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 i llustrative implementation strategies to attain alternative NAAQS for fine particulate matter (PM 2.5 

### ) 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 we lfare effects of changes in ambient air quality resulting from reductions in precursors to particulate matter (PM) including directly emitted carbonaceous particles, NO

x 

### , SO 2 

### , and NH 3 

### emissions? and (2) what is the estimated monetary value of the changes in these e ffects attributable to the revise d standards and a more stringent

alternative annual standa rd? This benefit analysis consti tutes one part of EPA’s thorough examination of the relative merits of this regulation.

The analysis presented in this chapter uses a methodology genera lly 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 uncertain ty associated with that estimate, the current analysis

follows the recommendation of NRC’s 2002 report “Estimating the Public Health Benefits of Proposed Air Pollution Regula tions” to begin moving the assessment of uncertainties from its ancillary analyses in to its main benefits presentation through the conduct of probab ilistic analyses.

2. Since the publication of CA IR, we have completed a full-scale expert elicitation designed to more fully characterize th e state of our understanding of the concentration-response function for PM-relate d premature mortality. The elicitation results form a major component of the curr ent 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 projec tions 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 ba se premature mortality benefits estimates are based on an assumed cutpoint in the long-term mortality concentration-response function at 10 μg/m 3 

### , and an assumed cutpoint in the short-term morbidity concentration-response functions at 10 μg/m 3 

### . We also show the results of a sensitivity analysis for premature mortalit y, with 4 alternative cutpoints, at 3 μg/m 3



### , 7.5 μg/m 3 

### , 12 μg/m 3 

### , and 14 μg/m 3



### . The benefits analysis takes as inputs the result s of the CMAQ air quality modeling described in Chapter 4. Reductions in certain PM 2.5 

### precursors such as NO

x 

### and VOC may also lead to changes in ambient concentrations of ozone. Thes e changes in ozone will also have health and5-2 

### welfare effects. However, for this RIA, becau se the majority of the illustrative strategies

evaluated do not affect NO

x 

### and VOC emissions (with the exce ption of nonattainment areas in parts of the western U.S., where we do not curren tly have adequate modeling data for ozone), we focus on estimating the health and welfare eff ects associated with changes in ambient PM 2.5



### . This adds some uncertainty to the overall result s, 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 PM 2.5



### . Potential human health effects associated with PM 2.5 

### range from premature mortality to morbidity effects linked to long-term (chroni c) and shorter-term ( acute) exposures (e.g., respiratory and cardiovascular symptoms resultin g in hospital admissions, asthma exacerbations, and acute and chronic bronchitis [CB]). Welf are 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. Alt hough methods exist for qua ntifying the benefits associated with many of these human health and welfare categories, not a ll can be evaluated at

this time because of limitations in methods a nd/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 an d alternative more stri ngent14/35 standards in 2020, when the standards are expected to be fully attained. Table 5-2 lists the full complement

of human health and welfare eff ects associated with PM (and its precursors) and identifies those effects that are quantified for the primary estima te 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 inve ntories for the emission species expected to affect ambient air quality, we use sophist icated 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 concentratio ns 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 ot her air quality statistics that are necessary

to estimate welfare effects.5-3 

### Table 5-1: Estimated Annual Monetized Benefi ts in 2020 of Illustrative Implementation Strategies for the Selected and Alternative PM 2.5 

### NAAQS, Incremental to Attainment of

the Current Standards

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

indicated in this table.

Total Full Attainment Benefits a, b (billions 1999$)

15/35 (μg/m3) 14/35 (μg/m3)

Based on Mortality Function from American Cancer Soci ety and Morbidity Functions from Epidemiology Literature c

$17 $30

Confidence Intervals Confidence Intervals Using a 3% discount rate ($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 ac ross 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 rang e illustrated by arraying the highest and lowest expert means. Likewise the 5th and 95th percentiles for these hi ghest 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 pr imary estimate in recent RIAs.5-4 

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

functions 1 

### to generate changes in the incidence of health effects, or changes in other exposure metrics are input to dose-response f unctions to generate changes in welfare

effects. Because these estimates contain uncertainty, we characterize the benefits estimates probabilistically when appr opriate information is available.

4. The resulting effects changes are then assigned monetary values, taking into account adjustments to values for growth in real inco me 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 welf are 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 represen t the estimates based upon illustrative attainment strategies for the final PM 2.5 

### standards (and an alternative set of more stringent standards). As explained in earlier chapters, we designed illustrative sets of c ontrols in and around areas that

need additional emission reductions to reach th e new standards in 2020. These strategies are evaluated after application of ex isting federal (such as CAIR), st ate, and local programs. As noted in earlier chapters, benef its (and costs) for the final PM 2.5 

### standards are evaluated incrementally relative to an illustrative scen ario of full attainment with the current PM 2.5 

### standards (15 μg/m 3 

### annual mean and 65 μg/m 3 

### daily 98 th 

### 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 regi on, 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 im pact 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 incide nce of the health effect and ambient pollution. We refer to the specific

value of the relative risk or estimate d coefficients in the epid emiological 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

PM 2.5 Standards a

Pollutant/Effect Quantified and Monet ized Effects Unquantified Effects

PM/Health b Premature mortality based on cohort study estimates c

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 em ergency 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

SO 2 /Health Hospital admissions for respiratory and cardiac

diseases Respiratory symptoms in asthmatics

NO
x /Health Lung irritation Lowered resistance to respiratory infection Hospital admissions for respiratory and cardiac

diseases

a Reductions in certain PM 2.5 precursors such as NO

x 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 illustrativ e strategies evaluated do not affect NO

x and VOC emissions, we focus on estimating the health and welfare effect s associated with changes in ambient PM 2.5 . 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 econom ic endpoints, there are a number of biological responses that have been associated with PM health effects including morphological changes and altered ho st defense mechanisms. The public health impact of these biological responses may be partly represented by our quantified endpoints.

c Cohort estimates are designed to ex amine the effects of long term exposur es to ambient pollu tion, 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 pr emature 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 ab le 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 standard s in residual nonattainment areas. To reflect different levels of confidence in the underlying data and models, bene fits will be presented as two components, representing the fully modeled pa rtial attainment component (referred to from this point forward at “modeled partial attainment”, and the extrapol ated residual attainment component (referred to from this point forward as “residual attainment”).

EPA is currently developing a comprehensive integr ated 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-re lated 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 func tions for this benefits analysis. The results of the ‘pilot’ for this expert elicitation were pres ented in RIAs for both the Nonroad Diesel and Clean Air Interstate Rules (U.S. EPA, 2004a, 2005) . The results of thes e elicitation projects, including peer review comments, ar e available on EPA’s Web site, at http://www.epa.gov/ttn/ecas/. In addition, similar to our approach in th e Nonroad Diesel and CAIR RIAs, we present a distri bution of benefits estimates ba sed 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 m odel, as discussed below). Use of the expert elicitation and incorpora tion of the standard errors appr oaches provide insights into the

likelihood of different outcomes and about the state of knowledge regarding the benefits estimates. Both approaches have different strengths and weaknesse s that are summarized later in this chapter.

The benefits estimates generated for the final PM 2.5 

### NAAQS rule are subject to a number of assumptions and uncertainties, which are discusse d throughout this document. For example, key assumptions underlying the data-derived concentr ation-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. Th e impacts of including a probabilistic representation of causality ar e explored using the result s of the expert elicitation.

2. All fine particles, regard less of their chemical compos ition, 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 rel eased from automotive engines and other

industrial sources 3 

### . 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 th e 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 w ith reductions in overall PM 2.5 

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

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

ambient concentrations under considerati on (above the assumed threshold of 10 μg/m 3 

### ). Thus, we assume that the CR functions are applicable to estimates of health benefits associated with reducing fine partic les in areas with vari ed concentrations of PM, including both regions that are in attainment with PM 2.5 

### 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 direct ly addressed in the expe rt elicitation, providing probabilistic characterizations of the likelihood of causality and the shape of the concentrationresponse function. The second of these is not di rectly addressed by the expert elicitation, and remains a significant source of unc ertainty in the state of knowle dge 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 ar e valid. Because we ar e projecting emissions and air quality out to 2020, there ar e 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 assessm ent tools, and we believe the results are highly useful in assessing the impacts of this rule.

In addition to the quantified and monetized bene fits summarized above, a number of additional categories associated with PM 2.5 

### and its precursor emissions are not currently amenable to quantification or valua tion. These include reduced acid a nd particulate deposition damage to cultural monuments and other materials, and envir onmental benefits due to reductions of impacts of acidification in lakes and streams and eutrophic ation in coastal areas. Additionally, we have not quantified a number of known or suspected health effects linked with PM for which appropriate health impact functi ons 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 benefi ts attributable to atta inment of alternative

standards.

Benefits estimates for attaining alternative sta ndards were generated using BenMAP, a computer program developed by EPA that integrates a numbe r of the modeling elements used in previous RIAs (e.g., interpolation functions , population projections, health impact functions, valuation functions, analysis and pooling methods) to transl ate modeled air concentr ation estimates into

3 Even within certain components such as directly emitte d 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 moneti zed benefits estimates. BenMAP provides estimates of both the mean impacts and the di stribution 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 ho w we incorporate uncertainty in to 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 ar e summarized in Table 5-1). Details on the emissions inventory and air mode ling 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 estimat es 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 i ndividual values. Total benefits are calculated simply as the sum of the values for all nonove rlapping 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 cha nges in other endpoints). The “damage-function” approach is the standard a pproach 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-functi on 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 de gree of uncertainty, due both to the randomness of environmental factors such as meteorology, and th e difficulty in measuring and predicting model inputs such as pollutant emissions. As such, where possible, we incorporate probabilistic representations of model i nputs and outputs. However, in many cases, probabilistic representations are not available. In these case s, we use the best available science and models, and characterize uncertainty using sensitivity analyses.

For the purposes of this RIA, the health impacts anal ysis is limited to those health effects that are directly linked to ambient levels of air po llution and specifically to those linked to PM 2.5 

### . There may be other, indirect health impacts associated with implementing emissions controls, such as occupational health impacts for coal miners. Th ese 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 lim ited 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 ecosy stems, but we are unable to place an economic value on these changes.

We note at the outset that EPA rarely has th e 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 estimate s are based on the best

available methods of benefits transfer. Benef its transfer is the scie nce and art of adapting primary research from similar contexts to obtai n the most accurate measure of benefits for the environmental quality change under analysis. Wh ere appropriate, adjustments are made for the level of environmental quality change, the soci odemographic and economic characteristics of the affected population, and other factors to impr ove 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 futu re adverse health effects by a fairly small amount for a large population. The appropriate economic measure is willingness to pay 4 

### (WTP) for changes in risk prior to the regulation (Freeman, 2003). 5 

### Adoption of WTP as the m easure of value implies that the value of environmental qual ity improvements depends on the individual preferences of the affected population and that the ex isting distribution of income (abi lity to pay) is appropriate. For some health effects, such as hospital admissi ons, WTP estimates are generally not available. In these cases, we use the cost of treating or m itigating the effect as the measure of benefits. These cost of illness (COI) estim ates generally (although not in ev ery 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 (Ha rrington and Portney, 1987; Berger et al., 1987).

One distinction in environmenta l benefits estimation is betwee n use values and nonuse values. Although no general agreement exists among econo mists on a precise distinction between the two (see Freeman [2003]), the gene ral nature of the difference is clear. Use values are those aspects of environmental quality that affect an individual’s we lfare directly. These effects include changes in product pri ces, quality, and availability; ch anges 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 envir onmental “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). Alte rnatively, 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. Ho wever, WTP is generally consid ered to be a more readily

available and conservative measure of benefits. In some cas es, such as the value of fatal risk reductions, we use WTA measures due to the difficulty in obtaining WTP estim ates. For cases where the changes in the good are small WTP and WTA are approximately equal.5-10 

### recreation and outdoor aesthetics; ch anges in health or life expect ancy; 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, direct ly or indirectly, in markets. For this reason, measuring nonuse values has proven to be significan tly more difficult than measuring use values. The air quality changes produced by atta inment strategies to attain the PM 2.5 

### NAAQS cause changes in both use and nonuse values, but th e monetary benefits estimates are almost exclusively for use values.

More frequently than not, the economic benef its from environmental quality changes are not traded in markets, so direct measurement t echniques cannot be used. There are three main nonmarket valuation methods used to develop valu es for endpoints considered in this analysis: stated preference (including cont ingent valuation [CV]), indir ect 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 compensati on is equivalent to an improvement in environmental quality. There is an extensive scie ntific literature and bod y of practice on both the theory and technique of statedpreference-based valuation. Well-designed and well-executed stated preference studies are va lid 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 in fer the benefits of pollution reduction. The most important application of this tec hnique for our analysis is the ca lculation of the VSL for use in estimating benefits from mortal ity risk reductions. No market exists where changes in the probability of death are directly exchanged. Ho wever, people make decisions about occupation, precautionary behavior, and other ac tivities associated with change s in the risk of death. By examining these risk changes and the other charact eristics of people’s choi ces, it is possible to infer information about the monetary values as sociated with changes in mortality risk (see Section 5.1.5).

Avoided cost methods are ways to estimate the costs of pollution by usin g the expenditures made necessary by pollution damage. For example, if buildings must be cl eaned or painted more frequently as levels of PM increase, then the ap propriately calculated increment of these costs is a reasonable lower-bound estimate (under most, a lthough not all, conditio ns) 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 th e 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 estimat es 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 co sts methods are also used to estimate some of

the health-related benefits related to morbid ity, 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 o ffers 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 re al income over time. Economic theory argues that WTP for most goods (such as environmenta l protection) will increa se if real incomes increase. There is substantial empirical evidence that the income elasticity 7 

### of WTP for health risk reductions is positive, although there is uncer tainty about its exact va lue. Thus, as real income increases, the WTP for environmental improvements also incr eases. Although many analyses assume that the income elasticity of WT P 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 estim ates 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, a nd through differences in income between study populations and the affected populati ons at a particular time. Empi rical evidence of the effect of real income on WTP gathered to date is ba sed on studies examining the former. The Environmental Economics Advisory Committee (EEAC) of the Sc ience 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 sensit ivity 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 c onflicting advice. While agreeing with “the general principle that the willingne ss to pay to reduce mortality risk s 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 empiri cal evidence, the Council does not support the use of the proposed adjustment s 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 sensit ivity analyses for alte rnative income growth adjustment factors.

Based on a review of the available income elastic ity literature, we adjusted the valuation of human health benefits upward to account for project ed growth in real U.S. income. Faced with a

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

derived from cross-sectional studi es 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 desc ription of the procedure we used to account for WTP for real income growth be tween 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 th at because of the variety of empirical sources used in deriving the income elasticities, there ma y 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 el asticity 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 fo r major illnesses and mortality risk reductions are base d on longer term measures of payment (such as wages or annual income). Economic theory suggests that relations hips become more elastic as the length of time grows, reflecting the ability to adjust spending ov er a longer time period. Based on this theory, it would be expected that WTP fo r reducing long term risks would be more elastic than WTP for reducing short term risks. We al so expect that the WTP for impr oved visibility in Class I areas would increase with growth in real income. Th e relative magnitude of the income elasticity of WTP for visibility compared with those for health effects suggests that visi bility is not as much of a necessity as health, thus, WT P is more elastic with respect to income. The elasticity values used to adjust estimates of benef its in 2020 are presented in Table 5-3.

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

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 dome stic product (GDP) and populations from 1990 to 2020 are needed to adjust benefits to reflect re al per capita income growth. For consistency with the emissions a nd 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 projec tions (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 na tional population in 2000. We used projections of real GDP provided in Kleckner and Ne umann (1999) for the years 1990 to 2010. 8 

### We used

8 U.S. Bureau of Economic Analysis, Table 2A (19 92$) (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 ch ained 1996 dollars) provided by St andard and Poor’s (2000) for the years 2010 to 2020. 9



### Using the method outlined in Kleckner and Neuma nn (1999) and the population and income data described above, we calculated WTP adjustment fact ors 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) ar e adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor . Table 5-4 lists the estimated ad justment factors. Note that, for premature mortality, we applied the income adju stment factor to the present discounted value

of the stream of avoided mortal ities occurring over the lag peri od. 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 benef its based on the COI approach or to work loss days and worker productivity. This assumption lead s us to underpredict benefits in future years because it is likely that increases in real U.S. in come 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 Growth a

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, lo cation, 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 ev ery other county in the United States to take into account patterns of economic growth and mi gration. The sum of growth in county-level populations is constrained to e qual a previously determined na tional population growth, based on Bureau of Census estimates (Hollman, Mulder , and Kallan, 2000). According to WP, linking county-level growth projections together and cons training to a national-leve l total growth avoids potential errors introduced by forecasting each county independently. County projections are developed in a four-stage process. First, nati onal-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, employ ment projections are made for 172 economic areas defined by the Bureau of Economic Anal ysis, using an “export-base” approach, which relies on linking industrial-sector production of nonlocally cons umed production items, such as outputs from mining, agriculture , and manufacturing with the national economy. The exportbased approach requires estimation of demand equati ons or calculation of hi storical growth rates for output and employment by sector. Third, pop ulation is projected for each economic area based on net migration rates derived from employment opportunities and following a cohortcomponent method based on fertility and morta lity in each area. Fourth, employment and population projections are repeat ed for counties, using the ec onomic 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-le vel population are based on historic al population data from 1969 through 1999 and do not include the 2000 Census resu lts. Given the availability of detailed 2000 Census data, we constructed adjusted county-le vel population projections for each future year using a two-stage process. Firs t, we constructed ratios of th e projected WP populations in a future year to the projected WP population in 200 0 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 containi ng the census block for each future year. This results in a set of future populati on projections that is consistent with the mo st recent detailed

Census data.

As noted above, values for environmental qualit y improvements are expected to increase with growth in real per capita income. Accounti ng 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 appreci ation of their inherent uncertainty. In response to these comments, EPA has initiated the de velopment of a comprehensive methodology for characterizing the aggregate imp act 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 th rough the conduct of probabilistic an alyses. In this final rule, EPA addressed key sources of uncertainty by Monte Carlo propagation of un certainty in the C-R

functions and economic valuation functions thro ugh 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 uncerta inties in the input parameters. Uncertainties associated with emissions, air qua lity modeling, populations, and baseline health5-15 

### effect incidence rates are not represented in the di stributions of benefits of attaining alternative standards. Issues such as correlation between input parameters and the identification of reasonable upper and lower bounds for input di stributions characterizing uncertainty in

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

In benefit analyses of air po llution regulations conducted to da te, the estimated impact of reductions in premature mortality has accounted for 85% to 95% of to tal benefits. Therefore, in characterizing the uncertainty related to the estimates of total benefits it is particularly important to attempt to characterize the unc ertainties associated with this e ndpoint. As such the analysis for this rule incorporates the results of our recent expert e licitation to characterize uncertainty in the effect estimates used to estimate premature mort ality resulting from exposures to PM into the main analysis. In collaboration with OMB, EPA completed a pilot e xpert 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 co mpleted 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 premat ure mortality function. This expert elicitation was designed to evaluate uncertainty in the underlying causal relations hip, the form of the mortality impact function (e.g., thre shold versus linear models) and the fit of a specific model to the data (e.g., confidence bounds for specific per centiles of the mortality effect estimates). Additional issues, such as the ab ility 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 alte rnate assumptions on the benefits estimates that are generated. It should be noted that the Monte Carlo-generated distributi ons of benefits reflect only some of the uncertainties in the input parameters. Uncertai nties associated with emissions, air quality modeling, populations, an d baseline health effect inci dence rates are not represented

in the distributions of benefits of attaining alternative standards.

Our distributions of total benefits do not comple tely 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 be nefits may not completely capture the shape and

location of the actual distri bution of total benefits.

5.1.4.1 Sources of Uncertainty

In any complex analysis using estimated paramete rs and inputs from numerous models, there are likely to be many sources of uncertainty. This an alysis is no exception. As outlined both in this and preceding chapters, many inputs were used to de rive the final estimate of benefits, including emission inventories, air quality models (wit h their associated pa rameters and inputs), epidemiological health effect estimates, estimate s of values (both from WTP and COI studies), population estimates, income estimates, and estimat es 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, ma y have a disproportionately large impact on final estimates of total benefits. For example, emissi ons estimates are used in the first stage of the5-16 

### 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 benefi ts analysis are the following:

• gaps in scientific data and inquiry;

• variability in estimated relationships, such as epidemiological effect estimates, introduced through differences in study desi gn and statistical modeling;

• errors in measurement and projection for va riables such as population growth rates;

• errors due to misspecification of model st ructures, including the use of surrogate variables, such as using PM 10 

### when PM 2.5 

### is not available, excluded variables, and simplification of co mplex functions; and

• biases due to omissions or other research limitations.

