From: "Frey, Nathan J." <Nathan_J._Frey@omb.eop.gov>

To: Lydia Wegman/RTP/USEPA/US@EPA, "Mancini, Dominic J." 

<Dominic_J._Mancini@omb.eop.gov>

Cc: Michael Ling/RTP/USEPA/US@EPA, Anna Wood/RTP/USEPA/US@EPA, Joseph-J 

Dougherty/DC/USEPA/US@EPA, "Lee, Amanda I." <Amanda_I._Lee@omb.eop.gov>

Date: 12/14/2012 08:06 AM

Subject: Summary of Interagency Comments Under EOs 12866 and 13563 -- PM NAAQs Final Rule RIA

Attached please find a summary of interagency comments under EOs 12866 and 13563 regarding the PM 

NAAQs final rule preamble and RIA. In these files, previous EPA edits were accepted and anything 

additional should show in tracked changes. Please let me know if you have any questions or if you would 

like to discuss further.
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E.O. 12866_PM NAAQS_2060-AO47_FRN_RIA_Chap5_20121120.dox

CHAPTER 5

HUMAN HEALTH BENEFITS ANALYSIS APPROACH AND RESULTS

5.1 Synopsis

This chapter presents the estimated human health benefits for the revised National 

Ambient Air Quality Standards (NAAQS) for particulate matter (PM). In this chapter, we quantify 

the health-related benefits of the fine particulate matter (PM2.5)-related air quality 

improvements resulting from the illustrative emission control scenarios that reduce emissions 

of directly emitted particles and precursor pollutants including SO2 and NOx to reach alternative

PM2.5 NAAQS levels in 2020.

1

These benefits are relative to an analytical baseline reflecting nationwide attainment of 

the current primary PM2.5 standards (i.e., annual standard of 15 μg/m3and 24-hour standard of 

35 μg/m3) that includes promulgated national regulations and illustrative emission controls to 

simulate attainment with 15/35 as well as a NOx emission adjustment to reflect expected 

reductions in mobile NOx emissions between 2020 and 2025. We project PM2.5 levels in certain 

areas that would exceed the revised annual standard of 12 μg/m3as well as alternative annual 

standards of 13 and 11 μg/m3after simulated attainment with 15/35 in the analytical baseline.

Table 5-1 summarizes the total monetized benefits of the revised and alternative PM2.5

standards in 2020. These estimates reflect the sum of the economic value of estimated PM2.5

mortality impacts identified and the value of all morbidity impacts.

The estimated benefits for the revised and alternative standards are in addition to the 

substantial benefits estimated for several recent implementation rules (U.S. EPA, 2009a, 2011c, 

2011d, 2011e). Rules such as the Mercury and Air Toxics Standard (MATS) and other emission 

reductions will have substantially reduced ambient PM2.5 concentrations by 2020 in the East, 

such that no additional controls would be needed in the East for the revised annual standard of 

12 μg/m3. Thus, all of the estimated benefits occur in California because this is the only state 

that needs additional air quality improvement beyond the analytical baseline after accounting 

 

1

The estimates in this chapter reflect incremental emissions reductions from an analytical baseline that gives an 

adjustment to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to 

occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised 

standards. Full benefits of the revised standards in those two areas will not be realized until 2025.
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for air quality improvements from recent rules. This fact does, however, introduce some added 

uncertainty, as discussed below.

Also of interest, is that the estimated benefits for the revised NAAQS are about double 

what was predicted in the proposal due to a combination of updates to the analytic baseline

(updated emissions inventories and ambient air quality data – as discussed in Chapter xx) 

,changes in the emissions reductions approach (as disussed in Chapter xx), and changes in this 

chapter, the most significant of which are xxxx. EPA believes that all of these improvements 

lead to a more refined estimate.

Table 5-1. Estimated Monetized Benefits of the of Revised and Alternative Annual PM2.5

Standards in 2020 Incremental to the Analytical Baseline (billions of 2010$)

a

Benefits Estimate 13 μg/m312 μg/m311 μg/m3

Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from 

Krewski et al. (2009)

3% discount rate $1.3 + B $4.0 +B $13 + B

7% discount rate $1.2 + B $3.6 +B $12 + B

Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from 

Lepeule et al. (2012)

3% discount rate $2.9 + B $9.1 +B $29 + B

7% discount rate $2.6 + B $8.2 +B $26 + B

a

Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized benefits here, 

which are discounted over the SAB-recommended 20-year segmented lag. It was not all possible to quantify all 

benefits due to data limitations in this analysis. “B” is the sum of all unquantified health benefits and welfare cobenefits.

As we describe in detail below, we estimate PM-related health impacts using 

concentration-response relationships drawn from the epidemiological literature. In general, we 

are more confident in the magnitude of the risks we estimate from simulated PM2.5 

concentrations that coincide with the bulk of the observed PM concentrations in the 

epidemiological studies that are used in the benefits estimates. Likewise, we are less confident

in the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the 

observed data in these studies. As noted in the preamble to the rule, the range from the 25th to 

10th percentiles of the air quality data in the epidemiology studies is a reasonable range below 

which we start to have appreciably less confidence in the magnitude of the associations 

observed in the epidemiological studies. Most of the estimated avoided premature deaths 
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occur at or above the lowest measured PM2.5 concentration in the two studies that are used to 

estimate mortality benefits.

In addition to PM2.5 benefits, implementation of emissions controls to reach some of the 

alternative PM2.5 standards would reduce other ambient pollutants, such as SO2, NO2, and 

ozone. However, because the method used in this analysis to simulate attainment does not 

account for changes in ambient concentrations of other pollutants, we were not able to 

quantify the co-benefits of reduced exposure to other pollutants. In addition, due to data and 

methodology limitations, we were unable to estimate additional health benefits associated 

with exposure to PM2.5 or the additional co-benefits from improvements in welfare effects (i.e., 

non-health effects) associated with emission reductions to attain the primary standard, such as 

visibility. We describe the unquantified health benefits in this chapter and the unquantified 

welfare co-benefits in Chapter 6.

5.2 Overview

This chapter contains a subset of the estimated health benefits of the revised and 

alternative PM2.5 standards in 2020 that the EPA was able to quantify, given the available

resources and methods. The analysis in this chapter aims to characterize the benefits of the air 

quality changes resulting from the implementation of new PM standards by answering two key 

questions:

1. What are the health effects of changes in ambient particulate matter (PM2.5) 

resulting from reductions in directly emitted PM2.5 and precursors due to the 

attainment of a new PM2.5 standard?

2. What is the economic value of these effects?

In this analysis, we consider an array of health impacts attributable to changes in PM2.5.

The Integrated Science Assessment for Particulate Matter (“PM ISA”)(U.S. EPA, 2009b) identifies

the human health effects associated with ambient particles, which include premature mortality 

and a variety of morbidity effects associated with acute and chronic exposures. Table 5-2

summarizes human health categories contained within the core benefits estimate as well as 

those categories that were unquantified due to limited data or resources. It is important to 

emphasize that the list of unquantified benefit categories is not exhaustive, nor is quantification 

of each effect complete. To identify human health benefits that we could quantify with 

confidence we selected endpoints that were classified as causal or likely causal in the PM ISA, 
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and we excluded effects not identified as having at least a causal, likely causal, or suggestive 

relationship with the affected pollutants in the most recent comprehensive scientific 

assessment, such as an Integrated Science Assessment (ISA). This decision does not imply that 

additional relationships between these and other human health and environmental co-benefits 

and the affected pollutants do not exist. Due to this decision criterion, we excluded some 

effects that were identified in previous lists of unquantified benefits in other RIAs (e.g., UVb 

exposure).

The benefits analysis in this chapter relies on an array of data inputs—including air 

quality modeling, health impact functions and valuation estimates among others—which are 

themselves subject to uncertainty and may also in turn contribute to the overall uncertainty in 

this analysis. We employ several techniques to characterize this uncertainty, which are 

described in detail in section 5.4.
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Table 5-2. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain

the Primary PM2.5 Standards

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized More Information

Improved Human Health

Reduced incidence 

of premature 

mortality from 

exposure to PM2.5

Adult premature mortality based on cohort 

study estimates and expert elicitation 

estimates (age >25 or age >30)

  Section 5.6

Infant mortality (age <1)   Section 5.6

Non-fatal heart attacks (age > 18)   Section 5.6

Hospital admissions—respiratory (all ages)   Section 5.6

Hospital admissions—cardiovascular (age >20)   Section 5.6

Emergency department visits for asthma (all 

ages)

  Section 5.6

Acute bronchitis (age 8–12)   Section 5.6

Lower respiratory symptoms (age 7–14)   Section 5.6

Upper respiratory symptoms (asthmatics age 

9–11)

  Section 5.6

Asthma exacerbation (asthmatics age 6–18)   Section 5.6

Lost work days (age 18–65)   Section 5.6

Minor restricted-activity days (age 18–65)   Section 5.6

Chronic Bronchitis (age >26) —

a —a

Section 5.6

Emergency department visits for 

cardiovascular effects (all ages)

—

a —a

Section 5.6

Strokes and cerebrovascular disease (age 50–

79)

—

a —a

Section 5.6

Other cardiovascular effects (e.g., other ages) — — PM ISAb

Other respiratory effects (e.g., pulmonary 

function, non-asthma ER visits, non-bronchitis 

chronic diseases, other ages and populations)

— — PM ISAb

Reproductive and developmental effects (e.g., 

low birth weight, pre-term births, etc.)

— — PM ISAb,c

Cancer, mutagenicity, and genotoxicity effects — — PM ISAb,c

(continued)
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Table 5-2. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain 

the Primary PM2.5 Standards (continued)

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized More Information

Reduced incidence 

of mortality from 

exposure to ozone

Premature mortality based on short-term 

study estimates (all ages)

— — Ozone CD, Draft 

Ozone ISAd

Premature mortality based on long-term study 

estimates (age 30–99)

— — Ozone CD, Draft 

Ozone ISAd

Hospital admissions—respiratory causes (age 

> 65)

— — Ozone CD, Draft 

ISAd

Hospital admissions—respiratory causes (age 

<2)

— — Ozone CD, Draft 

ISAd

Emergency department visits for asthma (all 

ages)

— — Ozone CD, Draft 

ISAd

Minor restricted-activity days (age 18–65) — — Ozone CD, Draft 

ISAd

School absence days (age 5–17) — — Ozone CD, Draft 

ISAd

Decreased outdoor worker productivity (age 

18–65)

— — Ozone CD, Draft 

ISAd

Other respiratory effects (e.g., premature 

aging of lungs)

— — Ozone CD, Draft 

ISAb

Cardiovascular and nervous system effects — — Ozone CD, Draft 

ISAc

Reproductive and developmental effects — — Ozone CD, Draft 

ISAc

Reduced incidence 

of morbidity from 

exposure to NO2

Asthma hospital admissions (all ages) — — NO2 ISAd

Chronic lung disease hospital admissions (age 

> 65)

— — NO2 ISAd

Respiratory emergency department visits (all 

ages)

— — NO2 ISAd

Asthma exacerbation (asthmatics age 4–18) — — NO2 ISAd

Acute respiratory symptoms (age 7–14) — — NO2 ISAd

Premature mortality — — NO2 ISAb,c
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Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations)

— — NO2 ISAb,c

(continued)
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Table 5-2. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain 

the Primary PM2.5 Standards (continued)

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized More Information

Reduced incidence 

of morbidity from 

exposure to SO2

Respiratory hospital admissions (age > 65) — — SO2 ISAd

Asthma emergency department visits (all 

ages)

— — SO2 ISAd

Asthma exacerbation (asthmatics age 4–12) — — SO2 ISAd

Acute respiratory symptoms (age 7–14) — — SO2 ISAd

Premature mortality — — SO2 ISAb,c

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations)

— — SO2 ISAb,c

Reduced incidence 

of morbidity from 

exposure to 

methylmercury 

(through role of 

sulfate in 

methylation)

Neurologic effects—IQ loss — — IRIS; NRC, 2000d

Other neurologic effects (e.g., developmental 

delays, memory, behavior)

— — IRIS; NRC, 2000b

Cardiovascular effects — — IRIS; NRC, 2000b,c

Genotoxic, immunologic, and other toxic 

effects

— — IRIS; NRC, 2000b,c

a We quantify these benefits in a sensitivity analysis, but not in the core analysis.

b We assess these benefits qualitatively because we do not have sufficient confidence in available data or 

methods.

c We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other 

significant concerns over the strength of the association.

d We assess these benefits qualitatively due to time and resource limitations for this analysis.

As described in Chapter 1 of this RIA, there are important differences worth noting in 

the design and analytical objectives of NAAQS RIAs compared to RIAs for implementation rules, 

such as the recent MATS rule (U.S. EPA, 2011d). The NAAQS RIAs illustrate the potential costs 

and benefits of attaining a revised air quality standard nationwide based on an array of 

emission reduction strategies for different sources including known and unknown controls, 

incremental to implementation of existing regulations and controls needed to attain the 

current standards. In short, NAAQS RIAs hypothesize, but do not predict, the emission 

reduction strategies that States may choose to enact when implementing a revised NAAQS. The 

setting of a NAAQS does not directly result in costs or benefits, and as such, NAAQS RIAs are 
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merely illustrative and the estimated costs and benefits are not intended to be added to the 

costs and benefits of other regulations that result in specific costs of control and emission 

reductions. By contrast, the emission reductions from implementation rules are generally for 

specific, well-characterized sources, such as the recent MATS rule addressing emissions from 

coal and oil-fired electricity generating units (U.S. EPA, 2011d). In general, the EPA is more 

confident in the magnitude and location of the emission reductions for implementation rules. 

As such, emission reductions achieved under promulgated implementation rules such as MATS 

have been reflected in the baseline of this NAAQS analysis. Subsequent implementation rules 

will be reflected in the baseline for the next PM NAAQS review. For this reason, the benefits 

estimated provided in this RIA and all other NAAQS RIAs should not be added to the benefits 

estimated for implementation rules.

5.3 Updated Methodology Presented in this RIA

The benefits analysis presented in this chapter incorporates an array of policy and 

technical changes that the Agency has adopted since the previous review of the PM2.5 standards 

in 2006, and since publication of the proposal RIA for this rulemaking. Below we note the 

aspects of this analysis that differ from the proposal RIA (U.S. EPA, 2012a):

1. Incorporation of the newest Harvard Six Cities mortality study. In 2012, Lepeule et al.

published an extended analysis of the Six Cities cohort. Compared to the study it 

replaces (Laden et al., 2006), this new analysis follows the cohort for a longer time 

and includes more years of PM2.5 monitoring data. The all-cause PM2.5 mortality risk 

coefficient drawn from Lepeule et al. (2012) is roughly similar to the Laden et al. 

(2006) risk coefficient applied in the EPA’s recent analyses of long-term PM2.5

mortality and has narrower confidence intervals.

2. Updated demographic data. We updated the population demographic data in 

BenMAP to reflect the 2010 Census and future projections based on economic 

forecasting models developed by Woods and Poole, Inc. (Woods and Poole, 2012). 

These data replace the earlier demographic projection data from Woods and Poole 

(2007).

3. Incorporation of new morbidity studies. Since the publication of the PM ISA (U.S. 

EPA, 2009) the epidemiological literature has produced several new studies 

examining the association between short-term PM2.5 exposure and respiratory and 

cardiovascular hospitalizations, respiratory and cardiovascular emergency 

department visits, and stroke. Upon careful evaluation of this new literature 

identified in the Provisional Science Assessment for Particulate Matter (“Provisional 
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Assessment”)(U.S. EPA, 2012b) we added several new studies to our health impact 

assessment.

4. Updated the survival rates for non-fatal acute myocardial infarctions. Based on 

recent data from 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 stratified by age.

These rates replace the survival rates from Rosamond et al. (1999).

5. Expanded uncertainty assessment. We clarified the comprehensive assessment of 

the various uncertain parameters and assumptions within the benefits analysis and 

expanded the evaluation of air quality benchmarks (previously the LML analysis).

Although the list above identifies the major changes implemented since the proposal 

RIA, the EPA has also updated several additional components of the benefits analysis since the 

2006 PM NAAQS RIA (U.S. EPA, 2006a). In the Portland Cement NESHAP proposal RIA (U.S. EPA, 

2009a), the Agency no longer assumed a concentration threshold in the concentrationresponse function for PM2.5-related health effects and began using the benefits derived from 

the two major cohort studies of PM2.5 and mortality as the core benefits estimates, while still 

including a range of sensitivity estimates based on the EPA’s PM2.5mortality expert elicitation.

In the NO2 NAAQS proposal RIA (U.S. EPA, 2009a), we revised the estimate used for the valueof-a-statistical life to be consistent with Agency guidance. In the proposed CSAPR (previously 

the “Transport Rule”) (U.S. EPA, 2010g), we incorporated the “lowest measured level”

assessment to help characterize uncertainty in estimates of benefits of reductions in PM2.5 at 

lower baseline concentrations of PM2.5. In the final CSAPR (U.S. EPA, 2011c), we updated the 

baseline incidence rates for hospital admissions and emergency department visits and asthma 

prevalence rates. We direct the reader to each of these RIAs for more information on these 

changes. In the proposal RIA for this NAAQS review (U.S. EPA, 2012a), we updated the 

American Cancer Society cohort study to Krewski et al. (2009), updated health endpoints in the 

core and sensitivity analyses, incorporated new morbidity studies, updated the median wage 

data in the cost-of-illness studies, and expanded the uncertainty assessment.
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5.4 Human Health Benefits Analysis Methods

We follow a “damage-function” approach in calculating total benefits of the modeled 

changes in environmental quality.2This approach estimates changes in individual health 

endpoints (specific effects that can be associated with changes in air quality) and assigns values 

to those changes assuming independence of the values for those individual endpoints. Total 

benefits are calculated simply as the sum of the values for all non-overlapping health 

endpoints. The “damage-function” approach is the standard method for assessing costs and 

benefits of environmental quality programs and has been used in several recent published 

analyses (Levy et al., 2009; Fann et al., 2012a; Tagaris et al., 2009).

To assess economic value in a damage-function framework, the changes in 

environmental quality must be translated into effects on people or on the things that people 

value. In some cases, the changes in environmental quality can be directly valued, as is the case 

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

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

values. For the purposes of this RIA, the health impacts analysis (HIA) is limited to those health 

effects that are directly linked to ambient levels of air pollution and specifically to those linked 

to PM2.5.

We note at the outset that the EPA rarely has the time or resources to perform 

extensive new research to measure directly either the health outcomes or their values for 

regulatory analyses. Thus, similar to Kunzli et al. (2000) and other, more recent health impact 

analyses, our estimates are based on the best available methods of benefits transfer. Benefits 

transfer is the science and art of adapting primary research from similar contexts to obtain the 

most accurate measure of benefits for the environmental quality change under analysis.

Adjustments are made for the level of environmental quality change, the socio-demographic 

and economic characteristics of the affected population, and other factors to improve the 

accuracy and robustness of benefits estimates.

5.4.1 Health Impact Assessment

The health impact assessment (HIA) quantifies the changes in the incidence of adverse 

health impacts resulting from changes in human exposure to PM2.5 and ozone air quality. HIAs 

 

2

The damage function approach is a more comprehensive method of estimating total benefits than the hedonic 

price approach applied to housing prices, which requires homebuyers to be knowledgeable of the full magnitude 

of health risks associated with their home purchase. See discussion of hedonic studies in Chapter 6.
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are a well-established approach for estimating the retrospective or prospective change in 

adverse health impacts expected to result from population-level changes in exposure to 

pollutants (Levy et al., 2009). PC-based tools such as the environmental Benefits Mapping and 

Analysis Program (BenMAP) can systematize health impact analyses by applying a database of 

key input parameters, including health impact functions and population projections—provided 

that key input data are available, including air quality estimates and risk coefficients (Abt 

Associates, 2010). Analysts have applied the HIA approach to estimate human health impacts 

resulting from hypothetical changes in pollutant levels (Hubbell et al., 2005; Tagaris et al., 2009; 

Fann et al., 2012a). The EPA and others have relied upon this method to predict future changes 

in health impacts expected to result from the implementation of regulations affecting air 

quality (e.g., U.S. EPA, 2011d). For this assessment, the HIA is limited to those health effects 

that are directly linked to ambient PM2.5 concentrations. There may be other indirect health 

impacts associated with implementing emissions controls, such as occupational health 

exposures.

The HIA approach used in this analysis involves three basic steps: (1) utilizing projections 

of PM2.5 air quality3and estimating the change in the spatial distribution of the ambient air 

quality; (2) determining the subsequent change in population-level exposure; (3) calculating 

health impacts by applying concentration-response relationships drawn from the 

epidemiological literature (Hubbell et al., 2009) to this change in population exposure.

A typical health impact function might look as follows:

(5.1)

where y0 is the baseline incidence rate for the health endpoint being quantified (for example, a 

health impact function quantifying changes in mortality would use the baseline, or background, 

mortality rate for the given population of interest); Pop is the population affected by the 

change in air quality; x is the change in air quality; and β is the effect coefficient drawn from 

the epidemiological study. Figure 5-1 provides a simplified overview of this approach.

 

3

Projections of ambient PM2.5 concentrations for this analysis were generated using the Community Multiscale Air 

Quality model (CMAQ). See Chapter 3 of this RIA for more information on the air quality modeling.
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Figure 5-1. Illustration of BenMAP Approach

Baseline Air Quality Post-Policy Scenario Air Quality

Incremental Air Quality

Improvement

PM2.5

Reduction

Population

Ages 30 -99

Background

Incidence

Rate

Effect

Estimate

Mortality 

Reduction
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5.4.2 Economic Valuation of Health Impacts

After quantifying the change in adverse health impacts, the final step is to estimate the 

economic value of these avoided impacts. The appropriate economic value for a change in a 

health effect depends on whether the health effect is viewed ex ante (before the effect has 

occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air 

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

population. The appropriate economic measure is therefore ex ante willingness to pay (WTP) 

for changes in risk. Epidemiological studies generally provide estimates of the relative risks of a 

particular health effect for a given increment of air pollution (often per 10 μg/m3for PM2.5). 

These relative risks can be used to develop risk coefficients that relate a unit reduction in PM2.5 

to changes in the incidence of a health effect. In order to value these changes in incidence, WTP 

for changes in risk need to be converted into WTP per statistical incidence. This measure is 

calculated by dividing individual WTP for a risk reduction by the related observed change in risk. 

For example, suppose a measure is able to reduce the risk of premature mortality from 2 in 

10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is 

$100, then the WTP for an avoided statistical premature mortality amounts to $1 million 

($100/0.0001 change in risk). Using this approach, the size of the affected population is 

automatically taken into account by the number of incidences predicted by epidemiological 

studies applied to the relevant population. The same type of calculation can produce values for 

statistical incidences of other health endpoints.

For some health effects, such as hospital admissions, WTP estimates are generally not 

available. In these cases, we use the cost of treating or mitigating the effect. For example, for 

the valuation of hospital admissions we use the avoided medical costs as an estimate of the 

value of avoiding the health effects causing the admission. These cost-of-illness (COI) estimates 

generally (although not necessarily in every case) understate the true value of reductions in risk 

of a health effect. They tend to reflect the direct expenditures related to treatment but not the 

value of avoided pain and suffering from the health effect.

We use the BenMAP model version 4.0.52 (Abt Associates, 2010) to estimate the health 

impacts and monetized health benefits for the proposed standard. Figure 5-2 shows the data 

inputs and outputs for the BenMAP model.
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Figure 5-2. Data Inputs and Outputs for the BenMAP Model

5.5 Uncertainty Characterization

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty. This analysis is no exception. As outlined 

both in this and preceding chapters, this analysis includes many data sources as inputs, 

including emission inventories, air quality data from models (with their associated parameters 

and inputs), population data, population estimates, health effect estimates from epidemiology 

studies, economic data for monetizing benefits, and assumptions regarding the future state of 

the world (i.e., regulations, technology, and human behavior). Each of these inputs may be 

uncertain and would affect the benefits estimate. When the uncertainties from each stage of 

the analysis are compounded, even small uncertainties can have large effects on the total 

quantified benefits.

After reviewing the EPA’s approach, the National Research Council (NRC) (2002, 2008),

which is part of the National Academies of Science, concluded that the EPA’s general 

Modeled Baseline 

and Post-Control 

Ambient PM2.5

PM2.5 Health 

Functions

Economic 

Valuation 

Functions

2020 Population 

Projections

PM2.5 Incremental Air 

Quality Change

PM2.5-Related Health 

Impacts

Woods & Poole 

Population 

Projections

Background 

Incidence and 

Prevalence Rates

Monetized PM2.5-

related Benefits

Blue identifies a user-selected input within the BenMAP model

Green identifies a data input generated outside of the BenMAP 

model

Census 

Population Data
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methodology for calculating the benefits of reducing air pollution is reasonable and informative 

in spite of inherent uncertainties. The NRC also highlighted the need to conduct rigorous 

quantitative analyses of uncertainty and to present benefits estimates to decision makers in 

ways that foster an appropriate appreciation of their inherent uncertainty. Since the 

publication of these reports, the EPA has continued work to improve the characterization of 

uncertainty in both health incidence and benefits estimates. In response to these 

recommendations, we have expanded our previous analyses to incorporate additional 

quantitative and qualitative characterizations of uncertainty. Although we have not yet been 

able to make as much progress towards a full, probabilistic uncertainty assessment as 

envisioned by the NAS as we had hoped, we have added a number of additional quantitative 

and qualitative analyses to highlight the impact that uncertain assumptions may have on the 

benefits estimates. In addition, for some inputs into the benefits analysis, such as the air quality 

data, it is difficult to address uncertainty probabilistically due to the complexity of the 

underlying air quality models and emission inputs. Therefore, we decline to make up alternative 

assumptions simply for the purpose of probabilistic uncertainty characterization when there is 

no scientific literature to support alternate assumptions.

To characterize uncertainty and variability, we follow an approach that combines 

elements from two recent analyses by the EPA (U.S. EPA, 2010b; 2011a), and uses a tiered 

approach developed by the World Health Organization (WHO) for characterizing uncertainty 

(WHO, 2008). We present this tiered assessment as well as an assessment of the potential 

impact and magnitude of each aspect of uncertainty In Appendix 5c. Data limitations prevent us 

from treating each source of uncertainty quantitatively and from reaching a full-probabilistic 

simulation of our results, but we were able to consider the influence of uncertainty in the risk 

coefficients and economic valuation functions by incorporating six quantitative analyses

described in more detail below:

1. A Monte Carlo assessment that accounts for random sampling error and between 

study variability in the epidemiological and economic valuation studies;

2. A concentration benchmark assessment that characterizes the distribution of 

avoided PM2.5-related deaths relative to specific concentrations in the long-term 

epidemiological studies used to estimate PM2.5-related mortality;

3. The quantification of PM-related mortality using alternative PM2.5 mortality effect 

estimates drawn from two long-term cohort studies and an expert elicitation;
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4. Sensitivity analyses of several aspects of PM-related benefits;

5. Distributional analyses of PM2.5-related benefits by location, race, income, and 

education; and

6. An analysis of the influence of various parameters on total monetized benefits.

5.5.1 Monte Carlo Assessment

Similar to other recent RIAs, we used Monte Carlo methods for characterizing random 

sampling error associated with the concentration response functions from epidemiological 

studies and random effects modeling to characterize both sampling error and variability across 

the economic valuation functions. The Monte Carlo simulation in the BenMAP software 

randomly samples from a distribution of incidence and valuation estimates to characterize the 

effects of uncertainty on output variables. Specifically, we used Monte Carlo methods to 

generate confidence intervals around the estimated health impact and monetized benefits. The 

reported standard errors in the epidemiological studies determined the distributions for 

individual effect estimates for endpoints estimated using a single study. For endpoints 

estimated using a pooled estimate of multiple studies, the confidence intervals reflect both the 

standard errors and the variance across studies. The confidence intervals around the monetized 

benefits incorporate the epidemiology standard errors as well as the distribution of the 

valuation function. These confidence intervals do not reflect other sources of uncertainty 

inherent within the estimates, such as baseline incidence rates, populations exposed and 

transferability of the effect estimate to diverse locations. As a result, the reported confidence 

intervals and range of estimates give an incomplete picture about the overall uncertainty in the 

benefits estimates.

5.5.2 Concentration Benchmark Analysis for PM2.5

We include a concentration benchmark assessment, which identifies the baseline (i.e.,

pre-rule) annual mean PM2.5 levels at which populations are exposed and specific 

concentrations in the two long-term cohort studies we use to quantify mortality impacts. This 

analysis characterizes avoided PM2.5-related deaths relative to the 10th and 25th percentiles of 

the air quality data used the Krewski et al. (2009) study as well as the lowest measured level 

(LML) of the Krewski et al. (2009) and Lepeule et al. (20) studies.
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5.5.3 Alternative Concentration-Response Functions for PM2.5–Related Mortality

We assign the greatest economic value to the reduction in PM2.5 relatedmortality risk. 

Therefore, it is particularly important to attempt to characterize the uncertainties associated 

with reductions in premature mortality. To better understand the concentration-response 

relationship between PM2.5 exposure and premature mortality, the EPA conducted an expert 

elicitation in 2006 (Roman et al., 2008; IEc, 2006).4In general, the results of the expert 

elicitation support the conclusion that the benefits of PM2.5 control are very likely to be 

substantial.

Alternative concentration-response functions are useful for assessing uncertainty 

beyond random statistical error, including uncertainty in the functional form of the model or 

alternative study design. Thus, we include the expert elicitation results as well as standard 

errors approaches to provide insights into the likelihood of different outcomes and about the 

state of knowledge regarding the benefits estimates. In this analysis, we present the results 

derived from the expert elicitation as indicative of the uncertainty associated with a major 

component of the health impact functions, and we provide the independent estimates derived 

from each of the twelve experts to better characterize the degree of variability in the expert 

responses.

In previous RIAs, the EPA presented benefits estimates using concentration response 

functions derived from the PM2.5 Expert Elicitation (Roman et al., 2008) as a range from the 

lowest expert value (Expert K) to the highest expert value (Expert E). However, this approach 

did not indicate the agency’s judgment on what the best estimate of PM2.5 benefits may be; as 

well, and the EPA’s independent Science Advisory Board (SAB) recommended refinements to 

the raised concerns about thisway EPA presentatedion the results of the elicitation (U.S. EPASAB, 2008). Therefore, we began to present the cohort-based studies (Krewski et al., 2009; 

Laden et al., 2006)5as our core estimates in the proposal RIA for the Portland Cement NESHAP 

(U.S. EPA, 2009a). Using alternate relationships between PM2.5 and premature mortality 

supplied by experts, higher and lower benefits estimates are plausible, but most of the expertbased estimates of the mean PM2.5 effect on mortality fall between the two epidemiologybased estimates (Roman et al., 2008). In addition to these studies, we have included a 

 

4

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

of multiple experts, are obtained (Ayyub, 2002).

5 We have since updated the the Harvard Six Cities cohort study from Laden et al. (2006) to use the most recent 

follow-up publication of this cohort (Lepeule et al, 2012).
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discussion or other recent multi-state cohort studies conducted in North America, but we have 

not estimated benefits using the effect coefficients from these studies. 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.

Even these multiple characterizations with confidence intervals omit the contribution to 

overall uncertainty from uncertainty in air quality changes, baseline incidence rates, and 

populations exposed. Furthermore, the approach presented here does not yet include methods 

for addressing correlation between input parameters and the identification of reasonable upper 

and lower bounds for input distributions characterizing uncertainty in additional model 

elements. As a result, the reported confidence intervals and range of estimates give an 

incomplete picture about the overall uncertainty in the estimates. This information should be 

interpreted within the context of the larger uncertainty surrounding the entire analysis.

5.5.4 Sensitivity Analyses

For some aspects of uncertainty, we have sufficient data to conduct sensitivity analyses.

In this analysis, we performed five such analyses for the revised standard level. In particular, 

we:

1. Assessed the sensitivity of the economic value of reductions in the risk of PM2.5-

related death according to differing assumptions regarding the lag between PM2.5

exposure and premature death. The timing of such premature deaths affects the 

magnitude of the discounted PM2.5-related mortality benefits. In this sensitivity 

assessment, we consider 6 alternative cessation lags.

2. Characterized the sensitivity of the economic value of the health endpoints valued 

using willingness-to-pay estimates to a higher and a lower assumption regarding 

income elasticity. As we discuss below, economic theory argues that individual 

willingness to pay increases as personal income grows. The relationship between 

growth in personal income and willingness-to-pay to reduce mortality and morbidity 

risk is characterized by the income growth factor.

3. Summarized the avoided cases of certain health endpoints for which we either 

lacked an appropriate economic value (cardiovascular hospital admissions and 
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stroke) or in which we no longer had sufficient confidence to retain in our primary 

benefits estimate (chronic bronchitis).

4. Assessed the sensitivity of the benefits results to the new population data from the 

2010 Census.

5. Assessed the sensitivity of the benefits results to an analysis year of 2025.

5.5.5 Distributional Assessment

In an Appendix to the proposal RIA, we characterized the distribution of PM2.5-related 

benefits based on the geographic distribution of race and education in areas where the 

illustrative emission reduction strategies would reduce PM2.5 concentrations. In that 

assessment, we aimed to answer two key questions:

1. What was the estimated distribution of PM2.5-related mortality risk based on the

race and education characteristics of the population living within areas projected to 

exceed alternative combinations of the proposed primary PM2.5 standards?

2. How would air quality improvements within these counties change the distribution 

of risk among populations of different races and educational attainment?

6

That assessment was generally consistent with the distributional assessments 

performed in support of MATS (U.S. EPA, 2011c), with one key difference. The environmental 

justice analyses accompanying the MATS RIA applied CMAQ-modeled PM2.5 predictions that 

represent the change in air quality after the implementation of each rule. By contrast, the 

proposal RIA aimed to illustrate the potential benefits and costs of attaining alternative primary 

PM2.5 standards; the states will ultimately implement attainment strategies, which may differ 

greatly from the least-cost strategy the EPA modeled here. Alternative emission reduction

strategies—particularly those that maximize benefits to human health and provide a more

equitable distribution of risk—are also available to the states, though not modeled here (Fann 

et al., 2012b). Due in part to time constraints, the EPA did not perform such an analysis for the 

final RIA, and instead refers readers to the Appendix noted above.

 

6

In this analysis we assess the change in risk among populations of different race and educational attainment. As 

we discuss further in the methodology, we consider this last variable because of the availability of educationmodified PM2.5 mortality risk estimates.
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5.5.6 Influence Analysis—Quantitative Assessment of Uncertainty

In the past few years, the EPA has initiated several projects to improve the 

characterization of uncertainty for benefits analysis. In particular, the EPA recently completed 

the first phase of a quantitative uncertainty analysis of benefits, hereafter referred to as the 

“Influence Analysis” (Mansfield et al., 2009). The Influence Analysis diagramed the uncertain 

components of each step within the benefits analysis process, identified plausible ranges for a 

sensitivity analysis, and assessed the sensitivity to total benefits to changes in each component.

Although this analysis does not quite fulfill the goal of a full probabilistic assessment, it 

accomplished the necessary first steps and identified the challenges to accomplishing that goal.

Below are some of the preliminary observations from the first phase of the project.

 The components that contribute the most to uncertainty of the monetized benefits 

and mortality incidence (in order of importance) are the value-of-a-statistical-life 

(VSL), the concentration-response (C-R) function for mortality, and change in PM2.5 

concentration.

 The components that contribute the least to uncertainty of the monetized benefits 

and mortality incidence are population, morbidity valuation, and income elasticity.

 The choice of a C-R function for mortality affects the mortality incidence and 

monetized benefits more than other sources of uncertainty within each C-R 

function.

 Alternative cessation lag structures for mortality have a moderate effect on the 

monetized benefits.

 Because the health impact function is essentially linear, the key components show 

the same sensitivity across all mortality C-R functions even if the midpoints differ 

significantly from one expert to another.

5.5.7 Qualitative Assessment of Uncertainty and Other Analysis Limitations

Although we strive to incorporate as many quantitative assessments of uncertainty as 

possible, there are several aspects we are only able to address qualitatively. These aspects are 

important factors to consider when evaluating the relative benefits of the emission reduction 

strategies for the revised and alternative annual standards:

The total monetized benefits presented in this chapter are based on our interpretation 

of the best available scientific literature and methods and supported by the EPA’s independent 
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SAB (Health Effects Subcommittee) (SAB-HES) (U.S. EPA- SAB, 2010a) and the National 

Academies of Science (NAS) (NRC, 2002). The benefits estimates are subject to a number of 

assumptions and uncertainties. For example, the key assumptions underlying the estimates for 

premature mortality, which account for over 98% of the total monetized benefits in this 

analysis, include the following:

1. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 varies considerably in composition across sources, but the scientific 

evidence is not yet sufficient to allow differentiation of effect estimates by particle 

type. The PM ISA, which was twice reviewed by Clean Air Scientific Advisory 

Committee (SAB-CASAC), concluded that “many constituents of PM2.5 can be linked 

with multiple health effects, and the evidence is not yet sufficient to allow 

differentiation of those constituents or sources that are more closely related to 

specific outcomes” (U.S. EPA, 2009b). These uncertainties are likely to be magnified 

in the current analysis to the extent that the emissions controls are less diverse 

when focusing on one small region of the country rather than EPA’s more typical 

NAAQS RIA’s that cover a broader geography with more diverse emissions sources 

and the application of a more diverse set of controls.

2. We assume that health impact functions based on national studies are 

representative of the experience in California. EPA has considered the cohort 

studies conducted in California specifically in addition to the national risk 

coefficients we use as our core estimates. Although we have not calculated the 

benefits results using these cohort studies, we provided the risk coefficients from 

these California cohorts to show how much the monetized benefits could have 

changed if we used these cohort studies. Most of the California cohort studies 

report central effect estimates similar to the (nation-wide) all-cause mortality risk 

estimate we applied from Krewski et al. (2009) and Lepeule et al. (2012) albeit with 

wider confidence intervals. Three cohort studies conducted in California indicate 

statistically significant higher risks than the risk estimates we applied from Lepeule 

et al. (2012), and four studies showed insignificant results.

3. We assume that the health impact function for fine particles is log-linear without a 

threshold in this analysis. Thus, the estimates include health benefits from reducing 

fine particles in areas with varied concentrations of PM2.5, including both areas that 

do not meet the fine particle standard and those areas that are in attainment, down 

to the lowest modeled concentrations.

3. We assume that there is a “cessation” lag between the change in PM exposures and

the total realization of changes in mortality effects. Specifically, we assume that 
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some of the incidences of premature mortality related to PM2.5 exposures occur in a 

distributed fashion over the 20 years following exposure based on the advice of the 

SAB-HES (U.S. EPA-SAB, 2004c), which affects the valuation of mortality benefits at 

different discount rates.

4. To characterize the uncertainty in the relationship between PM2.5 and premature 

mortality (which account for over 98% of total monetized benefits in this analysis), 

we include a set of twelve estimates based on results of the expert elicitation study 

in addition to our core estimates. Even these multiple characterizations omit the 

uncertainty in air quality estimates, baseline incidence rates, populations exposed 

and transferability of the effect estimate to diverse locations. As a result, the 

reported confidence intervals and range of estimates give an incomplete picture 

about the overall uncertainty in the PM2.5 estimates. This information should be 

interpreted within the context of the larger uncertainty surrounding the entire 

analysis.

As previously described, we strive to monetize as many of the benefits anticipated from 

the revised and alternative standards as possible given data and resource limitations, but the 

monetized benefits estimated in this RIA inevitably only reflect a portion of the benefits. 

Specifically, only certain benefits attributable to the health impacts associated with exposure to 

ambient fine particles have been monetized in this analysis. Data and methodological 

limitations prevented the EPA from quantifying or monetizing the benefits from several 

important health benefit categories from emission reduction strategies to reach the revised 

annual standard in this RIA, including potential co-benefits from reducing ozone exposure, NO2

exposure, SO2 exposure, and methylmercury exposure (see section 5.6.5 for more information). 

If we could fully monetize all of the benefit categories, the total monetized benefits would 

exceed the costs by an even greater margin than we currently estimate.

To more fully address all these uncertainties including those we cannot quantify, we 

apply a four-tiered approach using the WHO uncertainty framework (WHO, 2008), which 

provides a means for systematically linking the characterization of uncertainty to the 

sophistication of the underlying risk assessment. The EPA has applied similar approaches in 

analyses (U.S. EPA, 2010b, 2011a). Using this framework, we summarize the key uncertainties in 

the health benefits analysis, including our assessment of the direction of potential bias, 

magnitude of impact on the monetized benefits, degree of confidence in our analytical 

approach, and our ability to assess the source of uncertainty. More information on this 

approach and the uncertainty characterization are available in Appendix 5B.
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5.6 Benefits Analysis Data Inputs

In Figure 5-2, we summarized the key data inputs to the health impact and economic 

valuation estimate. Below we summarize the data sources for each of these inputs, including 

demographic projections, incidence and prevalence rates, effect coefficients, and economic 

valuation. We indicate where we have updated key data inputs since the benefits analysis

conducted for the MATS RIA (U.S. EPA, 2011d) and the proposal RIA (U.S. EPA, 2012a).

5.6.1 Demographic Data

Quantified and monetized human health impacts depend on the demographic 

characteristics of the population, including age, location, and income. We use population 

projections based on economic forecasting models developed by Woods and Poole, Inc.

(Woods and Poole, 2012). The Woods and Poole (WP) database contains county-level 

projections of population by age, sex, and race out to 2040, relative to a baseline using the 

2010 Census data; the proposal RIA incorporated WP projections relative to a baseline using 

2000 Census data. An analysis exploring the sensitivity of population and health impact 

estimates to this update can be found in Appendix 5.A. Projections in each county are 

determined simultaneously with every other county in the United States to take into account 

patterns of economic growth and migration. The sum of growth in county-level populations is 

constrained to equal a previously determined national population growth, based on Bureau of 

Census estimates (Hollman et al., 2000). According to WP, linking county-level growth 

projections together and constraining to a national-level total growth avoids potential errors 

introduced by forecasting each county independently. County projections are developed in a 

four-stage process:

 First, national-level variables such as income, employment, and populations are 

forecasted.

 Second, employment projections are made for 179 economic areas defined by the 

Bureau of Economic Analysis (U.S. BEA, 2004), using an “export-base” approach, 

which relies on linking industrial-sector production of non-locally consumed 

production items, such as outputs from mining, agriculture, and manufacturing with 

the national economy. The export-based approach requires estimation of demand 

equations or calculation of historical growth rates for output and employment by 

sector.
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 Third, population is projected for each economic area based on net migration rates 

derived from employment opportunities and following a cohort-component method 

based on fertility and mortality in each area.

 Fourth, employment and population projections are repeated for counties, using the 

economic region totals as bounds. The age, sex, and race distributions for each 

region or county are determined by aging the population by single year of age by sex 

and race for each year through 2040 based on historical rates of mortality, fertility, 

and migration.

5.6.2 Baseline Incidence and Prevalence Estimates

Epidemiological studies of the association between pollution levels and adverse health 

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

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

example, a typical result might be that a 10 μg/m3decrease in daily PM2.5 levels might be 

associated with a decrease in hospital admissions of 3%. The baseline incidence of the health 

effect is necessary to convert this relative change into a number of cases. A baseline incidence 

rate is the estimate of the number of cases of the health effect per year in the assessment 

location, as it corresponds to baseline pollutant levels in that location. To derive the total 

baseline incidence per year, this rate must be multiplied by the corresponding population 

number. For example, if the baseline incidence rate is the number of cases per year per million 

people, that number must be multiplied by the millions of people in the total population.

Table 5-3 summarizes the sources of baseline incidence rates and provides average 

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

prevalence data, we used age-specific rates where available. We applied concentrationresponse functions to individual age groups and then summed over the relevant age range to 

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

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

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

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

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

assumed to represent typical incidence at the national level. County, state and regional 

incidence rates are available for hospital admissions, and county-level data are available for 

premature mortality. 
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We projected mortality rates such that future mortality rates are consistent with our 

projections of population growth (Abt Associates, 2012). To perform this calculation, we began 

first with an average of 2004–2006 cause-specific mortality rates. Using Census Bureau 

projected national-level annual mortality rates stratified by age range, we projected these 

mortality rates to 2050 in 5-year increments (Abt Associates, 2012; U.S. Bureau of the Census 

2002).

The baseline incidence rates for hospital admissions and emergency department visits reflect 

the revised rates first applied in the CSAPR RIA (U.S. EPA, 2011c). In addition, we have revised 

the baseline incidence rates for acute myocardial infarction. These revised rates are more 

recent (AHRQ, 2007), which provides a better representation of the rates at which populations 

of different ages, and in different locations, visit the hospital and emergency department for air 

pollution-related illnesses. Also, the new baseline incidence rates are more spatially refined. For 

many locations within the U.S., these data are resolved at the county- or state-level, providing a 

better characterization of the geographic distribution of hospital and emergency department 

visits than the previous national rates. Lastly, these rates reflect unscheduled hospital 

admissions only, which represents a conservative assumption that most air pollution-related 

visits are likely to be unscheduled. If air pollution-related hospital admissions are scheduled, 

this assumption would underestimate these benefits.

For the set of endpoints affecting the asthmatic population, in addition to baseline 

incidence rates, prevalence rates of asthma in the population are needed to define the 

applicable population. Table 5-4 lists the prevalence rates used to determine the applicable 

population for asthma symptoms. Note that these reflect current asthma prevalence and 

assume no change in prevalence rates in future years. We updated these rates in the CSAPR RIA 

(U.S. EPA, 2011c).
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Table 5-3. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population

Endpoint Parameter

Rates

Value Source

Mortality Daily or annual mortality rate 

projected to 2020

a

Age-, cause-, and countyspecific rateCDC WONDER (2004–

2006)

U.S. Census bureau, 2000

Hospitalizations Daily hospitalization rate Age-, region-, state-, 

county- and cause-specific 

rate

2007 HCUP data filesb

ER Visits Daily ER visit rate for asthma and 

cardiovascular events

Age-, region-, state-, 

county- and cause-specific 

rate

2007 HCUP data filesb

Cerebrovascular 

events

Incidence of new cerebrovascular 

events among populations 50–79

0.001575I Table 3 of Miller et al. 

(2007)

Chronic BronchitiscAnnual prevalence rate per 

person

Aged 18–44

Aged 45–64

Aged 65 and older

0.0315

0.0549

0.0563

American Lung Association 

(2010a, 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+

Age-, region-, state-, and 

county-specific rate

2007 HCUP data files;

b

adjusted by 0.93 for 

probability of surviving 

after 28 days (Rosamond 

et al., 1999)

Asthma 

Exacerbations

Incidence among asthmatic 

African-American children

daily wheeze

daily cough

daily shortness of breath

0.173

0.145

0.074

Ostro et al. (2001) 

Acute Bronchitis Annual bronchitis incidence rate, 

children

0.043 American Lung Association 

(2002c, Table 11)

Lower Respiratory 

Symptoms

Daily lower respiratory symptom 

incidence among childrend

0.0012 Schwartz et al. (1994, 

Table 2)

Upper Respiratory 

Symptoms

Daily upper respiratory symptom 

incidence among asthmatic 

0.3419 Pope et al. (1991, Table 2)
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children

(continued)
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Table 5-3. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population (continued)

Endpoint Parameter

Rates

Value Source

Work Loss Days Daily WLD incidence rate per 

person (18–65)

Aged 18–24

Aged 25–44

Aged 45–64

0.00540

0.00678

0.00492

1996 HIS (Adams, 

Hendershot, and Marano, 

1999, Table 41); U.S. 

Census Bureau (2000)

School Loss Days Rate per person per year, 

assuming 180 school days per 

year

9.9 National Center for 

Education Statistics (1996) 

and 1996 HIS (Adams 

et al., 1999, Table 47); 

Minor RestrictedActivity DaysDaily MRAD incidence rate per 

person

0.02137 Ostro and Rothschild 

(1989, p. 243)

a Mortality rates are only available at 5-year increments.

b

Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional-level 

hospital and emergency department discharges for a variety of ICD codes (AHRQ, 2007).

c

Assessed in sensitivity analysis only. The rate numbers may be slightly different from those in Table 4 because 

we received more current estimates from ALA.

d

Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and 

wheeze.

Table 5-4. Asthma Prevalence Rates 

Population Group

Asthma Prevalence Rates

Value Source

All Ages 0.0780 American Lung Association (2010b, Table 7)

< 18 0.0941

5–17 0.1070

18–44 0.0719

45–64 0.0745

65+ 0.0716

African American, 5–17 0.1776 American Lung Association (2010b, Table 9)

African American, <18 0.1553 American Lung Associationa

a

Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008).
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5.6.3 Effect Coefficients

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 SAB-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) and the 

Provisional Assessment (U.S. EPA, 2012b).

7

In 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.

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 5-5. 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 

 

7

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

new studies from the PM ISA and Provisional Assessment for health endpoints not previously quantified in 

EPA’s RIAs are presented in a sensitivity analysis in Appendix 5B, but these new endpoints have not been 

incorporated into the core benefits analysis.
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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 

Table 5-5. Criteria Used When Selecting C-R Functions

Consideration Comments

Peer-Reviewed 

Research

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

process.

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

cohort studies are preferred over ecological studies because they control for important 

individual-level confounding variables that cannot be controlled for in ecological studies. 

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

preferred, because they have greater statistical power to detect effects. Studies that are 

more recent are also preferred because of possible changes in pollution mixes, medical 

care, and lifestyle over time. However, when there are only a few studies available, 

studies from all years will be included.

Population Attributes The most technically appropriate measures of benefits would be based on impact 

functions that cover the entire sensitive population but allow for heterogeneity across 

age or other relevant demographic factors. In the absence of effect estimates specific to 

age, sex, preexisting condition status, or other relevant factors, it may be appropriate to 

select effect estimates that cover the broadest population to match with the desired 

outcome of the analysis, which is total national-level health impacts. When available, 

multi-city studies are preferred to single city studies because they provide a more 

generalizable representation of the concentration-response 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, including 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. National estimates are most appropriate when benefits are nationally 

distributed; the impact of regional differences may be important when benefits only 

accrue to a single area.

Pollutants Included in 

Model

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

is important to use properly specified impact functions that include both pollutants. 

Using single-pollutant models in cases where both pollutants are expected to affect a 

health outcome can lead to double-counting when pollutants are correlated.

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

focus on reducing emissions of PM2.5 precursors, and because air quality modeling was 

conducted for this size fraction of PM. Where PM2.5 functions are not available, PM10

functions are used as surrogates, recognizing that there will be potential downward 

(upward) biases if the fine fraction of PM10 is more (less) toxic than the coarse fraction. 
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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.

Non-overlapping 

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 double-counting of benefits. 

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 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 effects pooling simply weights each study’s estimate by the inverse variance, 

giving more weight to studies with greater statistical power (lower variance). Random effects 

pooling accounts for both within-study variance and between-study variability, due, for 

example, to differences in population susceptibility. We used the fixed effects model as our null 

hypothesis and then determined whether the data suggest that we should reject this null 

hypothesis, in which case we would use the random effects model. 8Pooled impact functions 

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

 

8

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. 
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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 are drawn are 

included in Table 5-6. We highlight in blue those studies that have been added since the 

benefits analysis conducted for the MATS RIA (U.S. EPA, 2011d), and we highlight those studies 

in red that have been added since the proposal RIA (U.S. EPA, 2012a). 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. Table 5-7 summarizes those 

health endpoints and studies we have included as in sensitivity analyses.

Table 5-6. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts 

in the Core Analysis a

Endpoint Study Study Population

Risk Estimate

(95th Percentile 

Confidence Interval)a

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, 

total exposures

PM2.5 Expert Elicitation (Roman et al., 

2008)

>24 years Varies by expert

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)

Nonfatal heart 

attacks (cont’d)

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 18–64 years 1.02 (1.01–1.03) per 

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(Chronic lung disease) 36 μg/m3

Babin et al. (2007)—ICD 493 (asthma) <19 β=0.002 (0.004337)

Sheppard (2003)—ICD 493 (asthma) < 18 RR = 1.04 (1.01–1.06) per 

11.8 μg/m3

(continued)
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Table 5-6. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts 

in the Core Analysis a(continued)

Endpoint Study

Study 

Population

Risk Estimate

(95th Percentile 

Confidence Interval)a

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-, cerebro- and 

peripheral vascular disease)

β=0.00068

(0.000214)

Peng et al. (2008)—ICD 426-427; 428; 430-438; 

410-414; 429; 440-449 (Cardio-, cerebro- and 

peripheral vascular disease)

β=0.00071

(0.00013)

Bell et al. (2008)—ICD 426-427; 428; 430-438; 

410-414; 429; 440-449 (Cardio-, cerebro- and 

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 ER 

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

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

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 = 1.11 (1.58–1.58) 

per 15 μg/m3

a

Studies highlighted in blue represent updates incorporated since the RIA for MATS (U.S. EPA, 2011d). Studies 

highlighted in red represent updates incorporated since the proposal RIA (U.S. EPA, 2012a).

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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).
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Table 5-7. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts 

in the Sensitivity Analysisa

Endpoint Study Study Population

Chronic Illness

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

Stroke Miller et al. (2007) 50–79 years

Hospital Admissions

Cardiovascular ED Visits Metzger et al. (2004) 0–99

Tolbert et al. (2007) 0–99

Mathes et al. (2011) 40–99

a

Studies highlighted in blue represent updates incorporated since the RIA for MATS (U.S. EPA, 2011d). Studies 

highlighted in red represent updated incorporated since the proposal RIA (U.S. EPA, 2012a).

5.6.3.1 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 this review 

of the PM standards, which was twice reviewed by the SAB-CASAC (U.S. EPA-SAB, 2009b, 

2009c), 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 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-
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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 SABHES 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 (e.g., U.S. 

EPA, 2006a, 2010c, 2011c, 2011d). 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).
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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 SABHES 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, 2010b) 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, 2010b). The SAB-CASAC also provided extensive peer review of the risk 

assessment and supported the use of effect estimates from this study (U.S. EPA-SAB, 2009a, 

2010b, c). 

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

EPA, 2010b) which was reviewed by the SAB-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.
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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 all-cause 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 5.6.6.1). Second, following conclusions of the PM ISA, 

we assume that all fine particles are equally potent in causing premature mortality (see section 

5.7.2). 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 5.7.4).

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, 2009) and the Provisional 

Assessment (U.S. EPA, 2012b) (and thus not summarized here).9,10 Table 5-8 provides the effect 

 

9

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.

10 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. 
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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, 2006a), 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.

Table 5-8. 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/m3Change 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

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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 peernomination 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. 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 expert-derived 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”

11 according to the experts included:

 

11 These criteria are substantively similar to EPA’s study selection criteria identified in Table 5-5 of this chapter.
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 Geographic representation of the entire U.S. (e.g., monitoring sites across the 

country)

 Collection of information on individual risk factors and residential information both 

at the beginning and throughout the follow-up period

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

 Collection of genetic information from cohort members to identify and assess 

potential effect modifiers

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

 Collection of data on levels of several co-pollutants (not only those that are 

monitored for compliance purposes

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

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

 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 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. 
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(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]).

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 5-8. 

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 

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

Table 5-8. In addition, we now present the full distributions of the expert-derived results in a 

probabilistic graphic (rather than cascading the expert-derived results throughout the results 

tables as done in prior RIAs). We do not combine the expert results in order to preserve the 

breadth and diversity of opinion on the expert panel (Roman et. al., 2006). 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.12

The PM ISA states that less evidence is available regarding the potential impact of PM2.5 

 

12 For the purposes of this analysis, we only calculate benefits for infants age 0–1, not all children under 5 years 

old.
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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, 2004a). 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

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, 2004a).

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.
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5.6.3.2 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., 

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 
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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 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).

5.6.3.3 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.

The two main groups of hospital admissions estimated in this analysis are respiratory 

admissions and cardiovascular admissions. There is not much 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 
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to 64, while the remainder estimate risk among adults over 64.13 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 peripheralvascular diseases. To 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 peripheral-vascular 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.

 

13 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.
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5.6.3.4 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.

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,14 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). 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.

We estimate three types of acute respiratory symptoms: 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 

using an effect estimate from Schwartz and Neas (2000). Incidences of upper respiratory 

symptoms in asthmatic children aged 9 to 11 are estimated 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 

 

14 See http://www.nlm.nih.gov/medlineplus/ency/article/001087.htm, accessed April 2012. 
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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, 2009), but 

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

(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.

For this RIA, we have followed the SAB-HES recommendations regarding asthma 

exacerbations in developing the core estimate (U.S. EPA-SAB, 2004a). Although certain studies 

of acute respiratory events characterize these impacts among only asthmatic populations, 

others consider the full population, including both asthmatics and non-asthmatics. 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 loss days and 

MRADs. Finally, note also 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.

To characterize asthma exacerbations in children, 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).
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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 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 asthma exacerbation incidence 

estimate. 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.

