5D-1

APPENDIX 5D: DISCUSSION OF EFFECT ESTIMATES REFLECTED IN THE 

DEVELOPMENT OF DOLLAR-PER-TON VALUES USED IN MODELING PM2.5 

COBENEFITS

Overview

This section describes how we selected effect estimates to estimate the benefits of 

reducing PM2.5 (see section 5.4.4). This section mirrors that covering effect estimate selection for 

ozone presented in section 5.6.3 in terms of organization (in some cases, we have repeated 

introductory/setup content here to facilitate review by the reader).

The first step in selecting effect coefficients is to identify the health endpoints to be 

quantified. We base our selection of health endpoints on consistency with the EPA’s Integrated 

Science Assessments (which replace previous “Criteria Documents”), with input and advice from 

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

scientific literature in developing benefits analyses for the EPA’s Report to Congress on The 

Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a). In addition, we have 

included more recent epidemiology studies from the PM ISA (U.S. EPA, 2009b), the PM 

Provisional Assessment (U.S. EPA, 2012b).1In general, we follow a weight of evidence 

approach, based on the biological plausibility of effects, availability of concentration-response 

functions from well conducted peer-reviewed epidemiological studies, cohesiveness of results 

across studies, and a focus on endpoints reflecting public health impacts (like hospital 

admissions) rather than physiological responses (such as changes in clinical measures like 

Forced Expiratory Volume [FEV1]).

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

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

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

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

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

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

population-level impacts of reductions in ambient pollution levels in a health impact assessment.

 

1 The peer-reviewed studies in the Provisional Assessment have not yet undergone external review by the SAB. 
5D-2

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

the relationship between PM2.5 and adverse human health effects. We evaluated epidemiological 

studies using the selection criteria summarized in Table 5-6 (see section 5.6.3). These criteria 

include consideration of whether the study was peer-reviewed, the match between the pollutant 

studied and the pollutant of interest, the study design and location, and characteristics of the 

study population, among other considerations. In general, the use of concentration-response 

functions from more than a single study can provide a more representative distribution of the 

effect estimate. However, there are often differences between studies examining the same 

endpoint, making it difficult to pool the results in a consistent manner. For example, studies may 

examine different pollutants or different age groups. For this reason, we consider very carefully 

the set of studies available examining each endpoint and select a consistent subset that provides a 

good balance of population coverage and match with the pollutant of interest. In many cases, 

either because of a lack of multiple studies, consistency problems, or clear superiority in the 

quality or comprehensiveness of one study over others, a single published study is selected as the 

basis of the effect estimate.

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

selected, they are quantitatively combined or pooled to derive a more robust estimate of the 

relationship. The BenMAP Manual Technical Appendices for an earlier version of the program 

provides details of the procedures used to combine multiple impact functions (Abt Associates, 

2012). In general, we used fixed or random effects models to pool estimates from different single 

city studies of the same endpoint. Fixed effect pooling simply weights each study’s estimate by 

the inverse variance, giving more weight to studies with greater statistical power (lower 

variance). Random effects pooling accounts for both within-study variance and between-study 

variability, due, for example, to differences in population susceptibility. We used the fixed effect 

model as our null hypothesis and then determined whether the data suggest that we should reject 

this null hypothesis, in which case we would use the random effects model.2 Pooled impact 

functions are used to estimate hospital admissions and asthma exacerbations. When combining 

 

2 EPA recently changed the algorithm BenMAP uses to calculate study variance, which is used in the pooling 

process. Prior versions of the model calculated population variance, while the version used here calculated sample 

variance. This change did not affect the selection of random or fixed effects for the pooled incidence estimates 

between the proposal and final RIA.
5D-3

evidence across multi-city studies (e.g., cardiovascular hospital admission studies), we use equal 

weights pooling. The effect estimates drawn from each multi-city study are themselves pooled 

across a large number of urban areas. For this reason, we elected to give each study an equal 

weight rather than weighting by the inverse of the variance reported in each study. For more 

details on methods used to pool incidence estimates, see the BenMAP Manual Appendices (Abt 

Associates, 2012).

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

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

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

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

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

available.

The specific studies from which effect estimates for the core analysis for PM2.5

(generated using the dollar-per-ton approach – see section 5.4.4) are presented in Table 5-8 and 

repeated below for ease of access in in Table 5D-1. We highlight in red those studies that have 

been added since the benefits analysis conducted for the O3 reconsideration (U.S. EPA, 2010d) 

or the O3 NAAQS RIA (U.S. EPA, 2008). In all cases where effect estimates are drawn directly 

from epidemiological studies, standard errors are used as a partial representation of the 

uncertainty in the size of the effect estimate. 
5D-4

Table 5D-1. Health Endpoints and Epidemiological Studies Used to Quantify PM2.5-

related Health Impacts in the Core Analysis a

Endpoint Study

Study 

Population

Relative Risk or Effect Estimate (β)

(with 95th Percentile Confidence 

Interval or SE, respectively)

Premature Mortality

Premature mortality—

cohort study, all-cause

Krewski et al. (2009) > 29 years

> 24 years

RR = 1.06 (1.04–1.06) per 10 μg/m3

Lepeule et al. (2012) RR = 1.14 (1.07–1.22) per 10 μg/m3

Premature mortality—

all-cause

Woodruff et al. (1997) Infant (< 1 

year)

OR = 1.04 (1.02–1.07) per 10 μg/m3

Chronic Illness

Nonfatal heart attacks Peters et al. (2001) Adults (> 18 

years)

OR = 1.62 (1.13–2.34) per 20 μg/m3

Pooled estimate:

Pope et al. (2006) β = 0.00481 (0.00199)

Sullivan et al. (2005) β = 0.00198 (0.00224)

Zanobetti et al. (2009) β = 0.00225 (0.000591)

Zanobetti and Schwartz (2006) β = 0.0053 (0.00221)

Hospital Admissions 

Respiratory Zanobetti et al. (2009)—ICD 

460-519 (All respiratory)

> 64 years β=0.00207 (0.00446)

Kloog et al. (2012)—ICD 460-

519 (All Respiratory

β=0.0007 (0.000961)

Moolgavkar (2000)—ICD 490–

496 (Chronic lung disease)

18–64 years 1.02 (1.01–1.03) per 36 μg/m3

Babin et al. (2007)—ICD 493 

(asthma)

< 19 years β=0.002 (0.004337)

Sheppard (2003)—ICD 493 

(asthma) < 18 RR = 1.04 (1.01–1.06) per 11.8 μg/m3

Cardiovascular Pooled estimate:

Zanobetti et al. (2009)—ICD 

390-459 (all cardiovascular)

> 64 years

β=0.00189 (0.000283)

Peng et al. (2009)—ICD 426-

427; 428; 430-438; 410-414; 

429; 440-449 (Cardio-, cerebroand peripheral vascular disease)

β=0.00068

(0.000214)

Peng et al. (2008)—ICD 426-

427; 428; 430-438; 410-414; 

429; 440-449 (Cardio-, cerebroand peripheral vascular disease)

β=0.00071

(0.00013)

Bell et al. (2008)—ICD 426-

427; 428; 430-438; 410-414; 

429; 440-449 (Cardio-, cerebroand peripheral vascular disease)

β=0.0008

(0.000107)

Moolgavkar (2000)—ICD 390–

429 (all cardiovascular)

20–64 years RR=1.04 (t statistic: 4.1) per 10 μg/m3

Asthma-related 

emergency department 

visits

Pooled estimate:

Mar et al. (2010)