Some of the key uncertainties in the benef its 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 discusse d in detail in the following section.5-17 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 po llutant changes and populations in all locations.

● Similarity of future-year impact fu nctions 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 em issions, 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 PM 2.5

. 3. Uncertainties Associated with PM Mortality Risk

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

been determined.

● The extent to which adverse health effects are associated with low-leve l 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 PM 2.5 monitoring data in reflecting actual PM 2.5 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 ty pe 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 Epid emiological 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 eff ects. Health effects studies provide both a “best estimate” of this re lationship 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 e ffect being measured. The results of even the most well-designed epidemiological studies are characterized by th is type of uncertainty, though well-designed studies typically re port narrower uncertainty bounds around the best estimate than do studies of lesser quality. In selecting hea lth endpoints, we genera lly focus on endpoints where a statistically significant re lationship 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 th e fact that different published studies of the same pollutant/heal th effect relationship typically do not report id entical findings;

in some instances the differences are substantia l. These differences can exist even between equally well designed and executed studies and may result in health effect estimates that vary considerably. Across-study va riation can result from a variet y 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 wo rding). One possibility is that estimates of the single true relationship between a given pollutant and a heal th effect differ across studies because of differences in study design, random chance, or ot her factors. For example, a hypothetical study

conducted in New York and one conducted in Seat tle 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 pattern s). Alternatively, study results may differ because these two studies are in fact estimating di fferent relationships; that is, the same reduction in PM in New York and Seattle may result in di fferent reductions in premature mortality. This may result differences in the relative sensitiv ity 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 eff ect 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 un derestimates 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 scientific5-19 

### literature does not allow for a re gion-specific estimation of hea lth benefits for most health

outcomes. 10



### Extrapolation of Impact F unctions Across Populations. Epidemiological studies often focus on specific age ranges, either due to data ava ilability limitations (e.g., most hospital admission data come from Medicare records, which are lim ited to populations 65 and older), or to simplify data collection (e.g., some asthma symptom studi es focus on children at summer camps, which usually have a limited age range). We have assu med for the primary analysis that most impact functions should be applied only to those populat ions with ages that strictly match the populations in the underlying epidemiological stud ies. However, in many cases, there is no biological reason why the observe d health effect would not al so 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 biologi cal 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 w ith a specific effect estimate beyond the study population to cover the full age group (e.g., expa nding 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 substa ntial body of published sc ientific 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 It o [2001] and Bell et al. [2004]) have explored whether ozone may have mortality effects indepe ndent of PM. EPA is currently evaluating the epidemiological literature on the rela tionship 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 th e 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-sp ecific 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 concen trations well above the range of va lues for which the C-R function was fitted, avoided mortality may be misestimated.

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

would be no benefit to further reductions in PM 2.5 

### . Some researchers have hypothesized the presence of a threshold relationship. The na ture of the hypothesized relationship is the possibility that there exists a PM concentra tion level below which fu rther reductions no longer yield premature mortality reduction benefits. EPA’s most recent PM 2.5 

### Criteria Document

concludes that “the available evidence does not either support or re fute the existence of thresholds for the effects of PM on mortality ac ross the range of concentr ations in the studies” (U.S. EPA, 2004b, p. 9-44). EPA’s Science Advi sory Board (SAB) that provides advice on benefits analysis methods 11 

### has been to model premature mort ality associated with PM exposure as a non-threshold effect, that is, with harmful effects to exposed populat ions 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 refl ect regionally specific C-R functions resulting from regional differences in factors such as th e 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 ther e may be significant differences between PM produced via transported precursors, direct PM re leased from automotive e ngines, and direct PM from other industrial sources. The analysis also assumes that all components of fine particles have equal toxicity (because th e available epidemiological eff ect estimates are based on total

PM 2.5 

### 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 pr ovide 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 propor tions of benefits that are attributable to specific components of PM 2.5 

### , e.g., ammonium sulfate, ammoni um nitrate, elemental carbon, organic carbon, and crustal material (which includes metals). Th is 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 PM 2.5 

### 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 th e most careful work on this issue. They report that the associations between PM 2.5 and both all-cause and cardiopulm onary 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 real ization of changes in health e ffects. 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 existe nce of such a lag is important for the valuation of premature mortality inci dence because economic theory suggests that benefits occurring in the future should be discoun ted. There is no specific scientific evidence of

the existence or structure of a h ealth effects cessation lag for reducti ons in exposures to fine PM. Information about latency (the amount of time be tween 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 reduc tion associated with a given incremental change in PM exposure probab ly would not occur in the same year as the exposure reduction. The smoking-related literature al so 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 re duced exposure rather than co mplete cessation. The SAB-HES suggests that appropriate lag structures may be developed based on the distribution of causespecific deaths within the overa ll 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 deat hs, it is not clear what the appr opriate lag structure should be for different types of cardiopulmonary deaths, wh ich include both respirat ory 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 pr emature death in individuals with preexisting heart disease, which would imply very short cessation lags.

The SAB-HES provides several recommendations fo r future research that could support the development of defensible lag structures, incl uding 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 recommende d that until additional research has been completed, EPA should assume a segmented lag structure character ized by 30% of mortality reductions occurring in the first year, 50% occurring evenly ove r years 2 to 5 after the reduction in PM 2.5 

### , and 20% occurring evenly over the years 6 to 20 after the reduction in PM 2.5 

### (EPA-COUNCIL-LTR-05- 001, 2004). The distribution of deaths over the latency period is in tended to reflect the contribution of short-term exposures in the first y ear, 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 associat ed with air pollution. It is important to keep in mind that changes in the la g 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 induci ng premature mortality are still uncertain at

this time.

5.1.5 Health Benefits Assessment Methods

The largest monetized benefits of reducing ambien t concentrations of PM are attributable to

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

PM list numerous health effects know n to be linked to ambient con centrations of these pollutants (EPA, 2004; 2006). As discussed above, quantificati on of health impacts requires several inputs, including epidemiological effect estimates (con centration-response functi ons), baseline incidence and prevalence rates, potentially affected popul ations, and estimates of changes in ambient concentrations of air pollution. Previous sections have descri bed the population and air quality inputs. This section describes the effect estimates and baselin e incidence and prevalence inputs and the methods used to quantify and monetize cha nges in the expected number of incidences of various health effects. These include prem ature mortality, nonfatal heart attacks, chronic bronchitis, acute bronchitis, hos pital 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 anal ysis for three reasons: the possibility of doublecounting, uncertainties in applying effect relationships based on clin ical studies to the affected population, or a lack of an estab lished relationship between the hea lth effect and pollutant in the published epidemiological litera ture. Unquantifed effects ar e listed in Table 5-2. An improvement in ambient PM2.5 air quality may re duce the number of incidences within each unquantified effect category that the U.S. popula tion 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 quantif y these effects lends a

downward bias to the monetized bene fits 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 specifica lly established to provid e advice on the use of the scientific literature in developing be nefits analyses for ai r pollution regulations (http://www.epa.gov/sab/). In general, we follo w a weight of evidence approach, based on the biological plausibility of effect s, availability of concentration-response functions from wellconducted peer-reviewed epidemiol ogical studies, cohesiveness of results across studies, and a focus on endpoints reflecting public health imp acts (like hospital admi ssions) rather than physiological responses (such as changes in clin ical measures like Forced Expiratory Volume (FEV1)).

5.1.5.2 Sources of Information for Effect Estimates

There are several types of data that can suppor t the determination of types and magnitude of health effects associated with air pollution exposures. These sources of data include toxicological studies (includi ng animal and cellula r studies), human c linical trials, and observational epidemiology studies. All of these data sources provi de important c ontributions to the weight of evidence surrounding a particular health impact, however, only epidemiology studies provide direct concentr ation-response relationships whic h can be used to evaluate population-level impacts of reductions in ambient pollution levels.

However, standard environmental epidemiology st udies provide only a lim ited representation of the uncertainty associated with a specific C-R func tion, measuring only the st atistical 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 ambien t pollution and population le vel health outcomes, including many sources of model uncertainty, su ch as model specification, potential confounding

between factors that are both corr elated with the health outcome and each other, and many other factors. As such, in recent years, EPA has be gun investigating how expe rt elicitation methods can be used to integrate across various sour ces 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 f unction. Figure 5-1 shows how expert elicitation builds on both the direct empirical data on C-R re lationships and other le ss direct evidence to develop probabilistic distributions of C-R functi ons. EPA has used expert elicitation to inform the regulatory process in the past (see for exam ple the previous staff pa per for the lead NAAQS, U.S. EPA, 1990). In the current analysis, we ha ve only used expert elic itation 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 Da ta 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 p ublished scientific literature to ascertain the relationship between PM and adverse human heal th effects. We evaluated epidemiological studies using the selection criteria summarized in Table 5-6. These criteri a include consideration of whether the study was peer-reviewed, the ma tch between the pollutant studied and the pollutant of interest, the study design and location, and characte ristics 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 comp rehensiveness and scie ntific 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 e ffect. 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 ma y examine different pollutants or different age5-25 

### groups. For this reason, we consider very carefu lly the set of studies av ailable examining each endpoint and select a consistent subset that pr ovides 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 th e 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 an d a given health endpoint have been selected, they are quantitatively combined or pooled to derive a more robus t 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 fixe d 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, gi ving more weight to studies with greater statistical power (lower vari ance). Random effects pooling accounts for both within-study variance and between-study vari ability, due, for example, to differences in population susceptibility. We used the fixed effects mode l 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 availa ble 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 appl ies to both impact functions de fined 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 a nother (e.g., because of differences in population susceptibilities or differences in the compositi on of PM), location-specific eff ect 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 representati on 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 Consideratio ns 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 ex posure (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 pe riod 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, se x, preexisting condition status, or other relevant factors, it may be appropriate to select effect estimates that cover the broadest population to match with the de sired outcome of the analysis, which is total national-level health impacts. When available, multi-city studies are preferred to single city studies beca use 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 s pecified impact functions that include both pollutants. Using single-pollutant mode ls in cases where both pollutants are expected to affect a health outcome ca n lead to double-counting when pollutants

are correlated.

Measure of PM For this analysis, impact functions based on PM 2.5 are preferred to PM 10 because of the focus on reducing emissions of PM 2.5 precursors, and because air quality modeling was conducted for this size fraction of PM. Where PM 2.5 functions are not available, PM 10 functions are used as surrogates, recognizing that there will be potential downward (upw ard) biases if the fine fraction of PM 10 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 associat ed 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 e ndpoint quantified in this analysis.

Although a number of uncertainties remain to be addressed by continued research (NRC, 1998), a substantial body of published scie ntific literature documents th e correlation between elevated PM concentrations and increased mortality rate s (US EPA, 2004). Time-series methods have been used to relate short-term (often day-to-d ay) changes in PM concentrations and changes in daily mortality rates up to severa l days after a period of elevat ed PM concentrations. Cohort methods have been used to examine the poten tial relationship between community-level PM exposures over multiple years (i.e., long-term exposures) and community-level annual mortality

rates. Researchers have found st atistically significant associati ons between PM and premature mortality using both types of studi es. In general, the risk esti mates based on the cohort studies are larger than those derived from time-series studies. Cohort analys es 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 possibl y some component of short-term exposures (Kunzli et al., 2001; NRC, 2002). This section di scusses 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 th e 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 spons ored expert elicitation. The epidemiological studies from which these estimate s are drawn are described below. The expert elicitation project and the derivati on of effect estimates from the expert elicitation results are

described in the next section.

Over a dozen studies have found significant associ ations between various measures of long-term exposure to PM and elevated rates of annual mort ality, 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 suspe nded 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 inad equate control at the individual level for variables that are potentially im portant in causing mortality, such as wealth, smoking, and diet. Over the last 10 years, several studies using “p rospective 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 th e earlier work because th ey include individuallevel information with respect to health status and residence. The most extensive analyses have been based on data from two pr ospective cohort groups, often re ferred 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 da ta set comes from the California-based 7th Day Adventist Study (e.g., Abbey et al., 1999), which re ported associations between long-term PM exposure and mortality in men. Results from this cohort, however, have been inconsistent, and5-28 

### the air quality results are not geographically repr esentative 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). Th e characteristics of this group differ from the cohorts in the Six-Cities, ACS, and 7th Day A dventist studies with respect to income, race, health status, and smoking status. Unlike previo us 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 represen tative 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 ma tch populations based on the potential for demographic characteristics to modify the effect of air pollution on mo rtality risk. Thus, for the various ACS-based models, we are careful to apply the effect estima te 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% Afri can American, 24% current smokers).5-29 Table 5-7: Endpoints and Studies Used to Ca lculate Total Monetized Health Benefits

Endpoint Pollutant Study Study Population

Premature Mortality

Premature mortality—cohort study, allcause PM 2.5 (annual) Pope et al. (2002) Laden et al. (2006) >29 years >25 years

Premature mortality, total exposures PM 2.5 (annual) Expert Elicitation (IEc, 2006) >24 years

Premature mortality

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

Chronic Illness

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

Nonfatal heart attacks PM 2.5 (daily) Peters et al. (2001) Adults

Hospital Admissions

PM 2.5 (daily) Pooled estimate: Moolgavkar (2003)—ICD 490-496 (COPD) Ito (2003)—ICD 490-496 (COPD) >64 years

PM 2.5 (daily) Moolgavkar (2000)—ICD 490-496 (COPD) 20–64 years

PM 2.5 (daily) Ito (2003)—ICD 480-486 (pneumonia) >64 years Respiratory

PM 2.5 (daily) Sheppard (2003)—ICD 493 (asthma) <65 years

PM 2.5 (daily) Pooled estimate: Moolgavkar (2003)—ICD 390-429 (all cardiovascular) Ito (2003)—ICD 410-414, 427-428 (ischemic heart disease, dysrhythmia, heart failure) >64 years Cardiovascular

PM 2.5 (daily) Moolgavkar (2000)—ICD 390-429 (all cardiovascular) 20–64 years

Asthma-related ER

visits PM 2.5 Norris et al. (1999) 0–18 years

Other Health Endpoints

Acute bronchitis PM 2.5 Dockery et al. (1996) 8–12 years

Upper respiratory symptoms PM 10 Pope et al. (1991) Asthmatics, 9– 11 years

Lower respiratory symptoms PM 2.5 Schwartz and Neas (2000) 7–14 years

Asthma

exacerbations PM 2.5 Pooled estimate: Ostro et al. (2001) (cough, wheeze and shortness of breath) Vedal et al. (1998) (cough) 6–18 years a

Work loss days PM 2.5 Ostro (1987) 18–65 years

MRADs PM 2.5 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, a nd importance in informing the NAAQS development process, the Six-Cities and AC S 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 19 93) were subject to extensive reexamination and reanalysis by an independent team of scientific experts commissioned by HEI (Krewski et al., 2000). The final re sults of the reanalysis were th en 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 inve stigators. While the HEI reexamination lends credibility to the original studies, it also highlight s sensitivities concerning the relative impact of various pollutants, such as SO2, the potential role of educati on in mediating the association between pollution and mortality, and the infl uence of spatial correlation modeling.

Further confirmation and extension of the fi ndings 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 th e effect size from the first analysis and provided additional confirmation that reductions in PM 2.5 

### are likely to result in reductions in the risk of premature death. This additional evidence stems from the observed reductions in PM 2.5 

### in each city during the extended follow-up period. La den et al. (2006) found that mortality rates consistently went down at a rate propor tionate to the observed reductions in PM 2.5



### .

The extended analyses of the ACS cohort da ta (Pope et al., 2002, 2004) provides additional refinements to the analysis of PM-related mort ality by a) extending the follow-up period for the ACS study subjects to 16 years, wh ich triples the size of the mortality data set; b) substantially increasing exposure data, including additiona l measurement of cohort exposure to PM 2.5 

### following implementation of the PM 2.5 

### standard in 1999; c) controlling for a variety of personal risk factors including occupational exposure and di et; and d) using advan ced statistical methods to evaluate specific issues that can adversely a ffect risk estimates including the possibility of spatial autocorrelation of survival times in communities located near each other.

The NRC (2002) also recommended th at EPA review the database of cohort studies and consider developing a weighted mean estimate based on select ed studies. Because of the differences in the study designs and populations considered in the ACS and Harv ard 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 esti mating and valuing the pot ential reductions in mortality risk over the years, EPA consulted with the SAB-HES. That panel recommended using long-term prospective cohort studies in estima ting mortality risk reduction (U.S. EPA,1999b). This recommendation has been confirmed by a recent report from th e 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 becau se it includes a much larger sample size and longer exposure interval and covers more locations (e.g., 50 ci ties 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 Po pe et al. (2002) study as the basis for the primary mortality estimate for adults and suggests that alternate estimates of mortality generated5-31 

### using other cohort and time-series studies could be included as pa rt of the sensi tivity analysis (U.S. EPA-SAB, 2004b).

The SAB-HES also recommended using the specific es timated relative risks from the Pope et al. (2002) study based on the average exposure to PM 2.5 

### , measured by the average of two PM 2.5 

### 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 c oncerns regarding the stat istical reliability of the all-other cause mortality rela tive 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 base d 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 applicati on of the C-R function derived from the Harvard Six-cities study. In those analyses , the Six-cities estimate was incl uded as a sensitivity analysis in an appendix to the RIA. Following th e NAS advice to begin moving sensitivity and uncertainty analyses into the main body of the RI A, we are including a separate estimate based on the Six-cities study to complement the estimat e based on the ACS study. This also reflects the weight that was placed on bot h the ACS and Harvard Six-city studies by experts participating

in the PM 2.5 

### mortality expert elicitation.

As noted above, since the most recent SAB review, an extended followup of the Harvard Sixcities study has been published (L aden 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 elic itation responses. We note that because of the recent publication date of the Laden et al (2006) study, it has not undergone the CASA C and SAB-HES review received by the Pope et al (2002) and earlier Six-cities publicatio ns (see Dockery et al, 1993). However, it is clear from the expe rt elicitation that the results published in La den 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 as sessment of “new” science published since the closing date for the PM Criteria Document. The provisional asse ssment found that “new” studies generally strengthen the ev idence that acute and chronic e xposures to fine particles are associated with health effects. The provisional assessment found that the re sults reported in the studies do not dramatically diverge from prev ious findings, and, taken in context with the findings of the Criteria Document, the new info rmation and findings do not materially change any of the broad scientific conc lusions regarding the health effe cts of PM exposure made in the Criteria Document. The Laden et al (2006) st udy was included in this provisional assessment

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

assessment.

A number of additional analyses have been c onducted on the ACS cohort data (Jarrett et al., 2005; Krewski et al., 2005; Pope et al., 2004). These studies ha ve continued to find a strong significant relationship between PM 2.5 

### and mortality outcomes. Specifically, much of the recent5-32 

### research has suggested a stronger relationship between cardiovascular mortality and lung cancer mortality with PM 2.5 

### , and a less significant re lationship between respirat ory-related mortality and

PM 2.5



### .

EPA’s is committed to seeking the advice of its Science Advisory Board to review how EPA has incorporated expert elicitation resu lts into the benefits analysis, a nd 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 furt her, 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 recent ly published update, as the es timate 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 hea lth 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 pa rt of its efforts to improve the

characterization of uncertainties in its benef its estimates, EPA has conducted a study of the concentration-response (C-R) rela tionship between changes in PM 2.5 

### exposures and mortality using formally elicited expert judgments. The goal of the study was to elicit from a sample of health experts probabilistic dist ributions describing uncertainty in estimates of the reduction in mortality among the adult U.S. population resulti ng from reductions in ambient annual average

PM 2.5 

### levels. These distributions were obtained through a formal interview protocol using methods designed to elicit s ubjective expert judgments.

In 2003 and 2004, EPA conducted a pilo t-scale elicitation study with fi ve experts to explore the effectiveness of expert judgment techniques for characterizing uncer tainty and to explore the use of the expert judgment result s in the context of economic benefits analysis (Industrial Economics, 2004). EPA previously applied the resu lts of the pilot-scale study as part of its uncertainty analysis in the regulatory analys is accompanying the Clean Air Interstate Rule (CAIR) (U.S. EPA, 2005). EPA has recently comple ted 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 applie s 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 e xposures and mortality. These experts were selected through a peer-nomination process and included experts in ep idemiology, toxicology,

and medicine. The elicitation interview consisted of a protocol of carefully structured questions, both qualitative and quantitative , about the nature of the PM 2.5 

### -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, population5-33 

### questions requiring qualitative re sponses probed experts' belief s concerning key evidence and critical sources of uncertainty and enabled them to establish a conceptual basis supporting their quantitative judgments. Questions covered topi cs such as potential biological mechanisms linking PM 2.5 

### 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 resu lts; the likelihood of a causal relationship between

PM 2.5 

### and mortality, and the shape of the C-R func tion. 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. annua l, adult all-cause mortality resulting from a 1

μ g/m 3 

### change in annual average PM 2.5 

### exposure, assuming a range of baseline PM2.5 levels

between 4 and 30 μ g/m 3 

### . This quantitative question was designe d to yield results appropriate for application in EPA's quantitativ e health benefit analyses.