5.6.3.5 Effect Coefficients Selected for the Sensitivity Analyses

Chronic Bronchitis. Chronic bronchitis 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. Chronic bronchitis

affects an estimated 5% of the U.S. population (ALA, 1999). A limited number of studies have 

estimated the impact of air pollution on new incidences of chronic bronchitis. Schwartz (1993) 

and Abbey et al. (1995) provide evidence that long-term PM2.5 exposure gives rise to the 

development of chronic bronchitis in adults in the United States; these remain the two most 

recent studies observing a relationship between long-term exposure to PM2.5 and the onset of 

chronic bronchitis in the U.S. The absence of newer studies finding a relationship between longterm PM2.5 exposure and chronic bronchitis argues for moving this endpoint from the core

benefits analysis to a sensitivity analysis. In their review of the scientific literature on chronic 

obstructive pulmonary disease (COPD), which includes chronic bronchitis and emphysema, the 
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American Thoracic Society concluded that air pollution is “associated with COPD, but sufficient 

criteria for causation were not met” (Eisner et al., 2010).

Stroke. The PM ISA (U.S. EPA, 2009) includes several new studies that have examined 

the relationship between PM2.5 exposure and cerebrovascular events (U.S. EPA, 2009). Timeseries studies have generally been inconsistent with several studies showing positive 

associations (Dominici et al., 2006; Metzger et al., 2004; Lippman et al., 2000; Lisabeth et al.,

2008). Several other studies have demonstrated null or negative associations (Anderson et al.,

2001; Barnett et al., 2006; Peel et al., 2007). In general, these studies examined cerebrovascular 

disease as a group, though a few studies partition ischemic and hemorrhagic strokes separately 

(Lisabeth et al., 2008). A key limitation of these time-series studies is that they use hospital 

discharge rates as the diagnosis and relatively short lags (0–2 days)—this is problematic, as 

discharge rates are an imperfect diagnosis and strokes may occur several days before admission 

to the hospital.

Longer-term prospective cohort studies of PM2.5 and stroke include Miller et al. (2007).

Miller et al. (2007) estimated the change in risk among post-menopausal women enrolled in the 

Women’s Health Initiative (U.S. EPA, 2009b). After adjusting for age, race, smoking status, 

educational level, household income, body-mass index, diabetes, hypertension, and 

hypercholesterolemia, hazard ratios were estimated for the first cardiovascular event. Because 

this study considers first-time cardiovascular events, a key challenge to incorporating this study 

into the core health impact assessment is matching the baseline incidence rate correctly, and 

we have approximated this information using the data in the study.

In addition, Wellenius et al. (2012) examined the association of PM2.5 with neurologist 

confirmed ischemic stroke in Boston adults in a time-stratified case-crossover study. A key 

feature of this study is that it included the time of stroke symptom onset for most patients. 

Similar to the challenge with Miller et al. (2007), we do not have baseline incidence rates, and 

we do not have sufficient data from the study to approximate it. Three factors argue for 

treating this endpoint in the sensitivity analysis: (1) the epidemiological literature examining 

PM-related cerebrovascular events is still evolving; (2) there are special uncertainties associated 

with quantifying this endpoint; (3) we have not yet identified an appropriate method for 

estimating the economic value of this endpoint.
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Cardiovascular Emergency Department Visits. A large number of recent U.S.-based 

studies provide support for an association between short-term increases in PM2.5 and increased 

risk of ED visits for ischemic heart diseases (U.S. EPA, 2009b). Both Metzger et al. (2004) and 

Tolbert et al. (2007) published interim results from the Study of Particles and Health in Atlanta

(SOPHIA), finding a relationship between PM2.5 exposure and cardiovascular emergency 

department visits. These cardiovascular emergency department visits are distinct from 

cardiovascular hospital admissions and non-fatal heart attacks. To ensure no double-counting, 

we excluded ICD-9-411 (ischemic heart disease) from the baseline incidence rates for 

cardiovascular emergency department visits. Mathes et al. (2011) find relationships between 

PM2.5 levels and cardiovascular emergency department visits in New York City. The principal 

challenge to incorporating these studies is the absence of readily-available economic valuation 

estimates for cardiovascular emergency department visits. Until we develop an approach for 

estimating the economic value of this endpoint, we will quantify these ED visits in a sensitivity

analysis only.

5.6.4 Unquantified Human Health Benefits

The illustrative emission reduction strategies to reach the revised and alternative annual 

standards described in Chapter 4 would reduce emissions of directly emitted particles, as well 

as SO2, and NOx for an alternative standard for 11 μg/m3. The extent to which down wind 

exposure to secondary pollutants ozone, SO2, NOx, and/or methylmercury would actually be 

reduced would depend on the specific control strategy that States would use to reduce PM2.5 in 

a given area as well as local geographic and meteorological conditions. Although we have 

quantified many of the health benefits associated with reducing exposure to PM2.5, as shown in 

Table 5-2, we are unable to quantify the health benefits associated with reducing the potential 

for ozone exposure, SO2 exposure, NO2 exposure or contamination of local water bodies with 

mercurymethylmercury exposure due to the absence of air quality modeling data for these 

pollutants in this analysis. Although we applied the rollback method to simulate the impact of 

attaining the revised and alternative annual standards on ambient levels of PM2.5, this method 

does not simulate how the illustrative emission reductions would affect ambient levels of

ozone, SO2, or NO2. Furthermore, the air quality modeling conducted for this analysis did not 

assess mercury, so we are unable to estimate mercury deposition associated with the 

illustrative controls or subsequent bioaccumulation and exposure. Below we provide a 

qualitative description of these health benefits. In general, previous analyses have shown that 
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the monetized value of these additional health benefits is much smaller than PM2.5-related 

benefits (U.S. EPA, 2010a, 2010c, 2010d).

Reducing NOx emissions also reduces ozone concentrations in most areas. Reducing 

ambient ozone concentrations is associated with significant human health benefits, including 

mortality and respiratory morbidity (U.S. EPA, 2008a, 2010d). Epidemiological researchers have 

associated ozone exposure with adverse health effects in numerous toxicological, clinical and 

epidemiological studies (U.S. EPA, 2006b). When adequate data and resources are available, 

the EPA generally quantifies several health effects associated with exposure to ozone (e.g., U.S. 

EPA, 2008a, 2010d, 2011a, 2011c). These health effects include respiratory morbidity such as 

asthma attacks, hospital and emergency department visits, school loss days, as well as 

premature mortality. The scientific literature suggests that exposure to ozone is also associated 

with chronic respiratory damage and premature aging of the lungs, but the EPA has not 

quantified these effects in benefits analyses previously.

Following an extensive evaluation of health evidence from epidemiologic and laboratory 

studies, the Integrated Science Assessment for Sulfur Dioxide—Health Criteria (SO2 ISA) 

concluded that there is a causal relationship between respiratory health effects and short-term 

exposure to SO2 (U.S. EPA, 2008c). The immediate effect of SO2 on the respiratory system in 

humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO2 likely 

resulting from preexisting inflammation associated with this disease. A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO2

at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and 

percentage of asthmatics adversely affected. Based on our review of this information, we 

identified four short-term morbidity endpoints that the SO2 ISA identified as a “causal 

relationship”: asthma exacerbation, respiratory-related emergency department visits, and 

respiratory-related hospitalizations. The differing evidence and associated strength of the 

evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also 

concluded that the relationship between short-term SO2 exposure and premature mortality was 

“suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects 

to SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a 

relationship between SO2 exposure and mortality, there was a lack of robustness of the 

observed associations to adjustment for pollutants. We did not quantify these benefits due to 

data constraints.
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Epidemiological researchers have associated NO2 exposure with adverse health effects 

in numerous toxicological, clinical and epidemiological studies, as described in the Integrated 

Science Assessment for Oxides of Nitrogen—Health Criteria (NO2 ISA) (U.S. EPA, 2008b). The 

NO2 ISA provides a comprehensive review of the current evidence of health and environmental 

effects of NO2. The NO2 ISA concluded that the evidence “is sufficient to infer a likely causal 

relationship between short-term NO2 exposure and adverse effects on the respiratory system.”

These epidemiologic and experimental studies encompass a number of endpoints including 

emergency department visits and hospitalizations, respiratory symptoms, airway 

hyperresponsiveness, airway inflammation, and lung function. Effect estimates from 

epidemiologic studies conducted in the United States and Canada generally indicate a 2–20% 

increase in risks for ED visits and hospital admissions and higher risks for respiratory symptoms.

The NO2 ISA concluded that the relationship between short-term NO2 exposure and premature 

mortality was “suggestive but not sufficient to infer a causal relationship” because it is difficult 

to attribute the mortality risk effects to NO2 alone. Although the NO2 ISA stated that studies 

consistently reported a relationship between NO2 exposure and mortality, the effect was 

generally smaller than that for other pollutants such as PM. We did not quantify these benefits 

due to data constraints.

5.6.5 Economic Valuation Estimates

Reductions in ambient concentrations of air pollution generally lower the risk of future 

adverse health effects for a large population. Therefore, the appropriate economic measure is 

WTP for changes in risk of a health effect rather than WTP for a health effect that would occur 

with certainty (Freeman, 1993). Epidemiological studies generally provide estimates of the 

relative risks of a particular health effect that is avoided because of a reduction in air pollution. 

We converted those changes in risk to units of avoided statistical incidence for ease of 

presentation. We calculated the value of avoided statistical incidences by dividing individual 

WTP for a risk reduction by the related observed change in risk.

WTP estimates generally are not available for some health effects, such as hospital 

admissions. In these cases, we instead used the cost of treating or mitigating the effect to 

estimate the economic value. COI estimates generally (although not necessarily in all cases) 

understate the true value of reducing the risk of a health effect, because they reflect the direct 

expenditures related to treatment, but not the value of avoided pain and suffering (Harrington 

and Portney, 1987; Berger, 1987). 
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We provide unit values for health endpoints (along with information on the distribution 

of the unit value) in Table 5-9. All values are in constant year 2006 dollars, adjusted for growth 

in real income for WTP estimates out to 2020 using projections provided by Standard and 

Poor’s, which is discussed in further detail in Section 5.6.8. Economic theory argues that WTP 

for most goods (such as environmental protection) will increase if real income increases. 

Several of the valuation studies used in this analysis were conducted in the late 1980s and early 

1990s, and we are in the process of reviewing the literature to update these unit values. The 

discussion below provides additional details on valuing specific PM2.5-related related endpoints.

5.6.6.1 Mortality Valuation

Following the advice of the SAB’s Environmental Economics Advisory Committee (SABEEAC), the EPA currently uses the value of statistical life (VSL) approach in calculating the core

estimate of mortality benefits, because we believe this calculation provides the most 

reasonable single estimate of an individual’s willingness to trade off money for reductions in 

mortality risk (U.S. EPA-SAB, 2000). The VSL approach is a summary measure for the value of 

small changes in mortality risk experienced by a large number of people. For a period of time 

(2004–2008), the Office of Air and Radiation (OAR) valued mortality risk reductions using a VSL 

estimate derived from a limited analysis of some of the available studies. OAR arrived at a VSL 

using a range of $1 million to $10 million (2000$) consistent with two meta-analyses of the 

wage-risk literature. The $1 million value represented the lower end of the interquartile range 

from the Mrozek and Taylor (2002) meta-analysis of 33 studies. The $10 million value 

represented the upper end of the interquartile range from the Viscusi and Aldy (2003) metaanalysis of 43 studies. The mean estimate of $5.5 million (2000$) was also consistent with the 

mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. However, the 

Agency neither changed its official guidance on the use of VSL in rule-makings nor subjected the 

interim estimate to a scientific peer-review process through SAB or other peer-review group.
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Table 5-9. Unit Values for Economic Valuation of Health Endpoints (2010$)

a

Health Endpoint

Central Estimate of Value Per Statistical Incidence

1990 Income Level 2020 Income Level Derivation of Distributions of Estimates

Premature Mortality (Value of a 

Statistical Life)

$8,000,000 $9,600,000 The EPA currently recommends a central VSL of $4.8m (1990$, 1990 

income) based on a Weibull distribution fitted to 26 published VSL 

estimates (5 contingent valuation and 21 labor market studies). The 

underlying studies, the distribution parameters, and other useful 

information are available in Appendix B of the EPA’s Guidelines for 

Preparing Economic Analyses (U.S. EPA, 2010e).

Nonfatal Myocardial Infarction 

(heart attack)

3% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–64

Age 65 and over

7% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–64

Age 65 and over

$98,000

$110,000

$120,000

$200,000

$98,000

$97,000

$110,000

$110,000

$190,000

$97,000

$98,000

$110,000

$120,000

$200,000

$98,000

$97,000

$110,000

$110,000

$190,000

$97,000

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

values reflect lost earnings and direct medical costs over a 5-year 

period following a nonfatal MI. Lost earnings estimates are based on 

Cropper and Krupnick (1990). Direct medical costs are based 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 in 2000$:

age of onset: at 3% at 7%

25–44 $9,000 $8,000

45–54 $13,000 $12,000

55–65 $77,000 $69,000

Direct medical expenses (2000$): An average of:

1. Wittels et al. (1990) ($100,000—no discounting)

2. Russell et al. (1998), 5-year period ($22,000 at 3% discount rate; 

$21,000at 7% discount rate)

(continued)
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Table 5-9. Unit Values for Economic Valuation of Health Endpoints (2010$)

a

(continued)

Health Endpoint

Central Estimate of Value Per Statistical Incidence

2000 Income Level 2020 Income Level Derivation of Distributions of Estimates

Hospital Admissions 

Chronic Lung Disease (18–64) $21,000 $21,000 No distributional information available. The COI estimates (lost 

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of 

hospital stay, and weighted share of total chronic lung illnesses) 

reported in Agency for Healthcare Research and Quality (2007) 

(www.ahrq.gov). 

Asthma Admissions (0–64) $16,000 $16,000 No distributional information available. The COI estimates (lost 

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of 

hospital stay, and weighted share of total asthma category illnesses) 

reported in Agency for Healthcare Research and Quality (2007) 

(www.ahrq.gov). 

All Cardiovascular

Age 18–64

Age 65–99

$42,000

$41,000

$42,000

$41,000

No distributional information available. The COI estimates (lost 

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of 

hospital stay, and weighted share of total cardiovascular category 

illnesses) reported in Agency for Healthcare Research and Quality 

(2007) (www.ahrq.gov). 

All respiratory (ages 65+) $36,000 $36,000 No distributions available. The COI point estimates (lost earnings plus 

direct medical costs) are based on ICD-9 code level information (e.g., 

average hospital care costs, average length of hospital stay, and 

weighted share of total respiratory category illnesses) reported in 

Agency for Healthcare Research and Quality, 2007 (www.ahrq.gov).

Emergency Department Visits 

for Asthma

$430 $430 No distributional information available. Simple average of two unit 

COI values (2000$):

(1) $310, from Smith et al. (1997) and

(2) $260, from Stanford et al. (1999).

(continued)
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Table 5-9. Unit Values for Economic Valuation of Health Endpoints (2010$)

a

(continued)

Health Endpoint

Central Estimate of Value Per Statistical Incidence

2000 Income Level 2020 Income Level Derivation of Distributions of Estimates

Respiratory Ailments Not Requiring Hospitalization

Upper Respiratory Symptoms 

(URS)

$31 $33 Combinations of the three symptoms for which WTP estimates are 

available that closely match those listed by Pope et al. result in seven 

different “symptom clusters,” each describing a “type” of URS. A 

dollar value was derived for each type of URS, using mid-range 

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster 

and assuming additivity of WTPs. In the absence of information 

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

occurs within the URS symptom complex, we assumed a uniform 

distribution between $9.2 and $43 (2000$).

Lower Respiratory Symptoms 

(LRS)

$20 $21 Combinations of the four symptoms for which WTP estimates are 

available that closely match those listed by Schwartz et al. result in 11 

different “symptom clusters,” each describing a “type” of LRS. A 

dollar value was derived for each type of LRS, using mid-range 

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster 

and assuming additivity of WTPs. The dollar value for LRS is the 

average of the dollar values for the 11 different types of LRS. In the 

absence of information surrounding the frequency with which each of 

the 11 types of LRS occurs within the LRS symptom complex, we 

assumed a uniform distribution between $6.9 and $25 (2000$).

Asthma Exacerbations $54 $58 Asthma exacerbations are valued at $45 per incidence, based on the 

mean of average WTP estimates for the four severity definitions of a 

“bad asthma day,” described in Rowe and Chestnut (1986). This study 

surveyed asthmatics to estimate WTP for avoidance of a “bad asthma 

day,” as defined by the subjects. For purposes of valuation, an asthma 

exacerbation is assumed to be equivalent to a day in which asthma is 

moderate or worse as reported in the Rowe and Chestnut (1986) 

study. The value is assumed have a uniform distribution between $16 

and $71 (2000$).

(continued)
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Table 5-9. Unit Values for Economic Valuation of Health Endpoints (2010$)

a

(continued)

Health Endpoint

Central Estimate of Value Per Statistical Incidence

2000 Income Level 2020 Income Level Derivation of Distributions of Estimates

Respiratory Ailments Not Requiring Hospitalization (continued)

Acute Bronchitis $450 $480 Assumes a 6-day episode, with the distribution of the daily value 

specified as uniform with the low and high values based on those 

recommended for related respiratory symptoms in Neumann et al. 

(1994). The low daily estimate of $10 is the sum of the mid-range 

values recommended by IEc (1994) for two symptoms believed to be 

associated with acute bronchitis: coughing and chest tightness. The 

high daily estimate was taken to be twice the value of a minor 

respiratory restricted-activity day, or $110 (2000$). 

Work Loss Days (WLDs) Variable 

(U.S. median = $150)

Variable 

(U.S. median = $150)

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

median annual wages divided by 52 and then by 5—to get median 

daily wage. U.S. Year 2000 Census, compiled by Geolytics, Inc.

(Geolytics, 2002)

Minor Restricted Activity Days 

(MRADs)

$64 $68 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). 

Distribution is assumed to be triangular with a minimum of $22 and a 

maximum of $83, with a most likely value of $52 (2000$). Range is 

based on assumption that value should exceed WTP for a single mild 

symptom (the highest estimate for a single symptom—for eye 

irritation—is $16.00) and be less than that for a WLD. The triangular 

distribution acknowledges that the actual value is likely to be closer 

to the point estimate than either extreme.

a

All estimates are rounded to two significant digits. Unrounded estimates in 2000$ are available in the Appendix J of the BenMAP user manual (Abt 

Associates, 2012).
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During this time, the Agency continued work to update its guidance on valuing 

mortality risk reductions, including commissioning a report from meta-analytic experts 

to evaluate methodological questions raised by the EPA and the SAB on combining 

estimates from the various data sources. In addition, the Agency consulted several times 

with the SAB-EEAC on the issue. With input from the meta-analytic experts, the SABEEAC advised the Agency to update its guidance using specific, appropriate metaanalytic techniques to combine estimates from unique data sources and different 

studies, including those using different methodologies (i.e., wage-risk and stated 

preference) (U.S. EPA-SAB, 2007).

Until updated guidance is available, the Agency determined that a single, peerreviewed estimate applied consistently best reflects the SAB-EEAC advice it has 

received. Therefore, the Agency has decided to apply the VSL that was vetted and 

endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 

2000)15 while the Agency continues its efforts to update its guidance on this issue. This 

approach calculates a mean value across VSL estimates derived from 26 labor market 

and contingent valuation studies published between 1974 and 1991. The mean VSL 

across these studies is $4.8 million (1990$) or $6.3 million (2000$).16 The Agency is 

committed to using scientifically sound, appropriately reviewed evidence in valuing 

mortality risk reductions and has made significant progress in responding to the SABEEAC’s specific recommendations. In the process, the Agency has identified a number of 

important issues to be considered in updating its mortality risk valuation estimates. 

These are detailed in a white paper on “Valuing Mortality Risk Reductions in 

Environmental Policy,” which recently underwent review by the SAB-EEAC. A meeting 

with the SAB on this paper was held on March 14, 2011 and formal recommendations 

were transmitted on July 29, 2011 (U.S. EPA-SAB, 2011). Draft guidance responding to 

SAB recommendations will be developed shortly.

The economics literature concerning the appropriate method for valuing 

reductions in premature mortality risk is still developing. The adoption of a value for the 

 

15 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), EPA retained the VSL 

endorsed by the SAB with the understanding that further updates to the mortality risk valuation 

guidance would be forthcoming in the near future.

16 In this analysis, we adjust the VSL to account for a different currency year (2010$) and to account for 

income growth to 2020. After applying these adjustments to the $6.3 million value, the VSL is $8.9M. 
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projected reduction in the risk of premature mortality is the subject of continuing 

discussion within the economics and public policy analysis community. The EPA strives 

to use the best economic science in its analyses. Given the mixed theoretical finding and 

empirical evidence regarding adjustments to VSL for risk and population characteristics

(e.g., Smith et al., 2004; Alberini et al., 2004; Aldy and Viscusi, 2008), we use a single VSL 

for all reductions in mortality risk.

Although there are several differences between the labor market studies the EPA

uses to derive a VSL estimate and the PM2.5 air pollution context addressed here, those 

differences in the affected populations and the nature of the risks imply both upward 

and downward adjustments. Table 5-10 lists some of these differences and the expected 

effect on the VSL estimate for air pollution-related mortality. In the absence of a 

comprehensive and balanced set of adjustment factors, the EPA believes it is reasonable 

to continue to use the $4.8 million (1990$) value adjusted for inflation and income 

growth over time while acknowledging the significant limitations and uncertainties in 

the available literature.

Table 5-10. Influence of Applied VSL Attributes on the Size of the Economic Benefits 

of Reductions in the Risk of Premature Death (U.S. EPA, 2006a)

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 the EPA to “continue to use a wage-riskbased VSL as its primary estimate, including appropriate sensitivity analyses to reflect 

the uncertainty of these estimates,” and that “the only risk characteristic for which 

adjustments to the VSL can be made is the timing of the risk” (U.S. EPA-SAB, 2000). In 
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developing our core estimate of the benefits of premature mortality reductions, we 

have followed this advice. 

For premature mortality, we assume that there is a “cessation” lag between PM 

exposures and the total realization of changes in health effects. We assumed for this 

analysis that some of the incidences of premature mortality related to PM2.5 exposures 

occur in a distributed fashion over the 20 years following exposure and discounted over 

the period between exposure and premature mortality. Although the structure of the 

lag is uncertain, the EPA follows the advice of the SAB-HES to assume a segmented lag 

structure characterized by 30% of mortality reductions in the first year, 50% over years 2 

to 5, and 20% over the years 6 to 20 after the reduction in PM2.5 (U.S. EPA-SAB, 2004c). 

Additional cessation lag structures are described and assessed in Appendix 5.A of this 

RIA. To take this into account in the valuation of reductions in premature mortality, we 

discount the value of premature mortality occurring in future years using rates of 3% 

and 7%.17 Changes in the cessation lag assumptions do not change the total number of 

estimated deaths but rather the timing of those deaths. As such, the monetized benefits 

using a 7% discount rate are only approximately 10% less than the monetized benefits 

using a 3% discount rate. Further discussion of this topic appears in the EPA’s Guidelines 

for Preparing Economic Analyses (U.S. EPA, 2010e).

Uncertainties Specific to Premature Mortality Valuation. The economic benefits 

associated with reductions in the risk of premature mortality are the largest category of 

monetized benefits in this RIA. In addition, in prior analyses, the EPA has identified 

valuation of mortality-related benefits as the largest contributor to the range of 

uncertainty in monetized benefits (Mansfield et al., 2009).18 Because of the uncertainty 

in estimates of the value of reducing premature mortality risk, it is important to 

adequately characterize and understand the various types of economic approaches 

 

17 The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within 

the federal government. To comply with Circular A-4, EPA provides monetized benefits using discount 

rates of 3% and 7% (OMB, 2003). A 3% discount reflects reliance on a “social rate of time preference”

discounting concept. A 7% rate is consistent with an “opportunity cost of capital” concept to reflect the 

time value of resources directed to meet regulatory requirements. 

18 This conclusion was based on an assessment of uncertainty based on statistical error in epidemiological 

effect estimates and economic valuation estimates. Additional sources of model error such as those 

examined in the PM2.5 mortality expert elicitation (Roman et al., 2008) may result in different conclusions 

about the relative contribution of sources of uncertainty.
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available for valuing reductions in mortality risk. Such an assessment also requires an 

understanding of how alternative valuation approaches reflect that some individuals 

may be more susceptible to air pollution-induced mortality or reflect differences in the 

nature of the risk presented by air pollution relative to the risks studied in the relevant 

economics literature.

The health science literature on air pollution indicates that several human 

characteristics affect the degree to which mortality risk affects an individual. For 

example, some age groups appear to be more susceptible to air pollution than others 

(e.g., the elderly and children). Health status prior to exposure also affects susceptibility. 

An ideal benefits estimate of mortality risk reduction would reflect these human 

characteristics, in addition to an individual’s WTP to improve one’s own chances of 

survival plus WTP to improve other individuals’ survival rates. The ideal measure would 

also take into account the specific nature of the risk reduction commodity that is 

provided to individuals, as well as the context in which risk is reduced. To measure this 

value, it is important to assess how reductions in air pollution reduce the risk of dying 

from the time that reductions take effect onward and how individuals value these 

changes. Each individual’s survival curve, or the probability of surviving beyond a given 

age, should shift as a result of an environmental quality improvement. For example, 

changing the current probability of survival for an individual also shifts future 

probabilities of that individual’s survival. This probability shift will differ across 

individuals because survival curves depend on such characteristics as age, health state, 

and the current age to which the individual is likely to survive.

Although a survival curve approach provides a theoretically preferred method 

for valuing the benefits of reduced risk of premature mortality associated with reducing 

air pollution, the approach requires a great deal of data to implement. The economic 

valuation literature does not yet include good estimates of the value of this risk 

reduction commodity. As a result, in this study we value reductions in premature 

mortality risk using the VSL approach.
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Other uncertainties specific to premature mortality valuation include the 

following:

 Across-study variation: There is considerable uncertainty as to whether the 

available literature on VSL provides adequate estimates of the VSL for risk 

reductions from air pollution reduction. Although there is considerable 

variation in the analytical designs and data used in the existing literature, the 

majority of the studies involve the value of risks to a middle-aged working 

population. Most of the studies examine differences in wages of risky 

occupations, using a hedonic wage approach. Certain characteristics of both 

the population affected and the mortality risk facing that population are 

believed to affect the average WTP to reduce the risk. The appropriateness 

of a distribution of WTP based on the current VSL literature for valuing the 

mortality-related benefits of reductions in air pollution concentrations 

therefore depends not only on the quality of the studies (i.e., how well they 

measure what they are trying to measure), but also on the extent to which 

the risks being valued are similar and the extent to which the subjects in the 

studies are similar to the population affected by changes in pollution 

concentrations.

 Level of risk reduction: The transferability of estimates of the VSL from the 

wage-risk studies to the context of the PM NAAQS analysis rests on the 

assumption that, within a reasonable range, WTP for reductions in mortality 

risk is linear in risk reduction. For example, suppose a study provides a result 

that the average WTP for a reduction in mortality risk of 1/100,000 is $50, 

but that the actual mortality risk reduction resulting from a given pollutant 

reduction is 1/10,000. If WTP for reductions in mortality risk is linear in risk 

reduction, then a WTP of $50 for a reduction of 1/100,000 implies a WTP of 

$500 for a risk reduction of 1/10,000 (which is 10 times the risk reduction 

valued in the study). Under the assumption of linearity, the estimate of the 

VSL does not depend on the particular amount of risk reduction being

valued. This assumption has been shown to be reasonable provided the 

change in the risk being valued is within the range of risks evaluated in the 

underlying studies (Rowlatt et al., 1998).

 Voluntariness of risks evaluated: Although job-related mortality risks may 

differ in several ways from air pollution-related mortality risks, the most 

important difference may be that job-related risks are incurred voluntarily, or 

generally assumed to be, whereas air pollution-related risks are incurred 

involuntarily. Some evidence suggests that people will pay more to reduce 

involuntarily incurred risks than risks incurred voluntarily (e.g., Lichtenstein 

and Slovic, 2006). If this is the case, WTP estimates based on wage-risk 
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studies may understate WTP to reduce involuntarily incurred air pollutionrelated mortality risks.

 Sudden versus protracted death: A final important difference related to the 

nature of the risk may be that some workplace mortality risks tend to involve 

sudden, catastrophic events, whereas air pollution-related risks tend to 

involve longer periods of disease and suffering prior to death. Some evidence 

suggests that WTP to avoid a risk of a protracted death involving prolonged 

suffering and loss of dignity and personal control is greater than the WTP to 

avoid a risk (of identical magnitude) of sudden death (e.g., Tsuge et al., 2005; 

Alberini and Scasny, 2011). To the extent that the mortality risks addressed in 

this assessment are associated with longer periods of illness or greater pain 

and suffering than are the risks addressed in the valuation literature, the 

WTP measurements employed in the present analysis would reflect a 

downward bias.

 Self-selection and skill in avoiding risk: Recent research (Shogren and 

Stamland, 2002) suggests that VSL estimates based on hedonic wage studies 

may overstate the average value of a risk reduction. This is based on the fact 

that the risk-wage trade-off revealed in hedonic studies reflects the 

preferences of the marginal worker (i.e., that worker who demands the 

highest compensation for his risk reduction for a given job). This worker must 

have either a higher workplace risk than the average worker in a given 

occupation, a lower risk tolerance than the average worker in that 

occupation, or both. Conversely, the marginal worker should have a higher 

risk tolerance than workers employed in less-risky sectors. However, the risk 

estimate used in hedonic studies is generally based on average risk, so the 

VSL may be biased, in an ambiguous direction, because the wage differential 

and risk measures do not match.

 Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 

2006) finds that because individuals reevaluate their baseline risk of death as 

they age, the marginal value of risk reductions does not decline with age as 

predicted by some lifetime consumption models. This research supports 

findings in recent stated preference studies that suggest only small 

reductions in the value of mortality risk reductions with increasing age (e.g., 

Alberini et al., 2004).

5.6.6.2 Nonfatal Myocardial Infarctions Valuation

We were not able to identify a suitable WTP value for reductions in the risk of 

nonfatal heart attacks. Instead, we use a COI unit value with two components: the direct 
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medical costs and the opportunity cost (lost earnings) associated with the illness event. 

Because the costs associated with a myocardial infarction extend beyond the initial 

event itself, we consider costs incurred over several years. Using age-specific annual lost 

earnings estimated by Cropper and Krupnick (1990) and a 3% discount rate, we 

estimated a rounded present discounted value in lost earnings (in 2000$) over 5 years 

due to a myocardial infarction of $8,800 for someone between the ages of 25 and 44, 

$13,000 for someone between the ages of 45 and 54, and $75,000 for someone 

between the ages of 55 and 65. The rounded corresponding age-specific estimates of 

lost earnings (in 2000$) using a 7% discount rate are $7,900, $12,000, and $67,000, 

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, which provide significantly different values (see 

Table 5-11):

 Wittels et al. (1990) estimated expected total medical costs of myocardial 

infarction over 5 years to be $51,000 (rounded in 1986$) for people who 

were admitted to the hospital and survived hospitalization. (There does not 

appear to be any discounting used.) This estimated cost is based on a medical 

cost model, which incorporated therapeutic options, projected outcomes, 

and prices (using “knowledgeable cardiologists” as consultants). The model 

used medical data and medical decision algorithms to estimate the 

probabilities of certain events and/or medical procedures being used. The 

authors note that the average length of hospitalization for acute myocardial 

infarction has decreased over time (from an average of 12.9 days in 1980 to 

an average of 11 days in 1983). Wittels et al. used 10 days as the average in 

their study. It is unclear how much further the length of stay for myocardial 

infarction may have decreased from 1983 to the present. The average length 

of stay for ICD code 410 (myocardial infarction) in the year-2000 Agency for 

Healthcare Research and Quality (AHRQ) HCUP database is 5.5 days (AHRQ, 

2000). However, this may include patients who died in the hospital (not 

included among our nonfatal myocardial infarction cases), and whose length 

of stay was therefore substantially shorter than it would be if they had not 

died.

 Eisenstein et al. (2001) estimated 10-year costs of $45,000 in rounded 1997$ 

(using a 3% discount rate) for myocardial infarction patients, using statistical 
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prediction (regression) models to estimate inpatient costs. Only inpatient 

costs (physician fees and hospital costs) were included.

Table 5-11. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart 

Attacksa

Study Direct Medical Costs (2010$) Over an x-Year Period, for x =

Wittels et al. (1990) $160,000b5

Russell et al. (1998) $33,000c5

Average (5-year) costs $98,000 5

Eisenstein et al. (2001) $74,000c10

a

All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in appendix 

J of the BenMAP user manual (Abt Associates, 2012).

b Wittels et al. (1990) did not appear to discount costs incurred in future years.

c

Using a 3% discount rate. Discounted values as reported in the study.

As noted above, the estimates from these three studies are substantially 

different, and we have not adequately resolved the sources of differences in the 

estimates. Because the wage-related opportunity cost estimates from Cropper and 

Krupnick (1990) cover a 5-year period, we used estimates for medical costs that similarly 

cover a 5-year period (i.e., estimates from Wittels et al. (1990) and Russell et al. (1998). 

We used a simple average of the two 5-year estimates, or rounded to $85,000, and 

added it to the 5-year opportunity cost estimate. The resulting estimates are given in 

Table 5-12.

Table 5-12. Estimated Costs Over a 5-Year Period of a Nonfatal Myocardial Infarction 

(in 2010$)

a

Age Group Opportunity Cost Medical CostbTotal Cost

0–24 $0 $98,000 $98,000

25–44 $12,000c$98,000 $110,000

45–54 $17,000c$98,000 $120,000

55–65 $100,000c$98,000 $200,000

> 65 $0 $98,000 $98,000

a

All estimates rounded to two significant digits, so estimates may not sum across columns. Unrounded 

estimates in 2000$ are available in appendix J of the BenMAP user manual (Abt Associates, 2012).
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b

An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998).

c

From Cropper and Krupnick (1990), using a 3% discount rate for illustration.

5.6.6 Hospital Admissions and Emergency Department Valuation

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

for specific illnesses, we derive COI estimates for use in the benefits analysis. The 

International Classification of Diseases (WHO, 1977) code-specific COI estimates used in 

this analysis consist of estimated hospital charges and the estimated opportunity cost of 

time spent in the hospital (based on the average length of a hospital stay for the illness). 

We based all estimates of hospital charges and length of stays on statistics provided by 

the Agency for Healthcare Research and Quality’s Healthcare Utilization Project National 

Inpatient Sample (NIS) database (AHRQ, 2007). We estimated the opportunity cost of a 

day spent in the hospital as the value of the lost daily wage, regardless of whether the 

hospitalized individual is in the workforce. To estimate the lost daily wage, we divided 

the median weekly wage reported by the 2007 American Community Survey (ACS) by 

five and deflated the result to year 2010$ using the CPI-U “all items” (Abt Associates, 

2012). The resulting national average lost daily wage is $148. The total cost-of-illness 

estimate for an ICD code-specific hospital stay lasting n days, then, was the mean 

hospital charge plus $148 multiplied by n. In general, the mean length of stay has 

decreased since the 2000 database used in previous version of BenMAP while the mean 

hospital charge has increased. We provide the rounded unit values in 2010$ for the COI 

functions used in this analysis in Table 5-13.

Table 5-13. Unit Values for Hospital Admissions

End Point ICD Codes

Age Range Mean 

Hospital 

Charge 

(2010$)

Mean 

Length of 

Stay (days)

Total Cost of 

Illness (unit 

min. max. value in 2010$)

HA, Chronic Lung Disease 490–496 18 64 $19,000 3.9 $21,000

HA, Asthma 493 0 64 $14,000 3.0 $16,000

HA, All Cardiovascular 390–429 18 64 $40,000 4.1 $42,000

HA, All Cardiovascular 390–429 65 99 $37,000 4.9 $41,000

HA, All Respiratory 460–519 65 99 $31,000 6.1 $36,000

* All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in Appendix 

J of the BenMAP user manual (Abt Associates, 2012).
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To value asthma emergency department visits, we used a simple average of two 

estimates from the health economics literature. The first estimate comes from Smith et 

al. (1997), who reported approximately 1.2 million asthma-related emergency 

department visits in 1987, at a total cost of $186 million (1987$). The average cost per 

visit that year was $155; in 2010$, that cost was $464 (using the CPI-U for medical care 

to adjust to 2010$). The second estimate comes from Stanford et al. (1999), who 

reported the cost of an average asthma-related emergency department visit at $335, 

based on 1996–1997 data. A simple average of the two estimates yields a unit value of 

$388.

5.6.7 Minor Restricted Activity Days Valuation

No studies are reported to have estimated WTP to avoid a minor restricted 

activity day. However, Neumann et al. (1994) derived an estimate of willingness to pay 

to avoid a minor respiratory restricted activity day, using estimates from Tolley et al. 

(1986) of WTP for avoiding a combination of coughing, throat congestion and sinusitis. 

This estimate of WTP to avoid a minor respiratory restricted activity day is $38 (1990$), 

or about $68 (2010$). Although Ostro and Rothschild (1989) statistically linked ozone 

and minor restricted activity days, it is likely that most MRADs associated with ozone 

and PM2.5 exposure are, in fact, minor respiratory restricted activity days. For the 

purpose of valuing this health endpoint, we used the estimate of mean WTP to avoid a 

minor respiratory restricted activity day.

5.6.8 Growth in WTP Reflecting National Income Growth Over Time

Our analysis accounts for expected growth in real income over time. This is a 

distinct concept from inflation and currency year. Economic theory argues that WTP for 

most goods (such as environmental protection) will increase if real incomes increase. 

There is substantial empirical evidence that the income elasticity19 of WTP for health 

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

real income increases, the WTP for environmental improvements also increases. 

Although many analyses assume that the income elasticity of WTP is unit elastic (i.e., a 

10% higher real income level implies a 10% higher WTP to reduce risk changes), 

empirical evidence suggests that income elasticity is substantially less than one and thus 

 

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

change in income.
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relatively inelastic. As real income rises, the WTP value also rises but at a slower rate 

than real income.

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

estimates in two different ways: through real (national average) income growth 

between the year a WTP study was conducted and the year for which benefits are 

estimated, and through differences in income between study populations and the 

affected populations at a particular time. The SAB-EEAC advised the EPA to adjust WTP 

for increases in real income over time but not to adjust WTP to account for crosssectional income differences “because of the sensitivity of making such distinctions, and 

because of insufficient evidence available at present” (U.S. EPA-SAB, 2000). An advisory 

by another committee associated with the SAB, the Advisory Council on Clean Air 

Compliance Analysis (SAB-Council), has provided conflicting advice. While agreeing with 

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

increase with growth in real income” and that “[t]he same increase should be assumed 

for the WTP for serious nonfatal health effects,” they note that “given the limitations 

and uncertainties in the available empirical evidence, the Council does not support the 

use of the proposed adjustments for aggregate income growth as part of the primary 

analysis” (U.S. EPA-SAB, 2004b). Until these conflicting advisories have been reconciled, 

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

methods described below, while providing sensitivity analyses for alternative income 

growth adjustment factors.

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

valuation of human health benefits upward to account for projected growth in real U.S. 

income. Faced with a dearth of estimates of income elasticities derived from time-series 

studies, we applied estimates derived from cross-sectional studies in our analysis.

Details of the procedure can be found in Kleckner and Neumann (1999). We note that 

the literature has evolved since the publication of this memo and that an array of newer 

studies identifying potentially suitable income elasticity estimates are available (IEc, 

2012). The EPA anticipates seeking an SAB review of these studies, and its approach to 

adjusting WTP estimates to account for changes in personal income, in 2013. As such, 

these newer studies have not yet been incorporated into the benefits analysis. An 
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abbreviated description of the procedure we used to account for WTP for real income 

growth between 1990 and 2020 is presented below.

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

primary determinant of the strength of the relationship between changes in real income 

and WTP. As such, we use different elasticity estimates to adjust the WTP for minor 

health effects, severe and chronic health effects, and premature mortality. Note that 

because of the variety of empirical sources used in deriving the income elasticities, 

there may appear to be inconsistencies in the magnitudes of the income elasticities 

relative to the severity of the effects (a priori one might expect that more severe 

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

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

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

illnesses is often expressed as a short term payment to avoid a single episode. WTP for 

major illnesses and mortality risk reductions are based on longer term measures of 

payment (such as wages or annual income). Economic theory suggests that relationships 

become more elastic as the length of time grows, reflecting the ability to adjust 

spending over a longer time period. Based on this theory, it would be expected that 

WTP for reducing long term risks would be more elastic than WTP for reducing short 

term risks. The relative magnitude of the income elasticity of WTP for visibility 

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

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

values used to adjust estimates of benefits in 2020 are presented in Table 5-14.

20

Table 5-14. Elasticity Values Used to Account for Projected Real Income Growtha

Benefit Category Central Elasticity Estimate

Minor Health Effect 0.14

Severe and Chronic Health Effects 0.45

Premature Mortality 0.40

a

Derivation of estimates can be found in Kleckner and Neumann (1999). COI estimates are not adjusted 

for income growth.

 

20 We expect that the WTP for improved visibility in Class 1 areas would also increase with growth in real 

income (see Chapter 6).
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In addition to elasticity estimates, projections of real gross domestic product 

(GDP) and populations from 1990 to 2020 are needed to adjust benefits to reflect real 

per capita income growth. For consistency with the emissions and benefits modeling, 

we used national population estimates for the years 1990 to 1999 based on U.S. Census 

Bureau estimates (Hollman, Mulder, and Kallan, 2000). These population estimates are 

based on application of a cohort-component model applied to 1990 U.S. Census data 

projections (U.S. Bureau of Census, 2000). For the years between 2000 and 2020, we 

applied growth rates based on the U.S. Census Bureau projections to the U.S. Census 

estimate of national population in 2000. We used projections of real GDP provided in 

Kleckner and Neumann (1999) for the years 1990 to 2010.21 We used projections of real 

GDP (in chained 1996 dollars) provided by Standard and Poor’s (2000) for the years 2010 

to 2020.

22

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

and income data described above, we calculated WTP adjustment factors for each of the 

elasticity estimates listed in Table 5-15. Benefits for each of the categories (minor health 

effects, severe and chronic health effects, premature mortality, and visibility) are 

adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor.

For premature mortality, we applied the income adjustment factor specific to the 

analysis year, but we do not adjust for income growth over the 20-year cessation lag. 

Our approach could underestimate the benefits for the later years of the lag.

There is some uncertainty regarding the total costs of illness in the future. 

Specifically, the nature of medical treatment is changing, including a shift towards more 

outpatient treatment. Although we adjust the COI estimates for inflation, we do not 

have data to project COI estimates for the cost of treatment in the future or income 

growth over time, which leads to an inherent though unavoidable inconsistency 

between COI- and WTP-based estimates This approach may underpredict benefits in 

 

21 U.S. Bureau of Economic Analysis, Table 2A—Real Gross Domestic Product (1997) and U.S. Bureau of 

Economic Analysis, The Economic and Budget Outlook: An Update, Table 4—Economic Projections for 

Calendar Years 1997 Through 2007 (1997). Note that projections for 2007 to 2010 are based on average 

GDP growth rates between 1999 and 2007.

22 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.
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future years because it is likely that increases in real U.S. income would also result in 

increased COI (due, for example, to increases in wages paid to medical workers) and 

increased cost of work loss days and lost worker productivity (reflecting that if worker 

incomes are higher, the losses resulting from reduced worker production would also be 

higher). In addition, cost-of-illness estimates do not include sequelae costs or pain and 

suffering, the value of which would likely increase in the future. To the extent that costs 

would be expected to increase over time, this increase may be partially offset by 

advancement in medical technology that improves the effectiveness of treatment at 

lower costs. For these reasons, we believe that the cost-of-illness estimates in this RIA 

may underestimate (on net) the total economic value of avoided health impacts.

Table 5-15. Adjustment Factors Used to Account for Projected Real Income Growtha

Benefit Category 2020

Minor Health Effect 1.07

Severe and Chronic Health Effects 1.22

Premature Mortality 1.20

a

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

real GDP per capita.

5.7 Benefits Results

5.7.1 Benefits of the Revised and Alternative Annual Primary PM2.5 Standards

Applying the impact and valuation functions described previously in this chapter 

to the estimated changes in PM2.5 yields estimates of the changes in physical damages 

(e.g., premature mortalities, cases of acute bronchitis and hospital admissions) and the 

associated monetary values for those changes. Not all known PM health effects could be 

quantified or monetized. The monetized value of these unquantified effects is 

represented by adding an unknown “B” to the aggregate total. The estimate of total 

monetized health benefits is thus equal to the subset of monetized PM-related health 

benefits plus B, the sum of the non-monetized health benefits and welfare co-benefits; 

this B represents both uncertainty and a bias in this analysis, as it reflects those benefits 

categories that we are unable to monetize in this analysis.

Table 5-16 shows the population-weighted air quality change for the alternative

standards averaged across the continental U.S. Tables 5-17 through 5-22 present the
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benefits results for the annual PM2.5 standards. These benefits are relative to a 2020 

analytical baseline reflecting attainment nationwide of the current primary PM2.5 

standards (i.e., 15/35) that includes promulgated national regulations and illustrative 

emission controls to simulate attainment with 15/35 as well as an adjustment to NOx

emissions to reflect expected reductions in mobile NOx emissions between 2020 and 

2025.23 Figure 5-3 graphically displays the total monetized benefits of the revised annual

primary standard of 12 μg/m3, using alternative concentration-response functions at 

discount rates of 3% and 7%. Figure 5-4 graphically displays the cumulative distribution 

of total monetized benefits using the 2 epidemiology-derived and the 12 expert-derived 

relationships between PM2.5 and mortality for the revised standard, which provides the 

full range of uncertainty within and across the expert-derived relationships.

Table 5-16. Population-Weighted Air Quality Change for the Revised and Alternative

Annual Primary PM2.5 Standards Relative to Analytical Baseline

Standard Population-Weighted Air Quality Change

13 μg/m30.014 μg/m3

12 μg/m30.043 μg/m3

11 μg/m30.207 μg/m3

 

23 The estimates in this chapter reflect incremental emissions reductions from an analytical baseline that 

gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions 

reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate 

attainment with the revised standards. Full benefits of the revised standards in those two areas will 

not be realized until 2025.
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Table 5-17. Emission Reductions in Illustrative Emission Reduction Strategies for the 

Revised and Alternative Annual Primary PM2.5 Standards, by Pollutant and 

Region in 2020 (tons)a

13 μg/m312 μg/m311 μg/m3

Directly emitted PM2.5

East 0 0 8,200

West 0 0 160

CA 730 4,000 10,600

SO2

East 0 0 21,000

West 0 0 43

CA 0 0 0

NOx

East 0 0 9

West 0 0 0

CA 0 0 0

a

See Chapter 4 for more information on the illustrative emission reduction strategies. The emissions in 

this table reflect both known and unknown controls.
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Table 5-18. Estimated number of Avoided PM2.5 Health Impacts for the Revised and 

Alternative Annual Primary PM2.5 Standards (Incremental to the 

Analytical Baseline)

a

Health Effect

Revised and Alterative Annual Standards

(95th percentile confidence interval)

13 μg/m312 μg/m311 μg/m3

Avoided Mortality

Krewski et al. (2009)

(adult mortality age 30+)

140 460 1,500

(100--190) (320--590) (1,000--1,900)

Laden et al. (2006)

(adult mortality age 25+)

330 1,000 3,300

(180--480) (560--1,500) (1,800--4,800)

Woodruff et al. (1997)

(infant mortality)

0 1 4

(0--1) (1--2) (2--6)

Avoided Morbidity

Non-fatal heart attacks

Peters et al. (2001)

(age >18)

160 480 1,600

(49--260) (150--800) (480--2,600)

Pooled estimate of 4 studies

(age >18)

17 52 170

(8--41) (24--130) (78--410)

Hospital admissions—respiratory

(all ages)b

31 110 380

(-9--58) (-30--200) (-100--720)

Hospital admissions—cardiovascular

(age > 18)

43 140 480

(20--76) (66--240) (230--0,840)

Emergency department visits for 

asthma (age < 18)b

67 230 810

(-22--140) (-74--470) (-260--1,600)

Acute bronchitis 

(ages 8–12)b

280 870 2,700

(-36--580) (-110--1,800) (-350--5,500)

Lower respiratory symptoms

(ages 7–14)

3,500 11,000 34,000

(1500--5500) (4,900--17,000) (15,000--53,000)

Upper respiratory symptoms

(asthmatics ages 9–11)

5,100 16,000 49,000

(1300--8900) (4,100--28,000) (12,000--86,000)

Asthma exacerbation

(asthmatics ages 6–18)

13,000 40,000 120,000

(270--81000) (850--250,000) (2,600--770,000)

Lost work days 

(ages 18–65)

22,000 71,000 230,000

(19000--25000) (61,000--81,000) (190,000--260,000)

Minor restricted-activity days

(ages 18–65)

130,000 420,000 1,300,000

(110,000--150,000) (350,000--490,000) (1,100,000--1,600,000)

a

All incidence estimates are rounded to whole numbers with a maximum of two significant digits. These 

estimates reflect incremental emissions reductions from an analytical baseline that gives “an 

adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions 

expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment 

with the revised standards. Full benefits of the revised standards in those two areas will not be realized 

until 2025. Additional health endpoints, such as cardiovascular emergency department visits, are only 
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quantified in a sensitivity analysis in Table 5.A-6 because we do not yet have a valuation estimate for 

this endpoint. 

b

The negative estimates at the 5th percentile confidence estimates for these morbidity endpoints reflect 

the statistical power of the study used to calculate these health impacts. These results do not suggest 

that reducing air pollution results in additional health impacts.
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Table 5-19. Monetized PM2.5 Health Benefits for the Revised and Alternative Annual

Primary PM2.5 Standards (Incremental to Analytical Baseline) (Millions of 

2006$, 3% discount rate)

a

Health Effect

Revised and Annual Standards

(95th percentile confidence interval)

13 μg/m312 μg/m311 μg/m3

Avoided Mortalityb

Krewski et al. (2009)

(adult mortality age 30+)

$1,300 $4,000 $13,000

($120--$3,500) ($370--$11,000) ($1,200--$35,000)

Laden et al. (2006)

(adult mortality age 25+)

$2,900 $9,000 $29,000

($250--$8,100) ($800--$26,000) ($2,600--$82,000)

Woodruff et al. (1997)

(infant mortality)

$3.4 $11 $35

($0.29--$10) ($0.91--$32) ($3.0--$100)

Avoided Morbidity

Non-fatal heart attacks 

Peters et al. (2001)

(age >18)

$18 $55 $180

($3.0--$46) ($9.1--$140) ($31--$460)

Pooled estimate of 4 studies

(age >18)

$2.0 $6.0 $20.0

($0.43--$6.8) ($1.3--$21) ($4.4--$68)

Hospital admissions—respiratory

(all ages)c

$0.86 $3.0 $11

(-$0.22--$1.6) (-$0.8--$5.5) (-$2.7--$20)

Hospital admissions—cardiovascular 

(age > 18)

$1.70 $5.3 $18

($0.85--$2.8) ($2.7--$9.2) ($10--$32)

Emergency department visits for 

asthma

(age < 18)

$0.03 $0.10 $0.34

(-$0.0052--$0.061) (-$0.018--$0.21) (-$0.063--$0.73)

Acute bronchitis

(ages 8–12)c

$0.13 $0.42 $1.30

(-$0.0060--$0.37) (-$0.019--$1.2) (-$0.059--$3.5)

Lower respiratory symptoms

(ages 7–14)

$0.08 $0.24 $0.71

($0.025--$0.10) ($0.078--$0.47) ($0.24--$1.4)

Upper respiratory symptoms

(asthmatics ages 9–11)

$0.17 $0.54 $1.6

($0.038--$0.42) ($0.12--$1.30) ($0.36--$4.0)

Asthma exacerbation

(asthmatics ages 6–18)

$0.7 $2.30 $7.0

($0.027--$5.2) ($0.085--$16) ($0.26--$49)

Lost work days

(ages 18–65)

$3.3 $11 $35

($2.90--$3.70) ($9.4--$12) ($30--$39)

Minor restricted-activity days

(ages 18–65)

$8.8 $29 $91

($4.70--$13) ($15--$43) ($48--$140)

a

All estimates are rounded to two significant digits. Estimates do not include unquantified health benefits noted in 

Table 5-2 or Section 5.6.5 or welfare co-benefits noted in Chapter 6. These estimates reflect incremental emissions 

reductions from an analytical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in 

California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected 

to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will 

not be realized until 2025.

b

The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure.
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c

The negative estimates at the 5th percentile confidence estimates for this morbidity endpoint reflects the statistical 

power of the study used to calculate these health impacts. These results do not suggest that reducing air pollution 

results in additional health impacts.
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Table 5-20. Monetized PM2.5 Health Benefits for the Revised and Alternative Annual

Primary PM2.5 Standards (Incremental to Analytical Baseline) (Millions of 

2006$, 7% discount rate)

a

Health Effect

Revised and Alterative Annual Standards

(95th percentile confidence interval)

13 μg/m312 μg/m311 μg/m3

Avoided Mortalityb

Krewski et al. (2009)

(adult mortality age 30+)

$1,100 $3,600 $11,000

($110--$3,100) ($330--$9,800) ($1,100--$31,000)

Laden et al. (2006)

(adult mortality age 25+)

$2,600 $8,100 $26,000

($230--$7,300) ($720--$23,000) ($2,300--$74,000)

Woodruff et al. (1997)

(infant mortality)

$3.4 $11 $35

($0.29--$100) ($0.91--$32) ($3.0--$100)

Avoided Morbidity

Non-fatal heart attacks 

Peters et al. (2001)

(age >18)

$18 $54 $180

($2.8--$46) ($8.4--$140) ($28--$450)

Pooled estimate of 4 studies

(age >18)

$1.9 $5.9 $20

($0.40--$6.7) ($1.2--$20) ($4.1--$67)

Hospital admissions—respiratory

(all ages)c

$0.86 $3.0 $11

(-$0.22--$1.6) (-$0.8--$5.5) (-$2.7--$20)

Hospital admissions—cardiovascular 

(age > 18)

$1.70 $5.3 $18

($0.85--$2.8) ($2.7--$9.2) ($10--$32)

Emergency department visits for 

asthma

(age < 18)

$0.03 $0.10 $0.34

(-$0.0052--$0.061) (-$0.018--$0.21) (-$0.063--$0.73)

Acute bronchitis

(ages 8–12)c

$0.13 $0.42 $1.3

(-$0.0060--$0.37) (-$0.019--$1.2) (-$0.059--$3.5)

Lower respiratory symptoms

(ages 7–14)

$0.08 $0.24 $0.71

($0.025--$0.10) ($0.078--$0.47) ($0.24--$1.4)

Upper respiratory symptoms

(asthmatics ages 9–11)

$0.17 $0.54 $1.6

($0.038--$0.42) ($0.12--$1.30) ($0.36--$4.0)

Asthma exacerbation

(asthmatics ages 6–18)

$0.7 $2.30 $7.0

($0.027--$5.2) ($0.085--$16) ($0.26--$49)

Lost work days

(ages 18–65)

$3.3 $11 $35

($2.90--$3.70) ($9.4--$12) ($30--$39)

Minor restricted-activity days

(ages 18–65)

$8.8 $29 $91

($4.70--$13) ($15--$43) ($48--$140)

a

All estimates are rounded to two significant digits. Estimates do not include unquantified health benefits noted in 

Table 5-2 or Section 5.6.5 or welfare co-benefits noted in Chapter 6. These estimates reflect incremental emissions 

reductions from an analytical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in 

California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected 

to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will 

not be realized until 2025.

b

The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure.
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c

The negative estimates at the 5th percentile confidence estimates for this morbidity endpoint reflects the statistical 

power of the study used to calculate these health impacts. These results do not suggest that reducing air pollution 

results in additional health impacts.

Table 5-21. Total Estimated Monetized Benefits of the for Revised and Alternative 

Annual Primary PM2.5 Standards (Incremental to the Analytical Baseline) 

(billions of 2006$) a,b

Benefits Estimate 13 μg/m312 μg/m311 μg/m

Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality 

estimate from Krewski et al. (2009)

3% discount rate $1.3 + B $4.0 +B $13 + B

7% discount rate $1.2 + B $3.6 +B $12 + B

Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality 

estimate from Lepeule et al. (2012)

3% discount rate $2.9 + B $9.1 +B $29 + B

7% discount rate $2.6 + B $8.2 +B $26 + B

a

Rounded to two significant figures. The reduction in premature fatalities each year accounts for over 

98% of total monetized benefits in this analysis. Mortality risk valuation assumes discounting over the 

SAB-recommended 20-year segmented lag structure. Not all possible benefits are quantified and 

monetized in this analysis. B is the sum of all unquantified health and welfare co-benefits. Data 

limitations prevented us from quantifying these endpoints, and as such, these benefits are inherently 

more uncertain than those benefits that we were able to quantify.

b

These estimates reflect incremental emissions reductions from an analytical baseline that gives “an 

adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions 

expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment 

with the revised standards. Full benefits of the revised standards in those two areas will not be realized 

until 2025.