All ages RR = 1.04 (1.01–1.07) per 7 μg/m3

Slaughter et al. (2005) RR = 1.03 (0.98–1.09) per 10 μg/m3

Glad et al. (2012) β=0.00392 (0.002843)

Other Health Endpoints

Acute bronchitis Dockery et al. (1996) 8–12 years OR = 1.50 (0.91–2.47) per 14.9 μg/m3
5D-5

Asthma exacerbations Pooled estimate:

Ostro et al. (2001) (cough, 

wheeze and shortness of breath) 

b

6–18 years b

OR = 1.03 (0.98–1.07) 

OR = 1.06 (1.01–1.11) 

OR = 1.08 (1.00–1.17) per 30 μg/m3

Mar et al. (2004) (cough, 

shortness of breath)

RR = 1.21 (1–1.47) per 

RR = 1.13 (0.86–1.48) per 10 μg/m3

Work loss days Ostro (1987) 18–65 years β=0.0046 (0.00036)

Acute respiratory 

symptoms (MRAD)

Ostro and Rothschild (1989) 

(Minor restricted activity days) 18–65 years β=0.00220 (0.000658)

Upper respiratory 

symptoms Pope et al. (1991) Asthmatics, 9–11 years 1.003 (1–1.006) per 10 μg/m3

Lower respiratory 

symptoms Schwartz and Neas (2000) 7–14 years OR = 1.33 (1.11–1.58) per 15 μg/m3

a Studies highlighted in red represent updates incorporated since the O3 NAAQS RIA (U.S. EPA, 2008). These 

updates were introduced in the PM NAAQS RIA (U.S. EPA, 2012).

b The original study populations were 8 to 13 for the Ostro et al. (2001) study and 7 to 12 for the Mar et al. (2004) 

study. Based on advice from the SAB-HES, we extended the applied population to 6-18, reflecting the common 

biological basis for the effect in children in the broader age group. See: U.S. EPA-SAB (2004) and NRC (2002).

5D.1 PM2.5 Premature Mortality Effect Coefficients

Core Mortality Effect Coefficients for Adults. A substantial body of published scientific 

literature documents the association between elevated PM2.5 concentrations and increased 

premature mortality (U.S. EPA, 2009b). This body of literature reflects thousands of 

epidemiology, toxicology, and clinical studies. The PM ISA completed as part of the most recent 

review of the PM standards, which was twice reviewed by the SAB-CASAC (U.S. EPA-SAB, 

2009, 2009b), concluded that there is a causal relationship between mortality and both long-term 

and short-term exposure to PM2.5 based on the entire body of scientific evidence (U.S. EPA, 

2009b). The size of the mortality effect estimates from epidemiological studies, the serious 

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

risk reduction the most significant health endpoint quantified in this analysis. 

Researchers have found statistically significant associations between PM2.5 and 

premature mortality using different types of study designs. Time-series methods have been used 

to relate short-term (often day-to-day) changes in PM2.5 concentrations and changes in daily 

mortality rates up to several days after a period of elevated PM2.5 concentrations. Cohort 

methods have been used to examine the potential relationship between community-level PM2.5

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

rates that have been adjusted for individual level risk factors. When choosing between using 

short-term studies or cohort studies for estimating mortality benefits, cohort analyses are thought 
5D-6

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

account for the effects of long-term exposures as well as some fraction of short-term exposures 

(Kunzli et al., 2001; NRC, 2002). The NRC 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). The NRC further notes that “the overall effect estimates may be a combination of effects 

from long-term exposure plus some fraction from short-term exposure. The amount of overlap is 

unknown” (NRC, 2002, p. 108-9). To avoid double counting, we focus on applying the risk 

coefficients from the long-term cohort studies in estimating the mortality impacts of reductions 

in PM2.5.

Over the last two decades, several studies using “prospective cohort” designs have been 

published that are consistent with the earlier body of literature. Two prospective cohort studies, 

often referred to as the Harvard “Six Cities Study” (Dockery et al., 1993; Laden et al., 2006; 

Lepeule et al., 2012) and the “American Cancer Society” or “ACS study” (Pope et al., 1995; 

Pope et al., 2002; Pope et al., 2004; Krewski et al., 2009), provide the most extensive analyses of 

ambient PM2.5 concentrations and mortality. These studies have found consistent relationships 

between fine particle indicators and premature mortality across multiple locations in the United 

States. The credibility of these two studies is further enhanced by the fact that the initial 

published studies (Pope et al., 1995; Dockery et al., 1993) were subject to extensive 

reexamination and reanalysis by an independent team of scientific experts commissioned by the 

Health Effect Institute (HEI) and by a Special Panel of the HEI Health Review Committee 

(Krewski et al., 2000). Publication of studies confirming and extending the findings of the 1993 

Six Cities Study and the 1995 ACS study using more recent air quality and a longer follow-up 

period for the ACS cohort provides additional validation of the findings of these original studies 

(Pope et al., 2002, 2004; Laden et al., 2006; Krewski et al., 2009; Lepeule et al., 2012). The 

SAB-HES also supported using these two cohorts for analyses of the benefits of PM reductions, 

and concluded, “the selection of these cohort studies as the underlying basis for PM mortality 

benefit estimates to be a good choice. These are widely cited, well studied and extensively 

reviewed data sets” (U.S. EPA-SAB, 2010a). As both the ACS and Six Cities studies have 

inherent strengths and weaknesses, we present benefits estimates using relative risk estimates 

from the most recent extended reanalysis of these cohorts (Krewski et al., 2009; Lepeule et al., 

2012). Presenting results using both ACS and Six Cities is consistent with other recent RIAs 
5D-7

(e.g., U.S. EPA, 2006, 2010c, 2011b, 2011c). The PM ISA concludes that the ACS and Six 

Cities cohorts provide the strongest evidence of the association between long-term PM2.5

exposure and premature mortality with support from a number of additional cohort studies 

(described below).

The extended analyses of the ACS cohort data (Krewski et al., 2009) provides additional 

refinements to the analysis of PM-related mortality by (a) extending the follow-up period by 

2 years to the year 2000, for a total of 18 years; (b) incorporating almost double the number of 

urban areas (c) addressing confounding by spatial autocorrelation by incorporating ecological, or 

community-level, co-variates; (d) performing an extensive spatial analysis using land use 

regression modeling in two large urban areas. These enhancements make this analysis wellsuited for the assessment of mortality risk from long-term PM2.5 exposures for the EPA’s 

benefits analyses. 

In 2009, the SAB-HES again reviewed the choice of mortality risk coefficients for 

benefits analysis, concluding that “[t]he Krewski et al. (2009) findings, while informative, have 

not yet undergone the same degree of peer review as have the aforementioned studies. Thus, the 

SAB-HES recommends that EPA not use the Krewski et al. (2009) findings for generating the 

Primary Estimate” (U.S. EPA-SAB, 2010a). Since this time, the Krewski et al. (2009) has 

undergone additional peer review, which we believe strengthens the support for including this 

study in this RIA. For example, the PM ISA (U.S. EPA, 2009b) included this study among the 

key mortality studies. In addition, the risk assessment supporting the PM NAAQS (U.S. EPA, 

2010a) utilized risk coefficients drawn from the Krewski et al. (2009) study, the most recent 

reanalysis of the ACS cohort data. The risk assessment cited a number of advantages that 

informed the selection of the Krewski et al. (2009) study as the source of the core effect 

estimates, including the extended period of observation, the rigorous examination of model 

forms and effect estimates, the coverage for ecological variables, and the large dataset with over 

1.2 million individuals and 156 MSAs (U.S. EPA, 2010a). The CASAC also provided extensive 

peer review of the risk assessment and supported the use of effect estimates from this study (U.S. 