The results of the full-scale study c onsist of twelve individual dist ributions for the coefficient or slope of the C-R function relati ng changes in annual average PM 2.5 

### 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 applyi ng 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 PM 2.5 

### concentration below which

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

likelihood that the PM 2.5 

### -mortality relationship is or is not ca usal (unless the expert incorporated this last element directly in his slope distribution - see Indu strial Economics, 2006).

Based on the responses of the 12 experts (designated A through L), we constructed a corresponding set of 12 health im pact 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 constr uction 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 

### e y y β



### ,

Where y 0 

### 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

PM 2.5



### .

Some experts specified a piecewise log-linear fu nction, in which case we developed health impact functions that incorporate ambient concentr ation levels. For exampl e, 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 revi se 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/m 3 . As such, when applying the expert elicitation based functions, benefits are only estimated for starting concentrations greater than 4 μg/m 3

.5-34 

### a piecewise function with two segments, repres enting the concentration-response function for ambient concentrations between 4 and 10 μg/m 3 

### and between 10 and 30 μg/m 3 

### . In this case, the expert’s function can be translated into a health impact function of the form: 

# ( ) ( ) ⎪ ⎩ ⎪ ⎨ ⎧

≥

− ⋅ <

− ⋅

= ∆

∆

⋅ ∆

⋅



### 10 1 10 1

0 02 0 01

2 1



### Q if e y Q if e y y PM PM

β β



### ,

Where Q 0 

### is the baseline concentration of PM 2.5 

### , y 01 

### is the baseline inci dence for populations living in areas with baseline concentrations of PM 2.5 

### less than 10 μg/m 3 

### , y 02 

### is the baseline incidence for populations living in areas with baseline concentrations of PM 2.5 

### greater than or equal to10 μg/m 3 

### , and β 1 

### and β 2 

### are the effect estimates corresponding to the segments of the C-R function relating to ambient c oncentrations between 4 and 10 μg/m 3 

### and 10 and 30 μg/m 3



### , respectively.

A third form specified by one expert (Expert K) included both a piecewis e 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/m 3 

### (equal to 0.4) and the probability that a threshold existed between 5 and 10 μg/m 3 

### (equal to 0.1). The probability that a thre shold 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 equa l. 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/m 3 

### , and 16 to 30 μg/m 3 

### . Using this information, we translated Expert K’s responses into the following three conditional health impact functions:

(K1) 

# ( ) ( ) ⎪ ⎩ ⎪ ⎨ ⎧

≥

− ⋅ <

− ⋅

= ∆

∆

⋅ ∆

⋅



### 16 1 16 1

0 02 0 01

2 1



### Q if e y Q if e y y PM PM

β β



### (K2) 

# ( ) ( ) ( ) ( ) ( ) ( ) ( ) ⎪ ⎪ ⎪ ⎪ ⎪

⎩ ⎪ ⎪ ⎪ ⎪ ⎪

⎨ ⎧

≥

− ⋅ < ≤

− ⋅ < ≤ ×

− ⋅ < ≤ ×

− ⋅ < ≤ ×

− ⋅ < ≤ ×

− ⋅ < ≤ ×

− ⋅

= ∆

∆

⋅ ∆

⋅ ∆

⋅ ∆

⋅ ∆

⋅ ∆

⋅ ∆

⋅



### 16 1 16 5 1 5 4 8

. 0 1 4 3 6

. 0 1 3 2 4

. 0 1 2 1 2

. 0 1 1 0 0

. 0 1

0 02 0 01 0 01 0 01 0 01 0 01 0 01

2 1 1 1 1 1 1



### Q if e y Q if e y Q if e y Q if e y Q if e y Q if e y Q if e y

y

PM PM PM PM PM PM PM

β β β β β β β5-35 

### (K3) 

# ( ) ( ) ( ) ( ) ( ) ( ) ( ) ⎪ ⎪ ⎪ ⎪ ⎪

⎩ ⎪ ⎪ ⎪ ⎪ ⎪

⎨ ⎧

≥

− ⋅ < ≤ ×

− ⋅ < ≤ ×

− ⋅ < ≤ ×

− ⋅ < ≤ ×

− ⋅ < ≤ ×

− ⋅ < ≤ ×

− ⋅

= ∆

∆

⋅ ∆

⋅ ∆

⋅ ∆

⋅ ∆

⋅ ∆

⋅ ∆

⋅



### 16 1 16 10 0

. 1 1 10 9 8

. 0 1 9 8 6

. 0 1 8 7 4

. 0 1 7 6 2

. 0 1 6 0 0

. 0 1

0 02 0 01 0 01 0 01 0 01 0 01 0 01

2 1 1 1 1 1 1



### Q if e y Q if e y Q if e y Q if e y Q if e y Q if e y Q if e y

y

PM PM PM PM PM PM PM

β β β β β β β



### Function K1 is associated with a no threshold se gmented log-linear speci fication with a knot at 16 μg/m 3 

### . Function K2 represents the segmented log- linear function with a threshold between 0 and 5 μg/m 3 

### , with the cumulative probability of a thresh old at or below the initial concentration Q 0 

### increasing as Q 0 

### decreases (this will result in a declining expected value of the impact at lower initial concentrations). Likewise, func tion K3 represented the segmented log-linear function with a threshold between 5 and 10 μg/m 3 

### . 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 f unctions) of the effect size (in terms of the percent change in premature morta lity associated with a one microgram change in

annual mean PM 2.5 

### ). Six of the experts simply chose a normal distribution, which is completely specified with two parameters, the mean and sta ndard deviation (see Figure 5-2 for example). In one case, the expert specified a triangular di stribution, 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 expert s did not choose a parametric dist ribution, preferring instead to provide only effect estimates at particular percentiles of their di stributions. In these cases, we

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

we assume a continuous and smooth transiti on 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 Dist ribution for Expert D5-37 

### Figure 5-4. 

### Example Weibull Distribution for Expert J

Figure 5-5. 

### Example Custom Distribution for Expert B

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

2.5 th 

### percentile (the lower bound of a typical 95 percent confidence in terval), 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 distri bution 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 implie d truncated normal will be 1.34.5-38 

### Figure 5-6. 

### Truncated Normal Dist ribution for Expert C

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

between PM 2.5 

### and mortality was not causal, e.g., that reducing PM 2.5 

### would not actually reduce the risk of premature death. In these cases, th e distributions are unconditi onal, 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 re lationship. 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 vari ous components of an expert’s results are combined to produce an es timate of the distributi on of reduced mortality

associated with a reduction in ambient PM 2.5 

### , 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 displaye d in Figure 5-7. In Figure 5-7, the initial application of Expert K’s condi tional concentration-re sponse functions provides 3 distributions

associated with reductions in PM 2.5 

### concentrations in the range of starting concentrations from 4 to 16 μg/m 3 

### . These distributions are a ssigned weights based on the e xpert’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 distributi on of the change in mortality associated with

reductions in PM 2.5 

### in the range of starting concentrations from 16 to 30 μg/m 3 

### . This gives an

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

conditional on the existence of a causal relationship. This co nditional distribution is then combined with the expert’s judgment about causality (35 percent likelihood of a causal relationship), to derive the uncondi tional distribution of changes in mortality, which, as can be seen in the figure, is composed of a mass of probability at ze ro (reflecting the likelihood of no causal relationship), and a probability density function (PDF) over the re maining 35 percent of probability characterized by the conditional dist ribution. As expected, the unconditional5-39Mean = 249.07-76.4086.34249.07411.81574.54Range 4 to 16, Threshold 0 to 5Initial Distributions:Mean = 234.45-71.9181.27234.45387.63540.81Range 4 to 16, Threshold 5 to 10Mean = 435.44-223.54105.95435.44764.931,094.42Range 4 to 16, No ThresholdMean = 252.55-129.7161.42252.55443.68634.81Range 16 to 30, No ThresholdFrequency Chart.000.007.014.021.0280105.5211316.5422-182.6980.86344.40607.94871.4915,000 Trials193 OutliersForecast: Threshold Adjusted Distribution for rang+=Frequency Chart.000.006.012.018.025092.25184.5276.736939.32347.23655.15963.061,270.9815,000 Trials123 OutliersForecast: Total Range IncidenceMean = 0.35.000.163.325.488.6500.000.250.500.751.00Causality Probabilityp=0.4p=0.5p=0.1p=0.35p=0.65Final Distribution of Avoided Incidence of Premature Mortality for Expert K0.00%0.20%0.40%0.60%0.80%1.00%1.20%1.40%1.60%1.80%2.00% 0 63 125 187 248 310 372 4 34 4 95 557 619 680 742 804 866 927 9 89 1051 1113 1 174 123 6 129 8 1360 1421 1483 1545Avoided Premature Mortality

Relative Frequency0.000.100.200.300.400.500.600.700.800.901.00 Cumulative Probabilit yRelative Frequency at0 is 65%Mean=210Mean=590Mean=210Figure 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 strengt hened the case for an association between PM exposure and respiratory inflammation and infection leading to premature mortality in children under 5 years of age. Specifical ly, the SAB-HES noted the release of the WHO Global Burden of Disease Study focusing on ambien t 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 corr oborating findings linking PM exposure to increased respiratory inflammati on and infections in children. Recently, a study by Chay and Greenstone (2003) found that reduc tions 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 SA B-HES notes several st rengths of the study, including the use of a larger c ohort drawn from a large number of metropolitan areas and efforts to control for a variety of indi vidual 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-SA B, 2004b). A more recent study by Woodruff et al. (2006) continues to fi nd associations between PM 2.5 

### and infant mortality. The study also found the most significant relationships with resp iratory-related causes of death. We have not yet sought comment from the SAB on this more recen t 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 lim ited 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 give s rise to the development of CB in the United States. Because attainment strategies for th e PM NAAQS are expected to reduce primarily

PM 2.5 

### , this analysis uses only the Abbey et al. (1 995) study, because it is the only study focusing on the relationship between PM 2.5 

### and new incidences of CB.

Nonfatal Myocardial Infarct ions (heart attacks). 

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

### in the United States (Peters et al., 2001) and other countries (Poloniecki et al., 1997). We used a recent st udy by Peters et al. (2001) as the basis for the impact function estimating the relationship between PM 2.5 

### 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 PM 10, 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 me asure. Other studies, such as Domenici et al. (2006), Samet et al. (2000), and Moolgavkar (2000) , show a consistent relationship between all cardiov ascular hospital admissions, incl uding 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. PM 2.5 

### 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 relati onships between fine particles and cardiovascular effects both within and outside the United States. Several epid emiologic studies (Liao et al., 1999; Gold et al., 2000; Magari et al., 2001) have shown that heart rate vari ability (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 at tacks and other coronary heart diseases (Carthenon et al., 2002; Dekker et al., 2000; Liao et al., 19 97; Tsuji et al., 1996). As such, significant impacts of PM on heart rate variability are cons istent with an increased risk

of heart attacks.

Hospital and Emergency Room Admissions. 

### Because of the availabili ty of detailed hospital admission and discharge records, there is an extensive body of literature examining the relationship between hospital admi ssions and air pollution. Because of this, many of the hospital admission endpoints use pooled impact functions base d on the results of a number of studies. In addition, some studies have examined the re lationship 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 estimate d in this analysis are respiratory admissions

and cardiovascular admissions. There is not mu ch evidence linking PM with other types of hospital admissions. The only type of emergency r oom visits that have been consistently linked

to PM in the United States are asthma-related visits.

To estimate avoided incidences of PM 2.5 

### 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 a void double-counting benefits from reductions in myocardial infarctions when applying the impact function for car diovascular hospital admissions, we first adjusted the baseline cardiovascular hospital admissions to remove admissions for myocardial infarctions.

To estimate total avoided incidences of resp iratory hospital admissi ons, we used impact functions for several respiratory causes, incl uding chronic obstruc tive pulmonary disease (COPD), pneumonia, and asthma. As with cardiovascular admissions, additional published studies show a statistically si gnificant relationship between PM 10 

### and respiratory hospital admissions. We used only those focusing on PM 2.5 

### . 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 estimate s for people 65 and older with cardiovascular hospital admissions between the original and reanalyzed results, we do not expect the difference in the effect es timates 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 populati ons over 65, allowing us to pool the impact functions for this group. Only Moolgavkar (200 0) 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 hospita l admissions for populations under age 65. Total

avoided incidences of PM-relate d respiratory-related hospital ad missions is the sum of COPD, pneumonia, and asthma admissions.

To estimate the effects of PM air pollution reduc tions on asthma-related ER visits, we use the effect estimate from a study of children 18 and unde r by Norris et al. (1999). As noted earlier, there is another study by Schwartz examini ng a broader age group (less than 65), but the Schwartz study focused on PM 10 

### rather than PM 2.5 

### . 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 adul ts under 65, we will likely capture the majority of the impact of PM 2.5 

### on asthma emergency room visits in populations unde r 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 effect s not requiring hospitalization are associat ed 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 L ung Association, 2002c). Acute bronchitis is characterized by coughing, chest discomfort, slight fever, and ex treme 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 estima ted 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 estim ate from Schwartz and Neas (2000).

Because asthmatics have greater sensitivity to stimuli (including air po llution), 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 e ffects 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 resu lt in restrictions in activity le vels. 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/me dlineplus/ency/article/000124 .htm, accessed January 2002.

### 5-43 loss days (WLD). MRAD result when individual s 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 doi ng 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 PM 2.5 

### on MRAD was estimated using an ef fect estimate derived from Ostro and Rothschild (1989). Work loss days due to PM 2.5 

### 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 popu lation 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 endp oint. 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 studi es (Ostro et al., 2001; Vedal et al., 1998) that followed panels of asthma tic children. Ostro et al. (2001) followed a group of 138 African-American children in Lo s Angeles for 13 week s, recording daily occurrences of respiratory symp toms associated with asthma exacerbations (e.g., shortness of breath, wheeze, and cough). This study found a statistically significant association between

PM 2.5 

### , measured as a 12-hour average, and the daily prevalence of s hortness 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 Vancouv er 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 PM 10 

### and

18 Estimating asthma exacerbations associ ated with air pollu tion 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 ch ildren, 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 en dpoints. The SAB-HES, in commenting on the analytical blueprint for 812, acknowledged these challenges in evaluating asthmatic symp toms and appropriately adding them into the primary analysis (U.S. EPA-SAB, 2004b). Ho wever, despite these challe nges, the SAB- HES recommends the addition of asthma-related symptoms (i.e., asthma exace rbations) 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. No te also, that the SAB-HES, while supporting the incorporation of asthma exacerbation estimates, does not believe that the associatio n between ambient air polluti on, including ozone and PM, and the new onset of asthma is sufficiently strong to supp ort inclusion of this asthma-re lated endpoint in the primary

estimate.

### 5-44 respiratory symptoms for the asthmatic population was only re ported 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 cou gh-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 eff ect 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 th eir predictions. The pooled estimate from the Ostro et al. study is then pooled with the cough-related estimate ge nerated using the Vedal study. The rationale for this second pooling step is sim ilar to the first; both studies are attempting to quantify the same overall e ndpoint (asthma exacerbations).

Additional epidemiological studies are availa ble for characterizing asthma-related health endpoints (the full list of epidemiological st udies considered for modeling asthma-related incidence is presented in Tabl e 5-8). However, based on reco mmendations from the SAB-HES,

we decided not to use these addi tional studies in generating the primary estimate. In particular, the Yu et al. (2000) estimates show a much high er baseline incidence rate than other studies, which may lead to an overstatement of the expect ed 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 resp iratory symptoms in asthmatics but did not focus on specific exacerbations of asthma.

Treatment of Potential Threshol ds in Health Impact Functions

Unless specifically noted, our premature morta lity benefits estimates are based on an assumed cutpoint in the premature mortality c oncentration-response function at 10 μg/m 3 

### , and an assumed cutpoint of 10 μg/m 3 

### for the concentration-response functions for morbidity associated with short term exposure to PM2.5. The 10 μg/m 3 

### 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 primar y benefits estimates, we also constructed a sensitivity analysis by assigning different cutpoints below which changes in PM 2.5 

### are assumed to have no impact on premature mortality. In appl ying the cutpoints, we adjusted the mortality function slopes accordingly. 19 

### This sensitivity analysis allo ws us to determine the change (reduction) in avoided mortality cases and associated monetary benefits associated with alternative cutpoints. Five cut points (including the base case assu mption) were included in this sensitivity analysis: (a) 14 μg/m 3 

### (assumes no impacts below the alternative annual NAAQS), (b) 12 μg/m 3 

### (c) 10 μg/m 3 

### (reflects comments from CASAC - 2005), (d) 7.5 μg/m 3 

### (reflects recommendations from SAB-HES to consider estimating mortality benefits down to the lowest exposure levels considered in the Pope 2002 st udy used as the basi s for modeling chronic mortality) and (e) background or 3 μg/m 3 

### (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/m 3 , 12 μg/m 3 , and 14 μg/m 3
cutpoints since the 7.5 μg/m 3 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 PM 2.5 Ostro et al. (2001) African-American asthmatics, 8–13

Cough Prevalence of cough; incidence of cough PM 2.5 Ostro et al. (2001) African-American asthmatics, 8–13

Wheeze Prevalence of wheeze;

incidence of wheeze PM 2.5 Ostro et al. (2001) African-American asthmatics, 8–13

Asthma

exacerbation $ 1 mild asthma symptom: wheeze, cough, chest tightness,

shortness of breath PM 10 , PM 1.0 Yu et al. (2000) Asthmatics, 5–13

Cough Prevalence of cough PM 10 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 PM 10 Pope et al. (1991) Asthmatics, 9–11

Moderate or

worse asthma Probability of moderate (or worse) rating of

overall asthma status PM 2.5 Ostro et al. (1991) Asthmatics, all

ages

Acute

bronchitis $ 1 episodes of bronchitis in the past 12 months PM 2.5 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?” PM 2.5 McConnell et al. (1999) Asthmatics, 9–15

Asthma

attacks Respondent-defined

asthma attack PM 2.5 , ozone Whittemore and Korn (1980) Asthmatics, all

ages



### Baseline Health Effect Incidence Rates

The epidemiological studies of the associati on 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/m 3 

### decrease in daily PM 2.5 

### levels might decrease hospital admissions by 3%. To then to convert th is relative change into a number of cases, the

baseline incidence of the health effect is nece ssary. 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 asso ciation between polluti on levels and adverse health effects in a specific subpopulation, such as asthmatics or diabetics. In these cases, it is necessary to develop not only base line incidence rates, but also prevalence rates for the defining condition (e.g., asthma). For both baseline inci dence and prevalence data, we use age-specific rates where available. Impact functions are ap plied 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 me thods, 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 futu re incidence rates. However, for our primary benefits analysis, we continue to use current inci dence rates. The one exce ption is in the case of premature mortality. In this case, we have proj ected mortality rates such that future mortality rates are consistent with our pr ojections of population growth (A bt Associates, 2005). Compared with previous analyses, this will result in a re duction in the mortality related impacts of air pollution in future years.

Table 5-9 summarizes the baseline incidence data a nd sources used in the be nefits analysis. We use the most geographically disaggregated data av ailable. For premature mortality, county-level data are available. For hospital admissions, region al rates are available. However, for all other endpoints, a single national incidenc e 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 co mes 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 P opulation Prevalence Rates for Use in Impact Functions, General Population

Rates

Endpoint Parameter Value Source a

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 files b

Asthma ER Visits Daily asthma ER visit rate Age- and regionspecific visit rate 2000 NHAMCS public use data

files c ; 1999 NHDS public use data

files b

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 files b ; 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) Ostro et al. (2001) Asthma

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

children d 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 P opulation Prevalence Rates for Use in Impact Functions, General Population (continued)

Rates

Endpoint Parameter Value Source a

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 Hea lth 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 mo re 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 th e years 1996 through 1998. CDC maintains an online data repository of health statistics, CDC Wonde r, accessible at ht tp://wonder.cdc.gov/. The mortality rates provided are derived from U.S. death records and U.S. Census Bureau postcensal population estimates. Mortality ra tes 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 assu med that rates were uniform acr oss all ages in the reported age group. For example, to estimate mortality rate s for individuals ages 30 and up, we scaled the 25- to 34-year-old death count and population by one-half an d then generated a populationweighted mortality rate using data for the older age groups.

To estimate age- and county-specific mortalit y 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-speci fic mortality rates in 1996-1998 to corresponding rates for each future year. For the analysis year 2020, these ad justment 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 th e applicable population.