Table 5-22. Regional Breakdown of Monetized Benefits Results

Region

Revised and Alterative Annual Standards

13 μg/m312 μg/m311 μg/m3

Easta0% 0% 23%

Californiab100% 100% 77%

Rest of West 0% 0% <1%

a

Includes Texas and those states to the north and east. Several recent rules such as MATS and CSAPR will 

have substantially reduced PM2.5 levels by 2020 in the East, thus few additional controls would be 

needed to reach 12 μg/m3.
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b

For 12 and 13 μg/m3, all of the benefits occur in California because this highly populated area is where 

the most air quality improvement beyond the analytical baseline is needed to reach these levels. These 

estimates reflect incremental emissions reductions from an analytical baseline that gives “an 

adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions 

expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment 

with the revised standards. Full benefits of the revised standards in those two areas will not be realized 

until 2025.

Figure 5-3. Estimated PM2.5-Related Premature Mortalities Avoided According to 

Epidemiology or Expert-Derived PM2.5 Mortality Risk Estimate for 12 μg/m3in 2020 a

a

These estimates reflect incremental emissions reductions from an analytical baseline that gives “an 

adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions 

expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment 

with the revised standards. Full benefits of the revised standards in those two areas will not be realized 

until 2025.

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

Krewski et al.

Lepeuele et al.

Millions (2010$)

PM2.5 mortality benefits estimates derived from 2 epidemiology functions and 12 expert functions

Total Monetized PM2.5 Benefits of Standard at 12/35 μg/m3in 

2020*

3% Discount Rate

7% Discount Rate
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Figure 5-4. Total Monetized Benefits Using 2 Epidemiology-Derived and 12-Expert 

Derived Relationships Between PM2.5 and Premature Mortality for 12 μg/m3in 2020 a

a

These estimates reflect incremental emissions reductions from an analytical baseline that gives “an 

adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions 

expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment 

with the revised standards. Full benefits of the revised standards in those two areas will not be realized 

until 2025.

5.7.2 Uncertainty in Benefits Results

Mortality benefits account for 98% of total monetized benefits, in part because 

we are unable to quantify most of the non-health benefits. The next largest benefit is for 

reductions in chronic illness (nonfatal heart attacks), although this value is more than an 

order of magnitude lower than for premature mortality. Hospital admissions for 

respiratory and cardiovascular causes, MRADs and work loss days account for the 

majority of the remaining benefits. The remaining categories each account for a small 

percentage of total benefit; however, they represent a large number of avoided 

incidences affecting many individuals. A comparison of the incidence table to the 

0 %

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

$ 0 $ 5 $10 $15 $20 $25 $30 Cumulative Percentage of Monetized Benefits

Monetized Benefits (Billions of 2010$)

Total Monetized Benefits Using 2 Epidemiology-Derived and 12 Expert-Derived 

Relationships Between PM2.5 and Mortality for 12/35 μg/m3(3% discount rate)

Expert A

Expert B

Expert C

Expert D

Expert E

Expert F

Expert G

Expert H

Expert I

Expert J

Expert K

Expert L

Krewski et al.

Lepeule et al.
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monetary benefits table reveals that there is not always a close correspondence 

between the number of incidences avoided for a given endpoint and the monetary 

value associated with that endpoint. For example, we estimate almost 1,000 times more 

work loss days would be avoided than premature mortalities, yet work loss days account 

for only a very small fraction of total monetized benefits. This reflects the fact that many 

of the less severe health effects, while more common, are valued at a lower level than 

the more severe health effects. Also, some effects, such as hospital admissions, are 

valued using a proxy measure of WTP. As such, the true value of these effects may be 

higher than that reported in the tables above.

PM2.5 mortality benefits represent a substantial proportion of total monetized 

benefits (over 98% in this analysis), and these estimates have the following key 

assumptions and uncertainties.

1. We assume that all fine particles, regardless of their chemical 

composition, are equally potent in causing premature mortality. This is an 

important assumption, because PM2.5 produced varies considerably in 

composition across sources, but the scientific evidence is not yet 

sufficient to allow differential effects estimates by particle type. The PM 

ISA, which was twice reviewed by SAB-CASAC, concluded that “many 

constituents of PM2.5 can be linked with multiple health effects, and the 

evidence is not yet sufficient to allow differentiation of those 

constituents or sources that are more closely related to specific 

outcomes” (U.S. EPA, 2009b).

2. We assume that the health impact function for fine particles is log-linear 

without a threshold in this analysis. Thus, the estimates include health 

benefits from reducing fine particles in areas with varied concentrations 

of PM2.5, including both areas that do not meet the fine particle standard 

and those areas that are in attainment, down to the lowest modeled 

concentrations.

3. We assume that there is a “cessation” lag between the change in PM 

exposures and the total realization of changes in mortality effects. 

Specifically, we assume that some of the incidences of premature 

mortality related to PM2.5 exposures occur in a distributed fashion over 

the 20 years following exposure based on the advice of the SAB-HES (U.S. 

EPA-SAB, 2004c), which affects the valuation of mortality benefits at 

different discount rates.
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Given that reductions in premature mortality dominate the size of the overall 

monetized benefits, more focus on uncertainty in mortality-related benefits gives us 

greater confidence in our uncertainty characterization surrounding total benefits.

5.7.3 Estimated Life Years Gained and Reduction in the Percentage of Deaths 

Attributable to PM2.5

In their 2008 review of the EPA’s approach to estimating ozone-related mortality 

benefits, NRC indicated, “EPA should consider placing greater emphasis on reporting 

decreases in age-specific death rates in the relevant population and develop models for 

consistent calculation of changes in life expectancy and changes in number of deaths at 

all ages” (NRC, 2008). In addition, NRC noted in an earlier report that “[f]rom a publichealth perspective, life-years lost might be more relevant than annual number of 

mortality cases” (NRC, 2002). This advice is consistent with that of the SAB-HES, which 

agreed that “...the interpretation of mortality risk results is enhanced if estimates of lost 

life-years can be made” (U.S. EPA-SAB, 2004a). To address these recommendations, we 

use simplifying assumptions to estimate the number of life years that might be gained. 

We also estimate the reduction in the percentage of deaths attributed to PM2.5 resulting 

from the illustrative emission reduction strategies to reach the revised annual primary

standard. The EPA included similar estimates of life years gained in a previous 

assessment of PM2.5 benefits (U.S. EPA, 2006a, 2011a), the latter of which was peer 

reviewed by the SAB-HES (U.S. EPA-SAB, 2010a).

Changes in life years and changes in life expectancy at birth are frequently 

conflated, thus it is important to distinguish these two very different metrics at the 

outset. Life expectancy varies by age. CDC defines life expectancy as the “average 

number of years of life remaining for persons who have attained a given age” (CDC, 

2011). In other words, changes in life expectancy refer to an average change for the 

entire population, and refer to the future. Over the past 50 years, average life 

expectancy at birth in the U.S. has increased by 8.4 years (CDC, 2001). For example, life 

expectancy at birth was estimated in 2007 to be 77.9 years for an average person born 

in the U.S., but for people surviving to age 60, estimated life expectancy is 82.5 years 

(i.e., 4.6 years more than life expectancy at birth) (CDC, 2011). Life years, on the other 

hand, measure the amount of time that an individual loses if they die before the age of 

their life expectancy. Life years refer to individuals, and refer to the past, e.g., when the 
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individual has already died. If a 60-year old individual dies, we estimate that this 

individual would lose about 22.5 years of life (i.e., the average population life 

expectancy for an individual of this age minus this person’s age at death).

5.7.3.1 Estimated Life Years Gained

For estimating the potential life years gained by reducing exposure to PM2.5 in 

the U.S. adult population, we use the same general approach as Hubbell (2006) and 

Fann et al. (2012a). We have not estimated the change in average life expectancy at 

birth in this RIA. Hubbell (2006) estimated that reducing exposure to PM2.5 from air 

pollution regulations may result in an average gain of 15 years of life for those adults 

prematurely dying from PM2.5 exposure. In contrast, Pope et al. (2009) estimated 

changes in average life expectancy at birth over a twenty year period, suggesting that 

reducing exposure to air pollution may increase average life expectancy at birth by 

approximately 7 months, which was 15% of the overall increase in life expectancy at 

birth from 1980 through 2000. These results are not necessarily inconsistent because 

they are reporting different metrics. Because life expectancy is an average of the entire 

population (including both those whose deaths would likely be attributed to PM 

exposure as well as those whose deaths would not), average life expectancy changes 

associated with PM exposure would be expected to always be significantly smaller than 

the average number of life years lost by an individual who is projected to die 

prematurely from PM exposure.

To estimate the potential distribution of life years gained for population 

subgroups defined by the age range at which their reduction in PM2.5 exposure is 

modeled to occur we use standard life tables available from the CDC (2003) and the 

following formula:

(5.2)

where LEiis the average remaining life expectancy for age interval i, Miis the estimated 

change in number of deaths in age interval i, and n is the number of age intervals. 

To get Mi (the estimate the number of avoided premature deaths attributed to 

changes in PM2.5 exposure in 2020), we use a health impact function that incorporates 

risk coefficients estimated for the adult population in the U.S. and age-specific mortality 
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rates. That is, we use risk coefficients that do not vary by age, but use baseline mortality 

rates that do. Because mortality rates for younger populations are much lower than 

mortality rates for older populations, most but not all, of the avoided deaths tend to be 

in older populations. Table 5-23 summarizes the modeled number of life years gained by 

reducing PM2.5 exposure to 12 μg/m3in 2020. We then calculated the average number 

of life years gained per avoided premature mortality. Figure 5-5 shows the potential life 

years gained as a result of meeting a primary standard of 12 μg/m3in 2020, partitioned 

by the age when exposure reduction occurred, not necessarily age at death. 

By comparing the projected age distribution of the avoided premature deaths 

with the age distribution of life years gained, we observed that about half of the deaths 

occur in populations age 75–99, but half of the life years would occur in populations 

younger than 65. This is because the younger populations have the potential to lose 

more life years per death than older populations based on changes in PM2.5 exposure in 

2020. We estimate that the average individual who would otherwise have died 

prematurely from PM exposure would gain 16 additional years of life. However, this 

approach does not account for whether or not people who are older are more likely to 

be susceptible to the health effects of PM or whether that susceptibility was in and of 

itself caused by PM exposure (for a more complete discussion of this issue, see Kunzli et 

al., 2001). 
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Table 5-23. Sum of Life Years Gained by Age Range Attributed to the Revised Annual

Primary PM2.5 Standard of 12 μg/m3 in 2020a,b

Age Rangeb

Krewski et al. (2009) 

Risk Coefficient c

Lepeule et al. (2012)

Risk Coefficient

25–29 — 610

30–34 210 470

35–44 550 1,200

45–54 1,000 2,300

55–64 1,600 3,500

65–74 1,700 3,900

75–84 1,300 2,900

85–99 560 1,300

Total life years gained 7,000 16,000

Average life years gained per individual 15.0 16.0

a

Estimates rounded to two significant figures.

b

Because we assume that there is a “cessation” lag between PM exposures and the reduction in the risk 

of premature death, there is uncertainty regarding the specific ages that people die relative to the 

change in exposure.

c

The youngest age in the population cohort of this study is 30.
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Figure 5-5. Estimated Life Years Gained as a Result of the Revised Annual Primary 

PM2.5 Standard of 12 μg/m3in 2020, Partitioned by the Age When Exposure Reduction 

Occurred, Not Necessarily Age at Deatha

a

As shown in these charts, slightly more than half of the avoided premature deaths occur in populations 

age 75–99, but slightly more than half of the avoided life years occur in populations age <65 due to the 

fact that the younger populations would lose more life years per death than older populations. Results 

would be similar for other standard levels on a percentage basis. Because we assume that there is a 

“cessation” lag between PM exposures and the reduction in the risk of premature death, there is 

uncertainty regarding the specific ages that people die relative to the change in exposure.

b

The youngest age in the population cohort of this study used to estimate PM2.5 mortality incidence is 

30.

5.7.3.2 Percent of PM-related Mortality Reduced

To estimate the percentage of all-cause mortality attributed to reduced PM2.5

exposure in 2020 as a result of the illustrative emission reduction strategies, we use Mi

from the equation above, dividing the number of excess deaths estimated for each 

alternative standard by the total number of deaths in each county. Table 5-24 shows the 

reduction in all-cause mortality attributed to reducing PM2.5 exposure to the revised 

annual standard of 12 μg/m3in 2020. Figure 5-6 shows the percentage of avoided 

premature deaths attributed to meeting the revised primary annual standard of 12

μg/m3in 2020, partitioned by the age when exposure reduction occurred, not 

necessarily age at death.

Age 

30-34

Age

35-44

Age

45-54

Age

55-64

Age

65-74

Age

75-84

Age

85-99

Age

25-29

Age 

30-34

Age

35-44

Age

45-54

Age

55-64

Age

65-74

Age

75-84

Age

85-99

Calculated using Lepeule et al. (2012) risk 

coefficient Calculated using Krewski et al. (2009) risk 

coefficient b
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Table 5-24. Estimated Reduction in the Percentage of All-Cause Mortality Attributed 

to the Revised Annual Primary PM2.5 Standard of 12 μg/m3in 2020a,b

Age RangebKrewski et al. (2009) Risk CoefficientcLaden et al. (2006) Risk Coefficient

25–29 — 0.80%

30–34 0.35% 0.78%

35–44 0.32% 0.73%

45–54 0.32% 0.73%

55–64 0.33% 0.73%

65–74 0.33% 0.73%

75–84 0.32% 0.73%

85–99 0.29% 0.65%

a

Rounded to two significant figures. Results would be similar for other standard levels on a percentage 

basis. These estimates reflect incremental emissions reductions from an analytical baseline that gives 

“an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions 

expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment 

with the revised standards. Full benefits of the revised standards in those two areas will not be realized 

until 2025.

b

Because we assume that there is a “cessation” lag between PM exposures and the reduction in the risk 

of premature death, there is uncertainty regarding the specific ages that people die relative to the 

change in exposure.

c

The youngest age in the population cohort of this study is 30.

The relative distributions of the potential number of life years gained (Figure 5-5) 

and the estimated avoided mortalities (Figure 5-6) would be similar across alternative

annual standards. Because reduction in PM exposure is not associated with an 

immediate improvement in chronic health conditions, we assume that there is a 

“cessation” lag between PM exposures and the reduction in the risk of premature 

death. There is uncertainty regarding the specific ages at which people “avoid” death 

relative to the change in exposure. While the structure of the lag is uncertain, some 

studies suggest that most of the premature deaths are avoided within the first 3 years 

after the change in exposure, while others are unable to identify conclusively a critical 

window of exposure (U.S. EPA, 2004c; Schwartz, 2008; Krewski et al. 2009). These 

studies did not examine whether the cessation lag was modified by either age at the 

time when exposure is reduced or the extent of cumulative lifetime exposure.
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Figure 5-6. Estimate of Avoided Premature Deaths Attributed to the Revised 

Annual Primary PM2.5 Standard of 12 μg/m3in 2020, Partitioned by the Age When 

Exposure Reduction Occurred, Not Necessarily Age at Deatha

a

As shown in these charts, slightly more than half of the avoided premature deaths occur in populations 

age 75-99, but slightly more than half of the avoided life years occur in populations age <65 due to the 

fact that the younger populations would lose more life years per death than older populations. Results 

would be similar for other standard levels on a percentage basis. Because we assume that there is a 

“cessation” lag between PM exposures and the reduction in the risk of premature death, there is 

uncertainty regarding the specific ages that people die relative to the change in exposure.

b

The youngest age in the population cohort of this study is 30.

5.7.4 Evaluation of Mortality Impacts Relative to Various Concentration Benchmarks

In this analysis, we estimate the number of avoided PM2.5-related deaths 

occurring due to PM2.5 reductions down to various PM2.5 concentration benchmarks, 

including the Lowest Measured Level (LML) of each long-term PM2.5 mortality study. 

This analysis is one of several sensitivities that the EPA has historically performed that 

characterize the uncertainty associated with the PM-mortality relationship and the 

economic value of reducing the risk of premature death (Roman et al., 2008; U.S. EPA, 

2006a, 2011a; Mansfield, 2009).

Our review of the current body of scientific literature indicates that a log-linear 

no-threshold model provides the best estimate of PM-related long-term mortality. The 

Age

30-34 Age

35-44

Age

45-54

Age

55-64

Age 

65-74 Age 

75-84

Age 

85-99

Age

25-29

Age

30-34 Age

35-44

Age

45-54

Age

55-64

Age

65-74

Age

75-84

Age

85-99

Calculated using Krewski et al. (2009) risk 

coefficient b

Calculated using Lepeule et al. (2012) risk 

coefficient.
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PM ISA (U.S. EPA, 2009b), which was twice reviewed by the EPA’s Clean Air Scientific 

Advisory Committee (U.S. EPA-SAB, 2009a, 2009b), concluded that the evidence 

supports the use of a no-threshold log-linear model while also recognizing potential 

uncertainty about the exact shape of the concentration-response function.24 Consistent 

with this finding, we estimate benefits associated with the full range of PM2.5 exposure 

in conjunction with sensitivity analyses to recognize the potential uncertainty at lower 

concentrations. Specifically, we incorporated a LML assessment, a method the EPA has 

employed in several recent RIAs (U.S. EPA, 2010g, 2011c, 2011d). In addition, we have 

incorporated an assessment using specific concentration benchmarks identified in the 

EPA’s Policy Assessment for Particulate Matter (U.S. EPA, 2011b).

These two approaches summarize the distribution of avoided PM2.5-related 

mortality impacts relative to baseline (i.e., pre-rule) annual mean PM2.5 levels. The LML 

approach compares the percentage of avoided premature deaths estimated to occur 

above and below the minimum observed air quality level of each long-term cohort study 

we use to quantify PM. In the air quality benchmark approach, we summarize the 

impacts occurring at different points in the distribution of the air quality data used in 

these same epidemiology studies.

Our confidence in the estimated number of premature deaths avoided (but not 

in the existence of a causal relationship between PM and premature mortality) 

diminishes as we estimate these impacts in locations where PM2.5 levels are below the 

LML. This interpretation is consistent with the Policy Assessment (U.S. EPA, 2011b) and 

advice from SAB-CASAC during their peer review (U.S. EPA-SAB, 2010d). As noted in the 

preamble to the final rule, the Policy Assessment (U.S. EPA, 2011b) concludes that the 

range from the 25th to the 10th percentile is a reasonable range of the air quality 

distribution below which we start to have appreciably less confidence in the magnitude 

of the associations observed in the epidemiological studies. In general, we are more 

confident in the magnitude of the risks we estimate from simulated PM2.5 

concentrations that coincide with the bulk of the observed PM concentrations in the 

epidemiological studies at are used to estimate the benefits. Likewise, we are less 

 

24 For a summary of the scientific review statements regarding the lack of a threshold in the PM2.5-

mortality relationship, see the Technical Support Document (TSD) entitled Summary of Expert Opinions 

on the Existence of a Threshold in the Concentration-Response Function for PM2.5-related Mortality (U.S. 

EPA, 2010f).
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confident in the risk we estimate from simulated PM2.5 concentrations that fall below 

the bulk of the observed data in these studies. However, there are uncertainties 

inherent in identifying any particular point at which our confidence in reported 

associations becomes appreciably less, and the scientific evidence provides no clear 

dividing line. 

For these reasons, we consider the LML as well as one standard deviation below 

the mean25 air quality levels when characterizing the distribution of mortality impacts. It 

is important to emphasize that “less confidence” does not mean “no confidence.” In 

addition, while we may have less confidence in the magnitude of the risk, we still have 

high confidence that PM2.5 is causally associated with risk at those lower air quality 

concentrations. To clarify this concept, Figure 5-7 graphically displays the spectrum of 

confidence using illustrative concentration benchmarks from the major epidemiology 

studies cited in this chapter.

 

25 A range of one standard deviation around the mean represents approximately 68 percent of normally 

distributed data, and, below the mean falls between the 25th and 10th percentiles. 
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Figure 5-7. Relationship between the Size of the PM Mortality Estimates and the 

PM2.5 Concentration Observed in the Epidemiology Study

Although these types of concentration benchmark analyses (e.g., LML, one 

standard deviation below the mean, etc.) provide some insight into the level of 

uncertainty in the estimated PM2.5 mortality benefits, the EPA does not view these 

concentration benchmarks as a concentration threshold below which we would not 

quantify health benefits of air quality improvements Rather, the core benefits estimates 

reported in this RIA (i.e., those based on Krewski et al. (2009) and Lepeule et al. (2012)) 

are the best measures because they reflect the full range of modeled air quality 

concentrations associated with the emission reduction strategies. In reviewing the 

Policy Assessment, SAB-CASAC confirmed that “[a]lthough there is increasing 

uncertainty at lower levels, there is no evidence of a threshold (i.e., a level below which 

there is no risk for adverse health effects)” (U.S. EPA-SAB, 2010d). In addition, in 

reviewing the Costs and Benefits of the Clean Air Act (U.S. EPA, 2011a), the SAB-HES 

noted that “[t]his [no-threshold] decision is supported by the data, which are quite 

consistent in showing effects down to the lowest measured levels. Analyses of cohorts 

using data from more recent years, during which time PM concentrations have fallen, 

continue to report strong associations with mortality” (U.S. EPA-SAB, 2010a). Therefore, 
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the best estimate of benefits includes estimates below and above these concentration 

benchmarks but uncertainty is higher in the magnitude of health benefits estimated at 

lower concentrations, with the lowest confidence below the LML. Estimated health 

impacts reflecting air quality improvements below and above these concentration 

benchmarks are appropriately included in the total benefits estimate. In other words, 

our higher confidence in the estimated benefits above these concentration benchmarks 

should not imply an absence of confidence in the benefits estimated below these 

concentration benchmarks.

We estimate that most of the avoided PM-related impacts we estimate in this 

analysis occur among populations exposed at or above the LML of the Lepeule et al. 

(2012) study, while a majority of the impacts occur at or above the LML of the Krewski 

et al. (2009) study. We show the estimated reduction in incidence of premature 

mortality above and below the LML or air quality benchmarks of these studies in Tables 

5-25, and we graphically display the distribution of PM2.5-related mortality impacts for 

the selected standard in Figures 5-8 and 5-9. 

When interpreting these LML and air quality benchmarks results, it is important 

to understand that the avoided PM2.5 deaths are estimated to occur from PM2.5

reductions in the baseline air quality simulation, which assumes that 15/35 is already 

met. When simulating attainment with revised and alternative standards, we “roll back” 

the design value at each monitor exceeding the standard alternative to equal that 

standard and use an air quality interpolation technique to simulate the change in PM 

levels surrounding that monitor. This technique tends to simulate the greatest air 

quality changes nearest the monitor. We estimate benefits using modeled air quality 

data with 12 km grid cells, which is important because the grid cells are often 

substantially smaller than counties and PM2.5 concentrations vary spatially within a 

county. Therefore, there may be a small number of grid cells with concentrations slightly 

greater than 15 μg/m3 in the gridded baseline even though all monitors could meet an 

annual standard of 15 μg/m3. In addition, some grid cells in a county can be below the 

level of a standard even though the highest monitor value is above that standard. Thus, 

emission reductions can lead to benefits in grid cells that are below a standard even 

within a county with a monitor that exceeds that standard. Furthermore, our approach 

to simulating attainment can lead to benefits in counties that are below alternative 
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standard. Emission reduction strategies designed to reduce PM2.5 concentrations at a 

given monitor will frequently improve air quality in neighboring counties. In order to 

make a direct comparison between the benefits and costs of these emission reduction 

strategies, it is appropriate to include all the benefits occurring as a result of the 

emission reduction strategies applied regardless of where they occur. Therefore, it is not 

appropriate to estimate the fraction of benefits that occur only in counties that exceed 

the alternative standards because it would omit benefits attributable to emission 

reductions in exceeding counties. Figure 5-10 provides an illustration of this concept.

Table 5-25. Estimated Reduction in Incidence of Adult Premature Mortality Occurring 

Above and Below Various Concentration Benchmarks in the Underlying 

Epidemiology Studiesa

Revised and 

Alternative 

Standard Level Epidemiology Study

Total 

Reduced 

Mortality 

Incidence

Allocation of Reduced Mortality Incidence

Below 1 

Std.

Dev.

Below 

AQ 

Mean

At or Above 

1 Std. Dev.

Below AQ 

Mean

Below 

LML

At or 

Above 

LML

13 μg/m3Krewski et al. (2009)

140

79 66 6 140

(54%) (46%) (4%) (96%)

Lepeule et al. (2012)

330 N/A N/A

130 200

(38%) (62%)

12 μg/m3Krewski et al. (2009)

460

200 260 14 440

(43%) (57%) (3%) (97%)

Lepeule et al. (2012)

1,000 N/A N/A

310 720

(30%) (70%)

11 μg/m3Krewski et al. (2009)

1,500

690 770 27 1,400

(47%) (53%) (2%) (98%)

Lepeule et al. (2012)

3,300 N/A N/A

1,000 2,300

(31%) (69%)
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a Mortality incidence estimates are rounded to whole numbers and two significant digits, so estimates 

may not sum across columns. One standard deviation below the mean is equivalent to the middle of 

the range between the 10th and 25th percentile. For Krewski, the LML is 5.8 μg/m3and one standard 

deviation below the mean is 11.0 μg/m3. For Lepeule et al., the LML is 8 μg/m3and we do not have the 

data for one standard deviation below the mean. It is important to emphasize that although we have 

lower levels of confidence in levels below the LML for each study, the scientific evidence does not 

support the existence of a level below which health effects from exposure to PM2.5 do not occur.
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While the LML of each study is important to consider when characterizing and 

interpreting the overall level of PM2.5-related benefits, as discussed earlier in this 

chapter, the EPA believes that both of the cohort-based mortality estimates are suitable 

for use in air pollution health impact analyses. When estimating PM-related premature 

deaths avoided using risk coefficients drawn from the Lepeule et al. (2012) analysis of 

the Harvard Six Cities and the Krewski et al. (2009) analysis of the ACS cohorts there are 

innumerable other attributes that may affect the size of the reported effect estimates—

including differences in population demographics, the size of the cohort, activity 

patterns and particle composition among others. The LML assessment presented here 

provides a limited representation of one key difference between the two studies.

Of total PM2.5-Related deaths avoided for 12 μg/m3:

97% occur among populations exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) 

study.

70% occur among populations exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) 

study.

Figure 5-8. Number of Premature PM2.5-related Deaths Avoided for the Revised 

Annual Primary PM2.5 Standard of 12 μg/m3in 2020 According to the Baseline Level of 

PM2.5 and the Lowest Measured Air Quality Levels of Each Mortality Study

0%

5%

10%

15%

20%

25%

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 Percentage of Avoided PM2.5Mortalities

Baseline Annual Mean PM2.5 Level (μg/m3)

Percentage of Total PM-related Mortalities Avoided by Baseline Air 

Quality Level

LML of Lepeule et 

al. (2012) study

LML of Krewski et 

al. (2009) study
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Of total PM2.5-Related deaths avoided for 12 μg/m3:

97% occur among populations exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) 

study.

70% occur among populations exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) 

study.

Figure 5-9. Number of Premature PM2.5-related Deaths Avoided for the Revised 

Annual Primary PM2.5 Standard of 12 μg/m3in 2020 According to the Baseline Level of 

PM2.5 and the Lowest Measured Air Quality Levels of Each Mortality Study

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Cumulative percentage of avoided PM Mortalities

Baseline Annual Mean PM2.5 Level (μg/m3)

Cumulative Percentage of Total PM-related Mortalities Avoided by 

Baseline Air Quality Level

LML of Krewski et 

al. (2009) study

LML of Lepeuele et 

al. (2012) study
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As this illustration shows, because 12km grid cells are much smaller than counties and because 

PM2.5 concentrations vary within a county, there can be modeled grid cells with PM2.5 

concentrations below an standard level even when the county exceeds that level. Because we 

model benefits using grid cells, this is a key reason why the LML graphs show benefits at levels 

below the alternative standards. In addition, emission reductions in an exceeding county can have 

benefits in a neighboring county that does not exceed. 

Figure 5-10. Illustration of Relative Size of County with Exceeding Monitor and 

Modeled Grid Cells
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5.7.5 Additional Sensitivity Analyses

The details of these sensitivity analyses are provided in Appendix 5.A, and 

summarized here. The use of an alternate lag structure would change the PM2.5-related 

mortality benefits discounted at 3% discounted by between 10% and –27%; when 

discounted at 7%, these benefits change by between 22% and −52%. When applying 

higher and lower income growth adjustments, the monetary value of PM2.5-related 

premature mortality changes between 33% and −14%; the value of acute endpoints 

changes between 8% and −4%. Using the updated population projection data, the 

rounded estimates of total monetized benefits increases by 4.4% for 12 μg/m3. These 

sensitivity analysis results would be similar on a percentage basis for the alternative 

annual standards.

5.8 Discussion

The analysis in this Chapter demonstrates the potential for significant health 

benefits of the illustrative emission controls applied to simulate attainment with the 

revised annual primary PM2.5 standard. We estimate that by 2020 the emissions 

reductions to reach the revised annual primary standard would have reduced the 

number of PM2.5-related premature mortalities and produce substantial non-mortality 

benefits. This rule promises to yield significant welfare impacts as well (see Chapter 6), 

though the quantification of those endpoints is absent in this RIA. Even considering the 

quantified and unquantified uncertainties identified in this chapter, we believe that the 

implementing the revised annual PM2.5 standard of 12 μg/m3 would have substantial 

public health benefits that outweigh the costs (see Chapter 7).

Inherent in any complex RIA such as this one are multiple sources of uncertainty. 

Some of these we characterized through our quantification of statistical error in the 

concentration response relationships and our use of the expert elicitation-derived PM2.5 

mortality functions. Others, including the projection of atmospheric conditions and 

source-level emissions, the projection of baseline morbidity rates, incomes and 

technological development are unquantified. When evaluated within the context of 

these uncertainties, the health impact and monetized benefits estimates in this RIA can 

provide useful information regarding the public health benefits associated with the

revised PM NAAQS.
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There are important differences worth noting in the design and analytical 

objectives of NAAQS RIAs compared to RIAs for implementation rules, such as the 

recent MATS rule (U.S. EPA, 2011d). The NAAQS RIAs illustrate the potential costs and 

benefits of a revised air quality standard nationwide based on an array of emission 

reduction strategies for different sources, incremental to implementation of existing 

regulations and controls needed to attain the current standards. In short, NAAQS RIAs 

hypothesize, but do not predict, the emission reduction strategies that States may 

choose to enact when implementing a revised NAAQS. The setting of a NAAQS does not 

directly result in costs or benefits, and as such, NAAQS RIAs are merely illustrative and 

are not intended to be added to the costs and benefits of other regulations that result in 

specific costs of control and emission reductions. By contrast, the emission reductions 

from implementation rules are generally for specific, well-characterized sources, such as 

the recent MATS rule (U.S. EPA, 2011d). In general, the EPA is more confident in the 

magnitude and location of the emission reductions for implementation rules. As such, 

emission reductions achieved under promulgated implementation rules such as MATS 

have been reflected in the baseline of this NAAQS analysis. Subsequent implementation

rules will be reflected in the baseline for the next PM NAAQS review. For this reason, 

the benefits estimated provided in this RIA and all other NAAQS RIAs should not be 

added to the benefits estimated for implementation rules.

In setting the NAAQS, the EPA considers that PM2.5 concentrations vary over 

space and time. While the standard is designed to limit concentrations at the highest 

monitor in an area, it is understood that emission controls put in place to meet the 

standard of the highest monitor will simultaneously result in lower PM2.5 concentrations 

throughout the entire area. In fact, the Quantitative Risk and Exposure Assessment for 

Particulate Matter (U.S. EPA, 2010b) shows how different standard levels would affect 

the entire distribution of PM2.5 concentrations, and thus people’s exposures and risk, 

across urban areas. For this reason, it is inappropriate to use the NAAQS level as a bright 

line for health effects.

The NAAQS are not set at levels that eliminate the risk of air pollution 

completely. Instead, the Administrator sets the NAAQS at a level requisite to protect 

public health with an adequate margin of safety, taking into consideration effects on 

susceptible populations based on the scientific literature. The risk analysis prepared in 
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support of this PM NAAQS reported risks below these levels, while acknowledging that 

the confidence in those effect estimates is higher at levels closer to the standard (U.S. 

EPA, 2010b). While benefits occurring below the standard may be somewhat more 

uncertain than those occurring above the standard, the EPA considers these to be 

legitimate components of the total benefits estimate. Though there are greater 

uncertainties at lower PM2.5 concentrations, there is no evidence of a threshold in PM2.5-

related health effects in the epidemiology literature. Given that the epidemiological 

literature in most cases has not provided estimates based on threshold models, there 

would be additional uncertainties imposed by assuming thresholds or other non-linear 

concentration-response functions for the purposes of benefits analysis.

The estimated benefits for the revised and alternative annual standards are in 

addition to the substantial benefits estimated for several recent implementation rules 

(U.S. EPA, 2009a, 2011c, 2011d, 2011e). Rules such as the Mercury and Air Toxics 

Standard (MATS) and other emission reductions will have substantially reduced ambient 

PM2.5 concentrations by 2020 in the East, such that no additional controls would be 

needed to reach 12 μg/m3 in the East beyond the analytical baseline. These rules that 

have already been promulgated have tremendous combined benefits that explain why 

the number of avoided premature deaths associated with this NAAQS revision are 

smaller than were estimated in the previous PM NAAQS RIA (U.S. EPA, 2006a) for the 

year 2020 and even smaller than the mortality risks estimated for the current year in the 

PM REA (U.S. EPA, 2010b). In addition, because our analytical baseline excludes benefits 

associated with attaining the current annual and daily standards as well as the mobile 

NOx emissions anticipated by 2025, including the benefits associated with those air 

quality improvements would result in higher benefits than we have estimated here.

For the revised annual standard of 12 μg/m3, all of the estimated benefits occur 

in California because this is the only state that needs additional air quality improvement 

beyond the analytical baseline after accounting for the air quality improvements from

recent rules. Because all of the monetized human health benefits are projected to occur 

in California, we have considered the cohort studies conducted in California specifically

in addition to the national risk coefficients we use as our core estimates. Although we 

have not calculated the benefits results using these cohort studies, we provided the risk 

coefficients from these California cohorts to show how much the monetized benefits 
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could have changed if we used these cohort studies. Most of the California cohort 

studies report central effect estimates similar to the (nation-wide) all-cause mortality 

risk estimate we applied from Krewski et al. (2009) and Lepeule et al. (2012) albeit with 

wider confidence intervals. Three cohort studies conducted in California indicate 

statistically significant higher risks than the risk estimates we applied from Lepeule et al. 

(2012), and four studies showed insignificant results.

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E.O. 12866_PM NAAQS_2060-AO47_FRN_RIA_Executive Summary_20121120.dox

 EXECUTIVE SUMMARY

5.10 ES.1 Overview

Based on its review of the air quality criteria and the national ambient air quality 

standards (NAAQS) for particulate matter (PM), the U.S. Environmental Protection 

Agency (EPA) is making revisions to the primary standards for PM to provide requisite 

protection of public health and welfare. The EPA is revising the primary annual (healthbased) standard, retaining the primary 24-hour standard, and retaining the secondary 

(welfare-based) NAAQS for fine particles (generally referring to particles less than or

equal to 2.5 micrometers [μm] in diameter—PM2.5). The EPA is retaining the current 

primary and secondary 24-hour PM10 standards.

As has traditionally been done in NAAQS rulemakings, the EPA has conducted a 

Regulatory Impact Analysis (RIA) to provide the public with illustrative estimates of the 

potential costs and health and welfare benefits of attaining the revised annual standard 

along with two alternative standards. In NAAQS rulemakings, the RIA is prepared for 
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informational purposes only, and the decisions related to the setting of the PM NAAQS 

standards are not in any way based on consideration of the information or analyses in 

the RIA. The RIA fulfills the requirements of Executive Orders 12866 and 13563 and 

guidelines of the Office of Management and Budget’s (OMB) Circular A-4.

26

The control strategies presented in this RIA are illustrative and represent one set 

of control strategies states might choose to implement in order to meet the final 

standards. As a result, benefit and cost estimates provided in this RIA are not additive to 

benefits and costs from other regulations as each regulation is based on a different set 

of analytical assumptions and policy decisions. The costs and benefits identified in this 

RIA will not be realized until specific controls are mandated by future State 

Implementation Plans (SIPs) or other federal regulations.

5.11 ES.2 Existing and Revised PM Air Quality Standards

Two primary PM2.5 standards provide public health protection from effects 

associated with fine particle exposures: the annual standard and the 24-hour standard. 

The current annual standard is set at a level of 15.0 μg/m3, based on the 3-year average 

of annual arithmetic mean PM2.5 concentrations. The current 24-hour standard is set at 

a level of 35 μg/m3, based on the 3-year average of the 98th percentile of 24-hour PM2.5

concentration. In the RIA, the current primary PM2.5 standards, including both annual 

and 24-hour standards are denoted as 15/35μg/m3. Attainment of the 24-hour standard 

is analyzed only in developing the scenario of attainment with the existing standards of 

15/35μg/m3. All other scenarios evaluate additional emissions reductions needed to 

attain alternative annual standards only.

In this PM NAAQS review, the EPA has revised and lowered the level of the 

primary annual PM2.5 standard to 12 μg/m3in conjunction with retaining the level of the 

24-hour standard at 35 μg/m3and this standard is denoted as 12μg/m3. In addition to 

the revised annual standard of 12μg/m3, the RIA also analyzes the benefits and costs of 

incremental control strategies for two alternative annual standards of 13 μg/m3and 11

μg/m3.

 

26 U.S. Office of Management and Budget. Circular A-4, September 17, 2003. Available at 

http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf.
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Currently, the existing secondary (welfare-based) PM2.5 standards are identical in 

all respects to the primary standards. In this PM NAAQS review, the EPA is retaining the 

current suite of secondary standards for 24-hour and annual PM2.5. Thus, while the new 

primary annual standard will be revised to 12μg/m3, the secondary annual standard will 

remain at 15 μg/m3. Non-visibility welfare effects are addressed by this suite of 

secondary standards, and PM-related visibility impairment is addressed by the 

secondary 24-hour PM2.5 standard, which EPA is leaving unchanged at 35 μg/m3. The 

secondary standards will thus remain at 15/35 μg/m3.

With regard to the primary and secondary standards for particles less than or 

equal to 10 μm in diameter (PM10), the EPA is retaining the current primary and 

secondary 24-hour PM10 standards, which are both set at a level of 150 μg/m3, not to be 

exceeded more than once per year on average over 3 years (U.S. EPA, 1997)27. Since the 

benefit-cost analysis of the alternative PM10 standards was conducted when the 

standard was promulgated in 1997, this RIA does not repeat that analysis here.

5.11.1 ES.2.1 Establishing Attainment with the Current 15/35 μg/m3PM2.5 Standards

The RIA is intended to evaluate the costs and benefits of reaching attainment

with the alternative PM2.5 standards. In order to develop and evaluate control strategies 

for attaining a more stringent primary standard, it is important to first estimate PM2.5

levels in 2020 for attainment with the current NAAQS standards (15/35μg/m3) and 

projections of future air quality reflecting on-the-books Federal regulations, 

enforcement actions, state regulations, and population and economic growth.

Estimating the 2020 levels after attainment of the current standards is traditionally 

known as the baseline, or more specifically for this analysis the attainment baseline.

Establishing this baseline typically allows us to estimate the incremental costs and 

benefits of attaining any alternative primary standard. EPA anticipates attainment with 

15/35μg/m3in all but two areas in California, not expected to attain the current 

standard until 2025. While establishing the attainment of 15/35μg/m3is an important 

step in getting to the analytical baseline for evaluating costs and benefits of alternative 

 

27 U.S. Environmental Protection Agency. 1997. Regulatory Impact Analyses for the Particulate Matter and 

Ozone National Ambient Air Quality Standards and Proposed Regional Haze Rule. Available at: 

http://www.epa.gov/ttn/oarpg/naaqsfin/ria.html
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annual PM standards, evaluating the revised and alternative standards in these two 

areas in 2020 is not appropriate.

The attainment baseline includes reductions already achieved as a result of 

national regulations and reductions expected prior to 2020 from recently promulgated 

national regulations, referred to as the base case. In addition, the attainment baseline 

refers to the reductions from additional controls which the EPA estimates are needed to 

attain the current standard (15/35μg/m3). Reductions achieved as a result of state and 

local agency regulations and voluntary programs are reflected to the extent that they 

are represented in emission inventory information submitted to the U.S. EPA by state 

and local agencies. Below is a list of some of the major national rules reflected in the 

base case. Refer to Chapter 3, Section 3.2.1.4 for a more detailed discussion of the rules 

reflected in the 2020 base case emissions inventory.

 Light-Duty Vehicle Tier 2 Rule (U.S. EPA, 1999)

 Heavy Duty Diesel Rule (U.S. EPA, 2000)

 Clean Air Nonroad Diesel Rule (U.S. EPA, 2004)

 Regional Haze Regulations and Guidelines for Best Available Retrofit 

Technology Determinations (U.S. EPA, 2005b)

 NOx Emission Standard for New Commercial Aircraft Engines (U.S. EPA, 2005)

 Emissions Standards for Locomotives and Marine Compression-Ignition 

Engines (U.S. EPA, 2008)

 Control of Emissions for Nonroad Spark Ignition Engines and Equipment (U.S. 

EPA, 2008)

 C3 Oceangoing Vessels (U.S. EPA, 2010)

 Hospital/Medical/Infectious Waste Incinerators: New Source Performance 

Standards and Emission Guidelines: Final Rule Amendments (U.S. EPA, 2009)

 Reciprocating Internal Combustion Engines (RICE) NESHAPs (U.S. EPA, 2010)

 Mercury and Air Toxics Standards (U.S. EPA, 2011)
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 Cross-State Air Pollution Rule (U.S. EPA, 2011)28

The attainment baseline for this analysis does not assume emissions controls 

that might be implemented to meet the other NAAQS for O3, NOx, or SO2. To the extent 

that some of the estimated emissions reductions needed to meet the revised annual PM 

standard are also needed to meet the current standards for O3, NOx, or SO2, the costs 

and benefits of meeting the revised PM annual PM standard will be overstated. We did 

not conduct this analysis incremental to controls applied as part of previous NAAQS 

analyses (e.g., O3, NOx, or SO2) because the data and modeling on which these previous 

analyses were based are now considered outdated and are not compatible with the 

current PM2.5 NAAQS analysis. In addition, all control strategies analyzed in all NAAQS 

RIAs are hypothetical. The analysis presented here is just one scenario that states may 

employ but does not prescribe how attainment must be achieved.

5.11.2 ES.2.2 Establishing the Analytical Baseline for Evaluation of Alternative 

Standards

The goal of this RIA is to provide the best estimates of the costs and benefits of 

an illustrative attainment strategy to meet the revised annual standard of 12μg/m3, as 

well as two alternative standards of 13μg/m3and 11μg/m3, incremental to meeting the 

current standards of 15/35μg/m3, reflecting emissions projected to include the impact 

of economic growth and implementation of state and federal emissions controls. Most 

areas of the U.S. will be required to demonstrate attainment with the new standard by 

2020. As a result, for these areas, the correct baseline for estimating the incremental 

emissions reductions that would be needed to attain the more protective standard is a 

baseline with emissions projected to 2020 and adjusted to reflect the additional 

emissions reductions that would be needed to attain the current 15/35μg/m3standards.

For two areas in Southern California (South Coast and San Joaquin), the degree of 

projected non-attainment with the annual 12μg/m3standard is such that those counties 

are not expected to be able to demonstrate attainment of the new standard by 2020.

Instead, those two areas are likely to qualify for an extension of their attainment date of 

up to five years. If the areas are granted an attainment date extension, they will have 

until 2025 to demonstrate attainment of the revised annual standard of 12μg/m3. As a 

 

28 See Chapter 3, Section 3.2.1.4 for a discussion of the role CSAPR plays in the PM2.5 RIA and the reasons 

we believe CSAPR remains an appropriate proxy for this analysis.
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result, for these two areas, the correct baseline for estimating the incremental emission 

reductions needed to attain the more protective standard is a baseline with emissions

projected to 2025 adjusted to reflect the additional emissions reductions needed to 

attain the current 15/35μg/m3standards.

This difference in attainment year is important because between 2020 and 2025, 

emissions from mobile sources in California are expected to be reduced due to 

continued fleet turnover from older, higher emitting vehicles to newer, lower emitting 

vehicles. These reductions in emissions will occur as a result of previous state rules for 

which costs and benefits have already been counted, and thus will not be costs and 

benefits attributable to meeting the revised annual standard of 12μg/m329.

For the purposes of this analysis, we have constructed an analytical baseline 

which reflects attainment of 15/35μg/m3in 2020 for all areas except South Coast and 

San Joaquin. This analytical baseline is modified in those two areas to reflect an

“adjustment” for the reductions in NOx emissions that those areas are expected to see 

between 2020 and 2025. These reductions in NOx are not attributable to attainment of 

the current or revised PM standards, but rather reflect the impacts of other programs.

These NOx emissions changes will affect baseline PM concentrations, but will not affect 

costs or benefits of attaining the revised or alternative standards.

Since the RIA for the proposed rule, the EPA has made a number of technical 

improvements to its estimate of the analytic baseline, including x, y, and z, which jointly 

result in xxxxx.....

5.11.3 ES.2.3 Emission Reduction Estimates by Alternative Standards (2020)

Emission reductions were calculated for the revised annual standard of 

(12μg/m3) and two alternative annual standards (13μg/m3and 11μg/m3) from a 

baseline of attaining the current standards of 15/35μg/m3for all areas of the U.S. except 

the South Coast and San Joaquin areas of California, where the baseline is adjusted to 

reflect NOx emissions reductions expected to occur between 2020 and 2025. Emission 

reductions were calculated for the known control strategy analysis and the cost analysis 

for emission reductions needed beyond known controls (“extrapolated” costs) for each 

 

29 See Chapter 3, Section 3.2.1.4 for a listing of the EPA and California rules reflected in the 15/35 analysis.
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alternative standard being analyzed. The EPA estimates the national-scale emission 

reductions for revised annual standard of 12μg/m3and two alternative annual standards 

(13μg/m3and 11μg/m3) as shown in Table ES-1.

Due to the rules listed above and other emissions reductions should have 

substantially reduced ambient PM2.5 concentrations by 2020 in the East, no additional 

controls are anticipated to be needed outside of California (assuming no the absence of 

new sources). Specifically, our analysis estimates that in 2020 only xx counties, all in 

California, will be out of attainment with the revised annual standard of 12ug/m3. 

Emissions reductions are needed in more locations for the alternative standard of 

11ug/m3. See table xxx below.

Since the RIA for the proposed rule, the EPA has made a number of technical 

improvements to its estimates of needed reductions, which jointly result in .....

Table ES-1. Emission Reduction Estimates by Standard in 2020 (annual tons/year)a

Alternative Annual 

Standards (μg/m3) PM2.5 SO2 NOx

13 730 - -

12 4,000 - -

11 19,000 21,000 9

a

Estimates are rounded to two significant figures.

5.11.4

Table 5-17. Emission Reductions in Illustrative Emission Reduction Strategies for the 

Revised and Alternative Annual Primary PM2.5 Standards, by Pollutant and 

Region in 2020 (tons)a

13 μg/m312 μg/m311 μg/m3

Directly emitted PM2.5

East 0 0 8,200

West 0 0 160

CA 730 4,000 10,600

SO2

Comment [A1]: Please insert table 5-17
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East 0 0 21,000

West 0 0 43

CA 0 0 0

NOx

East 0 0 9

West 0 0 0

CA 0 0 0

5.11.5 aSee Chapter 4 for more information on the illustrative emission reduction 

strategies. The emissions in this table reflect both known and unknown controls.

5.11.6 ES.2.4 Health and Welfare Co-Benefits

The EPA estimated impacts on human health (e.g., mortality and morbidity 

effects) under full attainment of the three alternative annual PM2.5 standards. We 

considered an array of health impacts attributable to changes in PM2.5 exposure and 

estimated these benefits using the BenMAP model (Abt Associates, 2012), which has 

been used in many recent RIAs (e.g., U.S. EPA, 2006, 2011a, 2011b), and The Benefits 

and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011c). As noted above, because 

California is the only state that needs additional air quality improvement beyond the 

analytic baseline after accounting expected air quality improvements expected from 

recent rules, all of the benefits associated with the revised standard of 12ug/m3 occur in 

California.

Since the proposed rule, the EPA has incorporated an array of policy and 

technical updates to the benefits analysis approach applied in this RIA, including 

incorporation of the most recent follow-up to the Harvard Six Cities cohort study 

(Lepeule et al., 2012), more recent demographic data projections, additional

hospitalization and emergency department visit studies, and an expanded uncertainty 

assessment. Each of these updates is fully described in the health benefits chapter

(Chapter 5). Of most significance updating population estimates by using the 2010 

census rather than the 2000 census, updating the VSL from xxxx dollars to xxxx dollars. 

The combination of the changes and the changes already discussed to the analytic 

baseline and the emissions control strategies has lead to an almost doubling of the size 

of the estimated benefits between the proposal for this rule and this final analysis.
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The monetized benefits estimated in the core analysis include avoided 

premature deaths (derived from effect coefficients in two cohort studies [Krewski et al. 

(2009) and Lepeule et al. (2012)] for adults and one for infants Woodruff et al. (1997)) as 

well as avoided morbidity effects for ten non-fatal endpoints ranging in severity from 

lower respiratory symptoms to heart attacks.

Even though the primary standards are designed to protect against adverse 

effects to human health, the emission reductions will have welfare co-benefits in 

addition to the direct human health benefits. The term welfare co-benefits covers both 

environmental and societal benefits of reducing pollution, such as reductions in visibility 

impairment, materials damage, and ecosystem damage. Despite our attempts to 

quantify and monetize as many of the benefits as possible, the welfare co-benefits 

associated with meeting the alternative standards are not quantified or monetized in 

this analysis. Unquantified health benefits are discussed in Chapter 5, and unquantified 

welfare co-benefits are discussed in Chapter 6.

It is important to note that estimates of the health benefits from reduced PM2.5 

exposure reported here contain uncertainty, which are described in detail in Chapter 5 

and Appendix 5b. Below are two key assumptions in the benefits analysis:

1. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 varies considerably in composition across sources, but the 

scientific evidence is not yet sufficient to allow differentiation of effect estimates 

by particle type. The PM ISA, which was twice reviewed by CASAC, concluded 

that “many constituents of PM2.5 can be linked with multiple health effects, and 

the evidence is not yet sufficient to allow differentiation of those constituents or 

sources that are more closely related to specific outcomes” (U.S. EPA, 2009). 

These uncertainties are likely to be magnified in the current analysis to the 

extent that the emissions controls are less diverse when focusing on one small 

region of the country rather than EPA’s more typical NAAQS RIA’s that cover a 

broader geography with more diverse emissions sources and the application of a 

more diverse set of controls. 
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2. We assume that health impact functions based on national studies are 

representative of the experience in California. EPA has considered the cohort 

studies conducted in California specifically in addition to the national risk 

coefficients we use as our core estimates. Although we have not calculated 

the benefits results using these cohort studies, we provided the risk 

coefficients from these California cohorts to show how much the monetized 

benefits could have changed if we used these cohort studies. Most of the 

California cohort studies report central effect estimates similar to the (nationwide) all-cause mortality risk estimate we applied from Krewski et al. (2009) 

and Lepeule et al. (2012) albeit with wider confidence intervals. Three cohort 

studies conducted in California indicate statistically significant higher risks 

than the risk estimates we applied from Lepeule et al. (2012), and four 

studies showed insignificant results.

3. We assume that the health impact function for fine particles is log-linear without a 

threshold in this analysis. Thus, the estimates include health benefits from reducing fine 

particles in areas with varied concentrations of PM2.5, including both areas that do not 

meet the fine particle standard and those areas that are in attainment, down to the 

lowest modeled concentrations.

In general, we are more confident in the magnitude of the risks we estimate 

from simulated PM2.5 concentrations that coincide with the bulk of the observed PM 

concentrations in the epidemiological studies at are used to estimate the benefits. 

Likewise, we are less confident in the risk we estimate from simulated PM2.5

concentrations that fall below the bulk of the observed data in these studies. As noted 

in the preamble to the rule, the range from the 25th to 10th percentiles of the air quality 

data in the epidemiology studies is a reasonable range below which we start to have 

appreciably less confidence in the magnitude of the associations observed in the 

epidemiological studies. Concentration benchmark analyses (e.g., 25th percentile, 10th

percentile, one standard deviation below the mean30, and lowest measured level (LML)) 

provide some insight into the level of uncertainty in the estimated PM2.5 mortality 

benefits. Most of the estimated avoided premature deaths for this rulemaking occur at 

or above the lowest measured PM2.5 concentration in the two studies that are used to 

estimate mortality benefits. There are uncertainties inherent in identifying any 

particular point at which our confidence in reported associations becomes appreciably 

 

30 A range of one standard deviation around the mean represents approximately 68 percent of normally 

distributed data, and, below the mean falls between the 25th and 10th percentiles.
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less, and the scientific evidence provides no clear dividing line. However, the EPA does 

not view these concentration benchmarks as a concentration threshold below which we 

would not quantify health benefits of air quality improvements. Rather, the core

benefits estimates reported in this RIA (i.e., those based on Krewski et al. (2009) and 

Lepeule et al. (2012)) are the best measures because they reflect the full range of 

modeled air quality concentrations associated with the emission reduction strategies 

and because the current body of scientific literature indicates that a no-threshold model 

provides the best estimate of PM-related long-term mortality. It is important to 

emphasize that “less confidence” does not mean “no confidence.” 

The estimated benefits reflect illustrative control measures and emission 

reductions to lower PM2.5 concentrations at monitors projected to exceed the revised 

and alternative annual standards. The result is that air quality is expected to improve in 

counties that exceed these standards as well as surrounding areas that do not exceed 

the alternative standards. In order to make a direct comparison between the benefits 

and costs of the emission reduction strategies, it is appropriate to include all the 

benefits occurring as a result of the emission reduction strategies applied regardless of 

where they occur. Therefore, it is not appropriate to estimate the fraction of benefits 

that occur only in the counties that exceed the standards because it would omit benefits 

attributable to emission reduction in exceeding counties. In addition, we estimate 

benefits using modeled air quality data with 12km grid cells, which is important because 

the grid cells are often substantially smaller than counties and PM2.5 concentrations can 

vary spatially within a county. Some grid cells in a county can be below the level of the 

alternative standard even though the highest monitor exceeds the alternative standard. 

Thus, emission reductions can lead to benefits in grid cells that are below the alternative 

standards within an exceeding county. 

5.11.7 ES.2.5 Cost Analysis Approach

The EPA estimated total costs under partial and full attainment of the alternative 

PM2.5 standards. The engineering costs generally include the costs of purchasing, 

installing, and operating the referenced control technologies. The technologies and 

control strategies selected for analysis are illustrative of one way in which 

nonattainment areas could meet a revised standard. There are numerous ways to 

construct and evaluate potential control programs that would bring areas into 
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attainment with alternative standards, and the EPA anticipates that state and local 

governments will consider programs that are best suited for local conditions.

The partial attainment cost analysis reflects the costs associated with applying 

known controls. Costs for full attainment include estimates for the engineering costs of 

the additional tons of emissions reductions that are needed beyond identified controls, 

referred to as extrapolated costs. By definition, no cost data currently exists for the 

additional emissions reductions needed beyond known controls. We employ two 

methodologies for estimating the costs of unidentified future controls: a fixed-cost 

methodology and a hybrid methodology; both approaches assume that innovative 

strategies and new control options make possible the emissions reductions needed for 

attainment by 2020. The two approaches, however, implicitly reflect different 

assumptions about technological progress and innovation in emissions reductions 

strategies. The fixed-cost methodology uses a $15,000/ton estimate for each ton of 

PM2.5 reduced, and the hybrid methodology generates a total annual cost curve for 

PM2.5 for unknown future controls that might be applied in order to move toward 2020 

attainment. The hybrid methodology has the advantage of incorporating information 

about how significant the needed reductions from unspecified control technologies are 

relative to the known control measures and matching that information with expected 

increasing per-ton cost for applying unknown controls. Employing the fixed-cost 

methodology, approximately 90% of total costs for attaining the revised annual 

standard of 12 μg/m3are from unspecified control technologies. Employing the hybrid 

methodology, approximately 97% of total costs for attaining the revised annual 

standard of 12 μg/m3are from unspecified control technologies. The EPA recognizes 

that the extrapolated portion of the engineering cost estimates reflects substantial 

uncertainty about which sectors and which technologies might become available for 

cost-effective application in the future.

The engineering cost estimates are limited in their scope. Our analysis focuses on 

the emissions reductions needed for attainment of the revised and alternative 

standards. Also, the amendments to the ambient air monitoring regulations will revise 

the network design requirements for PM2.5 monitoring sites, resulting in moving 21 

monitors to established near-road monitoring stations by January 1, 2015. The 

incremental cost associated with moving these 21 monitors is a one-time cost of 
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$28,570. Lastly, EPA understands that some states will incur costs designing State 

Implementation Plans (SIPs) and implementing new control strategies to meet the 

revised standard. However, EPA does not know what specific actions states will take to 

design their SIPs to meet the revised standards; therefore we do not include estimated 

costs that government agencies may incur for managing the requirement, implementing 

these (or other) control strategies, or for offering incentives that may be necessary to 

encourage the implementation of specific technologies, especially for technologies that 

are not necessarily market driven.

