 

expert’s results are combined to produce an estimate of the distribution of reduced mortality 

associated with a reduction in ambient PM2.5, we provide an example calculation using the 

results from the partial attainment scenario for the 15/35 standards in California for Expert K. 

This example calculation is graphical displayed in Figure 5-7. In Figure 5-7, the initial 

application of Expert K’s conditional concentration-response functions provides 3 distributions 

associated with reductions in PM2.5 concentrations in the range of starting concentrations from 4 

to 16 μg/m3

. These distributions are assigned weights based on the expert’s judgments about the 

likelihood of a threshold existing in the ranges 0 to 5, 5 to 10, or not at all. These weights are 

used to develop a new distribution for the change in mortality for starting concentrations between 

4 and 16. These are then added to the distribution of the change in mortality associated with 

reductions in PM2.5 in the range of starting concentrations from 16 to 30 μg/m3

. This gives an 

overall distribution of reductions in mortality for the full range of starting concentrations, 

conditional on the existence of a causal relationship. This conditional distribution is then 

combined with the expert’s judgment about causality (35 percent likelihood of a causal 

relationship), to derive the unconditional distribution of changes in mortality, which, as can be 

seen in the figure, is composed of a mass of probability at zero (reflecting the likelihood of no 

causal relationship), and a probability density function (PDF) over the remaining 35 percent of 

probability characterized by the conditional distribution. As expected, the unconditional 
Mean = 249.07

-76.40 86.34 249.07 411.81 574.54

Range 4 to 16, Threshold 0 to 5

Initial Distributions:

Mean = 234.45

-71.91 81.27 234.45 387.63 540.81

Range 4 to 16, Threshold 5 to 10

Mean = 435

-223.54 105.95 435.44 

Range 4 to 16, No Thresh

Mean = 252.55

-129.71 61.42 252.55 443.68 634.81

Range 16 to 30, No Threshold

Frequency Chart

.000.007.014.021.028

0105.5211316.5422

-182.69 80.86 344.40 607.94 871.49

15,000 Trials 193 Outliers

Forecast: Threshold Adjusted Distribution for rang

+ =

Frequency C

.000.006.012.018.025

39.32 347.23 655.15 

15,000 Trials 

Forecast: Total Range I

Mean = 0.35

.000.163.325.488.650

0.00 0.25 0.50 0.75 1.00

Causality Probability

p=0.4

p=0.5 p=0.1

p

p=0.65

Final Distribution of Avoided Incidence of Pre

0.00%0.20%0.40%0.60%0.80%1.00%1.20%1.40%1.60%1.80%2.00%

0 631251872483103724344955576196807428048669279

Avoided Premature Mor

Relative Frequency

Relative Frequency at 0 is 65%

M

Figure 5-7. Example Calculations Expert K for California 15/35 Partial Attainment Scenario 
5-40 

distribution has a mean change in mortality that is 35 percent of the mean of the conditional 

distribution. 

Infant Mortality. Recently published studies have strengthened the case for an association 

between PM exposure and respiratory inflammation and infection leading to premature mortality 

in children under 5 years of age. Specifically, the SAB-HES noted the release of the WHO 

Global Burden of Disease Study focusing on ambient air, which cites several recently published 

time-series studies relating daily PM exposure to mortality in children (U.S. EPA-SAB, 2004b). 

The SAB-HES also cites the study by Belanger et al. (2003) as corroborating findings linking 

PM exposure to increased respiratory inflammation and infections in children. 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 EPA incorporate infant mortality into the primary 

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

the Woodruff et al. (1997) study (U.S. EPA-SAB, 2004b). A more recent study by Woodruff et 

al. (2006) continues to find associations between PM2.5 and infant mortality. The study also 

found the most significant relationships with respiratory-related causes of death. We have not 

yet sought comment from the SAB on this more recent study and as such continue to rely on the 

earlier 1997 analysis. 

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% 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 attainment strategies for the PM NAAQS 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 used 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. A more 

recent study by Zanobetti and Schwartz (2005) used a similar method to Peters et al. (2001), but 

focused on adults 65 and older, and used PM10 as the PM indicator. They found a significant 

relationship between nonfatal heart attacks and PM10, although the magnitude of the effect was 

much lower than Peters et al. This may reflect the use of PM10, the more limited age range, or 

the less precise diagnosis of heart attack used in defining the outcome measure. Other studies, 

such as Domenici et al. (2006), Samet et al. (2000), and Moolgavkar (2000), show a consistent 

relationship between all cardiovascular hospital admissions, including those for nonfatal heart 

attacks, and PM. Given the lasting impact of a heart attack on long-term health costs and 

earnings, we provide a separate estimate for nonfatal heart attacks. The estimate used in the PM 

NAAQS analysis is based on the single available U.S. PM2.5 effect estimate from Peters et al. 
5-41 

(2001). The finding of a specific impact on heart attacks is consistent with hospital admission 

and other studies showing relationships between fine particles and cardiovascular effects both 

within and outside the United States. 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 al., 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 visits. Since most emergency room visits do not result in an admission to the hospital (the 

majority of people going to the emergency room are treated and return home), we treat hospital 

admissions and emergency room visits separately, taking account of the fraction of emergency 

room visits that are admitted to the hospital. 

The two main groups of hospital admissions estimated in this analysis are respiratory admissions 

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

hospital admissions. The only type of emergency room visits that have been consistently linked 

to PM in the United States are asthma-related visits. 

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

populations aged 65 and older, we use effect estimates from studies by Moolgavkar (2003) and 

Ito (2003). However, only Moolgavkar (2000) provided a separate effect estimate for 

populations 20 to 64.16 Total cardiovascular hospital admissions are thus the sum of the pooled 

estimates from Moolgavkar (2003) and Ito (2003) for populations over 65 and the Moolgavkar 

(2000) based impacts for populations aged 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 used impact 

functions for several respiratory causes, including chronic obstructive pulmonary disease 

(COPD), pneumonia, and asthma. As with cardiovascular admissions, additional published 

studies show a statistically significant relationship between PM10 and respiratory hospital 

admissions. We used only those focusing on PM2.5. Both Moolgavkar (2000) and Ito (2003) 

 

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

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

Updates have been provided for the 65 and older population, and showed little difference. Given the very small 

(<5%) difference in the effect estimates for people 65 and older with cardiovascular hospital admissions between the 

original and reanalyzed results, we do not expect the difference in the effect estimates for the 20 to 64 population to 

differ significantly. As such, the choice to use the earlier, uncorrected analysis will likely not introduce much bias. 
5-42 

provide effect estimates for COPD in populations over 65, allowing us to pool the impact 

functions for this group. Only Moolgavkar (2000) provides a separate effect estimate for 

populations 20 to 64. Total COPD hospital admissions are thus the sum of the pooled estimate 

for populations over 65 and the single study estimate for populations 20 to 64. Only Ito (2003) 

estimated pneumonia and only for the population 65 and older. In addition, Sheppard (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) 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 emergency room visits in populations under 65, although there 

may still be significant impacts in the adult population under 65. 

Acute Health Events and Work Loss Days. As indicated in Table 5-1, 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 PM. The sources for the effect 

estimates used to quantify these effects are described below. 

Around 4% of U.S. children between the ages of 5 and 17 experience episodes of acute 

bronchitis annually (American Lung Association, 2002c). Acute bronchitis is characterized by 

coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a number of days. 

According to the MedlinePlus medical encyclopedia,17 with the exception of cough, most acute 

bronchitis symptoms abate within 7 to 10 days. Incidence of episodes of acute bronchitis in 

children between the ages of 5 and 17 were estimated using an effect estimate developed from 

Dockery et al. (1996). 

Incidences of lower respiratory symptoms (e.g., wheezing, deep cough) in children aged 7 to 14 

were estimated using an effect estimate from Schwartz and Neas (2000). 

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). 

Exposure to air pollution can result in restrictions in activity levels. These restrictions range 

from relatively minor changes in daily activities to serious limitations that can result in missed 

days of work (either from personal symptoms or from caring for a sick family member). We 

include two types of restricted activity days, minor restricted activity days (MRAD) and work 

 

17 See http://www.nlm.nih.gov/medlineplus/ency/article/000124.htm, accessed January 2002. 
5-43 

loss days (WLD). MRAD result when individuals reduce most usual daily activities and replace 

them with less strenuous activities or rest, yet not to the point of missing work or school. For 

example, a mechanic who would usually be doing physical work most of the day will instead 

spend the day at a desk doing paper and phone work because of difficulty breathing or chest 

pain. The effect of PM2.5 on MRAD was estimated using an effect estimate derived from Ostro 

and Rothschild (1989). Work loss days due to PM2.5 were estimated using an effect estimate 

developed from Ostro (1987). 

In analyzing attainment strategies for the PM NAAQS, we have followed the SAB-HES 

recommendations regarding asthma exacerbations in developing the primary estimate. To 

prevent double-counting, we focused the estimation on asthma exacerbations occurring in 

children and excluded adults from the calculation.18 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; 

Vedal et al., 1998) that followed panels of asthmatic children. Ostro et al. (2001) followed a 

group of 138 African-American children in Los Angeles for 13 weeks, recording daily 

occurrences of respiratory symptoms associated with asthma exacerbations (e.g., shortness of 

breath, wheeze, and cough). This study found a statistically significant association between 

PM2.5, measured as a 12-hour average, and the daily prevalence of shortness of breath and 

wheeze endpoints. Although the association was not statistically significant for cough, the 

results were still positive and close to significance; consequently, we decided to include this 

endpoint, along with shortness of breath and wheeze, in generating incidence estimates (see 

below). Vedal et al. (1998) followed a group of elementary school children, including 74 

asthmatics, located on the west coast of Vancouver Island for 18 months including measurements 

of daily peak expiratory flow (PEF) and the tracking of respiratory symptoms (e.g., cough, 

phlegm, wheeze, chest tightness) through the use of daily diaries. Association between PM10 and 

 

18 Estimating asthma exacerbations associated with air pollution exposures is difficult, due to concerns about 

double-counting of benefits. Concerns over double-counting stem from the fact that studies of the general 

population also include asthmatics, so estimates based solely on the asthmatic population cannot be directly added to 

the general population numbers without double-counting. In one specific case (upper respiratory symptoms in 

children), the only study available is limited to asthmatic children, so this endpoint can be readily included in the 

calculation of total benefits. However, other endpoints, such as lower respiratory symptoms and MRADs, are 

estimated for the total population that includes asthmatics. Therefore, to simply add predictions of asthma-related 

symptoms generated for the population of asthmatics to these total population-based estimates could result in 

double-counting, especially if they evaluate similar endpoints. The 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 (U.S. EPA-SAB, 2004b). 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. 
5-44 

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 is presented in Table 5-8). 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. 

Treatment of Potential Thresholds in Health Impact Functions

Unless specifically noted, our premature mortality benefits estimates are based on an assumed 

cutpoint in the premature mortality concentration-response function at 10 μg/m3

, and an assumed 

cutpoint of 10 μg/m3 for the concentration-response functions for morbidity associated with short 

term exposure to PM2.5. The 10 μg/m3 threshold reflects comments from CASAC (U.S. EPA 

Science Advisory Board, 2005). To consider the impact of a threshold in the response function 

for the chronic mortality endpoint on the primary benefits estimates, we also constructed a 

sensitivity analysis by assigning different cutpoints below which changes in PM2.5 are assumed 

to have no impact on premature mortality. In applying the cutpoints, we adjusted the mortality 

function slopes accordingly.19 This sensitivity analysis allows us to determine the change 

(reduction) in avoided mortality cases and associated monetary benefits associated with 

alternative cutpoints. Five cutpoints (including the base case assumption) were included in this 

sensitivity analysis: (a) 14 μg/m3

 (assumes no impacts below the alternative annual NAAQS), (b) 

12 μg/m3

 (c) 10 μg/m3 (reflects comments from CASAC - 2005), (d) 7.5 μg/m3 (reflects 

recommendations from SAB-HES to consider estimating mortality benefits down to the lowest 

exposure levels considered in the Pope 2002 study used as the basis for modeling chronic 

mortality) and (e) background or 3 μg/m3

 (reflects NRC recommendation to consider effects all 

the way to background). 

 

19 Note, that the adjustment to the mortality slopes was only done for the 10 μg/m3

 , 12 μg/m3 , and 14 μg/m3

cutpoints since the 7.5 μg/m3

 and background cutpoints are at or below the lowest measured exposure levels 

reported in the Pope 2002, for the combined exposure dataset. See Appendix H for a complete discussion of the 

slope adjustment procedure. 
5-45 

Table 5-8: Studies Examining Health Impacts in the Asthmatic Population Evaluated for Use in 

the Benefits Analysis 

Endpoint Definition Pollutant Study Study Population 

Asthma Attack Indicators 

Shortness of 

breath 

Prevalence of shortness 

of breath; incidence of 

shortness of breath 

PM2.5 Ostro et al. 

(2001) 

African-American 

asthmatics, 8–13 

Cough Prevalence of cough; 

incidence of cough 

PM2.5 Ostro et al. 

(2001) 

African-American 

asthmatics, 8–13 

Wheeze Prevalence of wheeze; 

incidence of wheeze 

PM2.5 Ostro et al. 

(2001) 

African-American 

asthmatics, 8–13 

Asthma 

exacerbation 

$1 mild asthma 

symptom: wheeze, 

cough, chest tightness, 

shortness of breath 

PM10, 

PM1.0

Yu et al. (2000) Asthmatics, 5–13 

Cough Prevalence of cough PM10 Vedal et al. 

(1998) 

Asthmatics, 6–13 

Other Symptoms/Illness Endpoints 

Upper 

respiratory 

symptoms 

$1 of the following: 

runny or stuffy nose; wet 

cough; burning, aching, 

or red eyes 

PM10 Pope et al. (1991) Asthmatics, 9–11 

Moderate or 

worse asthma 

Probability of moderate 

(or worse) rating of 

overall asthma status 

PM2.5 Ostro et al. 

(1991) 

Asthmatics, all 

ages 

Acute 

bronchitis 

$1 episodes of bronchitis 

in the past 12 months 

PM2.5 McConnell et al. 

(1999) 

Asthmatics, 9–15 

Phlegm “Other than with colds, 

does this child usually 

seem congested in the 

chest or bring up 

phlegm?” 

PM2.5 McConnell et al. 

(1999) 

Asthmatics, 9–15 

Asthma 

attacks 

Respondent-defined 

asthma attack 

PM2.5, 

ozone 

Whittemore and 

Korn (1980) 

Asthmatics, all 

ages 

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 μg/m3

 decrease in daily PM2.5 levels might decrease 

hospital admissions by 3%. To then to convert this relative change into a number of cases, the 

baseline incidence of the health effect is necessary. The baseline incidence 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 
5-46 

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, we continue to use current incidence rates. The one exception is in the case of 

premature mortality. In this case, we have projected mortality rates such that future mortality 

rates are consistent with our projections of population growth (Abt Associates, 2005). Compared 

with previous analyses, this will result in a reduction in the mortality related impacts of air 

pollution in future years. 

Table 5-9 summarizes the baseline incidence data and sources used in the benefits analysis. We 

use the most geographically disaggregated data available. For premature mortality, county-level 

data are available. For hospital admissions, regional rates are available. However, for all other 

endpoints, a single national incidence rate is used, due to a lack of more spatially disaggregated 

data. In these cases, 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. 
5-47 

Table 5-9: Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population 

Rates 

Endpoint Parameter Value Sourcea

Mortality Daily or annual mortality 

rate 

Age-, cause-, and 

county-specific rate 

CDC Wonder (1996–1998) 

Hospitalizations Daily hospitalization rate Age-, region-, and 

cause-specific rate 

1999 NHDS public use data filesb

Asthma ER Visits Daily asthma ER visit rate Age- and regionspecific visit rate 

2000 NHAMCS public use data 

filesc

; 1999 NHDS public use data 

filesb

Chronic Bronchitis Annual prevalence rate per 

person 

C Aged 18–44 

C Aged 45–64 

C Aged 65 and older 

0.0367 

0.0505 

0.0587 

1999 NHIS (American Lung 

Association, 2002b, Table 4) 

 Annual incidence rate per 

person 

0.00378 Abbey et al. (1993, Table 3) 

Nonfatal Myocardial 

Infarction (heart 

attacks) 

Daily nonfatal myocardial 

infarction incidence rate per 

person, 18+ 

C Northeast 

C Midwest 

C South 

C West 

0.0000159 

0.0000135 

0.0000111 

0.0000100 

1999 NHDS public use data filesb; 

adjusted by 0.93 for probability of 

surviving after 28 days (Rosamond 

et al., 1999) 

Incidence (and prevalence) 

among asthmatic AfricanAmerican children 

C daily wheeze 

C daily cough 

C daily dyspnea 

0.076 (0.173) 

0.067 (0.145) 

0.037 (0.074) 

Asthma Ostro et al. (2001) 

Exacerbations 

Prevalence among 

asthmatic children 

C daily wheeze 

C daily cough 

C daily dyspnea 

0.038 

0.086 

0.045 

Vedal et al. (1998) 

Acute Bronchitis Annual bronchitis incidence 

rate, children 

0.043 American Lung Association (2002c, 

Table 11) 

Lower Respiratory 

Symptoms 

Daily lower respiratory 

symptom incidence among 

childrend

0.0012 Schwartz et al. (1994, Table 2) 

Upper Respiratory 

Symptoms 

Daily upper respiratory 

symptom incidence among 

asthmatic children 

0.3419 Pope et al. (1991, Table 2) 

Work Loss Days Daily WLD incidence rate 

per person (18–65) 

C Aged 18–24 

C Aged 25–44 

C Aged 45–64 

0.00540 

0.00678 

0.00492 

1996 HIS (Adams, Hendershot, and 

Marano, 1999, Table 41); U.S. 