EPA-SAB, 2009, 2010b, c). 
5D-8

Consistent with the Quantitative Health Risk Assessment for Particulate Matter (U.S. 

EPA, 2010a) which was reviewed by the CASAC (U.S. EPA-SAB, 2009), we use the all-cause 

mortality risk estimate based on the random-effects Cox proportional hazard model that 

incorporates 44 individual and 7 ecological covariates (RR=1.06, 95% confidence intervals 1.04–

1.08 per 10μg/m3increase in PM2.5). The relative risk estimate (1.06 per 10μg/m3increase in 

PM2.5) is identical to the risk estimate drawn from the earlier Pope et al. (2002) study, though the 

confidence interval around the Krewski et al. (2009) risk estimate is tighter.

In the most recent Six Cities study, which was published after the last SAB-HES review, 

Lepeule et al. (2012) evaluated the sensitivity of previous Six-Cities results to model 

specifications, lower exposures, and averaging time using eleven additional years of cohort 

follow-up that incorporated recent lower exposures. The authors found significant associations 

between PM2.5 exposure and increased risk of all-cause, cardiovascular and lung cancer 

mortality. The authors also concluded that the concentration-response relationship was linear 

down to PM2.5 concentrations of 8 μg/m3, and that mortality rate ratios for PM2.5 fluctuated over 

time, but without clear trends, despite a substantial drop in the sulfate fraction. We use the allcause mortality risk estimate based on a Cox proportional hazard model that incorporates 3 

individual covariates. (RR=1.14, 95% confidence intervals 1.07–1.22 per 10 μg/m3increase in 

PM2.5). The relative risk estimate is slightly smaller than the risk estimate drawn from Laden et 

al. (2006), with relatively smaller confidence intervals.

Implicit in the calculation of PM2.5-related premature mortality impacts are several key 

assumptions, which are described in further detail later in this chapter. First, we assume that 

there is a “cessation” lag in time between the reduction in PM exposure and the full reduction in 

mortality risk that affects the timing (and thus discounted monetary valuation) of the resulting 

premature deaths (see section 5.6.4.1). Second, following conclusions of the PM ISA, we assume 

that all fine particles are equally potent in causing premature mortality (see section 5.7.3). Third, 

following conclusions of the PM ISA, we assume that the health impact function for fine 

particles is linear within the range of ambient concentrations affected by these standards (see 

section 5.7.3).
5D-9

Alternate Mortality Effect Coefficients for Adults. In addition to the ACS and Six Cities 

cohorts, several recent cohort studies conducted in North America provide evidence for the 

relationship between long-term exposure to PM2.5 and the risk of premature death. Many of these 

additional cohort studies are described in the PM ISA (U.S. EPA, 2009b) and the Provisional 

Assessment (U.S. EPA, 2012b) (and thus not summarized here).3,4 Table 5D-2 provides the 

effect estimates from each of these cohort studies for all-cause, cardiovascular, cardiopulmonary, 

and ischemic heart disease (IHD) mortality as well as the lowest measured air quality level 

(LML) and mean concentration in the study. 

We also draw upon the results of the 2006 expert elicitation sponsored by the EPA 

(Roman et al., 2008; IEc, 2006) to demonstrate the sensitivity of the benefits estimates to 12 

expert-defined concentration-response functions. The PM2.5 expert elicitation and the derivation 

of effect estimates from the expert elicitation results are described in detail in the 2006 PM2.5

NAAQS RIA (U.S. EPA, 2006), the elicitation summary report (IEc, 2006) and Roman et al. 

(2008), and so we summarize here the key attributes of this study relative to the interpretation of 

the estimates of PM-related mortality reported here. We describe also how the epidemiological

literature has evolved since the expert elicitation was conducted in 2005 and 2006. 

 

3

It is important to note that the newer studies in the Provisional Assessment are published in peer-reviewed journals 

and meet our study selection criteria, but they have not been assessed in the context of an Integrated Science 

Assessment nor gone through review by the SAB. In addition, only the ACS and H6C cohort studies have been 

recommended by the SAB as appropriate for benefits analysis of national rulemakings.

4

In this chapter, we only describe multi-state cohort studies. There are additional cohort studies that we have not 

included in this list, including cohort studies that focus on single cities (e.g., Gan et al., 2012) and cohort studies 

focusing on methods development. In Appendix 5A, we provide additional information regarding cohort studies in 

California, which is the only state for which we identified single state cohorts. 
5D-10

Table 5D-2. Summary of Effect estimates from Associated with Change in Long-Term 

Exposure to PM2.5 in Recent Cohort Studies in North America

Study Cohort (age)

LML 

(μg/m3)

Mean

(μg/m3)

Hazard Ratios per 10 μg/m3 Change in PM2.5

(95th percentile confidence intervals)

All Causes Cardiovascular Cardiopulmonary IHD

Pope et al. 

(2002)

ACS 

(age >30)

7.5 18.2 1.06

(1.02–1.11)

1.12

(1.08–1.15)

1.09

(1.03–1.16)

N/A

Laden et al. 

(2006)

Six Cities

(age > 25)

10 16.4 1.16

(1.07–1.26)

1.28

(1.13–1.44)

N/A N/A

Lipfert et al. 

(2006)a

Veterans

(age 39–63)

<14.1 14.3 1.15

(1.05–1.25)

N/A N/A N/A

Miller et al. 

(2007)b

WHI

(age 50–79)

3.4 13.5 N/A 1.76

(1.25–2.47)

N/A 2.21

(1.17–4.16)

Eftim et al. 

(2008)

Medicare (age > 

65)

6 13.6 1.21

(1.15–1.27)

N/A N/A N/A

Zeger et al. 

(2008)c

Medicare (age > 

65)

<9.8 13.2 1.068

(1.049–1.087)

N/A N/A N/A

Krewski et 

al. (2009)d

ACS

(age >30)

5.8 14 1.06

(1.04–1.08)

N/A 1.13

(1.10–1.16)

1.24

(1.19–1.29)

Puett et al. 

(2009)b

NHS 

(age 30–55)

5.8 13.9 1.26

(1.02–1.54)

N/A N/A 2.02

(1.07–3.78)

Crouse et al. 

(2011)d,e

Canadian 

census

1.9 8.7 1.06

(1.01–1.10)

N/A N/A N/A

Puett et al. 

(2011)f

Health 

Professionals 

(age 40–75)

<14.4 17.8 0.86

(0.70–1.00)

1.02

(0.84–1.23)

N/A N/A

Lepeule et 

al. (2012)d

Six Cities

(age > 25)

8 15.9 1.14

(1.07–1.22)

1.26

(1.14–1.40)

N/A N/A

a Low socio-economic status (SES) men only. Used traffic proximity as a surrogate of exposure. 

b Women only.

c Reflects risks in the Eastern U.S. Risks in the Central U.S. were higher, but the authors found no association in the 

Western U.S.

d Random effects Cox model with individual and ecologic covariates.

e Canadian population.

f Men with high socioeconomic status only.

The primary goal of the 2006 study was to elicit from a sample of health experts 

probabilistic distributions describing uncertainty in estimates of the reduction in mortality among 

the adult U.S. population resulting from reductions in ambient annual average PM2.5 levels. 