5.11.8

5.11.9 ES.2.6 Comparison of Benefits and Costs

The EPA’s illustrative analysis has estimated the health and welfare benefits and 

costs associated with the revised annual PM NAAQS. The results for 2020 suggest there 

will be significant health and welfare benefits and these benefits will outweigh the costs 

associated with the illustrative control strategies in 2020. In the analysis, we estimate 

the net benefits of the revised annual PM2.5 standard of 12μg/m3and alternative annual 

standards of 13μg/m3and 11μg/m3, incremental to the 2020 analytical baseline. For the 

revised annual standard of 12μg/m3, net benefits are estimated to be $4.0 billion to $8.8

billion at a 3% discount rate and $3.6 billion to $7.9 billion at a 7% discount rate in 2020

(2010 dollars). For an alternative annual standard of 13μg/m3, net benefits are 

estimated to be $1.3 billion to $2.8 billion at the 3% discount rate and $1.1 billion to 

$2.5 billion at the 7% discount rate. Net benefits of an alternative annual PM2.5 standard 

of 11μg/m3are estimated to be $13 billion to $27 billion at a 3% discount rate and $11 

billion to $25 billion at a 7% discount rate in 2020.

For the revised annual standard of 12μg/m3, the EPA estimates that the benefits 

of full attainment exceed the costs of full attainment by 10 to 150 times at a 7% 

discount rate. For the alternative annual standard of 13μg/m3, the EPA estimates that 

the benefits of full attainment exceed the costs of full attainment by 11 to 245 times at 

a 7% discount rate. For the alternative annual standards of 11μg/m3, the EPA estimates 

that the benefits of full attainment exceed the costs of full attainment by 7 to 83 times 

at a 7% discount rate.
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Figure ES -1 reflects the range of costs based of the calculation of costs using the 

fixed costs approach and the hybrid costs approach. Additionally, we see the difference 

in the calculation of benefits based on using various studies.

Table ES-2. Total Monetized Benefits, Total Costs, and Net Benefits in 2020 (millions 

of 2010$)—Full Attainment a

Alternative 

Annual 

Standard

(μg/m3)

Total Costsb Monetized Benefits d Net Benefits 

3% Discount 

Ratec

7% Discount 

Rate

3% Discount 

Rate

7% Discount 

Rate

3% Discount 

Rateb

7% Discount 

Rate

13 $11 to $100 $11 to $100 $1,300 to 

$2,900

$1,200 to 

$2,600

$1,300 to 

$2,800

$1,100 to 

$2,500

12 $53 to $350 $53 to 350 $4,000 to 

$9,100

$3,600 to 

$8,200

$4,000 to 

$8,800

$3,600 to 

$7,900

11 $320 to 

$1,700

$320 to 

$1,700

$13,000 to 

$29,000

$12,000 to

$26,000

$13,000 to 

$27,000

$11,000 to 

$25,000

a

These estimates reflect incremental emissions reductions from an analytical baseline that gives an 

“adjustment ” to the San Joaquin and South Coast areas in California for NOx emissions reductions 

expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment 

with the revised standards. Full benefits of the revised standards in those two areas will not be realized 

until 2025.

b

The two cost estimates do not represent lower and upper bound estimates, but represent estimates 

generated by two different methodologies. The lower estimate is generated using the fixed-cost 

methodology, which assumes that technological change and innovation will result in the availability of 

additional controls by 2020 that are similar in cost to the higher end of the cost range for current, 

known controls. The higher estimate is generated using the hybrid methodology, which assumes that 

while additional controls may become available by 2020, they become available at an increasing cost 

and the increasing cost varies by geographic area and by degree of difficulty associated with obtaining 

the needed emissions reductions. 

c

Due to data limitations, we were unable to discount compliance costs for all sectors at 3%. See Chapter 

7, Section 7.2.2 for additional details on the data limitations. As a result, the net benefit calculations at 

3% were computed by subtracting the costs at 7% from the monetized benefits at 3%.

d

The reduction in premature deaths each year accounts for over 90% of total monetized benefits. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag 

structure. Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the 

sum of all unquantified benefits. Data limitations prevented us from quantifying these endpoints, and 

as such, these benefits are inherently more uncertain than those benefits that we were able to 

quantify. The range of benefits reflects the range of the central estimates from two mortality cohort 

studies (i.e., Krewski et al. (2009) to Lepeule et al. (2012)).
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Figure ES-1. Monetized Benefit to Cost Comparison for 12 μg/m3in 2020 (7% 

Discount Rate)
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Table ES-3. Estimated Number of Avoided PM2.5 Health Impacts for Standard 

Alternatives—Full Attainmentin 2020a

Health Effect

Alternative Annual Standards

13 μg/m312 μg/m311 μg/m3

Adult Mortality 

Krewski et al. (2009) (adult) 140 460 1,500

Lepeule et al. (2012) (adult) 330 1,000 3,300

Woodruff et al. (1997) (infant) 0 1 4

Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 160 480 1,600

Pooled estimate of 4 studies 17 52 170

Hospital admissions—respiratory (all ages) 31 110 380

Hospital admissions—cardiovascular (age > 18) 43 140 480

Emergency department visits for asthma (age < 

18) 67 230 810

Acute bronchitis (age 8–12) 280 870 2,700

Lower respiratory symptoms (age 7–14) 3,500 11,000 34,000

Upper respiratory symptoms (asthmatics age 9–

11) 5,100 16,000 49,000

Asthma exacerbation (age 6–18) 13,000 40,000 120,000

Lost work days (age 18–65) 22,000 71,000 230,000

Minor restricted-activity days (age 18–65) 130,000 420,000 1,300,000

a

Incidence estimates are rounded to whole numbers with no more than two significant figures.

5.12 ES.3 Discussion and Conclusions

An extensive body of scientific evidence documented in the Integrated Science 

Assessment for Particulate Matter (PM ISA) indicates that PM2.5 can penetrate deep into 

the lungs and cause serious health effects, including premature death and other nonfatal illnesses (U.S. EPA, 2009). As described in the preamble to the rule, the revisions to 

the standards are based on an integrative assessment of an extensive body of new 

scientific evidence (U.S. EPA, 2009). Health studies published since the PM ISA (e.g., 

Pope et al. [2009]) confirm that recent levels of PM2.5 have had a significant impact on 
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public health. Based on the air quality analysis in this RIA, the EPA projects that nearly 

all counties with PM2.5 monitors in the U.S. would meet an annual standard of 12 μg/m3

without additional federal, state, or local PM control programs. This demonstrates the 

substantial progress that the U.S. has made in reducing air pollution emissions over the 

last several decades. Regulations such as the EPA’s recent Mercury and Air Toxics 

Standards (MATS) and other federal programs such as diesel standards will provide 

substantial improvements in regional concentrations of PM2.5. Our analysis shows a few 

areas would still need additional emissions reductions to address local sources of air 

pollution, including ports and uncontrolled industrial emissions. For this reason, we 

have designed the RIA analysis to focus on local controls in these few areas. We 

estimate that these additional local controls would yield benefits well in excess of costs.

The setting of a NAAQS does not compel specific pollution reductions, and as 

such does not directly result in costs or benefits. For this reason, NAAQS RIAs are merely 

illustrative. The NAAQS RIAs illustrate the potential costs and benefits of additional 

steps States could take to attain a revised air quality standard nationwide beyond rules 

already on the books. We base our illustrative estimates on an array of emission control 

strategies for different sources. The costs and benefits identified in this RIA will not be 

realized until specific controls are mandated by State Implementation Plans (SIPs) or 

other federal regulations. In short, NAAQS RIAs hypothesize, but do not prescribe, the 

control strategies that States may choose to enact when implementing a revised 

NAAQS.

It is important to emphasize that the EPA does not “double count” the costs or 

the benefits of our rules. Emission reductions achieved under rules that require specific 

actions from sources—such as MATS—are in the baseline of this NAAQS analysis, as are 

emission reductions needed to meet the current NAAQS. For this reason, the cost and 

benefits estimates provided in this RIA and all other NAAQS RIAs should not be added to 

the estimates for implementation rules.

In calculating the costs, EPA assumed the application of a significant number of 

unidentified future controls that would make possible the additional emissions

reductions needed for attainment in 2020. EPA used two methodologies – the fixed-cost 

and hybrid methodologies -- for estimating the costs of unidentified future controls, and 

both approaches assume that innovative strategies and new control options make 
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possible the emissions reductions needed for attainment by 2020. Estimates generated 

by the two approaches do not represent lower and upper bound estimates, but simply 

represent estimates generated by two different methodologies. The fixed-cost 

methodology implicitly assumes that technological change and innovation will result in 

the availability of additional controls by 2020 that are similar in cost to the higher end of 

the cost range for current controls. The hybrid methodology implicitly assumes that 

while additional controls become available by 2020, they become available at an 

increasing cost and the increasing cost varies by geographic area and by degree of 

difficulty associated with obtaining the needed emissions reductions. 

For the revised annual standard of 12μg/m3, the total cost estimates are 

comprised of between 90 and 97 percent extrapolated cost estimates, and the 

estimated total cost using the hybrid methodology is roughly 6.5 times more than the 

estimated total cost using the fixed-cost methodology.Because the hybrid methodology 

reflects increasing marginal costs in areas needing a higher ratio of emissions reductions 

from unknown to known controls, it could be more representative of total costs. In an 

effort to consider the potential fitness of the extrapolated cost estimates, we reviewed 

the South Coast Air Quality Management District’s (SCAQMD) 2012 Air Quality 

Management Plan (AQMP), and we located data on recent emission reduction credit 

(ERC) transactions in both the SCAQMD and San Joaquin Valley Air Pollution Control 

District (SJV APCD). While this information provides context for the extrapolated cost 

estimates, the current relationship between available controls and costs to reduce 

emissions may or may not be applicable in 2020 because of changes in innovation and 

advances in technology.

The SCAQMD’s 2012 AQMP includes information on control measures to meet 

the current 24-hour standard of 35μg/m3, including further PM2.5 controls for underfired charbroilers at a cost per ton reduced of $15,000. This control cost matches the 

parameter used in the fixed-cost methodology, as well as the initial value used for the 

hybrid methodology and is supportive of our selection of that value. In addition, the 

California Air Resources Board’s 2009 and 2010 Emission Reduction Offset Transaction 

Costs, Summary Report included PM10 ERC prices in both the SCAQMD and the SJV 

APCD. To some degree, ERC transaction prices reflect a choice between installing a more 

stringent control or purchasing ERCs. Between 2009 and 2010 PM10 ERC prices in SJV 
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APCD ranged from $40,000 per ton per year (tpy) to $70,000/tpy, and PM10 ERC prices in 

the SCAQMD ranged from $575,000/tpy to more than $1.9 million/tpy. These prices 

reflect both marginal costs that are higher than the fixed-cost estimates and marginal 

costs that are not inconsistent with the higher cost estimates generated using the 

hybrid methodology. For further discussion of the total cost estimates, refer to Section 

7.2.4 in Chapter 7 of this RIA.

Furthermore, the monetized benefits estimates presented in this RIA are not 

intended to capture the full burden of PM to public health, but rather represent the 

incremental benefits expected upon attaining the revised annual primary standard of 12

μg/m3. In comparison, modeling by Fann et al. (2012) estimated that 2005 levels of air 

pollution were responsible for between 130,000 and 320,000 PM2.5-related deaths, or 

between 6.1% and 15% of total deaths from all causes in the continental United States. 

The monetized benefits associated with attaining the proposed range of standards 

appear modest when viewed within the context of the potential overall public health 

burden of PM2.5 and ozone air pollution estimated by Fann et al. (2012), but this is 

primarily because regulations already on the books will make great strides toward 

reducing future levels of PM. One important distinction between the total public health 

burden estimated for 2005 air pollution levels and the estimated benefits in this RIA is 

that ambient levels of PM2.5 will have improved substantially by 2020, due to major 

emissions reductions resulting from implementation of Federal regulations. For 

example, we estimate that SO2 emissions (an important PM2.5 precursor) in the U.S. 

would fall from 14 million tons in 2005 to less than 5 million tons by 2020 (a reduction 

of 66%). For this reason, States will only need to achieve small air quality improvements 

to reach the proposed PM standards. As shown in the recent RIA for MATS (U.S. EPA, 

2011b), implementing other federal and state air quality actions will address a 

substantial fraction of the total public health burden of PM2.5 and ozone air pollution.

The NAAQS are not set at levels that eliminate the risk of air pollution. Instead, 

the Administrator sets the NAAQS at a level requisite to protect public health with an 

adequate margin of safety, taking into consideration effects on susceptible populations 

based on the scientific literature. The risk analysis prepared in support of this PM 

NAAQS reported risks below these levels, while acknowledging that the confidence in 

those effect estimates is higher at levels closer to the standard (U.S. EPA, 2010). While 
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benefits occurring below the standard are assumed to be more uncertain than those 

occurring above the standard, the EPA considers these to be legitimate components of 

the total benefits estimate. Though there are greater uncertainties at lower PM2.5 

concentrations, there is no evidence of a population-level threshold in PM2.5-related 

health effects in the epidemiology literature.

Lastly, EPA recognizes that there are uncertainties in both the cost and benefit 

estimates provided in this RIA. The EPA was unable to monetize fully all of the benefits 

associated with reaching these standards in this RIA, including other health effects of 

PM, visibility effects, ecosystem effects, and climate effects. If the EPA were able to 

monetize all of the benefits, the benefits would exceed the estimated costs by an even 

greater margin. 

5.13 ES.4 Caveats and Limitations

EPA acknowledges several important limitations of this analysis. These include:

5.13.1 ES.4.1 Benefits Caveats

 PM2.5 mortality benefits represent a substantial proportion of total 

monetized benefits (over 98%). To characterize the uncertainty in the 

relationship between PM2.5 and premature mortality, we include a set of 

twelve estimates of the concentration-response function based on results of 

the PM2.5 mortality expert elicitation study in addition to our core estimates. 

Even these multiple characterizations omit the uncertainty in air quality 

estimates, baseline incidence rates, populations exposed, chemical 

composition, transferability of the effect estimate to diverse locations, and 

additional uncertainty around the mean estimates expressed by the experts. 

As a result, the reported confidence intervals and range of estimates give an 

incomplete picture about the overall uncertainty in the PM2.5 estimates. This 

information should be interpreted within the context of the larger 

uncertainty surrounding the entire analysis.

 Most of the estimated avoided premature deaths occur at or above the 

lowest measured PM2.5 concentration in the two studies used to estimate 

mortality benefits. In general, we have greater confidence in risk estimates 
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based on PM2.5 concentrations where the bulk of the health and air quality 

data reside and somewhat less confidence where data density is lower.

 We analyzed full attainment in 2020, and projecting key variables introduces 

uncertainty. Inherent in any analysis of future regulatory programs are 

uncertainties in projecting atmospheric conditions and source-level 

emissions, as well as population, health baselines, incomes, technology, and 

other factors.

 There are uncertainties related to the health impact functions used in the 

analysis. These include within-study variability; pooling across studies; the 

application of C-R functions nationwide and for all particle species; 

extrapolation of impact functions across populations; and various 

uncertainties in the C-R function, including causality and shape of the 

function at low concentrations. Therefore, benefits may be under- or overestimates.

 This analysis omits certain unquantified effects due to lack of data, time, and 

resources. These unquantified endpoints include other health and ecosystem 

effects. The EPA will continue to evaluate new methods and models and 

select those most appropriate for estimating the benefits of reductions in air 

pollution.

 Full benefits of the revised standards in San Joaquin and South Coast will not 

be realized until 2025 when those areas are expected to demonstrate 

attainment with the revised standards. If we were to estimate the monetized 

benefits for 2025, those benefits would be higher due to population growth, 

aging of the population, and income growth over time.

5.13.2 ES.4.2 Control Strategy and Cost Analysis Caveats and Limitations

 The control technologies applied as part of the illustrative control strategies 

represent technologies that are available currently and may not reflect 

emerging devices that may be available in future years to aid in attainment 

of the revised standard. In addition the emission reductions calculated from 

the known controls (control efficiencies) assume that the control devices are 

properly installed and maintained. There is also variability in scale of 

application that is difficult to reflect for small area sources of emissions.

 The illustrative control strategy analysis estimates only one potential 

pathway to attainment. The control strategies are not recommendations for 

how the revised PM2.5 standard should be implemented, and states will make 
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all final decisions regarding implementation strategies for the revised 

NAAQS.

 The application of known controls is based upon source information obtained 

from the emissions inventory. To the extent the inventory is lacking data on 

baseline controls from State Implementation Plans (SIPs), we may analyze 

control options that are currently in place. 

 The future year emissions used as a basis for developing the control 

strategies in this RIA have implicit assumptions regarding emissions growth 

and control, which differ by sector. For some emission sectors these future 

year emissions may not reflect new sources locating in these areas.

 For two areas in California (South Coast Air Quality Management District and 

San Joaquin Valley Air Pollution Control District) the degree of projected 

nonattainment with the revised annual standard of 12μg/m3is such that 

these areas are not expected to be able to demonstrate attainment with the 

new standard by 2020. These areas may qualify for up to a five-year 

extension of their attainment date and are likely to have until 2025 to 

demonstrate attainment with the revised annual standard.

 The control technologies applied do not reflect potential effects of 

technological change that may be available in future years and the effects of 

“learning by doing” are not accounted for in the emissions reduction 

estimates. In our analysis, we do not have the necessary data for cumulative 

output, fuel sales, or emission reductions for all sectors included in order to 

properly generate control costs that reflect learning curve impacts. We 

believe the effect of including these impacts would be to lower our estimates 

of costs for our control strategies in 2020.

 In addition to the application of known controls, EPA assumes the application 

of unidentified future controls that make possible the additional emission 

reductions needed for attainment in 2020. By definition, no cost data 

currently exists for unidentified future technologies or innovative strategies

and the cost estimates for unidentified future controls reflect some 

uncertainty.

 Because we obtain control cost data from many sources, we are not always 

able to obtain consistent data across original data sources. If disaggregated 

control cost data is unavailable (i.e., where capital, equipment life value, and 

O&M costs are not separated out), EPA typically assumes that the estimated 

control costs are annualized using a 7% discount rate. When disaggregated 

control cost data is available (i.e., where capital, equipment life value, and 
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O&M costs are explicit) we can recalculate costs using a 3% discount rate. In 

general, we have some disaggregated data available for non-EGU point

source controls and we do not have any disaggregated control cost data for 

area source controls. In this analysis, for the revised annual standard of 

12μg/m3and the alternative standard of 13μg/m3 we did not have any 

disaggregated known control cost data, and as such we were not able to 

recalculate known control costs using a 3% discount rate. 

 EPA understands that some states will incur costs designing SIPs and 

implementing new control strategies to meet the revised annual standard. 

However, EPA does not know what specific actions states will take to design 

their SIPs to meet the revised standards; therefore we do not present 

estimated costs that government agencies may incur for managing the 

requirement, implementing these (or other) control strategies, or for offering 

incentives that may be necessary to encourage the implementation of 

specific technologies, especially for technologies that are not necessarily 

market driven.

5.13.3 ES.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. 

Fann, N., A. Lamson, K. Wesson, D. Risley, S.C. Anenberg, and B.J. Hubbell. 2012. 

“Estimating the National Public Health Burden Associated with Exposure to 

Ambient PM2.5 and Ozone. Risk Analysis.” Risk Analysis 32(1): 81-95.

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.

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.” Environ Health Perspect. In press. http://dx.doi.org/10.1289/ehp.1104660

Pope, CA III, E Majid, D Dockery. 2009. “Fine Particle Air Pollution and Life Expectancy in 

the United States.” New England Journal of Medicine 360: 376–386.

U.S. Environmental Protection Agency. 1997. Regulatory Impact Analyses for the 

Particulate Matter and Ozone National Ambient Air Quality Standards and 

Proposed Regional Haze Rule. Available at: 

http://www.epa.gov/ttn/oarpg/naaqsfin/ria.html
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U.S. Environmental Protection Agency (U.S. EPA). 2004. Final Regulatory Analysis: 

Control of Emissions from Nonroad Diesel Engines. EPA420-R-04-007. Available 

on the Internet at http://www.epa.gov/otaq/documents/nonroaddiesel/420r04007.pdf.

U.S. Environmental Protection Agency (U.S. EPA). November 17, 2005a. Federal Register. 

Vol. 70, No. 221. “Control of Air Pollution From Aircraft and Aircraft Engines; 

Emission Standards and Test Procedures.” 40 CFR Part 87. Available on the 

Internet at <http://www.epa.gov/fedrgstr/EPA-AIR/2005/November/Day17/a22704.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2005b. Regulatory Impact Analysis for 

the Final Clean Air Visibility Rule or the Guidelines for Best Available Retrofit 

Technology (BART) Determinations Under the Regional Haze Regulations. EPA452/R-05-004. Available on the Internet at 

<http://www.epa.gov/oar/visibility/pdfs/bart_ria_2005_6_15.pdf>.

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). 2009. Integrated Science Assessment 

for Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for 

Environmental Assessment—RTP Division. December. Available on the Internet 

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

U.S. Environmental Protection Agency (U.S. EPA). 2011a. Regulatory Impact Analysis for 

the Federal Implementation Plans to Reduce Interstate Transport of Fine 

Particulate Matter and Ozone in 27 States; Correction of SIP Approvals for 22 

States. June. Available on the Internet at 

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

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

the Final Mercury and Air Toxics Standards. EPA-452/R-11-011. December. 

Available on the Internet at 

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

U.S. Environmental Protection Agency (U.S. EPA). 2011c. 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 on the Internet at 

<http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>.
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U.S. Environmental Protection Agency (U.S. EPA). 2011d. Policy Assessment for the 

Review of the Particulate Matter National Ambient Air Quality Standards. EPA452/D-11-003. April. Available on the Internet at 

<http://www.epa.gov/ttnnaaqs/standards/pm/s_pm_2007_pa.html>.

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. Vol. 105 (6): 608-612.

I. Rationale for Final Decisions on the Primary PM2.5 Standards

This section presents the Administrator’s final decision regarding the need to 

revise the current primary PM2.5 standards and, more specifically, regarding revisions to 

the level and form of the existing primary annual PM2.5 standard in conjunction with 

retaining the existing primary 24-hour PM2.5 standard. As discussed more fully below, 

the rationale for the final decision is based on a thorough review, in the Integrated 

Science Assessment, of the latest scientific information, published through mid-2009, on 

human health effects associated with long- and short-term exposures to fine particles in 

the ambient air. The final decisions also take into account: (1) staff assessments of the 

most policy-relevant information presented and assessed in the Integrated Science 

Assessment and staff analyses of air quality and human risks presented in the Risk 

Assessment and the Policy Assessment, upon which staff conclusions regarding 

appropriate considerations in this review are based; (2) CASAC advice and 

recommendations, as reflected in discussions of drafts of the Integrated Science 

Assessment, Risk Assessment, and Policy Assessment at public meetings, in separate 
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written comments, and in CASAC’s letters to the Administrator; (3) the multiple rounds 

of public comments received during the development of these documents, both in 

connection with CASAC meetings and separately; and (4) extensive public comments 

received on the proposal. 

In developing this final rule, the Administrator recognizes that the CAA requires 

her to reach a public health policy judgment as to what standards would be requisite --

neither more nor less stringent than necessary -- to protect public health with an 

adequate margin of safety, based on scientific evidence and technical assessments that 

have inherent uncertainties and limitations. This judgment requires making reasoned 

decisions as to what weight to place on various types of evidence and assessments, and 

on the related uncertainties and limitations. Thus, in selecting the final standards, the 

Administrator is seeking not only to prevent fine particle concentrations that have been 

demonstrated to be harmful but also to prevent lower fine particle concentrations that 

may pose an unacceptable risk of harm, even if the risk is not precisely identified as to 

nature or degree. 

As discussed below, as well as in more detail in the proposal, a substantial 

amount of new research has been conducted since the close of the science assessment 

in the last review of the PM2.5 NAAQS (U.S. EPA, 2004), with important new information 

coming from epidemiological studies, in particular. This body of evidence includes 

hundreds of new epidemiological studies conducted in many countries around the 

world. In its assessment of the evidence judged to be most relevant to making decisions 
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on elements of the primary PM2.5 standards, the EPA has placed greater weight on U.S. 

and Canadian studies using PM2.5 measurements, since studies conducted in other 

countries may reflect different demographic and air pollution characteristics.31

The newly available research studies as well as the earlier body of scientific 

evidence presented and assessed in the Integrated Science Assessment have undergone 

intensive scrutiny through multiple layers of peer review and opportunities for public 

review and comment. In developing this final rule, the EPA has drawn upon an 

integrative synthesis of the entire body of evidence concerning exposure to ambient 

fine particles and a broad range of health endpoints (U.S. EPA, 2009a, Chapters 2, 4, 5, 

6, 7, and 8) focusing on those health endpoints for which the Integrated Science 

Assessment concludes that there is a causal or likely causal relationship with long- or 

short-term PM2.5 exposures. The EPA has also considered health endpoints for which the 

Integrated Science Assessment concludes there is evidence suggestive of a causal 

relationship with long-term PM2.5 exposures. 

The EPA has also drawn upon a quantitative risk assessment based upon the 

scientific evidence described and assessed in the Integrated Science Assessment. These 

analyses, discussed in the Risk Assessment (U.S. EPA, 2010a) and Policy Assessment 

(U.S. EPA, 2011a, chapter 2), have also undergone intensive scrutiny through multiple 

layers of peer review and multiple opportunities for public review and comment. 

 

31 Nonetheless, the Administrator recognizes the importance of all studies, including 

international studies, in the Integrated Science Assessment’s considerations of the 

weight of the evidence that informs causality determinations.
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Although important uncertainties remain in the qualitative and quantitative 

characterizations of health effects attributable to ambient fine particles, progress has 

been made in addressing these uncertainties in this review. The EPA’s review of this 

information has been extensive and deliberate. This intensive evaluation of the scientific 

evidence and quantitative assessments has provided a comprehensive and adequate 

basis for regulatory decision making at this time. 

This section describes the integrative synthesis of the evidence and technical 

information contained in the Integrated Science Assessment, the Risk Assessment, and 

the Policy Assessment with regard to the current and alternative standards. The EPA 

notes that the final decision for retaining or revising the current primary PM2.5 standards 

is a public health policy judgment made by the Administrator. The Administrator’s final 

decision draws upon scientific information and analyses related to health effects and 

risks; judgments about uncertainties that are inherent in the scientific evidence and 

analyses; CASAC advice; and comments received in response to the proposal.

In presenting the rationale for the final decisions on the primary PM2.5 standards, 

this section begins with a summary of the approaches used in setting the initial primary 

PM2.5 NAAQS in 1997 and in reviewing and revising those standards in 2006 (section 

III.A.1). The D.C. Circuit Court of Appeals remand of the primary annual PM2.5 standard 

in 2009 is discussed in section III.A.2. Taking into consideration this history, section 

III.A.3 describes the EPA’s general approach used in the current review for considering 
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the need to retain or revise the current suite of fine particle standards, taking into 

account public comment on the proposed approach. 

The scientific evidence and quantitative risk assessment were presented in 

sections III.B and III.C of the proposal, respectively (77 FR 38906 to 38917, June 29, 

2012) and are outlined in sections III.B and III.C below. Subsequent sections of this 

preamble provide a more complete discussion of the Administrator’s rationale, in light 

of key issues raised in public comments, for concluding that it is appropriate to revise 

the suite of current primary PM2.5 standards (section III.D), as well as a more complete 

discussion of the Administrator’s rationale for retaining or revising the specific elements 

of the primary PM2.5 standards, namely the indicator (section III.E.1); averaging time 

(section III.E.2); form (section III.E.3); and level (section III.E.4). A summary of the final 

decisions to revise the suite of primary PM2.5 standards is presented in section III.F. 

A. Background

There are currently two primary PM2.5 standards providing public health 

protection from effects associated with fine particle exposures. The annual standard is 

set at a level of 15.0 μg/m3, based on the 3-year average of annual arithmetic mean 

PM2.5 concentrations from single or multiple monitors sited to represent communitywide air quality. The 24-hour standard is set at a level of 35 μg/m3

, based on the 3-year 

average of the 98th percentile of 24-hour PM2.5 concentrations at each populationoriented monitor within an area. 
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The past and current approaches for reviewing the primary PM2.5 standards 

described below are all based most fundamentally on using information from 

epidemiological studies to inform the selection of PM2.5 standards that, in the 

Administrator’s judgment, protect public health with an adequate margin of safety. Such 

information can be in the form of air quality distributions over which health effect 

associations have been observed in scientific studies or in the form of concentrationresponse functions that support quantitative risk assessment. However, evidence- and 

risk-based approaches using information from epidemiological studies to inform 

decisions on PM2.5 standards are complicated by the recognition that no population 

threshold, below which it can be concluded with confidence that PM2.5-related effects 

do not occur, can be discerned from the available evidence.32 As a result, any general 

approach to reaching decisions on what standards are appropriate necessarily requires 

judgments about how to translate the information available from the epidemiological 

studies into a basis for appropriate standards. This includes consideration of how to 

weigh the uncertainties in the reported associations across the distributions of PM2.5

concentrations in the studies and the uncertainties in quantitative estimates of risk, in 

the context of the entire body of evidence before the Agency. Such approaches are 

consistent with setting standards that are neither more nor less stringent than 

necessary, recognizing that a zero-risk standard is not required by the CAA. 

 

32 The term "evidence-based" approach or consideration generally refers to using the 

information in the scientific evidence to inform judgments on the need to retain or 

revise the NAAQS. The term "risk-based" generally refers to using the quantitative 

information in the Risk Assessment to inform such judgments.
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1. General Approach Used in Previous Reviews

The general approach used to translate scientific information into standards in 

the previous PM NAAQS reviews focused on consideration of alternative standard levels 

that were somewhat below the long-term mean PM2.5 concentrations reported in key 

epidemiological studies (U.S. EPA, 2011a, section 2.1.1). This approach recognized that 

the strongest evidence of PM2.5-related associations occurs where the bulk of the data 

exists, which is over a range of concentrations around the long-term (i.e., annual) mean. 

In setting primary PM2.5 annual and 24-hour standards for the first time in 1997, 

the Agency relied primarily on an evidence-based approach that focused on 

epidemiological evidence, especially from short-term exposure studies of fine particles 

judged to be the strongest evidence at that time (U.S. EPA, 2011a, section 2.1.1.1). The 

EPA selected a level for the annual standard that was at or below the long-term mean 

PM2.5 concentrations in studies providing evidence of associations with short-term PM2.5

exposures, placing greatest weight on those short-term exposure studies that reported 

clearly statistically significant associations with mortality and morbidity effects. Further 

consideration of long-term mean PM2.5 concentrations associated with mortality and 

respiratory effects in children did not provide a basis for establishing a lower annual 

standard level. The EPA did not place much weight on quantitative risk estimates from 

the very limited risk assessment conducted, but did conclude that the risk assessment 

results confirmed the general conclusions drawn from the epidemiological evidence that 
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a serious public health problem was associated with ambient PM levels allowed under 

the then current PM10 standards (62 FR 38665/1, July 18, 1997).

The EPA considered the epidemiological evidence and data on air quality 

relationships to set an annual PM2.5 standard that was intended to be the “generally 

controlling” standard; i.e., the primary means of lowering both long- and short-term 

ambient concentrations of PM2.5.

33 In conjunction with the annual standard, the EPA 

also established a 24-hour PM2.5 standard to provide supplemental protection against 

days with high peak concentrations, localized “hotspots,” and risks arising from seasonal 

emissions that might not be well controlled by an annual standard (62 FR 38669/3). 

In 2006, the EPA used a different evidence-based approach to assess the 

appropriateness of the levels of the 24-hour and annual PM2.5 standards (U.S. EPA, 

2011a, section 2.1.1.2). Based on an expanded body of epidemiological evidence that 

was stronger and more robust than that available in the 1997 review, including 

additional studies of both short- and long-term exposures, the EPA decided that using 

evidence of effects associated with periods of exposure that were most closely matched 

 

33 In so doing, the EPA noted that because an annual standard would focus control 

programs on annual average PM2.5 concentrations, it would not only control longterm exposure levels, but would also generally control the overall distribution of 24-

hour exposure levels, resulting in fewer and lower 24-hour peak concentrations. 

Alternatively, a 24-hour standard that focused controls on peak concentrations could 

also result in lower annual average concentrations. Thus, the EPA recognized that 

either standard could provide some degree of protection from both short- and longterm exposures, with the other standard serving to address situations where the daily 

peaks and annual averages are not consistently correlated (62 FR 38669, July 18, 

1997). In the circumstances presented in that review, the EPA determined that it was 

appropriate to focus on the annual standard as the standard best suited to control 

both annual and daily air quality distributions (62 FR 38670).
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to the averaging time of each standard was the most appropriate public health policy 

approach for evaluating the scientific evidence to inform selecting the level of each 

standard. Thus, the EPA relied upon evidence from the short-term exposure studies as 

the principal basis for revising the level of the 24-hour PM2.5 standard from 65 to 35 

μg/m3to protect against effects associated with short-term exposures. The EPA relied 

upon evidence from long-term exposure studies as the principal basis for retaining the 

level of the annual PM2.5 standard at 15 μg/m3to protect against effects associated with 

long-term exposures. This approach essentially took the view that short-term studies 

were not appropriate to inform decisions relating to the level of the annual standard, 

and long-term studies were not appropriate to inform decisions relating to the level of 

the 24-hour standard. With respect to quantitative risk-based considerations, the EPA 

determined that the estimates of risks likely to remain upon attainment of the 1997 

suite of PM2.5 standards were indicative of risks that could be reasonably judged 

important from a public health perspective and, thus, supported revision of the 

standards. However, the EPA judged that the quantitative risk assessment had 

important limitations and did not provide an appropriate basis for selecting the levels of 

the revised standards in 2006 (71 FR 61174/1-2, October 17, 2006). 

2. Remand of Primary Annual PM2.5 Standard

As noted above in section II.B.2, several parties filed petitions for review in the 

U.S. Court of Appeals for the District of Columbia Circuit following promulgation of the 

revised PM NAAQS in 2006. These petitions challenged several aspects of the final rule 
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including the level of the primary PM2.5 annual standard. The primary 24-hour PM2.5

standard was not challenged by any of the litigants and, thus, was not considered in the 

court’s review and decision. 

On judicial review, the D.C. Circuit remanded the primary annual PM2.5 NAAQS to 

the EPA on grounds that the Agency failed to adequately explain why the annual 

standard provided the requisite protection from both short- and long-term exposures to 

fine particles including protection for at-risk populations. American Farm Bureau 

Federation v. EPA, 559 F. 3d 512 (D.C. Cir. 2009). With respect to human health 

protection from short-term PM2.5 exposures, the court considered the different 

approaches used by the EPA in the 1997 and 2006 PM NAAQS decisions, as summarized 

in section III.A.1 above. The court found that the EPA failed to adequately explain why a 

primary 24-hour PM2.5 standard by itself would provide the protection needed from 

short-term exposures and remanded the primary annual PM2.5 standard to the EPA “for 

further consideration of whether it is set at a level requisite to protect the public health 

while providing an adequate margin of safety from the risk of short-term exposures to 

PM2.5.” American Farm Bureau Federation v. EPA, 559 F. 3d at 520-24. 

With respect to protection from long-term exposure to fine particles, the court 

found that the EPA failed to adequately explain how the primary annual PM2.5 standard 

provided an adequate margin of safety for children and other at-risk populations. The 

court found that the EPA did not provide a reasonable explanation of why certain 

morbidity studies, including a study of children in Southern California showing lung 
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damage associated with long-term PM2.5 exposure (Gauderman et.al, 2000) and a multicity study (24-Cities Study) evaluating decreased lung function in children associated 

with long-term PM2.5 exposures (Raizenne et al., 1996), did not warrant a more stringent 

annual PM2.5 standard. Id. at 522-23. Specifically, the court found that:

EPA was unreasonably confident that, even though it relied solely upon longterm mortality studies, the revised standard would provide an adequate margin 

of safety with respect to morbidity among children. Notably absent from the 

final rule, moreover, is any indication of how the standard will adequately 

reduce risk to the elderly or to those with certain heart or lung diseases despite 

(a) the EPA’s determination in its proposed rule that those subpopulations are at 

greater risk from exposure to fine particles and (b) the evidence in the record 

supporting that determination. Id. at 525.

In addition, the court held that the EPA had not adequately explained its decision 

to base the level of the annual standard essentially exclusively on the results of longterm studies and the 24-hour standard level essentially exclusively on the results of 

short-term studies. See 559 F. 3d at 522 (“[e]ven if the long-term studies available today 

are useful for setting an annual standard,..., it is not clear why the EPA no longer 

believes it useful to look as well to short-term studies in order to design the suite of 

standards that will most effectively reduce the risks associated with short-term 

exposure”); see also Id. at 523-24 (holding that the EPA had not adequately explained 

why a standard based on levels in short-term exposure studies alone provided 

appropriate protection from health effects associated with short-term PM2.5 exposures 

given the stated need to lower the entire air quality distribution, and not just peak 

concentrations, in order to control against short-term effects). 
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In remanding the primary annual PM2.5 standard for reconsideration, the court 

did not vacate the standard, Id. at 530, so the standard remains in effect and is 

therefore the standard considered by the EPA in this review.

3. General Approach Used in the Policy Assessment for the Current Review

This review is based on an assessment of a much expanded body of scientific 

evidence, more extensive air quality data and analyses, and a more comprehensive 

quantitative risk assessment relative to the information available in past reviews, as 

presented and assessed in the Integrated Science Assessment and Risk Assessment and 

discussed in the Policy Assessment. As a result, the EPA’s general approach to reaching 

conclusions about the adequacy of the current suite of PM2.5 standards and potential 

alternative standards that are appropriate to consider was broader and more integrative 

than in past reviews. Our general approach also reflected consideration of the issues 

raised by the court in its remand of the primary annual PM2.5 standard as discussed in 

section III.A.2 above, since decisions made in this review, and the rationales for those 

decisions, will comprise the Agency’s response to the remand.

The EPA’s general approach took into account both evidence-based and riskbased considerations and the uncertainties related to both types of information, as well 

as advice from CASAC (Samet, 2010c,d) and public comments on the first and second 

draft Policy Assessments (U.S. EPA, 2010c,f). In so doing, the EPA staff developed a final 

Policy Assessment (U.S. EPA, 2011a) which provided as broad an array of policy options 

as was supported by the available information, recognizing that the selection of a 
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specific approach to reaching final decisions on the primary PM2.5 standards will reflect 

the judgments of the Administrator as to what weight to place on the various 

approaches and types of information available in the current review.

The Policy Assessment concluded it was most appropriate to consider the 

protection against PM2.5-related mortality and morbidity effects, associated with both 

long- and short-term exposures, afforded by the annual and 24-hour standards taken 

together, as was done in the 1997 review, rather than to consider each standard 

separately, as was done in the 2006 review (U.S. EPA, 2011a, section 2.1.3).34 As the EPA 

recognized in 1997, there are various ways to combine two standards to achieve an 

appropriate degree of public health protection. The extent to which these two 

standards are interrelated in any given area depends in large part on the relative levels 

of the standards, the peak-to-mean ratios that characterize air quality patterns in an 

area, and whether changes in air quality designed to meet a given suite of standards are 

likely to be of a more regional or more localized nature. 

In considering the combined effect of annual and 24-hour standards, the Policy 

Assessment recognized that changes in PM2.5 air quality designed to meet an annual 

 

34 By utilizing this approach, the Agency also is responsive to the remand of the 2006 

standard. As noted in section III.A.2, the D.C. Circuit, in remanding the 2006 primary 

annual PM2.5 standard, concluded that the Administrator had failed to adequately 

explain why an annual standard was sufficiently protective in the absence of 

consideration of the long-term mean PM2.5 concentrations in short-term exposure 

studies as well, and likewise had failed to explain why a 24-hour standard was 

sufficiently protective in the absence of consideration of the effect of an annual 

standard on reducing the overall distribution of 24-hour average PM2.5

concentrations. 559 F. 3d at 520-24. 
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standard would likely result not only in lower annual average PM2.5 concentrations but 

also in fewer and lower peak 24-hour PM2.5 concentrations. The Policy Assessment also 

recognized that changes designed to meet a 24-hour standard would result not only in 

fewer and lower peak 24-hour PM2.5 concentrations but also in lower annual average 

PM2.5 concentrations. Thus, either standard could be viewed as providing protection 

from effects associated with both short- and long-term exposures, with the other 

standard serving to address situations where the daily peak and annual average 

concentrations are not consistently correlated. 

In considering the currently available evidence, the Policy Assessment 

recognized that the short-term exposure studies were primarily drawn from 

epidemiological studies that associated variations in area-wide health effects with 

monitor(s) that measured the variation in daily PM2.5 concentrations over the course of 

several years. The strength of the associations in these data was demonstrably in the 

numerous “typical” days within the air quality distribution, not in the peak days. See also 

71 FR 61168, October 17, 2006 and American Farm Bureau Federation v EPA, 559 F. 3d 

at 523, 524 (making the same point). The quantitative risk assessments conducted for 

this and previous reviews demonstrated the same point; that is, much, if not most of the 

aggregate risk associated with short-term exposures results from the large number of 

days during which the 24-hour average concentrations are in the low-to mid-range, 

below the peak 24-hour concentrations (U.S. EPA, 2011a, section 2.2.2; U.S. EPA, 2010a, 

section 3.1.2.2). In addition, there was no evidence suggesting that risks associated with 
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long-term exposures were likely to be disproportionately driven by peak 24-hour 

concentrations.35

For these reasons, the Policy Assessment concluded that strategies that focused 

primarily on reducing peak days were less likely to achieve reductions in the PM2.5

concentrations that were most strongly associated with the observed health effects. 

Furthermore, the Policy Assessment concluded that a policy approach that focused on 

reducing peak exposures would most likely result in more uneven public health 

protection across the U.S. by either providing inadequate protection in some areas or 

overprotecting in other areas (U.S. EPA, 2011a, p. 2-9; U.S. EPA, 2010a, section 5.2.3). 

This is because, as discussed above, reductions based on control of peak days are less 

likely to control the bulk of the air quality distribution. 

The Policy Assessment concluded that a policy goal of setting a “generally 

controlling” annual standard that will lower a wide range of ambient 24-hour PM2.5

concentrations, as opposed to focusing on control of peak 24-hour PM2.5

concentrations, was the most effective and efficient way to reduce total population risk 

and so provide appropriate protection. This approach, in contrast to one focusing on a 

generally controlling 24-hour standard, would likely reduce aggregate risks associated 

with both long- and short-term exposures with more consistency and would likely avoid 

setting national standards that could result in relatively uneven protection across the 

 

35 In confirmation, a number of studies have presented analyses excluding higher PM 

concentration days and reported a limited effect on the magnitude of the effect 

estimates or statistical significance of the association (e.g., Dominici, 2006b; Schwartz 

et al, 1996; Pope and Dockery, 1992).
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country, due to setting standards that are either more or less stringent than necessary 

in different geographical areas (U.S. EPA, 2011a, p. 2-9).

The Policy Assessment also concluded that an annual standard intended to serve 

as the primary means for providing protection from effects associated with both longand short-term PM2.5 exposures cannot be expected to offer sufficient protection 

against the effects of all short-term PM2.5 exposures. As a result, in conjunction with a 

generally controlling annual standard, the Policy Assessment concluded it was 

appropriate to consider setting a 24-hour standard to provide supplemental protection, 

particularly for areas with high peak-to-mean ratios possibly associated with strong local 

or seasonal sources, or PM2.5-related effects that may be associated with shorter-thandaily exposure periods (U.S. EPA, 2011a, p. 2-10).

The Policy Assessment’s consideration of the protection afforded by the current 

and alternative suites of standards focused on PM2.5-related health effects associated 

with long-term exposures for which the magnitude of quantitative estimates of risks to 

public health generated in the risk assessment was appreciably larger in terms of overall 

incidence and percent of total mortality or morbidity effects than for short-term 

PM2.5-related effects. Nonetheless, the EPA also considered health effects and 

estimated risks associated with short-term exposures. In both cases, the Policy 

Assessment placed greatest weight on health effects that had been judged in the 

Integrated Science Assessment to have a causal or likely causal relationship with PM2.5

exposures, while also considering health effects judged to be suggestive of a causal 
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relationship or evidence that focused on specific at-risk populations. The Policy 

Assessment placed relatively greater weight on statistically significant associations that 

yielded relatively more precise effect estimates and that were judged to be robust to 

confounding by other air pollutants. In the case of short-term exposure studies, the 

Policy Assessment placed greatest weight on evidence from large multi-city studies, 

while also considering associations in single-city studies. 

In translating information from epidemiological studies into the basis for 

reaching staff conclusions on the adequacy of the current suite of standards, the Policy 

Assessment considered a number of factors. As an initial matter, the Policy Assessment

considered the extent to which the currently available evidence and related 

uncertainties strengthens or calls into question conclusions from the last review 

regarding associations between fine particle exposures and health effects. The Policy 

Assessment also considered evidence of health effects in at-risk populations and the 

potential impacts on such populations. Further, the Policy Assessment explored the 

extent to which PM2.5-related health effects had been observed in areas where air 

quality distributions extend to lower concentrations than previously reported or in areas 

that would likely have met the current suite of standards. 

In translating information from epidemiological studies into the basis for 

reaching staff conclusions on standard levels for consideration (U.S. EPA, 2011a, 

sections 2.1.3 and 2.3.4), the Policy Assessment first recognized the absence of 

discernible thresholds in the concentration-response functions from long- and short-
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term PM2.5 exposure studies (U.S. EPA, 2011a, section 2.4.3).36 In the absence of any 

discernible thresholds, the Agency’s general approach for identifying appropriate 

standard levels for consideration involved characterizing the range of PM2.5

concentrations over which we have the most confidence in the associations reported in 

epidemiological studies. In so doing, the Policy Assessment recognized that there is no 

single factor or criterion that comprises the “correct” approach, but rather there are 

various approaches that are reasonable to consider for characterizing the confidence in 

the associations and the limitations and uncertainties in the evidence. Identifying the 

implications of various approaches for reaching conclusions on the range of alternative 

standard levels that is appropriate to consider can help inform the final decisions to

either retain or revise the standards. Today’s final decisions also take into account 

public health policy judgments as to the degree of health protection that is to be 

achieved. 

In reaching staff conclusions on the range of annual standard levels that was 

appropriate to consider, the Policy Assessment focused on identifying an annual 

 

36 The epidemiological studies evaluated in the Integrated Science Assessment that 

examined the shape of concentration-response relationships and the potential 

presence of a threshold focused on cardiovascular-related hospital admissions and 

emergency department visits associated with short-term PM10 exposures and 

premature mortality associated with long-term PM2.5 exposure (U.S. EPA, 2009a, 

sections 6.5, 6.2.10.10 and 7.6). Overall, the Integrated Science Assessment 

concluded that the studies evaluated support the use of a no-threshold, log-linear 

model but recognized that “additional issues such as the influence of heterogeneity 

in estimates between cities, and the effect of seasonal and regional differences in PM 

on the concentration-response relationship still require further investigation” (U.S. 

EPA, 2009a, section 2.4.3).
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standard that provided requisite protection from effects associated with both long- and 

short-term exposures. In so doing, the Policy Assessment explored different approaches 

for characterizing the range of PM2.5 concentrations over which our confidence in the 

nature of the associations for both long- and short-term exposures is greatest, as well as 

the extent to which our confidence is reduced at lower PM2.5 concentrations. 

First, the Policy Assessment recognized that the approach that most directly 

addressed this issue considered studies that analyzed confidence intervals around 

concentration-response relationships and in particular, analyses that averaged across 

multiple concentration-response models rather than considering a single concentrationresponse model.37 The Policy Assessment explored the extent to which such analyses 

had been published for studies of health effects associated with long- or short-term 

PM2.5 exposures. Such analyses could potentially be used to characterize a 

concentration below which uncertainty in a concentration-response relationship 

substantially increases or is judged to be indicative of an unacceptable degree of 

uncertainty about the existence of a continuing concentration-response relationship. 

The Policy Assessment concluded that identifying this area of uncertainty in the 

concentration-response relationship could be used to inform identification of 

alternative standard levels that are appropriate to consider. 

 

37 This is distinct from confidence intervals around concentration-response relationships 

that are related to the magnitude of effect estimates generated at specific PM2.5

concentrations (i.e., point-wise confidence intervals) and that are relevant to the 

precision of the effect estimate across the air quality distribution, rather than to our 

confidence in the existence of a continuing concentration-response relationship across 

the entire air quality distribution on which a reported association was based. 
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Further, the Policy Assessment explored other approaches that considered 

different statistical metrics from epidemiological studies. The Policy Assessment first 

took into account the general approach used in previous PM reviews which focused on 

consideration of alternative standard levels that were somewhat below the long-term 

mean PM2.5 concentrations reported in epidemiological studies using air quality 

distributions based on composite monitor concentrations.38 This approach recognized 

that the strongest evidence of PM2.5-related associations occurs at concentrations 

around the long-term (i.e., annual) mean. In using this approach, the Policy Assessment

placed greatest weight on those long- and short-term exposure studies that reported 

statistically significant associations with mortality and morbidity effects.

In extending this approach, the Policy Assessment also considered information 

beyond a single statistical metric of PM2.5 concentrations (i.e., the mean) to the extent 

such information was available. Pursuant to an express comment from CASAC (Samet 

2010d, p. 2), the Policy Assessment utilized distributional statistics (i.e., statistical 

characterization of an entire distribution of data) to identify the broader range of PM2.5

concentrations that had the most influence on the calculation of relative risk estimates 

 

38 Using the term “composite monitor” does not imply that the EPA can identify one 

monitor that represents the air quality evaluated in a specific study area. Rather, the 

composite monitor concentration represents the average concentration across 

monitors within each area with more than one monitor included in a given study as 

typically reported in epidemiological studies. For multi-city studies, this metric 

reflects concentrations averaged across multiple monitors or from single monitors 

within each area and then averaged across study areas for an overall study mean 

PM2.5 concentration. This is consistent with the epidemiological evidence considered 

in other NAAQS reviews.
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in both long- and short-term exposure epidemiological studies. Thus, the Policy 

Assessment considered the part of the distribution of PM2.5 concentrations in which the 

data analyzed in the study (i.e., air quality and population-level data, as discussed 

below) were most concentrated, specifically, the range of PM2.5 concentrations around 

the long-term mean over which our confidence in the magnitude and significance of 

associations observed in the epidemiological studies was greatest. The Policy 

Assessment then focused on the lower part of the distribution to characterize where the 

data became appreciably more sparse and, thus, where our understanding of the 

magnitude and significance of the associations correspondingly became more uncertain. 

The Policy Assessment recognized there was no single percentile value within a given 

distribution that was most appropriate or “correct” to use to characterize where our 

confidence in the associations becomes appreciably lower. The Policy Assessment

concluded that the range from the 25th to 10th percentiles is a reasonable range to 

consider as a region where we had appreciably less confidence in the associations 

observed in epidemiological studies.39

 

39 In the PM NAAQS review completed in 2006, the Staff Paper similarly recognized that 

the evidence of an association in any epidemiological study is “strongest at and 

around the long-term average where the data in the study are most concentrated. 

For example, the interquartile range of long-term average concentrations within a 

study [with a lower bound of the 25th percentile] or a range within one standard 

deviation around the study mean, may reasonably be used to characterize the range 

over which the evidence of association is strongest” (U.S. EPA, 2005, p. 5-22). A range 

of one standard deviation around the mean represents approximately 68 percent of 

normally distributed data, and below the mean falls between the 25th and 10th

percentiles.
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In considering distributional statistics from epidemiological studies, the final 

Policy Assessment focused on two types of population-level metrics that CASAC advised 

were most useful to consider in identifying the PM2.5 concentrations most influential in 

generating the health effect estimates reported in the epidemiological studies.40

Consistent with CASAC advice, the most relevant information was the distribution of 

health events (e.g., deaths, hospitalizations) occurring within a study population in 

relation to the distribution of PM2.5 concentrations. However, in recognizing that access 

to health event data can be restricted, the Policy Assessment also considered the 

number of study participants within each study area as an appropriate surrogate for 

health event data.

The Policy Assessment recognized that an approach considering analyses of 

confidence intervals around concentration-response functions was intrinsically related 

to an approach that considered different distributional statistics. Both of these 

approaches could be employed to understand the broader distribution of PM2.5

concentrations which correspond to the health events reported in epidemiological 

 

40 The second draft Policy Assessment focused on the distributions of ambient PM2.5

concentrations and associated population data across areas included in several multicity studies for which such data were available in seeking to identify the most 

influential range of concentrations (U.S. EPA, 2010f, section 2.3.4.1). In its review of 

the second draft Policy Assessment, CASAC advised that it “would be preferable to 

have information on the concentrations that were most influential in generating the 

health effect estimates in individual studies” (Samet, 2010d, p.2). Therefore, in the 

final Policy Assessment, the EPA considered population-level data (i.e., area-specific 

health event data and study area population data) along with corresponding PM2.5

concentrations to generate a cumulative distribution of the population-level data 

relative to long-term mean PM2.5 concentrations to determine the most influential 

part of the air quality distribution (U.S. EPA, 2011a, Figure 2-7 and associated text).
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studies. In applying these approaches, the Policy Assessment, consistent with CASAC 

advice (Samet, 2010d, p.3), considered PM2.5 concentrations from long- and short-term 

PM2.5 exposure studies using composite monitor distributions. 

In reaching staff conclusions on alternative standard levels that were appropriate 

to consider, the Policy Assessment also included a broader consideration of the 

uncertainties and limitations of the current scientific evidence. Most notably, these 

uncertainties are related to the heterogeneity observed in the epidemiological studies in 

the eastern versus western parts of the U.S., the relative toxicity of PM2.5 components, 

and the potential role of co-pollutants (U.S. EPA, 2011a, pp. 2-25 to 2-26). The 

limitations and uncertainties associated with the currently available scientific evidence, 

including the availability of fewer studies toward the lower range of alternative annual 

standard levels being considered in this proposal, are summarized in section III.B below 

and further discussed in section III.B.2 of the proposal.

The Policy Assessment recognized that the level of protection afforded by the 

NAAQS relies both on the level and the form of the standard. The Policy Assessment 

concluded that a policy approach that used data based on composite monitor 

distributions to identify alternative standard levels, and then compared those levels to 

concentrations at maximum monitors to determine whether an area meets a given 

standard, inherently has the potential to build in some margin of safety (U.S. EPA, 
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2011a, p. 2-14).41 This conclusion was consistent with CASAC’s comments on the second 

draft Policy Assessment, in which CASAC expressed its preference for focusing on an 

approach using composite monitor distributions “because of its stability, and for the 

additional margin of safety it provides” when “compared to the maximum monitor 

perspective” (Samet, et al., 2010d, pp. 2 to 3). 

In reaching staff conclusions on alternative 24-hour standard levels that are 

appropriate to consider for setting a 24-hour standard intended to supplement the 

protection afforded by a generally controlling annual standard, the Policy Assessment

considered currently available short-term PM2.5 exposure studies. The evidence from 

these studies informed our understanding of the protection afforded by the suite of 

standards against effects associated with short-term exposures. In considering the 

short-term exposure studies, the Policy Assessment evaluated both the distributions of 

24-hour PM2.5 concentrations, with a focus on the 98th percentile concentrations (to the 

extent such data were available) to match the form of the current 24-hour PM2.5

 

41 Statistical metrics (e.g., means) based on composite monitor distributions may be 

identical to or below the same statistical metrics based on maximum monitor 

distributions. For example, some areas may have only one monitor, in which case the 

composite and maximum monitor distributions will be identical in those areas. Other 

areas may have multiple monitors that may be very close to the monitor measuring 

the highest concentrations, in which case the composite and maximum monitor 

distributions could be similar in those areas. As noted in Hassett-Sipple et al. (2010), 

for studies involving a large number of areas, the composite and maximum 

concentrations are generally within 5 percent of each other (77 FR 38905, fn. 30). Still 

other areas may have multiple monitors that may be separately impacted by local 

sources in which case the composite and maximum monitor distributions could be 

quite different (U.S. EPA, 2011a, p. 2-14). See further discussion of this issue in 

section III.E.4.c.i below.
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standard, as well as the long-term mean PM2.5 concentrations reported in these studies. 

In addition to considering the epidemiological evidence, the Policy Assessment also 

considered air quality information based on county-level 24-hour and annual design 

values42 to understand the policy implications of the alternative standard levels 

supported by the underlying science. In particular, the Policy Assessment considered the 

extent to which different combinations of alternative annual and 24-hour standards 

would support the policy goal of focusing on a generally controlling annual standard in 

conjunction with a 24-hour standard that would provide supplemental protection. In so 

doing, the Policy Assessment discussed the roles that each standard might be expected 

to play in the protection afforded by alternative suites of standards.