Bureau of the Census (2000) 

(continued) 
5-48 

Table 5-9: Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population (continued) 

Rates 

Endpoint Parameter Value Sourcea

Minor RestrictedActivity Days Daily MRAD incidence rate 

per person 

0.02137 Ostro and Rothschild (1989, p. 243) 

a

 The following abbreviations are used to describe the national surveys conducted by the National Center for 

Health Statistics: HIS refers to the National Health Interview Survey; NHDS—National Hospital Discharge 

Survey; NHAMCS—National Hospital Ambulatory Medical Care Survey. 

b

 See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/. 

c

 See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/. 

d

 Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and 

wheeze. 

Baseline age, cause, and county-specific mortality rates were obtained from the U.S. Centers for 

Disease Control and Prevention (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 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 populationweighted mortality rate using data for the older age groups. 

To estimate age- and county-specific mortality rates in years 2000 through 2020, we calculated 

adjustment factors, based on a series of Census Bureau projected national mortality rates, to 

adjust the CDC Wonder age- and county-specific mortality rates in 1996-1998 to corresponding 

rates for each future year. For the analysis year 2020, these adjustment factors ranged across age 

categories from 0.76 to 0.86 

For the set of endpoints affecting the asthmatic population, in addition to baseline incidence 

rates, prevalence rates of asthma in the population are needed to define the applicable population. 

Table 5-9 lists the baseline incidence rates and their sources for asthma symptom endpoints. 

Table 5-10 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. However, it should be noted that current trends in 

asthma prevalence do not lead us to expect that asthma prevalence rates will be more than 4% 

overall in 2020, or that large changes will occur in asthma prevalence rates for individual age 

categories (Mansfield et al., 2005). 
5-49 

Table 5-10: Asthma Prevalence Rates Used to Estimate Asthmatic Populations in Impact 

Functions 

Asthma Prevalence Rates 

Population Group Value Source 

All Ages 0.0386 American Lung Association (2002a, Table 7)—based on 1999 HIS 

< 18 0.0527 American Lung Association (2002a, Table 7)—based on 1999 HIS 

5–17 0.0567 American Lung Association (2002a, Table 7)—based on 1999 HIS 

18–44 0.0371 American Lung Association (2002a, Table 7)—based on 1999 HIS 

45–64 0.0333 American Lung Association (2002a, Table 7)—based on 1999 HIS 

65+ 0.0221 American Lung Association (2002a, Table 7)—based on 1999 HIS 

Male, 27+ 0.021 2000 HIS public use data filesa

African American, 5 to 17 0.0726 American Lung Association (2002a, Table 9)—based on 1999 HIS 

African American, <18 0.0735 American Lung Association (2002a, Table 9)—based on 1999 HIS 

a

 See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/. 

Selecting Unit Values for Monetizing Health Endpoints

The appropriate economic value for a change in a health effect depends on whether the health 

effect is viewed ex ante (before the effect has occurred) or ex post (after the effect has occurred). 

Reductions in ambient concentrations of air pollution generally lower the risk of future adverse 

health affects by a 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 in risk). Using this 

approach, the size of the affected population is automatically taken into account by the number 

of incidences predicted by epidemiological studies applied to the relevant population. The same 

type of calculation can produce values for statistical incidences of other health endpoints. 

For some health effects, such as hospital admissions, WTP estimates are generally not available. 

In these cases, we use the cost of treating or mitigating the effect 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 (although not in every case) understate the true value of reductions in risk of a health 

effect. They tend to reflect the direct expenditures related to treatment but not the value of 

avoided pain and suffering from the health effect. Table 5-11 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 out to 2020. 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 
Table 5-11: Unit Values Used for Economic Valuation of Health Endpoints (1999$) 

Central Estimate of Value Per 

Statistical Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Level Derivation of Distributions of Estimate

Premature Mortality (Value of a Statistical Life) 

$5,500,000 $6,600,000 Point estimate is the mean of a normal distribution with a 95% conf

$1 and $10 million. Confidence interval is based on two meta-analVSL literature: $1 million represents the lower end of the interquarMrozek and Taylor (2002) meta-analysis and $10 million represent

interquartile range from the Viscusi and Aldy (2003) meta-analysis. value of a small change in mortality risk aggregated over the affect

Chronic Bronchitis (CB) $340,000 $420,000 The WTP to avoid a case of pollution-related CB is calculated as 

WTP WTP e x x = − − 13 13 * β*( ) , where x is the severity of an average CB ca

for a severe case of CB, and  is the parameter relating WTP to sevregression results reported in Krupnick and Cropper (1992). The daverage severity-level case of CB was generated by Monte Carlo meach of three distributions: (1) WTP to avoid a severe case of CB probability of being each of the first nine deciles of the distribution oViscusi et al. (1991); (2) the severity of a pollution-related case of C

described in the Viscusi study) is assumed to have a triangular distlikely value at severity level 6.5 and endpoints at 1.0 and 12.0; and elasticity of WTP with respect to severity is normally distributed witstandard deviation = 0.0669 (from Krupnick and Cropper [1992]). Trationale for choosing it is described in detail in the Costs and Bene1990 to 2010 (EPA, 1999)., where x is the severity of an average CWTP for a severe case of CB, and  is the parameter relating WTP tregression results reported in Krupnick and Cropper (1992). The daverage severity-level case of CB was generated by Monte Carlo meach of three distributions: (1) WTP to avoid a severe case of CB probability of being each of the first nine deciles of the distribution oViscusi et al. (1991); (2) the severity of a pollution-related case of C

described in the Viscusi study) is assumed to have a triangular distlikely value at severity level 6.5 and endpoints at 1.0 and 12.0; and elasticity of WTP with respect to severity is normally distributed witstandard deviation = 0.0669 (from Krupnick and Cropper [1992]). Trationale for choosing it is described in detail in the Costs and Bene1990 to 2010 (U.S. EPA, 1999). 
Table 5-11: Unit Values Used for Economic Valuation of Health Endpoints (1999$) (continued) 

Central Estimate of Value Per 

Statistical Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Level Derivation of Distributions of Estimate

Nonfatal Myocardial Infarction (heart attack)  3% discount rate

 Age 0–24  Age 25–44  Age 45–54  Age 55–65  Age 66 and over  7% discount rate Age 0–24  Age 25–44  Age 45–54  Age 55–65  Age 66 and over 

$66,902 $74,676 $78,834 

$140,649 

$66,902 $65,293 $73,149 $76,871 

$132,214 

$65,293 

$66,902 $74,676 $78,834 

$140,649 

$66,902 $65,293 $73,149 $76,871 

$132,214 

$65,293 

No distributional information available. Age-specific cost-of-illness earnings and direct medical costs over a 5-year period following a earnings estimates are based on Cropper and Krupnick (1990). Dibased on simple average of estimates from Russell et al. (1998) an

Lost earnings: 

Cropper and Krupnick (1990). Present discounted value of 5 years age of onset: at 3% at 7%25-44 $8,774 $7,855 45-54 $12,932 $11,578 55-65 $74,746 $66,920 Direct medical expenses: An average of: 

1. Wittels et al. (1990) ($102,658—no discounting) 2. Russell et al. (1998), 5-year period ($22,331 at 3% discount rate

discount rate) 

Hospital Admissions 

Chronic Obstructive Pulmonary Disease (COPD) (ICD codes 490-492, 494-496) 

$12,378 $12,378 No distributional information available. The COI estimates (lost ear

costs) are based on ICD-9 code-level information (e.g., average hoaverage length of hospital stay, and weighted share of total COPD reported in Agency for Healthcare Research and Quality (2000) (w

Asthma Admissions $6,634 $6,634 No distributional information available. The COI estimates (lost ear

costs) are based on ICD-9 code-level information (e.g., average hoaverage length of hospital stay, and weighted share of total asthma reported in Agency for Healthcare Research and Quality (2000) (w

All Cardiovascular (ICD codes 390-429) 

$18,387 $18,387 No distributional information available. The COI estimates (lost ear

costs) are based on ICD-9 code-level information (e.g., average hoaverage length of hospital stay, and weighted share of total cardiov

illnesses) reported in Agency for Healthcare Research and Quality 

Emergency Room Visits for Asthma 

$286 $286 No distributional information available. Simple average of two unit 

(1) $311.55, from Smith et al. (1997) and (2) $260.67, from Stanford et al. (1999). 
Table 5-11: Unit Values Used for Economic Valuation of Health Endpoints (1999$) (continued) 

Central Estimate of Value Per 

Statistical Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Level Derivation of Distributions of Estimate

Respiratory Ailments Not Requiring Hospitalization 

Upper Respiratory Symptoms (URS) 

$25 $27 Combinations of the three symptoms for which WTP estimates are 

match those listed by Pope et al. result in seven different “symptom describing a “type” of URS. A dollar value was derived for each typrange estimates of WTP (IEc, 1994) to avoid each symptom in the additivity of WTPs. In the absence of information surrounding the feach of the seven types of URS occurs within the URS symptom couniform distribution between $10 and $45. 

Lower Respiratory Symptoms (LRS) 

$16 $18 Combinations of the four symptoms for which WTP estimates are a

match those listed by Schwartz et al. result in 11 different “symptom describing a “type” of LRS. A dollar value was derived for each typrange estimates of WTP (IEc, 1994) to avoid each symptom in the additivity of WTPs. The dollar value for LRS is the average of the d

different types of LRS. In the absence of information surrounding teach of the 11 types of LRS occurs within the LRS symptom compuniform distribution between $8 and $25. 

Asthma Exacerbations $42 $45 Asthma exacerbations are valued at $45 per incidence, based on t

WTP estimates for the four severity definitions of a “bad asthma daand Chestnut (1986). This study surveyed asthmatics to estimate W“bad asthma day,” as defined by the subjects. For purposes of valexacerbation is assumed to be equivalent to a day in which asthma 

reported in the Rowe and Chestnut (1986) study. The value is assdistribution between $17 and $73. 

Acute Bronchitis $360 $380 Assumes a 6-day episode, with the distribution of the daily value sp

the low and high values based on those recommended for related Neumann et al. (1994). The low daily estimate of $10 is the sum orecommended by IEc (1994) for two symptoms believed to be assobronchitis: coughing and chest tightness. The high daily estimate value of a minor respiratory restricted-activity day, or $110. 

Work Loss Days (WLDs) Variable (U.S. 

median=$110) 

No distribution available. Point estimate is based on county-specifdivided by 50 (assuming 2 weeks of vacation) and then by 5—to geU.S. Year 2000 Census, compiled by Geolytics, Inc. 
Table 5-11: Unit Values Used for Economic Valuation of Health Endpoints (1999$) (continued) 

Central Estimate of Value Per 

Statistical Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Level Derivation of Distributions of Estimate

Minor Restricted Activity Days (MRADs) 

$51 $54 Median WTP estimate to avoid one MRAD from Tolley et al. (1986)

assumed to be triangular with a minimum of $22 and a maximum ovalue of $55. Range is based on assumption that value should excmild symptom (the highest estimate for a single symptom—for eye be less than that for a WLD. The triangular distribution acknowledgis likely to be closer to the point estimate than either extreme. 
5-54 

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. In the following discussions, 

unit values are presented at 1990 levels of income for consistency with previous analyses. 

Equivalent future-year values can be obtained from Table 5-11. COI estimates are converted to 

constant 1999 dollar equivalents using the medical CPI. 

Valuing Reductions in Premature Mortality Risk. Following the advice of the EEAC of the 

SAB, 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, 2000a). The 

VSL approach is a summary measure for the value of small changes in mortality risk 

experienced by a large number of people. 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 (2002) metaanalysis. The $10 million upper confidence limit represents the upper end of the interquartile 

range from the Viscusi and Aldy (2003) meta-analysis. The mean estimate of $5.5 million is 

consistent with the mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. 

Because the majority of the studies in these meta-analyses are based on datasets from the early 

1990s or previous decades, we continue to assume that the VSL estimates provided by those 

meta-analyses are in 1990 income equivalents. Future research might provide income-adjusted 

VSL values for individual studies that can be incorporated into the meta-analyses. This would 

allow for a more reliable base-year estimate for use in adjusting VSL for aggregate changes in 

income over time. 

As indicated in the previous section on quantification of premature mortality benefits, we 

assumed for this analysis that some of the incidences of premature mortality related to PM 

exposures occur in a distributed fashion over the 20 years following exposure. To take this into 

account in the valuation of reductions in premature mortality, we applied an annual 3% discount 

rate to the value of premature mortality occurring in future years.20

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. EPA strives to use the best economic science in its analyses. Given 

the mixed theoretical finding and empirical evidence regarding adjustments to VSL for risk and 

population characteristics, we use a single VSL for all reductions in mortality risk. 

 

20 The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the 

federal government. EPA adopted a 3% 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% 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 EPA’s Guidelines for Preparing Economic Analyses (EPA, 2000b). 
5-55 

Although there are several differences between the labor market studies 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 5-

12 lists some of these differences and the expected effect on the VSL estimate for air pollutionrelated mortality. In the absence of a comprehensive and balanced set of adjustment factors, 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 5-12: Expected Impact on Estimated Benefits of Premature Mortality Reductions of 

Differences Between Factors Used in Developing Applied VSL and Theoretically Appropriate 

VSL 

Attribute Expected Direction of Bias 

Age Uncertain, perhaps overestimate 

Life Expectancy/Health Status Uncertain, perhaps overestimate 

Attitudes Toward Risk Underestimate 

Income Uncertain 

Voluntary vs. Involuntary Uncertain, perhaps underestimate 

Catastrophic vs. Protracted Death Uncertain, perhaps underestimate 

The SAB-EEAC has reviewed many potential VSL adjustments and the state of the economics 

literature. The SAB-EEAC advised EPA to “continue to use a wage-risk-based VSL as its 

primary estimate, including appropriate sensitivity analyses to reflect the uncertainty of these 

estimates,” and that “the only risk characteristic for which adjustments to the VSL can be made 

is the timing of the risk” (U.S. EPA, 2000a). 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 final PM NAAQS. 

In addition, in prior analyses, EPA has identified valuation of mortality benefits as the largest 

contributor to the range of uncertainty in monetized benefits (see U.S. EPA, 1999).21 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 pollutioninduced mortality or reflect differences in the nature of the risk presented by air pollution relative 

to the risks studied in the relevant economics literature. 

 

21 This conclusion was based on a assessment of uncertainty based on statistical error in epidemiological effect 

estimates and economic valuation estimates. Additional sources of model error such as those examined in the PM 

mortality expert elicitation may result in different conclusions about the relative contribution of sources of 

uncertainty. 
5-56 

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 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 hedonic wage approach. Certain characteristics of both the 

population affected and the mortality risk facing that population are believed to affect the 

average WTP to reduce the risk. The appropriateness of a distribution of WTP based on 

the current VSL literature for valuing the mortality-related benefits of reductions in air 

pollution concentrations therefore depends not only on the quality of the studies (i.e., how 

well they measure what they are trying to measure), but also on the extent to which the 

risks being valued are similar and the extent to which the subjects in the studies are 

similar to the population affected by changes in pollution concentrations. 

• Level of risk reduction: The transferability of estimates of the VSL from the wage-risk 

studies to the context of the PM NAAQS analysis rests on the assumption that, within a 

reasonable range, WTP for reductions in mortality risk is linear in risk reduction. For 

example, suppose a study provides a result that the average WTP for a reduction in 

mortality risk of 1/100,000 is $50, but that the actual mortality risk reduction resulting 

from a given pollutant reduction is 1/10,000. If WTP for reductions in mortality risk is 

linear in risk reduction, then a WTP of $50 for a reduction of 1/100,000 implies a WTP 

of $500 for a risk reduction of 1/10,000 (which is 10 times the risk reduction valued in 
5-57 

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). 

• Voluntariness of risks evaluated: Although job-related mortality risks may differ in 

several ways from air pollution-related mortality risks, the most important difference may 

be that job-related risks are incurred voluntarily, or generally assumed to be, whereas air 

pollution-related risks are incurred involuntarily. Some evidence suggests that people 

will pay more to reduce involuntarily incurred risks than risks incurred voluntarily. 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 and Stamland, 2002) 

suggests that VSL estimates based on hedonic wage studies may overstate the average 

value of a risk reduction. This is based on the fact that the risk-wage trade-off revealed in 

hedonic studies reflects the 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. 

• Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 2006) 

finds that because individuals reevaluate their baseline risk of death as they age, the 

marginal value of risk reductions does not decline with age as predicted by some lifetime 

consumption models. This research supports findings in recent stated preference studies 

that suggest only small reductions in the value of mortality risk reductions with 

increasing age. 