These distributions were obtained through a formal interview protocol using methods designed to 

elicit subjective expert judgments. These experts were selected through a peer-nomination 

process and included experts in epidemiology, toxicology, and medicine. The elicitation 

interview consisted of a protocol of carefully structured questions, both qualitative and 

quantitative, about the nature of the PM2.5-mortality relationship designed to build twelve 

individual distributions for the coefficient (or slope) of the C-R function relating changes in 

annual average PM2.5 exposures to annual, adult all-cause mortality. The elicitation also provided 

useful information regarding uncertainty characterization in the PM2.5-mortality relationship. 
5D-11

Specifically, during their interviews, the experts highlighted several uncertainties inherent within 

the epidemiology literature, such as causality, concentration thresholds, effect modification, the 

role of short- and long-term exposures, potential confounding, and exposure misclassification. In 

Appendix 5C, we evaluate each of these uncertainties in the context of this health impact 

assessment. For several of these uncertainties, such as causality, we are able to use the expertderived functions to quantify the impacts of applying different assumptions. The elicitation 

received favorable peer review in 2006 (Mansfield and Patil, 2006).

Prior to providing a quantitative estimate of the risk of premature death associated with 

long-term PM2.5 exposure, the experts answered a series of “conditioning questions.” One such 

question asked the experts to identify which epidemiological studies they found most 

informative. The “ideal study attributes”5according to the experts included:

1. Geographic representation of the entire U.S. (e.g., monitoring sites across the country)

2. Collection of information on individual risk factors and residential information both at 

the beginning and throughout the follow-up period

3. Large sample size that is representative of the general U.S. population

4. Collection of genetic information from cohort members to identify and assess potential 

effect modifiers

5. Monitoring of individual exposures (e.g., with a personal monitor)

6. Collection of data on levels of several co-pollutants (not only those that are monitored for 

compliance purposes

7. Accurate characterization of outcome (i.e., cause of death)

8. Follow-up for a long period of time, up to a lifetime

9. Prospective study design

Although no single epidemiological study completely satisfies each of these criteria, the 

experts determined that the ACS and Six Cities cohort studies best satisfy a majority of these 

ideal attributes. To varying degrees the studies examining these two cohorts: are geographically 

representative; have collected information on individual risk factors; include a large sample size; 

have collected data on co-pollutants in the case of the ACS study; have accurately characterized 

the health outcome; include a long (and growing) follow-up period; and, are prospective in 

nature. The experts also noted a series of limitations in these two cohort studies. In the case of 

 

5 These criteria are substantively similar to EPA’s study selection criteria identified in Table 5-5 of this chapter.
5D-12

the Six Cities study (Laden et al., 2006), the experts identified the “small sample size, limited 

number of cities, and concerns about representativeness of the six cities for the U.S. as a whole” 

as weaknesses. When considering the ACS study (Pope et al., 2002), the experts indicated that 

the “method of recruitment for the study, which resulted in a group with higher income, more 

education, and a greater proportion of whites than is representative of the general U.S. 

population” represented a shortcoming. Several experts also argued that because the ACS study 

relied upon “...whatever monitors were available to the study...a single monitor represent[ed] 

exposure for an entire metropolitan area...whereas [the Six Cities study] often had exposures 

assigned at the county level.” Despite these limitations, the experts considered the Pope et al. 

(2002) extended analysis of the ACS cohort and the Laden et al. (2006) extended analysis of the 

Six Cities cohort to be particularly influential in their opinions (see Exhibit 3-3 of the elicitation 

summary report [IEc, 2006]). 

Please note that the benefits estimates results presented are not the direct results from the 

studies or expert elicitation; rather, the estimates are based in part on the effect coefficients 

provided in those studies or by experts. In addition, the experts provided distributions around 

their mean PM2.5 effect estimates, which provides more information regarding the overall range 

of uncertainty, and this overall range is larger than the range of the mean effect estimates from 

each of the experts.

Since the completion of the EPA’s expert elicitation in 2006, additional epidemiology 

literature has become available, including 9 new multi-state cohort studies shown in Table 5-12. 

This newer literature addresses some of the weaknesses identified in the prior literature. For 

example, in an attempt to improve its characterization of population exposure the most recent 

extended analysis of the ACS cohort Krewski et al. (2009) incorporates two case studies that 

employ more spatially resolved estimates of population exposure. 

In light of the availability of this newer literature, we have updated the presentation of 

results in the RIA. Specifically, we focus the core analysis on results derived from the two most 

recent studies of the ACS and Six Cities cohorts (Krewski et al., 2009; Lepeule et al., 2012). 

Because the other multi-state cohorts generally have limited geography and age/gender 

representativeness, these limitations preclude us from using these studies in our core benefits 
5D-13

results, and we instead present the risk coefficients from these other multi-state cohorts in Table 

5D-2. In addition, we now include as a sensitivity analysis, mortality estimates based on 

application of the full set of expert-derived effect estimates (see Appendix 5B, section 5B.2). We 

do not combine the expert results in order to preserve the breadth and diversity of opinion on the 

expert panel (Roman et. al., 2008). This presentation of the expert-derived results is generally 

consistent with SAB advice (U.S. EPA-SAB, 2008), which recommended that the EPA 

emphasize that “scientific differences existed only with respect to the magnitude of the effect of 

PM2.5 on mortality, not whether such an effect existed” and that the expert elicitation “supports 

the conclusion that the benefits of PM2.5 control are very likely to be substantial”. Although it is 

possible that the newer literature could revise the experts’ quantitative responses if elicited again, 

we believe that these general conclusions are unlikely to change.

Mortality Effect Coefficients for Infants. In addition to the adult mortality studies described 

above, several studies show an association between PM exposure and premature mortality in 

children under 5 years of age.6 The PM ISA states that less evidence is available regarding the 

potential impact of PM2.5 exposure on infant mortality than on adult mortality and the results of 

studies in several countries include a range of findings with some finding significant 

associations. Specifically, the PM ISA concluded that evidence exists for a stronger effect at the 

post-neonatal period and for respiratory-related mortality, although this trend is not consistent 

across all studies. In addition, compared to avoided premature deaths estimated for adult 

mortality, avoided premature deaths for infants are significantly smaller because the number of 

infants in the population is much smaller than the number of adults and the epidemiology studies 

on infant mortality provide smaller risk coefficients associated with exposure to PM2.5.

In 2004, the SAB-HES noted the release of the WHO Global Burden of Disease Study 

focusing on ambient air, which cites several recently published time-series studies relating daily 

PM exposure to mortality in children (U.S. EPA-SAB, 2004). The SAB-HES also cites the study 

by Belanger et al. (2003) as corroborating findings linking PM exposure to increased respiratory 

inflammation and infections in children. A study by Chay and Greenstone (2003) found that 

reductions in TSP caused by the recession of 1981–1982 were statistically associated with 

 

6 For the purposes of this analysis, we only calculate benefits for infants age 0–1, not all children under 5 years old.
5D-14

reductions in infant mortality at the county level. With regard to the cohort study conducted by 

Woodruff et al. (1997), the SAB-HES notes several strengths of the study, including the use of a 

larger cohort drawn from a large number of metropolitan areas and efforts to control for a variety 

of individual risk factors in infants (e.g., maternal educational level, maternal ethnicity, parental 

marital status, and maternal smoking status). Based on these findings, the SAB-HES 

recommended that the EPA incorporate infant mortality into the primary benefits estimate and 

that infant mortality be evaluated using an impact function developed from the Woodruff et al. 

(1997) study (U.S. EPA-SAB, 2004).