Beyond these evidence-based considerations, the Policy Assessment also 

considered the quantitative risk estimates and the key observations presented in the 

Risk Assessment. This assessment included an evaluation of 15 urban case study areas 

and estimated risk associated with a number of health endpoints associated with longand short-term PM2.5 exposures (U.S. EPA, 2010a). As part of the risk-based 

considerations, the Policy Assessment considered estimates of the magnitude of PM2.5-

related risks associated with recent air quality levels and air quality simulated to just 

meet the current and alternative suites of standards using alternative simulation 

approaches. The Policy Assessment also characterized the risk reductions, relative to the 

risks remaining upon just meeting the current standards, associated with just meeting 

 

42 Design values are the metrics (i.e., statistics) that are compared to the NAAQS levels 

to determine compliance.
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alternative suites of standards. In so doing, the Policy Assessment recognized the 

uncertainties inherent in such risk estimates, and took such uncertainties into account 

by considering the sensitivity of the “core” risk estimates to alternative assumptions and 

methods likely to have substantial impact on the estimates. In addition, the Policy 

Assessment considered additional analyses characterizing the representativeness of the 

urban study areas within a broader national context to understand the roles that the 

annual and 24-hour standards may play in affording protection against effects related to 

both long- and short-term PM2.5 exposures.

Based on the approach discussed above, the Policy Assessment reached 

conclusions related to the primary PM2.5 standards that reflected an understanding of 

both evidence-based and risk-based considerations to inform two overarching questions 

related to: (1) the adequacy of the current suite of PM2.5 standards and (2) revisions to 

the standards that were appropriate to consider in this review to protect against health 

effects associated with both long- and short-term exposures to fine particles. When 

evaluating the health protection afforded by the current or any alternative suites of 

standards considered, the Policy Assessment took into account the four basic elements 

of the NAAQS: the indicator, averaging time, form, and level.

The general approach for reviewing the primary PM2.5 standards described above 

provided a comprehensive basis that helped to inform the Administrator’s judgments in 

reaching her proposed and final decisions to revise the current suite of primary fine 
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particle NAAQS and in responding to the remand of the 2006 primary annual PM2.5

standard.

B. Overview of Health Effects Evidence

This section outlines the key information presented in section III.B of the 

proposal (77 FR 38906 to 38911, June 29, 2012) and discussed more fully in the 

Integrated Science Assessment (Chapters 2, 4, 5, 6, 7, and 8) and the Policy Assessment

(Chapter 2) related to health effects associated with fine particle exposures. Section 

III.B. of the proposal discusses available information on the health effects associated 

with exposures to PM2.5, including the nature of such health effects (section III.B.1) and 

associated limitations and uncertainties (section III.B.2), at-risk populations (section 

III.B.3), and potential PM2.5-related impacts on public health (section III.B.4). As was true 

in the last two reviews, evidence from epidemiological, controlled human exposure and 

animal toxicological studies played a key role in the Integrated Science Assessment’s 

evaluation of the scientific evidence. 

The 2006 PM NAAQS review concluded that there was “strong epidemiological 

evidence” for linking long-term PM2.5 exposures with cardiovascular-related and lung 

cancer mortality and respiratory-related morbidity and for linking short-term PM2.5

exposures with cardiovascular-related and respiratory-related mortality and morbidity 

(U.S. EPA, 2004, p. 9-46; U.S. EPA, 2005, p. 5-4). Overall, the evidence from 

epidemiological, toxicological, and controlled human exposure studies supported “likely 

causal associations” between PM2.5 and both mortality and morbidity from 
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cardiovascular and respiratory diseases, based on “an assessment of strength, 

robustness, and consistency in results” (U.S. EPA, 2004, p. 9-48).43

In this review, based on the expanded body of evidence, the EPA finds that:

(1) In looking across the extensive new scientific evidence available in this review, 

our overall understanding of health effects associated with fine particle 

exposures has been greatly expanded. The currently available evidence is largely 

consistent with evidence available in the last review and substantially 

strengthens what is known about the effects associated with fine particle 

exposures. 

(2) A number of large multi-city epidemiological studies have been conducted 

throughout the U.S., including extended analyses of long-term exposure studies 

that were important to inform decision-making in the last review. The body of 

currently available scientific evidence has also been expanded greatly by the 

publication of a number of new multi-city, time-series studies that have used 

uniform methodologies to investigate the effects of short-term PM2.5 exposures 

on public health. This body of evidence provides a more expansive data base and 

considers multiple locations representing varying regions and seasons that 

provide evidence of the influence of different air pollution mixes on PM2.5-

associated health effects. These studies provide more precise estimates of the 

magnitude of effects associated with short-term PM2.5 exposure than most 

smaller-scale single-city studies that were more commonly available in the last 

review. These studies have reported consistent increases in morbidity and/or 

premature mortality related to ambient PM2.5 concentrations, with the strongest 

evidence reported for cardiovascular-related effects.

(3) In addition, the findings of new toxicological and controlled human exposure 

studies greatly expand and provide stronger support for a number of potential 

biological mechanisms or pathways for cardiovascular and respiratory effects 

associated with long- and short-term PM exposures. These studies provide 

 

43 The term “likely causal association” was used in the 2004 Criteria Document to 

summarize the strength of the available evidence available in the last review for 

PM2.5. However, this terminology was not based on a formal framework for 

evaluating evidence for inferring causation. Since the last review, the EPA has 

developed a more formal framework for reaching causal determinations with 

standardized language to express evaluation of the evidence (U.S. EPA, 2009a, 

section 1.5).
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coherence and biological plausibility for the effects observed in epidemiological 

studies.

(4) Using a more formal framework for reaching causal determinations than used in 

prior reviews,

44 the EPA concludes that a causal relationship exists between both 

long- and short-term exposures to PM2.5 and premature mortality and 

cardiovascular effects and a likely causal relationship exists between long- and 

short-term PM2.5 exposures and respiratory effects. Further, there is evidence 

suggestive of a causal relationship between long-term PM2.5 exposures and other 

health effects, including developmental and reproductive effects (e.g., low birth 

weight, infant mortality) and carcinogenic, mutagenic, and genotoxic effects 

(e.g., lung cancer mortality).45

(5) The newly available evidence significantly strengthens the link between longand short-term exposure to PM2.5 and premature mortality, while providing 

indications that the magnitude of the PM2.5-mortality association with long-term 

exposures may be larger than previously estimated. The strongest evidence 

comes from recent studies investigating long-term exposure to PM2.5 and 

cardiovascular-related mortality. The evidence supporting a causal relationship 

between long-term PM2.5 exposure and mortality also includes consideration of 

new studies that demonstrated an improvement in community health following 

reductions in ambient fine particles.

 

44 The causal framework draws upon the assessment and integration of evidence from 

across epidemiological, controlled human exposure, and toxicological studies, and 

the related uncertainties that ultimately influence our understanding of the evidence. 

This framework employs a five-level hierarchy that classifies the overall weight of 

evidence and causality using the following categorizations: causal relationship, likely 

to be causal relationship, suggestive of a causal relationship, inadequate to infer a 

causal relationship, and not likely to be a causal relationship (U.S. EPA, 2009a, Table 

1-3). The development of the causal framework reflects considerable input from 

CASAC and the public, with CASAC concluding that, “The five-level classification of 

strength of evidence for causal inference has been systemically applied [for PM]; this 

approach has provided transparency and a clear statement of the level of confidence 

with regard to causation, and we recommend its continued use in future ISAs” 

(Samet, 2009f, p. 1).

45 These causal inferences are based not only on the more expansive epidemiological 

evidence available in this review but also reflect consideration of important progress 

that has been made to advance our understanding of a number of potential biologic 

modes of action or pathways for PM-related cardiovascular and respiratory effects 

(U.S. EPA, 2009a, chapter 5).
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(6) Several new studies have examined the association between cardiovascular 

effects and long-term PM2.5 exposures in multi-city studies conducted in the U.S. 

and Europe. While studies were not available in the last review with regard to 

long-term exposure and cardiovascular-related morbidity, recent studies have 

provided new evidence linking long-term exposure to PM2.5 with an array of 

cardiovascular effects such as heart attacks, congestive heart failure, stroke, and 

mortality. This evidence is coherent with studies of short-term exposure to PM2.5

that have observed associations with a continuum of effects ranging from subtle 

changes in indicators of cardiovascular health to serious clinical events, such as 

increased hospitalizations and emergency department visits due to 

cardiovascular disease and cardiovascular mortality.

(7) Extended analyses of studies available in the last review as well as new 

epidemiological studies conducted in the U.S. and abroad provide stronger 

evidence of respiratory-related morbidity effects associated with long-term 

PM2.5 exposure. The strongest evidence for respiratory-related effects is from 

studies that evaluated decrements in lung function growth, increased respiratory 

symptoms, and asthma development. The strongest evidence from short-term 

PM2.5 exposure studies has been observed for increased respiratory-related 

emergency department visits and hospital admissions for chronic obstructive 

pulmonary disease (COPD) and respiratory infections. 

(8) The body of scientific evidence is somewhat expanded from the 2006 review but 

is still limited with respect to associations between long-term PM2.5 exposures 

and developmental and reproductive effects as well as cancer, mutagenic, and 

genotoxic effects. The strongest evidence for an association between PM2.5 and 

developmental and reproductive effects comes from epidemiological studies of 

low birth weight and infant mortality, especially due to respiratory causes during 

the post-neonatal period (i.e., 1 month -12 months of age). With regard to 

cancer effects, “[m]ultiple epidemiologic studies have shown a consistent 

positive association between PM2.5 and lung cancer mortality, but studies have 

generally not reported associations between PM2.5 and lung cancer incidence” 

(U.S. EPA 2009a p. 2-13 ).

(9) Efforts to evaluate the relationships between PM composition and health effects 

continue to evolve. While many constituents of PM2.5 can be linked with differing 

health effects, the evidence is not yet sufficient to allow differentiation of those 

constituents or sources that may be more closely related to specific health 

outcomes nor to exclude any individual component or group of components 

associated with any source categories from the fine particle mixture of concern.
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(10) Specific groups within the general population are at increased risk for 

experiencing adverse health effects related to PM exposures. The currently 

available evidence expands our understanding of previously identified at-risk 

populations (i.e., children, older adults, and individuals with pre-existing heart 

and lung disease) and supports the identification of additional at-risk populations 

(e.g., persons with lower socioeconomic status, genetic differences). Evidence 

for PM-related effects in these at-risk populations has expanded and is stronger 

than previously observed. There is emerging, though still limited, evidence for 

additional potentially at-risk populations, such as those with diabetes, people 

who are obese, pregnant women, and the developing fetus.

(11) The population potentially affected by PM2.5 is large. In addition, large subgroups 

of the U.S. population have been identified as at-risk populations. While 

individual effect estimates from epidemiological studies may be small in size, the 

public health impact of the mortality and morbidity associations can be quite 

large given the extent of exposure. Taken together, this suggests that exposure 

to ambient PM2.5 concentrations can have substantial public health impacts. 

(12) While the currently available scientific evidence is stronger and more consistent 

than in previous reviews, providing a strong basis for decision making in this 

review, the EPA recognizes that important uncertainties and limitations in the 

health effects evidence remain. Epidemiological studies evaluating health effects 

associated with long- and short-term PM2.5 exposures have reported 

heterogeneity in responses between cities and geographic regions within the 

U.S. This heterogeneity may be attributed, in part, to differences in the fine 

particle composition or related to exposure measurement error, which can 

introduce bias and increased uncertainty in associated health effect estimates. 

Variability in the associations observed across PM2.5 epidemiological studies may 

be due in part to exposure error related to measurement-related issues, the use 

of central fixed-site monitors to represent population exposure to PM2.5, models 

used in lieu of or to supplement ambient measurements, and our limited 

understanding of factors that may influence exposures (e.g., topography, the 

built environment, weather, source characteristics, ventilation usage, personal 

activity patterns, photochemistry). In addition, where PM2.5 and other pollutants 

(e.g., ozone, nitrogen dioxide, and carbon monoxide) are correlated, it can be 

difficult to distinguish the effects of the various pollutants in the ambient 

mixture (i.e., co-pollutant confounding).46

 

46 A copollutant meets the criteria for potential confounding in PM-health associations 

if: (1) it is a potential risk factor for the health effect under study; (2) it is correlated 
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While uncertainties and limitations still remain in the available health effects 

evidence, the Administrator judges the currently available scientific data base to be 

stronger and more consistent than in previous reviews providing a strong basis for 

decision making in this review. 

C. Overview of Quantitative Characterization of Health Risks

In addition to a comprehensive evaluation of the health effects evidence 

available in this review, the EPA conducted an expanded quantitative risk assessment 

for selected health endpoints to provide additional information and insights to inform 

decisions on the primary PM2.5 NAAQS.47 As discussed in section III.C of the proposal, 

the approach used to develop quantitative risk estimates associated with PM2.5

exposures was built on the approach used and lessons learned in the last review and 

focused on improving the characterization of the overall confidence in the risk 

estimates, including related uncertainties, by incorporating a number of enhancements, 

in terms of both the methods and data used in the analyses. 

The goals of this quantitative risk assessment were largely the same as those 

articulated in the risk assessment conducted for the last review. These goals included: 

(1) to provide estimates of the potential magnitude of premature mortality and/or 

 

with PM; and (3) it does not act as an intermediate step in the pathway between PM 

exposure and the health effect under study (U.S. EPA, 2004, p. 8-10). 

47 The quantitative risk assessment conducted for this review is more fully described and 

presented in the Risk Assessment (U.S. EPA, 2010a) and summarized in detail in the 

Policy Assessment (U.S. EPA, 2011a, sections 2.2.2. and 2.3.4.2). The scope and 

methodology for this risk assessment were developed over the last few years with 

considerable input from CASAC and the public as described in section II.B.3 above. 
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selected morbidity effects in the population associated with recent ambient levels of 

PM2.5 and with simulating just meeting the current and alternative suites of PM2.5

standards in 15 selected urban study areas,

48 including, where data were available, 

consideration of impacts on at-risk populations; (2) to develop a better understanding of 

the influence of various inputs and assumptions on the risk estimates to more clearly 

differentiate among alternative suites of standards; and (3) to gain insights into the 

distribution of risks and patterns of risk reductions and the variability and uncertainties 

in those risk estimates. In addition, the quantitative risk assessment included 

nationwide estimates of the potential magnitude of premature mortality associated 

with long-term exposure to recent ambient PM2.5 concentrations to more broadly 

characterize this risk on a national scale and to support the interpretation of the more 

detailed risk estimates generated for selected urban study areas. 

The expanded and updated risk assessment conducted in this review included 

estimates of risk for: (1) all-cause, ischemic heart disease-related, cardiopulmonaryrelated, and lung cancer-related mortality associated with long-term PM2.5 exposure; (2) 

 

48 The Risk Assessment concluded that these 15 urban study areas were generally 

representative of urban areas in the U.S. likely to experience relatively elevated levels of 

risk related to ambient PM2.5 exposure with the potential for better characterization at 

the higher end of that distribution (U.S. EPA, 2011a, p. 2-42; U.S. EPA, 2010a, section 

4.4, Figure 4-17). The representativeness analysis also showed that the 15 urban study 

areas do not capture areas with the highest baseline morality risks or the oldest 

populations (both of which can result in higher PM2.5-related mortality estimates). 

However, some of the areas with the highest values for these attributes had relatively 

low PM2.5 concentrations (e.g., urban areas in Florida) and, consequently, the Risk 

Assessment concluded failure to include these areas in the set of urban study areas was 

unlikely to exclude high PM2.5-risk locations (U.S. EPA, 2010a, section 4.4.1).
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non-accidental, cardiovascular-related, and respiratory-related mortality associated 

with short-term PM2.5 exposure; and (3) cardiovascular-related and respiratory-related 

hospital admissions and asthma-related emergency department visits associated with 

short-term PM2.5 exposure.49

The Risk Assessment included a core set of risk estimates supplemented by an 

alternative set of risk results generated using single-factor and multi-factor sensitivity 

analyses. The core set of risk estimates was developed using the combination of 

modeling elements and input data sets identified in the Risk Assessment as having 

higher confidence relative to inputs used in the sensitivity analyses. The results of the 

sensitivity analyses provided information to evaluate and rank the potential impacts of 

key sources of uncertainty on the core risk estimates. In addition, the sensitivity 

analyses represented a set of reasonable alternatives to the core set of risk estimates 

that fell within an overall set of plausible risk estimates surrounding the core estimates. 

The EPA recognized that there were many sources of variability and uncertainty 

inherent in the inputs to its quantitative risk assessment.50 The design of the risk 

assessment included a number of elements to address these issues in order to increase 

the overall confidence in the risk estimates generated for the 15 urban study areas, 

 

49 The evidence available for these selected health effect endpoints generally focused on 

the entire population, although some information was available to support analyses 

that considered differences in estimated risk for at-risk populations including older 

adults and persons with pre-existing cardiovascular and respiratory diseases.

50 Variability refers to the heterogeneity of a variable of interest within a population or 

across different populations. Uncertainty refers to the lack of knowledge regarding 

the actual values of inputs to an analysis (U.S. EPA, 2010a, p. 3-63). 
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including using guidance from the World Health Organization (WHO, 2008) as a 

framework for characterizing uncertainty in the analyses.51

With respect to the sources of variability, the Risk Assessment considered those 

that contributed to differences in risk across urban study areas, but did not directly 

affect the degree of risk reduction associated with the simulation of just meeting 

current or alternative standard levels (e.g., differences in baseline incidence rates, 

demographics and population behavior). The Risk Assessment also focused on factors 

that not only introduced variability into risk estimates across study areas, but also 

played an important role in determining the magnitude of risk reductions upon 

simulation of just meeting current or alternative standard levels (e.g., peak-to-mean 

ratios of ambient PM2.5 concentrations within individual urban study areas and the 

nature of the rollback approach used to simulate just meeting the current or alternative 

standards). Key sources of potential variability that were likely to affect population risks 

included the following: (1) intra-urban variability in ambient PM2.5 concentrations, 

including PM2.5 composition; (2) variability in the patterns of reductions in PM2.5

concentrations associated with different rollback approaches when simulating just 

meeting the current or alternative standards; co-pollutant exposures; (3) factors related 

to demographic and socioeconomic status; (4) behavioral differences across urban study 

areas (e.g., time spent outdoors); (5) baseline incidence rates; and (6) longer-term 

 

51 The extent to which key sources of potential variability were (or were not) fully 

captured in the design of the risk assessment are discussed in section 3.5.2 of the Risk 

Assessment (U.S. EPA, 2010a, pp. 3-67 to 3-69).
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temporal variability in ambient PM2.5 concentrations reflecting meteorological trends as 

well as future changes in the mix of PM2.5 sources, including changes in air quality 

related to future regulatory actions. 

With regard to uncertainties, single and multi-factor sensitivity analyses were 

combined with a qualitative analysis to assess the impact of potential sources of 

uncertainty on the core risk estimates. Key sources of uncertainty included: (1) 

characterizing intra-urban population exposure in the context of epidemiological studies 

linking PM2.5 to specific health effects; (2) statistical fit of the concentration-response 

functions for short-term exposure-related health endpoints; (3) shape of the 

concentration-response functions; (4) specifying the appropriate lag structure for shortterm exposure studies; (5) transferability of concentration-response functions from 

study locations to urban study area locations for long-term exposure-related health 

endpoints; (6) use of single-city versus multi-city studies in the derivation of 

concentration-response functions; (7) impact of historical air quality on estimates of 

health risk associate with long-term PM2.5 exposures; and (8) potential variation in effect 

estimates reflecting compositional differences in PM2.5.

Beyond characterizing uncertainty and variability, a number of design elements 

were included in the risk assessment to increase the overall confidence in the risk 

estimates generated for the 15 urban study areas (U.S. EPA, 2011a, pp. 2-38 to 2-41). 

These elements included: (1) use of a deliberative process for specifying components of 

the risk model that reflects consideration of the latest research on PM2.5 exposure and 
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risk (U.S. EPA, 2010a, section 5.1.1); (2) integration of key sources of variability into the 

design as well as the interpretation of risk estimates (U.S.EPA, 2010a, section 5.1.2); (3) 

assessment of the degree to which the urban study areas are representative of areas in 

the U.S. experiencing higher PM2.5-related risk (U.S.EPA, 2010a, section 5.1.3); and (4) 

identification and assessment of important sources of uncertainty and the impact of 

these uncertainties on the core risk estimates (U.S.EPA, 2010a, section 5.1.4). Further, 

additional analyses examined potential bias and overall confidence in the risk estimates. 

Greater confidence is associated with risk estimates based on simulated annual mean 

PM2.5 concentrations that are within the region of the air quality distribution used in 

deriving the concentration-response functions where the bulk of the data reside (e.g., 

within one standard deviation around the long-term mean PM2.5 concentration) (U.S.

EPA, 2011a, p. 2-38).

Key observations and insights from the PM2.5 risk assessment, together with 

important caveats and limitations, were discussed in section III.C.3 of the proposal. In 

general, in considering the set of quantitative risk estimates and related uncertainties 

and limitations related to long- and short-term PM2.5 exposure together with 

consideration of the health endpoints which could not be quantified, the Policy 

Assessment concluded this information provided strong evidence that risks estimated to 

remain upon simulating just meeting the current suite of PM2.5 standards are important 

from a public health perspective, both in terms of severity and magnitude of effects. 

Patterns of increasing estimated risk reductions were generally observed as either the 
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annual or 24-hour standard level, or both, were reduced over the ranges considered in 

the Risk Assessment.

The magnitude of both long- and short-term exposure-related risk estimated to 

remain upon just meeting the current suite of standards as well as alternative standard 

levels was strongly associated with the simulated change in annual mean PM2.5

concentrations. Although long- and short-term exposure-related mortality rates have 

similar patterns in terms of the subset of urban study areas experiencing risk reductions 

for the current suite of standard levels, the magnitude of risk remaining is higher for 

long-term exposure-related mortality and substantially lower for short-term exposurerelated mortality. Short-term exposure-related morbidity risk estimates were greater for 

cardiovascular-related than respiratory-related events and emergency department visits 

for asthma-related events were significant: Furthermore, most of the aggregate risk 

associated with short-term exposures was not primarily driven by the small number of 

days with PM2.5 concentrations in the upper tail of the air quality distribution, but rather 

by the large number of days with PM2.5 concentrations at and around the mean of the 

distribution, that is, the 24-hour average concentrations that are in the low-to midrange, well below the peak 24-hour concentrations (U.S. EPA, 2011a, p. 2-3).

With regard to characterizing estimates of PM2.5-related risk associated with 

simulation of alternative standards, the Policy Assessment recognized that greater 

overall confidence was associated with estimates of risk reduction than for estimates of 

absolute risk remaining (U.S. EPA, 2011a, p. 2-94). Furthermore, the Policy Assessment 
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recognized that estimates of absolute risk remaining for each of the alternative standard 

levels considered, particularly in the context of long-term exposure-related mortality, 

may be underestimated52. In addition, the Policy Assessment observed that in 

considering the overall confidence associated with the quantitative analyses, the Risk 

Assessment recognized that: (1) substantial variability existed in the magnitude of risk 

remaining across urban study areas and (2) in general, higher confidence was associated 

with risk estimates based on PM2.5 concentrations near the mean PM2.5 concentrations 

in the underlying epidemiological studies providing the concentration-response 

functions (e.g., within one standard deviation of the mean PM2.5 concentration 

reported). Furthermore, although the Risk Assessment estimated that the alternative 

24-hour standard levels considered (when controlling) would result in additional 

estimated risk reductions beyond those estimated for alternative annual standard levels 

alone, these additional estimated reductions were highly variable. Conversely, the Risk 

Assessment recognized that alternative annual standard levels, when controlling, 

resulted in more consistent risk reductions across urban study areas, thereby potentially 

 

52 Based on the consideration of both the qualitative and quantitative assessments of 

uncertainty, the Risk Assessment concluded that it is unlikely that the estimated risks 

are over-stated, particularly for premature mortality related to long-term PM2.5

exposures. In fact, the Policy Assessment and the Risk Assessment concluded that the 

core risk estimates for this category of health effects may well be biased low based 

on consideration of alternative model specifications evaluated in the sensitivity 

analyses (U.S. EPA, 2011a, p. 2-41; U.S. EPA, 2010a, p. 5-16; Figures 4-7 and 4-8). In 

addition, the Policy Assessment recognized that the currently available scientific 

information included evidence for a broader range of health endpoints and at-risk 

populations beyond those included in the quantitative risk assessment, including 

decrements in lung function growth and respiratory symptoms in children as well as 

reproductive and developmental effects (U.S. EPA, 2011a, section 2.2.1).
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providing a more consistent degree of public health protection (U.S. EPA, 2010a, p. 5-

17).

D. Conclusions on the Adequacy of the Current Primary PM2.5 Standards

1. Introduction

The initial issue to be addressed in the current review of the primary PM2.5

standards is whether, in view of the advances in scientific knowledge and other 

information reflected in the Integrated Science Assessment, the Risk Assessment, and 

the Policy Assessment, the existing standards should be retained or revised. In 

considering the adequacy of the current suite of PM2.5 standards, the Administrator has 

considered the large body of evidence presented and assessed in the Integrated Science 

Assessment (U.S. EPA, 2009a), the quantitative assessment of risks, staff conclusions 

and associated rationales presented in the Policy Assessment, views expressed by 

CASAC, and public comments. The Administrator has taken into account both evidenceand risk-based considerations53 in developing final conclusions on the adequacy of the 

current primary PM2.5 standards.

 

53 Evidence-based considerations include the assessment of epidemiological, 

toxicological, and controlled human exposure studies evaluating long- or short-term 

exposures to PM2.5, with supporting evidence related to dosimetry and potential 

pathways/modes of action, as well as the integration of evidence across each of 

these disciplines, as assessed in the Integrated Science Assessment (U.S. EPA, 2009a) 

and focus on the policy-relevant considerations as discussed in section III.B above and 

in the Policy Assessment (U.S. EPA, 2011a, section 2.2.1). Risk-based considerations 

draw from the results of the quantitative analyses presented in the Risk Assessment 

(U.S. EPA, 2010a) and focus on the policy-relevant considerations as discussed in 

section III.C above and in the Policy Assessment (U.S. EPA, 2011a, section 2.2.2).
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a. Evidence- and Risk- based Considerations in the Policy Assessment

In considering the available epidemiological evidence in this review, the Policy 

Assessment took a broader approach than was used in the last review. This approach 

reflected the more extensive and stronger body of evidence available since the last 

review on health effects related to both long- and short-term exposure to PM2.5. As 

discussed in section III.A.3 above, this broader approach focused on setting the annual 

standard as the “generally controlling” standard for lowering both short- and long-term 

PM2.5 concentrations and so providing requisite protection to public health. In 

conjunction with such an annual standard, this approach focused on setting the 24-hour 

standard to provide supplemental protection against days with high peak PM2.5

concentrations.

In addressing the question whether the evidence now available in this review 

supports consideration of standards that are more protective than the current PM2.5

standards, the Policy Assessment considered whether: (1) statistically significant health 

effects associations with long- or short-term exposures to fine particles occur in areas 

that would likely have met the current PM2.5 standards [see American Trucking 

Association, 283 F. 3d at 369, 376 (revision of level of PM NAAQS justified when health 

effects are observed in areas meeting the existing standard)], and (2) associations with 

long-term exposures to fine particles extend down to lower air quality concentrations 

than had previously been observed. With regard to associations observed in long-term 

PM2.5 exposure studies, the Policy Assessment recognized that extended follow-up 
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analyses of the ACS and Harvard Six Cities studies provided consistent and stronger 

evidence of an association with mortality at lower air quality distributions than had 

previously been observed (U.S. EPA, 2011a, pp. 2-31 to 2-32). The original and reanalysis 

of the ACS study reported positive and statistically significant effects associated with a 

long-term mean PM2.5 concentration of 18.2 μg/m3across 50 metropolitan areas for 

1979 to 1983 (Pope et al., 1995; Krewski et al., 2000).54 In extended analyses, positive 

and statistically significant effects of approximately similar magnitude were associated 

with declining PM2.5 concentrations, from an aggregate long-term mean in 58 

metropolitan areas of 21.2 μg/m3in the original monitoring period (1979 to 1983) to 

14.0 μg/m3for 116 metropolitan areas in the most recent years evaluated (1999-2000), 

with an overall average across the two study periods in 51 metropolitan areas of 17.7 

μg/m3(Pope et al., 2002; Krewski et al., 2009). With regard to the Harvard Six Cities 

Study, the original and reanalysis reported positive and statistically significant effects 

associated with a long-term mean PM2.5 concentration of 18.0 μg/m3for 1980 to 1985 

(Dockery et al., 1993; Krewski et al., 2000). In an extended follow-up of this study, the 

aggregate long-term mean concentration across all years evaluated was 16.4 μg/m3for 

1980 to 198855 (Laden et al., 2006). In an additional analysis of the extended follow-up 

of the Harvard Six Cities study, investigators reported that the concentration-response 

 

54 The study periods referred to in the Policy Assessment (U.S. EPA, 2011a) and in this 

final rule reflect the years of air quality data that were included in the analyses, 

whereas the study periods identified in the Integrated Science Assessment (U.S. EPA, 

2009a) reflect the years of health event data that were included.

55 Aggregate mean concentration provided by study author (personal communication 

from Dr. Francine Laden, 2009).
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relationship was linear and “clearly continuing below the level” of the current annual 

standard (U.S. EPA, 2009a, p. 7-92; Schwartz et al., 2008). 

Cohort studies conducted since the last review provided additional evidence of 

mortality associated with air quality distributions that are generally lower than those 

reported in the ACS and Harvard Six Cities studies, with effect estimates that were 

similar or, in some studies, significantly greater in magnitude than in the ACS and 

Harvard Six Cities studies (see also, section III.D.1.a of the proposal, 77 FR 38918 to 

28919; U.S. EPA, 2011a, pp. 2-32 to 2-33). The Women’s Health Initiative (WHI) study 

reported positive and most often statistically significant associations between long-term 

PM2.5 exposure and cardiovascular-related mortality as well as morbidity effects, with 

much larger relative risk estimates for mortality than in the ACS and Harvard Six Cities 

studies, at an aggregate long-term mean PM2.5 concentration of 12.9 μg/m3for 2000 

(Miller et al., 2007).56

Using the Medicare cohort, Eftim et al. (2008) reported somewhat higher effect 

estimates than in the ACS and Harvard Six Cities studies with aggregate long-term mean 

concentrations of 13.6 μg/m3and 14.1 μg/m3, respectively, for 2000 to 2002. Zeger et 

al. (2008) reported associations between long-term PM2.5 exposure and mortality for 

the eastern region of the U.S. at an aggregated long-term PM2.5 median concentration 

 

56 The Policy Assessment noted that in comparison to other long-term exposure studies, 

the Miller et al. (2007) study was more limited in that it was based on only one year 

of air quality data (U.S. EPA, 2011a, p. 2-82). The proposal further noted that the air 

quality data considered were extrapolated from that one single year of air quality 

data (2000) to the whole study, and that the air quality data post-dated the years of 

health events considered (i.e., 1994 to 1998) (77 FR 38918, fn 62).
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of 14.0 μg/m3, although no association was reported for the western region with an 

aggregate long-term PM2.5 median concentration of 13.1 μg/m3(U.S. EPA, 2009a, p. 7-

88).57

Premature mortality in children reported in a national infant mortality study as 

well as mortality in a cystic fibrosis cohort including both children and adults reported 

positive but statistically nonsignificant effects associated with long-term aggregate 

mean concentrations of 14.8 μg/m3and 13.7 μg/m3, respectively (Woodruff et al., 2008; 

Goss et al., 2004).

With respect to respiratory morbidity effects associated with long-term PM2.5

exposure, the across-city mean of 2-week average PM2.5 concentrations reported in the 

initial Southern California Children’s Health Study was approximately 15.1 μg/m3(Peters 

et al., 1999). These results were found to be consistent with results of cross-sectional 

analyses of the 24-Cities Study (Dockery et al., 1996; Raizenne et al., 1996), which 

reported a long-term cross-city mean PM2.5 concentration of 14.5 μg/m3.

58 In this 

review, extended analyses of the Southern California Children’s Health Study provide 

stronger evidence of PM2.5-related respiratory effects, at lower air quality 

 

57 Zeger et al. (2008) also reported positive and statistically significant effects for the 

central region, with an aggregate long-term mean PM2.5 concentration of 10.7 μg/m3. 

However, in contrast to the eastern and western risk estimates, the central risk 

estimate increased with adjustment for COPD (used as a proxy for smoking status). 

Due to the potential for confounding bias influencing the risk estimate for the central 

region, the Policy Assessment did not focus on the results reported in the central 

region to inform the adequacy of the current suite of standards or alternative annual 

standard levels (U.S. EPA, 2011a, p. 2-32).

58 See American Farm Bureau Federation v. EPA, 559 F. 3d at 525 (noting the importance 

of these studies, as well as EPA’s failure to properly take them into account).
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concentrations than had previously been reported, with a four-year aggregate mean 

concentration of 13.8 μg/m3across the 12 study communities (McConnell et al., 2003; 

Gauderman et al., 2004, U.S. EPA, 2009a, Figure 7-4). 

In also considering health effects for which the Integrated Science Assessment 

concluded evidence was suggestive of a causal relationship, the Policy Assessment 

noted a limited number of birth outcome studies that reported positive and statistically 

significant effects related to aggregate long-term mean PM2.5 concentrations down to 

approximately 12 μg/m3(U.S. EPA, 2011a, p. 2-33).

Collectively, the Policy Assessment concluded that currently available evidence 

provided support for associations between long-term PM2.5 exposure and mortality and 

morbidity effects that extend to distributions of PM2.5 concentrations that are lower 

than those that had previously been associated with such effects, with aggregate longterm mean PM2.5 concentrations extending to well below the level of the current annual 

standard.

The Policy Assessment also considered the long-term mean PM2.5 concentrations 

in short-term exposure studies in assessing the appropriateness of the level of the 

current annual standard. See American Farm Bureau Federation v. EPA, 559 F. 3d at 522, 

523-24 (remanding 2006 standard because the EPA had not adequately explained its 

choice not to consider long-term means of short-term exposure studies in assessing 

adequacy of primary annual PM2.5 standard). In light of the mixed findings reported in 

single-city, short-term exposure studies, the Policy Assessment placed comparatively 
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greater weight on the results from multi-city studies in considering the adequacy of the 

current suite of standards (U.S. EPA, 2011a, pp. 2-34 to 2-35). 

With regard to associations reported in short-term PM2.5 exposure studies, the 

Policy Assessment recognized that long-term mean concentrations reported in new 

multi-city U.S. and Canadian studies provided evidence of associations between shortterm PM2.5 exposure and mortality at similar air quality distributions to those that had 

previously been observed in an 8-cities Canadian study (Burnett and Goldberg, 2003; 

aggregate long-term mean PM2.5 concentration of 13.3 μg/m3). In a multi-city timeseries analysis of 112 U.S. cities, Zanobetti and Schwartz (2009) reported a positive and 

statistically significant association with all-cause, cardiovascular-related (e.g., heart 

attacks, stroke), and respiratory-related mortality and short-term PM2.5 exposure, in 

which the aggregate long-term mean PM2.5 concentration was 13.2 μg/m3(U.S. EPA, 

2009a, Figure 6-24). Furthermore, city-specific effect estimates indicated the association 

between short-term exposure to PM2.5 and total mortality and cardiovascular- and 

respiratory-related mortality was consistently positive for an overwhelming majority (99 

percent) of the 112 cities across a wide range of air quality concentrations (long-term 

mean concentrations ranging from 6.6 μg/m3to 24.7 μg/m3; U.S. EPA, 2009a, Figure 6-

24, p. 6-178 to 179). The EPA staff noted that for all-cause mortality, city-specific effect 

estimates were statistically significant for 55 percent of the 112 cities, with long-term 

city-mean PM2.5 concentrations ranging from 7.8 μg/m3to 18.7 μg/m3and 24-hour 
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PM2.5 city-mean 98th percentile concentrations ranging from 18.4 to 64.9 μg/m3

(personal communication with Dr. Antonella Zanobetti, 2009).59

With regard to cardiovascular and respiratory morbidity effects, in the first 

analysis of the Medicare cohort conducted by Dominici et al. (2006a) across 204 U.S. 

counties, investigators reported a statistically significant association with 

hospitalizations for cardiovascular and respiratory diseases and short-term PM2.5

exposure, in which the aggregate long-term mean PM2.5 concentration was 13.4 μg/m3. 

Furthermore, a sub-analysis restricted to days with 24-hour average concentrations of 

PM2.5 at or below 35 μg/m3indicated that, in spite of a reduced statistical power from a 

smaller number of study days, statistically significant associations were still observed 

between short-term exposure to PM2.5 and hospital admissions for cardiovascular and 

respiratory diseases (Dominici, 2006b).60 In an extended analysis of this cohort, Bell et al 

(2008) reported a positive and statistically significant increase in cardiovascular 

hospitalizations associated with short-term PM2.5 exposure, in which the aggregate longterm mean PM2.5 concentration was 12.9 μg/m3

. These results, along with the 

observation that approximately 50 percent of the 204 county-specific mean 98th

 

59 Single-city Bayes-adjusted effect estimates for the 112 cities analyzed in Zanobetti 

and Schwartz (2009) were provided by the study authors (personal communication 

with Dr. Antonella Zanobetti, 2009; see also U.S. EPA, 2009a, Figure 6-24). 

60 This sub-analysis was not included in the original publication (Dominici et al., 2006a). 

The study authors provided sub-analysis results for the Administrator’s consideration 

as a letter to the docket following publication of the proposed rule in January 2006 

(personal communication with Dr. Francesca Dominici, 2006b). As noted in section 

III.A.3, this study is part of the basis for the conclusion that there is no evidence 

suggesting that risks associated with long-term exposures are likely to be 

disproportionately driven by peak 24-hour concentrations.
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percentile PM2.5 concentrations in the study aggregated across all years were below the 

24-hour standard of 35 μg/m3, not only indicated that effects are occurring in areas that 

would meet the current standards but also suggested that the overall health effects 

observed across the U.S. are not primarily driven by the higher end of the PM2.5 air 

quality distribution (Bell, 2009a, personal communication from Dr. Michelle Bell 

regarding air quality data for Bell et al., 2008 and Dominici et al., 2006a). 

Collectively, the Policy Assessment concluded that the findings from short-term 

PM2.5 exposure studies provided evidence of PM2.5-associated health effects occurring in 

areas that would likely have met the current suite of PM2.5 standards (U.S. EPA, 2011a, 

p. 2-35). These findings were further bolstered by evidence of statistically significant 

PM2.5-related health effects occurring in analyses restricted to days in which 24-hour 

average PM2.5 concentrations were below 35 μg/m3(Dominici, 2006b). 

In evaluating the currently available scientific evidence, as summarized in section 

III.B of the proposal, the Policy Assessment first concluded that there was stronger and 

more consistent and coherent support for associations between long- and short-term 

PM2.5 exposures and a broad range of health outcomes than was available in the last 

review, providing the basis for fine particle standards at least as protective as the 

current PM2.5 standards (U.S. EPA, 2011a, p. 2-26). Having reached this initial conclusion, 

the Policy Assessment addressed the question of whether the available evidence 

supported consideration of standards that were more protective than the current 

standards. In so doing, the Policy Assessment considered whether there was now 
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evidence that health effect associations have been observed in areas that likely met the 

current suite of PM2.5 standards. As discussed above, long- and short-term PM2.5

exposure studies provided evidence of associations with mortality and cardiovascular 

and respiratory effects both at lower ambient PM2.5 concentrations than had been 

observed in the previous review and at concentrations allowed by the current standards 

(U.S. EPA, 2011a, p. 2-35). 

In reviewing this information, the Policy Assessment recognized that important 

limitations and uncertainties associated with this expanded body of scientific evidence, 

as discussed in section III.B.2 of the proposal, needed to be carefully considered in 

determining the weight to be placed on the body of studies available in this review. 

Taking these limitations and uncertainties into consideration, the Policy Assessment 

concluded that the currently available evidence clearly calls into question whether the 

current suite of primary PM2.5 standards protects public health with an adequate margin 

of safety from effects associated with long- and short-term exposures. Furthermore, the 

Policy Assessment concluded this evidence provides strong support for considering fine 

particle standards that would afford increased protection beyond that afforded by the 

current standards (U.S. EPA, 2011a, p. 2-35).

In addition to evidence-based consideration, the Policy Assessment also 

considered the extent to which health risks estimated to occur upon simulating just 

meeting the current PM2.5 standards may be judged to be important from a public 

health perspective, taking into account key uncertainties associated with the 
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quantitative health risk estimates. In so doing, the Policy Assessment first noted that the 

quantitative risk assessment addresses: (1) the core PM2.5-related risk estimates; (2) the 

related uncertainty and sensitivity analyses, including additional sets of reasonable risk 

estimates generated to supplement the core analysis; (3) an assessment of the 

representativeness of the urban study areas within a national context;61 and (4) 

consideration of patterns in design values and air quality monitoring data to inform 

interpretation of the risk estimates, as discussed in section III.C above. 

In considering the health risks estimated to remain upon simulation of just 

meeting the current suite of standards and considering both the qualitative and 

quantitative assessment of uncertainty completed as part of the assessment, the Policy 

Assessment concluded these risks are important from a public health standpoint and 

provided strong support for consideration of alternative standards that would provide 

increased protection beyond that afforded by the current PM2.5 (U.S. EPA, 2011a, pp. 2-

47 to 2-48). This conclusion reflected consideration of both the severity and the 

magnitude of the effects. For example, the Risk Assessment indicated the possibility that 

premature deaths related to ischemic heart disease associated with long-term PM2.5

exposure alone would likely be on the order of thousands of deaths per year in the 15 

 

61 Based on analyses of the representativeness of the 15 urban study areas in the 

broader national context, the Policy Assessment concludes that these study areas are 

generally representative of urban areas in the U.S. likely to experience relatively 

elevated levels of risk related to ambient PM2.5 exposures (U.S. EPA, 2011a, p. 2-42). 
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urban study areas upon simulating just meeting the current standards62 (U.S. EPA, 

2011a, pp. 2-46 to 2-47). Moreover, additional risks were anticipated for premature 

mortality related to cardiopulmonary effects and lung cancer associated with long-term 

PM2.5 exposure as well as mortality and cardiovascular- and respiratory-related 

morbidity effects (e.g., hospital admissions, emergency department visits) associated 

with short-term PM2.5 exposures. Based on the consideration of both qualitative and 

quantitative assessments of uncertainty completed as part of the quantitative risk 

assessment, the Risk Assessment concluded that it was unlikely that the estimated risks 

are over-stated, particularly for mortality related to long-term PM2.5 exposure, and may 

well be biased low based on consideration of alternative model specifications evaluated 

in the sensitivity analyses (U.S. EPA, 2010a, p. 5-16; U.S. EPA, 2011a, p. 2-41). 

Furthermore, the currently available scientific information summarized in section III.B of 

the proposal provided evidence for a broader range of health endpoints and at-risk 

populations beyond those included in the quantitative risk assessment (U.S. EPA, 2011a, 

p. 2-47).

b. CASAC Advice

The CASAC, based on its review of drafts of the Integrated Science Assessment, 

the Risk Assessment, and the Policy Assessment, provided an array of advice both with 

regard to interpreting the scientific evidence and quantitative risk assessment, as well as 

 

62 Premature mortality for all causes attributed to PM2.5 exposure was estimated to be 

on the order of tens of thousands of deaths per year on a national scale based on 

2005 air quality data (U.S. EPA, 2010a, Appendix G, Table G-1).
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with regard to consideration of the adequacy of the current PM2.5 standards (Samet, 

2009a b,c,d,e,f; Samet 2010a,b,c,d). With regard to the adequacy of the current 

standards, CASAC concluded that the “currently available information clearly calls into 

question the adequacy of the current standards” (Samet, 2010d, p. i) and that the 

current standards are “not protective” (Samet, 2010d, p. 1). Further, in commenting on 

the first draft Policy Assessment, CASAC noted:

With regard to the integration of evidence-based and risk-based considerations, 

CASAC concurs with EPA’s conclusion that the new data strengthens the 

evidence available on associations previously considered in the last round of the 

assessment of the PM2.5 standard. CASAC also agrees that there are significant 

public health consequences at the current levels of the standard that justify 

consideration of lowering the PM2.5 NAAQS further (Samet, 2010c, p.12). 

c. Administrator's Proposed Conclusions Concerning the Adequacy of the Current 

Primary PM2.5 Standards

At the time of the proposal, in considering the body of scientific evidence, the 

Administrator concluded there was stronger and more consistent and coherent support 

for associations between long- and short-term PM2.5 exposure and a broader range of 

health outcomes than was available in the last review, providing the basis for fine 

particle standards at least as protective as the current PM2.5 standards. In particular, the 

Administrator recognized in section III.D.4 of the proposal that the Integrated Science 

Assessment concluded that the results of epidemiological and experimental studies 

form a plausible and coherent data set that supports a causal relationship between 

long- and short-term PM2.5 exposures and mortality and cardiovascular effects and a 

likely causal relationship between long- and short-term PM2.5 exposures and respiratory 
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effects. Furthermore, the Administrator reflected that effects had been observed at 

lower ambient PM2.5 concentrations than what had been observed in the last review, 

including at ambient PM2.5 concentrations in areas that likely met the current PM2.5

NAAQS. With regard to the results of the quantitative risk assessment, the 

Administrator noted that the Risk Assessment concluded that the risks estimated to 

remain upon simulation of just meeting the current standards were important from a 

public health standpoint in terms of both the severity and magnitude of the effects. 

At the time of the proposal, in considering whether the current suite of PM2.5

standards should be revised to provide requisite public health protection, the 

Administrator carefully considered the staff conclusions and rationales presented in the 

Policy Assessment, the advice and recommendations from CASAC, and public comments 

to date on this issue. In so doing, the Administrator placed primary consideration on the 

evidence obtained from the epidemiological studies and provisionally found the 

evidence of serious health effects reported in long- and short-term exposure studies 

conducted in areas that would have met the current standards to be compelling, 

especially in light of the extent to which such studies are part of an overall pattern of 

positive and frequently statistically significant associations across a broad range of 

studies that collectively represent a strong and robust body of evidence. 

As discussed in the Integrated Science Assessment and Policy Assessment, the 

Administrator recognized that much progress has been made since the last review in 

addressing some of the key uncertainties that were important considerations in 
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establishing the current suite of PM2.5 standards. For example, progress made since the 

last review provides increased confidence in the long- and short-term exposure studies 

as a basis for considering whether any revision of the annual standard is appropriate 

and increased confidence in the short-term exposure studies as a basis for considering 

whether any revision of the 24-hour standard is appropriate.63

Based on her consideration of these conclusions, as well as consideration of 

CASAC’s conclusion that the evidence and risk assessment clearly called into question 

the adequacy of the public health protection provided by the current PM2.5 NAAQS and 

public comments on the proposal, the Administrator provisionally concluded that the 

current primary PM2.5 standards, taken together, were not requisite to protect public 

health with an adequate margin of safety and that revision was needed to provide 

increased public health protection. The Administrator provisionally concluded that the 

scientific evidence and information on risk provided strong support for consideration of 

alternative standards that would provide increased public health protection beyond that 

afforded by the current PM2.5 standards.

2. Comments on the Need for Revision

This section addresses general comments based on relevant facts that either 

support or oppose any change to the current suite of primary PM2.5 standards. 

 

63 The EPA notes that this increased confidence in the long- and short-term associations 

generally reflects less uncertainty as to the likely causal nature of such associations, 

but does not address directly the question of the extent to which such associations 

remain toward the lower end of the range of ambient PM2.5 concentrations. This 

question is central to the Agency’s evaluation of the relevant evidence to determine 

appropriate standards levels, as discussed below in section III.E.4.
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Comments on specific long- and short-term exposure studies that relate to 

consideration of the appropriate levels of the annual and 24-hour standards are 

addressed in section III.E.4 below. Many public comments asserted that the current 

PM2.5 standards are insufficient to protect public health with an adequate margin of 

safety and that revisions to the standards are therefore appropriate, indeed 

necessitated. 

Among those calling for revisions to the current standards were the Children’s 

Health Protection Advisory Committee (CHPAC); major medical and public health groups 

including the American Heart Association (AHA), American Lung Association (ALA), 

American Public Health Association (APHA), American Thoracic Society (ATS); the 

Physicians for Social Responsibility (PSR); major environmental groups such as the Clean 

Air Council, Clean Air Task Force, Earthjustice, Environmental Defense Fund (EDF), 

National Resources Defense Council (NRDC), and Sierra Club; many environmental 

justice organizations as well as medical doctors, academic researchers, health 

professionals, and many private citizens. For example, the American Heart Association 

and other major national public health and medical organizations stated that, “[o]ur 

organizations are keenly aware of the public health and medical threats from particulate 

matter” and called on the EPA to “significantly strengthen” both the annual and 24-hour 

PM2.5 standards “to help us protect the health of our patients and our nation” (AHA et 

al., 2012, pp 1 and 13). AHA et al. and ALA et al., as well as a group of more than 350 
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physicians, environmental health researchers, and public health and medical 

professionals articulated similar comments on the available evidence:

Ample scientific evidence supports adopting tighter standards to protect the 

health of people who are most susceptible to the serious health effects of these 

pollutants. More than 10,000 peer-reviewed scientific studies have been 

published since 1997 when EPA adopted the current annual standard. These 

studies validate and extend earlier epidemiologic research linking both acute and 

chronic fine particle pollution with serious morbidity and mortality. The newer 

research has also expanded our understanding of the range of health outcomes 

associated with PM and has identified adverse respiratory and cardiovascular 

health effects at lower exposure levels than previously reported. As discussed 

and interpreted in the EPA’s 2009 Integrated Science Assessment for Particulate 

Matter, the new evidence reinforces already strong existing studies and supports 

the conclusion that PM2.5 is causally associated with numerous adverse health 

effects in humans at exposure levels far below the current standard. Such a 

conclusion demands prompt action to protect human health. (AHA et al., 2012, 

pp. 1 to 2; ALA et al., pp. 4 to 5; similar comment submitted by Rom et al., 2012, 

p. 1).

All of these medical and public health commenters stated that the current PM2.5

standards need to be revised, and that even more protective standards than those 

proposed by the EPA are needed to adequately protect public health, particularly for atrisk populations. Many environmental justice organizations and individual commenters 

also expressed such views.

The National Association of Clean Air Agencies (NACAA), the Northeast States for 

Coordinated Air Use Management (NESCAUM), and many State and local air agencies 

and health departments who commented on the PM2.5 standards supported revision of 
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the suite of current PM2.5 standards, as did five state attorneys general (Schneiderman 

et al., 2012) and the National Tribal Air Association (NTAA). 

These commenters based their views chiefly on the body of evidence and 

technical analyses presented and discussed in the Integrated Science Assessment, the 

Risk Assessment, and the Policy Assessment finding the available scientific information 

to be stronger and more compelling than in the last review. These commenters 

generally placed much weight on CASAC’s recommendation to revise the PM2.5

standards to provide increased public health protection and on the EPA staff conclusions 

presented in the final Policy Assessment.

Some of these commenters specifically mentioned extended analyses of seminal 

long-term exposure studies - the ACS (Krewski et al., 2009), Harvard Six Cities (Laden et 

al., 2006), and Southern California Children’s Health (Gauderman et al., 2004) studies. 

These commenters also highlighted the availability of additional long-term exposure

studies in this review, specifically a study of premature mortality in older adults (Eftim et 

al., 2008) and the WHI study of cardiovascular morbidity and mortality effects in women 

(Miller et al., 2007) providing stronger evidence of mortality and morbidity effects 

associated with long-term PM2.5 exposures at lower concentrations than had previously 

been observed, including studies of effects in at-risk populations. For example, some 

commenters asserted:

Evidence during the last review showed clearly that the annual average standard 

needed to be much lower than the standard of 15 μg/m3that was first set in 

1997. The evidence has only grown since then. Multiple, multi-city studies over 
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long periods of time have shown clear evidence of premature death, 

cardiovascular and respiratory harm as well as reproductive and developmental 

harm at contemporary concentrations far below the level of the current (annual) 

standard (ALA et al., 2012, p. 39; AHA et al., 2012, p. 10). 

These commenters also highlighted the availability of a number of short-term 

PM2.5 exposure studies as providing evidence of mortality and morbidity effects at 

concentrations below the level of the current 24-hour PM2.5 standard. Specifically, these 

commenters made note of multi-city studies of premature mortality (Zanobetti and 

Schwartz, 2009) and increased hospitalizations for cardiovascular and respiratoryrelated effects in older adults (Bell et al., 2008). These commenters also asserted the 

importance of many of the single-city studies, arguing that these studies “provide 

valuable information regarding impacts on susceptible populations and on health risk in 

areas with high peak to mean concentration ratios” (ALA, et al., 2012, p. 65). 

Collectively, considering the multi- and single-city short-term exposure studies, these 

commenters asserted “the record clearly supports a more stringent 24-hour standard of 

25 μg/m3to provide uniform protection in all regions of the country particularly from 

short-term spikes in pollution and from the sub-daily exposures that trigger heart 

attacks and strokes” (ALA et al., 2012, p. 62). A group of more than 350 physicians, 

environmental health researchers, and public health and medical professionals argued, 

“[s]tudies of short-term exposure demonstrate that PM2.5 air pollution increases the risk 

of hospital admissions for heart and lung problems even when you exclude days with 

pollution concentrations at or above the current daily standard of 35 μg/m3. Daily 
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concentrations must be capped at lower levels to protect against peak exposure days 

that occur due to local and seasonal sources of emissions” (Rom et al., 2012, p. 2).

In addition, many of these commenters generally concluded that progress had 

been made in reducing many of the uncertainties identified in the last review, in better 

understanding mechanisms by which PM2.5 may be causing the observed health effects, 

and in improving our understanding of at-risk populations. Further, a number of 

commenters argued that by making the standards more protective, the PM2.5 NAAQS 

would be more consistent with other existing standards (e.g., California's annual 

average standard of 12 μg/m3) (CARB, 2012; CA OEHHA, 2012). Other commenters 

argued that revising the primary PM2.5 standards would be more consistent with the 

recommendations of the World Health Organization (WHO) and/or Canada (e.g., ALA et 

al., 2012, p. 62; ISEE, 2012, p. 2; MOE-Ontario, 2012, p. 1). 

With regard to the scope of the literature reviewed for PM2.5-related health 

effects, some commenters asserted that the EPA inappropriately narrowed the scope of 

the review by excluding a number of categories of relevant studies, specifically related 

to studies of diesel pollution and traffic-related pollution (ALA, et al., 2012, p. 17). These 

commenters argued that, based upon the exclusion of these types of studies, the 

Integrated Science Assessment “came to the erroneous conclusion that the causal 

relationship between PM and cancer is merely suggestive. This conclusion does not 

square with the International Agency Research on Cancer (IARC) finding that diesel 

emissions are a known human carcinogen nor with the conclusions of the extended 
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analyses of the [Harvard] Six Cities and ACS cohort studies that report positive and 

statistically significant associations between PM2.5 and lung cancer.” Id.

Some of these commenters also noted the results of the EPA’s quantitative risk 

assessment, concluding that it showed that the risks estimated to remain when the 

current standards are met are large and important from a public health perspective and 

warrant increased protection. For example, ALA et al., noted that the Risk Assessment 

indicated the quantitative risk analyses likely underestimated PM2.5-related mortality 

(U.S. EPA, 2010a, p. 5-16) and argued that “the measurements of risk should be treated 

conservatively” (ALA, et al., 2012, p. 73). These commenters also summarized an 

expanded analysis of alternative PM2.5 standard levels that they argued documented the 

need for more protective standards (McCubbin, 2011). 

In general, all of these commenters agreed on the importance of results from the 

large body of scientific studies reviewed in the Integrated Science Assessment and on 

the need to revise the suite of PM2.5 standards as articulated in the EPA’s proposal, 

while generally differing with the EPA’s proposed judgments about the extent to which 

the standards should be revised based on this evidence, specifically for providing 

protection for at-risk populations. 

The EPA generally agrees with these commenters’ conclusion regarding the need 

to revise the current suite of PM2.5 standards. The scientific evidence noted by these 

commenters was generally the same as that assessed in the Integrated Science 

Assessment and the Policy Assessment, and the EPA agrees that this evidence provides a 
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strong basis for concluding that the current PM2.5 standards, taken together, are not 

requisite to protect public health with an adequate margin of safety, and they need to 

be revised to provide increased protection. For reasons discussed in section III.E.4.c 

below, however, the EPA disagrees with aspects of these commenters’ views on the 

level of protection that is appropriate.

The EPA disagrees with these commenters’ views that diesel exhaust studies 

were excluded from the Integrated Science Assessment and were not considered when 

making the causality determination for cancer, mutagenicity, and genotoxicity. As 

discussed in section 7.5 of the Integrated Science Assessment, diesel exhaust studies 

were integrated within the broader body of scientific evidence that was considered in 

reaching the causality determination for these health endpoints. Additionally, as 

discussed in section 1.5.3 of the Integrated Science Assessment, the evidence from 

diesel exhaust studies was also considered as part of the collective evidence evaluated 

when making determinations for other, noncancer health outcomes (e.g., cardiovascular 

and respiratory effects).64 Specifically, when evaluating this evidence, the focus was on 

understanding the effects of diesel exhaust particles. 