Valuing Reductions in the Risk of Chronic Bronchitis. The best available estimate of WTP to 

avoid a case of CB comes from Viscusi, Magat, and Huber (1991). The Viscusi, Magat, and 

Huber 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, Magat, and Huber (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 
5-58 

Krupnick and Cropper (1992) study. Details of this adjustment procedure are provided in the 

Benefits TSD for the Nonroad Diesel rulemaking (Abt Associates, 2003). 

We use the mean of a distribution of WTP estimates as the central tendency estimate of WTP to 

avoid a pollution-related case of CB in this analysis. The distribution incorporates uncertainty 

from three sources: the WTP to avoid a case of severe CB, as described by Viscusi, Magat, and 

Huber; the severity level of an average pollution-related case of CB (relative to that of the case 

described by Viscusi, Magat, and Huber); 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. 

Valuing Reductions in Nonfatal 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 use a COI unit value with two components: the 

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

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

we consider costs incurred over several years. Using age-specific annual lost earnings estimated 

by Cropper and Krupnick (1990) and a 3% discount rate, we estimated a present discounted 

value in lost earnings (in 2000$) over 5 years due to a 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% 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 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 
5-59 

present. The average length of stay for ICD code 410 (myocardial infarction) in the year2000 Agency for Healthcare Research and Quality (AHRQ) 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 5-13). 

Table 5-13: Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart Attacks 

Study Direct Medical Costs (2000$) Over an x-Year Period, for x = 

Wittels et al. (1990) $109,474a

 5 

Russell et al. (1998) $22,331b 5 

Eisenstein et al. (2001) $49,651b

 10 

Russell et al. (1998) $27,242b

 10 a

 Wittels et al. (1990) did not appear to discount costs incurred in future years. 

b

 Using a 3% discount rate. Discounted values as reported in the study. 

As noted above, the estimates from these three studies are substantially different, and we have 

not adequately resolved the sources of differences in the estimates. Because the wage-related 

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

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

(1990) and Russell et al. (1998). We used a simple average of the two 5-year estimates, or 

$65,902, and added it to the 5-year opportunity cost estimate. The resulting estimates are given 

in Table 5-14. 

Table 5-14: Estimated Costs Over a 5-Year Period (in 2000$) of a Nonfatal Myocardial Infarction 

Age Group Opportunity Cost Medical Costa

 Total Cost 

0–24 $0 $65,902 $65,902 

25–44 $8,774b $65,902 $74,676 

45–54 $12,253b

 $65,902 $78,834 

55–65 $70,619b

 $65,902 $140,649 

> 65 $0 $65,902 $65,902 

a

 An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998). 

b

 From Cropper and Krupnick (1990), using a 3% discount rate. 
5-60 

5.1.6 Human Welfare Impact Assessment 

PM and PM precursor emissions 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, NOx, and SO2 

list numerous physical and ecological effects known to be linked to ambient concentrations of 

these pollutants (U.S. EPA, 2005; 1993) This section describes individual effects and how we 

quantify and monetize them. These effects include changes in nitrogen and sulfate deposition, 

and visibility. 

Visibility Benefits

Changes in the level of ambient PM caused by the reduction in emissions associated with 

attainment strategies for the PM NAAQS will change the level of visibility throughout the 

United States. 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 Mountains 

National Park. This section discusses the measurement of the economic benefits of improved 

visibility. 

It is difficult to quantitatively define a visibility endpoint that can be used for valuation. 

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

components of the atmosphere 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, the deciview, 

based directly on the degree of measured light absorption. Deciviews are standardized for a 

reference distance in such a way that one deciview corresponds to a change of about 10% 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 control options.22

EPA considers benefits from two categories of visibility changes: residential visibility and 

recreational visibility. In both cases economic benefits are believed to consist of use values and 

nonuse values. Use values include the aesthetic benefits of better visibility, improved road and 

air safety, and enhanced recreation in activities like hunting and birdwatching. Nonuse values 

are based on people’s beliefs that the environment ought to exist free of human-induced haze. 

 

22 A change of less than 10% 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% 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 that, when considered together, amount to perceptible changes 

in visibility. 
5-61 

Nonuse values may be more important 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.23 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 United States are assumed to derive some benefit from improvements 

in Class I areas. Values are assumed to be higher if the Class I area is located close to their 

home.24

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). 

Although 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 use the CV 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.25 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 (U.S. EPA-SAB, 1999). Residential visibility benefits are not calculated for 

this 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 WTP to protect 

national parks or NPS-managed wilderness areas within a particular region. The survey used 

photographs reflecting different visibility levels in the specified recreational areas. The visibility 

levels in these photographs were later converted to deciviews for the current analysis. The 

survey data collected were used to estimate a WTP equation for improved visibility. In addition 

to the visibility change variable, the estimating equation also included household income as an 

explanatory variable. 

 

23 The Clean Air Act designates 156 national parks and wilderness areas as Class I areas for visibility protection. 24 For details of the visibility estimates discussed in this chapter, please refer to the Benefits TSD for the Nonroad 

Diesel rulemaking (Abt Associates, 2003). 

25 An SAB advisory letter indicates that “many members of the Council believe that the Chestnut and Rowe study is 

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

approve use of these estimates because of concerns about the peer-reviewed status of the study. EPA believes the 

study has received adequate review and has been cited in numerous peer-reviewed publications (Chestnut and 

Dennis, 1997). 
5-62 

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 United States. 

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. 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 Chestnut and Rowe study (Chestnut and Rowe, 1990a; 1990b), although representing the 

best available estimates, has a number of limitations. These include the following: 

• The age of the study (late 1980s) will increase the uncertainty about the correspondence 

of the estimated values to those that might be provided by current or future populations. 

• The survey focused only on populations in five states, so the application of the estimated 

values to populations outside those states requires that preferences of populations in the 

five surveyed states be similar to those of nonsurveyed states. 

• There is an inherent difficulty in separating values expressed for visibility improvements 

from an overall value for improved air quality. The Chestnut and Rowe study attempted 

to control for this by informing respondents that “other households are being asked about 

visibility, human health, and vegetation protections in urban areas and at national parks in 

other regions.” However, most of the respondents did not feel that they were able to 

segregate visibility at national parks entirely from residential visibility and health effects. 

• It is not clear exactly what visibility improvements the respondents to the Chestnut and 

Rowe survey were valuing. For the purpose of the benefits analysis for this rule, EPA 

assumed that respondents provided values for changes in annual average visibility. 

Because most policies will result in a shift in the distribution of visibility (usually 

affecting the worst days more than the best days), the annual average may not be the most 

relevant metric for policy analysis. 

• The WTP question asked about changes in average visibility. However, the survey 

respondents were shown photographs of only summertime conditions, when visibility is 

generally at its worst. It is possible that the respondents believed those visibility 

conditions held year-round, in which case they would have been valuing much larger 

overall improvements in visibility than what otherwise would be the case. 

• The survey did not include reminders of possible substitutes (e.g., visibility at other 

parks) or budget constraints. These reminders are considered to be best practice for 

stated preference surveys. 

• The Chestnut and Rowe survey focused on visibility improvements in and around 

national parks and wilderness areas. The survey also focused on visibility improvements 

of national parks in the southwest United States. Given that national parks and 

wilderness areas exhibit unique characteristics, it is not clear whether the WTP estimate 

obtained from Chestnut and Rowe can be transferred to other national parks and 

wilderness areas, without introducing additional uncertainty. 
5-63 

In general, the survey design and implementation reflect the period in which the survey was 

conducted. Since that time, many improvements to the stated preference methodology have been 

developed. As future survey efforts are completed, EPA will incorporate values for visibility 

improvements reflecting the improved survey designs. 

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 reductions in precursor emissions 

associated with attainment strategies for the PM NAAQS. A general WTP 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 attainment 

strategies for the PM NAAQS. 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% increase in income 

is associated with a 0.9% increase in WTP for a given change in visibility. A more detailed 

explanation of the visibility benefits methodology is provided in Appendix I. 

Using the methodology outlined above, EPA estimates that the total WTP for the visibility 

improvements in Southeastern Class I areas brought about by attainment strategies for the PM 

NAAQS is $530 million in 2020 for attainment of the 15/35 option and $1,200 million for 

attainment of the 14/35 option. 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 United States living outside the 

state containing the Class I area, and the value accounts for growth in real income. 

One major source of uncertainty for the visibility benefits estimate is the benefits transfer process 

used. Judgments used to choose the functional form and key parameters of the estimating 

equation for WTP 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. 

Agricultural, Forestry, and Other Vegetation-Related Benefits

Certain illustrative attainment strategies which reduce 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 oversaturation due to an 

abundance of on-farm nitrogen production, primarily from animal manure. In these areas, 

reductions in atmospheric deposition of nitrogen represent additional agricultural benefits. 
5-64 

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 the deposition of nitrogen could have negative effects on forest and 

vegetation growth in ecosystems where nitrogen is a limiting factor (EPA, 1993). 

On the other hand, there is evidence that forest ecosystems in some areas of the United States are 

nitrogen saturated (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. 

Benefits from Reductions in Materials Damage

The 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. Particulate matter 

also has 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. 

Previous EPA benefits 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 1970s) are too out of date to provide a reliable estimate of 

current household soiling damages (U.S. EPA, 1998). 

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. 

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. 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 in 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 
5-65 

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 stated preference 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. 

5.2 Benefits Analysis—Results and Probabilistic Uncertainty Analysis 

5.2.1 Results of National Assessment 

Applying the impact and valuation functions described previously in this chapter to the estimated 

changes in PM yields estimates of the changes in health and environmental endpoints (e.g., 

premature mortalities, cases, admissions, and change in light extinction) and the associated 

monetary values for those changes. As noted earlier, benefits are provided for three regions of 

the U.S. (Eastern, Western excluding CA, and CA). Benefits are also separately provided for the 

modeled scenarios (which result in only partial attainment for a limited number of areas) and for 

residual attainment based on “rolling back” PM2.5 design values to the level of the standards (see 

Chapter 4). Because of the differences in the sources of effect estimates for mortality versus 

morbidity (mortality includes both epidemiology and expert elicitation based impact functions), 

mortality estimates are presented separately from morbidity. 

Estimates of mortality and morbidity impacts are presented in Tables 5-16 through 5-19. For 

mortality, results based on concentration response functions from the American Cancer Society 

Study (ACS), Six Cities, and Expert Elicitation are being provided in each table to give an 

indication of the sensitivity of the benefits estimates to alternative assumptions. Following the 

recommendations of the NRC report (NRC, 2002), we identify those estimates which are based 

on empirical data, and those which are based on expert judgments. EPA intends to ask its 

Science Advisory Board to evaluation how EPA has incorporated expert elicitation results into 

the benefits analysis, and the extent to which they find the presentation in this RIA responsive to 

the NRC (2002) guidance to incorporate uncertainty into the main analysis and further, whether 

the agency should move toward presenting a central estimate with uncertainty bounds or 

continue to provide separate estimates for each of the 12 experts as well as from the ACS and Six 

Cities studies, and if so, the appropriateness of using Laden et al 2006, the most recently 

published update, as the estimate for the Six Cities based model. 

Monetized values for both health and welfare endpoints are presented in Tables 5-20 through 5-

26, along with total aggregate monetized benefits in Table 5-27. Figures 5-8 and 5-9 provide a 

graphical view of the results of the benefits analysis. The graphs show the relative proportions 

of total benefits in each area accounted for by the modeled and residual benefits and also shows 
5-66 

the relative magnitudes of benefits across the three regions of the U.S. Finally, the graphs allow 

for comparison across the sources of data for the mortality concentration-response function. 

All of the monetary benefits are in constant-year 1999 dollars. For each endpoint and total 

benefits, we provide both the mean estimate and the 95% confidence interval. Note that in the 

case of the premature mortality estimates derived from the expert elicitation, we report the 95% 

credible interval, which encompasses a broader representation of uncertainty relative to the 

statistical confidence intervals provided for the effect estimates derived from the epidemiology 

literature. 
Table 5-16: Illustrative Strategy to Attain 15/35: Estimated Reduction in Premature Mortality (Incremental to 15/65 

90th Percentile Confidence Intervals Provided in Parenthesesa

 Eastern U.S. Western U.S. Excluding CA California National Total 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Res

Attai

Mortality Impact Functions Derived from Epidemiology Literature ACS Studyb 360 

(140 – 600) 

17 

(7 – 27) 

80 

(30 – 120) 

15 

(6 – 24) 

520 

(200 – 830) 

1,600 

(610 – 2,490) 

960 

(370 – 1,500) 

1,

(620 – 

Harvard Six-City Studyc 800 

(450 – 1,200) 

38 

(21 – 55) 

200 

(90 – 300) 

30 

(18 – 50) 

1,200 

(640 – 1,700) 

3,500 

(1,900 – 5,000) 

2,200 

(1,180 – 3,100) 

3,

(1,900 

Woodruff et al., 1997 (infant mortality) 

1 

(1 – 2) 

0.02 

(0.01 – 0.03) 

0.7 

(0.4 – 1.1) 

0.3 

(0.2 – 0.5) 

1 

(1 – 2) 

4.8 

(2.3 – 7.2) 

3 

(1 – 5) 

(3 

Mortality Impact Functions Derived from Expert Elicitation Expert A 1,700 

(300 – 3,100) 

41 

(8 – 75) 

1,400 

(300 – 2,500) 

370 

(70 – 660) 

1,600 

(300 – 2,800) 

5,100 

(900 – 9,100) 

4,600 

(900 – 8,400) 

5,

(1,000 

Expert B 1,400 

(200 – 2,800) 

34 

(5 – 67) 

1,100 

(100 – 2,200) 

290 

(30 – 600) 

1,300 

(200 – 2,500) 

4,100 

(600 – 8,200) 

3,700 

(400 – 7,600) 

4,

(600 – 

Expert C 1,400 

(230 – 2,800) 

34 

(6 – 67) 

1,100 

(190 – 2,200) 

300 

(50 – 600) 

1,300 

(210 – 2,500) 

4,200 

(700 – 8,200) 

3,800 

(630 – 7,500) 

4,

(760 – 

Expert D 920 

(190 – 1,500) 

23 

(5 – 36) 

750 

(150 – 1,200) 

200 

(41 – 320) 

850 

(170 – 1,400) 

2,800 

(570 – 4,400) 

2,500 

(510 – 4,000) 

3,

(610 – 

Expert E 2,100 

(1,100 – 3,200) 

52 

(26 – 78) 

1,700 

(870 – 2,600) 

460 

(230 – 690) 

2,000 

(980 – 2,900) 

6,400 

(3,200 – 9,500) 

5,800 

(2,900 – 8,700) 

6,

(3,500 – 

Expert F 1,200 

(820 – 1,700) 

30 

(20 – 41) 

1,000 

(660 – 1,400) 

270 

(180 – 360) 

1,100 

(760 – 1,600) 

3,700 

(2,500 – 5,100) 

3,400 

(2,200 – 4,600) 

4,

(2,700 

Expert G 750 

(0 – 1,400) 

18 

(0 – 34) 

610 

(0 – 1,100) 

160 

(0 – 300) 

690 

(0 – 1,300) 

2,300 

(0 – 4,200) 

2,000 

(0 – 3,800) 

2,

(0 – 

Expert H 920 

(0 – 2,200) 

22 

(0 – 53) 

750 

(0 – 1,800) 

200 

(0 – 470) 

850 

(0 – 2,000) 

2,800 

(0 – 6,500) 

2,500 

(0 – 6,000) 

3,

(0 – 

Expert I 1,300 

(200 – 2,300) 

32 

(5 – 55) 

1,100 

(200 – 1,800) 

280 

(40 – 490) 

1,200 

(200 – 2,100) 

3,900 

(600 – 6,800) 

3,600 

(600 – 6,200) 

4,

(700 – 

Expert J 1,200 

(310 – 2,300) 

28 

(7 – 56) 

900 

(250 – 1,800) 

250 

(66 – 490) 

1,100 

(280 – 2,100) 

3,500 

(930 – 6,800) 

3,200 

(840 – 6,200) 

3,

(1,000 

Expert K 190 

(0 – 960) 

5 

(0 – 23) 

160 

(0 – 780) 

41 

(0 – 210) 

200 

(0 – 940) 

580 

(0 – 2,880) 

540 

(0 – 2,700) 

6

(0 – 

Expert L 910 

(100 – 1,700) 

25 

(5 – 42) 

660 

(0 – 1,400) 

180 

(10 – 380) 

920 

(200 – 1,600) 

2,900 

(500 – 5,200) 

2,500 

(300 – 4,700) 

3,

(500 – 

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. b The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), whi

as the primary estimate in recent RIAs 

c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha

estimate.
Table 5-17: Illustrative Strategy to Attain 15/35: Estimated Reductions in Morbidity (Incremental to 15/65 Attainme

Percentile Confidence Intervals Provided in Parenthesesa

 Eastern U.S. Western U.S. Excluding CA California National Total 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Res

Attai

Morbidity Functions Derived from Epidemiology Literature Chronic bronchitis (age >25 and over) 

360 

(66 – 650) 

8 

(1 – 14) 

240 

(45 – 440) 

87 

(16 – 160) 