In 2010, the SAB-HES again noted the increasing body of literature relating infant 

mortality and PM exposure and supported the inclusion of infant mortality in the monetized 

benefits (U.S. EPA-SAB, 2010a). The SAB-HES generally supported the approach of estimating 

infant mortality based on Woodruff et al. (1997) and noted that a more recent study by Woodruff 

et al. (2006) continued to find associations between PM2.5 and infant mortality in California. The 

SAB-HES also noted, “when PM10 results are scaled to estimate PM2.5 impacts, the results yield 

similar risk estimates.” Consistent with the Costs and Benefits of the Clean Air Act (U.S. EPA, 

2011a), we continue to rely on the earlier 1997 study in part due to the national–scale of the 

earlier study.

5D.2 Hospital Admissions and Emergency Department Visits

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

an extensive body of literature examining the relationship between hospital admissions and air 

pollution. For this reason, we pool together the incidence estimates using several different 

studies for many of the hospital admission endpoints. In addition, some studies have examined 

the relationship between air pollution and emergency department visits. Since most emergency 

department visits do not result in an admission to the hospital (i.e., most people going to the 

emergency department are treated and return home), we treat hospital admissions and emergency 

department visits separately, taking account of the fraction of emergency department visits that 

are admitted to the hospital. Specifically, within the baseline incidence rates, we parse out the 

scheduled hospital visits from unscheduled ones as well as the hospital visits that originated in 

the emergency department.
5D-15

The two main groups of hospital admissions are estimated in this analysis for PM2.5: 

respiratory admissions and cardiovascular admissions. There is not sufficient evidence linking 

PM2.5 with other types of hospital admissions. Both asthma- and cardiovascular-related visits 

have been linked to PM2.5 in the United States, though as we note below, we are able to assign an 

economic value to asthma-related events only. To estimate the effects of PM2.5 air pollution 

reductions on asthma-related ER visits, we use the effect estimate from a study of children 18 

and under by Mar et al. (2010), Slaughter et al. (2005), and Glad et al. (2012). The first two 

studies examined populations 0 to 99 in Washington State, while Glad et al. examined 

populations 0-99 in Pittsburgh, PA. Mar and colleagues perform their study in Tacoma, while 

Slaughter and colleagues base their study in Spokane. We apply random/fixed effects pooling to 

combine evidence across these two studies.

To estimate avoided incidences of cardiovascular hospital admissions associated with 

PM2.5, we used studies by Moolgavkar (2000), Zanobetti et al. (2009), Peng et al. (2008, 2009) 

and Bell et al., (2008). Only Moolgavkar (2000) provided a separate effect estimate for adults 20 

to 64, while the remainder estimate risk among adults over 64.7 Total cardiovascular hospital 

admissions are thus the sum of the pooled estimate for adults over 65 and the single study 

estimate for adults 20 to 64. Cardiovascular hospital admissions include admissions for 

myocardial infarctions. To avoid double-counting benefits from reductions in myocardial 

infarctions when applying the impact function for cardiovascular hospital admissions, we first 

adjusted the baseline cardiovascular hospital admissions to remove admissions for myocardial 

infarctions. We applied equal weights pooling to the multi-city studies assessing risk among 

adults over 64 because these studies already incorporated pooling across the city-level estimates. 

One potential limitation of our approach is that while the Zanobetti et al. (2009) study assesses 

all cardiovascular risk, Bell et al. (2008), and Peng et al., (2008, 2009) studies estimate a subset 

of cardiovascular hospitalizations as well as certain cerebro- and peripheral-vascular diseases. To 

 

7 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. Given the very small (<5%) difference in the effect estimates for people 65 and older with cardiovascular 

hospital admissions between the original and reanalyzed results, we do not expect this choice to introduce much 

bias. For a discussion of the GAM convergence criteria, and how it affected the size of effect coefficients reported 

by time series epidemiological studies using NMMAPS data, see: http://www.healtheffects.org/Pubs/sttimeseries.htm.
5D-16

address the potential for the pooling of these four studies to produce a biased estimate, we match 

the pooled risk estimate with a baseline incidence rate that excludes cerebro- and peripheralvascular disease. An alternative approach would be to use the Zanobetti et al. (2009) study alone, 

though this would prevent us from drawing upon the strengths of the three multi-city studies.

To estimate avoided incidences of respiratory hospital admissions associated with PM2.5, 

we used a number of studies examining total respiratory hospital admissions as well as asthma 

and chronic lung disease. We estimated impacts among three age groups: adults over 65, adults 

18 to 64 and children 0 to 17. For adults over 65, the multi-city studies by Zanobetti et al. (2009) 

and Kloog et al. (2012) provide effect coefficients for total respiratory hospital admissions 

(defined as ICD codes 460–519). We pool these two studies using equal weights. Moolgavkar et 

al. (2003) examines PM2.5 and chronic lung disease hospital admissions (less asthma) in Los 

Angeles, CA among adults 18 to 64. For children 0 to 18, we pool two studies using 

random/fixed effects. The first is Babin et al. (2007) which assessed PM2.5 and asthma hospital 

admissions in Washington, DC among children 1 to 18; we adjusted the age range for this study 

to apply to children 0 to 18. The second is Sheppard et al. (2003) which assessed PM2.5 and 

asthma hospitalizations in Seattle, Washington, among children 0 to 18.

5D.3 Acute Health Events and School/Work Loss Days

In addition to mortality, chronic illness, and hospital admissions, a number of acute 

health effects not requiring hospitalization are associated with exposure to PM2.5. The sources for 

the effect estimates used to quantify these effects are described below.

Asthma exacerbations. For this RIA, we have followed the SAB-HES recommendations 

regarding asthma exacerbations in developing the core estimate (U.S. EPA-SAB, 2004). 

Although certain studies of acute respiratory events characterize these impacts among only 

asthmatic populations, others consider the full population, including both asthmatics and nonasthmatics. For this reason, incidence estimates derived from studies focused only on asthmatics 

cannot be added to estimates from studies that consider the full population—to do so would 

double-count impacts. To prevent such double-counting, we estimated the exacerbation of 

asthma among children and excluded adults from the calculation. Asthma exacerbations 

occurring in adults are assumed to be captured in the general population endpoints such as work 
5D-17

loss days and MRADs. Finally, we note the important distinction between the exacerbation of 

asthma among asthmatic populations, and the onset of asthma among populations not previously 

suffering from asthma; in this RIA, we quantify the exacerbation of asthma among asthmatic 

populations and not the onset of new cases of asthma.

Based on advice from the SAB-HES (2004), regardless of the age ranges included in the 

source epidemiology studies, we extend the applied population to ages 6 to 18, reflecting the 

common biological basis for the effect in children in the broader age group. This age range 

expansion is also supported by NRC (2002, pp. 8, 116).

To characterize asthma exacerbations in children from exposure to PM2.5, we selected 

two studies (Ostro et al., 2001; Mar et al., 2004) that followed panels of asthmatic children. 

Ostro et al. (2001) followed a group of 138 African-American children in Los Angeles for 13 

weeks, recording daily occurrences of respiratory symptoms associated with asthma 

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

significant association between PM2.5, measured as a 12-hour average, and the daily prevalence 

of shortness of breath and wheeze endpoints. Although the association was not statistically 

significant for cough, the results were still positive and close to significance; consequently, we 

decided to include this endpoint, along with shortness of breath and wheeze, in generating 

incidence estimates (see below).