Table 5-9 lists the baseline inci dence rates and their sources fo r asthma symptom endpoints. Table 5-10 lists the prevalence rates used to de termine the applicable population for asthma symptom endpoints. Note that th ese reflect current asthma preval ence and assume no change in prevalence rates in future years. As noted a bove, we are investigating methods for projecting asthma prevalence rates in future years. Howeve r, 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 occu r 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 Associat ion (2002a, Table 7)—based on 1999 HIS

< 18 0.0527 American Lung Association (2002a, Table 7)—based on 1999 HIS

5–17 0.0567 American Lung Association (2002a, Table 7)—based on 1999 HIS

18–44 0.0371 American Lung Association (2002a, Table 7)—based on 1999 HIS

45–64 0.0333 American Lung Association (2002a, Table 7)—based on 1999 HIS

65+ 0.0221 American Lung Association (2002a, Table 7)—based on 1999 HIS

Male, 27+ 0.021 2000 HIS public use data files a

African American, 5 to 17 0.0726 American Lung Association (2002a, Table 9)—based on 1999 HIS

African American, <18 0.0735 American Lung A ssociation (2002a, Table 9)—based on 1999 HIS

a See ftp://ftp.cdc.gov/pub/Health_S tatistics/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 pollu tion generally lower the risk of future adverse health affects by a small amount for a large popu lation. The appropriate economic measure is

therefore ex ante WTP for changes in risk. However, ep idemiological 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 premat ure mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk re duction 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 affect ed population is automatically ta ken into account by the number of incidences predicted by epid emiological studies applied to th e relevant population. The same type of calculation can produce values for statis tical incidences of ot her health endpoints.

For some health effects, such as hospital admissi ons, WTP estimates are generally not available. In these cases, we use the cost of treating or m itigating the effect as a primary estimate. For example, for the valuation of hospital admissi ons we use the avoided medical costs as an estimate of the value of avoiding the health e ffects causing the admission. These COI estimates generally (although not in every case ) understate the true value of re ductions 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 eff ect. Table 5-11 summarizes the value estimates per health effect that we used in this analysis. Values are pr esented both for a 1990 base income level and adjusted for income growth out to 2020. Note that the un it 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 for5-50Table 5-11: Unit Values Used for EconomicValuation of Health Endpoints (1999$)Central Estimate of Value PerStatistical IncidenceHealth Endpoint1990 IncomeLevel2020 IncomeLevel Derivation of Distributions of EstimatesPremature Mortality (Valueof a Statistical Life)$5,500,000 $6,600,000 Point estimate isthe mean of a normal distribution witha 95% confidence interval between$1 and $10 million. Confidence interval is based on two meta-analyses of the wage-riskVSL literature: $1 million represents the lower end of the interquartile range from theMrozek and Taylor (2002) meta-analysis and $10 million represents the upper end of theinterquartile range from the Viscusi and Aldy(2003) meta-analysis. The VSL represents thevalue of a small change in mortality riskaggregated over the affected population.Chronic Bronchitis (CB) $340,000 $420,000 The WTP to avoid a case of pollution-related CB is calculated asWTP WTP exx=− −1313**( )

β, where x is the severity of an average CB case, WTP13is the WTPfor a severe case of CB, andis the parameter relating WTP to severity, based on theregression results reported in Krupnick and Cropper (1992). The distribution of WTP for anaverage severity-level case of CB was generated by Monte Carlo methods, drawing fromeach of three distributions: (1) WTP to avoid a severe case of CB is assigned a 1/9probability of being each of the first nine deciles of the distribution of WTP responses inViscusi et al. (1991); (2) the severity of a pollution-related case of CB(relative to the casedescribed in the Viscusi study) is assumed tohave a triangular distribution, with the mostlikely value at severity level 6.5 and endpoints at 1.0 and 12.0; and (3) the constant in theelasticity of WTP with respect to severityis normally distributed with mean = 0.18 andstandard deviation = 0.0669 (from Krupnick andCropper [1992]). This process and therationale for choosing it is described in detail in theCosts and Benefits of the Clean Air Act,1990 to 2010(EPA, 1999)., where x is the severity of an average CB case, WTP13 is theWTP for a severe case of CB, andis the parameter relating WTP to severity, based on theregression results reported in Krupnick and Cropper (1992). The distribution of WTP for anaverage severity-level case of CB was generated by Monte Carlo methods, drawing fromeach of three distributions: (1) WTP to avoid a severe case of CB is assigned a 1/9probability of being each of the first nine deciles of the distribution of WTP responses inViscusi et al. (1991); (2) the severity of a pollution-related case of CB(relative to the casedescribed in the Viscusi study) is assumed tohave a triangular distribution, with the mostlikely value at severity level 6.5 and endpoints at 1.0 and 12.0; and (3) the constant in theelasticity of WTP with respect to severityis normally distributed with mean = 0.18 andstandard deviation = 0.0669 (from Krupnick andCropper [1992]). This process and therationale for choosing it is described in detail in the Costs and Benefits of the Clean Air Act,1990 to 2010 (U.S. EPA, 1999).(continued)5-51Table 5-11: Unit Values Used for Economic Valuation of Health Endpoints (1999$) (continued)Central Estimate of Value PerStatistical IncidenceHealth Endpoint1990 IncomeLevel2020 IncomeLevel Derivation of Distributions of EstimatesNonfatal MyocardialInfarction (heart attack)3% discount rateAge 0–24Age 25–44Age 45–54Age 55–65Age 66 and over7% discount rateAge 0–24Age 25–44Age 45–54Age 55–65Age 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$65,293No distributional information available. Age-specific cost-of-illness values reflect lostearnings and direct medical costs over a 5-year period following a nonfatal MI. Lostearnings estimates are based on Cropper and Krupnick (1990). Direct medical costs arebased on simple average of estimates from Russell et al. (1998) and Wittels et al. (1990).Lost earnings:Cropper and Krupnick (1990). Present discounted value of 5 years of lost earnings:age of onset: at 3%at 7%25-44 $8,774 $7,85545-54 $12,932 $11,57855-65 $74,746 $66,920Direct 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; $21,113 at 7%discount rate)Hospital AdmissionsChronic ObstructivePulmonary Disease(COPD)(ICD codes 490-492, 494-496)$12,378 $12,378No distributional information available. The COI estimates (lost earnings plus direct medicalcosts) are based on ICD-9 code-level information (e.g., average hospital care costs,average length of hospital stay, and weightedshare of total COPD category illnesses)reported in Agency for Healthcare Research and Quality (2000) (www.ahrq.gov).Asthma Admissions $6,634 $6,634No distributional information available. The COI estimates (lost earnings plus direct medicalcosts) are based on ICD-9 code-level information (e.g., average hospital care costs,average length of hospital stay, and weightedshare of total asthmacategory illnesses)reported in Agency for Healthcare Research and Quality (2000) (www.ahrq.gov).All Cardiovascular(ICD codes 390-429)$18,387 $18,387No distributional information available. The COI estimates (lost earnings plus direct medicalcosts) are based on ICD-9 code-level information (e.g., average hospital care costs,average length of hospital stay, and weighted share of total cardiovascular categoryillnesses) reported in Agency for Healthcare Research and Quality (2000) (www.ahrq.gov).Emergency Room Visitsfor Asthma$286 $286 No distributional information available. Simple average of two unit COI values:(1) $311.55, from Smith et al. (1997) and(2) $260.67, from Stanford et al. (1999).(continued)5-52Table 5-11: Unit Values Used for Economic Valuation of Health Endpoints (1999$) (continued)Central Estimate of Value PerStatistical IncidenceHealth Endpoint1990 IncomeLevel2020 IncomeLevel Derivation of Distributions of EstimatesRespiratory Ailments Not Requiring HospitalizationUpper RespiratorySymptoms (URS)$25 $27Combinations of the three symptoms for which WTP estimates are available that closelymatch those listed by Pope et al. result inseven different “symptom clusters,” eachdescribing a “type” of URS. A dollar value was derived for each type of URS, using midrange estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and assumingadditivity of WTPs. In the absence of information surrounding the frequency with whicheach of the seven types of URS occurs within the URS symptom complex, we assumed auniform distribution between $10 and $45.Lower RespiratorySymptoms (LRS)$16 $18Combinations of the four symptoms for which WTP estimates are available that closelymatch those listed by Schwartz et al. resultin 11 different “symptom clusters,” eachdescribing a “type” of LRS. A dollar valuewas derived for each type of LRS, using midrange estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and assumingadditivity of WTPs. The dollar value for LRS isthe average of the dollar values for the 11different types of LRS. In the absence ofinformation surrounding the frequency with whicheach of the 11 types of LRS occurs within the LRS symptom complex, we assumed auniform distribution between $8 and $25.Asthma Exacerbations $42 $45Asthma exacerbations are valued at $45 perincidence, based on the mean of averageWTP estimates for the four severity definitions of a “bad asthma day,” described in Roweand Chestnut (1986). This study surveyed asthmatics to estimate WTP for avoidance of a“bad asthma day,” as defined by the subjects. For purposes of valuation, an asthmaexacerbation is assumed to be equivalent to a day in which asthma is moderate or worse asreported in the Rowe and Chestnut (1986) study. The value is assumed have a uniformdistribution between $17 and $73.Acute Bronchitis $360 $380Assumes a 6-day episode, with the distributionof the daily value specified as uniform withthe low and high values based on those recommended for related respiratory symptoms inNeumann et al. (1994). The low daily estimateof $10 is the sum of the mid-range valuesrecommended by IEc (1994) for two symptoms believed to be associated with acutebronchitis: coughing and chest tightness. The high daily estimate was taken to be twice thevalue of a minor respiratory restricted-activity day, or $110.Work Loss Days (WLDs)Variable (U.S.median=$110)No distribution available. Point estimate isbased on county-specific median annual wagesdivided by 50 (assuming 2 weeks of vacation) and then by 5—to get median daily wage.U.S. Year 2000 Census, compiled by Geolytics, Inc.(continued)5-53Table 5-11: Unit Values Used for Economic Valuation of Health Endpoints (1999$) (continued)Central Estimate of Value PerStatistical IncidenceHealth Endpoint1990 IncomeLevel2020 IncomeLevel Derivation of Distributions of EstimatesMinor Restricted ActivityDays (MRADs)$51 $54Median WTP estimate to avoid one MRAD from Tolley et al. (1986). Distribution isassumed to be triangular with a minimum of $22 and a maximum of $83, with a most likelyvalue of $55. Range is based on assumption that value should exceed WTP for a singlemild symptom (the highest estimate for a single symptom—for eye irritation—is $16.00) andbe less than that for a WLD. The triangular distribution acknowledges that the actual valueis 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 nonfat al myocardial infarctions (heart attacks) and school absence days are provided because these e ndpoints have only recently been added to the analysis and the valuation met hods 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 fr om Table 5-11. COI estimates are converted to constant 1999 dollar equivale nts 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 provid es 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 m ean value of avoiding one statistical death is
assumed to be $5.5 million in 1999 dollars. This re presents a central valu e consistent with the range of values suggested by recent meta-analy ses of the wage-risk VSL literature. The

distribution of VSL is characte rized by a confidence interval from $1 to $10 million, based on two meta-analyses of the wage-risk VSL litera ture. The $1 million lower confidence limit represents the lower end of th e interquartile range from the Mrozek and Taylor (2002) metaanalysis. The $10 million upper confidence limit re presents the upper end of the interquartile range from the Viscusi and Aldy (2003) meta-ana lysis. The mean estimate of $5.5 million is

consistent with the mean VSL of $5.4 million estimated in the Koch i et al. (2006) meta-analysis. Because the majority of the studies in these me ta-analyses are based on datasets from the early 1990s or previous decades, we continue to a ssume 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 quantif ication of premature mo rtality benefits, we assumed for this analysis that some of the in cidences 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 premat ure mortality, we applied an annual 3% discount rate to the value of premature mo rtality occurring in future years. 20



### The economics literature concerni ng the appropriate method for va luing 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 conti nuing discussion within the economics and public policy analysis community. EPA strives to use th e best economic science in its analyses. Given the mixed theoretical finding and empirical eviden ce 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 addr essed here, those differences in the affected populations and the nature of th e risks imply both upward and downward adjustments. Table 5- 12 lists some of these differences and the expect ed effect on the VSL estimate for air pollutionrelated mortality. In the absence of a comprehens ive 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 Be nefits 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 “c ontinue 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 ri sk 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 fo llowed this advice and discounted over the lag period between exposure and premature mortality.

Uncertainties Specific to Premat ure 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 be nefits 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 pr emature mortality avoidance, it is important to adequately characterize and understand the variou s types of economic approaches available for mortality valuation. Such an assessment also requires an understand ing of how alternative valuation approaches reflect that some indivi duals may be more sus ceptible to air pollutioninduced mortality or reflect differences in the natu re 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 s ources of model error such as those examined in the PM mortality expert elicitation may result in different conc lusions about the relative contribution of sources of uncertainty.

### 5-56 The health science literature on air pollution indi cates that several human characteristics affect the degree to which mortality risk affects an indi vidual. For example, some age groups appear to be more susceptible to air pollution than others (e.g., the elderly and child ren). Health status prior to exposure also affects susceptibility. An ideal benefits estimate of mortality risk

reduction would reflect these human characteris tics, 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 accoun t 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 polluti on reduce the risk of dying from the time that reductions take effect onw ard and how individuals value these changes. Each individual’s survival curve, or the probabi lity 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 sh ifts 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 wh ich the individual is likely to

survive.

Although a survival curve approach provides a th eoretically preferred method for valuing the benefits of reduced risk of premature morta lity associated with reducing air pollution, the approach requires a great deal of data to impl ement. The economic valuation literature does not yet include good estimates of the va lue 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 estimat es of the VSL saved by air pollution reduction. Although there is c onsiderable variation in the analytical designs and data used in the existing literature, the majority of the studies i nvolve the value of risks to a middle-aged working population. Most of th e studies examine differences in wages of risky occupations, using a hedonic wage appro ach. Certain characteristics of both the population affected and the mortality risk faci ng that population are be lieved to affect the average WTP to reduce the risk. The appropr iateness of a distribu tion of WTP based on the current VSL literature for valuing the mortal ity-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 m easure), 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 anal ysis 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 th e 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 assump tion of linearity, the estimate of the VSL does not depend on the particular amount of ri sk reduction being valued. This assumption has been shown to be reasonable provided the change in the ri sk being valued is within the range of risks evaluated in the underlying st udies (Rowlatt et al., 1998).

• Voluntariness of risks evaluated: Although job-related mortality risks may differ in several ways from air pollution-related morta lity 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 involuntar ily. 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 incu rred 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-re lated risks tend to involve lo nger periods of disease and suffering prior to death. Some evidence suggest s 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 magn itude) of sudden death. To the extent that the mortality risks addressed in this assessm ent are associated w ith 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 (Shogr en and Stamland, 2002) suggests that VSL estimates based on hedoni c 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 th e marginal worker (i.e., that worker who demands the highest compensation for his risk re duction). 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 thei r baseline risk of death as they age, the marginal value of risk reductions does not de cline with age as predicted by some lifetime consumption models. This research supports fi ndings 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 se vere case of CB to the survey respondents. We therefore employ an estimate of WTP to avoid a pollutio n-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 th e elasticity of WTP with respect to severity reported in the

### 5-58 Krupnick and Cropper (1992) study. Details of this adjustment pr ocedure 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 se vere 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, a nd Huber); and the elasticity of WT P with respect to severity of the illness. Based on assumptions about the di stributions of each of these three uncertain components, we derive a dist ribution of WTP to avoid a pol lution-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 cen tral tendency estimate of WTP to avoid a

PM-related case of CB.

Valuing Reductions in Nonfatal Myocar dial Infarctions (Heart Attacks). 

### The Agency has recently incorporated into its analyses the impa ct 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 suitabl e 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 (los t earnings) associated with the illness event.

Because the costs associated w ith a myocardial infarction exte nd 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 es timated a present discounted value in lost earnings (in 2000$) over 5 years due to a myocar dial 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 appe ar to be any discounting used.) Wittels et al. was used to value coronary heart diseas e in the 812 Retrospec tive 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 an d/or medical procedures being used. The authors note that the average length of hospitalization for acu te 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 in farction may have decreased from 1983 to the

### 5-59 present. The average length of stay for ICD code 410 (myocardi al infarction) in the year2000 Agency for Healthcare Research and Qualit y (AHRQ) HCUP database is 5.5 days. However, this may include patients who di ed in the hospital ( not included among our nonfatal myocardial infarction cases), whose le ngth of stay was th erefore substantially shorter than it would be if they had not died.

• Eisenstein et al. (2001) estimated 10-y ear costs of $44,663 in 1997$, or $49,651 in 2000$ for myocardial infarction patie nts, using statistical predic tion (regression) models to estimate inpatient costs. Only inpatient cost s (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 provide d significantly different values (see Table 5-13).

Table 5-13: Alternative Direct Medical Cost of Illness Estimates for No nfatal Heart Attacks

Study Direct Medical Costs (2000$) Over an x-Year Period, for x =

Wittels et al. (1990) $109,474 a 5
Russell et al. (1998) $22,331 b 5
Eisenstein et al. (2001) $49,651 b 10
Russell et al. (1998) $27,242 b 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 differen ces in the estimates. Because the wage-related opportunity cost estimates from Cropper and Krupni ck (1990) cover a 5-y ear 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 simp le 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 Cost a Total Cost

0–24 $0 $65,902 $65,902

25–44 $8,774 b $65,902 $74,676

45–54 $12,253 b $65,902 $78,834

55–65 $70,619 b $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 numerou s documented effects on environmental quality

that affect human welfare. These welfare eff ects include direct damage s to property, either through impacts on material structures or by soil ing 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 re creational experiences or the existence value of important resources. EPA’s Criteria Documents for PM, NOx, and SO2 list numerous physical and ecologica l effects known to be linked to ambient concentrations of these pollutants (U.S. EPA, 2005; 1993) This sec tion describes individual effects and how we quantify and monetize them. These effects includ e 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 ch ange 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 site s of unique public value, such as the Great Smokey Mountains

National Park. This section discusses the meas urement 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. Mo re light absorption means that the clarity of visual images and visu al range is reduced, ceteris paribus. Light absorption is a variable that can be accurately measured. Sisler (1996) crea ted 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 decivi ew corresponds to a change of about 10% in available light. Sisler characteri zed 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, measur ed in deciviews, of the areas affected by the control options. 22



### EPA considers benefits from two categories of vi sibility changes: resi dential visibility and recreational visibility. In both cases economic benefits are believe d to consist of use values and

nonuse values. Use values include the aesthetic be nefits of better visibility, improved road and air safety, and enhanced recreat ion in activities like hunting a nd birdwatching. Nonuse values are based on people’s beliefs that the environm ent 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, am ount to perceptible changes in visibility.

### 5-61 Nonuse values may be more important for recrea tional areas, particular ly national parks and

monuments.

Residential visibility bene fits are those that occur from visib ility changes in urban, suburban, and

rural areas and also in re creational 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 r ecreational and residential benefits is that only those people living in resi dential areas are assumed to receive benefits from residential visibility,

while all households in the United States are as sumed to derive some benefit from improvements in Class I areas. Values are assumed to be highe r 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 visi bility conducted in 1990 (McClelland et al., 1993) and the other is a 1988 survey on recreational visi bility value (Chestnut and Rowe, 1990a; 1990b). Although there are a number of othe r studies in the literature, th ey were conducted in the early

1980s and did not use methods that are considered defensible by current standards. Both the

Chestnut and Rowe and McClella nd et al. studies use the CV me thod. There has been a great deal of controversy and significant developmen t 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 va lid CV study and is suff iciently reliable to serve as the basis for monetary estimates of the benefits of visibility changes in recreational

areas. 25 

### This study serves as an esse ntial 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. st udy 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-SA B, 1999). Residential visibility benefits are not calculated for this analysis.

The Chestnut and Rowe study measured the dema nd for visibility in Class I areas managed by the National Park Service (NPS) in three broa d regions of the count ry: California, the Southwest, and the Southeast. Respondents in fi ve states were asked about their WTP to protect national parks or NPS-managed wilderness areas w ithin a particular region. The survey used photographs reflecting different visib ility levels in the specified re creational 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 estimati ng equation also included ho usehold income as an explanatory variable.

23 The Clean Air Act designates 156 national parks and wild erness 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 ar eas in the study regions. A comple te description of the benefits

transfer method used to infer values for visibi lity changes in Class I areas outside the study regions is provided in the Benefits TSD for th e 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 increas e the uncertainty a bout 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 applica tion of the estimated values to populations outside those states requires that pr eferences of populations in the five surveyed states be similar to those of nonsurveyed states.