440 

(81 – 800) 

1,500 

(280 – 2,700) 

1,000 

(190 – 1,900) 

1,

(300 – 

Nonfatal myocardial infarction (age >17) 

800 

(420 – 1,100) 

38 

(20 – 55) 

140 

(76 – 200) 

30 

(16 – 44) 

1,000 

(560 – 1,500) 

3,000 

(1,600 – 4,300) 

1,900 

(1,100 – 2,800) 

3,

(1,700 

Hospital admissions—respiratory (all ages) 

86 

(43 – 130) 

4 

(2 – 6) 

13 

(7 – 20) 

3 

(1 – 4) 

104 

(52 – 160) 

320 

(160 – 480) 

200 

(100 – 310) 

3

(160 

Hospital admissions—cardiovascular (age >17) 

190 

(120 – 260) 

9 

(6 – 12) 

30 

(19 – 42) 

6 

(4 – 9) 

220 

(140 – 300) 

650 

(400 – 887) 

440 

(280 – 600) 

6

(410 

Emergency room visits for asthma (age <19) 

290 

(170 – 410) 

7 

(4 – 11) 

25 

(15 – 35) 

6 

(3 – 8) 

210 

(130 – 300) 

690 

(410 – 970) 

530 

(310 – 740) 

7

(417 

Acute bronchitis (age 8–12) 

870 

(–30 – 1,800) 

17 

(–1 – 34) 

650 

(–20 – 1,300) 

280 

(–10 – 560) 

1,240 

(–40 – 2,500) 

4,300 

(–150 – 8,500) 

2,800 

(–90 – 5,600) 

4,

(–160 

Lower respiratory symptoms (age 7–14) 

4,900 

(2,400 – 7,500) 

180 

(86 – 270) 

1,400 

(660 – 2,100) 

300 

(150 – 460) 

11,600 

(5,600 – 

17,600) 

38,000 

(18,000 – 

57,000) 

18,000 

(8,600 – 

27,000) 

38

(19,

57,

Upper respiratory symptoms (asthmatic children age 9–18) 

3,600 

(1,100 – 6,100) 

130 

(41 – 220) 

1,000 

(320 – 1,700) 

220 

(70 – 370) 

8,500 

(2,700 – 

14,300) 

28,000 

(8,800 – 

47,000) 

13,000 

(4,100 – 

22,000) 

28

(8,900 – 

Asthma exacerbation (asthmatic children age 6–18) 

4,400 

(500 – 13,000) 

160 

(18 – 0) 

1,200 

(130 – 3,500) 

270 

(30 – 780) 

10,400 

(1,200 – 

30,200) 

34,000 

(3,800 – 

99,000) 

16,000 

(1,800 – 

47,000) 

35

(3,8

100

Work loss days (age 18–65) 

33,000 

(29,000 – 

37,000) 

1,300 

(1,100 – 1,400) 

7,900 

(6,900 – 8,900) 

1,800 

(1,600 – 2,000) 

73,500 

(64,000 – 

82,900) 

230,000 

(200,000 – 

260,000) 

110,000 

(100,000 – 

130,000) 

230

(200

260

Minor restrictedactivity days (age 18–65) 

200,000 

(170,000 – 

230,000) 

8,000 

(6,000 – 9,000) 

46,000 

(39,000 – 

53,000) 

10,000 

(9,000 – 

12,000) 

430,000 

(360,000 – 

500,000) 

1,300,000 

(1,100,000 – 

1,500,000) 

680,000 

(570,000 – 

780,000) 

1,40

(1,100

1,60

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. 
Table 5-18: Illustrative Strategy to Attain 14/35: Estimated Reduction in Premature Mortality (Incremental to 15/65 

90th Percentile Confidence Intervals Provided in Parentheses a

 Eastern U.S. Western U.S. Excluding CA California National Total 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Re

Atta

Mortality Impact Functions Derived from Epidemiology Literature ACS Studyb 2,100 

(820 – 3,400) 

70 

(29 – 120) 

77 

(30 – 120) 

15 

(6 – 24) 

500 

(200 – 810) 

1,600 

(650 – 2,600) 

2,700 

(1,000 – 4,300) 

1

(680 – 

Harvard Six-City Studyc 4,700 

(2,600 – 6,900) 

170 

(90 – 250) 

170 

(95 – 250) 

34 

(18 – 49) 

1,100 

(620 – 1,700) 

3,700 

(2,000 – 5,400) 

6,000 

(3,300 – 8,800) 

3

(2,100 

Woodruff et al 1997 (infant mortality) 

8 

(4 – 11) 

0.2 

(0.1 – 0.3) 

0.7 

(0.3 – 1.1) 

0 

(0 – 1) 

1.3 

(1.0 – 1.9) 

5 

(2 – 8) 

10 

(5 – 14) 

(3 

Mortality Impact Functions Derived from Expert ElicitationExpert A 10,000 

(1,900 – 18,000) 

180 

(30 – 330) 

1,400 

(250 – 2,400) 

370 

(67 – 660) 

1,500 

(300 – 2,800) 

5,300 

(1,000 – 9,600) 

13,000 

(2,400 – 24,000) 

5

(1,100 

Expert B 8,100 

(1,000 – 17,000) 

150 

(20 – 300) 

1,000 

(100 – 2,200) 

290 

(29 – 600) 

1,200 

(200 – 2,500) 

4,300 

(600 – 8,600) 

10,000 

(1,200 – 21,000) 

4

(600 – 

Expert C 8,400 

(1,400 – 17,000) 

150 

(25 – 300) 

1,100 

(190 – 2,200) 

300 

(50 – 600) 

1,300 

(210 – 2,500) 

4,400 

(730 – 8,600) 

11,000 

(1,800 – 21,000) 

4

(810 – 

Expert D 5,500 

(1,100 – 8,800) 

100 

(20 – 160) 

740 

(150 – 1,200) 

200 

(41 – 320) 

830 

(170 – 1,300) 

2,900 

(590 – 4,600) 

7,100 

(1,400 – 11,000) 

3

(650 – 

Expert E 13,000 

(6,400 – 19,000) 

230 

(110 – 300) 

1,700 

(850 – 2,500) 

460 

(230 – 690) 

1,900 

(960 – 2,900) 

6,700 

(3,400 – 10,000) 

16,000 

(8,200 – 25,000) 

7

(3,700 

Expert F 7,300 

(4,900 – 10,000) 

130 

(90 – 180) 

980 

(650 – 1,300) 

270 

(180 – 360) 

1,100 

(740 – 1,500) 

3,900 

(2,600 – 5,300) 

9,400 

(6,300 – 13,000) 

4

(2,900 

Expert G 4,500 

(0 – 8,300) 

80 

(0 – 150) 

600 

(0 – 1,100) 

160 

(0 – 300) 

670 

(0 – 1,200) 

2,400 

(0 – 4,400) 

5,700 

(0 – 11,000) 

2

(0 – 

Expert H 5,500 

(0 – 13,000) 

100 

(0 – 230) 

740 

(0 – 1,700) 

200 

(1 – 470) 

830 

(0 – 2,000) 

2,900 

(0 – 6,800) 

7,100 

(0 – 17,000) 

3

(0 – 

Expert I 7,900 

(1,200 – 14,000) 

140 

(20 – 240) 

1,000 

(160 – 1,800) 

280 

(44 – 490) 

1,200 

(200 – 2,000) 

4,100 

(600 – 7,100) 

10,000 

(1,600 – 17,000) 

4

(700 – 

Expert J 7,000 

(1,800 – 14,000) 

120 

(33 – 240) 

930 

(240 – 1,800) 

250 

(66 – 490) 

1,000 

(270 – 2,000) 

3,700 

(970 – 7,100) 

8,900 

(2,300 – 17,000) 

4

(1,070 

Expert K 1,100 

(0 – 5,700) 

21 

(0 – 100) 

150 

(0 – 760) 

41 

(0 – 210) 

190 

(0 – 920) 

610 

(0 – 3,000) 

1,500 

(0 – 7,400) 

6

(0 – 

Expert L 5,400 

(700 – 10,000) 

110 

(20 – 180) 

650 

(0 – 1,400) 

180 

(13 – 380) 

890 

(200 – 1,500) 

3,100 

(500 – 5,400) 

7,000 

(900 – 13,000) 

3

(600 – 

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. b The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), whi

as the primary estimate in recent RIAs 

c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha

estimate.
Table 5-19: Illustrative Strategy to Attain 14/35: Estimated Reductions in Morbidity (Incremental to 15/65 Attainme

Percentile Confidence Intervals Provided in Parentheses a

 Eastern U.S. Western U.S. Excluding CA California National Total 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Res

Attai

Morbidity Impact Function Derived frm Epidemiology Literature Chronic bronchitis (age >25 and over) 

2,200 

(410 – 4,100) 

40 

(7 – 70) 

240 

(44 – 430) 

87 

(16 – 160) 

430 

(79 – 780) 

1,600 

(290 – 2,800) 

2,900 

(540 – 5,300) 

1,

(320 – 

Nonfatal myocardial infarction (age >17) 

4,200 

(2,300 – 6,100) 

150 

(80 – 220) 

140 

(77 – 200) 

30 

(16 – 44) 

1,000 

(540 – 1,500) 

3,200 

(1,800 – 4,600) 

5,300 

(2,900 – 7,800) 

3,

(1,900 

Hospital admissions—respiratory (all ages) 

500 

(250 – 750) 

18 

(9 – 27) 

13 

(7 – 20) 

3 

(1 – 4) 

100 

(50 – 150) 

340 

(170 – 510) 

620 

(310 – 930) 

3

(180 

Hospital admissions—cardiovascular (age >17) 

1,100 

(680 – 1,500) 

37 

(24 – 51) 

31 

(19 – 42) 

6 

(4 – 9) 

210 

(130 – 291) 

690 

(430 – 940) 

1,300 

(830 – 1,800) 

7

(460 – 

Emergency room visits for asthma (age <19) 

2,200 

(1,300 – 3,000) 

76 

(45 – 110) 

25 

(15 – 36) 

6 

(3 – 8) 

210 

(120 – 290) 

740 

(438 – 1,040) 

2,400 

(1,400 – 3,400) 

8

(486 – 

Acute bronchitis (age 8–12) 

5,900 

(–200 – 12,000) 

110 

(–4 – 220) 

640 

(–20 – 1,300) 

280 

(–10 – 560) 

1,200 

(–40 – 2,400) 

4,500 

(–160 – 9,000) 

7,700 

(–260 – 16,000) 

4,

(–170 

Lower respiratory symptoms (age 7–14) 

34,000 

(16,000 – 

51,000) 

1,200 

(600 – 1,800) 

1,400 

(670 – 2,100) 

300 

(150 – 460) 

11,000 

(5,400 – 

17,100) 

40,000 

(20,000 – 

61,000) 

46,000 

(22,400 – 

70,000) 

42

(20,

63,

Upper respiratory symptoms (asthmatic children age 9–18) 

25,000 

(7,800 – 42,000) 

900 

(270 – 1,500) 

1,000 

(320 – 1,700) 

220 

(70 – 370) 

8,300 

(2,600 – 

13,900) 

30,000 

(9,400 – 

50,000) 

34,000 

(11,000 – 

57,000) 

31

(9,800 – 

Asthma exacerbation (asthmatic children age 6–18) 

30,000 

(3,400 – 89,000) 

1,000 

(120 – 3,000) 

1,200 

(140 – 3,600) 

270 

(30 – 780) 

10,100 

(1,100 – 

29,300) 

36,000 

(4,100 – 

106,000) 

42,000 

(4,600 – 

120,000) 

38

(4,2

110

Work loss days (age 18–65) 

220,000 

(190,000 – 

250,000) 

7,000 

(6,000 – 8,000) 

8,000 

(7,000 – 9,000) 

1,800 

(1,600 – 2,000) 

71,300 

(62,100 – 

80,400) 

240,000 

(210,000 – 

280,000) 

300,000 

(260,000 – 

340,000) 

250

(220

290

Minor restrictedactivity days (age 18–65) 

1,300,000 

(1,100,000 – 

1,500,000) 

44,000 

(37,000 – 

51,000) 

47,000 

(40,000 – 

54,000) 

10,000 

(8,800 – 

12,000) 

420,000 

(350,000 – 

480,000) 

1,400,000 

(1,200,000 – 

1,700,000) 

1,800,000 

(1,500,000 – 

2,000,000) 

1,50

(1,300

1,70

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. 
Table 5-20: Illustrative Strategy to Attain 15/35: Estimated Monetary Value of Reductions in Risk of Premature Mo

Discount Rate, in millions of 1999$) 90th Percentile Confidence Intervals Provided in Parentheses a

 Eastern U.S. Western U.S. Excluding CA California National To

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Mortality Impact Functions Derived from Epidemiology Literature ACS Studyb $2,100 ($470 – $4,400) $97 ($22 – $200) $440 ($99 – $920) $87 ($19 – $180) $3,000 ($670 – $6,200) $9,000 ($2,000 – 

$19,000) 

$5,500 

($1,200 – 

$12,000) 

Harvard Six-City Studyc

$4,800 

($1,200 – $9,200) 

$220 

($57 – $430) 

$1,000 

($260 – $1,900) 

$200 

($51 – $380) 

$6,800 

($1,800 – 

$13,000) 

$20,000 

($5,300 – 

$39,000) 

$13,000 

($3,300 – 

$24,000) 

Woodruff et al 1997 (infant mortality) 

$6 

($1 – $11) 

$0 

($0 – $0) 

$4 

($1 – $8) 

$2 

($0 – $4) 

$8 

($2 – $15) 

$28 

($7 – $55) 

$17 

($4 – $35) 

Mortality Impact Functions Derived from Expert ElicitationExpert A $9,800 

($1,300 – $22,000) 

$240 

($32 – $540) 

$8,000 

($1,100 – 

$18,000) 

$2,100 

($280 – $4,800) 

$9,000 

($1,200 – 

$20,000) 

$29,000 

($4,000 – 

$67,000) 

$27,000 

($3,600 – 

$61,000) 

Expert B $7,800 

($650 – $21,000) 

$200 

($21 – $510) 

$6,100 

($390 – $17,000) 

$1,700 

($120 – $4,500) 

$7,400 

($740 – $19,000) 

$24,000 

($2,300 – 

$62,000) 

$21,000 

($1,800 – 

$57,000) 

Expert C $8,100 

($980 – $20,000) 

$200 

($24 – $480) 

$6,600 

($800 – $16,000) 

$1,800 

($210 – $4,200) 

$7,500 

($900 – $18,000) 

$24,000 

($3,000 – 

$59,000) 

$22,000 

($2,700 – 

$54,000) 

Expert D $5,300 

($800 – $11,000) 

$130 

($19 – $270) 

$4,300 

($650 – $9,100) 

$1,200 

($170 – $2,400) 

$4,900 

($730 – $10,000) 

$16,000 

($2,400 – 

$34,000) 

$15,000 

($2,200 – 

$31,000) 

Expert E $12,000 

($3,100 – $24,000) 

$300 

($76 – $600) 

$10,000 

($2,500 – 

$20,000) 

$2,700 

($670 – $5,300) 

$11,000 

($2,800 – 

$22,000) 

$37,000 

($9,300 – 

$73,000) 

$34,000 

($8,500 – 

$67,000) 

Expert F $7,200 

($1,900 – $13,000) 

$170 

($47 – $330) 

$5,800 

($1,600 – 

$11,000) 

$1,500 

($420 – $2,900) 

$6,600 

($1,800 – 

$12,000) 

$22,000 

($5,900 – 

$40,000) 

$19,000 

($5,300 – 

$37,000) 

Expert G $4,300 

($0 – $11,000) 

$110 

($0 – $260) 

$3,500 

($0 – $8,700) 

$940 

($0 – $2,300) 

$4,000 

($0 – $9,800) 

$13,000 

($0 – $32,000) 

$12,000 

($0 – $29,000) 

($

Expert H $5,300 

($17 – $15,000) 

$130 

($0 – $370) 

$4,300 

($14 – $12,000) 

$1,200 

($4 – $3,300) 

$4,900 

($16 – $14,000) 

$16,000 

($52 – $46,000) 

$15,000 

($47 – $42,000) 

($5

Expert I $7,600 

($900 – $17,000) 

$190 

($22 – $410) 

$6,200 

($730 – $14,000) 

$1,600 

($190 – $3,600) 

$7,000 

($830 – $15,000) 

$23,000 

($2,700 – 

$50,000) 

$21,000 

($2,500 – 

$46,000) 

Expert J $6,800 

($1,100 – $16,000) 

$160 

($28 – $390) 

$5,500 

($930 – $13,000) 

$1,500 

($250 – $3,500) 

$6,200 

($1,100 – 

$15,000) 

$20,000 

($3,500 – 

$48,000) 

$18,000 

($3,100 – 

$44,000) 

Expert K $1,100 

($0 – $6,000) 

$27 

($0 – $150) 

$900 

($0 – $4,800) 

$240 

($0 – $1,300) 

$1,100 

($0 – $6,000) 

$3,400 

($0 – $18,000) 

$3,100 

($0 – $17,000) 

($

Expert L $5,300 

($480 – $13,000) 

$140 

($20 – $330) 