Mar et al. (2004) studied the effects of various size fractions of particulate matter on 

respiratory symptoms of adults and children with asthma, monitored over many months. The 

study was conducted in Spokane, Washington, a semiarid city with diverse sources of particulate 

matter. Data on respiratory symptoms and medication use were recorded daily by the study’s 

subjects, while air pollution data was collected by the local air agency and Washington State 

University. Subjects in the study consisted of 16 adults—the majority of whom participated for 

over a year—and nine children, all of whom were studied for over eight months. Among the 

children, the authors found a strong association between cough symptoms and several metrics of 

particulate matter, including PM2.5. However, the authors found no association between 

respiratory symptoms and PM of any metric in adults. Mar et al. therefore concluded that the 

discrepancy in results between children and adults was due either to the way in which air quality 
5D-18

was monitored, or a greater sensitivity of children than adults to increased levels of PM air 

pollution.

We employed the following pooling approach in combining estimates generated using 

effect estimates from the two studies to produce a single estimate for PM-related asthma 

exacerbation incidence. First, we used random/fixed effects pooling to combine the Ostro and 

Mar estimates for shortness of breath and cough. Next, we pooled the Ostro estimate of wheeze 

with the pooled cough and shortness of breath estimates to derive an overall estimate of asthma 

exacerbation in children.

Acute Respiratory Symptoms. We estimate three types of acute respiratory symptoms related 

to PM2.5 exposure: lower respiratory symptoms, upper respiratory symptoms, and minor 

restricted activity days (MRAD). 

Incidences of lower respiratory symptoms (e.g., wheezing, deep cough) in children aged 

7 to 14 were estimated for PM2.5 using an effect estimate from Schwartz and Neas (2000). 

Incidences of upper respiratory symptoms in asthmatic children aged 9 to 11 are estimated for 

PM2.5 using an effect estimate developed from Pope et al. (1991). Because asthmatics have 

greater sensitivity to stimuli (including air pollution), children with asthma can be more 

susceptible to a variety of upper respiratory symptoms (e.g., runny or stuffy nose; wet cough; and 

burning, aching, or red eyes). Research on the effects of air pollution on upper respiratory 

symptoms has thus focused on effects in asthmatics. 

Minor restricted activity days (MRAD) result when individuals reduce most usual daily 

activities and replace them with less strenuous activities or rest, yet not to the point of missing 

work or school. For example, a mechanic who would usually be doing physical work most of the 

day will instead spend the day at a desk doing paper and phone work because of difficulty 

breathing or chest pain. The effect of PM2.5 on MRAD was estimated using an effect estimate 

derived from Ostro and Rothschild (1989).

More recently published literature examining the relationship between short-term PM2.5

exposure and acute respiratory symptoms was available in the PM ISA (U.S. EPA, 2009b), but 

proved to be unsuitable for use in this benefits analysis. In particular, the best available study 
5D-19

(Patel et al., 2010) specified a population aged 13–20, which overlaps with the population in 

which we asses asthma exacerbation. As we describe in detail below, to avoid the chance of 

double-counting impacts, we do not estimate changes in acute respiratory symptoms and asthma 

exacerbation among populations of the same age.

Acute Bronchitis. Approximately 4% of U.S. children between the ages of 5 and 17 experience 

episodes of acute bronchitis annually (ALA, 2002). Acute bronchitis is characterized by 

coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a number of days. 

According to the MedlinePlus medical encyclopedia,8 with the exception of cough, most acute 

bronchitis symptoms abate within 7 to 10 days. Incidence of episodes of acute bronchitis in 

children between the ages of 5 and 17 were estimated using an effect estimate developed from 

Dockery et al. (1996). 

Work Loss Days. Health effects from air pollution can also result in missed days of work 

(either from personal symptoms or from caring for a sick family member). Days of work lost due 

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

also be absent from school because of respiratory or other diseases caused by exposure to air 

pollution, but we have not quantified these effects for this rule.

Work loss days. Health effects from air pollution can also result in missed days of work (either 

from personal symptoms or from caring for a sick family member). Days of work lost due to 

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

estimated the impact of PM2.5 on the incidence of work loss days in a national sample of the adult 

working population, ages 18 to 65 living in metropolitan areas. Ostro reported that two-week 

average PM2.5 levels were significantly linked to work loss days, but there was some year-to-year 

variability in the results.

5D.4 Nonfatal Acute Myocardial Infarctions (AMI) (Heart Attacks)

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

States (Mustafić et al., 2012; Peters et al., 2001; Sullivan et al., 2005; Pope et al., 2006; 

Zanobetti and Schwartz, 2006; Zanobetti et al., 2009) and other countries (Poloniecki et al., 

 

8 See http://www.nlm.nih.gov/medlineplus/ency/article/001087.htm, accessed April 2012. 
5D-20

1997; Barnett et al., 2006; Peters et al., 2005). In previous health impact assessments, we have 

relied upon a study by Peters et al. (2001) as the basis for the impact function estimating the 

relationship between PM2.5 and nonfatal heart attacks. The Peters et al. (2001) study exhibits a 

number of strengths. In particular, it includes a robust characterization of populations 

experiencing acute myocardial infarctions (AMIs). The researchers interviewed patients within 4 

days of their AMI events and, for inclusion in the study, patients were required to meet a series 

of criteria including minimum kinase levels, an identifiable onset of pain or other symptoms and 

the ability to indicate the time, place and other characteristics of their AMI pain in an interview.

Since the publication of Peters et al. (2001), a number of other single and multi-city 

studies have appeared in the literature. These studies include Sullivan et al. (2005), which 

considered the risk of PM2.5-related hospitalization for AMIs in King County, WA; Pope et al. 

(2006), based in Wasatch Range, UT; Zanobetti and Schwartz (2006), based in Boston, MA; and, 

Zanobetti et al. (2009), a multi-city study of 26 U.S. communities. Each of these single and 

multi-city studies, with the exception of Pope et al. (2006), measure AMIs using hospital 

discharge rates. Conversely, the Pope et al. (2006) study is based on a large registry with 

angiographically characterized patients—arguably a more precise indicator of AMI. Because the 

Pope et al. (2006) study reflected both myocardial infarctions and unstable angina, this produces 

a more comprehensive estimate of acute ischemic heart disease events than the other studies. 

However, unlike the Peters study (Peters et al., 2006), Pope and colleagues did not measure the 

time of symptom onset, and PM2.5 data were not measured on an hourly basis.

As a means of recognizing the strengths of the Peters study while also incorporating the 

newer evidence found in the four single and multi-city studies, we present a range of AMI 

estimates. The upper end of the range is calculated using the Peters study while the lower end of 

the range is the result of an equal-weights pooling of these four newer studies. It is important to 

note that when calculating the incidence of nonfatal AMI, the fraction of fatal heart attacks is 

subtracted to ensure that there is no double-counting with premature mortality estimates. 

Specifically, we apply an adjustment factor in the concentration-response function to reflect the 

probability of surviving a heart attack. Based on recent data from the Agency for Healthcare 

Research and Quality’s Healthcare Utilization Project National Inpatient Sample database 

(AHRQ, 2009), we identified death rates for adults hospitalized with acute myocardial infarction 
5D-21

stratified by age (e.g., 1.852% for ages 18–44, 2.8188% for ages 45–64, and 7.4339% for ages 

65+). These rates show a clear downward trend over time between 1994 and 2009 for the 

average adult and thus replace the 7% survival rate previously applied across all age groups from 

Rosamond et al. (1999).

5D.5 References

Abt Associates, Inc. 2012. BenMAP User’s Manual Appendices. Prepared for U.S. Environmental Protection 

Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. September. Available at 

<http://www.epa.gov/air/benmap/models/BenMAPAppendicesOct2012.pdf>.

Agency for Healthcare Research and Quality (AHRQ). 2009. HCUPnet, Healthcare Cost and Utilization Project.