• There is an inherent difficulty in separating values expresse d for visibility improvements from an overall value for improved air quality. The Chestnut and Rowe study attempted to control for this by informing respondents th at “other households are being asked about visibility, human health, and ve getation protections in urban areas and at national parks in other regions.” However, most of the responde nts 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 impr ovements 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 resu lt in a shift in the distribut ion 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 av erage visibility. However, the survey respondents were shown photographs of only su mmertime conditions, when visibility is generally at its worst. It is possible th at the respondents belie ved those visibility conditions held year-round, in which case th ey would have been valuing much larger overall improvements in visibility than what otherwise would be the case.

• The survey did not include reminders of possi ble substitutes (e.g., visibility at other parks) or budget constraints. These reminde rs 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 surv ey 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 intr oducing additional uncertainty.

### 5-63 In general, the survey design and implementatio n 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 comple ted, EPA will incorporate values for visibility improvements reflecting the improved survey designs.

The estimated relationship from the Chestnut and Rowe study is only direc tly applicable to the populations represented by survey respondents. EPA used benefits transfer methodology to extrapolate these results to th e population affected by the reduc tions 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 impr ovement, and household income. The behavioral parameters of this equation were taken from an alysis 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 eviden ce indicates that households are willing to pay more for a given visibility impr ovement as their income increases (Chestnut, 1997). The benefits estimates here incorporate Ch estnut’s estimate that a 1% increase in income

is associated with a 0.9% increase in WTP for a given change in visibi lity. A more detailed explanation of the visibility benefits methodology is provided in Appendix I.

Using the methodology outlined above, EPA estima tes 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 incl udes the value to househol ds 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 va lue 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 functiona l form and key parameters of the estimating equation for WTP for the affected population coul d have significant effect s 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 Ot her Vegetation-Related Benefits

Certain illustrative attainment strategies which reduce NO

x 

### emissions will also reduce nitrogen deposition on agricultural land and forests. There is some evidence that nitrogen deposition may have positive effects on agricultu ral output through passive fert ilization. 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 availabl e, but it is likely that the ove rall value is very small relative

to other health and welfare effects. The sh are of nitrogen requir ements provided by this deposition is small, and the marginal cost of pr oviding 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, primar ily 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 passi ve 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 nitr ogen 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 ec osystems 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 acidificati on, which can lead to leach ing 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 mode led are expected to produce econom ic benefits in the form of reduced materials damage. There are two importa nt 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/industr ial buildings and structures of cultural and historical significance. The e ffects on historic buildings an d outdoor works of art are of particular concern because of the uniqueness a nd 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 comm ercial 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 h ousehold soiling benefits.

Benefits from Reduced Ecosystem Damage

The effects of air pollution on th e health and stability of eco systems are potentially very important but are at present poor ly understood and difficult to m easure. Excess nutrient loads, especially of nitrogen, cause a variety of advers e consequences to the health of estuarine and

coastal waters. These effects include toxic an d/or noxious algal blooms such as brown and red tides, low (hypoxic) or zero (anoxic) concentrat ions of dissolved oxygen in bottom waters, the loss of submerged aquatic vegeta tion due to the light-filtering e ffect of thick algal mats, and fundamental shifts in phytoplankton comm unity 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 enviro nmental responses. Because neither appropriate

### 5-65 functions nor sufficient information to estimate th e 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 defi ned, 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 sulfat e loadings can be linked to measurable and definable changes in fish pop ulations or definable indexes of biodiversity, then stated preference studies can be designed to elicit individu als’ WTP for changes in these effects. This is an important area for furt her 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 describe d previously in this chapter to the estimated changes in PM yields estimates of the change s in health and environmental endpoints (e.g., premature mortalities, cases, admissions, and cha nge in light extinction) and the associated monetary values for those changes. As noted ear lier, benefits are provid ed 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” PM 2.5 

### design values to the level of the standards (see Chapter 4). Because of the differences in the so urces of effect estimates for mortality versus morbidity (mortality includes bo th epidemiology and expert elic itation based impact functions), mortality estimates are presented separately from morbidity.

Estimates of mortality and morb idity impacts are presented in Tables 5-16 through 5-19. For mortality, results based on concentration respons e 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 es timates 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 th ey 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 en dpoints 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 gr aphs show the relative proportions of total benefits in each area accounted for by the modeled and residual be nefits 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 con centration-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 re presentation of uncertainty relative to the

statistical confidence intervals provided for the effect estimates derived from the epidemiology

literature.5-67Table 5-16: Illustrative Strategy to Attain 15/35: Estimated Reductionin Premature Mortality (Incremental to 15/65 Attainment Strategy)90thPercentile Confidence Intervals Provided in ParenthesesaEastern U.S. Western U.S. Excluding CA California National TotalModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentNational Total FullAttainmentMortality Impact Functions Derived from Epidemiology LiteratureACS Studyb360(140 – 600)17(7 – 27)80(30 – 120)15(6 – 24)520(200 – 830)1,600(610 – 2,490)960(370 – 1,500)1,600(620 – 2,500)2,500(1,000 – 4,100)Harvard Six-City Studyc800(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)3,600(1,900 – 5,100)5,700(3,100 – 8,300)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)5(3 – 8)8(4 – 12)Mortality Impact Functions Derived from Expert ElicitationExpert 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)5,500(1,000 – 9,900)10,000(1,900 – 18,000)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)4,400(600 – 8,900)8,100(1,000 – 16,000)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)4,500(760 – 8,900)8,400(1,400 – 16,000)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)3,000(610 – 4,800)5,500(1,100 – 8,800)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)6,900(3,500 – 10,000)13,000(6,400 – 19,000)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)4,000(2,700 – 5,500)7,400(4,900 – 10,000)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)2,400(0 – 4,500)4,500(0 – 8,300)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)3,000(0 – 7,100)5,500(0 – 13,000)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)4,300(700 – 7,300)7,900(1,200 – 13,000)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)3,800(1,000 – 7,300)7,000(1,800 – 14,000)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)630(0 – 3,100)1,200(0 – 5,800)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)3,100(500 – 5,600)5,600(800 – 10,000)aAll estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns.bThe 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),which has previously been reportedas the primary estimate in recent RIAscBased 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 whathas previously been a sensitivityestimate.5-68Table 5-17: Illustrative Strategy to Attain 15/35: Estimated Reductions in Morbidity (Incremental to 15/65 Attainment Strategy) 90thPercentile Confidence Intervals Provided in ParenthesesaEastern U.S. Western U.S. Excluding CA California National TotalModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentNational Total FullAttainmentMorbidity Functions Derived from Epidemiology LiteratureChronic 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)1,600(300 – 2,900)2,600(490 – 4,800)Nonfatal myocardialinfarction (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)3,100(1,700 – 4,400)5,000(2,700 – 7,200)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)330(160 – 490)530(260 – 800)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)660(410 – 910)1,100(690 – 1,500)Emergency room visitsfor asthma (age <19)290(170 – 410)7(4 – 11)25(15 – 35)6(3 – 8)210(130 – 300)690(410 – 970)530(310 – 740)700(417 – 990)1,200(730 – 1,700)Acute bronchitis (age8–12)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)4,500(–160 – 9,100)7,300(–260 – 15,000)Lower respiratorysymptoms (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)38,000(19,000 –57,000)56,000(27,000 – 84,000)Upper respiratorysymptoms (asthmaticchildren 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)28,000(8,900 – 48,000)41,000(13,000 – 70,000)Asthma exacerbation(asthmatic childrenage 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)35,000(3,800 –100,000)51,000(5,600 – 150,000)Work loss days (age18–65)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)230,000(200,000 –260,000)350,000(300,000 –390,000)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)1,400,000(1,100,000 –1,600,000)2,000,000(1,700,000 –2,300,000)a All estimates are rounded to 2 significant digits. All roundingoccurs after final summing of unrounded estimates. As such, totals will not sum across columns.5-69Table 5-18: Illustrative Strategy to Attain 14/35: Estimated Reductionin Premature Mortality (Incremental to 15/65 Attainment Strategy)90thPercentile Confidence Intervals Provided in ParenthesesaEastern U.S. Western U.S. Excluding CA California National TotalModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentNational Total FullAttainmentMortality Impact Functions Derived from Epidemiology LiteratureACS Studyb2,100(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)1,700(680 – 2,800)4,400(1,700 – 7,100)Harvard Six-City Studyc4,700(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)3,900(2,100 – 5,700)9,900(5,400 – 14,000)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)6(3 – 8)15(7 – 23)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)5,900(1,100 – 10,600)19,000(3,500 – 34,000)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)4,700(600 – 9,500)15,000(1,900 – 31,000)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)4,900(810 – 9,500)16,000(2,600 – 31,000)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)3,200(650 – 5,100)10,000(2,100 – 16,000)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)7,400(3,700 – 11,000)24,000(12,000 – 35,000)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)4,300(2,900 – 5,800)14,000(9,100 – 19,000)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)2,600(0 – 4,800)8,300(0 – 15,000)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)3,200(0 – 7,600)10,000(0 – 24,000)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)4,600(700 – 7,800)15,000(2,300 – 25,000)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)4,000(1,070 – 7,800)13,000(3,400 – 25,000)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)670(0 – 3,300)2,200(0 – 11,000)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)3,300(600 – 5,900)10,000(1,400 – 19,000)aAll estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns.bThe 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),which has previously been reportedas the primary estimate in recent RIAscBased 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 whathas previously been a sensitivityestimate.5-70Table 5-19: Illustrative Strategy to Attain 14/35: Estimated Reductions in Morbidity (Incremental to 15/65 Attainment Strategy)90thPercentile Confidence Intervals Provided in ParenthesesaEastern U.S. Western U.S. Excluding CA California National TotalModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentNational Total FullAttainmentMorbidity Impact Function Derived frm Epidemiology LiteratureChronic 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)1,700(320 – 3,100)4,600(850 – 8,300)Nonfatal myocardialinfarction (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)3,400(1,900 – 4,900)8,700(4,800 – 13,000)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)360(180 – 540)980(490 – 1,500)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)730(460 – 1,000)2,100(1,300 – 2,800)Emergency room visitsfor 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)820(486 – 1,200)3,200(1,900 – 4,500)Acute bronchitis (age8–12)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)4,900(–170 – 9,800)13,000(–440 – 25,000)Lower respiratorysymptoms (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)42,000(20,000 –63,000)88,000(43,000 –130,000)Upper respiratorysymptoms (asthmaticchildren 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)31,000(9,800 – 52,000)65,000(20,000 –110,000)Asthma exacerbation(asthmatic childrenage 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)38,000(4,200 –110,000)79,000(8,900 – 230,000)Work loss days (age18–65)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)250,000(220,000 –290,000)550,000(480,000 –620,000)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)1,500,000(1,300,000 –1,700,000)3,300,000(2,700,000 –3,800,000)a All estimates are rounded to 2 significant digits. All roundingoccurs after final summing of unrounded estimates. As such, totals will not sum across columns.5-71Table 5-20: Illustrative Strategy to Attain15/35: Estimated Monetary Value of Reductions in Risk of Premature Mortality (3 PercentDiscount Rate, in millions of 1999$) 90thPercentile Confidence Intervals Provided in ParenthesesaEastern U.S. Western U.S. Excluding CA California National TotalModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentNational Total FullAttainmentMortality Impact Functions Derived from Epidemiology LiteratureACS 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)$9,200($2,000 –$19,000)$15,000($3,300 –$31,000)Harvard Six-CityStudyc$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)$21,000($5,400 –$40,000)$33,000($8,600 –$64,000)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)$30($7 – $59)$47($12 – $94)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)$32,000($4,300 –$72,000)$59,000($7,900 –$130,000)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)$26,000($2,400 –$68,000)$47,000($4,200 –$120,000)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)$26,000($3,200 –$63,000)$48,000($5,900 –$120,000)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)$17,000($2,600 –$36,000)$32,000($4,800 –$67,000)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)$40,000($10,000 –$79,000)$74,000($19,000 –$150,000)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)$23,000($6,300 –$44,000)$43,000($12,000 –$80,000)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)$14,000($0 – $35,000)$26,000($0 – $64,000)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)$17,000($56 – $49,000)$32,000($100 – $91,000)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)$25,000($2,900 –$54,000)$45,000($5,400 –$100,000)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)$22,000($3,700 –$52,000)$40,000($6,900 –$95,000)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)$3,600($0 – $20,000)$6,800($0 – $36,000)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)$18,000($2,200 –$43,000)$32,000($3,500 –$79,000)aAll estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns.bThe estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002),which has previously been reportedas the primary estimate in recent RIAscBased on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating whathas previously been a sensitivityestimate.5-72Table 5-21: Illustrative Strategy to Attain 15/35: Estimated Monetary Value of Reductions in Risk of Premature Mortality (7 PercentDiscount Rate, in millions of 1999$) 90thPercentile Confidence Intervals Provided in ParenthesesaEastern U.S. Western U.S. Excluding CA California National TotalModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentNational Total FullAttainmentMortality Impact Functions Derived from Epidemiology LiteratureACS 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)$7,700($1,700 –$16,000)$12,000($2,800 –$26,000)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)$17,000($4,500 –$34,000)$28,000($7,300 –$54,000)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)$25($6 – $50)$40($10 – $79)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)$27,000($3,600 –$61,000)$49,000($6,700 –$110,000)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)$22,000($2,100 –$57,000)$40,000($3,600 –$110,000)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)$22,000($2,700 –$53,000)$41,000($5,000 –$98,000)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)$15,000($2,200 –$31,000)$27,000($4,000 –$56,000)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)$34,000($8,500 –$66,000)$62,000($16,000 –$120,000)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)$20,000($5,300 –$37,000)$36,000($9,800 –$67,000)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)$12,000($0 – $29,000)$22,000($0 – $54,000)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)$15,000($47 – $41,000)$27,000($87 – $77,000)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)$21,000($2,500 –$45,000)$38,000($4,600 –$84,000)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)$18,000($3,100 –$44,000)$34,000($5,800 –$80,000)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)$3,100($0 – $16,000)$5,700($0 – $31,000)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)$15,000($1,800 –$36,000)$27,000($2,900 –$66,000)aAll estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns.bThe estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002),which has previously been reportedas the primary estimate in recent RIAscBased on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating whathas previously been a sensitivityestimate.5-73Table 5-22: Illustrative Strategy to Attain 15/35: Estimated Monetary Value of Morbidity Reductions (in millions of 1999$) 90thPercentileConfidence Intervals Provided in ParenthesesaEastern U.S. Western U.S. Excluding CA California National TotalModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentNational Total FullAttainmentMorbidity Impact Function Derived frm Epidemiology LiteratureChronic 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)$640($50 – $2,300)$1,100($83 – $3,700)Nonfatal myocardialinfarction (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)$260($71 – $560)$420($110 – $910)Nonfatal myocardialinfarction (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)$250($66 – $550)$410($110 – $890)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)$5.2($2.6 – $7.8)$8.5($4.2 – $13.0)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)$14.0($8.7 – $19.0)$23.0($14.0 – $32.0)Emergency room visitsfor 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)$0.19($0.11 – $0.29)$0.34($0.19 – $0.51)Acute bronchitis (age8–12)$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)$1.70(–$0.06 – $4.20)$2.70(–$0.10 – $6.70)Lower respiratorysymptoms (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)$0.62($0.23 – $1.10)$0.90($0.34 – $1.70)Upper respiratorysymptoms (asthmaticchildren 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)$0.76($0.20 – $1.60)$1.10($0.29 – $2.40)Asthma exacerbation(asthmatic childrenage 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)$1.40($0.16 – $4.70)$2.10($0.23 – $7.00)Work loss days (age18–65)$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)$29($26 – $33)$43($37 – $48)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)$34($3 – $66)$51($5 – $99)a All estimates are rounded to 2 significant digits. All roundingoccurs after final summing of unrounded estimates. As such, totals will not sum across columns.5-74Table 5-23: Illustrative Strategy to Attain14/35: Estimated Monetary Value of Reductions in Risk of Premature Mortality (3 PercentDiscount Rate, in millions of 1999$) 90thPercentile Confidence Intervals Provided in ParenthesesaEastern U.S. Western U.S. Excluding CA California National TotalModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentNational Total FullAttainmentMortality Impact Functions Derived from Epidemiology LiteratureACS 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)$10,000($2,200 –$21,000)$26,000($5,700 –$53,000)Harvard Six-CityStudyc$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)$23,000($5,900 –$44,000)$57,000($15,000 –$110,000)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)$32($8 – $63)$87($21 – $170)Mortality Impact Functions Derived from Expert ElicitationExpert 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)$34,000($4,600 –$77,000)$110,000($15,000 –$250,000)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)$27,000($2,600 –$72,000)$87,000($7,700 –$230,000)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)$28,000($3,400 –$68,000)$90,000($11,000 –$220,000)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)$19,000($2,800 –$39,000)$59,000($8,900 –$120,000)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)$43,000($11,000 –$84,000)$140,000($34,000 –$270,000)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)$25,000($6,700 –$46,000)$79,000($22,000 –$150,000)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)$15,000($0 – $37,000)$48,000($0 – $120,000)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)$18,000($60 – $53,000)$59,000($190 –$170,000)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)$26,000($3,100 –$58,000)$85,000($10,000 –$190,000)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)$23,000($4,000 –$55,000)$75,000($13,000 –$180,000)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)$3,900($0 – $21,000)$12,000($0 – $67,000)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)$19,000($2,300 –$46,000)$60,000($6,100 –$150,000)aAll estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns.bThe estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002),which has previously been reportedas the primary estimate in recent RIAscBased on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating whathas previously been a sensitivityestimate.5-75Table 5-24: Illustrative Strategy to Attain14/35: Estimated Monetary Value of Reductions in Risk of Premature Mortality (7 PercentDiscount Rate, in millions of 1999$) 90thPercentile Confidence Intervals Provided in ParenthesesaEastern U.S. Western U.S. Excluding CA California National TotalModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentNational Total FullAttainmentMortality Impact Functions Derived from Epidemiology LiteratureACS 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)$8,500($1,900 –$18,000)$21,000($4,800 –$45,000)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)$19,000($5,000 –$37,000)$48,000($13,000 –$94,000)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)$27($7 – $53)$73($18 – $140)Mortality Impact Functions Derived from Expert ElicitationExpert 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)$29,000($3,900 –$65,000)$92,000($12,000 –$210,000)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)$23,000($2,200 –$61,000)$74,000($6,500 –$200,000)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)$24,000($2,900 –$57,000)$76,000($9,200 –$180,000)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)$16,000($2,300 –$33,000)$50,000($7,500 –$100,000)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)$36,000($9,000 –$71,000)$120,000($29,000 –$230,000)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)$21,000($5,700 –$39,000)$67,000($18,000 –$130,000)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)$13,000($0 – $31,000)$41,000($0 – $100,000)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)$16,000($51 – $44,000)$50,000($160 –$140,000)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)$22,000($2,600 –$48,000)$71,000($8,500 –$160,000)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)$20,000($3,400 –$46,000)$63,000($11,000 –$150,000)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)$3,300($0 – $18,000)$10,000($0 – $56,000)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)$16,000($2,000 –$39,000)$50,000($5,100 –$120,000)aAll estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns.bThe estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002),which has previously been reportedas the primary estimate in recent RIAscBased on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating whathas previously been a sensitivityestimate.5-76Table 5-25: Illustrative Strategy to Attain 14/35: Estimated Monetary Value of Morbidity Reductions (in millions of 1999$) 90thPercentileConfidence Intervals Provided in ParenthesesaEastern U.S. Western U.S. Excluding CA California National TotalModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentModeled PartialAttainmentResidualAttainmentNational Total FullAttainmentMorbidity Impact Function Derived frm Epidemiology LiteratureChronic 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)$680($54 – $2,400)$1,900($150 – $6,600)Nonfatal myocardialinfarction (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)$280($79 – $620)$730($200 – $1,600)Nonfatal myocardialinfarction (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)$280($72 – $600)$700($180 – $1,600)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)$5.8($2.9 – $8.7)$16.0($7.8 – $23.0)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)$15.0($10.0 – $21.0)$43.0($27.0 – $59.0)Emergency room visitsfor 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)$0.23($0.12 – $0.34)$0.88($0.48 – $1.30)Acute bronchitis (age8–12)$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)$1.80(–$0.07 – $4.50)$4.60(–$0.17 – $12.00)Lower respiratorysymptoms (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)$0.68($0.26 – $1.30)$1.40($0.54 – $2.70)Upper respiratorysymptoms (asthmaticchildren 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)$0.84($0.22 – $1.80)$1.80($0.45 – $3.70)Asthma exacerbation(asthmatic childrenage 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)$1.60($0.17 – $5.20)$3.30($0.36 – $11.00)Work loss days (age18–65)$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)$32($28 – $36)$65($56 – $73)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)$37($3 – $72)$81($7 – $160)a All estimates are rounded to 2 significant digits. All roundingoccurs after final summing of unrounded estimates. As such, totals will not sum across columns.5-77Table 5-26: Monetary Benefits Associated with Improvements inVisibility in Selected Federal Class I Areas in 2020 Incrementalto15/65 Attainment Strategy (in millions of 1999$)aSuite of Standards CaliforniaSouthwest Southeast Total15/35 $320 $120 $91 $53014/35 $320 $130 $770 $1,200a All estimates are rounded to 2 significant digits. All roundingoccurs after final summing of unrounded estimates. As such, totals will not sum across columns.5-78Table 5-27: Ranges of Total Monetized Benefits (Health and Visibility) Associated with Full Attainment of 15/35 and 14/35 Standards Incremental to Attainment ofCurrent 15/65 Standards in 2020 (in millions of 1999$) 90thPercentile Confidence Intervals Provided in Parenthesesa3% Discount Rate 7% Discount RateSource of Mortality Effect Estimate15/35 14/35 15/35 14/35Data DerivedACS Studyb$17,000 $30,000 $15,000 $26,000($4,100 - $36,000) ($7,300 - $63,000)($3,500 - $31,000) ($6,400 - $54,000)Harvard Six-City Studyc$35,000 $62,000 $30,000 $52,000($9,400 - $70,000) ($17,000 - $120,000) ($8,100 - $59,000) ($14,000 - $100,000)Expert Elicitation DerivedExpert A $61,000 $110,000 $51,000 $96,000($8,700 - $140,000) ($16,000 - $260,000) ($7,400 - $120,000) ($14,000 - $220,000)Expert B $49,000 $91,000 $42,000 $78,000($5,000 - $130,000) ($9,300 - $240,000) ($4,300 - $110,000) ($8,100 - $210,000)Expert C $51,000 $94,000 $43,000 $80,000($6,700 - $120,000) ($13,000 - $230,000) ($5,800 - $100,000) ($11,000 - $190,000)Expert D $34,000 $64,000 $29,000 $54,000($5,600 - $72,000) ($11,000 - $130,000) ($4,800 - $62,000) ($9,100 - $110,000)Expert E $76,000 $140,000 $64,000 $120,000($19,000 - $150,000) ($36,000 - $280,000) ($16,000 - $130,000) ($31,000 - $240,000)Expert F $45,000 $84,000 $38,000 $71,000($12,000 - $86,000) ($23,000 - $160,000) ($11,000 - $73,000) ($20,000 - $140,000)Expert G $28,000 $52,000 $24,000 $45,000($800 - $69,000) ($1,700 - $130,000) ($790 - $59,000) ($1,600 - $110,000)Expert H $34,000 $63,000 $29,000 $54,000($900 - $96,000) ($1,900 - $180,000) ($880 - $82,000) ($1,800 - $150,000)Expert I $48,000 $89,000 $40,000 $75,000($6,200 - $110,000) ($12,000 - $200,000) ($5,300 - $89,000) ($10,000 - $170,000)Expert J $42,000 $79,000 $36,000 $67,000($7,700 - $100,000) ($14,000 - $190,000) ($6,600 - $86,000) ($12,000 - $160,000)Expert K $9,000 $17,000 $7,900 $15,000($800 - $42,000) ($1,700 - $77,000)($790 - $36,000) ($1,600 - $66,000)Expert L $35,000 $64,000 $29,000 $54,000($4,300 - $84,000) ($7,700 - $160,000) ($3,700 - $72,000) ($6,800 - $130,000)aAll estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not sum across columns.bThe 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),which has previously been reportedas the primary estimate in recent RIAscBased 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 whathas previously been a sensitivityestimate.5-79Figure 5-8. Comparison of Benefits ofIllustrative Attainment Strategy for the Revised Standards (15/35) Across Regions andSources of Mortality Effect Estimates5-80Figure 5-9. Comparison of Benefits of Illustrative Attainment Strategy for the MoreStringent Alternative Standards (14/35) AcrossRegions and Sources of Mortality Effect Estimates5-81Table 5-28: Mortality Threshold Sensitivity Analysis for 15/35 Scenario (Using Pope et al., 2002 Effect Estimate with Slope Adjustmentfor Thresholds Above 7.5 ug) 90thPercentile Confidence Intervals Provided in ParenthesesaEstimated Reduction in Mortality IncidenceEastern U.S. Western U.S. Excluding CA California National TotalLevel ofAssumedThresholdModeledPartialAttainmentResidualAttainmentModeledPartialAttainmentResidualAttainmentResidualAttainmentModeledPartialAttainmentResidualAttainmentModeledPartialAttainmentNationalTotal FullAttainmentLess CertaintyThat Benefits Areat Least as LargeNoThreshold620(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)1,700(670 – 2,700)3,700(1,500 – 6,000)Thresholdat 7.5 μg610(240 – 980)15(6 – 24)320(130 – 520)110(44 – 180)2,000(790 – 3,200)560(220 – 900)1,900(740 – 3,000)1,500(590 – 2,400)3,500(1,400 – 5,600)Thresholdat 10 μg360(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)960(370 – 1,500)2,500(1,000 – 4,100)Thresholdat 12 μg38(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)480(190 – 800)1,700(680 – 2,800)More CertaintyThat Benefits areat Least as LargeThresholdat 14 μg10(4 – 16)2(1 – 3)9(3 – 14)0(0 – 0)440(170 – 700)390(150 – 0,600)440(170 – 700)410(160 – 700)840(330 – 1,400)a All estimates are rounded to 2 significant digits. All roundingoccurs after final summing of unrounded estimates. As such, totals will not sum across columns.5-82Table 5-29: Mortality Threshold Sensitivity Analysis for 14/35 Scenario (Using Pope et al., 2002 Effect Estimate with Slope Adjustmentfor Thresholds Above 7.5 ug)90thPercentile Confidence Intervals Provided in ParenthesesaEstimated Reduction in Mortality IncidenceEastern U.S. Western U.S. Excluding CA California National TotalLevel ofAssumedThresholdModeledPartialAttainmentResidualAttainmentModeledPartialAttainmentResidualAttainmentModeledPartialAttainmentResidualAttainmentModeledPartialAttainmentResidualAttainmentNationalTotal FullAttainmentLess CertaintyThat Benefits Areat Least as LargeNoThreshold3,700(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)2,200(850 – 3,500)7,000(2,700 –11,200)Thresholdat 7.5 μg3,500(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)2,100(840 – 3,400)6,500(2,600 –10,500)Thresholdat 10 μg2,100(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)1,700(680 – 2,800)4,400(1,730 – 7,100)Thresholdat 12 μg220(87 – 360)60(24 – 100)12(5 – 19)0(0 – 1)420(160 – 670)1,300(530 – 2,200)650(250 – 1,000)1,400(550 – 2,300)2,100(810 – 3,300)More CertaintyThat Benefits areat Least as LargeThresholdat 14 μg54(21 – 87)44(17 – 70)9(3 – 14)0(0 – 0)370(140 – 600)480(190 – 800)430(170 – 700)530(210 – 800)960(370 – 1,500)a All estimates are rounded to 2 significant digits. All roundingoccurs after final summing of unrounded estimates. As such, totals will not sum across columns.5-83Table 5-30: Sensitivity of Monetized Benefits of Reductionsin Mortality Risk to Assumed Thresholds for 15/35 Scenario (UsingPope etal., 2002 Effect Estimate with Slope Adjustment for Thresholds Above 7.5 ug)90thPercentile Confidence Intervals Provided inParenthesesaMillions of 1999$Eastern U.S.Western U.S. ExcludingCA CaliforniaTotal NationwideAttainmentLevel ofAssumedThresholdDiscountRateModeledPartialAttainmentResidualAttainmentModeledPartialAttainmentResidualAttainmentModeledPartialAttainmentResidualAttainmentModeledPartialAttainmentResidualAttainmentNationalTotal FullAttainment3%$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)$12,000($2,600 –$24,000)$22,000($4,800 –$45,000)Less Certain thatBenefits Are at Leastas LargeNoThreshold7%$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)$9,900($2,200 –$21,000)$18,000($4,100 –$38,000)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)$12,000($2,600 –$24,000)$20,000($4,500 –$42,000)Threshold at7.5 ug7%$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)$9,800($2,200 –$20,000)$17,000($3,800 –$35,000)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)$9,200($2,000 –$19,000)$15,000($3,300 –$31,000)Threshold at10 ug7%$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)$7,700($1,700 –$16,000)$12,000($2,800 –$26,000)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)$7,200($1,600 –$15,000)$10,000($2,200 –$21,000)Threshold at12 ug7%$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)$6,100($1,400 –$13,000)$8,400($1,900 –$18,000)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)$2,500($560 –$5,300)$4,900($1,100 –$10,000)More Certain thatBenefits Are at Leastas LargeThreshold at14 ug7%$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)$2,100($470 –$4,400)$4,100($910 –$8,500)a All estimates are rounded to 2 significant digits. All roundingoccurs after final summing of unrounded estimates. As such, totals will not sum across columns.5-84Table 5-31: Sensitivty of Monetized Beneifts of Reductions inMortality Risk to Assumed Thresholds for 14/35 Scenario (Using Pope etal., 2002 Effect Estimate with Slope Adjustment for Thresholds Above 7.5 ug)90thPercentile Confidence Intervals Provided inParenthesesaMillions of 1999$Eastern U.S.Western U.S. ExcludingCA CaliforniaTotal NationwideAttainmentLevel ofAssumedThresholdDiscountRateModeledPartialAttainmentResidualAttainmentModeledPartialAttainmentResidualAttainmentModeledPartialAttainmentResidualAttainmentModeledPartialAttainmentResidualAttainmentNationalTotal FullAttainment3%$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)$13,000($2,800 –$26,000)$40,000($9,000 –$84,000)Less Certain thatBenefits Are at Leastas LargeNoThreshold7%$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)$11,000($2,400 –$22,000)$34,000($7,500 –$70,000)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)$12,000($2,800 –$26,000)$38,000($8,400 –$79,000)Threshold at7.5 ug7%$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)$10,000($2,300 –$22,000)$32,000($7,100 –$66,000)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)$10,000($2,200 –$21,000)$26,000($5,700 –$53,000)Threshold at10 ug7%$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)$8,500($1,900 –$18,000)$21,000($4,800 –$45,000)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)$8,200($1,800 –$17,000)$12,000($2,700 –$25,000)Threshold at12 ug7%$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)$6,900($1,500 –$14,000)$10,000($2,200 –$21,000)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)$3,000($680 –$6,300)$5,600($1,200 –$12,000)More Certain thatBenefits Are at Leastas LargeThreshold at14 ug7%$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)$2,600($570 –$5,300)$4,700($1,000 –$9,700)a All estimates are rounded to 2 significant digits. All roundingoccurs after final summing of unrounded estimates. As such, totals will not sum across columns.