$3,800 

($110 – $10,000) 

$1,100 

($59 – $2,800) 

$5,300 

($720 – $12,000) 

$17,000 

($2,100 – 

$40,000) 

$14,000 

($1,300 – 

$36,000) 

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. b The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), whi

as the primary estimate in recent RIAs 

c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha

estimate.
Table 5-21: Illustrative Strategy to Attain 15/35: Estimated Monetary Value of Reductions in Risk of Premature 

Discount Rate, in millions of 1999$) 90th Percentile Confidence Intervals Provided in Parentheses a 

 Eastern U.S. Western U.S. Excluding CA California National Total 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Re

Atta

Mortality Impact Functions Derived from Epidemiology Literature ACS Studyb $1,800 ($390 – $3,700) $82 ($18 – $170) $370 ($83 – $770) $73 ($16 – $150) $2,500 ($560 – $5,200) $7,600 ($1,700 – 

$16,000) 

$4,700 

($1,000 – 

$9,700) 

$7

($1

$16

Harvard Six-City Studyc $4,000 ($1,000 – 

$7,800) 

$180 

($48 – $360) 

$840 

($220 – $1,600) 

$160 

($43 – $320) 

$5,700 

($1,500 – 

$11,000) 

$17,000 

($4,400 – 

$33,000) 

$11,000 

($2,700 – 

$20,000) 

$17

($4

$34

Woodruff et al 1997 (infant mortality) 

$5 

($1 – $10) 

$0 

($0 – $0) 

$4 

($1 – $7) 

$2 

($0 – $3) 

$6 

($2 – $13) 

$23 

($6 – $46) 

$15 

($4 – $29) 

$

($6 

Mortality Impact Functions Derived from Expert ElicitationExpert A $8,300 

($1,100 – 

$19,000) 

$200 

($27 – $460) 

$6,700 

($900 – 

$15,000) 

$1,800 

($240 – 

$4,100) 

$7,600 

($1,000 – 

$17,000) 

$25,000 

($3,400 – 

$56,000) 

$23,000 

($3,000 – 

$51,000) 

$27

($3

$61

Expert B $6,600 

($550 – 

$18,000) 

$170 

($17 – $430) 

$5,200 

($320 – 

$14,000) 

$1,400 

($100 – 

$3,800) 

$6,200 

($630 – 

$16,000) 

$20,000 

($1,900 – 

$53,000) 

$18,000 

($1,500 – 

$48,000) 

$22

($2

$57

Expert C $6,900 

($830 – 

$17,000) 

$170 

($20 – $400) 

$5,500 

($670 – 

$13,000) 

$1,500 

($180 – 

$3,600) 

$6,300 

($760 – 

$15,000) 

$20,000 

($2,500 – 

$49,000) 

$19,000 

($2,300 – 

$45,000) 

$22

($2

$53

Expert D $4,500 

($670 – $9,400) 

$110 

($16 – $230) 

$3,600 

($540 – $7,600) 

$970 

($140 – 

$2,000) 

$4,100 

($620 – $8,600) 

$14,000 

($2,000 – 

$28,000) 

$12,000 

($1,800 – 

$26,000) 

$15

($2

$31

Expert E $10,000 

($2,600 – 

$21,000) 

$250 

($64 – $500) 

$8,400 

($2,100 – 

$17,000) 

$2,200 

($560 – 

$4,400) 

$9,500 

($2,400 – 

$19,000) 

$31,000 

($7,800 – 

$61,000) 

$28,000 

($7,100 – 

$56,000) 

$34

($8

$66

Expert F $6,000 

($1,600 – 

$11,000) 

$150 

($40 – $280) 

$4,900 

($1,300 – 

$9,100) 

$1,300 

($350 – 

$2,400) 

$5,500 

($1,500 – 

$10,000) 

$18,000 

($4,900 – 

$34,000) 

$16,000 

($4,400 – 

$31,000) 

$20

($5

$37

Expert G $3,700 

($0 – $9,000) 

$89 

($0 – $220) 

$3,000 

($0 – $7,300) 

$790 

($0 – $1,900) 

$3,300 

($0 – $8,300) 

$11,000 

($0 – $27,000) 

$10,000 

($0 – $25,000) 

$12

($0 – $

Expert H $4,500 

($14 – $13,000) 

$110 

($0 – $310) 

$3,600 

($12 – $10,000) 

$970 

($3 – $2,800) 

$4,100 

($13 – $12,000) 

$13,000 

($44 – $38,000) 

$12,000 

($40 – $35,000) 

$15

($47 – 

Expert I $6,400 

($760 – 

$14,000) 

$160 

($18 – $340) 

$5,200 

($620 – 

$11,000) 

$1,400 

($160 – 

$3,000) 

$5,900 

($700 – 

$13,000) 

$19,000 

($2,300 – 

$42,000) 

$18,000 

($2,100 – 

$38,000) 

$2

($2

$45

Expert J $5,700 

($960 – 

$14,000) 

$140 

($23 – $330) 

$4,600 

($780 – 

$11,000) 

$1,200 

($210 – 

$2,900) 

$5,200 

($880 – 

$12,000) 

$17,000 

($2,900 – 

$40,000) 

$16,000 

($2,600 – 

$37,000) 

$18

($3

$44

Expert K $930 

($0 – $5,000) 

$23 

($0 – $120) 

$760 

($0 – $4,100) 

$200 

($0 – $1,100) 

$950 

($0 – $5,000) 

$2,800 

($0 – $15,000) 

$2,600 

($0 – $14,000) 

$3

($0 – $

Expert L $4,400 

($410 – 

$11,000) 

$120 

($17 – $280) 

$3,200 

($91 – $8,800) 

$890 

($50 – $2,400) 

$4,500 

($600 – 

$10,000) 

$14,000 

($1,700 – 

$33,000) 

$12,000 

($1,100 – 

$30,000) 

$15

($1

$36

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. b The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), whi

as the primary estimate in recent RIAs 

c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha

estimate.
Table 5-22: Illustrative Strategy to Attain 15/35: Estimated Monetary Value of Morbidity Reductions (in millions of 

Confidence Intervals Provided in Parentheses a

 Eastern U.S. Western U.S. Excluding CA California National Total 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Res

Attai

Morbidity Impact Function Derived frm Epidemiology Literature Chronic bronchitis (age >25 and over) 

$140 

($11 – $510) 

$3 

($0 – $11) 

$97 

($8 – $340) 

$35 

($3 – $120) 

$180 

($14 – $630) 

$600 

($47 – $2,100) 

$420 

($33 – $1,500) 

$6

($50 – 

Nonfatal myocardial infarction (age >17) 3% Discount Rate 

$63 

($17 – $140) 

$3 

($1 – $7) 

$11 

($3 – $24) 

$2 

($1 – $5) 

$87 

($24 – $190) 

$250 

($70 – $540) 

$160 

($43 – $350) 

$2

($71 – 

Nonfatal myocardial infarction (age >17) 7% Discount Rate 

$61 

($15 – $140) 

$3 

($1 – $7) 

$11 

($3 – $24) 

$2 

($1 – $5) 

$84 

($22 – $190) 

$240 

($64 – $540) 

$160 

($40 – $350) 

$2

($66 – 

Hospital admissions—respiratory (all ages) 

$1.4 

($0.7 – $2.1) 

$0.1 

($0.0 – $0.1) 

$0.2 

($0.1 – $0.3) 

$0.1 

($0.0 – $0.1) 

$1.7 

($0.8 – $2.5) 

$5.1 

($2.5 – $7.7) 

$3.3 

($1.6 – $4.9) 

$

($2.6 

Hospital admissions—cardiovascular (age >17) 

$3.9 

($2.5 – $5.4) 

$0.2 

($0.1 – $0.3) 

$0.6 

($0.4 – $0.9) 

$0.1 

($0.1 – $0.2) 

$4.6 

($2.9 – $6.3) 

$14.0 

($8.4 – $19.0) 

$9.0 

($5.7 – $13.0) 

$1

($8.7 – 

Emergency room visits for asthma (age <19) 

$0.08 

($0.04 – $0.12) 

$0.00 

($0.00 – $0.00) 

$0.01 

($0.00 – $0.01) 

$0.00 

($0.00 – $0.00) 

$0.06 

($0.03 – $0.09) 

$0.19 

($0.10 – $0.29) 

$0.14 

($0.08 – $0.22) 

$0

($0.11 

Acute bronchitis (age 8–12) 

$0.32 

(–$0.01 – $0.81) 

$0.01 

($0.00 – $0.02) 

$0.24 

(–$0.01 – 

$0.60) 

$0.10 

($0.00 – $0.26) 

$0.46 

(–$0.02 – 

$1.10) 

$1.60 

(–$0.06 – 

$3.90) 

$1.00 

(–$0.04 – 

$2.60) 

$1

(–$0.06 

Lower respiratory symptoms (age 7–14) 

$0.08 

($0.03 – $0.15) 

$0.00 

($0.00 – $0.01) 

$0.02 

($0.01 – $0.04) 

$0.00 

($0.00 – $0.01) 

$0.19 

($0.07 – $0.35) 

$0.61 

($0.23 – $1.10) 

$0.29 

($0.11 – $0.54) 

$0

($0.23 

Upper respiratory symptoms (asthmatic children age 9–18) 

$0.10 

($0.03 – $0.21) 

$0.00 

($0.00 – $0.01) 

$0.03 

($0.01 – $0.06) 

$0.01 

($0.00 – $0.01) 

$0.23 

($0.06 – $0.48) 

$0.75 

($0.20 – $1.60) 

$0.35 

($0.09 – $0.75) 

$0

($0.20 

Asthma exacerbation (asthmatic children age 6–18) 

$0.19 

($0.02 – $0.61) 

$0.01 

($0.00 – $0.02) 

$0.05 

($0.01 – $0.17) 

$0.01 

($0.00 – $0.04) 

$0.43 

($0.05 – $1.40) 

$1.40 

($0.15 – $4.70) 

$0.67 

($0.07 – $2.20) 

$1

($0.16 

Work loss days (age 18–65) 

$3 

($3 – $4) 

$0.13 

($0.11 – $0.15) 

$0.9 

($0.8 – $1.0) 

$0.19 

($0.17 – $0.22) 

$9 

($8 – $10) 

$29 

($25 – $33) 

$14 

($12 – $15) 

$

($26 

Minor restrictedactivity days (age 18–65) 

$5 

($0 – $10) 

$0.19 

($0.02 – $0.37) 

$1.2 

($0.1 – $2.2) 

$0.26 

($0.02 – $0.51) 

$11 

($1 – $21) 

$33 

($3 – $65) 

$17 

($2 – $33) 

$

($3 – 

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. 
Table 5-23: Illustrative Strategy to Attain 14/35: Estimated Monetary Value of Reductions in Risk of Premature Mo

Discount Rate, in millions of 1999$) 90th Percentile Confidence Intervals Provided in Parentheses a

 Eastern U.S. Western U.S. Excluding CA California National Total 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Res

Attai

Mortality Impact Functions Derived from Epidemiology Literature ACS Studyb $12,000 ($2,700 – 

$25,000) 

$430 

($96 – $900) 

$450 

($100 – $930) 

$87 

($19 – $180) 

$2,900 

($650 – $6,000) 

$9,500 

($2,100 – 

$20,000) 

$15,000 

($3,400 – 

$32,000) 

$10

($2,

$21

Harvard Six-City Studyc

$27,000 

($7,100 – 

$53,000) 

$980 

($250 – $1,900) 

$1,000 

($260 – $2,000) 

$200 

($51 – $380) 

$6,600 

($1,700 – 

$13,000) 

$21,000 

($5,600 – 

$42,000) 

$35,000 

($9,100 – 

$68,000) 

$23

($5,

$44

Woodruff et al., 1997 (infant mortality) 

$43 

($11 – $86) 

$1 

($0 – $2) 

$4 

($1 – $8) 

$2 

($0 – $4) 

$7 

($2 – $15) 

$29 

($7 – $58) 

$55 

($14 – $110) 

$

($8 – 

Mortality Impact Functions Derived from Expert Elicitation Expert A $59,000 

($7,900 – 

$130,000) 

$1,100 

($140 – $2,400) 

$7,800 

($1,100 – 

$18,000) 

$2,100 

($280 – $4,800) 

$8,800 

($1,200 – 

$20,000) 

$31,000 

($4,200 – 

$70,000) 

$75,000 

($10,000 – 

$170,000) 

$34

($4,

$77

Expert B $47,000 

($3,900 – 

$130,000) 

$860 

($91 – $2,200) 

$6,000 

($380 – 

$17,000) 

$1,700 

($120 – $4,500) 

$7,200 

($720 – 

$19,000) 

$25,000 

($2,400 – 

$65,000) 

$60,000 

($5,000 – 

$160,000) 

$27

($2,

$72

Expert C $49,000 

($5,900 – 

$120,000) 

$870 

($110 – $2,100) 

$6,500 

($790 – 

$16,000) 

$1,800 

($210 – $4,200) 

$7,300 

($880 – 

$18,000) 

$25,000 

($3,100 – 

$61,000) 

$62,000 

($7,500 – 

$150,000) 

$28

($3,

$68

Expert D $32,000 

($4,800 – 

$67,000) 

$570 

($85 – $1,200) 

$4,300 

($640 – $8,900) 

$1,200 

($170 – $2,400) 

$4,800 

($710 – 

$10,000) 

$17,000 

($2,500 – 

$35,000) 

$41,000 

($6,100 – 

$86,000) 

$19

($2,

$39

Expert E $74,000 

($18,000 – 

$150,000) 

$1,300 

($330 – $2,600) 

$9,800 

($2,500 – 

$19,000) 

$2,700 

($670 – $5,300) 

$11,000 

($2,800 – 

$22,000) 

$39,000 

($9,700 – 

$76,000) 

$95,000 

($24,000 – 

$190,000) 

$43

($11

$84

Expert F $43,000 

($12,000 – 

$80,000) 

$770 

($210 – $1,400) 

$5,700 

($1,500 – 

$11,000) 

$1,500 

($420 – $2,900) 

$6,400 

($1,700 – 

$12,000) 

$23,000 

($6,100 – 

$42,000) 

$55,000 

($15,000 – 

$100,000) 

$25

($6,

$46

Expert G $26,000 

($0 – $64,000) 

$460 

($0 – $1,100) 

$3,500 

($0 – $8,500) 

$940 

($0 – $2,300) 

$3,900 

($0 – $9,600) 

$14,000 

($0 – $34,000) 

$33,000 

($0 – $82,000) 

$15

($0 – $

Expert H $32,000 

($100 – $91,000) 

$570 

($2 – $1,600) 

$4,300 

($14 – $12,000) 

$1,200 

($4 – $3,300) 

$4,800 

($15 – $14,000) 

$17,000 

($55 – $48,000) 

$41,000 

($130 – 

$120,000) 

$18

($60 – 

Expert I $45,000 

($5,400 – 

$100,000) 

$810 

($96 – $1,800) 

$6,100 

($720 – 

$13,000) 

$1,600 

($190 – $3,600) 

$6,800 

($810 – 

$15,000) 

$24,000 

($2,900 – 

$52,000) 

$58,000 

($6,900 – 

$130,000) 

$26

($3,

$58

Expert J $40,000 

($6,800 – 

$96,000) 

$720 

($120 – $1,700) 

$5,400 

($920 – 

$13,000) 

$1,500 

($250 – $3,500) 

$6,000 

($1,000 – 

$14,000) 

$21,000 

($3,600 – 

$50,000) 

$52,000 

($8,800 – 

$120,000) 

$23

($4,

$55

Expert K $6,600 

($0 – $35,000) 

$120 

($0 – $640) 

$880 

($0 – $4,800) 

$240 

($0 – $1,300) 

$1,100 

($0 – $5,800) 

$3,500 

($0 – $19,000) 

$8,600 

($0 – $46,000) 

$3

($0 – $

Expert L $31,000 

($2,900 – 

$78,000) 

$630 

($90 – $1,400) 

$3,700 

($110 – 

$10,000) 

$1,100 

($59 – $2,800) 

$5,200 

($700 – 

$12,000) 

$18,000 

($2,200 – 

$42,000) 

$40,000 

($3,700 – 

$100,000) 

$19

($2,

$46

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. b The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), whi

as the primary estimate in recent RIAs 

c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha

estimate.
Table 5-24: Illustrative Strategy to Attain 14/35: Estimated Monetary Value of Reductions in Risk of Premature Mo

Discount Rate, in millions of 1999$) 90th Percentile Confidence Intervals Provided in Parentheses a

 Eastern U.S. Western U.S. Excluding CA California National Total 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Res

Attai

Mortality Impact Functions Derived from Epidemiology Literature ACS Studyb $10,000 ($2,300 – 

$21,000) 

$360 

($81 – $760) 

$380 

($84 – $780) 

$73 

($16 – $150) 

$2,400 

($540 – $5,100) 

$8,000 

($1,800 – 

$17,000) 

$13,000 

($2,900 – 

$27,000) 

$8

($1,

$18

Harvard Six-City Studyc $23,000 ($6,000 – 

$45,000) 

$820 

($210 – $1,600) 

$850 

($220 – $1,600) 

$160 

($43 – $320) 