Rockville, MD. Available at <http://hcupnet.ahrq.gov>.

American Lung Association (ALA). 2002. Trends in Asthma Morbidity and Mortality. American Lung Association, 

Best Practices and Program Services, Epidemiology and Statistics Unit. Available at 

<http://www.lungusa.org/data/asthma/ASTHMAdt.pdf>.

Babin, S. M., H. S. Burkom, et al. 2007. “Pediatric Patient Asthma-related Emergency Department Visits and 

Admissions in Washington, DC, from 2001–2004, and Associations with Air Quality, Socio-economic Status 

and Age Group.” Environmental Health 6: 9.

Barnett, Adrian G. et al. 2006. “The Effects of Air Pollution on Hospitalizations for Cardiovascular Disease in 

Elderly People in Australian and New Zealand Cities.” Environmental Health Perspectives 114(7): 1018-1023.

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.

Bell, M. L., K. Ebisu, et al. 2008. “Seasonal and Regional Short-term Effects of Fine Particles on Hospital 

Admissions in 202 US Counties, 1999–2005.” American Journal of Epidemiology 168(11): 1301-1310.

Chay, K.Y., and M. Greenstone. 2003. “The Impact of Air Pollution on Infant Mortality: Evidence from Geographic 

Variation in Pollution Shocks Induced by a Recession.” Quarterly Journal of Economics 118(3).

Crouse DL, Peters PA, van Donkelaar A, Goldberg MS, Villeneuve PJ, Brion O, et al. 2012. “Risk of Nonaccidental and Cardiovascular Mortality in Relation to Long-term Exposure to Low Concentrations of Fine 

Particulate Matter: A Canadian National-level Cohort Study.” Environmental Health Perspectives 120(5):708-

14.

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.

Eftim SE, Samet JM, Janes H, McDermott A, Dominici F. 2008. “Fine particulate matter and mortality: a 

comparison of the Six Cities and American Cancer Society cohorts with a Medicare cohort.” Epidemiology

19:209–216.
5D-22

Gan, W. Q., H. W. Davies, et al. 2012. “Association of Long-term Exposure to Community Noise and Trafficrelated Air Pollution with Coronary Heart Disease Mortality.” American Journal of Epidemiology. 1;175 

(9):898-906.

Glad, J.A., L.L. Brink, E.O. Talbott, P.C. Lee, X. Xu, M. Saul, and J. Rager. 2012. “The Relationship of Ambient O3

and PM2.5 Levels and Asthma Emergency Department Visits: Possible Influence of Gender and Ethnicity.”

Archives of Environmental & Occupational Health 62 (2): 103-108.

Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the ConcentrationResponse Relationship Between PM2.5 Exposure and Mortality. Prepared for: Office of Air Quality Planning and 

Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. September. Available at 

<http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_tsd_expert_interview_summaries.pdf>.

Kloog, I., B.A. Coull, A. Zanobetti, P. Koutrakis, J.D. Schwartz. 2012. “Acute and Chronic Effects of Particles on 

Hospital Admissions in New England.” PLoS ONE. Vol 7 (4): 1-8.

Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. Extended follow-up and spatial analysis of 

the American Cancer Society study linking particulate air pollution and mortality. HEI Research Report, 140, 

Health Effects Institute, Boston, MA.

Krewski, D., R.T. Burnett, M.S. Goldbert, K. Hoover, J. Siemiatycki, M. Jerrett, M. Abrahamowicz, and W.H. 

White. 2000. Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate 

Air Pollution and Mortality. Special Report to the Health Effects Institute. Cambridge MA. July.

Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr, and M. Studnicka. 2001. “Assessment of Deaths 

Attributable to Air Pollution: Should We Use Risk Estimates Based on Time Series or on Cohort Studies?”

American Journal of Epidemiology 153(11):1050-55.

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate Air Pollution and 

Mortality.” American Journal of Respiratory and Critical Care Medicine 173:667-672.

Lepeule J, Laden F, Dockery D, Schwartz J. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended 

Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspectives 120

(7):965-70.

Lipfert, F.W.; Wyzga, R.E.; Baty, J.D.; Miller, J.P. 2006. “Traffic Density as a Surrogate Measure of Environmental 

Exposures in Studies of Air Pollution Health Effects: Long-Term Mortality in a Cohort of US Veterans.”

Atmospheric Environment 40: 154-169.

Mansfield, Carol and Sumeet Patil, 2006. Peer Review of Expert Elicitation. Memo to Ron Evans, EPA. September 

29, 2006. RTI International. Available at 

<http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_peer_review_summary.pdf>.

Mar, T. F., J. Q. Koenig and J. Primomo. 2010. “Associations between asthma emergency visits and particulate 

matter sources, including diesel emissions from stationary generators in Tacoma, Washington.” Inhalation 

Toxicology 22 (6): 445-8.

Mar, T. F., T. V. Larson, et al. 2004. “An analysis of the association between respiratory symptoms in subjects with 

asthma and daily air pollution in Spokane, Washington.” Inhalation Toxicology 16(13): 809-15.

Miller, Kristin A., David S. Siscovick, Lianne Sheppard, Kristen Shepherd, Jeffrey H. Sullivan, Garnet L. Anderson, 

and Joel D. Kaufman. 2007. “Long-Term Exposure to Air Pollution and Incidence of Cardiovascular Events in 

Women.” New England Journal of Medicine 356 (5):447-458.
5D-23

Moolgavkar, S.H. 2000. “Air Pollution and Hospital Admissions for Diseases of the Circulatory System in Three 

U.S. Metropolitan Areas.” Journal of the Air and Waste Management Association 50:1199-1206.

Moolgavkar, S.H. 2003. “Air Pollution and Daily Deaths and Hospital Admissions in Los Angeles and Cook 

Counties.” In Revised Analyses of Time-Series Studies of Air Pollution and Health. Special Report. Boston, 

MA: Health Effects Institute.

Mustafić H, Jabre P, Caussin C et al. 2012. “Main Air Pollutants and Myocardial Infarction: A Systematic Review 

and Meta-analysis” Journal of the American Medical Association 2012;307(7):713-721.

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution 

Regulations. Washington, DC: The National Academies Press. Washington, DC.

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.

Ostro, B.D. 1987. “Air Pollution and Morbidity Revisited: A Specification 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.

Patel, M. M., S. N. Chillrud, J. C. Correa, Y. Hazi, M. Feinberg, D. Kc, S. Prakash, J. M. Ross, D. Levy and P. L. 

Kinney. 2010. “Traffic-Related Particulate Matter and Acute Respiratory Symptoms among New York City 

Area Adolescents.” Environmental Health Perspectives 118(9):1338-43.

Peng, R. D., H. H. Chang, et al. 2008. “Coarse particulate matter air pollution and hospital admissions for 

cardiovascular and respiratory diseases among Medicare patients.” Journal of the American Medical 

Association 299(18): 2172-9.

Peters A. 2005. “Particulate matter and heart disease: evidence from epidemiological studies.” Toxicology and 

Applied Pharmacology Sept 1;207(2 Suppl):477-82.

Peters A. 2006. “When a myocardial infarction comes out of the not-so-blue air.” Circulation 114(23):2430-1

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 (23): 2810-5.

Poloniecki, J.D., R.W. Atkinson., A.P. de Leon., and H.R. Anderson. 1997. “Daily Time Series for Cardiovascular 

Hospital Admissions and Previous Day’s Air Pollution in London, UK.” Occupational and Environmental 

Medicine 54(8):535-540

Pope, C. A., III, J. B. Muhlestein, et al. 2006. “Ischemic heart disease events triggered by short-term exposure to 

fine particulate air pollution.” Circulation 114(23): 2443-8.

Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. 1991. “Respiratory Health and PM10 Pollution:

A Daily Time Series Analysis.” American Review of Respiratory Diseases 144:668-674.

Pope, C.A., III, M.J. Thun, M.M. Namboodiri, D.W. Dockery, J.S. Evans, F.E. Speizer, and C.W. Heath, Jr. 1995. 

“Particulate Air Pollution as a Predictor of Mortality in a Prospective Study of U.S. Adults.” American Journal 

of Respiratory Critical Care Medicine 151:669-674.

Pope, C.A., III, R.T. Burnett, 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.
5D-24

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung Cancer, 

Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the 

American Medical Association 287:1132-1141.

Puett RC, Hart JE, Suh H, Mittleman M, Laden F. 2011. “Particulate Matter Exposures, Mortality and 

Cardiovascular Disease in the Health Professionals Follow-up Study.” Environmental Health Perspectives

119(8): 1130–1135.

Puett RC, Hart JE, Yanosky JD, Paciorek C, Schwartz J, Suh H, et al. 2009. “Chronic fine and coarse particulate 

exposure, mortality and coronary heart disease in the Nurses’ Health Study.” Environmental Health 

Perspectives 117:1697–1701.

Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, Bryan J. Hubbell, and 

Patrick L. Kinney. 2008. “Expert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine 

Particulate Matter in the U.S.” Environmental Science & Technology 42(7):2268-2274.

Rosamond, W., G. Broda, E. Kawalec, S. Rywik, A. Pajak, L. Cooper, and L. Chambless. 1999. “Comparison of 

Medical Care and Survival of Hospitalized Patients with Acute Myocardial Infarction in Poland and the United 

States.” American Journal of Cardiology 83:1180-1185

Schwartz, J., and L.M. Neas. 2000. “Fine Particles are More Strongly Associated than Coarse Particles with Acute 

Respiratory Health Effects in Schoolchildren.” Epidemiology 11:6-10.

Sheppard, L. 2003. “Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in Seattle, Washington, 

1987-1994.” In Revised Analyses of Time-Series Studies of Air Pollution and Health. Special Report. Boston, 

MA: Health Effects Institute.

Slaughter, James C et al. 2005. “Association between particulate matter and emergency room visits, hospital 

admissions and mortality in Spokane, Washington.” Journal of Exposure Analysis and Environmental 

Epidemiology 15, 153–159. 

Sullivan, J., L. Sheppard, et al. 2005. “Relation between short-term fine-particulate matter exposure and onset of 

myocardial infarction.” Epidemiology 16(1): 41-8.

U.S. Environmental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 National Ambient Air 

Quality Standards for Particulate Matter, Chapter 5. Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. October. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205—Benefits.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2008. Regulatory Impact Analysis, 2008 National Ambient Air 

Quality Standards for Ground-level O3, Chapter 6. Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. March. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/6-O3riachapter6.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter 

(Final Report). EPA-600-R-08-139F. National Center for Environmental Assessment—RTP Division.

December. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.

U.S. Environmental Protection Agency (U.S. EPA). 2010a. Quantitative Health Risk Assessment for Particulate 

Matter—Final Report. EPA-452/R-10-005. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. September. Available at 

<http://www.epa.gov/ttnnaaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2010b. Final Regulatory Impact Analysis (RIA) for the SO2 

National Ambient Air Quality Standards (NAAQS). Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. June. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/fso2ria100602full.pdf>.
5D-25

U.S. Environmental Protection Agency (U.S. EPA). 2010c. Regulatory Impact Analysis (RIA) for the Proposed 

Reconsideration of the O3 National Ambient Air Quality Standards (NAAQS). Office of Air Quality Planning 

and Standards, Research Triangle Park, NC. January. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/s1-supplemental_analysis_full.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011a. The Benefits and Costs of the Clean Air Act 1990 to 

2020: EPA Report to Congress. Office of Air and Radiation, Office of Policy, Washington, DC. March. 

Available at <http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011b. Regulatory Impact Analysis for the Federal 

Implementation Plans to Reduce Interstate Transport of Fine Particulate Matter and O3 in 27 States; 

Correction of SIP Approvals for 22 States. June. Available at 

<http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011c. Regulatory Impact Analysis for the Final Mercury and 

Air Toxics Standards. EPA-452/R-11-011. December. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2012. Regulatory Impact Analysis for the Final Revisions to the 

National Ambient Air Quality Standards for Particulate Matter. EPA-452/R-12-003. Office of Air Quality 

Planning and Standards, Health and Environmental Impacts Division. December. Available at: 

<http://www.epa.gov/pm/2012/finalria.pdf>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2008. Characterizing 

Uncertainty in Particulate Matter Benefits Using Expert Elicitation. EPA-COUNCIL-08-002. July. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/43B91173651AED9E85257487004EA6CB/$File/EPACOUNCIL-08-002-unsigned.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: National Emission 

Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing Industry. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. April. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2009b. Integrated Science Assessment for Particulate Matter 

(Final Report). EPA-600-R-08-139F. National Center for Environmental Assessment—RTP Division.

December. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010a. Review of EPA’s DRAFT 

Health Benefits of the Second Section 812 Prospective Study of the Clean Air Act. EPA-COUNCIL-10-001. 

June. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/59e06b6c5ca66597852575

e7006c5d09!OpenDocument&TableRow=2.3#2.>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010b. Review of Risk 

Assessment to Support the Review of the Particulate Matter (PM) Primary National Ambient Air Quality 

Standards—External Review Draft (September 2009). EPA-CASAC-10-003. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/BC1ECC5D539EF723852

57678006D5754/$File/EPA-CASAC-10-003-unsigned.pdf>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010c. CASAC Review of 

Quantitative Health Risk Assessment for Particulate Matter—Second External Review Draft (February 2010). 

EPA-CASAC-10-008. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/BC4F6E77B63851558525

77070002F09F/$File/EPA-CASAC-10-008-unsigned.pdf>.
5D-26

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004. 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. Available at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400433D5D/$File/counc

il_adv_04002.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2009a. Review of EPA’s 

Integrated Science Assessment for Particulate Matter (First External Review Draft, December 2008). EPACOUNCIL-09-008. May. Available at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/73ACCA834AB44A

10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2009b. Review of Integrated 

Science Assessment for Particulate Matter (Second External Review Draft, July 2009). EPA-CASAC-10-001. 

November. Available at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/151B1F83B0231455

85257678006836B9/$File/EPA-CASAC-10-001-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011. Review of Valuing 

Mortality Risk Reductions for Environmental Policy: A White Paper (December 10, 2010). EPA-SAB-11-011 

July. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/$File/EPA-SAB-11-

011-unsigned.pdf>.

Woodruff TJ, Parker JD, Schoendorf KC. 2006. “Fine particulate matter (PM2.5) air pollution and selected causes of 

postneonatal infant mortality in California.” Environmental Health Perspectives 114(5):786-90.

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The relationship between Selected of postneonatal infant 

mortality and particulate air pollution in the United States.” Environmental Health Perspectives 105 (6): 608-

612.

Zanobetti A. and Schwartz, J. 2006. “Air pollution and emergency admissions in Boston, MA.” Journal of 

Epidemiology and Community Health 60(10): 890-5.

Zanobetti, A., M. Franklin, et al. 2009. “Fine particulate air pollution and its components in association with causespecific emergency admissions.” Environmental Health 8: 58-60.