 

64 In developing the second draft Integrated Science Assessment, the EPA reexamined 

the controlled human exposure and toxicological studies of fresh diesel and gasoline 

exhaust This information, in addition to other considerations, supported a change in 

the causal determinations for ultrafine particles. Specifically, in reevaluating the 

causal determinations for short-term ultrafine particle exposures and cardiovascular 

and respiratory effects, the EPA changed the classification from “inadequate” to 

“suggestive” for both categories of health outcomes (Vandenberg, 2009, p. 3). CASAC 

agreed with the EPA’s rationale for revising these causal determinations (Samet, 

2009f, p. 10).
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It is important to recognize that the Integrated Science Assessment focused on 

experimental studies of diesel exhaust that evaluated exposures that were relevant to 

ambient concentrations, i.e., “within one or two orders of magnitude of ambient PM 

concentrations” (U.S. EPA, 2009a, section 1.3). The causal determination for cancer, 

mutagenicity, and genotoxicity presented in the Integrated Science Assessment 

represents an integration of experimental and observational evidence of exposures to 

ambient PM concentrations. The EPA fully considered the findings of studies that 

assessed these and other health effects associated with exposure to diesel particles in 

reaching causality determinations regarding health outcomes associated with PM2.5

exposures. Furthermore, CASAC supported the EPA’s change to the causal 

determination for cancer and long-term PM2.5 concentrations from “inadequate” to

“suggestive” (Samet, 2009f, p. 2).

With regard to traffic studies, the EPA disagrees with the commenters’ views 

that traffic studies that focused on exposure indicators such as distance to roadways 

should have been included in the Integrated Science Assessment. These studies were 

excluded from consideration because they did not measure ambient concentrations of 

specific air pollutants, including PM2.5, but instead were studies evaluating exposure to 

the undifferentiated “traffic related air pollution” mixture (ALA et al., 2012, p.17) (U.S.

EPA, 2009a, section 1.3). As a result, these studies do not add to the collective body of 

evidence on the relationship between long- or short-term exposure to ambient 

concentrations of PM2.5 and health effects.
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Some of these commenters also identified “new” studies that were not included 

in the Integrated Science Assessment as providing further support for the need to revise 

the primary PM2.5 standards. As discussed in section II.B.3 above, the EPA notes that, as 

in past NAAQS reviews, the Agency is basing the final decisions in this review on the 

studies and related information included in the PM air quality criteria that have 

undergone CASAC and public review and will consider the “new” studies for purposes of 

decision making in the next PM NAAQS review. Nonetheless, in provisionally evaluating 

commenters’ arguments (see Response to Comments document), the EPA notes that its 

provisional assessment of “new” science found that such studies did not materially 

change the conclusions in the Integrated Science Assessment (U.S. EPA, 2012b).

Another group of commenters opposed revising the current PM2.5 standards. 

These views were most extensively presented in comments from the Utility Air 

Regulatory Group (UARG), representing a group of electric generating companies and 

organizations and several national trade associations; the American Petroleum Institute 

(API) representing more than 500 oil and natural gas companies; the National 

Association of Manufacturers (NAM), the American Chemistry Council (ACC), the 

American Fuel & Petroleum Manufacturers (AFPM), the Alliance of Automobile 

Manufacturers (AAM), and other manufacturing associations; the Electric Power 

Research Institute (EPRI); and the Texas Commission on Environmental Quality (Texas 

CEQ). These commenters generally mentioned many of the same studies that were cited 

by the commenters who supported revising the standards, as well as other studies, but 
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highlighted different aspects of these studies in reaching substantially different 

conclusions about their strength and the extent to which progress has been made in 

reducing uncertainties in the evidence since the last review. Furthermore, they asserted 

that the evidence that has become available since the last review does not establish a 

more certain risk or a risk of effects that are significantly different in character to those 

that provided a basis for the current standards, nor does the evidence demonstrate that 

the risk to public health upon attainment of the current standards would be greater 

than was understood when the EPA established the current standards in 2006. 

These commenters generally expressed the view that the current standards 

provide the requisite degree of public health protection. In supporting their view, these 

commenters generally argued that the EPA’s conclusions are inconsistent with the 

current state of the science and questioned the underlying scientific evidence including 

the causal determinations reached in the Integrated Science Assessment. More 

specifically, this group of commenters argued that: (1) the EPA did not apply its 

framework for causal determination consistently across studies or health outcomes and, 

in the process, the EPA relied on a selective group of long- and short-term exposure 

studies to reach conclusions regarding causality; (2) toxicological and controlled human 

exposure studies do not provide supportive evidence that the health effects observed in 

epidemiological studies are biologically plausible; (3) uncertainties in the underlying 

health science are as great or greater than in 2006; (4) there is no evidence of greater 

risk since the last review to justify tightening the current annual PM2.5 standard; and (5) 
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“new” studies not included in the Integrated Science Assessment continue to increase 

uncertainty about possible health risks associated with exposure to PM2.5. These 

comments are discussed in turn below.

(l) Some of these commenters asserted that the EPA did not apply its framework 

for causal determinations consistently across studies or health outcomes (ACC, 2012, 

Attachment A, pp. 1 to 2; API, 2012, Attachment 1, p. 30; NAM et al., 2012, pp. 22 to 25; 

Texas CEQ, 2012, pp 2 to 3).65 These commenters argued that the EPA downplayed 

epidemiological studies with null or inconsistent results, inappropriately used the Hill 

criteria when evaluating the epidemiological evidence, and used the same study and the 

same underlying database to conclude that there was a causal association between 

mortality and multiple criteria pollutants.

The EPA disagrees with these commenters’ views. First, the EPA recognizes that 

the evaluation of the scientific evidence and its application of the causal framework 

used in the current PM NAAQS review was the subject of exhaustive and detailed review 

by CASAC and the public. As summarized in section II.B.3 above, prior to finalizing the 

Integrated Science Assessment, two drafts were released for CASAC and public review 

to evaluate the scientific integrity of the documents. Evidence related to the substantive

issues raised by CASAC and public commenters with regard to the content of the first 

 

65 The EPA notes that the same concerns about the causal determinations presented in 

the Integrated Science Assessment were raised in comments to CASAC on the draft 

Integrated Science Assessments (e.g., UARG, 2009; API, 2009; ACC, 2012, Appendix 

B). CASAC, therefore, had the opportunity to consider these comments in reaching 

consensus conclusions on this issue. 
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and second draft Integrated Science Assessments were discussed at length during these 

public CASAC meetings and considered in developing the final Integrated Science 

Assessment. CASAC supported the development of the EPA’s causality framework and 

its use in the current PM NAAQS review and concluded:

The five-level classification of strength of evidence for causal inference has been 

systematically applied; this approach has provided transparency and a clear 

statement of the level of confidence with regard to causation, and we 

recommend its continued use in future Integrated Science Assessments (Samet 

2009f, p. 1). 

These commenters asserted that during the application of the causal framework

the EPA inappropriately relied on a selective group of long- and short-term exposure 

studies in reaching causal inferences (API, 2012, pp 12 to 17; ACC, 2012, Attachment A, 

pp 1 to 2; NAM et al., 2012, pp. 22 to 25; Texas CEQ, 2012, pp 2 to 3). Additionally, these 

commenters expressed the view that the EPA focused on a subset of epidemiological 

studies that reported positive and statistically significant results while ignoring other 

studies, especially those that reported no statistically significant associations, those that 

reported potential thresholds, or those that highlighted uncertainties and limitations in 

study design or results. Furthermore, some of these commenters argued that 

epidemiological studies are observational in nature and cannot provide evidence of a 

causal association.

The EPA disagrees with these commenters’ views on assessing the health effects 

evidence and on the conclusions regarding the causality determinations reached in the 

Integrated Science Assessment. In conducting a comprehensive evaluation of the 
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evidence in the Integrated Science Assessment, the EPA recognized the distinction 

between the evaluation of the relative scientific quality of individual study results and 

the evaluation of the pattern of results within the broader body of scientific evidence

and considered both in reaching causality determinations. The more detailed 

characterizations of individual studies included an assessment of the quality of the study 

based on specific criteria as described in the Integrated Science Assessment (U.S. EPA, 

2009a, section 1.5.3). 

In developing an integrated assessment of the health effects evidence for PM, 

the EPA emphasized the importance of examining the pattern of results across various 

studies and did not focus solely on statistical significance66 as a criterion of study 

strength. This approach is consistent with views clearly articulated throughout the 

epidemiological and causal inference literature, specifically, that it is important not to 

focus on results of statistical tests to the exclusion of other information.67 The concepts 

underlying the EPA’s approach to evaluating statistical associations have been discussed 

 

66 Statistical significance is an indicator of the precision of a study’s results, which is 

influenced by a variety of factors including, but not limited to, the size of the study, 

exposure and measurement error, and statistical model specifications. Studies 

typically calculate “p-values” to determine whether the study results are statistically 

significant or whether the study results are likely to occur simply by chance. In 

general practice, effects are considered statistically significant if p values are less than 

0.05.

67 For example, Rothman (1998) stated, “Many data analysts appear to remain oblivious 

to the qualitative nature of significance testing [and that]... statistical significance is 

itself only a dichotomous indicator. As it has only two values, significant or not 

significant ....Nevertheless, p-values still confound effect size with study size, the two 

components of estimation that we believe need to be reported separately.” As a 

result, Rothman recommended that p-values be omitted as long as point and interval 

estimates are available. 
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in numerous publications, including a report by the U.S. Surgeon General on the health 

consequences of smoking (Centers for Disease Control and Prevention, 2004). This 

report cautions against over-reliance on statistical significance in evaluating the overall 

evidence for an exposure-response relationship. Criteria characterized by Hill (1965) 

also addressed the value, or lack thereof, of statistical tests in the determination of 

cause: 

No formal tests of significance can answer those [causal] questions. Such tests 

can, and should, remind us of the effects the play of chance can create, and they 

will instruct us in the likely magnitude of those effects. Beyond that, they 

contribute nothing to the ‘proof’ of our hypothesis (Hill, 1965, p. 299). 

The statistical significance of individual study findings has played an important 

role in the EPA’s evaluation of the study’s results and the EPA has placed greater 

emphasis on studies reporting statistically significant results. However, in the broader 

evaluation of the evidence from many epidemiological studies, and subsequently during 

the process of forming causality determinations in integrating evidence across 

epidemiological, controlled human exposure, and toxicological studies, the EPA has 

emphasized the pattern of results across epidemiological studies, and whether the 

effects observed were coherent across the scientific disciplines for drawing conclusions 

on the relationship between PM2.5 and different health outcomes. Thus, the EPA did not 

limit its focus or consideration to just studies that reported positive associations or 

where the results were statistically significant. 

In addition, some commenters asserted that the EPA inappropriately used the 

Hill criteria by failing to consider the limitations of studies with weak associations, 
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thereby overstating the consistency of the observed associations (API, 2012, 

Attachment 1, pp. 30 to 35). These commenters argued that risk estimates greater than 

3 to 4 reflect strong associations supportive of a causal link, while smaller risk estimates 

(i.e., 1.5 to 3) are considered to be weak and require other lines of evidence to 

demonstrate causality. 

As discussed in section 1.5.3 of the Integrated Science Assessment, the EPA 

thoroughly considered the limitations of all studies during its evaluation of the scientific 

literature (U.S. EPA,, 2009a, p. 1-14). This collective body of evidence, including known 

uncertainties and limitations of the studies evaluated, were considered during the 

process of forming causality determinations as discussed in chapters 6 and 7 of the 

Integrated Science Assessment. For example, the EPA concluded that “a causal 

relationship exists between short-term PM2.5 exposure and cardiovascular effects,” 

however, in reaching this conclusion, the Agency recognized and considered limitations 

of the current evidence that still requires further examination (U.S. EPA, 2009a., in 

section 6.2.12.1). Therefore, the EPA disagrees with these commenters’ views that the 

Hill criteria were inappropriately used in that the limitations of studies were not 

considered. 

The EPA also disagrees with the commenters’ assertion that the magnitude of 

the association must be large to support a determination of causality. As discussed in 

the Integrated Science Assessment, the strength of the observed association is an 

important aspect to aid in judging causality and “while large effects support causality, 
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modest effects therefore do not preclude it” (U.S. EPA, 2009a, Table 1-2, section 1.5.4). 

The weight of evidence approach used by the EPA encompasses a multitude of factors 

of which the magnitude of the association is only one component (U.S. EPA, 2009a, 

Table 1-3). An evaluation of the association across multiple investigators and locations 

supports the “reproducibility of findings [which] constitutes one of the strongest 

arguments for causality” (U.S. EPA, 2009a, Table 1-2). Even though the risk estimates for 

air pollution studies may be modest, the associations are consistent across hundreds of 

studies as demonstrated in the Integrated Science Assessment. Furthermore, the 

causality determinations rely on different lines of evidence, by integrating evidence 

across disciplines, including animal toxicological studies and controlled human exposure 

studies.

Furthermore, as summarized in section III.B above and discussed more fully in 

section III.B.3 of the proposal, the EPA recognizes that the population potentially 

affected by PM2.5 is considerable, including large subgroups of the U.S. population that 

have been identified as at-risk populations (e.g., children, older adults, persons with 

underlying cardiovascular or respiratory disease). While individual effect estimates from 

epidemiological studies may be modest in size, the public health impact of the mortality 

and morbidity associations can be quite large given that air pollution is ubiquitous. 

Indeed, with the large population exposed, exposure to a pollutant causally associated 

at a population level with mortality and serious illness has significant public health 

consequences, virtually regardless of the relative risk. Taken together, this information 
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indicates that exposure to ambient PM2.5 concentrations has substantial public health 

impacts.

In addition, these commenters believed that the EPA downplayed null or 

inconsistent findings in numerous long-term mortality studies with reported PM2.5

concentrations above and below the level of the current annual standard. The EPA 

disagrees that studies with null or inconsistent findings were not accurately presented 

and considered in the Integrated Science Assessment. For example, as discussed 

throughout section 7.6 and depicted in Figures 7-6 and 7-7 of the Integrated Science 

Assessment, the EPA presented the collective evidence from all studies that examined 

the association between long-term PM2.5 exposure and mortality. Overall, across these 

studies there was evidence of consistent positive associations in different cohorts. That 

evidence, in combination with the biological plausibility provided by experimental and 

toxicological studies evaluated in sections 7.1 and 7.2 of the Integrated Science 

Assessment, supported a causal relationship exists between long-term PM2.5 exposure 

and mortality. 

Lastly, some of these commenters argued that in some cases, the EPA used the 

same study and the same underlying database to conclude that there is a causal 

association between mortality and multiple criteria pollutants. These commenters

argued, “[i]n doing so, EPA attributes the cause of the mortality effects observed to 

whichever criteria pollutant it is reviewing at the time” (API, 2012, pp. 14 to 16). 
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The EPA strongly disagrees that the Agency “attributes the cause of mortality 

effects observed to whichever criteria pollutant it is reviewing at the time.” The EPA 

consistently recognizes that other pollutants are also associated with health outcomes, 

as is reflected in the fact that the EPA has established regulations to limit emissions of 

particulate criteria pollutants as well as other gaseous criteria pollutants. 

Epidemiological studies often examine the association between short- and long-term 

exposures to multiple air pollutants and mortality within a common dataset in an 

attempt to identify the air pollutant(s) of the complex mixture most strongly associated 

with mortality. In evaluating these studies, the EPA employs specific study selection 

criteria to identify those studies most relevant to the review of the NAAQS. In its 

assessment of the health evidence regarding PM2.5, the EPA has carefully evaluated the 

potential for confounding, effect measure modification, and the role of PM2.5 as a 

component of a complex mixture of air pollutants (U.S. EPA, 2009, p. 1-9). The EPA used

a rigorous weight of evidence approach to inform causality that evaluated consistency 

across studies within a discipline, evidence for coherence across disciplines, and 

biological plausibility. Additionally, during this process, the EPA assessed the limitations 

of each study in the context of the collective body of evidence. It was the collective 

evidence, not one individual study that ultimately determined whether a causal 

relationship exists between a pollutant and health outcome. In the Integrated Science 

Assessment, the combination of epidemiological and experimental evidence formed the 

basis for the Agency concluding for the first time that a causal relationship exists 
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between short- or long-term exposure to a criteria pollutant and mortality (U.S. EPA, 

2009, sections 2.3.1.1 and 2.3.1.2). 

Additionally, while the EPA has evaluated some of the studies used to inform the 

causality determination for PM in the Integrated Science Assessments for other criteria 

air pollutants, the Agency has done so in the context of examining the collective body of 

evidence for each of the respective criteria air pollutants. As such, the body of evidence 

to inform causality has varied from pollutant to pollutant resulting in the association 

between each criteria air pollutant and mortality being classified at a different level of 

the five-level hierarchy used to inform causation (e.g., U.S. EPA, 2008e, U.S. EPA, 2008f, 

U.S. EPA, 2010k). 

The EPA notes that the final causality determinations presented in the Integrated 

Science Assessment reflected CASAC’s recommendations on the second draft Integrated 

Science Assessment (Samet, 2009f, pp. 2 to 3). Specifically, CASAC supported the EPA’s 

changes (in the second versus first draft Integrated Science Assessment) from “likely 

causal” to “causal” for long-term exposure to PM2.5 and cardiovascular effects and for 

cancer and PM2.5 (from “inadequate” to “suggestive”). Id. Furthermore, CASAC 

recommended “upgrading” the causal classification for PM2.5 and total mortality to 

“causal” for both the short- and long-term timeframes. Id. With regard to mortality, the 

“EPA carefully reevaluated the body of evidence, including the collective evidence for 

biological plausibility for mortality effects, and determined that a causal relationship 
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exists for short- and long-term exposure to PM2.5 and mortality, consistent with the 

CASAC comments” (Jackson, 2012). 

(2) With regard to toxicological and controlled human exposure studies, these 

commenters argued that the available evidence does not provide coherence or 

biological plausibility for health effects observed in epidemiological studies (API, 2012, 

pp. 21 to 22, Attachment 1, pp. 25 to 29; AAM, 2012, pp. 15 to 16; Texas CEQ, 2012, p. 

3). With regard to the issue of mechanisms, these commenters noted that although the 

EPA recognizes that new evidence is now available on potential mechanisms and 

plausible biological pathways, the evidence provided by toxicological and controlled 

human exposure studies still does not resolve all questions about how PM2.5 at ambient 

concentrations could produce the mortality and morbidity effects observed in 

epidemiological studies. More specifically, for example, some of these commenters 

argued that:

A review of the Integrated Science Assessment, however, suggests that the 

experimental evidence is inconsistent and not coherent with findings in 

epidemiology studies. Specifically, the findings of mild and reversible effects in 

most experimental studies conducted at elevated exposures are not consistent 

with the more serious associations described in epidemiology studies (e.g., 

hospital admissions and mortality). Also, both animal studies and controlled 

human exposure studies have identified no effect levels for acute and chronic 

exposure to PM and PM constituents at concentrations considerably above 

ambient levels. EPA should consider the experimental findings in light of these 

higher exposure levels and what the relevance may be for ambient exposures 

(API, 2012, Attachment 1, p. 25).

The EPA notes that in the review completed in 1997, the Agency considered the 

lack of demonstrated biological mechanisms for the varying effects observed in 
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epidemiological studies to be an important caution in its integrated assessment of the 

health evidence upon which the standards were based (71 FR 61157, October 17, 2006). 

In the review completed in 2006, the EPA recognized the findings from additional 

research that indicated that different health responses were linked with different 

particle characteristics and that both individual components and complex particle 

mixtures appeared to be responsible for many biologic responses relevant to fine 

particle exposures. Id. Since that review, there has been a great deal of research 

directed toward advancing our understanding of biologic mechanisms. While this 

research has not resolved all questions, and further research is warranted (U.S. EPA, 

2011a, section 2.5), it has provided important insights as discussed in section III.B.1 of 

the proposal (77 FR at 38906 to 38909) and discussed more fully in the Integrated 

Science Assessment (U.S. EPA, 2009a, Chapter 5). 

As noted in the proposal, toxicological studies provide evidence to support the 

biological plausibility of cardiovascular and respiratory effects associated with long- and 

short-term PM exposures observed in epidemiological studies (77 FR 38906) and 

provide supportive mechanistic evidence that the cardiovascular morbidity effects 

observed in long-term exposure epidemiological studies are coherent with studies of 

cardiovascular-related mortality (77 FR 38907). The Integrated Science Assessment 

concluded that the new evidence available in this review “greatly expands” upon the 

evidence available in the last review “particularly in providing greater understanding of 

the underlying mechanisms for PM2.5 induced cardiovascular and respiratory effects for 
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both short- and long-term exposures” (U.S. EPA, 2009a, p. 2-17). The mechanistic 

evidence now available, taken together with newly available epidemiological evidence, 

increases the Agency’s confidence that a causal relationship exists between long- and 

short-term exposure to PM2.5 and cardiovascular effects and mortality.68 In addition, 

CASAC supported the Integrated Science Assessment approach and characterization of 

potential mechanisms or modes of action (Samet, 2009e, pp. 7 to 8; Samet, 2009f, p. 

11), as well as the findings of a causal relationship at the population level between 

exposure to PM2.5 and mortality and cardiovascular effects (Samet, 2009f, pp. 2 to 3).69

Additionally, the EPA disagrees with commenters that the mild and reversible 

effects observed in controlled human exposure studies are inconsistent with the more 

serious effects observed in epidemiological studies. Ethical considerations regarding the 

types of studies that can be performed with human subjects generally limit the effects 

that can be evaluated to those that are transient, reversible, and of limited short-term 

consequence. The relatively small number of subjects recruited for controlled exposure 

 

68 See American Trucking Association v. EPA, 175 F. 3d 1027, 1055-56 (D.C. Cir. 1999) 

reversed in part and affirmed in part sub nom, Whitman v. American Trucking Assn’s, 

531 U.S. 457 (2001) holding that the EPA could establish NAAQS without identifying a 

biological mechanism (“To begin with, the statute itself requires no such proof. The 

Administrator may regulate air pollutants "emissions of which, in his judgment, cause 

or contribute to air pollution which may reasonably be anticipated to endanger public 

health or welfare." (emphasis added by the court). Moreover, this court has never 

required the type of explanation petitioners seek from EPA. In fact, we have expressly 

held that EPA's decision to adopt and set air quality standards need only be based on 

‘reasonable extrapolations from some reliable evidence’.... Indeed, were we to accept 

petitioners' view, EPA (or any agency for that matter) would be powerless to act 

whenever it first recognizes clear trends of mortality or morbidity in areas dominated 

by a particular pathogen.”).
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studies should also be expected to have less variability in health status and risk factors 

than occurring in the general population.70 Consequently, the severity of health effects 

observed in controlled human exposure studies evaluating the effects of PM should be 

expected to be less than observed in epidemiologic studies. Nonetheless, that effects 

are observed in relatively healthy individuals participating in controlled exposure studies 

serves as an indicator that PM is initiating health responses and that more severe 

responses may reasonably be expected in a more diverse population. 

It should also be noted that there is a small body of toxicological evidence 

demonstrating mortality in rodents exposed to PM (e.g., Killingsworth et al. 1997; 

Godleski et al. 1996). Overall it is not surprising that lethality is not induced in more 

toxicological research, as these types of studies do not readily lend themselves to this 

endpoint. Epidemiological studies have observed associations between PM and 

 

70 For example, the EPA excludes from its controlled human exposure studies involving 

exposure to PM2.5 any individual with a significant risk factor for experiencing adverse 

effects from such exposure. Thus, the EPA excludes a priori the following categories 

of persons: those with a history of angina, cardiac arrhythmias, and ischemic 

myocardial infarction or coronary bypass surgery; those with a cardiac pacemaker; 

those with uncontrolled hypertension (greater than 150 systolic and 90 diastolic); 

those with neurogenetive diseases; those with a history of bleeding diathesis; those 

taking beta-blockers; those using oral anticoagulants; those who are pregnant, 

attempting to become pregnant, or breastfeeding; those who have experienced a 

respiratory infection within four weeks of exposure; those experiencing eye or 

abdominal surgery within six weeks of exposure; those with active allergies; those 

with a history of chronic illnesses such as diabetes, cancer, rheumatologic diseases, 

immunodeficiency state, known cardiovascular disease, or chronic respiratory 

diseases; smokers. The EPA “Application for Independent Review Board Approval of 

Human Subjects Research: Cardiopulmonary Effects of healthy Older GSTM1 Null and 

Sufficient individuals to Concentrated Ambient Air Particles (CAPTAIN)”, Nov. 9, 2011, 

p. 9.
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mortality in communities with populations in the range of many thousands to millions of 

people. Clearly, it is not feasible to expose hundreds (if not thousands) of animals to 

ambient PM (potentially over many years) in a laboratory setting to induce enough 

lethalities to distinguish between natural deaths and those attributable to PM. 

Furthermore, the heterogeneous human populations sampled in epidemiological studies 

are comprised of individuals with different physical, genetic, health, and socioeconomic 

backgrounds which may impact the outcome. However, in toxicological studies, the 

rodent groups are typically inbred, such that inter-individual variability is minimized. 

Thus, if the rodent strain used is quite robust, PM-induced effects may not be observed 

at low exposure concentrations.

(3) In asserting that the uncertainties in the underlying health science are as 

great or greater than in the last review and therefore do not support revision to the 

standards at this time, commenters in this group variously discussed a number of issues 

related to: (a) confounding, (b) heterogeneity in risk estimates, (c) exposure 

measurement error, (d) model specification, (e) the concentration-response 

relationship, and (f) understanding the relative toxicity of components within the 

mixture of fine particles. Each of these issues is addressed below and some are 

discussed in more detail in the Response to Comments document. 

In summary, these commenters concluded that the substantial uncertainties 

present in the last review have not been resolved and/or that the uncertainty about the 

possible health risks associated with PM2.5 exposure has not diminished. As discussed 
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below, the EPA believes that the overall uncertainty about possible health risks 

associated with both long- and short-term PM2.5 exposure has diminished to an 

important degree since the last review. While the EPA agrees that important 

uncertainties remain, and that future research directed toward addressing these 

uncertainties is warranted, the EPA disagrees with commenters’ views that the 

remaining uncertainties in the scientific evidence are too great to warrant revising the 

current PM2.5 NAAQS.

(a) Confounding 

Some commenters have criticized the EPA for not adequately addressing the 

issue of confounding in both long- and short-term exposure studies of mortality and 

morbidity. This includes confounding due to copollutants, as well as unmeasured 

confounding.71

With regard to copollutant confounding, these commenters asserted that the 

EPA has not adequately interpreted the results from studies that examined the effect of 

copollutants on the relationship between long- and short-term PM2.5 exposures and 

mortality and morbidity outcomes. These commenters contend that the EPA has 

 

71 The Integrated Science Assessment defines confounding as “a confusion of effects. 

Specifically, the apparent effect of the exposure of interest is distorted because the 

effect of an extraneous factor is mistaken for or mixed with the actual exposure 

effect (which may be null) (Rothman and Greenland, 1998)” (U.S. EPA, 2009a, p. 1-

16). Epidemiological analyses attempt to adjust or control for these characteristics 

(i.e., potential confounders) that differ between exposed and non-exposed 

individuals (U.S. EPA, 2009a, section 1.5.3). Not all risk factors can be controlled for 

within a study design/model and are termed “unmeasured confounders.” An 

unmeasured confounder is a confounder that has not previously been measured and 

therefore is not included in the study design/model. 
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inappropriately concluded that PM2.5-related mortality and morbidity associations are 

generally robust to confounding. The commenters stated that statistically significant 

PM2.5 associations in single-pollutant models in epidemiological studies do not remain 

statistically significant in copollutant models. 

The loss of statistical significance or the reduction in the magnitude of the effect 

estimate when a co-pollutant model is used may be the result of factors other than 

confounding. These changes do not prove either the existence or absence of 

confounding. These impacts must be evaluated in a broader context that considers the 

entire body of evidence. The broader examination of this issue in the Integrated Science 

Assessment included a focus on evaluating the stability of the size of the effect 

estimates in epidemiological studies conducted by a number of research groups using 

single- and copollutant models (U.S. EPA, 2009a, sections 6.2.10.9, 6.3.8.5, and 6.5, 

Figures 6-5, 6-9, and 6-15). This examination found that, for most epidemiological 

studies, there was little change in effect estimates based on single- and copollutant 

models, although the Integrated Science Assessment recognized that in some cases, the 

PM2.5 effect estimates were markedly reduced in size and lost statistical significance. 

Additionally, the EPA notes that these comments do not adequately reflect the 

complexities inherent in assessing the issue of copollutant confounding. As discussed in 

the proposal (77 FR 38907, 38909, and 38910) and more fully in the Integrated Science 

Assessment (U.S.EPA, 2009a, sections 6.2, 6.3, and 6.5), although copollutant models 

may be useful tools for assessing whether gaseous copollutants may be potential 
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confounders, such models alone cannot determine whether copollutants are in fact 

confounders. Interpretation of the results of copollutant models is complicated by 

correlations that often exist among air pollutants, by the fact that some pollutants play 

a role in the atmospheric reactions that form other pollutants such as secondary fine 

particles, and by the statistical power of the studies in question inherent in the study 

methodology. For example, the every-third or sixth-day sampling schedule often 

employed for PM2.5 measurements compared to daily measurements of gaseous 

copollutants drastically reduces the overall sample size to assess the effect of 

copollutants on the PM2.5-morbidity or mortality relationship, such that the reduced 

sample size can lead to less precise effect estimates (e.g., wider confidence intervals).

The EPA recognizes that when PM2.5 is correlated with gaseous pollutants it can 

be difficult to identify the effect of individual pollutants in the ambient mixture (77 FR 

38910). However, based on the available evidence, the EPA concludes epidemiological 

studies continue to support the conclusion that PM2.5 associations with mortality and 

morbidity outcomes are robust to the inclusion of gaseous copollutants in statistical 

models. The EPA evaluated the potential confounding effects of gaseous copollutants 

and, although it is recognized that uncertainties and limitations still remain, the Agency 

concluded the collective body of scientific evidence is “stronger and more consistent 

than in previous reviews providing a strong basis for decision making in this review" (77 

FR 38910/1). 
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Several commenters offered detailed comments on the long-term PM2.5

exposure studies arguing that associations from mortality studies are subjected to 

unmeasured confounding and as a result are not appropriately characterized as 

providing evidence of a causal relationship between long-term PM2.5 exposure and 

mortality (e.g., UARG, 2012, pp. 10 to 11, Attachment A, pp. 17 to 23; API, 2012, pp. 13 

to 14, Attachment 1, pp. 11 to 14, Attachment 7, pp. 2-10; ACC, 2012, p. 18 to 21; AFPM, 

2012, p. 8; Texas CEQ, 2012, p. 4). Specifically, commenters cited two studies (i.e., Janes 

et al., 2007 and Greven et al., 2011) that used a new type of statistical analysis to 

examine associations between annual (long-term) and monthly (sub-chronic) PM2.5

exposure and mortality. The commenters interpreted the results of these analyses as 

evidence of unmeasured confounding in the long-term PM2.5 exposure-mortality 

relationship. These commenters interpreted these studies as raising fundamental 

questions regarding the EPA’s determination that a causal relationship exists between 

long-term PM2.5 exposure and mortality. In addition to the commenters mentioned 

above, all of the authors of the publications by Janes et al. (2007) and Greven et al. 

(2011) (i.e., Francesca Dominici, Scott Zeger, Holly Janes, and Sonja Greven) submitted a 

joint comment to the public docket in order to clarify specific points regarding these two 

studies (Dominici et al., 2012).

The first study, Janes et al. (2007), was evaluated in the Integrated Science 

Assessment (U.S. EPA, 2009a, p. 7-88). The second study, Greven et al. (2011), an 

extension of the Janes et al. (2007) study adding three more years of data, is a “new” 
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study discussed in the Provisional Science Assessment (U.S. EPA, 2012). Both studies 

used nationwide Medicare mortality data to examine the association between monthly 

average PM2.5 concentrations over the preceding 12 months and monthly mortality 

rates in 113 U.S. counties and examined whether community-specific trends in monthly 

PM2.5 concentrations and mortality declined at the same rate as the national rate. The 

investigators examined this by decomposing the association between PM2.5 and 

mortality into two components: (1) national trends, defined as the association between 

the national average trend in monthly PM2.5 concentrations averaged over the previous 

12 months and the national average trend in monthly mortality rates, and (2) local 

trends, defined as county-specific deviations in monthly PM2.5 concentrations and 

monthly mortality rates from national trends. 

The EPA does not question the results of the national trends analyses conducted 

by Janes et al. (2007) and Greven et al. (2011).72 Both Janes et al. (2007) and Greven et 

al. (2011) observed positive and statistically significant associations between long-term 

exposure to PM2.5 and mortality in their national analyses. However, Janes et al. (2007) 

and Greven et al. (2011) eliminated all of the spatial variation in air pollution and 

mortality in their data set when estimating the national effect, focusing instead on both 

chronic (yearly) and sub-chronic (monthly) temporal differences in the data (Dominici et 

 

72 In its evaluation of Janes et al. (2007) in the Integrated Science Assessment, the EPA 

did not identify limitations in the statistical methods used per se (U.S. EPA, 2009a, p. 

7-88) and included the results of the national-scale analyses in that study in the body 

of evidence that supported the determination that there is a causal relationship 

between long-term PM2.5 exposure and mortality.
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al. 2012). Janes et al. (2007) (Table 1) highlighted that over 90 percent of the variance in 

the data set used for the analyses conducted by both Janes et al. (2007) and Greven et 

al. (2011) was attributable to spatial variability, which the authors chose to discard. As 

noted above, the focus of the analyses by Janes et al. (2007) and Greven et al. (2011) 

was on two components: (1) a temporal or time component, i.e., the “national” trends 

analysis, which examined the association between the national average trend in 

monthly PM2.5 concentrations averaged over the previous 12 months and the national 

average trend in monthly mortality rates and (2) a space-by-time component, i.e., the 

“local” trends analysis, which examined county-specific deviations in monthly PM2.5

concentrations and monthly mortality rates from national trends. These two 

components combined comprised less than 10 percent of the variance in the data set.

The authors included a focus on the space-by-time component, which represented 

approximately 5 percent of the variance in the data set, in an attempt to identify, absent 

confounding, if PM2.5 was associated with mortality at this unique exposure window. 

Thus, these studies are not directly comparable to other cohort studies investigating the 

relationship between long-term exposure to PM2.5 and mortality, which make use of 

spatial variability in air pollution and mortality data.73 This point was highlighted by the 

 

73 Though not directly comparable, the national effect estimates for mortality reported 

by Janes et al. (2007) and Greven et al. (2011) are coincidentally similar in magnitude 

to those previously reported. It is important to note that previous cohort studies 

have focused on identifying spatial differences in PM2.5 concentrations between 

cities, while Janes et al. (2007) and Greven et al. (2011) focus primarily on temporal 

differences in PM2.5 concentrations. In fact, Greven et al. (2011) state “We do not 

focus here on a third type [of statistical approach] used in cohort studies, measuring 
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study authors who stated that “when one considers that this wealth of information is 

not accounted for in [Janes 2007], it is not as surprising that... vastly different estimates 

of the PM2.5/mortality relationship [were observed] than in other studies that do exploit 

that variability” (Dominici et al., 2012, p. 2). 

The EPA notes that the results of the local trends analyses conducted by Janes et 

al. (2007) and Greven et al. (2011) are limited by the monthly timescale used in these 

analyses. This view is consistent with comments on the Janes et al. (2007) study 

articulated in Pope and Burnett (2007),74 which noted that an important limitation of the 

local scale analysis conducted by Janes et al. (2007) and subsequently by Greven et al. 

(2011) was the subchronic exposure window considered in these analyses. Both studies 

used annual average PM2.5 concentrations to characterize long-term national trends 

which was consistent with exposure windows considered in other studies of long-term 

exposure to PM2.5 and mortality.75 However, the local scale analyses used monthly 

average PM2.5 concentrations to characterize county-specific deviations from national 

trends (the local scale). The use of monthly average data likely does not provide enough 

exposure contrast to observe temporal changes in mortality at the local scale. It also 

 

the association between average PM2.5 levels and average age-adjusted mortality 

rates across cities (purely spatial or cross-sectional association).”

74 Some commenters argued that there were flaws in the criticisms offered by Pope and 

Burnett (2007) on the paper by Janes et al. (2007) (UARG, 2012, Attachment A, pp. 19 

to 23). The EPA responds to each of these specific comments in the Response to 

Comments document.

75 As noted above, however, Janes et al. (2007) and Greven et al. (2011) focused on 

temporal variability and other studies of long-term exposure to PM2.5 and mortality 

focus on spatial variability.
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represents a different exposure window than considered in the large body of evidence 

of health effects related to short-term (from less than one day to up to several days) 

and chronic (one or more years) measures of PM2.5. 

Furthermore, the EPA disagrees with commenters that studies by Janes et al. 

(2007) and Greven et al. (2011) provide evidence that other studies of long-term 

exposure to PM2.5 and mortality are affected by unmeasured confounding. As noted 

above, the design of the studies conducted by Janes et al (2007) and Greven et al (2011) 

are fundamentally different than those used in other studies of long-term exposure to 

PM2.5 and mortality, including the ACS cohort and the Harvard Six Cities study. Studies, 

such as the ACS and Harvard Six Cities studies, used the spatial variation between cities 

to measure the effect of long-term (annual) exposures to PM2.5 on mortality risk, and 

did not conduct any analyses relying on the temporal variation in PM2.5. The opposite is 

true of the Janes et al. (2007) and Greven et al. (2011) studies which first removed the 

spatial variability in PM2.5 and then examined the temporal variation at both the 

national and local scale to measure the effects of temporal differences in PM2.5 on 

mortality risk. Janes et al. (2007) and Greven et al. (2011) focus on changes in PM2.5

concentrations over time and, therefore, control for confounders would be based on 

including variables that vary over time rather than over space. As a result, any evidence 

of potential confounding of the PM2.5-mortality risk relationship derived from Janes et 

al. (2007) and Greven et al. (2011) cannot be extrapolated to draw conclusions related 
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to potential spatial confounding in studies based on the spatial variation in PM2.5

concentrations.

As detailed in the Integrated Science Assessment (U.S. EPA, 2009a, section 7.6), 

and recognized by the authors of Janes et al. (2007) and Greven et al. (2011), the cohort 

studies that informed the causality determination for long-term PM2.5 exposure and 

mortality “have developed approaches to adjust for measured and unmeasured 

confounders” (Dominici et al., 2012, p. 2). These approaches were specifically designed 

to adjust for spatial confounding. The hypothesis that the authors of Janes et al. (2007) 

and Greven et al. (2011) chose to examine was that differences in the local and national 

effects indicated unmeasured temporal confounding in either the local or national effect 

estimate. This hypothesis was specific to these two studies that examined temporal 

variability in exposure to air pollution and did not include known potential confounders 

at either the national or local scale as time-varying covariates in the statistical model. 

The authors acknowledged that the interpretation of either the national or local 

estimates needs to occur with an appreciation of the potential confounding effects of 

national and local scale covariates that were omitted from the model (Dominici et al., 

2012).

It is important to recognize that because Janes et al. (2007) and Greven et al. 

(2011) focused on variations in PM2.5 over time and not space, the results from these 

two studies do not provide any indication that other studies of long-term exposure to 
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PM2.5 and mortality exhibit spatial confounding, or that PM2.5 does not cause mortality.76

The authors of Janes et al. (2007) and Greven et al. (2011) recognized that “it is entirely 

possible that these papers are looking for an association at a timescale for which no 

association truly exists” (Dominici et al.,. 2012, p. 3). Furthermore, as highlighted in the 

Integrated Science Assessment and discussed by Pope and Burnett (2007), the 

conclusions of Janes et al. (2007) “are overstated. . . [T]heir analysis tells us little or 

nothing about unmeasured confounding in those and related studies because the 

methodology of Janes et al. largely excludes the sources of variability that are exploited 

in those other studies. By using monthly mortality counts and lagged 12-month average 

pollution concentrations, the authors eliminate the opportunity to exploit short-term or 

day-to-day variability.” 

In conclusion, the EPA interprets the results of the analyses conducted by Janes 

et al. (2007) and Greven et al. (2011) as being consistent with prior knowledge of 

examining associations with long-term exposure to PM2.5 at the national scale using 

long-term average PM2.5 concentrations. For the reasons presented above and discussed 

in more detail in the Response to Comments document, the Agency disagrees with the 

commenters’ assumption that the results of Janes et al. (2007) and Greven et al. (2011) 

indicate unmeasured confounding in the results of other cohort studies of long-term 

exposure to PM2.5 and mortality. Therefore, the EPA concludes that these studies do not 

 

76 Further, the EPA notes that Janes et al. (2007) and Greven et al. (2011) provide no 

information relevant to examining confounding in studies of short-term exposure to 

PM2.5.
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invalidate the large body of epidemiological evidence that supports the EPA’s 

determination that a causal relationship exists between long-term PM2.5 exposure and 

mortality.77

(b) Heterogeneity in Risk Estimates

Some commenters argued that the heterogeneity in risk estimates observed in 

multi-city epidemiological studies and the lack of statistical significance in many regional 

or seasonal estimates highlights a potential bias associated with combined multi-city 

epidemiological study results (e.g., API, 2012, Attachment 1, pp. 15 to 19). These 

commenters further argued that more refined intra-urban exposure estimates 

conducted for two of the largest cities included in the ACS study, Los Angeles and New 

York City, based on land-use regression models and/or kriging methods (Krewski et al., 

2009) “underscore the importance of considering city-specific health estimates, which 

may account for heterogeneity in PM2.5 concentrations or other differences among 

cities, rather than relying on pooled nationwide results from multi-city studies” (API, 

2012, Attachment 1, p. 17).

With respect to understanding the nature and magnitude of PM2.5-related risks, 

the EPA agrees that epidemiological studies evaluating health effects associated with 

long- and short-term PM2.5 exposures have reported heterogeneity in responses 

between cities and effect estimates across geographic regions of the U.S. (U.S. EPA, 

 

77 The EPA notes that the EPA’s conclusion with regard to interpretation of the results 

from Janes et al. (2007) and Greven et al. (2012) is supported by the study authors’ 

conclusion that “[o]ur results do not invalidate previous epidemiological studies”

(Dominici, 2012, p. 1 (emphasis original)).
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2009a, sections 6.2.12.1, 6.3.8.1, 6.5.2, and 7.6.1; U.S. EPA, 2011a, p. 2-25). For 

example, when focusing on short-term PM2.5 exposure, the Integrated Science 

Assessment found that multi-city studies that examined associations with mortality and 

cardiovascular and respiratory hospital admissions and emergency department visits 

demonstrated greater cardiovascular effects in the eastern versus the western U.S. 

(Dominici, et al., 2006; Bell et al., 2008; Franklin et al. (2007, 2008)). 

In addition, the Integrated Science Assessment evaluated studies that provided 

some evidence for seasonal differences in PM2.5 risk estimates, specifically in the 

northeast. The Integrated Science Assessment found evidence indicating that individuals 

may be at greater risk of dying from higher exposures to PM2.5 in the warmer months, 

and at greater risk of PM2.5 associated hospitalization for cardiovascular and respiratory 

diseases during colder months of the year. The limited influence of seasonality on PM

risk estimates in other regions of the U.S. may be due to a number of factors including 

varying PM composition by season, exposure misclassification due to regional 

tendencies to spend more or less time outdoors and air conditioning usage, and the 

prevalence of infectious diseases during the winter months (U.S. EPA, 2009a, p. 3-182). 

Overall, the EPA took note in the proposal that uncertainties still remain 

regarding various factors that contribute to heterogeneity observed in epidemiological 

studies (77 FR 38909/3). Nonetheless, the EPA recognizes that this heterogeneity could 

be attributed, at least in part, to differences in PM2.5 composition across the U.S., as well 

as to exposure differences that vary regionally such as personal activity patterns, 
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microenvironmental characteristics, and the spatial variability of PM2.5 concentrations in 

urban areas (U.S. EPA, 2009a, section 2.3.2; 77 FR 38910). 

As recognized in the Policy Assessment, the current epidemiological evidence 

and the limited amount of city-specific speciated PM2.5 data do not allow conclusions to 

be drawn that specifically differentiate effects of PM2.5 in different locations (U.S. EPA, 

2011a, p. 2-25). Furthermore, the Integrated Science Assessment concluded “that many 

constituents of PM2.5 can be linked with multiple health effects, and the evidence is not 

yet sufficient to allow differentiation of those constituents or sources that are more 

closely related to specific health outcomes” (U.S. EPA, 2009a, p. 2-17). CASAC 

thoroughly reviewed the EPA’s presentation of the scientific evidence indicating 

heterogeneity in PM2.5 effect estimates in epidemiological studies and concurred with 

the overall conclusions presented in the Integrated Science Assessment.

(c) Exposure Measurement Error 

Some commenters argued that the EPA did not adequately consider exposure 

measurement error, which they asserted is an important source of bias in 

epidemiological studies that can bias effect estimates in either direction (e.g., API, 2012, 

Attachment 1, pp. 19 to 20). 

The EPA agrees that exposure measurement error is an important source of 

uncertainty and that the variability in risk estimates observed in multi-city studies could 

be attributed, in part, to exposure error due to measurement-related issues (77 FR 

38910). However, the Agency disagrees with the commenters’ assertion that exposure 
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measurement error was not adequately considered in this review. The Integrated 

Science Assessment included an extensive discussion that addresses issues of exposure 

measurement error (U.S. EPA, 2009a, sections 2.3.2 and 3.8.6). Exposure measurement 

error may lead to bias in effect estimates in epidemiological studies. A number of 

studies evaluated in the last review (U.S. EPA, 2004, section 8.4.5) and in the current 

review (U.S. EPA, 2009a, section 3.8.6) have discussed the direction and magnitude of 

bias resulting from specified patterns of exposure measurement error (Armstrong 1998; 

Thomas et al. 1993; Carroll et al. 1995) and have generally concluded “classical” (i.e., 

random, within-person) exposure measurement error can bias effect estimates towards 

the null. Therefore, consistent with conclusions reached in the last review, the 

Integrated Science Assessment concluded “in most circumstances, exposure error tends 

to bias a health effect estimate downward” (U.S. EPA, 2009a, sections 2.3.2 and 3.8.6) 

(emphasis added). Thus, the EPA has both considered and accounted for the possibility 

of exposure measurement error, and the possible bias would make it more difficult to 

detect true associations, not less difficult. 

(d) Model Specification 

Commenters contended that the EPA did not account for the fact that “selecting 

an appropriate statistical model for epidemiologic studies of air pollution involves 

several choices that involve much ambiguity, scant biological evidence, and a profound 

impact on analytic results, given that many estimated associations are weak” (ACC, 

2012, p. 5) For short-term exposure studies, the EPA recognizes, as summarized in the 
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HEI review panel commentary that selecting a level of control to adjust for time-varying 

factors, such as temperature, in time-series epidemiological studies involves a trade-off 

(HEI, 2003). For example, if the model does not sufficiently adjust for the relationship 

between the health outcome and temperature, some effects of temperature could be 

falsely ascribed to the pollution variable. Conversely, if an overly aggressive approach is 

used to control for temperature, the result would possibly underestimate the pollutionrelated effect and compromise the ability to detect a small but true pollution effect (U.S. 

EPA, 2004, p. 8-236; HEI, 2003, p. 266). The selection of approaches to address such 

variables depends in part on prior knowledge and judgments made by the investigators, 

for example, about weather patterns in the study area and expected relationships 

between weather and other time-varying factors and health outcomes considered in the 

study. As demonstrated in section 6.5 of the Integrated Science Assessment, the EPA 

thoroughly considered each of these issues and the overall effect of different model 

specifications on the association between short-term PM2.5 exposure and mortality. 

Regardless of the model employed, consistent positive associations were observed 

across studies that controlled for the potential confounding effects of time and weather 

using different approaches (U.S. EPA 2009, Figure 6-27). The EPA also considered the 

influence of model specification in the examination of long-term PM2.5 exposure studies. 

For example, in section 7.6 of the Integrated Science Assessment, Figures 7-6 and 7-7 

summarize the collective evidence that evaluated the association between long-term 

PM2.5 exposure and mortality. Regardless of the model used, these studies collectively 
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found evidence of consistent positive associations between long-term PM2.5 exposure 

and mortality. 

The EPA, therefore, disagrees with commenters that model specification was not 

considered when evaluating the epidemiological evidence used to form causality 

determinations. The EPA specifically points out that the process of assessing the 

scientific quality and relevance of epidemiological studies includes examining 

“important methodological issues (e.g., lag or time period between exposure and 

effects, model specifications, thresholds, mortality displacement) related to 

interpretation of the health evidence (U.S. EPA, 2009, p. 1-9).” Consistent with the 

conclusions of the 2004 PM Air Quality Criteria Document, the EPA recognizes that there 

is still no clear consensus at this time as to what constitutes appropriate control of 

weather and temporal trends in short-term exposure studies, and that no single 

statistical modeling approach is likely to be most appropriate in all cases (U.S. EPA, 

2004, p. 8-238). However, the EPA believes that the available evidence interpreted in 

light of these remaining uncertainties does provide increased confidence relative to the 

last review in the reported associations between short- and long-term PM2.5 exposures 

and mortality and morbidity effects, alone and in combination with other pollutants.

(e) Concentration-response Relationship

Additionally, commenters questioned the interpretation of the shape of the 

concentration-response relationship, specifically stating that multiple studies have 

demonstrated that there is a threshold in the PM-health effect relationship and that the 
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log-linear model is not biologically plausible (API, 2012, Attachment 9; ACC, 2012, 

Appendix A, pp. 7 to 8). The EPA disagrees with this assertion due to the number of 

studies evaluated in the Integrated Science Assessment that continue to support the use 

of a no-threshold, log-linear model to most appropriately represent the PM 

concentration-response relationship (U.S. EPA, 2009a, section 2.4.3). While recognizing 

that uncertainties remain, the EPA believes that our understanding of this issue for both 

long-and short-term exposure studies has advanced since the last review. As discussed 

in the Integrated Science Assessment, both long- and short-term exposure studies have 

employed a variety of statistical approaches to examine the shape of the concentrationresponse function and whether a threshold exists. While the EPA recognizes that there 

likely are individual biological thresholds for specific health responses, the Integrated 

Science Assessment concluded the overall evidence from existing epidemiological 

studies does not support the existence of thresholds at the population level, for effects 

associated with either long-term or short-term PM exposures within the ranges of air 

quality observed in these studies (U.S. EPA, 2009a, section 2.4.3).

78 The Integrated 

Science Assessment concluded that this evidence collectively supported the conclusion 

that a no-threshold, log-linear model is most appropriate (U.S. EPA, 2009a, sections 

6.2.10.10, 6.5.2.7, and 7.6.4). CASAC likewise advised that “[a]lthough there is increasing 

uncertainty at lower levels, there is no evidence of a threshold (Samet, 2010d, p. ii).

 

78 While epidemiological analyses have not identified a population threshold in the 

range of air quality concentrations evaluated in these studies, the EPA recognizes 

that it is possible that such thresholds exist towards the lower end of these ranges (or 

below these ranges).
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The EPA recognizes that some short-term exposure studies have examined the 

PM2.5 concentration-response relationship in individual cities or on a city-to-city basis 

and observed heterogeneity in the shape of the concentration-response curve across 

cities. As discussed in (b) above, these findings are a source of uncertainty that the EPA 

agrees requires further investigation. Nonetheless, the Integrated Science Assessment 

concluded that “the studies evaluated further support the use of a no-threshold, loglinear model, but additional issues such as the influence of heterogeneity in estimates 

between cities and the effects of seasonal and regional differences in PM on the 

concentration-response-relationship still require further investigation” (U.S. EPA, 2009a, 

p. 2-25). 

(f) Relative Toxicity of PM2.5 Components 

Some commenters highlighted uncertainties in understanding the role of 

individual constituents within the mix of fine particles. These commenters asserted that 

a mass-based standard may not be appropriate due to the growing body of evidence 

indicating that certain PM2.5 components may be more closely related to specific health 

outcomes (e.g., EC and OC) (EPRI, 2012, p. 2). 

With regard to questions about the role of individual constituents within the mix 

of fine particles, as a general matter, the EPA recognizes that although new research 

directed toward this question has been conducted since the last review, important 

questions remain and the issue remains an important element in the Agency’s ongoing 

research program. At the time of the last review, the Agency determined that it was 
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appropriate to continue to control fine particles as a group, as opposed to singling out 

any particular component or class of fine particles (71 FR 61162 to 61164, October 17, 

2006). This distinction was based largely on epidemiological evidence of health effects 

using various indicators of fine particles in a large number of areas that had significant 

contributions of differing components or sources of fine particles, together with some 

limited experimental studies that provided some evidence suggestive of health effects 

associated with high concentrations of numerous fine particle components. 

In this review, as discussed in the proposal (77 FR 38922 to 38923) and in section 

III.E.1 below, while most epidemiological studies continue to be indexed by PM2.5 mass, 

several recent epidemiological studies included in the Integrated Science Assessment 

have used PM2.5 speciation data to evaluate health effects associated with fine particle 

exposures. In the Integrated Science Assessment, the EPA thoroughly evaluated the 

scientific evidence that examined the effect of different PM2.5 components and sources 

on a variety of health outcomes (U.S. EPA, 2009a, section 6.6) and observed that the 

available information continues to suggest that many different chemical components of 

fine particles and a variety of different types of source categories are all associated with, 

and probably contribute to, effects associated with PM2.5. The Integrated Science 

Assessment concluded that the current body of scientific evidence indicated that “many 

constituents of PM can be linked with differing health effects and the evidence is not yet 

sufficient to allow differentiation of those constituents or sources that are more closely 

related to specific health outcomes” (U.S. EPA, 2009a, p. 2-26 and 6-212). Furthermore, 
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the Policy Assessment concluded that the evidence is not sufficient to support 

eliminating any component or group of components associated with any specific source 

categories from the mix of fine particles included in the PM2.5 indicator (U.S. EPA, 2009a, 

p. 2-56). CASAC agreed that it was reasonable to retain PM2.5 as an indicator for fine 

particles in this review as “[t]here was insufficient peer-reviewed literature to support 

any other indicator at this time” (Samet, 2010c, p. 12).

This information is relevant to the Agency’s decision to retain PM2.5 as the 

indicator for fine particles as discussed in section III.E.1 below. The EPA also believes 

that it is relevant to the Agency’s conclusion as to whether revision of the suite of 

primary PM2.5 standards is appropriate. While there remain uncertainties about the role 

and relative toxicity of various components of fine PM, the current evidence continues 

to support the view that fine particles should be addressed as a group for purposes of 

public health protection. 

In summary, in considering the above issues related to uncertainties in the 

underlying health science, on balance, the EPA believes that the available evidence 

interpreted in light of these remaining uncertainties does provide increased confidence 

relative to the last review in the reported associations between long- and short- term 

PM2.5 exposures and mortality and morbidity effects, alone and in combination with 

other pollutants, and supports stronger inferences as to the causal nature of the 

associations. The EPA also believes that this increased confidence, when taken in 

context of the entire body of available health effects evidence and in light of the 
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evidence from epidemiological studies of associations observed in areas meeting the 

current primary PM2.5 standards, specifically in areas meeting the current primary 

annual PM2.5 standard, adds support to its conclusion that the current suite of PM2.5

standards needs to be revised to provide increased public health protection. 

(4) In asserting that there is no evidence of greater risk since the 2006 review to 

justify lowering the current annual PM2.5 standard, some commenters argued that, “if 

the current primary PM2.5 annual standard of 15 μg/m3 was considered to be adequately 

protective of public health in 2006, given relative risk estimates that EPA was using at 

that time, then that standard would surely still be adequately protective of the public 

health if relative risk estimates remain at the same level (or lower)” (UARG, 2012, 

Attachment 1, p. 24). These commenters compared risk coefficients used for mortality 

in the EPA’s risk assessment done in the last review with those from the Agency’s core 

risk assessment done as part of this review, and they concluded that “the entire range 

of the core relative risk for long-term mortality is lower now than it was in the prior 

review” (UARG, 2012, Attachment 1, p. 24). These commenters used this conclusion as 

the basis for a claim that there is no reason to revise the current annual PM2.5 standard.

The EPA believes that this claim is fundamentally flawed. In comparing the 

scientific understanding of the risk presented by exposure to PM2.5 between the last and 

current reviews, one must examine not only the quantitative estimate of risk from those 

exposures (e.g., the numbers of premature deaths or increased hospital admissions at 

various concentrations), but also the degree of confidence that the Agency has that the 
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observed health effects are causally linked to PM2.5 exposure at those concentrations. 

As documented in the Integrated Science Assessment and in the recommendations and 

conclusions of CASAC, the EPA recognizes significant advances in our understanding of 

the health effects of PM2.5, based on evidence that is stronger than in the last review. As 

a result of these advances, the EPA is now more certain that fine particles, alone or in 

combination with other pollutants, present a significant risk to public health at 

concentrations allowed by the current primary PM2.5 standards. From this more 

comprehensive perspective, since the risks presented by PM2.5 are more certain, similar 

or even somewhat lower relative risk estimates would not be a basis to conclude that no 

revision to the suite of PM2.5 standards is “requisite” to protect public health with an 

adequate margin of safety. This also ignores that the relative risk estimate is only one 

factor considered by the Administrator, e.g. it ignores that epidemiological studies since 

the last review indicate associations between PM2.5 and mortality and morbidity in areas 

meeting the current annual standard.

In any case, the commenters’ reliance on the flawed 2006 review is misplaced. 