$5,500 

($1,400 – 

$11,000) 

$18,000 

($4,700 – 

$35,000) 

$29,000 

($7,600 – 

$57,000) 

$19

($5,

$37

Woodruff et al., 1997 (infant mortality) 

$36 

($9 – $72) 

$1 

($0 – $2) 

$3 

($1 – $7) 

$2 

($0 – $3) 

$6 

($2 – $12) 

$24 

($6 – $48) 

$46 

($11 – $92) 

$

($7 – 

Mortality Impact Functions Derived from Expert Elicitation Expert A $49,000 

($6,600 – 

$110,000) 

$880 

($120 – $2,000) 

$6,600 

($890 – 

$15,000) 

$1,800 

($240 – $4,100) 

$7,400 

($990 – 

$17,000) 

$26,000 

($3,500 – 

$59,000) 

$63,000 

($8,500 – 

$140,000) 

$29

($3,

$65

Expert B $39,000 

($3,300 – 

$110,000) 

$730 

($76 – $1,900) 

$5,100 

($320 – 

$14,000) 

$1,400 

($100 – $3,800) 

$6,000 

($610 – 

$16,000) 

$21,000 

($2,000 – 

$55,000) 

$50,000 

($4,200 – 

$140,000) 

$23

($2,

$61

Expert C $41,000 

($4,900 – 

$99,000) 

$730 

($88 – $1,800) 

$5,500 

($660 – 

$13,000) 

$1,500 

($180 – $3,600) 

$6,100 

($740 – 

$15,000) 

$21,000 

($2,600 – 

$52,000) 

$52,000 

($6,300 – 

$130,000) 

$24

($2,

$57

Expert D $27,000 

($4,000 – 

$56,000) 

$480 

($72 – $1,000) 

$3,600 

($530 – $7,500) 

$970 

($140 – $2,000) 

$4,000 

($600 – $8,400) 

$14,000 

($2,100 – 

$30,000) 

$34,000 

($5,100 – 

$72,000) 

$16

($2,

$33

Expert E $62,000 

($16,000 – 

$120,000) 

$1,100 

($280 – $2,200) 

$8,300 

($2,100 – 

$16,000) 

$2,200 

($560 – $4,400) 

$9,300 

($2,300 – 

$18,000) 

$32,000 

($8,200 – 

$64,000) 

$80,000 

($20,000 – 

$160,000) 

$36

($9,

$71

Expert F $36,000 

($9,700 – 

$67,000) 

$640 

($170 – $1,200) 

$4,800 

($1,300 – 

$9,000) 

$1,300 

($350 – $2,400) 

$5,400 

($1,500 – 

$10,000) 

$19,000 

($5,100 – 

$35,000) 

$46,000 

($12,000 – 

$86,000) 

$21

($5,

$39

Expert G $22,000 

($0 – $54,000) 

$390 

($0 – $960) 

$2,900 

($0 – $7,200) 

$790 

($0 – $1,900) 

$3,300 

($0 – $8,100) 

$11,000 

($0 – $28,000) 

$28,000 

($0 – $69,000) 

$13

($0 – $

Expert H $27,000 

($86 – $77,000) 

$480 

($2 – $1,400) 

$3,600 

($12 – $10,000) 

$970 

($3 – $2,800) 

$4,000 

($13 – $11,000) 

$14,000 

($46 – $40,000) 

$34,000 

($110 – 

$98,000) 

$16

($51 – 

Expert I $38,000 

($4,500 – 

$84,000) 

$680 

($81 – $1,500) 

$5,100 

($610 – 

$11,000) 

$1,400 

($160 – $3,000) 

$5,700 

($680 – 

$13,000) 

$20,000 

($2,400 – 

$44,000) 

$49,000 

($5,800 – 

$110,000) 

$22

($2,

$48

Expert J $34,000 

($5,700 – 

$80,000) 

$610 

($100 – $1,400) 

$4,500 

($770 – 

$11,000) 

$1,200 

($210 – $2,900) 

$5,100 

($860 – 

$12,000) 

$18,000 

($3,000 – 

$42,000) 

$44,000 

($7,400 – 

$100,000) 

$20

($3,

$46

Expert K $5,600 

($0 – $30,000) 

$100 

($0 – $540) 

$740 

($0 – $4,000) 

$200 

($0 – $1,100) 

$930 

($0 – $4,900) 

$3,000 

($0 – $16,000) 

$7,200 

($0 – $39,000) 

$3

($0 – $

Expert L $26,000 

($2,500 – 

$66,000) 

$530 

($76 – $1,200) 

$3,200 

($91 – $8,700) 

$890 

($50 – $2,400) 

$4,300 

($590 – 

$10,000) 

$15,000 

($1,800 – 

$35,000) 

$34,000 

($3,100 – 

$85,000) 

$16

($2,

$39

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. b The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), whi

as the primary estimate in recent RIAs 

c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha

estimate.
Table 5-25: Illustrative Strategy to Attain 14/35: Estimated Monetary Value of Morbidity Reductions (in millions of 

Confidence Intervals Provided in Parentheses a

 Eastern U.S. Western U.S. Excluding CA California National Total 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Residual 

Attainment 

Modeled Partial 

Attainment 

Res

Attai

Morbidity Impact Function Derived frm Epidemiology Literature Chronic bronchitis (age >25 and over) 

$900 

($70 – $3,200) 

$16 

($1 – $58) 

$95 

($7 – $340) 

$35 

($3 – $120) 

$170 

($13 – $610) 

$630 

($50 – $2,200) 

$1,200 

($91 – $4,100) 

$6

($54 – 

Nonfatal myocardial infarction (age >17) 3% Discount Rate 

$350 

($92 – $760) 

$12 

($3 – $28) 

$11 

($3 – $25) 

$2 

($1 – $5) 

$84 

($23 – $180) 

$270 

($75 – $580) 

$440 

($120 – $970) 

$2

($79 – 

Nonfatal myocardial infarction (age >17) 7% Discount Rate 

$330 

($85 – $750) 

$12 

($3 – $27) 

$11 

($3 – $24) 

$2 

($1 – $5) 

$82 

($21 – $180) 

$260 

($69 – $570) 

$430 

($110 – $950) 

$2

($72 – 

Hospital admissions—respiratory (all ages) 

$8.0 

($4.0 – $12.0) 

$0.3 

($0.1 – $0.4) 

$0.2 

($0.1 – $0.3) 

$0.1 

($0.0 – $0.1) 

$1.6 

($0.8 – $2.4) 

$5.4 

($2.7 – $8.2) 

$10.0 

($4.9 – $15.0) 

$

($2.9 

Hospital admissions—cardiovascular (age >17) 

$22.0 

($14.0 – $31.0) 

$0.8 

($0.5 – $1.1) 

$0.6 

($0.4 – $0.9) 

$0.1 

($0.1 – $0.2) 

$4.4 

($2.8 – $6.1) 

$14.0 

($9.0 – $20.0) 

$27.0 

($17.0 – $38.0) 

$1

($10.0 

Emergency room visits for asthma (age <19) 

$0.59 

($0.32 – $0.90) 

$0.02 

($0.01 – $0.03) 

$0.01 

($0.00 – $0.01) 

$0.00 

($0.00 – $0.00) 

$0.06 

($0.03 – $0.09) 

$0.20 

($0.11 – $0.31) 

$0.66 

($0.36 – $1.00) 

$0

($0.12 

Acute bronchitis (age 8–12) 

$2.10 

(–$0.08 – $5.40) 

$0.04 

($0.00 – $0.10) 

$0.23 

(–$0.01 – 

$0.59) 

$0.10 

($0.00 – $0.26) 

$0.44 

(–$0.02 – 

$1.10) 

$1.60 

(–$0.06 – 

$4.10) 

$2.80 

(–$0.10 – 

$7.10) 

$1

(–$0.07 

Lower respiratory symptoms (age 7–14) 

$0.55 

($0.21 – $1.00) 

$0.02 

($0.01 – $0.04) 

$0.02 

($0.01 – $0.04) 

$0.00 

($0.00 – $0.01) 

$0.18 

($0.07 – $0.34) 

$0.65 

($0.25 – $1.20) 

$0.75 

($0.28 – $1.40) 

$0

($0.26 

Upper respiratory symptoms (asthmatic children age 9–18) 

$0.67 

($0.17 – $1.40) 

$0.02 

($0.01 – $0.05) 

$0.03 

($0.01 – $0.06) 

$0.01 

($0.00 – $0.01) 

$0.22 

($0.06 – $0.47) 

$0.81 

($0.21 – $1.70) 

$0.90 

($0.24 – $1.90) 

$0

($0.22 

Asthma exacerbation (asthmatic children age 6–18) 

$1.30 

($0.14 – $4.20) 

$0.04 

($0.00 – $0.14) 

$0.05 

($0.01 – $0.17) 

$0.01 

($0.00 – $0.04) 

$0.42 

($0.05 – $1.40) 

$1.50 

($0.16 – $5.00) 

$1.70 

($0.19 – $5.80) 

$1

($0.17 

Work loss days (age 18–65) 

$23 

($20 – $26) 

$0.8 

($0.7 – $0.9) 

$0.9 

($0.8 – $1.0) 

$0.2 

($0.2 – $0.2) 

$9 

($8 – $10) 

$31 

($27 – $35) 

$33 

($28 – $37) 

$

($28 

Minor restrictedactivity days (age 18–65) 

$32 

($3 – $63) 

$1.1 

($0.1 – $2.1) 

$1.2 

($0.1 – $2.3) 

$0.3 

($0.0 – $0.5) 

$10 

($1 – $20) 

$36 

($3 – $69) 

$44 

($4 – $86) 

$

($3 – 

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. 
Table 5-26: Monetary Benefits Associated with Improvements in Visibility in Selected Federal Class I Areas in 2015/65 Attainment Strategy (in millions of 1999$)a

Suite of Standards California Southwest Southeast Total 

15/35 $320 $120 $91 $530 

14/35 $320 $130 $770 $1,200 

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. 
Table 5-27: Ranges of Total Monetized Benefits (Health and Visibility) Associated with Full Attainment of 15/35 and 14/35 Standards IncremeCurrent 15/65 Standards in 2020 (in millions of 1999$) 90th Percentile Confidence Intervals Provided in Parentheses a

3% Discount Rate 7% Discou

Source of Mortality Effect Estimate 15/35 14/35 15/35 Data Derived ACS Studyb $17,000 $30,000 $15,000 

($4,100 - $36,000) ($7,300 - $63,000) ($3,500 - $31,000) 

Harvard Six-City Studyc $35,000 $62,000 $30,000  ($9,400 - $70,000) ($17,000 - $120,000) ($8,100 - $59,000) Expert Elicitation Derived Expert A $61,000 $110,000 $51,000  ($8,700 - $140,000) ($16,000 - $260,000) ($7,400 - $120,000) Expert B $49,000 $91,000 $42,000  ($5,000 - $130,000) ($9,300 - $240,000) ($4,300 - $110,000) Expert C $51,000 $94,000 $43,000  ($6,700 - $120,000) ($13,000 - $230,000) ($5,800 - $100,000) Expert D $34,000 $64,000 $29,000  ($5,600 - $72,000) ($11,000 - $130,000) ($4,800 - $62,000) Expert E $76,000 $140,000 $64,000  ($19,000 - $150,000) ($36,000 - $280,000) ($16,000 - $130,000) Expert F $45,000 $84,000 $38,000  ($12,000 - $86,000) ($23,000 - $160,000) ($11,000 - $73,000) Expert G $28,000 $52,000 $24,000  ($800 - $69,000) ($1,700 - $130,000) ($790 - $59,000) Expert H $34,000 $63,000 $29,000  ($900 - $96,000) ($1,900 - $180,000) ($880 - $82,000) Expert I $48,000 $89,000 $40,000  ($6,200 - $110,000) ($12,000 - $200,000) ($5,300 - $89,000) Expert J $42,000 $79,000 $36,000  ($7,700 - $100,000) ($14,000 - $190,000) ($6,600 - $86,000) Expert K $9,000 $17,000 $7,900  ($800 - $42,000) ($1,700 - $77,000) ($790 - $36,000) Expert L $35,000 $64,000 $29,000  ($4,300 - $84,000) ($7,700 - $160,000) ($3,700 - $72,000) a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. b The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), whi

as the primary estimate in recent RIAs 

c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha

estimate.
Figure 5-8. Comparison of Benefits of Illustrative Attainment Strategy for the Revised Standards (15/35) Acros

Sources of Mortality Effect Estimates 
Figure 5-9. Comparison of Benefits of Illustrative Attainment Strategy for the More Stringent Alternative Stand

Regions and Sources of Mortality Effect Estimates 
Table 5-28: Mortality Threshold Sensitivity Analysis for 15/35 Scenario (Using Pope et al., 2002 Effect Estimate wit

for Thresholds Above 7.5 ug) 90th Percentile Confidence Intervals Provided in Parentheses a

 Estimated Reduction in Mortality Incidence  Eastern U.S. Western U.S. Excluding CA California National T

Level of 

Assumed 

Threshold 

Modeled 

Partial 

Attainment 

Residual 

Attainment 

Modeled 

Partial 

Attainment 

Residual 

Attainment 

Residual 

Attainment 

Modeled 

Partial 

Attainment 

Residual 

Attainment 

A

Less Certainty That Benefits Are at Least as Large 

No Threshold 

620 

(240 – 1,000)

15 

(6 – 24) 

510 

(200 – 810) 

140 

(53 – 220) 

2,000 

(800 – 3,300) 

570 

(220 – 920) 

1,900 

(740 – 3,000)

(6

 Threshold 

at 7.5 μg 

610 

(240 – 980) 

15 

(6 – 24) 

320 

(130 – 520) 

110 

(44 – 180) 

2,000 

(790 – 3,200) 

560 

(220 – 900) 

1,900 

(740 – 3,000)

(5

 Threshold 

at 10 μg 

360 

(140 – 580) 

17 

(7 – 27) 

80 

(30 – 120) 

15 

(6 – 24) 

1,600 

(620 – 2,500) 

520 

(200 – 0,800)

1,600 

(610 – 2,500)

(3

 Threshold 

at 12 μg 

38 

(15 – 62) 

2 

(1 – 3) 

12 

(5 – 19) 

0 

(0 – 0) 

1,200 

(490 – 2,000) 

430 

(170 – 0,700)

1,200 

(490 – 2,000)

(

More Certainty That Benefits are at Least as Large 

Threshold at 14 μg 

10 

(4 – 16) 

2 

(1 – 3) 

9 

(3 – 14) 

0 

(0 – 0) 

440 

(170 – 700) 

390 

(150 – 0,600)

440 

(170 – 700) 

(

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. 
Table 5-29: Mortality Threshold Sensitivity Analysis for 14/35 Scenario (Using Pope et al., 2002 Effect Estimate wit

for Thresholds Above 7.5 ug) 90th Percentile Confidence Intervals Provided in Parentheses a

 Estimated Reduction in Mortality Incidence  Eastern U.S. Western U.S. Excluding CA California National T

Level of 

Assumed 

Threshold 

Modeled 

Partial 

Attainment 

Residual 

Attainment 

Modeled 

Partial 

Attainment 

Residual 

Attainment 

Modeled 

Partial 

Attainment 

Residual 

Attainment 

Modeled 

Partial 

Attainment 

A

Less Certainty That Benefits Are at Least as Large 

No Threshold 

3,700 

(1,500 – 6,000)

70 

(26 – 110) 

500 

(200 – 800) 

130 

(53 – 220) 

560 

(220 – 900) 

2,000 

(770 – 3,200)

4,800 

(1,900 – 7,700)

(8

 Threshold 

at 7.5 μg 

3,500 

(1,400 – 5,700)

70 

(26 – 110) 

320 

(120 – 510) 

110 

(44 – 180) 

550 

(210 – 880) 

2,000 

(770 – 3,200)

4,400 

(1,700 – 7,100)

(8

 Threshold 

at 10 μg 

2,100 

(820 – 3,400)

70 

(29 – 120) 

80 

(30 – 120) 

15 

(6 – 24) 

500 

(200 – 810) 

1,600 

(650 – 2,600)

2,700 

(1,000 – 4,300)

(6

 Threshold 

at 12 μg 

220 

(87 – 360) 

60 

(24 – 100) 

12 

(5 – 19) 

0 

(0 – 1) 

420 

(160 – 670) 

1,300 

(530 – 2,200)

650 

(250 – 1,000)

(5

More Certainty That Benefits are at Least as Large 

Threshold at 14 μg 

54 

(21 – 87) 

44 

(17 – 70) 

9 

(3 – 14) 

0 

(0 – 0) 

370 

(140 – 600) 

480 

(190 – 800) 

430 

(170 – 700) 

(2

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. 
Table 5-30: Sensitivity of Monetized Benefits of Reductions in Mortality Risk to Assumed Thresholds for 15/35 Sceal., 2002 Effect Estimate with Slope Adjustment for Thresholds Above 7.5 ug) 90th Percentile Confidence Intervals P

Parentheses a

 Millions of 1999$  Eastern U.S. 