### 5-85 We provide likelihood distributions both for th e total dollar benefits estimate and for the incidence of premature mortality to show the uncer tainty described by each expert’s judgment as well as the range of uncertainty as sociated with the standard errors in the Pope et al. (2002) amd Laden et al (2006) studies. The uncertainty abou t the total dollar benef it associated with any single endpoint combines the uncertainties fr om 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 th e wide range of sources of uncertainty that we have not incorporated, including uncertainty in emissions, air quality, and

baseline health eff ect incidence rates.

We are unable at this time to characterize the uncertainty in the estimate of benefits of improvements in visibility at Cla ss I areas. As such, we treat the visibility benefits as fixed and add them to all percentiles of th e health benefits distribution.

Given this unequal treatment of endpoints, it is likely that these distri butions do not capture the full range of benefits, and in f act are likely to understate the un certainty, especially on the high end of the range due to omission of pote ntially significant benefit categories.

Following these tables, we also provide a more comprehensive graphical presentation of the distributions of benefits genera ted using the available information from empirical studies and expert elicitation. Not all known PM-related hea lth and welfare effects could be quantified or monetized. The monetized value of these unquan tified 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 an d welfare benefits.

Total monetized benefits are dominated by benefi ts of mortality risk reductions. Based on the full range of expert elicitation resu lts, the range of mean estimates across the full set of mortality effect estimates projects that attainment of the final standard s of 15/35 will result in 1,200 to 13,000 avoided premature deaths annually in 2020 in cremental to the 15/65 attainment strategy, and that an attainment strategy for the more stringent 14 μg/m 3 

### annual standard would result in 2,200 to 24,000 avoided premature deaths incremen tal to the 15/65 attain ment strategy with 1,000 to 11,000 avoided premature deaths incrementa l to attainment of th e final 15/35 standards.

The threshold sensitivity analysis shows that mo rtality impacts are fairly sensitive to assumed thresholds, especially in the Western U.S. (exc luding 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/m 3 

### threshold leads to a reducti on in estimated incidence of mortality of almost 85 percent compared with the no threshold case. In the East, impacts of the 10 μg/m 3 

### threshold are smaller, but still significant, with a reduction of over 40 percent. In California, where annual mean levels are gene rally quite high, the impact of the 10 μg/m 3


### threshold is small, with a reduction of only 10 per cent. Nationwide, the av erage 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 distribu tion. The total dollar benefit for that iteration is the product of the two. If this is repeated for many (e.g., th ousands of) iterations, the distribution of total dollar benefits associated with the endpoint is generated.

### 5-86 μg/m 3 

### threshold is a reduction in premature mortal ity 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% di scount rate and $8 to $64 billion using a 7% discount rate. Health bene fits account for 97% of to tal benefits, in part because we are unable to quantify most of the nonhealth benefits. Th ese unquantified benefits may be substantial, although the magnitude of th ese benefits is highly unc ertain. 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 attack s), although this value is in some cases more than an order of magnitude lower than for premature mortalit y. Hospital admissions for respiratory and cardiovascular causes, visibility, MRADs, and wo rk 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 be nefits table reveals that there is not always a close correspondence between th e number of incidences avoided for a given endpoint and the monetary value associated with that endpoint. For exam ple, there are almost 100 times more work loss days than premature mo rtalities, 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 hospita l 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 benefi ts-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 bene fits 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 th e 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 mode led attainment strategy, and $1,200 million for visibility improvements due to the 14/35 modeled attainment strategy. Th e value of visibility

benefits in areas where we were unable to m onetize benefits could also be substantial (see Appendix J).

Figures 5-10 and 5-11 presents box plots of the distributions of the reduction in PM 2.5 

### -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 th e statistical error associ ated 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 inte rquartile range (bounded by the 25th and 75th percentiles), and the whis kers defining the 90% confidence interval (bounded by the 5th and 95th percen tiles of the distribution).5-885,6001,2007,0007,9005,5004,5007,40012,7005,5008,4008,10010,1005,7002,50005,00010,00015,00020,00025,00030,00035,00040,000Pope etal(2002)Ladenet al(2006)ExpertAExpertBExpertCExpertDExpertEExpertFExpertGExpertHExpertIExpertJExpertKExpertL

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) arebased on the means and standard errors of the C-R functions from the studies. The red dotted lines enclose a range bounded bythe means of the two data-deriveddistributions.DistributionsDerived fromEpidemiologyStudiesDistributions Based on Expert ElicitationFigure 5-10.Results of Application of Expert Elicitation: Annual Reductions in Premature Mortality in 2020 Associated withIllustrative Strategies to Attain 15/35, Incremental to Attainment of the 1997 Standards5-8910,3002,20013,00014,60010,3008,30013,70023,70010,30015,60015,10018,9009,9004,40005,00010,00015,00020,00025,00030,00035,00040,000Pope etal(2002)Ladenet al(2006)ExpertAExpertBExpertCExpertDExpertEExpertFExpertGExpertHExpertIExpertJExpertKExpertL

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) arebased on the means and standard errors of the C-R functions fromthe studies. The red dotted lines enclose a range bounded bythe means of the two data-deriveddistributions.DistributionsDerived fromEpidemiologyStudiesDistributions Based on Expert ElicitationFigure 5-11.Results of Application of Expert Elicitation: Annual Reductions in Premature Mortality in 2020 Associated withIllustrative Strategies to Attain 14/35, Incremental to Attainment of the 1997 Standards

### 5-90 For the 15/35 attainment strategy, the data-deriv ed 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 expe rts. 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 rela tionship associated with one of the expert’s

distributions.

For the 14/35 attainment strategy, the data-deriv ed 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 expe rts. 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 rela tionship associated with one of the expert’s distributions. The statistical uncertainty bounds of all of the estimat es, including the dataderived distributions, overlap. Although the uncertainty bounds for some experts include zero, and some distributions have signif icant percentiles at zero, all of the distributions have a positive

mean estimate.

The statistical uncertainty bounds of all of the esti mates, 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 th e distributions have a positive mean estimate.

Figure 5-12 and 5-13 present box plots of the distributions of moneti zed benefits of reductions in premature mortality associated w ith use of the Pope et al. (2002) , Laden et al. (2006), and expertjudgment based mortality inciden ce distributions. For the 15/35 attainment strategy (Figure 5- 12), the data-derived esti mates 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 (bas ed 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 bill ion. Mean annual benefits from the expert elicitation range from $17 billion (Expert K) to $140 billion (E xpert 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 La den et al (2006) results.5-91$76,000$34,000$51,000$49,000$61,000$35,000$17,000$45,000$28,000$34,000$48,000$42,000$9,000$35,000$0$50,000$100,000$150,000$200,000$250,000$300,000Pope etal(2002)Ladenet al(2006)ExpertAExpertBExpertCExpertDExpertEExpertFExpertGExpertHExpertIExpertJExpertKExpertL

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 values based onempirical data. Visibility benefits are included as a constant. Mortality distributions labeled Expert A - Expert L are basedon individual expert responses. The mortality distributionslabeled 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 adjusted upwards toaccount for growth in real income out to 2020.The red dotted lines enclose a range bounded by the means of the two data-deriveddistributions.DistributionsDerived fromEpidemiologyStudiesDistributions Based on Expert Elicitation ResultsFigure 5-12.Results of Probabilistic Uncertainty Analysis: Dollar Valueof Health and Welfare Impacts Associated with IllustrativeStrategies to Attain 15/35 (Full attainment),Incremental to Attainment of the 1997 Standards5-92$140,000$64,000$94,000$91,000$110,000$62,000$30,000$84,000$52,000$63,000$89,000$79,000$17,000$64,000$0$50,000$100,000$150,000$200,000$250,000$300,000Pope etal(2002)Ladenet al(2006)ExpertAExpertBExpertCExpertDExpertEExpertFExpertGExpertHExpertIExpertJExpertKExpertL

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 values basedon empirical data. Visibility benefits are included as a constant. Mortality distributions labeled Expert A - Expert L are based on individual expert responses. The mortalitydistributions labeled Pope et al. (2002) and Laden et al (2006)are based on the means and standard errors of the C-R functionsfrom the studies. Dollar benefits have beenadjusted upwards to account for growth in real income out to 2020.The red dotted lines enclose a range bounded by the means ofthe two data-derived distributions.DistributionsDerived fromEpidemiologyStudiesDistributions Based on Expert Elicitation ResultsFigure 5-13.Results of Probabilistic Uncertainty Analysis: Dollar Valueof Health and Welfare Impacts Associated with IllustrativeStrategies 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.5-9400.10.20.30.40.50.60.70.80.91$0 $50,000 $100,000 $150,000 $200,000 $250,000 $300,000 $350,000Million ($1999)

Cumulative Probability
.Expert AExpert BExpert CExpert DExpert EExpert FExpert GExpert HExpert IExpert JExpert KExpert LPope et al (2002)Laden et al (2006)Note: All non-mortality distributions are based onclassical statistical error derived from the standarderrors reported in epidemiology studies and distributionsof unit values based on empirical data. Visibility benefitsare included as a constant. Mortality distributionslabeled Expert A - Expert L are based on individualexpert responses. The distributions labeled Pope et al(2002) and Laden et al (2006) are based on the meansand standard errors of the C-R function from the studies.Dollar benefits have been adjusted upwards to accountfor growth in real income out to 2020.Figure 5-14.Results of Probabilistic Uncertainty Analysis: Cumulative Distributions of Dollar Value of Health and Welfare ImpactsAssociated with Illustrative Strategies to Attain 15/35, Incremental to Attainment of the 1997 Standards5-9500.10.20.30.40.50.60.70.80.91$0 $50,000 $100,000 $150,000 $200,000 $250,000 $300,000 $350,000Million ($1999)