As discussed in section III.A.2 above, the D.C. Circuit remanded Administrator Johnson’s 

2006 decision to retain the primary annual PM2.5 standard because the Agency failed to 

adequately explain why the annual standard provided the requisite protection from 

both short- and long-term exposure to fine particles including protection for at-risk 

populations. The 2006 standard was also at sharp odds with CASAC advice and 

recommendations as to the requisite level of protection (Henderson, 2006a,b). In other 
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words, the 2006 primary annual PM2.5 standard is not an appropriate benchmark for 

comparison. 

(5) Some of these commenters also identified “new” as well as older studies that 

had been included in prior reviews as providing additional evidence that the causality 

determinations presented in the Integrated Science Assessment did not consider the 

totality of the scientific literature, further supporting their view that a revision of the 

PM2.5 is unwarranted. As discussed in section II.B.3 above, the EPA notes that, as in past 

NAAQS reviews, the Agency is basing the final decisions in this review on the studies and 

related information included in the Integrated Science Assessment that have undergone 

CASAC and public review, and will consider newly published studies for purposes of 

decision making in the next PM NAAQS review. In provisionally evaluating commenters’ 

arguments (see Response to Comments document), the EPA notes that its provisional 

assessment of “new” science found that such studies did not materially change the 

conclusions reached in the Integrated Science Assessment (U.S. EPA, 2012b). 

3. Administrator’s Final Conclusions Concerning the Adequacy of the Current 

Primary PM2.5 Standards

Having carefully considered the public comments, as discussed above, the 

Administrator believes the fundamental scientific conclusions on the effects of PM2.5

reached in the Integrated Science Assessment, and discussed in the Policy Assessment, 

are valid. In considering whether the suite of primary PM2.5 standards should be revised, 

the Administrator places primary consideration on the evidence obtained from the 

epidemiological studies. The Administrator believes that this literature, combined with 
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the other scientific evidence discussed in the Integrated Science Assessment, collectively 

represents a strong and generally robust body of evidence of serious health effects 

associated with both long- and short-term exposures to PM2.5. As discussed in the 

Integrated Science Assessment and Policy Assessment, the EPA believes that much 

progress has been made since the last review in reducing some of the major 

uncertainties that were important considerations in establishing the current suite of 

PM2.5 standards. In that context, the Administrator finds the evidence of serious health 

effects reported in exposure studies conducted in areas with long-term mean

concentrations ranging from approximately at or above the level of the annual standard 

to long-term mean concentrations significantly below the level of the annual standard 

to be compelling, especially in light of the extent to which such studies are part of an 

overall pattern of positive and frequently statistically significant associations across a 

broad range of studies. The information in the quantitative risk assessment lends 

support to this conclusion.

There has been extensive critical review of this body of evidence, the 

quantitative risk assessment, and related uncertainties, including review by CASAC and 

the public. The public comments on the basis for the EPA’s proposed decision to revise 

the suite of primary PM2.5 standards have identified a number of issues about which 

different parties disagree including issues for which additional research is warranted. 

Having weighed all comments and the advice of CASAC, the Administrator believes that 

since the last review the overall uncertainty about the public health risks associated 
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with both long- and short-term exposure to PM2.5 has been diminished to an important 

degree. The remaining uncertainties in the available evidence do not diminish 

confidence in the associations between exposure to fine particles and mortality and 

serious morbidity effects. Based on her increased confidence in the association between 

exposure to PM2.5 and serious public health effects, combined with evidence of such an 

association in areas that would meet the current standards, the Administrator agrees 

with CASAC that revision of the current suite of PM2.5 standards to provide increased 

public health protection is necessary. Based on these considerations, the Administrator 

concludes that the current suite of primary PM2.5 standards is not sufficient, and thus 

not requisite, to protect public health with an adequate margin of safety, and that 

revision is needed to increase public health protection. 

It is important to note that this conclusion, and the reasoning on which it is 

based, do not resolve the question of what specific revisions are appropriate. That 

requires looking specifically at the current 24-hour and annual PM2.5 standards, 

including their indicator, averaging times, forms, and levels, and evaluating the scientific 

evidence and other information relevant to determining the appropriate revision of the 

suite of standards.

E. Conclusions on the Elements of the Primary Fine Particle Standards

1. Indicator
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In initially setting standards for fine particles in 1997, the EPA concluded it was 

appropriate to control fine particles as a group, rather than singling out any particular 

component or class of fine particles. The EPA noted that community health studies had 

found significant associations between various indicators of fine particles, and that 

health effects in a large number of areas had significant mass contributions of differing 

components or sources of fine particles. In addition, a number of toxicological and 

controlled human exposure studies had reported health effects associations with high 

concentrations of numerous fine particle components. It was also not possible to rule 

out any component within the mix of fine particles as not contributing to the fine 

particle effects found in the epidemiologic studies (62 FR 38667, July 18, 1977). In 

establishing a size-based indicator in 1977 to distinguish fine particles from particles in 

the coarse mode, the EPA noted that the available epidemiological studies of fine 

particles were based largely on PM2.5 and also considered monitoring technology that 

was generally available. The selection of a 2.5 μm size cut reflected the regulatory 

importance of defining an indicator that would more completely capture fine particles 

under all conditions likely to be encountered across the U.S., especially when fine 

particle concentrations and humidity are likely to be high, while recognizing that some 

small coarse particles would also be captured by current methods to monitor PM2.5 (62 

FR 38666 to 38668, July 18, 1997). In the last review, based on the same considerations, 

the EPA again recognized that the available information supported retaining the PM2.5

indicator and remained too limited to support a distinct standard for any specific PM2.5
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component or group of components associated with any source categories of fine 

particles (71 FR 61162 to 61164, October 17, 2006).

In this current review, the same considerations continue to apply for selection of 

an appropriate indicator for fine particles. As an initial matter, the Policy Assessment 

recognizes that the available epidemiological studies linking mortality and morbidity 

effects with long- and short-term exposures to fine particles continue to be largely 

indexed by PM2.5. For the same reasons discussed in the last two reviews, the Policy 

Assessment concluded that it was appropriate to consider retaining a PM2.5 indicator to 

provide protection from effects associated with long- and short-term fine particle 

exposures (U.S. EPA, 2011, p. 2-50).

The Policy Assessment also considered the expanded body of evidence available 

in this review to consider whether there was sufficient evidence to support a separate 

standard for ultrafine particles79 or whether there was sufficient evidence to establish 

distinct standards focused on regulating specific PM2.5 components or a group of 

components associated with any source categories of fine particles (U.S. EPA, 2011a, 

section 2.3.1).

A number of studies available in this review have evaluated potential health 

effects associated with short-term exposures to ultrafine particles. As noted in the 

Integrated Science Assessment, the enormous number and larger, collective surface 

 

79 Ultrafine particles, generally including particles with a mobility diameter less than or 

equal to 0.1 μm, are emitted directly to the atmosphere or are formed by nucleation

of gaseous constituents in the atmosphere (U.S. EPA, 2009a, p. 3-3).
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area of ultrafine particles are important considerations for focusing on this particle size 

fraction in assessing potential public health impacts (U.S. EPA, 2009a, p. 6-83). Per unit 

mass, ultrafine particles may have more opportunity to interact with cell surfaces due to 

their greater surface area and their greater particle number compared with larger 

particles (U.S. EPA, 2009a, p. 5-3). Greater surface area also increases the potential for 

soluble components (e.g., transition metals, organics) to adsorb to ultrafine particles 

and potentially cross cell membranes and epithelial barriers (U.S. EPA, 2009a, p. 6-83). 

In addition, evidence available in this review suggests that the ability of particles to 

enhance allergic sensitization is associated more strongly with particle number and 

surface area than with particle mass (U.S. EPA, 2009a, p. 6-127). 

New evidence, primarily from controlled human exposure and toxicological 

studies, expands our understanding of cardiovascular and respiratory effects related to 

short-term ultrafine particle exposures. However, the Policy Assessment concluded that 

this evidence was still very limited and largely focused on exposure to diesel exhaust, for 

which the Integrated Science Assessment concluded it was unclear whether the effects 

observed are due to ultrafine particles, larger particles within the PM2.5 mixture, or the 

gaseous components of diesel exhaust (U.S. EPA, 2009a, p. 2-22). In addition, the 

Integrated Science Assessment noted uncertainties associated with the controlled 

human exposure studies using concentrated ambient particle systems which have been

shown to modify the composition of ultrafine particles (U.S. EPA, 2009a, p. 2-22, see 

also section 1.5.3). 
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The Policy Assessment recognized that there are relatively few epidemiological 

studies that have examined potential cardiovascular and respiratory effects associated 

with short-term exposures to ultrafine particles (U.S. EPA, 2011a, p. 2-51). These studies 

have reported inconsistent and mixed results (U.S. EPA, 2009a, section 2.3.5).

Collectively, in considering the body of scientific evidence available in this 

review, the Integrated Science Assessment concluded that the currently available 

evidence was suggestive of a causal relationship between short-term exposures to 

ultrafine particles and cardiovascular and respiratory effects. Furthermore, the 

Integrated Science Assessment concluded that evidence was inadequate to infer a 

causal relationship between short-term exposure to ultrafine particles and mortality as 

well as long-term exposure to ultrafine particles and all outcomes evaluated (U.S. EPA, 

2009a, sections 2.3.5, 6.2.12.3, 6.3.10.3, 6.5.3.3, 7.2.11.3, 7.3.9, 7.4.3.3, 7.5.4.3, and 

7.6.5.3; Table 2-6). 

With respect to our understanding of ambient ultrafine particle concentrations, 

at present, there is no national network of ultrafine particle samplers; thus, only 

episodic and/or site-specific data sets exist (U.S. EPA, 2009a, p. 2-2). Therefore, the 

Policy Assessment recognized a national characterization of concentrations, temporal 

and spatial patterns, and trends was not possible at this time, and the availability of 

ambient ultrafine measurements to support health studies was extremely limited (U.S.

EPA, 2011a, p. 2-51). In general, measurements of ultrafine particles are highly 

dependent on monitor location and, therefore, more subject to exposure error than 
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accumulation mode particles (U.S. EPA, 2009a, p. 2-22). Furthermore, the number of 

ultrafine particles generally decreases sharply downwind from sources, as ultrafine 

particles may grow into the accumulation mode by coagulation or condensation (U.S.

EPA, 2009a, p. 3-89). Limited studies of ambient ultrafine particle measurements have 

suggested that these particles exhibit a high degree of spatial and temporal 

heterogeneity driven primarily by differences in nearby source characteristics (U.S. EPA, 

2009a, p. 3-84). Internal combustion engines and, therefore, roadways are a notable 

source of ultrafine particles, so concentrations of these particles near roadways are 

generally expected to be elevated (U.S. EPA, 2009a, p. 2-3). Concentrations of ultrafine 

particles have been reported to drop off much more quickly with distance from 

roadways than fine particles (U.S. EPA, 2009a, p. 3-84).

In considering both the currently available health effects evidence and the air 

quality data, the Policy Assessment concluded that this information was still too limited 

to provide support for consideration of a distinct PM standard for ultrafine particles 

(U.S. EPA, 2011a, p. 2-52).

In addressing the issue of particle composition, the Integrated Science 

Assessment concluded that, “[f]rom a mechanistic perspective, it is highly plausible that 

the chemical composition of PM would be a better predictor of health effects than 

particle size” (U.S. EPA, 2009a, p. 6-202). Heterogeneity of ambient concentrations of 

PM2.5 constituents (e.g., elemental carbon, organic carbon, sulfates, nitrates) observed 

in different geographical regions as well as regional heterogeneity in PM2.5-related 
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health effects reported in a number of epidemiological studies are consistent with this 

hypothesis (U.S. EPA, 2009a, section 6.6). 

With respect to the availability of ambient measurement data for fine particle 

components in this review, the Policy Assessment noted that there were now more 

extensive ambient PM2.5 speciation measurement data available through the Chemical 

Speciation Network (CSN) than in previous reviews (U.S. EPA, 2011a, section 1.3.2 and 

Appendix B, section B.1.3). The Integrated Science Assessment observed that data from 

the CSN provided further evidence of spatial and seasonal variation in both PM2.5 mass 

and composition among cities and geographic regions (U.S. EPA, 2009a, pp. 3-50 to 3-

60; Figures 3-12 to 3-18; Figure 3-47). Some of this variation may be related to regional 

differences in meteorology, sources, and topography (U.S. EPA, 2009a, p. 2-3). 

The currently available epidemiological, toxicological, and controlled human 

exposure studies evaluated in the Integrated Science Assessment on the health effects 

associated with ambient PM2.5 constituents and categories of fine particle sources used 

a variety of quantitative methods applied to a broad set of PM2.5 constituents, rather 

than selecting a few constituents a priori (U.S. EPA, 2009a, p. 2-26). Epidemiological 

studies have used measured ambient PM2.5 speciation data, including monitoring data 

from the CSN, while all of the controlled human exposure and most of the toxicological 

studies have used concentrated ambient particles and analyzed the constituents therein 
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(U.S. EPA, 2009a, p. 6-203).80 The CSN provides PM2.5 speciation measurements 

generally on a one-in-three or one-in-six day sampling schedule and, thus, does not 

capture data every day at most sites.81

The Policy Assessment recognized that several new multi-city studies evaluating 

short-term exposures to fine particle constituents are now available. These studies 

continued to show an association between mortality and cardiovascular and/or 

respiratory morbidity effects and short-term exposures to various PM2.5 components 

including nickel, vanadium, elemental carbon, organic carbon, nitrates, and sulfates 

(U.S. EPA, 2011a, section 2.3.1; U.S. EPA, 2009a, sections 6.5.2.5 and 6.6). 

Limited evidence is available to evaluate the health effects associated with longterm exposures to PM2.5 components (U.S. EPA, 2009a, section 7.6.2). The Policy 

Assessment noted the most significant new evidence was provided by a study that 

evaluated multiple PM2.5 components and an indicator of traffic density in an 

 

80 Most studies considered between 7 to 20 ambient PM2.5 constituents, with elemental 

carbon, organic carbon, sulfates, nitrates, and metals most commonly measured. Many 

of the studies grouped the constituents with various factorization or source 

apportionment techniques to examine the relationship between the grouped 

constituents and various health effects. However, not all studies labeled the constituent 

groupings according to their presumed source and a small number of controlled human 

exposure and toxicological studies did not use any constituent grouping. These 

differences across studies substantially limit any integrative interpretation of these 

studies (U.S. EPA, 2009a, p. 6-203). 

81 To expand our understanding of the role of specific PM2.5 components and sources 

with respect to the observed health effects, researchers have expressed a strong 

interest in having access to PM2.5 speciation measurements collected more frequently 

(U.S. EPA, 2011a, p. 2-53, including footnote 47). 
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assessment of health effects related to long-term exposure to PM2.5 (Lipfert et al., 

2006). Using health data from a cohort of U.S. military veterans and PM2.5 measurement 

data from the CSN, Lipfert et al. (2006) reported positive associations between mortality 

and long-term exposures to nitrates, elemental carbon, nickel, and vanadium as well as 

traffic density and peak ozone concentrations (U.S. EPA, 2011a, p. 2-54; U.S. EPA, 2009a, 

pp. 7-89 to 7-90). 

With respect to source categories of fine particles potentially associated with a 

range of health endpoints, the Integrated Science Assessment reported that the 

currently available evidence suggests associations between cardiovascular effects and a 

number of specific PM2.5–related source categories, including oil combustion, wood or 

biomass burning, motor vehicle emissions, and crustal or road dust sources (U.S. EPA, 

2009a, section 6.6; Table 6-18). In addition, a few studies have evaluated associations 

between PM2.5-related source categories and mortality. For example, one study

reported an association between mortality and a PM2.5 coal combustion factor (Laden et 

al., 2000), while other studies linked mortality to a secondary sulfate long-range 

transport PM2.5 source (Ito et al., 2006; Mar et al., 2006) (U.S. EPA, 2009a, section 

6.6.2.1). Other studies have looked different components of particulate matter. There 

was less consistency in associations observed between selected sources of fine particles 

and respiratory health endpoints, which may be partially due to the fact that fewer 

studies have evaluated respiratory-related outcomes and measures. However, there 

was some evidence for PM2.5-related associations with secondary sulfate and 
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decrements in lung function in asthmatic and healthy adults (U.S. EPA, 2009a, p. 6-211; 

Gong et al., 2005; Lanki et al., 2006). A couple of studies have observed an association 

between respiratory endpoints in asthmatics and surrogates for the crustal/soil/road 

dust and traffic sources of PM (U.S. EPA, 2009a, p. 6-205; Gent et al., 2009; Penttinen et 

al., 2006). 

Recent studies have shown that source apportionment methods have the 

potential to add useful insights into which sources and/or PM constituents may 

contribute to different health effects. Of particular interest are several epidemiological 

studies that compared source apportionment methods and reported consistent results 

across research groups (U.S. EPA, 2009a, p. 6-211; Hopke et al., 2006; Ito et al., 2006; 

Mar et al., 2006; Thurston et al., 2005). These studies reported associations between 

total mortality and secondary sulfate in two cities for two different lag times. The sulfate 

effect was stronger for total mortality in Washington D.C. and for cardiovascular-related 

mortality in Phoenix (U.S. EPA, 2009a, p. 6-204). These studies also found some 

evidence for associations with mortality and a number of source categories (e.g., 

biomass/wood combustion, traffic, copper smelter, coal combustion, sea salt) at various 

lag times (U.S. EPA, 2009a, p. 6-204). Sarnat et al. (2008) compared three different 

source apportionment methods and reported consistent associations between 

emergency department visits for cardiovascular diseases with mobile sources and 

biomass combustion as well as increased respiratory-related emergency department 

visits associated with secondary sulfate (U.S. EPA, 2009a, pp. 6-204 and 6-211).
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Collectively, in considering the currently available evidence for health effects 

associated with specific PM2.5 components or groups of components associated with any 

source categories of fine particles as presented in the Integrated Science Assessment, 

the Policy Assessment concluded that additional information available in this review 

continues to provide evidence that many different constituents of the fine particle 

mixture as well as groups of components associated with specific source categories of 

fine particles are linked to adverse health effects (U.S. EPA, 2011a, p. 2-55). However, as 

noted in the Integrated Science Assessment, while “[t]here is some evidence for trends 

and patterns that link particular ambient PM constituents or sources with specific health 

outcomes...there is insufficient evidence to determine whether these patterns are 

consistent or robust” (U.S. EPA, 2009a, p. 6-210). Assessing this information, the 

Integrated Science Assessment concluded that “the evidence is not yet sufficient to 

allow differentiation of those constituents or sources that are more closely related to 

specific health outcomes” (U.S. EPA, 2009a, pp. 2-26 and 6-212). Therefore, the Policy 

Assessment concluded that the currently available evidence is not sufficient to support 

consideration of a separate indicator for a specific PM2.5 component or group of 

components associated with any source category of fine particles. Furthermore, the 

Policy Assessment concluded that the evidence is not sufficient to support eliminating 

any component or group of components associated with any source categories of fine 

particles from the mix of fine particles included in the PM2.5 indicator (U.S. EPA, 2011a, 

p. 2-56). 
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The CASAC agreed with the EPA staff conclusions presented in the Policy 

Assessment and concluded that it is appropriate to consider retaining PM2.5 as the 

indicator for fine particles and further asserted, “There [is] insufficient peer-reviewed 

literature to support any other indicator at this time” (Samet, 2010c, p. 12). CASAC 

expressed a strong desire for the EPA to “look ahead to future review cycles and 

reinvigorate support for the development of evidence that might lead to newer 

indicators that may correlate better with the health effects associated with ambient air 

concentrations of PM ...” (Samet, 2010c, p 2). 

Consistent with the staff conclusions presented in the Policy Assessment and

CASAC advice, the Administrator proposed to retain PM2.5 as the indicator for fine 

particles. Further, the Administrator provisionally concluded that currently available 

scientific information does not provide a sufficient basis for supplementing mass-based, 

primary fine particle standards with standards using a separate indicator for ultrafine 

particles or a separate indicator for a specific PM2.5 component or group of components 

associated with any source categories of fine particles. In addition, the Administrator 

also provisionally concluded that the currently available scientific information did not 

provide a sufficient basis for eliminating any individual component or group of 

components associated with any source categories from the mix of fine particles 

included in the PM2.5 mass-based indicator. 
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The EPA received comparatively few public comments on issues related to the 

indicator for fine particles.82 Some commenters emphasized the need to conduct 

additional research to more fully understand the effect of specific PM2.5 components 

and/or sources on public health. These commenters expressed views about the 

importance of evaluating health effect associations with various fine particle 

components and types of source categories as a basis for focusing ongoing and future 

research to reduce uncertainties in this area and for considering whether alternative 

indicator(s) may be appropriate to consider in future PM NAAQS reviews for standards 

intended to protect against the array of health effects that have been associated with 

fine particles as indexed by PM2.5. For example, the PSR encouraged more research and 

monitoring related to PM2.5 components and noted the importance of components 

associated with coal combustion (PSR, 2012, pp. 5 to 6). EPRI asserted that “new” 

studies support focusing on EC and OC and encouraged the EPA to seriously consider 

the mass-based approach (EPRI, 2012, p. 2). Likewise, Georgia Mining Association 

supported additional monitoring and research efforts related to PM2.5 composition and 

specifically encouraged the evaluation of using particle number (e.g., particle count) 

(GMA, 2012, pp. 2 to 3). 

The Administrator agrees with CASAC as well as these commenters that the 

results of additional research and monitoring efforts will be helpful for informing future 

PM NAAQS reviews. Information from such studies could also help inform the 

 

82 No public comments were submitted regarding the use of a different size cut for fine 

particles.
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development of strategies that emphasize control of specific types of emission sources 

so as to address particles of greatest concern to public health. However, based upon the 

scientific information considered in the Integrated Science Assessment as well as the 

public comments summarized above, the Administrator continues to take note there is 

evidence that many different constituents of the fine particle mixture as well as groups 

of components associated with specific sources of fine particles are linked to adverse 

health effects. Furthermore, she recognizes that the evidence is not yet sufficient to 

differentiate those constituents or sources that are most closely related to specific 

health outcomes nor to exclude any PM2.5 components or sources of fine particles from 

the mix of particles included in the PM2.5 indicator. 

Having considered the public comments on this issue, the Administrator concurs 

with the Policy Assessment conclusions and CASAC recommendations and concludes 

that it is appropriate to retain PM2.5 as the indicator for fine particles. 

2. Averaging Time

In 1997, the EPA initially set both an annual standard, to provide protection from 

health effects associated with both long- and short-term exposures to PM2.5, and a 24-

hour standard to supplement the protection afforded by the annual standard (62 FR 

38667 to 38668, July, 18, 1997). In the last review, the EPA retained both annual and 24-

hour averaging times (71 FR 61164, October 17, 2006). These decisions were based, in 

part, on evidence of health effects related to both long-term (from a year to several 

years) and short-term (from less than one day to up to several days) measures of PM2.5.
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The overwhelming majority of studies conducted since the last review continue 

to utilize annual (or multi-year) and 24-hour averaging times, reflecting the averaging 

times of the current PM2.5 standards. These studies continue to provide evidence that 

health effects are associated with annual and 24-hour averaging times. Therefore, the 

Policy Assessment concluded it is appropriate to retain the current annual and 24-hour 

averaging times to provide protection from effects associated with both long- and shortterm PM2.5 exposures (U.S. EPA, 2011a, p. 2-57).

In considering whether the information available in this review supports 

consideration of different averaging times for PM2.5 standards specifically with regard to 

considering a standard with an averaging time less than 24 hours to address health 

effects associated with sub-daily PM2.5 exposures, the Policy Assessment noted there 

continues to be a growing body of studies that provide additional evidence of effects 

associated with exposure periods less than 24-hours (U.S. EPA, 2011a, p. 2-57). Relative 

to information available in the last review, recent studies provide additional evidence 

for cardiovascular effects associated with sub-daily (e.g., one to several hours) exposure 

to PM, especially effects related to cardiac ischemia, vasomotor function, and more 

subtle changes in markers of systemic inflammation, hemostasis, thrombosis and 

coagulation (U.S. EPA, 2009a, section 6.2). Because these studies have used different 

indicators (e.g., PM2.5, PM10, PM10-2.5, ultrafine particles), averaging times (e.g., 1, 2, and 

4 hours), and health outcomes, it is difficult to draw conclusions about cardiovascular 

effects associated specifically with sub-daily exposures to PM2.5. 
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With regard to respiratory effects associated with sub-daily PM2.5 exposures, the 

currently available evidence was much sparser than for cardiovascular effects and 

continues to be very limited. The Integrated Science Assessment concluded that for 

several studies of hospital admissions or medical visits for respiratory diseases, the 

strongest associations were observed with 24-hour average or longer exposures, not 

with less than 24-hour exposures (U.S. EPA, 2009a, section 6.3). 

Collectively, the Policy Assessment concluded that this information, when 

viewed as a whole, is too unclear, with respect to the indicator, averaging time and 

health outcome, to serve as a basis for consideration of establishing a primary PM2.5

standard with an averaging time shorter than 24-hours at this time (U.S. EPA, 2011a, p. 

2-57). 

With regard to health effects associated with PM2.5 exposure across varying 

seasons in this review, Bell et al. (2008) reported higher PM2.5 risk estimates for 

hospitalization for cardiovascular and respiratory diseases in the winter compared to 

other seasons. In comparison to the winter season, smaller statistically significant 

associations were also reported between PM2.5 and cardiovascular morbidity for spring 

and autumn, and a positive, but statistically non-significant association was observed for 

the summer months. In the case of mortality, Zanobetti and Schwartz (2009) reported a 

4-fold higher effect estimate for PM2.5-associated mortality for the spring as compared 

to the winter. Taken together, these results provided emerging but limited evidence 
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that individuals may be at greater risk of dying from higher exposures to PM2.5 in the 

warmer months and may be at greater risk of PM2.5-associated hospitalization for 

cardiovascular and respiratory diseases during colder months of the year (U.S. EPA, 

2011a, p. 2-58). 

Overall, the Policy Assessment observed that there are few studies presently 

available to deduce a general pattern in PM2.5-related risk across seasons. In addition, 

these studies utilized 24-hour exposure periods within each season to assess the 

PM2.5-associated health effects and do not provide information on health effects 

associated with a season-long exposure to PM2.5. Due to these limitations in the 

currently available evidence, the Policy Assessment concluded that there was no basis 

to consider a seasonal averaging time separate from a 24-hour averaging time.

Based on the above considerations, the Policy Assessment concluded that the 

currently available information provided strong support for consideration of retaining 

the current annual and 24-hour averaging times but does not provide support for 

considering alternative averaging times (U.S. EPA, 2011a, p. 2-58). In addition, CASAC 

considered it appropriate to retain the current annual and 24-hour averaging times for 

the primary PM2.5 standards (Samet, 2010c, pp. 2 to 3). At the time of the proposal, the 

Administrator concurred with the staff conclusions and CASAC advice and proposed that 

the averaging times for the primary PM2.5 standards should continue to include annual 

and 24-hour averages to protect against health effects associated with long- and shortterm exposures. Furthermore, the Administrator provisionally concluded, consistent 
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with conclusions reached in the Policy Assessment and by CASAC, that the currently 

available information was too limited to support consideration of alternative averaging 

times to establish a national standard with a shorter-than 24-hour averaging time or 

with a seasonal averaging time. 

The EPA received no significant public comments on the issue of averaging time 

for the PM2.5 primary standards. The Administrator concurs with recommendations 

made by CASAC and the staff conclusions presented in the Policy Assessment and 

concludes, as proposed, that it is appropriate to retain the current annual and 24-hour 

averaging times for the primary PM2.5 standards to protect against health effects 

associated with long- and short-term exposure periods.

3. Form

The “form” of a standard defines the air quality statistic that is to be compared 

to the level of the standard in determining whether an area attains the standard. In this 

review, the EPA considers whether currently available information supports retaining or 

revising the forms for the annual or 24-hour PM2.5 standards. 

a. Annual Standard

In 1997, the EPA established the form of the annual PM2.5 standard as an annual 

arithmetic mean, averaged over 3 years, from single or multiple community-oriented 

monitors. This form was intended to represent a relatively stable measure of air quality 

and to characterize longer-term area-wide PM2.5 concentrations, in conjunction with a 

24-hour standard designed to provide adequate protection against localized peak or 
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seasonal PM2.5 concentrations. The level of the standard was to be compared to 

measurements made at each community-oriented monitoring site, or, if specific criteria 

were met, measurements from multiple community-oriented monitoring sites could be 

averaged (i.e., spatial averaging)83 (62 FR 38671 to 38672, July 18, 1997). The constraints 

were intended to ensure that spatial averaging would not result in inequities in the level 

of protection provided by the standard (62 FR 38672, July 18, 1997). This approach was 

consistent with the epidemiological studies on which the PM2.5 standard was primarily 

based, in which air quality data were generally averaged across multiple monitors in an 

area or were taken from a single monitor that was selected to represent communitywide exposures. 

In the last review, the EPA tightened the criteria for use of spatial averaging to 

provide increased protection for vulnerable populations exposed to PM2.5. This change 

was based in part on an analysis of the potential for disproportionate impacts on 

potentially at-risk populations, which found that the highest concentrations in an area 

tend to be measured at monitors located in areas where the surrounding population is 

more likely to have lower education and income levels and higher percentages of 

minority populations (71 FR 61166/2, October 17, 2006; U.S. EPA, 2005, section 5.3.6.1).

In this review, as outlined in section III.B above and discussed more fully in 

section III.B.3 of the proposal, there now exist more health data such that the 

 

83 Spatial averaging as part of the form of the annual PM2.5 standard is unique to this 

standard and is not used with other PM standards nor with other NAAQS.
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Integrated Science Assessment has identified persons from lower socioeconomic strata 

as an at-risk population (U.S. EPA, 2009a, section 8.1.7; U.S. EPA, 2011a, section 2.2.1). 

Moreover, there now exist more years of PM2.5 air quality data than were available in 

the last review. Consideration in the Policy Assessment of the spatial variability across 

urban areas that was revealed by this expanded data base has raised questions as to 

whether an annual standard that allows for spatial averaging, even within specified 

constraints as narrowed in 2006 (71 FR 61165 to 61167, October 17, 2006), would 

provide appropriate public health protection. 

In considering the potential for disproportionate impacts on at-risk populations, 

the Policy Assessment considered an update of an air quality analysis conducted for the 

last review (U.S. EPA, 2011a, pp. 2-59 to 60; Schmidt, 2011, Analysis A). This analysis 

focused on determining whether the spatial averaging provisions, as modified in 2006, 

could introduce inequities in protection for at-risk populations exposed to PM2.5. 

Specifically, the Policy Assessment considered whether persons of lower socioeconomic 

status, minority groups, or different age groups (i.e., children or older adults) are more 

likely than the general population to live in areas in which the monitors recording the 

highest air quality values in an area are located. Data used in this analysis included 

demographic parameters measured at the Census Block or Census Block Group level, 

including percent minority population, percent minority subgroup population, percent 

of persons living below the poverty level, percent of persons 18 years of age or older, 

and percent of persons 65 years of age and older. In each candidate geographic area, 
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data from the Census Block(s) or Census Block Group(s) surrounding the location of the 

monitoring site (as delineated by radii buffers of 0.5, 1.0, 2.0, and 3.0 miles) in which the 

highest air quality value was monitored were compared to the average of monitored 

values in the area. This analysis looked beyond areas that would meet the current 

spatial averaging criteria and considered all urban areas (i.e., Core Based Statistical 

Areas or CBSAs) with at least two valid annual design value monitors (Schmidt, 2011, 

Analysis A). Recognizing the limitations of such cross-sectional analyses, the Policy 

Assessment observed that the highest concentrations in an area tend to be measured at 

monitors located in areas where the surrounding populations are more likely to live 

below the poverty line and to have higher percentage of minorities (U.S. EPA, 2011a, p. 

2-60). 

Based upon the analysis described above, the Policy Assessment concluded that 

the existing constraints on spatial averaging, as modified in 2006, may be inadequate to 

avoid substantially greater exposures in some areas, potentially resulting in 

disproportionate impacts on at-risk populations of persons with lower SES levels as well 

as minorities. Therefore, the Policy Assessment concluded that it was appropriate to 

consider revising the form of the annual PM2.5 standard such that it did not allow for the 

use of spatial averaging across monitors. In doing so, the level of the annual PM2.5

standard would be compared to measurements made at the monitoring site that 
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represents area-wide air quality recording the highest PM2.5 concentrations84 (U.S. EPA, 

2011a, p. 2-60). 

The CASAC agreed with staff conclusions that it was “reasonable” for the EPA to 

eliminate the spatial averaging provisions (Samet, 2010d, p. 2). Further, in CASAC’s 

comments on the first draft Policy Assessment, it noted, “Given mounting evidence 

showing that persons with lower SES levels are a susceptible group for PM-related 

health risks, CASAC recommends that the provisions that allow for spatial averaging 

across monitors be eliminated for the reasons cited in the (first draft) Policy 

Assessment” (Samet, 2010c, p. 13). In its review of the second draft Policy Assessment, 

CASAC recognized “although much of the epidemiological research has been conducted 

using community-wide averages, several key studies reference the nearest 

measurement site, so that some risk estimates are not necessarily biased by the 

averaging process. Further, the number of such studies is likely to expand in the future” 

(Samet, 2010d, pp. 1 to 2).

Only two areas in the country used the initial spatial averaging provisions for 

demonstrating attainment with the primary annual PM2.5 standard set in 1997 (70 FR 

19847, April 14, 2005; U.S. EPA, 2006c). Since these provisions were tightened in 2006,

no area has used spatial averaging to demonstrate attainment. No areas in the country 

 

84 As discussed in section VIII.B.1 below, the EPA is revising several terms associated 

with PM2.5 monitor placement. Specifically, the EPA is revoking the term “communityoriented” and replacing it with the term “area-wide” monitoring. 
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are currently using the spatial averaging provisions to demonstrate attainment with the 

current primary annual PM2.5 standard.

In considering the Policy Assessment’s conclusions based on the results of the 

analysis discussed above and concern over the evidence of potential disproportionate 

impacts on at-risk populations as well as CASAC advice, the Administrator proposed to 

revise the form of the annual PM2.5 standard to eliminate the use of spatial averaging. 

Thus, the Administrator proposed revising the form of the annual PM2.5 standard to 

compare the level of the standard with measurements from each “appropriate” monitor 

in an area85 with no allowance for spatial averaging. Thus, for an area with multiple 

monitors, the appropriate reporting monitor with the highest design value would 

determine the attainment status for that area.

Of the commenters noted in section III.D.2 above who supported a more 

stringent annual PM2.5 standard, those who commented on the form of the annual PM2.5

standard supported the EPA’s proposal to eliminate the spatial averaging provisions. 

These commenters contended that the EPA’s analyses of the potential impacts of spatial 

averaging, discussed above and in the proposal (77 FR 38924), demonstrated that the 

current form results in uneven public health protection leading to disproportionate 

impacts on at-risk populations. Specifically, the ALA and other environmental and public 

health commenters contended that “spatial averaging allows exposure of people to 

 

85 As discussed in section VIII.B.2.b below, the EPA concludes that PM2.5 monitoring sites 

at micro- and middle-scale locations should be comparable to the annual standard 

unless the monitoring site has been approved by the Regional Administrator as a 

“relatively unique micro-scale, or localized hot-spot, or unique middle-scale site.”
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unhealthy levels of pollution at specific locales even within an area meeting the 

standard” (ALA et al., 2012, p. 23). These commenters particularly focused on the 

importance for low-income and minority populations of eliminating the spatial 

averaging provisions. They concluded that spatial averaging “is an environmental justice 

concern because poor people are more likely to live near roads, depots, factories, ports, 

and other pollution sources.” Id. p. 24.

Other commenters (e.g., AAM, 2012; Dow, 2012) also supported the elimination 

of spatial averaging in order to “avoid potential disproportionate impacts on at-risk 

populations” and to maximize “the benefits to public health of reducing the annual 

PM2.5 standard.” However, these groups expressed concern that the elimination of 

spatial averaging, in combination with the requirement for near road monitors (as 

discussed in section VIII.B.3.b.i of the proposal), would effectively and inappropriately 

increase the stringency of the annual PM2.5 standard. 

This concern was also shared by other commenters who disagreed with the 

elimination of spatial averaging. For example, the Class of ’85 RRG emphasized concerns 

about increasing the stringency of the standard while providing few health benefits if 

spatial averaging is eliminated, particularly in combination with the requirement for 

near-road monitors. These commenters contended that “[b]ecause EPA proposes to use 

the readings from the highest single worst case monitor (rather than the average of all 

community area monitors), and since roadway monitoring locations will likely be worst 
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case monitors, the proposed NAAQS will become more stringent without targeting the 

PM2.5 species most harmful to human health” (Class of ’85 RRG, 2012, p. 6).

Several commenters also maintained that because spatial averaging is consistent 

with how air quality data are considered in the underlying epidemiological studies, such 

averaging should not be eliminated. Specifically, commenters including NAM et al., 

AFPM, and ACC pointed out that PM2.5 epidemiological studies use spatially averaged 

multi-monitor concentrations, rather than the single highest monitor, when evaluating 

health effects. Therefore, these commenters contended that allowing spatial averaging 

would make the PM2.5 standard more consistent with the approaches used in the

epidemiological studies upon which the standard is based. In addition, some 

commenters also contended that the EPA failed to consider whether modifying, rather 

than eliminating, the constraints on spatial averaging would have been sufficient to 

protect the public health. If so, these commenters argued that “elimination of spatial 

averaging would go beyond what is requisite to protect the public health” (NAM et al., 

2012, p. 20).

In considering the public comments on the form of the annual standard, the EPA 

recognizes a number of commenters agreed with the basis for the EPA’s proposal to 

eliminate spatial averaging. While other commenters expressed disagreement or 

concern with the proposed decision to eliminate the spatial averaging provisions, the 

Agency notes that these commenters did not challenge the analyses or considerations 

that provided the fundamental basis for the Administrator’s proposed decision. Rather, 
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these commenters generally raised concerns that eliminating the option for spatial 

averaging would increase the stringency of the standard, especially in light of additional 

monitoring sites in near-road environments (as discussed in section VIII.B.3.b.1 below). 

The EPA does not agree with the comment that siting some monitors in near 

roadway environments makes the standard more stringent or impermissibly more 

stringent. As discussed in section VIII.B.3.b.i below, a significant fraction of the 

population lives in proximity to major roads, and these exposures occur in locations that 

represent ambient air. Monitoring in such areas does not make the standard more 

stringent than warranted, but rather affords the intended protection to the exposed 

populations, among them at-risk populations, exposed to fine particles in these areas. 

Thus, the EPA has made a determination that monitors representative of area-wide air 

quality may be compared to the annual standard. The 24-hour and annual NAAQS are 

designed to protect the public with an adequate margin of safety, and this siting provision 

is fully consistent with providing the protection the standard is designed to provide and 

does not make the standard more stringent or more stringent than necessary. 

Monitors that are representative of area-wide air quality may be compared to the 

annual standard. This is consistent with the use of monitoring data in the epidemiological 

studies that provide the primary basis for determining the level of the annual standard. In 

addition, the EPA notes that the annual standard is designed to protect against both longand short-term exposures through controlling the broad distribution of air quality across 
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an area over time.86 It is fully consistent with the protection the standard is designed to 

provide for near road monitors to be compared to the annual standard if the monitor is 

representative of area-wide air quality. This does not make the standard either more 

stringent or impermissibly more stringent.

In further considering these comments, the EPA notes that the stringency or level 

of protection provided by each NAAQS is not based solely on the form of the standard; 

rather, the four elements of the standard that together serve to define each standard (i.e., 

indicator, averaging time, form, and level) must be considered collectively in evaluating 

the protection afforded by each standard. Therefore, the EPA considers these comments 

are also appropriate to discuss collectively with other issues related to the appropriate 

level for annual standard, and are discussed below in sections III.E.4.c-d. 

In reaching a final decision on the form of the annual standard, the 

Administrator considers the available analyses, CASAC advice, and public comments on 

form as discussed above. She also considers related issues in the public comments on 

the level of the annual standard as discussed in section III.E.4.c below. She notes that 

even when the annual PM2.5 standard was first set in 1997, the spatial averaging 

provisions included constraints intended to ensure that inequities in the level of 

protection would not result. These constraints on spatial averaging were tightened in 

the last review, based on an analysis showing the potential for spatial averaging to allow 

higher PM2.5 concentrations in locations where subgroups within the general population 

 

86 This is in contrast to the 24-hour standard which is designed to provide supplemental protection, 

addressing peak exposures that might not otherwise be addressed by the annual standard. Consistent 

with this, monitors are not required to be representative of area-wide air quality to be compared to 

the 24-hour standard.
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were potentially disproportionately exposed and hence, at disproportionate risk (e.g., 

low income and minority communities). The Administrator notes that in proposing to 

eliminate spatial averaging altogether in this review, she has relied on further analyses 

in the current review (Schmidt, 2011, Analysis A). As discussed above and in the 

proposal (77 FR 38924), these analyses showed that the current constraints on spatial 

averaging may be inadequate in some areas to avoid substantially greater exposures for 

people living near monitors recording the highest PM2.5 concentrations. Such exposures 

could result in disproportionate impacts to at-risk populations, including low-income 

populations as well as minority groups. 

On this basis, the Administrator concludes that public health would not be 

protected with an adequate margin of safety in all locations, as required by law, if 

disproportionately higher exposure concentrations in at-risk populations such as low 

income communities as well as minority communities were averaged together with 

lower concentrations measured at other sites in a large urban area. See ALA v. EPA, 134 

F. 3d 388, 389 (D.C. Cir., 1998) (“this court has held that ‘NAAQS must protect not only 

average healthy individuals, but also sensitive citizens such as children,’ and ‘if a 

pollutant adversely affects the health of these sensitive individuals, EPA must 

strengthen the entire national standard’”) and Coalition of Battery Recyclers Association 

v. EPA, 604 F 3d. 613, 617 (D.C. Cir., 2010) (“Petitioners’ assertion that the revised lead 

NAAQS is overprotective because it is more stringent than necessary to protect the 

entire population of young U.S. children ignores that the Clean Air Act allows protection 
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of sensitive subpopulations.”) In reaching this conclusion, the Administrator further 

notes that her concern over possible disproportionate PM2.5-related health impacts in 

at-risk populations extends to populations living near important sources of PM2.5, 

including the large populations that live near major roadways.87

In light of all of the above considerations, including consideration of available 

analyses, CASAC advice, and public comments, the Administrator concludes that the 

current form of the annual PM2.5 standard should be revised to eliminate spatial 

averaging provisions. Thus, the level of the revised annual PM2.5 standard established 

with this notice will be compared with measurements from each appropriate monitor in 

an area, with no allowance for spatial averaging. The Administrator’s conclusions with 

regard to the appropriate level of the annual PM2.5 standard to set in conjunction with 

this form are discussed below in section III.E.4.d.

b. 24-Hour Standard

In 1997, the EPA established the form of the 24-hour PM2.5 standard as the 98th

percentile of 24-hour concentrations at each population-oriented monitor within an 

area, averaged over three years (62 FR at 38671 to 38674, July 18, 1997). The Agency 

selected the 98th percentile as an appropriate balance between adequately limiting the 

occurrence of peak concentrations and providing increased stability which, when 

averaged over 3 years, facilitated effective health protection through the development 

of more stable implementation programs. By basing the form of the standard on 

 

87Section VIII.B.3.b.i below discusses public comments specifically related to the 

proposed requirement for near-road monitors. 
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concentrations measured at population-oriented monitoring sites, the EPA intended to 

provide protection for people residing in or near localized areas of elevated 

concentrations. In the last review, in conjunction with lowering the level of the 24-hour 

standard, the EPA retained this form based in part on a comparison with the 99th

percentile form.88

In revisiting the stability of a 98th versus 99th percentile form for a 24-hour 

standard intended to provide supplemental protection for a generally controlling annual 

standard, an analysis presented in the Policy Assessment considered air quality data 

reported in 2000 to 2008 to update our understanding of the ratio between peak-tomean PM2.5 concentrations. This analysis provided evidence that the 98th percentile 

value was a more stable metric than the 99th percentile (U.S. EPA, 2011a, Figure 2-2, p. 

2-62). 

At the time of the proposal, the Agency recognized that the selection of the 

appropriate form of the 24-hour standard includes maintaining adequate protection 

against peak 24-hour concentrations while also providing a stable target for risk 

management programs, which serves to provide for the most effective public health 

 

88 In reaching this final decision, the EPA recognized a technical problem associated with 

a potential bias in the method used to calculate the 98th percentile concentration for 

this form. The EPA adjusted the sampling frequency requirement in order to reduce 

this bias. Accordingly, the Agency modified the final monitoring requirements such 

that areas that are within 5 percent of the standards are required to increase the 

sampling frequency to every day (71 FR 61164 to 61165, October 17, 2006).
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protection in the long run.89 As in previous reviews, the EPA recognized that a 

concentration-based form, compared to an exceedance-based form, was more 

reflective of the health risks posed by elevated pollutant concentrations because such a 

form gives proportionally greater weight to days when concentrations are well above 

the level of the standard than to days when the concentrations are just above the level 

of the standard. Further, the Agency provisionally concluded that a concentration-based 

form, when averaged over three years, provided an appropriate balance between 

limiting peak pollutant concentrations and providing a stable regulatory target, thus 

facilitating the development of more stable implementation programs. 

In considering the information provided in the Policy Assessment and 

recognizing that the degree of public health protection likely to be afforded by a 

standard is a result of the combination of the form and the level of the standard, the 

Administrator proposed to retain the 98th percentile form of the 24-hour standard. The 

Administrator provisionally concluded that the 98th percentile form represents an 

appropriate balance between adequately limiting the occurrence of peak concentrations 

and providing increased stability relative to an alternative 99th percentile form. 

Few public commenters commented specifically on the form of the 24-hour 

standard. None of the public commenters raised objections to continuing the use of a 

concentration-based form for the 24-hour standard. Many of the individuals and groups 

 

89 See ATA III, 283 F.3d at 374-376 which concludes that it is legitimate for the EPA to 

consider overall stability of the standard and its resulting promotion of overall 

effectiveness of NAAQS control programs in setting a standard that is requisite to 

protect the public health. 
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who supported a more stringent 24-hour PM2.5 standard noted in section III.D.2 above, 

however, recommended a more restrictive concentration-based percentile form, 

specifically a 99th percentile form. The limited number of these commenters who 

provided a specific rationale for this recommendation generally expressed their concern 

that the 98th percentile form could allow too many days where concentrations exceeded 

the level of the standard, and thus fail to adequately protect public health. Other public 

commenters representing state and local air agencies and industry groups generally 

supported retaining the current 98th percentile form. In most cases, these groups 

expressed the overall view that the current 24-hour PM2.5 standard, including the form 

of the current standard, should be retained. 

The EPA notes that the viewpoints represented in this review are similar to 

comments submitted in the last review and through various NAAQS reviews. The EPA 

recognizes that the selection of the appropriate form includes maintaining adequate 

protection against peak 24-hour values while also providing a stable target for risk 

management programs, which serves to provide for the most effective public health 

protection in the long run.90 Nothing in the commenters’ views has provided a reason to 

change the Administrator’s previous conclusion regarding the appropriate balance 

represented in the proposed form of the 24-hour PM2.5 standard. Therefore, the 

 

90 As just noted above, it is legitimate for the EPA to consider promotion of overall 

effectiveness of risk management programs designed to attain the NAAQS, including 

their overall stability, in setting a standard that is requisite to protect the public 

health. The context for the court’s discussion in ATA III is identical to that here; 

whether to adopt a 98th percentile form for a 24-hour standard intended to provide 

supplemental protection for a generally controlling annual standard.
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Administrator concurs with staff conclusions presented in the Policy Assessment and 

CASAC recommendations and concludes that it is appropriate to retain the 98th

percentile form for the 24-hour PM2.5 standard.

4. Level

In the last review, the EPA selected levels for the annual and the 24-hour PM2.5

standards using evidence of effects associated with periods of exposure that were most 

closely matched to the averaging time of each standard. Thus, as discussed in section 

III.A.1, the EPA relied upon evidence from long-term exposure studies as the principal 

basis for selecting the level of the annual PM2.5 standard that would protect against 

effects associated with long-term exposures. The EPA relied upon evidence from the 

short-term exposure studies as the principal basis for selecting the level of the 24-hour 

PM2.5 standard that would protect against effects associated with short-term exposures. 

As summarized in section III.A.2 above, the 2006 decision to retain the level of the 

annual PM2.5 standard at 15 μg/m3 91 was challenged and on judicial review, the D.C. 

Circuit remanded the primary annual PM2.5 standard to the EPA, finding that EPA’s 

explanation for its approach to setting the level of the annual standard was inadequate. 

a. General Approach for Considering Standard Levels

 

91 Throughout this section, the annual standard levels are denoted as integer values for 

simplicity, although, as noted above in section II.B.1, Table 1, the annual standard 

level is defined to one decimal place, such that the current annual standard level is 

15.0 μg/m3. Alternative annual standard levels discussed in this section are similarly 

defined to one decimal place.
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Building upon the lessons learned in the previous PM NAAQS reviews, in 

considering alternative standard levels supported by the currently available scientific 

information, the Policy Assessment used an approach that integrated evidence-based 

and risk-based considerations, took into account CASAC advice, and considered the 

issues raised by the court in remanding the primary annual PM2.5 standard. Following 

the general approach outlined in section III.A.3 above, for the reasons discussed below, 

the Policy Assessment concluded it was appropriate to consider the protection afforded 

by the annual and 24-hour standards taken together against mortality and morbidity 

effects associated with both long- and short-term PM2.5 exposures. This was consistent 

with the approach taken in the review completed in 1997 rather than considering each 

standard separately, as was done in the review completed in 2006. 

Beyond looking directly at the relevant epidemiologic evidence, the Policy 

Assessment considered the extent to which specific alternative PM2.5 standard levels 

were likely to reduce the nature and magnitude of both long-term exposure-related 

mortality risk and short-term exposure-related mortality and morbidity risk (U.S. EPA, 

2011a, section 2.3.4.2; U.S.EPA, 2010a, section 4.2.2). As noted in section III.C.3 above, 

patterns of increasing estimated risk reductions were generally observed as either the 

annual or 24-hour standard, or both, were reduced below the level of the current 

standards (U.S. 2011a, Figures 2-11 and 2-12; U.S. EPA, 2010a, sections 4.2.2, 5.2.2, and 

5.2.3). 
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Based on the quantitative risk assessment, the Policy Assessment observed, as 

discussed in section III.A.3, that analyses conducted for this and previous reviews 

demonstrated that much, if not most, of the aggregate risk associated with short-term 

exposures results from the large number of days during which the 24-hour average 

concentrations are in the low-to mid-range, below the peak 24-hour concentrations 

(U.S. EPA, 2011a, p. 2-9). Furthermore, as discussed in section III.C above and in section 

III.C.3 of the proposal, the Risk Assessment observed that alternative annual standard 

levels, when controlling, resulted in more consistent risk reductions across urban study 

areas, thereby potentially providing a more consistent degree of public health 

protection (U.S. EPA, 2010a, pp. 5-15 to 5-16). In contrast, the Risk Assessment noted 

that the results of simulating alternative suites of PM2.5 standards including different 

combinations of alternative annual and 24-hour standard levels suggested that an 

alternative 24-hour standard level can produce additional estimated risk reductions 

beyond that provided by an alternative annual standard alone. However, the degree of 

estimated risk reduction provided by alternative 24-hour standard levels was highly 

variable, in part due to the choice of rollback approached used (U.S. EPA, 2010a, p. 5-

17). 

Based on its review of the second draft Policy Assessment, CASAC agreed with 

the EPA staff’s general approach for translating the available epidemiological evidence, 

risk information, and air quality information into the basis for reaching conclusions on 

alternative standards for consideration. Furthermore, CASAC agreed “that it is 
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appropriate to return to the strategy used in 1997 that considers the annual and the 

short-term standards together, with the annual standard as the controlling standard, 

and the short-term standard supplementing the protection afforded by the annual 

standard” and “considers it appropriate to place the greatest emphasis” on health 

effects judged to have evidence supportive of a causal or likely causal relationship as 

presented in the Integrated Science Assessment (Samet, 2010d, p. 1). 

Therefore, the Policy Assessment concluded, consistent with specific CASAC 

advice, that it was appropriate to set a “generally controlling” annual standard that will 

lower a wide range of ambient 24-hour concentrations. The Policy Assessment 

concluded this approach would likely reduce aggregate risks associated with both longand short-term exposures with more consistency than a generally controlling 24-hour 

standard and would be the most effective and efficient way to reduce total 

PM2.5-related population risk and so provide appropriate protection. The staff believed 

this approach, in contrast to one focusing on a generally controlling 24-hour standard, 

would likely reduce aggregate risks associated with both long- and short-term exposures 

with more consistency and would likely avoid setting national standards that could 

result in relatively uneven protection across the country due to setting standards that 

were either more or less stringent than necessary in different geographical areas. 

The Policy Assessment recognized that an annual standard intended to serve as 

the primary means for providing protection against effects associated with both longand short-term PM2.5 exposures cannot be expected to offer an adequate margin of 
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safety against the effects of all short-term PM2.5 exposures. As a result, in conjunction 

with a generally controlling annual standard, the Policy Assessment concluded it was 

appropriate to consider setting a 24-hour standard to provide supplemental protection, 

particularly for areas with high peak-to-mean ratios possibly associated with strong local 

or seasonal sources, or PM2.5-related effects that may be associated with shorter-thandaily exposure periods.

At the time of the proposal, the Administrator agreed with the approach 

discussed in the Policy Assessment as summarized in section III.A.3 above, and 

supported by CASAC, of considering the protection afforded by the annual and 24-hour 

standards taken together for mortality and morbidity effects associated with both longand short-term exposures to PM2.5. Furthermore, based on the evidence and 

quantitative risk assessment, the Administrator provisionally concluded it was 

appropriate to set a “generally controlling” annual standard that will lower a wide range 

of ambient 24-hour concentrations, with a 24-hour standard focused on providing 

supplemental protection, particularly for areas with high peak-to-mean ratios possibly 

associated with strong local or seasonal sources, or PM2.5-related effects that may be 

associated with shorter-than daily exposure periods. The Administrator provisionally 

concluded this approach would likely reduce aggregate risks associated with both longand short-term exposures more consistently than a generally controlling 24-hour 

standard and would be the most effective and efficient way to reduce total PM2-5-

related population risk.
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The Administrator is mindful that considering what standards are requisite to 

protect public health with an adequate margin of safety requires public health policy 

judgments that neither overstate nor understate the strength and limitations of the 

evidence or the appropriate inferences to be drawn from the evidence. At the time of 

the proposal, in considering how to translate the available information into appropriate 

standard levels, the Administrator weighed the available scientific information and 

associated uncertainties and limitations. For the purpose of determining what standard 

levels were appropriate to propose, the Administrator recognized, as did the EPA staff in 

the Policy Assessment, that there was no single factor or criterion that comprised the 

“correct” approach to weighing the various types of available evidence and information, 

but rather there were various approaches that were appropriate to consider. The 

Administrator further recognized that different evaluations of the evidence and other 

information before the Administrator could reflect placing different weight on the 

relative strengths and limitations of the scientific information, and different judgments 

could be made as to how such information should appropriately be used in making 

public health policy decisions on standard levels. This recognition led the Administrator 

to consider various approaches to weighing the evidence so as to identify appropriate 

standard levels to propose. In so doing, the Administrator encouraged extensive public 

comment on alternative approaches to weighing the evidence and other information so 

as to inform her public health policy judgments before reaching final decisions on 

appropriate standard levels.
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b. Proposed Decisions on Standard Levels

i. Consideration of the Alternative Standard Levels in the Policy Assessment

In recognizing the absence of a discernible population threshold below which 

effects would not occur, the Policy Assessment’s general approach for identifying 

alternative annual standard levels that were appropriate to consider focused on 

characterizing the part of the distribution of PM2.5 concentrations in which we had the 

most confidence in the associations reported in the epidemiological studies and 

conversely where our confidence in the association became appreciably lower. The 

most direct approach to address this issue, consistent with CASAC advice (Samet, 2010c, 

p.10), was to consider epidemiological studies reporting confidence intervals around 

concentration-response relationships (U.S. EPA, 2011a, p. 2-63). Based on a thorough 

search of the available evidence, the Policy Assessment identified only one study 

(Schwartz et al., 2008) that conducted a multi-model analysis to characterize confidence 

intervals around the estimated concentration-response relationship. The Policy 

Assessment concluded that this single relevant analysis was too limited to serve as the 

principal basis for identifying alternative standard levels in this review (U.S. EPA, 2011a, 

p. 2-70).

The Policy Assessment explored other approaches that considered different 

heuristic approaches to characterizing the part of the distributions of long-term mean 

PM2.5 concentrations that were most influential in generating health effect estimates in 

long- and short-term epidemiological studies, and placed greatest weight on those 
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studies that reported positive and statistically significant associations (U.S. EPA, 2011a, 

p. 2-63). First, as discussed in section III.A.3 above, the Policy Assessment considered 

the statistical metric used in previous reviews. This approach recognized our assumption 

that the strongest evidence of associations occurs at concentrations around the longterm mean concentration. Thus, in earlier reviews, the EPA focused on identifying 

standard levels that were somewhat below the long-term mean concentrations 

reported in PM2.5 epidemiological studies. The long-term mean concentrations 

represented air quality data typically used in epidemiological analyses and provided a 

direct link between PM2.5 concentrations and the observed health effects. Further, these 

data were available for all long- and short-term exposure studies analyzed and, 

therefore, represented the data set available for the broadest set of epidemiological 

studies. 