Western U.S. Excluding 

CA California 

Total N

Atta

Level of 

Assumed 

Threshold 

Discount 

Rate 

Modeled 

Partial 

Attainment 

Residual 

Attainment 

Modeled 

Partial 

Attainment 

Residual 

Attainment 

Modeled 

Partial 

Attainment 

Residual 

Attainment 

Modeled 

Partial 

Attainment 

3% 

$3,600 

($800 – $7,500) 

$88 

($20 – 

$180) 

$2,900 

($650 – $6,100) 

$780 

($170 – $1,600) 

$3,300 

($740 – $6,900) 

$11,000 

($2,400 – 

$23,000) 

$9,900 

($2,200 – 

$21,000) 

Less Certain that Benefits Are at Least as Large No 

Threshold 

7% 

$3,000 

($680 – $6,300) 

$74 

($16 – 

$150) 

$2,500 

($550 – $5,100) 

$660 

($150 – $1,400) 

$2,800 

($620 – $5,800) 

$9,200 

($2,000 – 

$19,000) 

$8,300 

($1,800 – 

$17,000) 

3% 

$3,500 

($780 – $7,300) 

$88 

($20 – 

$180) 

$1,900 

($420 – $3,900) 

$650 

($140 – $1,300) 

$3,200 

($720 – $6,800) 

$11,000 

($2,400 – 

$23,000) 

$8,600 

($1,900 – 

$18,000) 

Threshold at 

7.5 ug 

7% 

$3,000 

($660 – $6,100) 

$74 

($16 – 

$150) 

$1,600 

($350 – $3,300) 

$550 

($120 – $1,100) 

$2,700 

($610 – $5,700) 

$9,100 

($2,000 – 

$19,000) 

$7,300 

($1,600 – 

$15,000) 

3% 

$2,100 

($470 – $4,400) 

$97 

($22 – 

$200) 

$440 

($99 – 

$920) 

$87 

($19 – 

$180) 

$3,000 

($670 – $6,200) 

$9,000 

($2,000 – 

$19,000) 

$5,500 

($1,200 – 

$12,000) 

Threshold at 

10 ug 

7% 

$1,800 

($390 – $3,700) 

$82 

($18 – 

$170) 

$370 

($83 – 

$770) 

$73 

($16 – 

$150) 

$2,500 

($560 – $5,200) 

$7,600 

($1,700 – 

$16,000) 

$4,700 

($1,000 – 

$9,700) 

3% 

$220 

($49 – 

$460) 

$10 

($2 – $21) 

$67 

($15 – 

$140) 

$3 

($1 – $6) 

$2,500 

($560 – $5,200) 

$7,200 

($1,600 – 

$15,000) 

$2,800 

($620 – $5,800) 

Threshold at 

12 ug 

7% 

$190 

($42 – 

$390) 

$9 

($2 – $18) 

$57 

($13 – 

$120) 

$2 

($1 – $5) 

$2,100 

($470 – $4,400) 

$6,100 

($1,400 – 

$13,000) 

$2,400 

($520 – $4,900) 

3% 

$59 

($13 – 

$120) 

$12 

($3 – $24) 

$50 

($11 – 

$100) 

$0 

($0 – $0) 

$2,200 

($500 – $4,700) 

$2,500 

($560 – $5,200) 

$2,400 

($520 – $4,900) 

More Certain that Benefits Are at Least as Large 

Threshold at 

14 ug 

7% 

$49 

($11 – 

$100) 

$10 

($2 – $20) 

$42 

($9 – $87) 

$0 

($0 – $0) 

$1,900 

($420 – $3,900) 

$2,100 

($470 – $4,400) 

$2,000 

($440 – $4,100) 

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. 
Table 5-31: Sensitivty of Monetized Beneifts of Reductions in Mortality Risk to Assumed Thresholds for 14/35 Sce

al., 2002 Effect Estimate with Slope Adjustment for Thresholds Above 7.5 ug) 90th Percentile Confidence Intervals P

Parentheses a

 Millions of 1999$  Eastern U.S. 

Western U.S. Excluding 

CA California 

Total N

Atta

Level of 

Assumed 

Threshold 

Discount 

Rate 

Modeled 

Partial 

Attainment 

Residual 

Attainment 

Modeled 

Partial 

Attainment 

Residual 

Attainment 

Modeled 

Partial 

Attainment 

Residual 

Attainment 

Modeled 

Partial 

Attainment 

3% 

$22,000 

($4,800 – 

$45,000) 

$390 

($86 – 

$800) 

$2,900 

($640 – $6,000) 

$780 

($170 – $1,600) 

$3,200 

($720 – $6,700) 

$11,000 

($2,500 – 

$24,000) 

$28,000 

($6,200 – 

$58,000) 

Less Certain that Benefits Are at Least as Large No 

Threshold 

7% 

$18,000 

($4,000 – 

$38,000) 

$320 

($72 – 

$670) 

$2,400 

($540 – $5,000) 

$660 

($150 – $1,400) 

$2,700 

($610 – $5,700) 

$9,600 

($2,100 – 

$20,000) 

$23,000 

($5,200 – 

$48,000) 

3% 

$20,000 

($4,500 – 

$42,000) 

$390 

($86 – 

$800) 

$1,800 

($410 – $3,800) 

$650 

($140 – $1,300) 

$3,200 

($710 – $6,600) 

$11,000 

($2,500 – 

$24,000) 

$25,000 

($5,700 – 

$53,000) 

Threshold at 

7.5 ug 

7% 

$17,000 

($3,800 – 

$36,000) 

$320 

($72 – 

$670) 

$1,600 

($350 – $3,200) 

$550 

($120 – $1,100) 

$2,700 

($590 – $5,500) 

$9,600 

($2,100 – 

$20,000) 

$21,000 

($4,800 – 

$44,000) 

3% 

$12,000 

($2,700 – 

$25,000) 

$430 

($96 – 

$900) 

$450 

($100 – 

$930) 

$87 

($19 – 

$180) 

$2,900 

($650 – $6,000) 

$9,500 

($2,100 – 

$20,000) 

$15,000 

($3,400 – 

$32,000) 

Threshold at 

10 ug 

7% 

$10,000 

($2,300 – 

$21,000) 

$360 

($81 – 

$760) 

$380 

($84 – 

$780) 

$73 

($16 – 

$150) 

$2,400 

($540 – $5,100) 

$8,000 

($1,800 – 

$17,000) 

$13,000 

($2,900 – 

$27,000) 

3% 

$1,300 

($290 – $2,700) 

$350 

($79 – 

$740) 

$68 

($15 – 

$140) 

$3 

($1 – $6) 

$2,400 

($540 – $5,000) 

$7,800 

($1,700 – 

$16,000) 

$3,800 

($840 – $7,800) 

Threshold at 

12 ug 

7% 

$1,100 

($240 – $2,200) 

$300 

($66 – 

$620) 

$57 

($13 – 

$120) 

$2 

($1 – $5) 

$2,000 

($450 – $4,200) 

$6,600 

($1,500 – 

$14,000) 

$3,200 

($700 – $6,600) 

3% 

$310 

($69 – 

$650) 

$250 

($56 – 

$520) 

$50 

($11 – 

$100) 

$0 

($0 – $0) 

$2,100 

($480 – $4,500) 

$2,800 

($620 – $5,800) 

$2,500 

($560 – $5,200) 

More Certain that Benefits Are at Least as Large 

Threshold at 

14 ug 

7% 

$260 

($58 – 

$550) 

$210 

($47 – 

$440) 

$42 

($9 – $87) 

$0 

($0 – $0) 

$1,800 

($400 – $3,700) 

$2,400 

($520 – $4,900) 

$2,100 

($470 – $4,400) 

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns. 
5-85 

We provide likelihood distributions both for the total dollar benefits estimate and for the 

incidence of premature mortality to show the uncertainty described by each expert’s judgment as 

well as the range of uncertainty associated with the standard errors in the Pope et al. (2002) amd 

Laden et al (2006) studies. The uncertainty about the total dollar benefit associated with any 

single endpoint combines the uncertainties from two sources—the C-R relationship and the 

valuation—and is estimated with a Monte Carlo method.26 Our estimates of the likelihood 

distributions for total benefits should be viewed within the context of the wide range of sources 

of uncertainty that we have not incorporated, including uncertainty in emissions, air quality, and 

baseline health effect incidence rates. 

We are unable at this time to characterize the uncertainty in the estimate of benefits of 

improvements in visibility at Class I areas. As such, we treat the visibility benefits as fixed and 

add them to all percentiles of the health benefits distribution. 

Given this unequal treatment of endpoints, it is likely that these distributions do not capture the 

full range of benefits, and in fact are likely to understate the uncertainty, especially on the high 

end of the range due to omission of potentially significant benefit categories. 

Following these tables, we also provide a more comprehensive graphical presentation of the 

distributions of benefits generated using the available information from empirical studies and 

expert elicitation. Not all known PM-related health and welfare effects could be quantified or 

monetized. The monetized value of these unquantified effects is represented by adding an 

unknown “B” to the aggregate total. The estimate of total monetized health benefits is thus equal 

to the subset of monetized PM-related health 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. Based on the 

full range of expert elicitation results, the range of mean estimates across the full set of mortality 

effect estimates projects that attainment of the final standards of 15/35 will result in 1,200 to 

13,000 avoided premature deaths annually in 2020 incremental to the 15/65 attainment strategy, 

and that an attainment strategy for the more stringent 14 μg/m3

 annual standard would result in 

2,200 to 24,000 avoided premature deaths incremental to the 15/65 attainment strategy with 

1,000 to 11,000 avoided premature deaths incremental to attainment of the final 15/35 standards. 

The threshold sensitivity analysis shows that mortality impacts are fairly sensitive to assumed 

thresholds, especially in the Western U.S. (excluding CA), where annual average concentrations 

are low relative to California and the Eastern U.S. For the 15/35 attainment scenario, in the 

West, the assumption of a 10 μg/m3 threshold leads to a reduction in estimated incidence of 

mortality of almost 85 percent compared with the no threshold case. In the East, impacts of the 

10 μg/m3 threshold are smaller, but still significant, with a reduction of over 40 percent. In 

California, where annual mean levels are generally quite high, the impact of the 10 μg/m3 

threshold is small, with a reduction of only 10 percent. Nationwide, the average impact of the 10 

 

26 In each iteration of the Monte Carlo procedure, a value is randomly drawn from the incidence distribution, and a 

value is randomly drawn from the unit dollar value distribution. The total dollar benefit for that iteration is the 

product of the two. If this is repeated for many (e.g., thousands of) iterations, the distribution of total dollar benefits 

associated with the endpoint is generated. 
5-86 

μg/m3 threshold is a reduction in premature mortality incidence of approximately 32 percent. 

Threshold impacts are similar for the 14/35 attainment scenario. 

Including the expert elicitation results, the estimated range of total incremental monetized 

benefits in 2020 for the final rule is $9 to $75 billion using a 3% discount rate and $8 to $64 

billion using a 7% discount rate. Health benefits account for 97% of total benefits, in part 

because we are unable to quantify most of the nonhealth benefits. These unquantified benefits 

may be substantial, although the magnitude of these benefits is highly uncertain. The monetized 

benefit associated with reductions in the risk of premature mortality, which accounts for $6.8 to 

74 billion in 2020 is between 80 to 99 percent of total monetized health benefits, depending on 

the source of the mortality impact function. The next largest benefit is for reductions in chronic 

illness (CB and nonfatal heart attacks), although this value is in some cases more than an order of 

magnitude lower than for premature mortality. Hospital admissions for respiratory and 

cardiovascular causes, visibility, MRADs, and work loss days account for the majority of the 

remaining benefits. The remaining categories each account for a small percentage of total 

benefit; however, they represent a large number of avoided incidences affecting many 

individuals. A comparison of the incidence table to the monetary benefits table reveals that there 

is not always a close correspondence between the number of incidences avoided for a given 

endpoint and the monetary value associated with that endpoint. For example, there are almost 

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 Tables 5-20 

through 5-27. 

In addition to unquantified and unmonetized health benefit categories, Table 5-2 shows a number 

of welfare benefit categories that are omitted from the monetized benefit estimates for this rule. 

Only a subset of the expected visibility benefits-those for Class I areas in the southeastern and 

southwestern (including California) United States are included in the monetary benefits estimates 

we project for this rule. We believe the benefits associated with these non-health benefit 

categories are likely significant. For example, we are able to quantify significant visibility 

improvements in Class I areas in the Northeast and Midwest, but are unable at present to place a 

monetary value on these improvements. Similarly, we anticipate improvement in visibility in 

urban areas for which we are currently unable to monetize benefits. For the Class I areas in the 

southeastern and southwestern U.S., we estimate annual incremental benefits of $530 million for 

visibility improvements due to the 15/35 modeled attainment strategy, and $1,200 million for 

visibility improvements due to the 14/35 modeled attainment strategy. The value of visibility 

benefits in areas where we were unable to monetize benefits could also be substantial (see 

Appendix J). 

Figures 5-10 and 5-11 presents box plots of the distributions of the reduction in PM2.5-related 

premature mortality based on the C-R distributions provided by each expert, as well as that from 

the data-derived health impact functions, based on the statistical error associated with Pope et al. 

(2002) and Laden et al. (2006). 
5-87 

The distributions are depicted as box plots with the diamond symbol (—) showing the mean, the 

dash (–) showing the median (50th percentile), the box defining the interquartile range (bounded 

by the 25th and 75th percentiles), and the whiskers defining the 90% confidence interval 

(bounded by the 5th and 95th percentiles of the distribution). 
1,200

7,000

7,900

5,500

4,500

7,400

12,700

5,500

8,400 8,100

10,100

5,700

2,500

0

5,000

10,00015,00020,00025,00030,00035,00040,000

Pope et

al

(2002)

Laden

et al

(2006)

Expert

A

Expert

B

Expert

C

Expert

D

Expert

E

Expert

F

Expert

G

Expert

H

Expert 

I

Expert

J

Expert

K

E

Reduction in Premature Mortality in 2020 . 

Note: Distributions labeled Expert A - Expert L are based on individual expert responses. The distributions labeled Pope et al. (2002) and Laden et al (200based on the means and standard errors of the C-R functions from the studies. The red dotted lines enclose a range bounded by the means of the two datdistributions.

Distributions Derived from 

Epidemiology 

Studies

Distributions Based on Expert Elicitation

Figure 5-10. Results of Application of Expert Elicitation: Annual Reductions in Premature Mortality in 2020 A

Illustrative Strategies to Attain 15/35, Incremental to Attainment of the 1997 Standards 
2,200

13,000

14,600

10,300

8,300

13,700

23,700

10,300

15,600 15,100

18,900

9,900

4,400

0

5,000

10,00015,00020,00025,00030,00035,00040,000

Pope et

al

(2002)

Laden

et al

(2006)

Expert

A

Expert

B

Expert

C

Expert

D

Expert

E

Expert

F

Expert

G

Expert

H

Expert 

I

Expert

J

Expert

K

Expe

L

Reduction in Premature Mortality in 2020 . 

Note: Distributions labeled Expert A - Expert L are based on individual expert responses. The distributions labeled Pope et al. (2002) and Laden et al (2006) arbased on the means and standard errors of the C-R functions from the studies. The red dotted lines enclose a range bounded by the means of the two data-derdistributions.

Distributions Derived from 

Epidemiology 

Studies

Distributions Based on Expert Elicitation

Figure 5-11. Results of Application of Expert Elicitation: Annual Reductions in Premature Mortality in 2020 A

Illustrative Strategies to Attain 14/35, Incremental to Attainment of the 1997 Standards
5-90 

For the 15/35 attainment strategy, the data-derived estimates based on Pope et al. (2002) and 

Laden et al. (2006) show that the mean predicted number of premature deaths avoided in 2020 

ranges from 2,500 to 5,700. The lower end of this range is higher than one of the experts and the 

upper end of this range is lower than seven experts. The range falls within the uncertainty 

bounds of all but two experts.. The figure shows that the average annual number of premature 

deaths avoided in 2020 ranges from approximately 1,200 (based on the judgments of Expert K) 

to 12,700 (based on the judgments of Expert E). The medians span zero to 12,700, with the zero 

value due to the low probability of a causal relationship associated with one of the expert’s 

distributions. 

For the 14/35 attainment strategy, the data-derived estimates based on Pope et al. (2002) and 

Laden et al. (2006) show that the mean predicted number of premature deaths avoided in 2020 

ranges from 4,400 to 9,900. The lower end of this range is higher than one of the experts and the 

upper end of this range is lower than seven experts. The range falls within the uncertainty 

bounds of all but two experts. The figure shows that the average annual number of premature 

deaths avoided in 2020 ranges from approximately 2,200 (based on the judgments of Expert K) 

to 23,700 (based on the judgments of Expert E). The medians span zero to 23,700, with the zero 

value due to the low probability of a causal relationship associated with one of the expert’s 

distributions. The statistical uncertainty bounds of all of the estimates, including the dataderived distributions, overlap. Although the uncertainty bounds for some experts include zero, 

and some distributions have significant percentiles at zero, all of the distributions have a positive 

mean estimate. 

The statistical uncertainty bounds of all of the estimates, including the data-derived distributions, 

overlap. Although the uncertainty bounds for some experts include zero, and some distributions 

have significant percentiles at zero, all of the distributions have a positive mean estimate. 