Cumulative Probability
.Expert AExpert BExpert CExpert DExpert EExpert FExpert GExpert HExpert IExpert JExpert KExpert LPope et al (2002)Laden et al (2006)Note: All non-mortality distributions are based onclassical statistical error derived from the standarderrors reported in epidemiology studies and distributionsof unit values based on empirical data. Visibility benefitsare included as a constant. Mortality distributionslabeled Expert A - Expert L are based on individualexpert responses. The distributions labeled Pope et al(2002) and Laden et al (2006) are based on the meansand standard errors of the C-R function from the studies.Dollar benefits have been adjusted upwards to accountfor growth in real income out to 2020.Figure 5-15.Results of Probabilistic Uncertainty Analysis: Cumulative Distributions of Dollar Value of Health and Welfare ImpactsAssociated 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 sour ces of the benefits associated with PM attainment strategies, we provide a breakout of benefits by the majo r PM component species, including ammonium sulfate, ammonium nitrate, elemental carbon, or ganic carbon, and crustal material. This is accomplished by apportioning total benefits based on the proportion of the population weighted change in total PM 2.5 

### 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 to tal 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 bene fits 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 ha ve demonstrated that it is possi ble to reach full attainment using only direct PM controls, and as su ch, all of the full attainment benefits in that region can be assigned to the direct PM relate d species, including el emental 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 Strategy5-98Table 5-32: Apportionment of Monetized Health Benefits of Modeled Attainment Strategies to PM Component Species -- 3 PercentDiscount Rate*15/35 Modeled Attainment Strategy 14/35 Modeled Attainment StrategyAmmoniumSulfateAmmoniumNitrateCrustalElementalCarbonOrganicCarbonAmmoniumSulfateAmmoniumNitrateCrustalElementalCarbonOrganicCarbonPercent of MonetizedBenefits23.5% 18.5% 12.0% 5.2% 40.7%73.2% 6.4% 4.1% 1.9% 14.5%Apportioned Benefits:Source of MortalityEffect EstimateData DerivedACS Studya$1,500 $1,100 $700 $300 $2,500$12,600 $1,100 $700 $300 $2,500Harvard Six-City Studyb$3,100 $2,400 $1,600 $700 $5,400$26,800 $2,300 $1,500 $700 $5,300Expert Elicitation DerivedExpert A $6,400 $5,100 $3,300 $1,400 $11,200 $56,400 $4,900 $3,100 $1,500 $11,100Expert B $5,200 $4,100 $2,600 $1,200 $9,000 $45,200 $3,900 $2,500 $1,200 $8,900Expert C $5,400 $4,200 $2,700 $1,200 $9,300 $46,900 $4,100 $2,600 $1,200 $9,300Expert D $3,600 $2,800 $1,800 $800 $6,200 $31,200 $2,700 $1,700 $800 $6,200Expert E $8,100 $6,400 $4,100 $1,800 $14,000 $70,500 $6,100 $3,900 $1,900 $13,900Expert F $4,700 $3,700 $2,400 $1,100 $8,200 $41,300 $3,600 $2,300 $1,100 $8,200Expert G $2,900 $2,300 $1,500 $700 $5,100 $25,600 $2,200 $1,400 $700 $5,100Expert H $3,600 $2,800 $1,800 $800 $6,200 $31,200 $2,700 $1,700 $800 $6,200Expert I $5,000 $4,000 $2,600 $1,100 $8,700 $44,000 $3,800 $2,500 $1,200 $8,700Expert J $4,500 $3,500 $2,300 $1,000 $7,800 $39,100 $3,400 $2,200 $1,000 $7,700Expert K $900 $700 $500 $200 $1,500 $7,600 $700 $400 $200 $1,500Expert L $3,500 $2,800 $1,800 $800 $6,100 $30,900 $2,700 $1,700 $800 $6,100•Does not include residual benefits of full attainment in areasthat were not modeled to attain using illustrative control strategies.aThe estimate is based on the concentration-response (C-R) functiondeveloped from the study of the American Cancer Society cohort reported in Pope et al (2002),which has previously been reportedas the primary estimate in recent RIAsbBased 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 whathas previously been a sensitivityestimate.5-99Table 5-33: Apportionment of Monetized Health Benefits of Modeled Attainment Strategies to PM Component Species -- 7 PercentDiscount Rate*15/35 Modeled Attainment Strategy 14/35 Modeled Attainment StrategyAmmoniumSulfateAmmoniumNitrateCrustalElementalCarbonOrganicCarbonAmmoniumSulfateAmmoniumNitrateCrustalElementalCarbonOrganicCarbonPercent of MonetizedBenefits23.5% 18.5% 12.0% 5.2% 40.7%73.2% 6.4% 4.1% 1.9% 14.5%Apportioned Benefits:Source of MortalityEffect EstimateData DerivedACS Studya$1,200 $1,000 $600 $300 $2,200$10,800 $900 $600 $300 $2,100Harvard Six-City Studyb$2,600 $2,100 $1,300 $600 $4,500$22,800 $2,000 $1,300 $600 $4,500Expert Elicitation DerivedExpert A $5,400 $4,300 $2,800 $1,200 $9,400 $47,600 $4,100 $2,700 $1,300 $9,400Expert B $4,400 $3,400 $2,200 $1,000 $7,600 $38,200 $3,300 $2,100 $1,000 $7,600Expert C $4,500 $3,600 $2,300 $1,000 $7,900 $39,600 $3,400 $2,200 $1,000 $7,800Expert D $3,000 $2,400 $1,600 $700 $5,300 $26,500 $2,300 $1,500 $700 $5,200Expert E $6,800 $5,400 $3,500 $1,500 $11,800 $59,500 $5,200 $3,300 $1,600 $11,800Expert F $4,000 $3,200 $2,100 $900 $6,900 $34,900 $3,000 $1,900 $900 $6,900Expert G $2,500 $2,000 $1,300 $600 $4,300 $21,700 $1,900 $1,200 $600 $4,300Expert H $3,000 $2,400 $1,600 $700 $5,200 $26,400 $2,300 $1,500 $700 $5,200Expert I $4,300 $3,400 $2,200 $1,000 $7,400 $37,200 $3,200 $2,100 $1,000 $7,400Expert J $3,800 $3,000 $1,900 $800 $6,600 $33,100 $2,900 $1,800 $900 $6,500Expert K $800 $600 $400 $200 $1,300 $6,600 $600 $400 $200 $1,300Expert L $3,000 $2,400 $1,500 $700 $5,200 $26,200 $2,300 $1,500 $700 $5,200* Does not include residual benefits of full attainment in areas that were not modeled to attain using illustrative control strategies.aThe estimate is based on the concentration-response (C-R) functiondeveloped from the study of the American Cancer Society cohort reported in Pope et al (2002),which has previously been reportedas the primary estimate in recent RIAsbBased 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 whathas previously been a sensitivityestimate.

### 5-100 As discussed in previous chapters, the 15/35 atta inment 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 E GU and non-EGU SO2 in the Eastern U.S., the benefits for the 14/35 strategy are much more h eavily 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 PM 2.5 

### precursors. The result suggests there will be significant a dditional health and welfare benefits arising from reducing emissi ons from a variety of sources in and around projected nonattaining coun ties in 2020. While 2020 is the expected date th at states would need

to demonstrate attainment with the revised standard s, it is expected that benefits (and costs) will begin occurring much earlier, as states begin im plementing control measures to show reasonable progress towards attainment. Using the full range of benefits (including the results of the expert elicitation), our estimate thatbetween 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 risk s associated with exceeding the standards.

There are several important fact ors to consider when evaluating the relative benefits of the attainment strategies for the revised 15/35 and mo re stringent 14/35 standard s. 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 un ique 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” categ ory 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 st rategies, because we applied an estimation approach that reduced concentrations only at the specific viol ating monitors and not surrounding monitors that did not vi olate the standards. The magn itude of this underestimate is

unknown.

Another important factor to not e is the geographic scope of th e controls applied in the two illustrative attainment strategies. Comparing the be nefits of the two attainment strategies, it is clear that the incremental impact of the attainment strategy for th e tighter annual standard is to

almost double the total benefits. This should not be construed to indi cate 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 th e tighter annual standard. Be cause a regional SO2 program for EGU and nonEGU sources was evaluated, this resu lted in much more widespread reductions in

ambient PM 2.5 

### 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 PM 2.5 mass. This issue is an activ e area of research for EPA. The Agency is exploring ways to estimate the im portance 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 se nsitivity analysis including this factor among a number of other potentially important input pa rameters. 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 an alysis 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 mo re robust strategy than one that achieves reductions in only one component Until a more robus t scientific basis exists for making reliable judgments about the relative toxi city of PM, it will not be pos sible to determine whether the strategy of reducing a wide array of PM types is the optimal approach.

Inherent in any analysis of futu re regulatory programs are uncerta inties in projecting atmospheric conditions and source-level emissions, as we ll as population, health baselines, incomes, technology, and other factors. The assumptions used to capture these elements are reasonable based on the available evidence. However, da ta limitations prevent an overall quantitative estimate of the uncertainty associated with esti mates 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 PM 2.5 

### precursor emissions.

EPA will continue to evaluate new methods and models and select thos e most appropriate for estimating the health benefits of reductions in ai r pollution. It is important to continue improving

benefits transfer methods in te rms of transferring economic va lues and transferring estimated impact functions. The development of both bette r 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 essen tial 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, ep idemiologists, toxicologists, and economists should result in a more tightly integrated analyt ical framework for measur ing health benefits of air pollution policies.

5.4 References



### Abbey, D.E., S.D. Colome, P.K. Mills, R. Bu rchette, W.L. Beeson, and Y. Tian. 1993. “Chronic Disease Associated With Long-Term Concentrations of Nitrogen Dioxide.” Journal of Exposure Analysis and Environmental Epidemiology 3(2):181-202.

### 5-102 Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. Mills. 1995. “Estimated Long-Term Ambient Concentrations of PM (10) and Development of Respiratory Symptoms in a Nonsmoking Population.” Archives of Environmental Health 50(2): 139-

152.

Abbey, D.E., N. Nishino, W.F. McDonnell, R.J. Burchette, S.F. Knutsen, W. Lawrence Beeson, and J.X. Yang. 1999. “Long-term inhalable pa rticles and other air pollutants related to mortality in nonsmokers [see comments].” American Journal of Respiratory and Critical

Care Medicine 159(2):373-382.

Abt Associates, Inc. April 2003. Proposed Nonroad Land-based Diesel Engine Rule: Air Quality Estimation, Selected Health and Welf are Benefits Methods, and Benefit Analysis

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

Abt Associates, Inc. October 2005. Methodology for County-level Mo rtality Rate Projections. Memorandum to Bryan Hubbell and Zachary Pekar, U.S. EPA.

Adams, P.F., G.E. Hendershot, and M.A. Marano . 1999. “Current Estimates from the National Health Interview Survey, 1996.” Vital Health Statistics 10(200):1-212.

Agency for Healthcare Research and Quality ( AHRQ). 2000. HCUPnet, Healthcare Cost and Utilization Project.

American Lung Association. 1999. “Chronic Bronchitis.” Available at http://www.lungusa.org/dis eases/lungchronic.html.

American Lung Association. 2002a. Trends in Asthma Morbidity and Mortality . American Lung Association, Best Pract ices and Program Services, Epidemiology and Statistics

Unit.

American Lung Association. 2002b. Trends in Chronic Bronchitis and Emphysema: Morbidity and Mortality . American Lung Association, Best Practices and Program Services, Epidemiology and Statistics Unit.

American Lung Association. 2002c. Trends in Morbidity and Mortal ity: Pneumonia, Influenza, and Acute Respiratory Conditions . American Lung Association, Best Practices and Program Services, Epidemiology and Statistics Unit.

Ayyub, B.M. 2002. Elicitation of Expert Opinions for Uncertainty and Risk . CRC Press,

Florida.

Banzhaf, S., D. Burtraw, and K. Palmer. Octo ber 2002. “Efficient Emission Fees in the U.S. Electricity Sector.” Resources fo r the Future Discussion Paper 02-45.

Belanger, K., W. Beckett, E. Triche, M.B. Bracken , T. Holford, P. Ren, J.E. McSharry, D.R. Gold, T.A. Platts-Mills, and B.P. Leaderer. 2003. “Symptoms of Wheeze and Persistent Cough in the First Year of Life : Associations with Indoor Allergens, Air Contaminants, and Maternal History of Asthma.” American Journal of Epidemiology 158:195-202.

### 5-103 Bell, M.L., A. McDermott, S.L. Zeger, J.M. Same t, and F. Dominici. 2004. “Ozone and Shortterm Mortality in 95 U.S. Urban Communities, 1987–2000.” Journal of the American

Medical Association 292:2372-2378.

Berger, M.C., G.C. Blomquist, D. Kenkel, and G.S. Tolley. 1987. “Valuing Changes in Health Risks: A Comparison of Alternative Measures.” The Southern Economic Journal

53:977-984.

Bricker, S.B., C.G. Clement, D.E. Pirhalla, S.P. Orlando, and D.R.G. Farrow. 1999. National Estuarine Eutrophication Assessm ent: Effects of Nutrient Enrichment in the Nation’s

Estuaries . National Oceanic and Atmospheric Admi nistration, National Ocean Service, Special Projects Office and the National Cent ers for Coastal Ocean Science. Silver Spring, Maryland.

Carnethon, M.R., D. Liao, G.W. Evans, W.E. Ca scio, L.E. Chambless, W.D. Rosamond, and G.

Heiss. 2002. “Does the Cardiac Autonomi c Response to Postural Change Predict Incident Coronary Heart Disease and Mort ality? The Atherosclerosis Risk in Communities Study.” American Journal of Epidemiology 155(1):48-56.

CDC Wonder, Wide-ranging OnLi ne Data for Epidemiologic Research (Wonder) (data from years 1996-1998), Centers for Disease Control a nd Prevention (CDC), U.S. Department

of Health and Human Serv ices, http://wonder.cdc.gov.

Chay, K.Y., and M. Greenstone. 2003. “The Impact of Air Pollution on Infant Mortality: Evidence from Geographic Variation in Po llution Shocks Indu ced by a Recession.” Quarterly Journal of Economics 118(3).

Chestnut, L.G. April 15, 1997. Draft Memora ndum: Methodology for Estimating Values for Changes in Visibility at National Parks.

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

Rain Provisions of the 1990 Clean Air Act Amendments.” Journal of the Air and Waste Management Association 47:395-402.

Chestnut, L.G., and R.D. Rowe. 1990a. “A New National Park Visibility Value Estimates.” In Visibility and Fine Particles , Transactions of an AWMA/EPA International Specialty Conference, C.V. Mathai, ed. Air and Wa ste Management Association, Pittsburgh.

Chestnut, L.G., and R.D. Rowe. 1990b. Preservation Values for Visibility Protection at the National Parks: Draft Final Report. Prepared for Office of Air Quality Planning and Standards, U.S. Environmental Protection Ag ency, Research Triangle Park, NC, and Air Quality Management Division, National Park Service, Denver, CO.

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

Disease.” Resources for the Future. Wa shington, DC. Discussion Paper QE 89-16-

REV.

### 5-104 Dekker, J.M., R.S. Crow, A.R. Folsom, P.J. Hann an, D. Liao, C.A. Swenne, and E.G. Schouten. 2000. “Low Heart Rate Variability in a 2- Minute Rhythm Strip Predicts Risk of Coronary Heart Disease and Mortality Fr om Several Causes: The ARIC Study.”

Circulation 2000 102:1239-1244.

Dockery, D.W., C.A. Pope, X.P. Xu, J.D. Spengler , J.H. Ware, M.E. Fay, B.G. Ferris, and F.E. Speizer. 1993. “An Association between Air Pollution and Mortality in Six U.S. Cities.” New England Journal of Medicine 329(24):1753-1759.

Dockery, D.W., J. Cunningham, A.I. Damokosh, L. M. Neas, J.D. Spengler, P. Koutrakis, J.H. Ware, M. Raizenne, and F.E. Speizer. 1996. “Health Effects of Acid Aerosols On North American Children-Respiratory Symptoms.” Environmental Health Perspectives 104(5):500-505.

Domenici, F., R.D. Peng, M.L. Bell, L. Pham, A. McDermott, S.L. Zeger, J.M. Samet. 2006.

“Fine Particulate Air Pollution and Ho spital Admission for Cardiovascular and Respiratory Diseases. Journal of the Americ an Medical Association 295: 1127-1134.

Eisenstein, E.L., L.K. Shaw, K.J. Anstrom, C. L. Nelson, Z. Hakim, V. Hasselblad and D.B. Mark. 2001. “Assessing the Clinical and Econo mic Burden of Coronary Artery Disease:

1986-1998.” Medical Care 39(8):824-35.

Freeman, A.M. III. 2003. The Measurement of Environmental and Resource Values, Second

Edition . Washington, DC: Resources for the Future.

Gold, D.R., A. Litonjua, J. Schwartz, E. Lovett, A. Larson, B. Nearing, G. Allen, M. Verrier, R. Cherry., and R. Verrier. 2000. “Ambient Pollution and Heart Rate Variability.”

Circulation 101(11):1267-73.

Grosclaude, P., and N.C. Soguel. 1994. “Val uing Damage to Historic Buildings Using a Contingent Market: A Case Study of Road Traffic Externalities.” Journal of Environmental Planning and Management 37: 279-287.

Guo, Y.L., Y.C. Lin, F.C. Sung, S.L. Huang, Y.C. Ko, J.S. Lai, H.J. Su, C.K. Shaw, R.S. Lin, and D.W. Dockery. 1999. “Climate, Tr affiC-Related Air Pollutants, and Asthma

Prevalence in Middle-School Children in Taiwan.” Environmental Health Perspectives

107:1001-1006.

Harrington, W., and P.R. Portney. 1987. “V aluing the Benefits of Health and Safety Regulation.” Journal of Urban Economics 22:101-112.

Hollman, F.W., T.J. Mulder, and J.E. Kallan. January 2000. “Methodology and Assumptions for the Population Projections of the United States: 1999 to 2100.” Population Division Working Paper No. 38, Population Projections Branch, Population Division, U.S. Census Bureau, Department of Commerce.

Hubbell, B., A. Hallberg, D.R. McCubbin, and E. Post. (2005) Health-Related Benefits of Attaining the 8-Hr Ozone Standard. Environmental Health Perspectives 113:73–82.

### 5-105 Ibald-Mulli, A., J. Stieber, H. -E. Wichmann, W. Koenig, and A. Peters. 2001. “Effects of Air

Pollution on Blood Pressure: A P opulation-Based Approach.” American Journal of

Public Health 91:571-577.

Industrial Economics, Incorporated (IEc). Ma rch 31., 1994. Memorandum to Jim DeMocker, Office of Air and Radiation, Office of Polic y Analysis and Review, U.S. Environmental Protection Agency.

Industrial Economics, Incorporated (IEc). Ap ril 2004. “Expert Judgment Assessment of the Relationship Between PM 2.5 

### Exposure and Mortality.” Available at <www.epa.gov/ttn/ecas/benefits.html>.

Industrial Economics, Incorporated (IEc). August 2006. Expanded Expert Judgment Assessment of the Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Peer Review Draft. Prepared for: Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC.

Ito, K. 2003. “Associations of Particulat e Matter Components with Daily Mortality and Morbidity in Detroit, Michigan.” In Revised Analyses of Time-Series Studies of Air

Pollution and Health . Special Report. Health Effects Institute, Boston, MA.

Jerrett M, Burnett RT, Ma R, Pope CA 3rd, Krewski D, Newbold KB, Thurston G, Shi Y, Finkelstein N, Calle EE, Thun MJ. 2005. Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology . 2005 Nov;16(6):727-36.

Kleckner, N., and J. Neumann. June 3, 1999. “Recommended Approach to Adjusting WTP Estimates to Reflect Changes in Real Inco me.” Memorandum to Jim Democker, U.S.

EPA/OPAR.

Kochi, I., B. Hubbell, and R. Kramer. 2006. An Empirical Bayes Approach to Combining Estimates of the Value of Statistical Life for Environmental Policy Analysis.

Environmental and Resource Economics . 34: 385-406.

Krewski D., R.T. Burnett, M.S. Goldbert, K. Hoover, J. Siemiatycki, M. Jerrett, M. Abrahamowicz, and W.H. White. July 2000. Reanalysis of the Harv ard Six Cities Study and the American Cancer Society Study of Particulate Air Pollution and Mortality

. Special Report to the Health E ffects Institute, Cambridge MA.

Krewski, D., R. Burnett, M. Jerrett, C.A. Pope , D. Rainham, E. Calle, G. Thurston, M. Thun. 2005. Mortality and Long-term Exposure to Ambient Air Pollution: Ongoing Analyses Based on the American Cancer Society Cohort. Journal of Toxicology and
Environmental Health, Part A. 68: 1093-1109.