Figure 5-12 and 5-13 present box plots of the distributions of monetized benefits of reductions in 

premature mortality associated with use of the Pope et al. (2002), Laden et al. (2006), and expertjudgment based mortality incidence distributions. For the 15/35 attainment strategy (Figure 5-

12), the data-derived estimates based on Pope et al. (2002) and Laden et al. (2006) show that the 

mean annual benefit ranges from $17 billion to $35 billion. Mean annual benefits for each 

expert range from approximately $9 billion (based on judgments of Expert K) to $75 billion 

(based on the judgments of Expert E). For the 14/34 attainment strategy (Figure 5-13), the dataderived estimates range from $30 billion to $62 billion. Mean annual benefits from the expert 

elicitation range from $17 billion (Expert K) to $140 billion (Expert E). As with the mortality 

incidence estimates, with the exception of Expert K, all of the expert based distributions have 

means greater than the Pope et al (2002) result, and 10 of the 12 expert based results are greater 

than or equal to the Laden et al (2006) results.
$76,000

$34,000

$51,000 $49,000

$61,000

$35,000

$17,000

$45,000

$28,000

$34,000

$48,000

$42,000

$9,

$0

$50,000

$100,000$150,000$200,000$250,000$300,000

Pope et

al

(2002)

Laden

et al

(2006)

Expert

A

Expert

B

Expert

C

Expert

D

Expert

E

Expert

F

Expert

G

Expert

H

Expert 

I

Expert

J

Expert

K

Millions of $1999 .

Note: All non-mortality distributions are based on classical statistical error derived from the standard errors reported in epidemiology studies and distributions of uniempirical data. Visibility benefits are included as a constant. Mortality distributions labeled Expert A - Expert L are based on individual expert responses. The mortlabeled Pope et al. (2002) and Laden et al (2006) are based on the means and standard errors of the C-R functions from the studies. Dollar benefits have been adjuaccount for growth in real income out to 2020.The red dotted lines enclose a range bounded by the means of the two data-derived distributions.

Distributions Derived from 

Epidemiology 

Studies

Distributions Based on Expert Elicitation Results

Figure 5-12. Results of Probabilistic Uncertainty Analysis: Dollar Value of Health and Welfare Impacts Associ

Strategies to Attain 15/35 (Full attainment), Incremental to Attainment of the 1997 Standards 
$140,000

$64,000

$94,000 $91,000

$110,000

$62,000

$30,000

$84,000

$52,000

$63,000

$89,000

$79,000

$17,0

$0

$50,000

$100,000$150,000$200,000$250,000$300,000

Pope et

al

(2002)

Laden

et al

(2006)

Expert

A

Expert

B

Expert

C

Expert

D

Expert

E

Expert

F

Expert

G

Expert

H

Expert 

I

Expert

J

Expert

K

E

Millions of $1999 .

Note: All non-mortality distributions are based on classical statistical error derived from the standard errors reported in epidemiology studies and distributions of unit von empirical data. Visibility benefits are included as a constant. Mortality distributions labeled Expert A - Expert L are based on individual expert responses. The mortdistributions labeled Pope et al. (2002) and Laden et al (2006) are based on the means and standard errors of the C-R functions from the studies. Dollar benefits have 

adjusted upwards to account for growth in real income out to 2020.The red dotted lines enclose a range bounded by the means of the two data-derived distributions.

Distributions Derived from 

Epidemiology 

Studies

Distributions Based on Expert Elicitation Results

Figure 5-13. Results of Probabilistic Uncertainty Analysis: Dollar Value of Health and Welfare Impacts Associ

Strategies to Attain 14/35 (Full attainment), Incremental to Attainment of the 1997 Standards
5-93 

These distributions can also be displayed in terms of cumulative distribution functions. The 

cumulative distributions of monetized benefits are provided in Figures 5-14 and 5-15 for the 

15/35 and 14/35 attainment scenarios, respectively. 
0

0.10.20.30.40.50.60.70.80.9

1

$0 $50,000 $100,000 $150,000 $200,000 $250,000 $300,000 

Million ($1999)

Cumulative Probability .

Expert A Expert B Expert C Expert DExpert E Expert F Expert G Expert HExpert I Expert J Expert K Expert LPope et al (2002) Laden et al (2006)

Note: All non-mortality distributions are based on classical statistical error derived from the standard errors reported in epidemiology studies and distribuof unit values based on empirical data. Visibility benare included as a constant. Mortality distributions labeled Expert A - Expert L are based on individual expert responses. The distributions labeled Pope e(2002) and Laden et al (2006) are based on the meand standard errors of the C-R function from the stuDollar benefits have been adjusted upwards to accofor growth in real income out to 2020.

Figure 5-14. Results of Probabilistic Uncertainty Analysis: Cumulative Distributions of Dollar Value of Health 

Associated with Illustrative Strategies to Attain 15/35, Incremental to Attainment of the 1997 Standards 
0

0.10.20.30.40.50.60.70.80.9

1

$0 $50,000 $100,000 $150,000 $200,000 $250,000 $300,000 

Million ($1999)

Cumulative Probability .

Expert A Expert B Expert C Expert DExpert E Expert F Expert G Expert HExpert I Expert J Expert K Expert LPope et al (2002) Laden et al (2006)

Note: All non-mortality distributions are based on classical statistical error derived from the standarerrors reported in epidemiology studies and distriof unit values based on empirical data. Visibility bare included as a constant. Mortality distributions labeled Expert A - Expert L are based on individuexpert responses. The distributions labeled Pope (2002) and Laden et al (2006) are based on the mand standard errors of the C-R function from the Dollar benefits have been adjusted upwards to acfor growth in real income out to 2020.

Figure 5-15. Results of Probabilistic Uncertainty Analysis: Cumulative Distributions of Dollar Value of Health 

Associated with Illustrative Strategies to Attain 14/35, Incremental to Attainment of the 1997 Standards
5-96 

5.2.2 Benefits by Major PM Component 

In order to better understand the sources of the benefits associated with PM attainment strategies, 

we provide a breakout of benefits by the major PM component species, including ammonium 

sulfate, ammonium nitrate, elemental carbon, organic carbon, and crustal material. This is 

accomplished by apportioning total benefits based on the proportion of the population weighted 

change in total PM2.5 accounted for by each component species. This is not exact, but provides a 

reasonable approximation of the proportion of benefits associated with each species. 

Figure 5-16 shows the proportion of total benefits associated with each species for the nation as a 

whole for the partial attainment scenarios for 15/35 and 14/35. It is not possible to accurately 

assess the composition of benefits for the full attainment scenario, due to the unknown 

composition of controls that might be used to reach full attainment in California and Salt Lake 

City. In the Eastern U.S., we have demonstrated that it is possible to reach full attainment using 

only direct PM controls, and as such, all of the full attainment benefits in that region can be 

assigned to the direct PM related species, including elemental carbon, organic carbon, and crustal 

materials. 

Tables 5-32 and 5-33 provide the total benefits broken out by species for the nation for the 15/35 

and 14/35 partial attainment scenarios. 
5-97 

Figure 5-16. Proportion of Population-weighted Reduction in Ambient Annual PM2.5 

Associated with PM2.5 Components for Modeled Attainment Strategies

Ammonium Sulfate

23%

Ammonium Nitrate

19%

Crustal

12%

Elemental Carbon

5%

Organic Carbon

41%

15/35 Attainment Strategy

Ammonium Sulfate

74%

Ammonium Nitrate

6%

Crustal

4%

Elemental Carbon

2%

Organic Carbon

14%

14/35 Attainment Strategy

Ammonium Sulfate

23%

Ammonium Nitrate

19%

Crustal

12%

Elemental Carbon

5%

Organic Carbon

41%

15/35 Attainment Strategy

Ammonium Sulfate

74%

Ammonium Nitrate

6%

Crustal

4%

Elemental Carbon

2%

Organic Carbon

14%

14/35 Attainment Strategy
Table 5-32: Apportionment of Monetized Health Benefits of Modeled Attainment Strategies to PM Component SpDiscount Rate* 

15/35 Modeled Attainment Strategy 14/35 Modeled Attainment Stra

Ammonium 

Sulfate 

Ammonium 

Nitrate Crustal 

Elemental 

Carbon 

Organic 

Carbon 

Ammonium 

Sulfate 

Ammonium 

Nitrate Crustal 

Elem

Ca

Percent of Monetized Benefits 23.5% 18.5% 12.0% 5.2% 40.7% 73.2% 6.4% 4.1% 1.Apportioned Benefits: Source of Mortality Effect Estimate Data Derived ACS Studya $1,500 $1,100 $700 $300 $2,500 $12,600 $1,100 $700 $3Harvard Six-City Studyb $3,100 $2,400 $1,600 $700 $5,400 $26,800 $2,300 $1,500 $7Expert Elicitation Derived Expert A $6,400 $5,100 $3,300 $1,400 $11,200 $56,400 $4,900 $3,100 $1,Expert B $5,200 $4,100 $2,600 $1,200 $9,000 $45,200 $3,900 $2,500 $1,Expert C $5,400 $4,200 $2,700 $1,200 $9,300 $46,900 $4,100 $2,600 $1,Expert D $3,600 $2,800 $1,800 $800 $6,200 $31,200 $2,700 $1,700 $8Expert E $8,100 $6,400 $4,100 $1,800 $14,000 $70,500 $6,100 $3,900 $1,Expert F $4,700 $3,700 $2,400 $1,100 $8,200 $41,300 $3,600 $2,300 $1,Expert G $2,900 $2,300 $1,500 $700 $5,100 $25,600 $2,200 $1,400 $7Expert H $3,600 $2,800 $1,800 $800 $6,200 $31,200 $2,700 $1,700 $8Expert I $5,000 $4,000 $2,600 $1,100 $8,700 $44,000 $3,800 $2,500 $1,Expert J $4,500 $3,500 $2,300 $1,000 $7,800 $39,100 $3,400 $2,200 $1,Expert K $900 $700 $500 $200 $1,500 $7,600 $700 $400 $2Expert L $3,500 $2,800 $1,800 $800 $6,100 $30,900 $2,700 $1,700 $8

• Does not include residual benefits of full attainment in areas that were not modeled to attain using illustrative control strategies. 

a The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), whi

as the primary estimate in recent RIAs 

b Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha

estimate.
Table 5-33: Apportionment of Monetized Health Benefits of Modeled Attainment Strategies to PM Component SpeDiscount Rate* 

15/35 Modeled Attainment Strategy 14/35 Modeled Attainment Stra

Ammonium 

Sulfate 

Ammonium 

Nitrate Crustal 

Elemental 

Carbon 

Organic 

Carbon 

Ammonium 

Sulfate 

Ammonium 

Nitrate Crustal 

Elem

Ca

Percent of Monetized Benefits 23.5% 18.5% 12.0% 5.2% 40.7% 73.2% 6.4% 4.1% 1.Apportioned Benefits: Source of Mortality Effect Estimate Data Derived ACS Studya $1,200 $1,000 $600 $300 $2,200 $10,800 $900 $600 $3Harvard Six-City Studyb $2,600 $2,100 $1,300 $600 $4,500 $22,800 $2,000 $1,300 $6Expert Elicitation Derived Expert A $5,400 $4,300 $2,800 $1,200 $9,400 $47,600 $4,100 $2,700 $1,Expert B $4,400 $3,400 $2,200 $1,000 $7,600 $38,200 $3,300 $2,100 $1,Expert C $4,500 $3,600 $2,300 $1,000 $7,900 $39,600 $3,400 $2,200 $1,Expert D $3,000 $2,400 $1,600 $700 $5,300 $26,500 $2,300 $1,500 $7Expert E $6,800 $5,400 $3,500 $1,500 $11,800 $59,500 $5,200 $3,300 $1,Expert F $4,000 $3,200 $2,100 $900 $6,900 $34,900 $3,000 $1,900 $9Expert G $2,500 $2,000 $1,300 $600 $4,300 $21,700 $1,900 $1,200 $6Expert H $3,000 $2,400 $1,600 $700 $5,200 $26,400 $2,300 $1,500 $7Expert I $4,300 $3,400 $2,200 $1,000 $7,400 $37,200 $3,200 $2,100 $1,Expert J $3,800 $3,000 $1,900 $800 $6,600 $33,100 $2,900 $1,800 $9Expert K $800 $600 $400 $200 $1,300 $6,600 $600 $400 $2Expert L $3,000 $2,400 $1,500 $700 $5,200 $26,200 $2,300 $1,500 $7

* Does not include residual benefits of full attainment in areas that were not modeled to attain using illustrative control st

a The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), whi

as the primary estimate in recent RIAs 

b Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what ha

estimate.
5-100 

As discussed in previous chapters, the 15/35 attainment strategy focused more on local controls 

of direct PM compared to the attainment strategy for 14/35. As such, the proportion of benefits 

accounted for by carbon and crustal components is much greater. Because the 14/35 strategy 

included a significant regional reduction in EGU and non-EGU SO2 in the Eastern U.S., the 

benefits for the 14/35 strategy are much more heavily comprised of sulfate reductions. In both 

cases, elemental carbon contributes only a small fraction of the benefits. 

5.3 Discussion 

This analysis has estimated the health and welfare benefits of reductions in ambient 

concentrations of particulate matter resulting from a set of illustrative control strategies to reduce 

emissions of PM2.5 precursors. The result suggests there will be significant additional health and 

welfare benefits arising from reducing emissions from a variety of sources in and around 

projected nonattaining counties in 2020. While 2020 is the expected date that states would need 

to demonstrate attainment with the revised standards, it is expected that benefits (and costs) will 

begin occurring much earlier, as states begin implementing control measures to show reasonable 

progress towards attainment. Using the full range of benefits (including the results of the expert 

elicitation), our estimate that between 1,200 and 13,000 additional premature mortalities would 

be avoided annually when the emissions reductions from implementing the new standards are 

fully realized provides additional evidence of the important role that implementation of the 

standards plays in reducing the health risks associated with exceeding the standards. 

There are several important factors to consider when evaluating the relative benefits of the 

attainment strategies for the revised 15/35 and more stringent 14/35 standards. First, California 

accounts for a large share of the total benefits for both of the evaluated standards. As noted in 

this and other chapters, California presented a unique challenge for modeling attainment with the 

standards because of the severe nature of the air quality problem and difficulties in modeling the 

impacts of emissions controls on air quality. Because we were only able to model a small 

fraction of the emissions controls that might be needed to reach attainment in California, the 

proportion of California benefits in the “residual attainment” category are large relative to other 

areas of the U.S. These benefits are likely to be more uncertain than the modeled benefits, and 

they are likely to understate the actual benefits of attainment strategies, because we applied an 

estimation approach that reduced concentrations only at the specific violating monitors and not 

surrounding monitors that did not violate the standards. The magnitude of this underestimate is 

unknown. 

Another important factor to note is the geographic scope of the controls applied in the two 

illustrative attainment strategies. Comparing the benefits of the two attainment strategies, it is 

clear that the incremental impact of the attainment strategy for the tighter annual standard is to 

almost double the total benefits. This should not be construed to indicate that tightening the 

annual standard by one microgram is equivalent to tightening the daily standard by thirty 

micrograms. Much of the difference in benefits is due to the regional nature of the illustrative 

control strategy evaluated for the tighter annual standard. Because a regional SO2 program for 

EGU and nonEGU sources was evaluated, this resulted in much more widespread reductions in 

ambient PM2.5 concentrations relative to the more localized emissions reductions programs 

evaluated for the 15/35 attainment strategy. Depending on the types and locations of controls 
5-101 

selected by states to reach attainment, benefits of attaining either the revised or alternative 

standards can vary greatly from our projections. 

As noted above, there continues to be scientific uncertainty about the specific toxicity of 

different components of overall PM2.5 mass. This issue is an active area of research for EPA. 

The Agency is exploring ways to estimate the importance of this assumption on the certainty of 

human health benefits and its implications for control strategy development and assessment. The 

agency has recently conducted an exploratory sensitivity analysis including this factor among a 

number of other potentially important input parameters. The preliminary findings of this 

analysis can be found in the draft report at located in the PM NAAQS RIA docket. 

While EPA has not performed formal sensitivity analysis of the assumption of equal toxicity for 

this RIA, we can, nonetheless, suggest that in the face of uncertainties regarding differential 

toxicity, strategies that reduce a wide array of types of PM and precursor emissions will have 

more certain health benefits than strategies that are more narrowly focused. The illustrative 

attainment strategy for the revised standards results in a balanced mix of reductions in different 

PM2.5 components, suggesting it may be a more robust strategy than one that achieves 

reductions in only one component Until a more robust scientific basis exists for making reliable 

judgments about the relative toxicity of PM, it will not be possible to determine whether the 

strategy of reducing a wide array of PM types is the optimal approach. 

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 benefits estimates presented here can be useful 

information in expanding the understanding of the public health impacts of reducing PM2.5

precursor emissions. 

EPA will continue to evaluate new methods and models and select those most appropriate for 

estimating 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, high blood pressure, and adverse birth 

outcomes (such as low birth weight) 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, toxicologists, and economists 

should result in a more tightly integrated analytical framework for measuring health benefits of 

air pollution policies. 

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