Krupnick, A.J., and M.L. Cropper. 1992. “T he Effect of Information on Health Risk

Valuations.” Journal of Risk and Uncertainty 5(2):29-48.

### 5-106 Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr, and M. Studnicka. 2001. “Assessment

of Deaths Attributable to Air Pollution: Should We Use Risk Estimates Based on Time

Series or on Cohort Studies?” American Journal of Epidemiol ogy 153(11):1050-55.

Kunzli, N., R. Kaiser, S. Medina, M. Studnicka, O. Chanel, P. Filliger, M. Herry, F. Horak Jr., V. Puybonnieux-Texier, P. Quenel, J. Schneider , R. Seethaler, J-C Vergnaud, and H. Sommer. 2000. “Public-Health Impact of Ou tdoor and TraffiC-Related Air Pollution: A European Assessment.” The Lancet 356:795-801.

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. Reduction in Fine Particulate Air Pollution and Mortality. American Journal of Respiratory and Critical Care Medicine.

173: 667-672.

Lave, L.B., and E.P. Seskin. 1977. Air Pollution and Human Health . Baltimore: Johns Hopkins University Press for Resources for the Future.

Levy, J.I., J.K. Hammitt, Y. Yanagisawa, and J.D. Spengler. 1999. “Development of a New Damage Function Model for Power Plan ts: Methodology and Applications.” Environmental Science and Technology 33:4364-4372.

Levy, J.I., T.J. Carrothers, J.T. Tuomisto, J.K. Hammitt, and J.S. Evans. 2001. “Assessing the

Public Health Benefits of Reduced Ozone Concentrations.” Environmental Health Perspectives 109:1215-1226.

Liao, D., J. Cai, W.D. Rosamond, R.W. Barnes, R.G. Hutchinson, E.A. Whitsel, P. Rautaharju,

and G. Heiss. 1997. “Cardiac Autonomi c Function and Incident Coronary Heart Disease: A Population-Based Ca se-Cohort Study. The ARIC Study. Atherosclerosis Risk in Communities Study.” American Journal of Epidemiology 145(8):696-706.

Liao, D., J. Creason, C. Shy, R. Williams, R. Wa tts, and R. Zweidinger. 1999. “Daily Variation

of Particulate Air Pollution and Poor Card iac Autonomic Control in the Elderly.” Environ Health Perspect 107:521-5.

Lipfert, F.W., S.C. Morris, and R.E. Wyzga. 1989. “Acid Aerosols—the Next Criteria Air

Pollutant.” Environmental Science & Technology 23(11):1316-1322.

Lipfert, F.W., H. Mitchell Perry Jr., J. Philip Miller, Jack D. Baty, Ronald E. Wyzg, and Sharon E. Carmody. 2000. “The Washington Un iversity-EPRI Veterans’ Cohort Mortality Study: Preliminary Results.” Inhalation Toxicology 12:41-74.

Lipfert, F.W.; Perry, H.M., Jr.; Miller, J.P.; Ba ty, J.D.; Wyzga, R.E.; Carmody, S.E. 2003. “Air Pollution, Blood Pressure, and Their Long- Term Associations with Mortality” Inhalation Toxicology . 15, 493-512.

Lipfert, F.W.; Wyzga, R.E.; Baty, J.D.; Mill er, J.P. 2006. “Traffic Density as a Surrogate Measure of Environmental Exposures in Studi es of Air Pollution Health Effects: LongTerm Mortality in a Cohort of US Veterans” Atmospheric Environment 40: 154-169.

### 5-107 Magari, S.R., R. Hauser, J. Schwartz, P.L. Willia ms, T.J. Smith, and D.C. Christiani. 2001.

“Association of Heart rate Va riability with Occupational an d Environmental Exposure to

Particulate Air Pollution.” Circulation 104(9):986-91.

Mansfield, C., K. Thomas, M. Farrelly, L. Cl ayton, T. Wilcosky. 2005. Development of Projections of Asthma Prevalence, Chronic Bronchitis Prevalence, and Condition-Related Hospitalizations, 2004–2030, Final Report. Research Triangle Park, NC. RTI International. Prepared for U.S. EPA Office of Air and Radiation.

McClelland, G., W. Schulze, D. Waldman, J. Irw in, D. Schenk, T. Stewart, L. Deck, and M. Thayer. September 1993. Valuing Eastern Visibility: A Field Test of the Contingent

Valuation Method . Prepared for Office of Policy, Planning and Evaluation, U.S. Environmental Protection Agency.

McConnell, R., K. Berhane, F. Gilliland, S.J. London, H. Vora, E. Avol, W.J. Gauderman, H.G. Margolis, F. Lurmann, D.C. Thomas, and J. M. Peters. 1999. “Air Pollution and Bronchitic Symptoms in Southern California Children with Asthma.” Environmental Health Perspectives 107(9):757-760.

Moolgavkar, S.H. 2000. “Air Pollution and Hospita l Admissions for Diseases of the Circulatory System in Three U.S. Metropolitan Areas.” Journal of the Air and Waste Management

Association 50:1199-1206.

Moolgavkar, S.H. 2003. “Air Pollution and Daily Deaths and Hospital Admissions in Los Angeles and Cook Counties.” In Revised Analyses of Time -Series Studies of Air

Pollution and Health . Special Report. Boston, MA : Health Effects Institute.

Mrozek, J.R., and L.O. Taylor. 2002. “What Dete rmines the Value of Life? A Meta-Analysis.” Journal of Policy Analysis and Management 21(2):253-270.

National Research Council (NRC). 1998. Research Priorities for Airborne Particulate Matter: I. Immediate Priorities and a Long-Range Research Portfolio . Washington, DC: The

National Academies Press.

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . Washington, DC: The National Academies Press.

Neumann, J.E., M.T. Dickie, and R.E. Unsworth . March 31, 1994. “Linkage Between Health Effects Estimation and Morbidity Valuati on in the Section 812 Analysis—Draft

Valuation Document.” Industrial Economics Incorporated (IEc) Memorandum to Jim DeMocker, U.S. Environmental Protection Ag ency, Office of Air and Radiation, Office of Policy Analysis and Review.

Norris, G., S.N. YoungPong, J.Q. Koenig, T.V. La rson, L. Sheppard, and J.W. Stout. 1999. “An

Association between Fine Pa rticles and Asthma Emergency Department Visits for

Children in Seattle.” Environmental Health Perspectives 107(6):489-493.

### 5-108 Ostro, B.D. 1987. “Air Pollution and Morb idity Revisited: A Sp ecification Test.” Journal of Environmental Economics Management 14:87-98.

Ostro, B.D. and S. Rothschild. 1989. “Air Pollution and Acute Respiratory Morbidity: An Observational Study of Multiple Pollutants.” Environmental Research 50:238-247.

Ostro, B.D., M.J. Lipsett, M.B. Wiener, and J. C. Selner. 1991. “Asthmatic Responses to

Airborne Acid Aerosols.” American Journal of Public Health 81(6):694-702.

Ostro, B., and L. Chestnut. 1998. “Assessing the Health Benefits of Reducing Particulate

Matter Air Pollution in the United States.” Environmental Research, Section A, 76:94-

106.

Ostro, B., M. Lipsett, J. Mann, H. Braxton- Owens, and M. White. 2001. “Air Pollution and Exacerbation of Asthma in African-American Children in Los Angeles.” Epidemiology 12(2):200-208.

Ozkaynak, H., and G.D. Thurston. 1987. “Asso ciations between 1980 U.S. Mortality Rates and

Alternative Measures of Airbor ne Particle Concentration.” Risk Analysis 7(4):449-461.

Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mittleman. 2001. “Increased Particulate Air Pollution and the Triggering of Myocardial Infarction.” Circulation 103:2810-2815.

Poloniecki, J.D., R.W. Atkinson., A.P. de Leon., and H.R. Anderson. 1997. “Daily Time Series for Cardiovascular Hospital Admissions a nd Previous Day’s Air Pollution in London,

UK.” Occupational and Environmental Medicine 54(8):535-540.

Pope, C.A., III, M.J. Thun, M.M. Namboodiri, D. W. Dockery, J.S. Evans, F.E. Speizer, and C.W. Heath, Jr. 1995. “Particulate Air Po llution as a Predictor of Mortality in a Prospective Study of U.S. Adults.” American Journal of Respiratory Critical Care

Medicine 151:669-674.

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mortal ity, and Long-term Exposure to Fine

Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141.

Pope, C.A., III, D.W. Dockery, J.D. Spengler, an d M.E. Raizenne. 1991. “Respiratory Health

and PM 10 

### Pollution: A Daily Time Series Analysis.” American Review of Respiratory

Diseases 144:668-674.

Pope, C.A., III, R.T. Burnett, G.D. Thurston, M.J. Thun, E.E. Calle, D. Krewski, and J.J. Godleski. 2004. “Cardiovascular Mortality and Long-term Exposure to Particulate Air

Pollution.” Circulation 109: 71-77.

Rosamond, W., G. Broda, E. Kawal ec, S. Rywik, A. Pajak, L. Cooper, and L. Chambless. 1999. “Comparison of Medical Care and Surviv al of Hospitalized Patients with Acute Myocardial Infarction in Pola nd and the United States.” American Journal of Cardiology

83:1180-1185.

### 5-109 Rowe, R.D., and L.G. Chestnut. 1986. “Oxida nts and Asthmatics in Los Angeles: A Benefits Analysis—Executive Summary.” Prepared by Energy and Resource Consultants, Inc. Report to the U.S. Environmental Protecti on Agency, Office of Policy Analysis. EPA230-09-86-018. Washington, DC.

Rowlatt, et al. 1998. “Valuation of Deaths from Air Pollution.” NERA and CASPAR for

DETR.

Russell, M.W., D.M. Huse, S. Drowns, E.C. Hame l, and S.C. Hartz. 1998. “Direct Medical Costs of Coronary Artery Disease in the United States.” American Journal of Cardiology 81(9):1110-1115.

Samet, J.M., S.L. Zeger, F. Dominici, F. Currier o, I. Coursac, D.W. Dockery, J. Schwartz, and

A. Zanobetti. June 2000. The National Morbidity, Mortality and Air Pollution Study: Part II: Morbidity, Mortality and Air Pollution in the United States . Research Report No. 94, Part II. Health Effects Institute, Cambridge MA.

Schwartz, J. 1993. “Particulate Air Po llution and Chronic Respiratory Disease.” Environmental

Research 62:7-13.

Schwartz, J., and L.M. Neas. 2000. “Fine Partic les are More Strongly As sociated than Coarse Particles with Acute Respiratory He alth Effects in Schoolchildren.” Epidemiology 11:6-

10.

Schwartz J, Dockery DW, Neas LM, Wypij D, Wa re JH, Spengler JD, Koutrakis P, Speizer FE,

Ferris BG Jr. 1994. Acute effects of summ er air pollution on respiratory symptom reporting in children. American Journal of Respiratory and Critical Care Medicine 150(5 Pt 1):1234-42.

Sheppard, L. 2003. “Ambient Air Pollution an d Nonelderly Asthma Hospital Admissions in Seattle, Washington, 1987-1994.” In Revised Analyses of Time-Series Studies of Air

Pollution and Health . Special Report. Boston, MA : Health Effects Institute.

Shogren, J., and T. Stamland. 2002. “Skill and the Value of Life.” Journal of Political Economy 110:1168-1197.

Sisler, J.F. 1996. Spatial and Seasonal Patterns and Long Term Variability of the Composition of the Haze in the United States: An Anal ysis of Data from the IMPROVE Network. Colorado State University, Cooperative Institut e for Research in the Atmosphere. Fort Collins, CO. July. See EPA Air Docket A-96-56, Document No. VI-B-09-(ee).

Smith, D.H., D.C. Malone, K.A. Lawson, L.J. Okam oto, C. Battista, and W.B. Saunders. 1997.

“A National Estimate of the Economic Costs of Asthma.” American Journal of Respiratory and Critical Care Medicine 156(3 Pt 1):787-793.

Smith, V.K., G. Van Houtven, and S.K. Pattana yak. 2002. “Benefit Transfer via Preference

Calibration.” Land Economics 78:132-152.

### 5-110 Smith, V.K., S.K. Pattanayak, and G. Van Houtven. 2006. “Structural benefit transfer: An example using VSL estimates.” Ecological Economics . Forthcoming. doi:10.1016/j.ecolecon.2006.04.002

Standard and Poor’s. 2000. “The U.S. Econo my: The 25 Year Focus.” Winter. Web site: http://www2standardandpoors.com.

Stanford, R., T. McLaughlin, and L.J. Okamoto. 1999. “The Cost of Asthma in the Emergency Department and Hospital.” American Journal of Respirat ory and Critical Care Medicine 160(1):211-215.

Thurston, G.D., and K. Ito. 2001. “Epidemi ological Studies of Acute Ozone Exposures and Mortality.” Journal of Exposure Analysis and Environmental Epidemiology 11(4):286-

294.

Tolley, G.S. et al. January 1986. Valuation of Reductions in Human Health Symptoms and Risks. University of Chicago . Final Report for the U.S. Environmental Protection Agency.

Tsuji, H., M.G. Larson, F.J. Venditti, Jr., E.S. Manders, J.C. Evans, C.L. Feldman, D. Levy. 1996. “Impact of Reduced Heart Rate Vari ability on Risk for Cardiac Events. The Framingham Heart Study.” Circulation 94(11):2850-2855.

U.S. Bureau of Census. 2000. Population Projec tions of the United States by Age, Sex, Race, Hispanic Origin and Nativity: 1999 to 2100. Population Projections Program, Population Division, U.S. Census Bur eau, Washington, DC. Available at: <http://www.census.gov/population/project ions/nation/summary/np-t.txt.>

U.S. Environmental Protection Agency (EPA). 1990. Review of the National Ambient Air Quality Standards for Lead: Assessment of Scientific and Technical Information. OAQPS Staff Paper U.S. EPA, Research Triangle Park, NC, EPA-450/2-89-022.

December.

U.S. Environmental Protection Agency (EPA). 1993. External Draft, Air Quality Criteria for
Ozone and Related Photochemical Oxidants. Volume II. U.S. EPA, Office of Health and

Environmental Assessment. Research Triangle Park, NC, EPA/600/AP-93/004b.3v.

U.S. Environmental Protection Agency (EPA). 1996a. Review of the National Ambient Air Quality Standards for Ozone: Assessment of Scientific and Technical Information. Office of Air Quality Planning and Standards, Re search Triangle Park, NC, EPA report no.

EPA/4521R-96-007.

U.S. Environmental Protection Agency (EPA). 1996b. Review of the National Ambient Air Quality Standards for Particulate Matter: Assessment of Scientific and Technical Information . Office of Air Quality Planning and St andards, Research Triangle Park, NC; EPA report no. EPA/4521R-96-013.

### 5-111 U.S. Environmental Protection Agency (EPA ). 1998. “Advisory Council on Clean Air Compliance Analysis Advisory on the Cl ean Air Act Amendments (CAAA) of 1990 Section 812 Prospective Study: Overview of Air Quality and Emissions Estimates: Modeling, Health and Ecological Valuati on Issues Initial Studies.” EPA-SABCOUNCIL-ADV-98-003.

U.S. Environmental Protection Agency (EPA). 1999a. The Benefits and Costs of the Clean Air
Act, 1990–2010. Prepared for U.S. Congress by U.S. EPA, Office of Air and Radiation/Office of Policy Analysis and Review, Washington, DC, November; EPA report no. EPA-410-R-99-001.

U.S. Environmental Protection Agency (EPA). 1999b. “An SAB Advisory: The Clean Air Act Section 812 Prospective Study Heal th and Ecological Initial Studies.” Prepared by the Health and Ecological Eff ects Subcommittee (HEES) of the Advisory Council on the Clean Air Compliance Analysis, Science Adviso ry Board, U.S. Environmental Protection Agency. Washington DC. EPA-SAB-COUNCIL-ADV-99-005.

U.S. Environmental Protection Agency (EPA). July 2000a. “An SAB Report on EPA’s White Paper Valuing the Benefits of Fatal Cancer Risk Reduction.” EPA-SAB-EEAC-00-013.

U.S. Environmental Protection Agency (EPA). September 2000b. Guidelines for Preparing Economic Analyses. EPA 240-R-00-003.

U.S. Environmental Protection Agency Science Advisory Board. 1999. The Clean Air Act Amendments (CAAA) Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Advisory Council on Cl ean Air Compliance Analysis: Costs and

Benefits of the CAAA. EPA-SA B-COUNCIL-ADV-00-002. October.

U.S. Environmental Protection Agency, 2004a. Fi nal Regulatory Analysis: Control of Emissions from Nonroad Diesel Engines. EPA420-R- 04-007. Prepared by Office of Air and

Radiation. Available at http://www.epa.gov/nonro ad-diesel/2004fr/420r04007.pdf

U.S. Environmental Protection Agency, 2004b. Air Quality Criteria fo r Particulate Matter

Volume II of II. National Center for Envir onmental Assessment, Office of Research and Development, U.S. Environmental Protecti on Agency, Research Triangle Park, NC

EPA/600/P-99/002bF

U.S. Environmental Protection Agency, 2005. Re gulatory Impact Analysis for the Clean Air Interstate Rule. EPA 452/-03-001. Prepared by Office of Air and Radiation. Available

at: http://www.epa.gov/intersta teairquality/tsd0175.pdf

U.S. Environmental Protection Agency, 2006. Air Quality Criteria for Ozone and Related

Photochemical Oxidants Volume I of III. National Center for Environmental Assessment, Office of Research and Deve lopment, U.S. Environmental Protection Agency, Research Triangle Park, NC EPA 600/R-05/004aF

### 5-112 U.S. Environmental Protection Agency Science Advisory Board. 2004a. Review of the Draft Analytical Plan for EPA's Second Prospective Analysis – Benefits and Costs of the Clean Air Act, 1990-2020: An Advisory by the Advisory Council for Clean Air Compliance Analysis. EPA-SAB-COUNCI L-ADV-04-004. December.

U.S. Environmental Protection Agency Science A dvisory Board. 2004b. Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analysis –

Benefits and Costs of the Clean Air Act, 1990-2020. EPA- SAB-COUNCIL-ADV-04-

002 March.

U.S. Environmental Protection Agency Scien ce Advisory Board. 2004c. Advisory Council onClean Air Compliance Analysis Response to Agency Request on Cessation Lag. EPACOUNCIL-LTR-05-001. December.

U.S. Environmental Protection Agency Science Advisory Board. 2005. EPA’s Review of the National Ambient Air Quality Standards for Particulate Matter (S econd Draft PM Staff Paper, January 2005). EPA-SAB-CASAC-05-007. June.

U.S. Office of Management and Budget (OMB). 2003. Circular A-4 Guidance for Federal Agencies Preparing Regulatory Analyses, Available at: <http://www/whitehouse.gov/omb/inforeg/iraguide.html>.

Vedal, S., J. Petkau, R. White, and J. Blair. 199 8. “Acute Effects of Am bient Inhalable Particles

in Asthmatic and Nonasthmatic Children.” American Journal of Respiratory and Critical

Care Medicine 157(4):1034-1043.

Viscusi, V.K., and J.E. Aldy. 2003. “The Valu e of a Statistical Life: A Critical Review of Market Estimates Throughout the World.” Journal of Risk and Uncertainty 27(1):5-76.

Viscusi, W.K., W.A. Magat, and J. Huber. 1991. “Pricing Environmental Health Risks: Survey

Assessments of Risk-Risk and Risk-Dollar Trade-Offs for Chronic Bronchitis.” Journal of Environmental Economics and Management 21:32-51.

Whittemore, A.S., and E.L. Korn. 1980. “Asthm a and Air Pollution in the Los Angeles Area.” American Journal of Public Health 70:687-696.

Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. 1990. “Medical Costs of Coronary Artery Disease

in the United States.” American Journal of Cardiol ogy 65(7):432-440.

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected Causes of Postneonatal Infant Mortality and Particulate Air Pollution in the United

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

Woodruff TJ, Parker JD, Schoendorf KC. 2006. Fi ne particulate matter (PM2.5) air pollution and selected causes of postneonatal infant mortality in California. Environmental Health Perspectives 114(5):786-90.

### 5-113 Yu, O., L. Sheppard, T. Lumley, J.Q. Koenig, a nd G.G. Shapiro. 2000. “Effects of Ambient Air Pollution on Symptoms of Asthma in S eattle-Area Children Enrolled in the CAMP Study.” Environmental Health Perspect ives 108(12):1209-1214.

Zanobetti, A. and J. Schwartz. 2005. The Eff ect of Particulate Air Pollution on Emergency Admsissions for Myocardial Infarction: A Multicity Case-Crossover Analysis. Environmental Health Perspectives. 113: 978-982.

### 5-114