However, consistent with CASAC’s comments on the second draft Policy 

Assessment92 (Samet, 2010d, p. 2), in preparing the final Policy Assessment, the EPA 

staff explored ways to take into account additional information from epidemiological 

studies, when available (Rajan et al., 2011). These analyses focused on evaluating 

different statistical metrics, beyond the long-term mean concentration, to characterize 

the part of the distribution of PM2.5 concentrations in which staff continued to have 

confidence in the associations observed in epidemiological studies and below which 

 

92 While CASAC expressed the view that it would be most desirable to have information 

on concentration-response relationships, they recognized that it would also be 

“preferable to have information on the concentrations that were most influential in 

generating the health effect estimates in individual studies” (Samet, 2010d, p. 2). 
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there was a comparative lack of data such that the staff’s confidence in the relationship 

was appreciably less. This would also be the part of the distribution of PM2.5

concentrations which had the most influence on generating the health effect estimates 

reported in epidemiological studies. As discussed in section III.A.3 above, the Policy 

Assessment recognized there was no one percentile value within a given distribution 

that was the most appropriate or “correct” way to characterize where our confidence in 

the associations becomes appreciably lower. The Policy Assessment concluded that 

focusing on concentrations within the lower quartile of a distribution, such as the range 

from the 25th to the 10th percentile, was reasonable to consider as a region within which 

we begin to have appreciably less confidence in the associations observed in 

epidemiological studies.93 In the EPA staff’s view, considering lower PM2.5

concentrations, down to the lowest concentration observed in a study, would be a 

highly uncertain basis for selecting alternative standard levels (U.S. EPA, 2009a, p. 2-71).

 

93 In the last review, staff believed it was appropriate to consider a level for an annual 

PM2.5 standard that was somewhat below the averages of the long-term 

concentrations across the cities in each of the key long-term exposures studies, 

recognizing that the evidence of an association in any such study was strongest at 

and around the long-term average where the data in the study are most 

concentrated. For example, the interquartile range of long-term average 

concentrations within a study and a range within one standard deviation around the 

study mean were considered reasonable approaches for characterizing the range 

over which the evidence of association is strongest (U.S. EPA, 2005, pp. 5-22 to 5-23). 

In this review, the Policy Assessment noted the interrelatedness of the distributional 

statistics and a range of one standard deviation around the mean which contains 

approximately 68 percent of normally distributed data, in that one standard 

deviation below the mean falls between the 25th and 10th percentiles (U.S. EPA, 

2011a, p. 2-71). 
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As outlined in section III.A.3 above, the Policy Assessment recognized that there 

were two types of population-level information to consider in identifying the range of 

PM2.5 concentrations which have the most influence on generating the health effect 

estimates reported in epidemiological studies. The most relevant information to 

consider was the number of health events (e.g., deaths, hospitalizations) occurring 

within a study population in relation to the distribution of PM2.5 concentrations likely 

experienced by study participants. However, in recognizing that access to health event 

data may be restricted, and consistent with advice from CASAC (Samet 2010d, p.2), EPA 

staff also considered the number of participants within each study area, in relation to 

the distribution of PM2.5 concentrations (i.e., study population data), as a surrogate for 

health event data. 

In applying this approach, the Policy Assessment focused on identifying the part 

of the distribution of PM2.5 concentrations which had the most influence on generating 

health effect estimates in epidemiological studies, as discussed in section III.A.3 above. 

As discussed below, in working with study investigators, the EPA staff was able to obtain 

health event data for three large multi-city studies (Krewski et al., 2009; Zanobetti and 

Schwartz, 2009; Bell et al., 2008) and population data for the same three studies and 

one additional long-term exposure study (Miller et al., 2007), as documented in a staff 

memorandum (Rajan et al., 2011).94 For the three studies for which both health event 

 

94 The distributional statistical analysis of population-level data built upon an earlier 

analysis that evaluated the distributions of air quality and associated population data 
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and study population data were available, the EPA staff analyzed the reliability of using 

study population data as a surrogate for health event data. Based on these analyses, the 

EPA staff recognized that the 10th and 25th percentiles of the health event and study 

population distributions are nearly identical and concluded that the distribution of 

population data can be a useful surrogate for event data, providing support for 

consideration of the study population data for Miller et al. (2007), for which health 

event data were not available (Rajan et al., 2011, Analysis 1 and Analysis 2, in particular, 

Table 1 and Figures 1 and 2).

With regard to the long-term mean PM2.5 concentrations which are relevant to 

the first approach, Figures 1 through 3 (U.S. EPA, 2011a, Figures 2-4, 2-5, 2-6, and 2-8) 

summarize data available for multi-city, long- and short-term exposure studies that 

evaluated endpoints classified in the Integrated Science Assessment as having evidence 

of a causal or likely causal relationship or evidence suggestive of a causal relationship, 

showing the studies with long-term mean PM2.5 concentrations below 17 μg/m3.

95 As 

discussed in more detail in section III.E.4.b of the proposal, Figures 1 and 3 summarize 

the health outcomes evaluated, relative risk estimates, air quality data, and geographic 

scope for long- and short-term exposure studies, respectively, that evaluated mortality 

(evidence of a causal relationship); cardiovascular effects (evidence of a causal 

 

for three long-term exposure studies and three short-term exposure studies (Schmidt 

et al., 2010, Analysis 2). 

95 Additional studies presented and assessed in the Integrated Science Assessment 

report effects at higher long-term mean PM2.5 concentrations (e.g., U.S. EPA, 2009a, 

Figures 2-1, 2-2, 7-6, and 7-7).
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relationship); and respiratory effects (evidence of a likely causal relationship) in the 

general population, as well as in older adults, an at-risk population. Figure 2 provides 

this same summary information for long-term exposure studies that evaluated 

respiratory effects (evidence of a likely causal relationship) in children, an at-risk 

population, as well as developmental effects (evidence suggestive of a causal 

relationship). 
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Figure 1. Summary of Effect Estimates (per 10 μg/m3) and Air Quality Distributions for Multi-City, Long-term PM2.5 Exposure Studies of 

the General Population and Older Adults96, 97, 98

 

96 The air quality data for the Miller et al. (2007) study is discussed more fully in the Response to Comments document.

97 The Goss et al. (2004) study patients over the age of 6 years enrolled in the Cystic Fibrosis Foundation National Patient Registry in 1999 

and 2000. The average age of the study participants was 18.4 (SD 10).

98 The air quality data reported in Figure 1 of the proposal incorrectly identified the long-term mean PM2.5 concentration for this study as 14.3 μg/m3

, which was the 

long-term mean PM2.5 concentration for Lipfert et al. (2006b). This mistake was also made in the Figure 2-2 of the ISA (U.S. EPA, 2009a, p. 2-15). The EPA notes that 

footnote a for Figure 4 of Lipfert et al. (2006a) identifies that the mortality risks for PM2.5 are based on one year of air quality data (1999). However, this study does 

not report the long-term mean PM2.5 concentration for 1999. Rather, in Table 1 of Lipfert et al. (2006a), the study authors reported long-term mean PM2.5

concentrations of 14.3 μg/m3for the years 1989 to 1996 and 14.6 μg/m3for the years 1997 to 2001.
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Source: U.S. EPA, 2011a, Figure 2-4
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Figure 2. Summary of Effect Estimates (per 10 μg/m3) and Air Quality Distributions for Multi-City, Long-term PM2.5 Exposure Studies of 

Children

Source: U.S. EPA, 2011a, Figure 2-5
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Figure 3. Summary of Effect Estimates (per 10 μg/m3) and Air Quality Distributions for Multi-City, Short-term PM2.5 Exposure Studies of 

the General Population and Older Adults
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Source: U.S. EPA, 2011a, Figure 2-6
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With regard to consideration of additional information from epidemiological studies which was 

relevant to the second approach, the EPA staff compiled a summary of the range of PM2.5

concentrations corresponding with the 25th to 10th percentiles of health event or study population data 

from the four multi-city studies, for which distributional statistics are available99 (U.S. EPA, 2011a, 

Figure 2-7; Rajan et al., 2011, Table 1). By considering this approach, one could focus on the range of 

PM2.5 concentrations below the long-term mean ambient concentrations over which we continue to 

have confidence in the associations observed in epidemiological studies (e.g., above the 25th

percentile) where commensurate public health protection could be obtained for PM2.5-related effects 

and, conversely, identify the range in the distribution below which our confidence in the associations is 

appreciably less, to identify alternative annual standard levels. 

The mean PM2.5 concentrations associated with the studies summarized in Figures 1, 2, and 3 

and with the distributional statistics analyses (Rajan et al., 2011) are based on concentrations averaged 

across ambient monitors within each area included in a given study and then averaged across study 

areas to calculate an overall study mean concentration, as discussed above. Figure 4, discussed in more 

detail in section III.E.4.a of the proposal, summarizes statistical metrics for those key studies100 included 

in

 

99 The EPA staff obtained health event data (e.g., number of deaths, hospitalizations) occurring in a 

study population for three multi-city studies (Krewski et al., 2009; Zanobetti and Schwartz, 2009; 

Bell et al., 2008) and study population data were obtained for the same three studies and one 

additional study (Miller et al., 2007) (U.S. EPA, 2011a, p.2-71). If health event or study population 

data were available for additional studies, the EPA could employ distributional statistics to identify 

the broader range of PM2.5 concentrations that were most influential in generating health effect 

estimates in those studies. 

100 Long- and short-term exposure studies considered “key” studies for consideration are summarized 

in Figure 4 and include those studies observing effects for which the evidence supported a causal or 
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likely causal association. This figure represents the subset of multi-city studies included in Figures 1 

through 3 that provided evidence of positive and generally statistically significant effects associated in 

whole or in part with more recent air quality data, generally representing health effects associated 
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Figure 4. Translating Epidemiological Evidence from Multi-City Exposure Studies into an Annual PM2.5 Standard101

 

101 The air quality data for the Miller et al. (2007) study is discussed more fully in the Response to Comments document.
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Source: U.S. EPA, 2011a, Figure 2-
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Figures 1, 2, and 3 that provide evidence of positive and generally statistically significant PM2.5-

related effects, which are relevant to the two approaches for translating epidemiological 

evidence into potential standard levels as discussed above. The top of Figure 4 includes 

information for long-term exposure studies evaluating health outcomes classified as having 

evidence of a causal or likely causal relationship with PM2.5 exposures (long-term mean PM2.5

concentrations indicated by diamond symbols). The middle of Figure 4 includes information for 

short-term exposure studies evaluating health outcomes classified as having evidence of a 

causal or likely causal relationship with PM2.5 exposures (long-term mean PM2.5 concentrations 

indicated by triangle symbols). The bottom of Figure 4 includes information for long-term

exposures studies evaluating health outcomes classified as having evidence suggestive of a 

causal relationship (long-term mean PM2.5 concentrations indicated by square symbols). Figure 

4 also summarizes the range of PM2.5 concentrations corresponding with the 25th (indicated by 

solid circles) to 10th (indicated by open circles) percentiles of the health event or study 

population data from the four multi-city studies (highlighted in bold text) for which 

distributional statistics are available.

In considering the evidence, the Policy Assessment recognized that NAAQS are 

standards set so as to provide requisite protection, neither more nor less stringent than 

necessary to protect public health, with an adequate margin of safety. This judgment, 

ultimately made by the Administrator, involves weighing the strength of the evidence and the 

inherent uncertainties and limitations of that evidence. Therefore, depending on the weight 
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placed on different aspects of the evidence and inherent uncertainties, consideration of 

different alternative standard levels could be supported. 

Given the currently available evidence discussed in more detail in section III.E.4.b of the 

proposal and considering the various approaches discussed above, the Policy Assessment 

concluded it was appropriate to focus on an annual standard level within a range of about 12 to 

11 μg/m3(U.S. EPA, 2011a, pp. 2-82, 2-101, and 2-106). As illustrated in Figure 4, the Policy 

Assessment recognized that a standard level of 12 μg/m3, at the upper end of this range, was 

somewhat below the long-term mean PM2.5 concentrations reported in all the multi-city, longand short-term exposure studies that provided evidence of positive and statistically significant 

associations with health effects classified as having evidence of a causal or likely causal 

relationship, including premature mortality and hospitalizations and emergency department 

visits for cardiovascular and respiratory effects as well as respiratory effects in children. 

Further, a level of 12 μg/m3 would reflect consideration of additional population-level 

information from such epidemiological studies in that it generally corresponded with 

approximately the 25th percentile of the available distributions of health events data in the 

studies for which population-level information was available. In addition, a level of 12 μg/m3

would reflect some consideration of studies that provided more limited evidence of 

reproductive and developmental effects, which were suggestive of a causal relationship, in that 

it was about at the same level as the lowest long-term mean PM2.5 concentrations reported in 

such studies (see Figure 4). 
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Alternatively the Policy Assessment recognized that an annual standard level of 11 

μg/m3, at the lower end of this range, was well below the lowest long-term mean PM2.5

concentrations reported in all multi-city long- and short-term exposure studies that provide 

evidence of positive and statistically significant associations with health effects classified as 

having evidence of a causal or likely causal relationship. A level of 11 μg/m3 would reflect 

placing more weight on the distributions of health event and population data, in that this level 

was within the range of PM2.5 concentrations corresponding to the 25th and 10th percentiles of 

all the available distributions of such data. In addition, a level of 11 μg/m3 was somewhat below 

the lowest long-term mean PM2.5 concentrations reported in reproductive and developmental 

effects studies that are suggestive of a causal relationship. Thus, a level of 11 μg/m3 would 

reflect an approach to translating the available evidence that places relatively more emphasis 

on margin of safety considerations and less certain causal relationships than would a standard 

set at a higher level. Such a policy approach would tend to weigh uncertainties in the evidence 

in such a way as to avoid potentially underestimating PM2.5-related risks to public health. 

Further, recognizing the uncertainties inherent in identifying any particular point at which our 

confidence in reported associations becomes appreciably less, the Policy Assessment concluded 

that the available evidence did not provide a sufficient basis to consider alternative annual 

standard levels below 11 μg/m3(U.S. EPA, 2011a, p. 2-81). 

The Policy Assessment also considered the extent to which the available evidence 

provided a basis for considering alternative annual standard levels above 12 μg/m3. As 

discussed below, the Policy Assessment concluded that it could be reasonable to consider a 
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standard level up to 13 μg/m3based on a policy approach that weighed uncertainties in the 

evidence in such a way as to avoid potentially overestimating PM2.5-related risks to public 

health, especially to the extent that primary emphasis was placed on long-term exposure 

studies as a basis for an annual standard level. A level of 13 μg/m3 was somewhat below the 

long-term mean PM2.5 concentrations reported in all but one of the long-term exposure studies 

providing evidence of positive and statistically significant associations with PM2.5-related health 

effects classified as having a causal or likely causal relationship. As shown in Figure 4, the one 

long-term exposure study with a long-term mean PM2.5 concentration just below 13 μg/m3 was 

the Miller et al., (2007) study. However, as noted in section III.D.1.a of the proposal and 

discussed in more detail in the Response to Comments document, the Policy Assessment 

observed that in comparison to other long-term exposure studies, the Miller et al. study was 

more limited in that it was based on only one year of air quality data and the one year was after 

the health outcomes were reported (U.S. EPA, 2011a, pp. 2-81 to 2-82). Thus, to the extent that 

less weight was placed on the Miller et al. study than on other long-term exposure studies with 

more robust air quality data, a level of 13 μg/m3could be considered as being protective of 

long-term exposure related effects classified as having a causal or likely causal relationship. In 

also considering short-term exposure studies, however, the Policy Assessment noted that a 

level of 13 μg/m3 was below the long-term mean PM2.5 concentrations reported in most but not 

all such studies. In particular, two studies -- Burnett et al. (2004) and Bell et al. (2008) --

reported long-term mean PM2.5 concentrations of 12.8 and 12.9 μg/m

3

, respectively. In 

considering these studies, the Policy Assessment found no basis to conclude that these two 
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studies were any more limited or uncertain than the other short-term exposure studies shown 

in Figures 3 and 4 (U.S. EPA, 2011a, p. 2-82). On this basis, as discussed below, the Policy 

Assessment concluded that consideration of an annual standard level of 13 μg/m3 would have 

implications for the degree of protection that would need to be provided by the 24-hour 

standard, in order that the suite of PM2.5 standards, taken together, would provide appropriate 

protection from effects on public health related to short-term exposure to PM2.5 (U.S. EPA, 

2011a, p. 2-82).

The Policy Assessment also noted that a standard level of 13 μg/m3 would reflect a 

judgment that the uncertainties in the epidemiological evidence as summarized in section III.B 

above and discussed in more detail in section III.B.2 of the proposal, including uncertainties 

related to the heterogeneity observed in the epidemiological studies in the eastern versus 

western parts of the U.S., the relative toxicity of PM2.5 components, and the potential role of 

co-pollutants, are too great to warrant placing any weight on the distributions of health event 

and population data that extend down below the long-term mean concentrations into the 

lower quartile of the data. This level would also reflect a judgment that the evidence from 

reproductive and developmental effects studies that is suggestive of a causal relationship was 

too uncertain to support consideration of any lower level.

Beyond evidence-based considerations, the Policy Assessment also considered the 

extent to which the quantitative risk assessment supported consideration of these alternative 

standard levels or provided support for lower levels. In considering simulations of just meeting 

alternative annual standard levels within the range of 13 to 11 μg/m3(in conjunction with the 
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current 24-hour standard level of 35 μg/m3), the Policy Assessment concluded that important 

public health improvements are associated with risk reductions estimated for standard levels of 

13 and 12 μg/m3and noted that the level of 11 μg/m3 was not included in the quantitative risk 

assessment. The Policy Assessment noted that the overall confidence in the quantitative risk 

estimates varied for the different alternative standard levels evaluated and was stronger for the 

higher levels and substantially lower for the lowest level evaluated (i.e., 10 μg/m3). Based on 

the above considerations, the Policy Assessment concluded that the quantitative risk 

assessment provided support for considering alternative annual standard levels within a range 

of 13 to 11 μg/m3, but did not provide strong support for considering lower alternative 

standard levels (U.S. EPA, 2011a, pp. 2-102 to 2-103).

Taken together, the Policy Assessment concluded that consideration of alternative 

annual standard levels in the range of 13 to 11 μg/m3 may be appropriate. Furthermore, the 

Policy Assessment concluded that the currently available evidence most strongly supported 

consideration of an alternative annual standard level in the range of 12 to 11 μg/m3(U.S. EPA, 

2011a, p. 2-82). The Policy Assessment concluded that an alternative level within the range of 

12 to 11 μg/m3 would more fully take into consideration the available information from all longand short-term PM2.5 exposure studies, including studies of at-risk populations, than would a 

higher level. This range also reflected placing weight on information from studies that helped to 

characterize the range of PM2.5 concentrations over which we continue to have confidence in 

the associations observed in epidemiological studies, as well as the extent to which our 

confidence in the associations was appreciably less at lower concentrations. 
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As recognized in sections III.A.3 and III.E.4.a above, an annual standard intended to 

serve as the primary means for providing protection from effects associated with both longand short-term PM2.5 exposures is not expected to provide appropriate protection against the 

effects of all short-term PM2.5 exposures (unless established at a level so low as to undoubtedly 

provide more protection than necessary for long-term exposures). Of particular concern are 

areas with high peak-to-mean ratios possibly associated with strong local or seasonal sources, 

or PM2.5-related effects that may be associated with shorter-than-daily exposure periods. As a 

result, the Policy Assessment concluded that it was appropriate to consider alternative 24-hour 

PM2.5 standard levels that would supplement the protection provided by an annual standard. 

As outlined in section III.A.3 above, the Policy Assessment considered the available 

evidence from short-term PM2.5 exposure studies, as well as the uncertainties and limitations in 

that evidence, to assess the degree to which alternative annual and 24-hour PM2.5 standards 

can be expected to reduce the estimated risks attributed to short-term fine particle exposures. 

In considering the available epidemiological evidence, the Policy Assessment took into account 

information from multi-city studies as well as single-city studies. The Policy Assessment 

considered the distributions of 24-hour PM2.5 concentrations reported in short-term exposure 

studies, focusing on the 98th percentile concentrations to match the form of the 24-hour 

standard as discussed in section III.E.3.b above. In recognizing that the annual and 24-hour 

standards work together to provide protection from effects associated with short-term PM2.5

exposures, the Policy Assessment also considered information on the long-term mean PM2.5

concentrations from these studies. 
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In addition to considering the epidemiological evidence, the Policy Assessment 

considered air quality information, specifically peak-to-mean ratios using county-level 24-hour 

and annual design values, to characterize air quality patterns in areas possibly associated with 

strong local or seasonal sources. These patterns helped in understanding the extent to which 

different combinations of annual and 24-hour standards would be consistent with the policy 

goal of setting a generally controlling annual standard with a 24-hour standard that provides 

supplemental protection especially for areas with high peak-to-mean ratios (U.S. EPA, 2011a, p. 

2-14). 

In considering the information provided by the short-term exposure studies, the Policy 

Assessment recognized that to the extent these studies were conducted in areas that likely did 

not meet one or both of the current standards, such studies did not help inform the 

characterization of the potential public health improvements of alternative standards set at 

lower levels. Therefore, in considering the short-term exposure studies to inform staff 

conclusions regarding levels of the 24-hour standard that are appropriate to consider, the 

Policy Assessment placed greatest weight on studies conducted in areas that likely met both 

the current annual and 24-hour standards.

With regard to multi-city studies that evaluated effects associated with short-term PM2.5

exposures, as summarized in Figure 3 above and discussed in more detail in section III.E.4.c of 

the proposal, the Policy Assessment noted that, to the extent air quality distributions were 

reduced to reflect just meeting the current 24-hour standard, additional protection would be 

anticipated for the effects observed in the three multi-city studies with 98th percentile values 
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greater than 35 μg/m3(Burnett et al., 2004; Burnett and Goldberg, 2003; Franklin et al., 2008). 

In the three additional studies with 98th percentile values below 35 μg/m3, specifically 98th

percentile concentrations of 34.2, 34.3, and 34.8 μg/m3, the Policy Assessment noted that these 

studies reported long-term mean PM2.5 concentrations of 12.9, 13.2, and 13.4 μg/m3, 

respectively (Bell et al., 2008; Zanobetti and Schwartz, 2009; Dominici et al., 2006a). To the 

extent that consideration was given to revising the level of the annual standard, as discussed in 

section III.E.4.b of the proposal, the Policy Assessment recognized that potential changes 

associated with meeting such an alternative annual standard would result in lowering risks 

associated with both long- and short-term PM2.5 exposures. Consequently, in considering a 24-

hour standard that would operate in conjunction with an annual standard to provide 

appropriate public health protection, the Policy Assessment noted that to the extent that the 

level of the annual standard was revised to within a range of 13 to 11 μg/m3, in particular in the 

range of 12 to 11 μg/m

3

, additional protection would be provided for the long-term effects 

observed in these multi-city studies (U.S. EPA, 2011a, p. 2-84). 

Based on this information, the Policy Assessment concluded that the multi-city, shortterm exposure studies generally provided support for retaining the 24-hour standard level at 35 

μg/m3so long as the standard is in conjunction with an annual standard level revised to within 

a range of 12 to 11 μg/m3(U.S. EPA, 2011a, p. 2-84). Alternatively, in conjunction with an 

annual standard level of 13 μg/m3, the Policy Assessment concluded that the multi-city studies 

provided limited support for revising the 24-hour standard level somewhat below 35 μg/m3, 

such as down to 30 μg/m3, based on one study (Bell et al., 2008) that reported positive and 
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statistically significant effects with an overall 98th percentile value below the level of the 

current 24-hour standard and an overall long-term mean concentration slightly less than 13 

μg/m3(Figure 3; U.S. EPA, 2011a, p. 2-84). 

In reaching staff conclusions regarding alternative 24-hour standard levels that were 

appropriate to consider, the Policy Assessment also took into account relevant information 

from single-city studies that evaluated effects associated with short-term PM2.5 exposures. The 

Policy Assessment recognized that these studies may provide additional insights regarding 

impacts on at-risk populations and/or on areas with isolated peak concentrations. 

As discussed in more detail in section III.E.4.c of the proposal, although a number of 

single-city studies reported effects at appreciably lower PM2.5 concentrations than multi-city 

short-term exposure studies, the uncertainties and limitations associated with the single-city 

studies were considerably greater than those associated with the multi-city studies and, thus, 

the Policy Assessment concluded there was less confidence in using these studies as a basis for 

setting the level of a standard. Therefore, the Policy Assessment concluded that the multi-city 

short-term exposure studies provided the strongest evidence to inform decisions on the level of 

the 24-hour standard, and the single-city studies did not warrant consideration of 24-hour 

standard levels different from those supported by the multi-city studies (U.S. EPA, 2011a, p. 2-

88). 

In addition to considering the epidemiological evidence, the Policy Assessment took into 

account air quality information based on county-level 24-hour and annual design values to 

understand the public health implications of the alternative standard levels supported by the 
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currently available scientific evidence, as discussed in section III.E.4.b above. Consistent with 

the general approach discussed in section III.A.3 above, the Policy Assessment considered the 

extent to which different combinations of alternative annual and 24-hour standard levels based 

on the evidence would support the policy goal of lowering annual and 24-hour air quality 

distributions by using the annual standard to be the “generally controlling” standard in 

conjunction with setting the 24-hour standard to provide supplemental protection (U.S. EPA, 

2011a, pp 2-88 to 2-91, Figure 2-10). 

Using information on the relationship of the 24-hour and annual design values, the 

Policy Assessment examined the implications of three alternative suites of PM2.5 standards 

identified as appropriate to consider based on the currently available scientific evidence, as 

discussed above. The Policy Assessment concluded that an alternative suite of PM2.5 standards 

that would include an annual standard level of 11 or 12 μg/m3and a 24-hour standard with a 

level of 35 μg/m3(i.e., 11/35 or 12/35) would result in the annual standard being the generally 

controlling standard in most areas although the 24-hour standard would continue to be the 

generally controlling standard in the Northwest (U.S. EPA, 2011a, pp. 2-89 to 2-91 and Figure 2-

10). These Northwest counties generally represented areas where the annual mean PM2.5

concentrations have historically been low but where relatively high 24-hour concentrations 

occur, often related to seasonal wood smoke emissions. Alternatively, combining an alternative 

annual standard of 13 μg/m3 with a 24-hour standard of 30 μg/m3 would result in many more 

areas across the country in which the 24-hour standard would likely become the controlling 

standard (the standard driving air quality distributions lower) than if an alternative annual 
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standard of 12 or 11 μg/m3 were paired with the current level of the 24-hour standard (i.e., 35 

μg/m3). 

The Policy Assessment concluded that consideration of retaining the 24-hour standard 

level at 35 μg/m3 would reflect placing greatest weight on evidence from multi-city studies that 

reported positive and statistically significant associations with health effects classified as having 

a causal or likely causal relationship. In conjunction with lowering the annual standard level, 

especially within a range of 12 to 11 μg/m3, this alternative recognized additional public health 

protection against effects associated with short-term PM2.5 exposures which would be provided 

by lowering the annual standard such that revision to the 24-hour standard would not be 

warranted (U.S. EPA, 2011a, p. 2-91). 

Beyond evidence-based considerations, the Policy Assessment also considered the 

extent to which the quantitative risk assessment supported consideration of retaining the 

current 24-hour standard level or provided support for lower standard levels. In considering 

simulations of just meeting the current 24-hour standard level of 35 μg/m3or alternative levels 

of 30 or 25 μg/m3(in conjunction with alternative annual standard levels within a range of 13 to 

11 μg/m3), the Policy Assessment noted that the overall confidence in the quantitative risk 

estimates varied for the different standard levels evaluated and was stronger for the higher 

levels and substantially lower for the lowest level evaluated (i.e., 25 μg/m3). Based on this 

information, the Policy Assessment concluded that the quantitative risk assessment provided 

support for considering a 24-hour standard level of 35 or 30 μg/m3(in conjunction with an 
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alternative standard level within a range of 13 to 11 μg/m3) but did not provide strong support 

for considering lower alternative 24-hour standard levels (U.S. EPA, 2011a, pp. 2-102 to 2-103).

Taken together, the Policy Assessment concluded that while it was appropriate to 

consider an alternative 24-hour standard level within a range of 35 to 30 μg/m3, the currently 

available evidence most strongly supported consideration for retaining the current 24-hour 

standard level at 35 μg/m3in conjunction with lowering the level of the annual standard within 

a range of 12 to 11 μg/m3(U.S. EPA, 2011a, p. 2-92).

ii. CASAC Advice

Based on its review of the second draft Policy Assessment, CASAC agreed with the 

general approach for translating the available epidemiological evidence, risk information, and 

air quality information into the basis for reaching conclusions on alternative standards for 

consideration. Furthermore, CASAC agreed “that it is appropriate to return to the strategy used 

in 1997 that considers the annual and the short-term standards together, with the annual 

standard as the controlling standard, and the short-term standard supplementing the 

protection afforded by the annual standard” and “considers it appropriate to place the greatest 

emphasis” on health effects judged to have evidence supportive of a causal or likely causal 

relationship as presented in the Integrated Science Assessment (Samet, 2010d, p. 1). 

CASAC concluded that the range of levels presented in the second draft Policy 

Assessment (i.e., alternative annual standard levels within a range of 13 to 11 μg/m3and 

alternative 24-hour standard levels within a range of 35 to 30 μg/m3) “are supported by the 

epidemiological and toxicological evidence, as well as by the risk and air quality information 
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compiled” in the Integrated Science Assessment, Risk Assessment, and second draft Policy 

Assessment. CASAC further noted that “[a]lthough there is increasing uncertainty at lower 

levels, there is no evidence of a threshold (i.e., a level below which there is no risk for adverse 

health effects)” (Samet, 2010d, p. ii). 

Although CASAC supported the alternative standard level ranges presented in the 

second draft Policy Assessment, it did not express support for any specific levels or 

combinations of standards. Rather, CASAC encouraged the EPA to develop a clearer rationale in 

the final Policy Assessment for staff conclusions regarding annual and 24-hour standards that 

were appropriate to consider, including consideration of the combination of these standards 

supported by the available information (Samet, 2010d, p. ii). Specifically, in commenting on a 

distributional statistical analysis of air quality and associated population data presented in the 

second draft Policy Assessment, CASAC encouraged staff to focus on information related to the 

concentrations that were most influential in generating the health effect estimates in individual 

studies to inform alternative standard levels. CASAC urged that the EPA redo that analysis using 

health event or study population data (Samet, 2010d, p. 2). CASAC also commented that the 

approach presented in the second draft Policy Assessment to identify alternative 24-hour 

standard levels which focused on peak-to-mean ratios was not relevant for informing the actual 

level (Samet 2010d, p. 4). Further, they expressed the concern that the combinations of annual 

and 24-hour standard levels discussed in the second draft Policy Assessment (i.e., in the range 

of 13 to 11 μg/m3for the annual standard, in conjunction with retaining the current 24-hour 

PM2.5 standard level of 35 μg/m3; alternatively, revising the level of the 24-hour standard to 30 
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μg/m3in conjunction with an annual standard level of 11 μg/m3) “may not be adequately 

inclusive” and “[i]t was not clear why, for example a daily standard of 30 μg/m3should only be 

considered in combination with an annual level of 11 μg/m3” (Samet, 2010d, p. ii). CASAC 

encouraged the EPA to more clearly explain its rationale for identifying the 24-hour/annual 

combinations that are appropriate for consideration (Samet 2010d, p. ii). 

In considering CASAC’s advice as well as public comment on the second draft Policy 

Assessment, the EPA staff conducted additional analyses and modified their conclusions 

regarding alternative standard levels that were appropriate to consider. The staff conclusions in 

the final Policy Assessment (U.S. EPA, 2011a, section 2.3.4.4) differed somewhat from the 

alternative standard levels discussed in the second draft Policy Assessment (U.S. EPA, 2010f, 

section 2.3.4.3), upon which CASAC based its advice. Changes made in the final Policy 

Assessment were primarily focused on improving and clarifying the approach for translating the 

epidemiological evidence into a basis for staff conclusions on the broadest range of alternative 

standard levels supported by the available scientific information and more clearly articulating 

the rationale for the staff’s conclusions (Wegman, 2011, pp. 1 to 2). Consistent with CASAC’s 

advice to consider more information from epidemiological studies, as discussed in section 

III.E.4.b.1 above, the EPA analyzed additional population-level data obtained from several study 

authors (Rajan et al., 2011). In transmitting the final Policy Assessment to CASAC, the Agency 

notified CASAC that the final staff conclusions reflected consideration of CASAC’s advice and 

that those staff conclusions were based, in part, on the specific distributional analysis that 

CASAC had urged the EPA to conduct (Wegman, 2011, p.2). Thus, CASAC had an opportunity to 
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comment on the final Policy Assessment, but chose not to provide any additional comments or 

advice after receiving it. 

iii. Administrator’s Proposed Decisions on the Primary PM2.5 Standard Levels

In reaching her conclusions regarding appropriate alternative standard levels to 

consider, the Administrator considered the epidemiological and other scientific evidence, 

estimates of risk reductions associated with just meeting alternative annual and/or 24-hour 

standards, air quality analyses, related limitations and uncertainties, staff conclusions as 

presented in the Policy Assessment, and the advice of CASAC. As an initial matter, the 

Administrator agreed with the general approach discussed in the Policy Assessment as 

summarized in sections III.A.3 and III.E.4.a above, and supported by CASAC, of considering the 

protection afforded by the annual and 24-hour standards taken together for mortality and 

morbidity effects associated with both long- and short-term exposures to PM2.5 (77 FR 38939). 

Furthermore, based on the evidence and quantitative risk assessment, the Administrator 

provisionally concluded it is appropriate to set a “generally controlling” annual standard that 

will lower a wide range of ambient 24-hour concentrations, with a 24-hour standard focused on 

providing supplemental protection, particularly for areas with high peak-to-mean ratios 

possibly associated with strong local or seasonal sources, or PM2.5-related effects that may be 

associated with shorter-than daily exposure periods. The Administrator provisionally concluded 

this approach would likely reduce aggregate risks associated with both long- and short-term 

exposures more consistently than a generally controlling 24-hour standard and would be the 

most effective and efficient way to reduce total PM2-5-related population risk. Id.
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In reaching decisions on alternative standard levels to propose, the Administrator 

judged that it was most appropriate to examine where the evidence of associations observed in 

the epidemiological studies was strongest and, conversely, where she had appreciably less 

confidence in the associations observed in the epidemiological studies. Based on the 

characterization and assessment of the epidemiological and other studies presented and 

assessed in the Integrated Science Assessment and the Policy Assessment, the Administrator 

recognized the substantial increase in the number and diversity of studies available in this 

review including extended analyses of the seminal studies of long-term PM2.5 exposures (i.e., 

ACS and Harvard Six Cities studies) as well as important new long-term exposure studies (as 

summarized in Figures 1 and 2). Collectively, the Administrator noted that these studies, along 

with evidence available in the last review, provided consistent and stronger evidence of an 

association with premature mortality, with the strongest evidence related to cardiovascularrelated mortality, at lower ambient concentrations than previously observed. The Administrator 

also recognized the availability of stronger evidence of morbidity effects associated with longterm PM2.5 exposures, including evidence of cardiovascular effects from the WHI study and 

respiratory effects, including decreased lung function growth, from the extended analyses for 

the Southern California Children’s Health Study. Furthermore, the Administrator recognized 

new U.S. multi-city studies that greatly expanded and reinforced our understanding of mortality 

and morbidity effects associated with short-term PM2.5 exposures, providing stronger evidence 

of associations at ambient concentrations similar to those previously observed (as summarized 

in Figure 3). Id. at 38939-40.
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The newly available scientific evidence built upon the previous scientific data base to 

provide evidence of generally robust associations and to provide a basis for greater confidence 

in the reported associations than in the last review. The Administrator recognized that the 

weight of evidence, as evaluated in the Integrated Science Assessment, was strongest for health 

endpoints classified as having evidence of a causal relationship. These relationships included 

those between long- and short-term PM2.5 exposures and mortality and cardiovascular effects. 

She recognized that the weight of evidence was also strong for health endpoints classified as 

having evidence of a likely causal relationship, which included those between long- and shortterm PM2.5 exposures and respiratory effects. In addition, the Administrator made note of the 

much more limited evidence for health endpoints classified as having evidence suggestive of a 

causal relationship, including developmental, reproductive and carcinogenic effects. Id .at 

38940.

Based on information discussed and presented in the Integrated Science Assessment, 

the Administrator recognized that health effects may occur over the full range of 

concentrations observed in the long- and short-term epidemiological studies and that no 

discernible threshold for any effects can be identified based on the currently available evidence 

(U.S. EPA, 2009a, section 2.4.3). She also recognized, in taking note of CASAC advice and the 

distributional statistics analysis discussed in section III.E.4.b.i above and in the Policy 

Assessment, that there was significantly greater confidence in observed associations over 

certain parts of the air quality distributions in the studies, and conversely, that there was 
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significantly diminished confidence in ascribing effects to concentrations toward the lower part 

of the distributions.

Consistent with the general approach summarized in section III.A.3 above, and 

supported by CASAC as discussed in section III.E.4.a above, the Administrator generally agreed 

that it was appropriate to consider a level for an annual standard that was somewhat below the 

long-term mean PM2.5 concentrations reported in long- and short-term exposure studies. In 

recognizing that the evidence of an association in any such study was strongest at and around 

the long-term average where the data in the study are most concentrated, she understood that 

this approach did not provide a bright line for reaching decisions about appropriate standard 

levels. The Administrator noted that long-term mean PM2.5 concentrations were available for 

each study considered and, therefore, represented the most robust data set to inform her 

decisions on appropriate annual standard levels. She also noted that the overall study mean 

PM2.5 concentrations were generally calculated based on monitored concentrations averaged 

across monitors in each study area with multiple monitors, referred to as a composite monitor 

concentration, in contrast to the highest concentration monitored in each study area, referred 

to as a maximum monitor concentration, which are used to determine whether an area meets a 

given standard. In considering such long-term mean concentrations, the Administrator 

understood that it was appropriate to consider the weight of evidence for the health endpoints 

evaluated in such studies in giving weight to this information. Id.

Based on the information summarized in Figure 4 above and presented in more detail in 

the Policy Assessment (U.S. EPA, 2011a, chapter 2) for effects classified in the Integrated 
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Science Assessment as having a causal or likely causal relationship with PM2.5 exposures, the 

Administrator observed an overall pattern of statistically significant associations reported in 

studies of long-term PM2.5 exposures with long-term mean concentrations ranging from 

somewhat above the current standard level of 15 μg/m3down to the lowest mean 

concentration in such studies of 12.9 μg/m3(in Miller et al., 2007).102 She observed a similar 

pattern of statistically significant associations in studies of short-term PM2.5 exposures with 

long-term mean concentrations ranging from around 15 μg/m3down to 12.8 μg/m3(in Burnett 

et al., 2004). With regard to effects classified as providing evidence suggestive of a causal 

relationship, the Administrator observed a small number of long-term exposure studies related 

to developmental and reproductive effects that reported statistically significant associations 

with overall study mean PM2.5 concentrations down to 11.9 μg/m3(in Bell et al., 2007).103 Id.

The Administrator also considered additional information from epidemiological studies, 

consistent with CASAC advice, to take into account the broader distribution of PM2.5

concentrations and the degree of confidence in the observed associations over the broader air 

 

102 The EPA notes that the Miller et al., (2007) study provides strong evidence of cardiovascular 

related effects associated with long-term PM2.5 exposures. At the time of the proposal, the 

EPA recognized the limited nature of the air quality data considered in this study (77 FR 

38918, fn. 62). The EPA has reviewed those limitations, in conjunction with consideration of 

public comments received on the proposal as discussed in section III.E.4.c, in conjunction 

with reaching a final decision on the level of the annual standard. 

103 With respect to suggestive evidence related to cancer, mutagenic, and genotoxic effects, the 

PM2.5 concentrations reported in studies generally included ambient concentrations that are 

equal to or greater than ambient concentrations observed in studies that reported mortality 

and cardiovascular and respiratory effects (U.S. EPA, 2009a, section 7.5), such that in 

selecting alternative standard levels that provide protection from mortality and 

cardiovascular and respiratory effects, it is reasonable to anticipate that protection will also 

be provided for carcinogenic effects.
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quality distribution. In considering this additional information, she understood that the Policy 

Assessment presented information on the 25th and 10th percentiles of the distributions of PM2.5

concentrations available from four multi-city studies to provide a general frame of reference as 

to the part of the distribution in which the data become appreciably more sparse and, thus, 

where her confidence in the associations observed in epidemiological studies would become 

appreciably less. 

As summarized in Figure 4 above, the Administrator took note of additional populationlevel data that were available for four studies (Krewski et al., 2009; Miller et al., 2007; Bell et al., 

2008; Zanobetti and Schwartz, 2009), each of which reported statistically significant 

associations with health endpoints classified as having evidence of a causal relationship. In 

considering the long-term PM2.5 concentrations associated with the 25th percentile values of 

the population-level data for these four studies, she observed that these values ranged from 

somewhat above to somewhat below 12 μg/m3. The Administrator recognized that three of 

these four studies represented some of the strongest evidence available within the overall body 

of scientific evidence and noted that three of these studies (Krewski et al., 2009; Bell et al., 

2008; Zanobetti and Schwartz, 2009) were used as the basis for concentration-response 

functions used in the quantitative risk assessment (U.S. EPA, 2010a, section 3.3.3). 

In considering this information, the Administrator noted that CASAC advised that 

information about the long-term PM2.5 concentrations that were most influential in generating 

the health effect estimates in epidemiological studies can help to inform selection of an 

appropriate annual standard level. However, the Administrator also recognized that additional 
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population-level data were available for only these four studies and, therefore, she believed 

that these studies comprised a more limited data set than one based on long-term mean PM2.5

concentrations for which data were available for all studies considered, as discussed above. 

The Administrator recognized, as summarized in section III.B.2 above, that important 

uncertainties remain in the evidence and information considered in this review of the primary 

fine particle standards. These uncertainties are generally related to understanding the relative 

toxicity of the different components in the fine particle mixture, the role of PM2.5 in the 

complex ambient mixture, exposure measurement errors inherent in epidemiological studies 

based on concentrations measured at fixed monitor sites, and the nature, magnitude, and 

confidence in estimated risks related to increasingly lower ambient PM2.5 concentrations. 

Furthermore, the Administrator noted that epidemiological studies have reported 

heterogeneity in responses both within and between cities and geographic regions across the 

U.S. She recognized that this heterogeneity may be attributed, in part, to differences in fine 

particle composition in different regions and cities. The Administrator also recognized that 

there are additional limitations associated with evidence for reproductive and developmental 

effects, identified as being suggestive of a causal relationship with long-term PM2.5 exposures,

including: the limited number of studies evaluating such effects; uncertainties related to 

identifying the relevant exposure time periods of concern; and limited toxicological evidence 

providing little information on the mode of action(s) or biological plausibility for an association 

between long-term PM2.5 exposures and adverse birth outcomes. Id. at 38941.
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The Administrator was mindful that considering what standards were requisite to 

protect public health with an adequate margin of safety required public health policy 

judgments that neither overstated nor understated the strength and limitations of the evidence 

or the appropriate inferences to be drawn from the evidence. In considering how to translate 

the available information into appropriate standard levels, the Administrator weighed the 

available scientific information and associated uncertainties and limitations. For the purpose of 

determining what standard levels were appropriate to propose, the Administrator recognized, 

as did EPA staff in the Policy Assessment, that there was no single factor or criterion that 

comprised the sole “correct” approach to weighing the various types of available evidence and 

information, but rather there were various approaches that are appropriate to consider. The 

Administrator further recognized that different evaluations of the evidence and other 

information before the Administrator could reflect placing different weight on the relative 

strengths and limitations of the scientific information, and different judgments could be made 

as to how such information should appropriately be used in making public health policy 

decisions on standard levels. This recognition led the Administrator to consider various 

approaches to weighing the evidence so as to identify appropriate standard levels to propose. 

In so doing, the Administrator encouraged extensive public comment on alternative approaches 

to weighing the evidence and other information so as to inform her public health policy 

judgments before reaching final decisions on appropriate standard levels.

In considering the available information, the Administrator noted the advice of CASAC 

that the currently available scientific information, including epidemiological and toxicological 
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evidence as well as risk and air quality information, provided support for considering an annual 

standard level within a range of 13 to 11 μg/m3and a 24-hour standard level within a range of 

35 to 30 μg/m3. In addition, the Administrator recognized that the Policy Assessment concluded 

that the available evidence and risk-based information support consideration of annual 

standard levels in the range of 13 to 11 μg/m3, and that the Policy Assessment also concluded 

that the evidence most strongly supported consideration of an annual standard level in the 

range of 12 to 11 μg/m3. In considering how the annual and 24-hour standards work together 

to provide appropriate public health protection, the Administrator observed that CASAC did not 

express support for any specific levels or combinations of standards within these ranges. Nor 

did CASAC choose to comment on additional information and analyses presented in the final 

Policy Assessment prepared in response to CASAC’s recommendations on the second draft 

Policy Assessment (Wegman, 2011). 

In considering the extent to which the currently available evidence and information 

provided support for specific standard levels within the ranges identified by CASAC and the 

Policy Assessment as appropriate for consideration, the Administrator initially considered 

standard levels within the range of 13 to 11 μg/m3for the annual standard. In so doing, the 

Administrator first considered the long-term mean PM2.5 concentrations reported in studies of 

effects classified as having evidence of a causal or likely causal relationship, as summarized in 

Figure 4 above and discussed more broadly above. She noted that a level at the upper end of 

this range would be below most but not all the overall study mean concentrations from the 

multi-city studies of long- and short-term exposures, whereas somewhat lower levels within 
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this range would be below all such overall study mean concentrations. In considering the 

appropriate weight to place on this information, the Administrator again noted that the 

evidence of an association in any such study was strongest at and around the long-term 

average where the data in the study are most concentrated, and that long-term mean PM2.5

concentrations were available for each study considered and, therefore, represented the most 

robust data set to inform her decisions on appropriate annual standard levels. Further, she was 

mindful that this approach did not provide a bright line for reaching decisions about 

appropriate standard levels. Id.

In considering the long-term mean PM2.5 concentrations reported in studies of effects 

classified as having evidence suggestive of a causal relationship, as summarized in Figure 4 for 

reproductive and developmental effects, the Administrator noted that a level at the upper end 

of this range would be below the overall study mean concentration in one of the three studies, 

while levels in the mid- to lower part of this range would be below the overall study mean 

concentrations in two or three of these studies. In considering the appropriate weight to place 

on this information, the Administrator noted the very limited nature of this evidence of such 

effects and the additional uncertainties in these epidemiological studies relative to the studies 

that provide evidence of causal or likely causal relationships.

The Administrator also considered the distributional analyses of population-level 

information that were available from four of the epidemiological studies that provide evidence 

of effects identified as having a causal relationship with long- or short-term PM2.5

concentrations for annual standard levels within the same range of 13 to 11 μg/m3. In so doing, 
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the Administrator first noted that a level in the mid-part of this range generally corresponds 

with approximately the 25thpercentile of the distributions of health events data available in 

three of these studies. The Administrator also noted that standard levels toward the upper part 

of this range would reflect placing substantially less weight on this information, whereas 

standard levels toward the lower part of this range would reflect placing substantially more 

weight on this information. In considering this information, the Administrator noted that there 

was no bright line that delineates the part of the distribution of PM2.5 concentrations within 

which the data become appreciably more sparse and, thus, where her confidence in the 

associations observed in epidemiological studies became appreciably less.

In considering mean PM2.5 concentrations and distributional analyses from the various 

sets of epidemiological studies noted above, the Administrator was mindful, as noted above, 

that such studies typically report concentrations based on composite monitor distributions, in 

which concentrations may be averaged across multiple ambient monitors that may be present 

within each area included in a given study. Thus, a policy approach that used data based on 

composite monitors to identify potential alternative standard levels would inherently build in a 

margin of safety of some degree relative to an alternative standard level based on 

measurements at the monitor within an area that records the highest concentration, or the 

maximum monitor, since once a standard was set, concentrations at appropriate maximum 

monitors within an area were generally used to determine whether an area meets a given 

standard.
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The Administrator also recognized that judgments about the appropriate weight to 

place on any of the factors discussed above should reflect consideration not only of the relative 

strength of the evidence but also on the important uncertainties that remained in the evidence 

and information being considered in this review. The Administrator noted that the extent to 

which these uncertainties influenced judgments about appropriate annual standard levels 

within the range of 13 to 11 μg/m3 would likely be greater for standard levels in the lower part 

of this range which would necessarily be based on fewer available studies than would higher 

levels within this range.

Based on the above considerations, the Administrator concluded that it was appropriate 

to propose to set a level for the primary annual PM2.5 standard within the range of 12 to 13 

μg/m3. The Administrator provisionally concluded that a standard set within this range would 

reflect alternative approaches to appropriately placing the most weight on the strongest 

available evidence, while placing less weight on much more limited evidence and on more 

uncertain analyses of information available from a relatively small number of studies. Further, 

she provisionally concluded that a standard level within this range would reflect alternative 

approaches to appropriately providing an adequate margin of safety for the populations at risk 

for the serious health effects classified as having evidence of a causal or likely causal 

relationship, depending in part on the emphasis placed on margin of safety considerations. The 

Administrator recognized that setting an annual standard level at the lower end of this range

would reflect an approach that placed more emphasis on the entire body of the evidence, 

including the analysis of the distribution of air quality concentrations most influential in 
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generating health effect estimates in the studies, and on margin of safety considerations, than 

would setting a level at the upper end of the range. Conversely, an approach that would 

support a level at the upper end of this range would generally support a view that the 

uncertainties remaining in the evidence are such that the evidence does not warrant setting a 

lower annual standard level. Id. at 38942.

At the time of the proposal, while the Administrator recognized that CASAC advised, and 

the Policy Assessment concluded, that the available scientific information provided support for 

considering a range that extended down to 11 μg/m3, she concluded that proposing such an 

extended range would reflect a public health policy approach that placed more weight on 

relatively limited evidence and more uncertain information and analyses than she considered 

appropriate at this time. Nonetheless, the Administrator solicited comment on a level down to 

11 μg/m3as well as on approaches for translating scientific evidence and rationales that would 

support such a level. Such an approach might reflect a view that the uncertainties associated 

with the available scientific information warrant a highly precautionary public health policy

response that would incorporate a large margin of safety.

The Administrator recognized that potential air quality changes associated with meeting 

an annual standard set at a level within the range of 12 to 13 μg/m3 will result in lowering risks 

associated with both long- and short-term PM2.5 exposures. However, the Administrator 

recognized that such an annual standard intended to serve as the primary means for providing 

protection from effects associated with both long- and short-term PM2.5 exposures would not 

by itself be expected to offer requisite protection with an adequate margin of safety against the 
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effects of all short-term PM2.5 exposures. As a result, in conjunction with proposing an annual 

standard level in the range of 12 to 13 μg/m3, the Administrator provisionally concluded that it 

was appropriate to continue to provide supplemental protection by means of a 24-hour 

standard set at the appropriate level, particularly for areas with high peak-to-mean ratios 

possibly associated with strong local or seasonal sources, or for PM2.5-related effects that may 

be associated with shorter-than-daily exposure periods. 

Based on the approach discussed in section III.A.3 above, at the time of the proposal the 

Administrator relied upon evidence from the short-term exposure studies as the principal basis 

for selecting the level of the 24-hour standard. In considering these studies as a basis for the 

level of a 24-hour standard, and having selected a 98th percentile form for the standard, the 

Administrator agreed with the focus in the Policy Assessment of looking at the 98th percentile 

values, as well as at the long-term mean PM2.5 concentrations in these studies.

In considering the information provided by the short-term exposure studies, the 

Administrator recognized that to the extent these studies were conducted in areas that likely 

did not meet one or both of the current standards, such studies did not help inform the 

characterization of the potential public health improvements of alternative standards set at 

lower levels. By reducing the PM2.5 concentrations in such areas to just meet the current 

standards, the Administrator anticipated that additional public health protection would occur. 

Therefore, the Administrator focused on studies that reported positive and statistically 

significant associations in areas that would likely have met both the current 24-hour and annual 

standards. She also considered whether or not these studies were conducted in areas that 
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would likely have met an annual standard level of 12 to 13 μg/m3to inform her decision 

regarding an appropriate 24-hour standard level. As discussed in section III.E.4.a, consistent 

with the Policy Assessment, the Administrator concluded that multi-city, short-term exposure 

studies provided the strongest data set for informing her decisions on appropriate 24-hour 

standard levels. The Administrator viewed the single-city, short-term exposure studies as a 

much more limited data set providing mixed results and, therefore, she had less confidence in 

using those studies as a basis for setting the level of a 24-hour standard. With regard to the 

limited number of single-city studies that reported positive and statistically significant 

associations for a range of health endpoints related to short-term PM2.5 concentrations in areas 

that would likely have met the current suite of PM2.5 standards, the Administrator recognized 

that many of those studies had significant limitations (e.g., limited statistical power, limited 

exposure data) or equivocal results (mixed results within the same study area) that made them 

unsuitable to form the basis for setting the level of a 24-hour standard. 

With regard to multi-city studies that evaluated effects associated with short-term PM2.5

exposures, the Administrator observed an overall pattern of positive and statistically significant 

associations in studies with 98th percentile values averaged across study areas in the range of 

45.8 to 34.2 μg/m3(Burnett et al., 2004; Zanobetti and Schwartz, 2009; Bell et al., 2008; 

Dominici et al., 2006a, Burnett and Goldberg, 2003; Franklin et al., 2008). The Administrator 

noted that, to the extent air quality distributions were reduced to reflect just meeting the 

current 24-hour standard, additional protection would be anticipated for the effects observed 

in the three multi-city studies with 98th percentile values greater than 35 μg/m3(Burnett et al., 
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2004; Burnett and Goldberg, 2003; Franklin et al., 2008). In the three additional studies with 

98th percentile values below 35 μg/m3, specifically 98th percentile concentrations of 34.2, 34.3, 

and 34.8 μg/m3, the Administrator noted that these studies reported long-term mean PM2.5

concentrations of 12.9, 13.2, and 13.4 μg/m3, respectively (Bell et al., 2008; Zanobetti and 

Schwartz, 2009; Dominici et al., 2006a).

In proposing to revise the level of the annual standard to within the range of 12 to 13 

μg/m3, as discussed above, the Administrator recognized that additional protection would be 

provided for the short-term effects observed in these multi-city studies in conjunction with an 

annual standard level of 12 μg/m3, and in two of these three studies in conjunction with an 

annual standard level of 13 μg/m3. She noted that the study-wide mean concentrations were 

based on averaging across monitors within study areas and that compliance with the standard 

would be based on concentrations measured at the monitor reporting the highest 

concentration within each area. The Administrator believed it would be reasonable to conclude 

that revision to the 24-hour standard would not be appropriate in conjunction with an annual 

standard within this range. Based on the above considerations related to the epidemiological 

evidence, the Administrator provisionally concluded that it was appropriate to retain the level 

of the 24-hour standard at 35 μg/m3, in conjunction with a revised annual standard level in the 

proposed range of 12 to 13 μg/m3.

In addition to considering the epidemiological evidence, the Administrator also took into 

account air quality information based on county-level 24-hour and annual design values to 

understand the public health implications of retaining the 24-hour standard level at 35 μg/m3in 
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conjunction with an annual standard level within the proposed range of 12 to 13 μg/m3. She 

considered whether these suites of standards would meet a public health policy goal which 

included setting the annual standard to be the “generally controlling” standard in conjunction 

with setting the 24-hour standard to provide supplemental protection to the extent that 

additional protection is warranted. As discussed above, the Administrator provisionally 

concluded that this approach was the most effective and efficient way to reduce total 

population risk associated with both long- and short-term PM2.5 exposures, resulting in more 

uniform protection across the U.S. than the alternative of setting the 24-hour standard to be 

the controlling standard. 

In considering the air quality information, the Administrator first recognized that there 

was no annual standard within the proposed range of levels, when combined with a 24-hour 

standard at the proposed level of 35 μg/m3, for which the annual standard would be the 

generally controlling standard in all areas of the country. She further observed that such a suite 

of PM2.5 standards with an annual standard level of 12 μg/m3 would result in the annual 

standard as the generally controlling standard in most regions across the country, except for 

certain areas in the Northwest, where the annual mean PM2.5 concentrations have historically 

been low but where relatively high 24-hour concentrations occur, often related to seasonal 

wood smoke emissions (U.S. EPA, 2011a, pp. 2-89 to 2-91, Figure 2-10). Although not explicitly 

delineated on Figure 2-10 in the Policy Assessment, an annual standard of 13 μg/m3 would be 

somewhat less likely to be the generally controlling standard in some regions of the U.S. 

outside the Northwest in conjunction with a 24-hour standard level of 35 μg/m3.
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Taking the above considerations into account, the Administrator proposed to revise the 

level of the primary annual PM2.5 standard from 15.0 μg/m3to within the range of 12.0 to 13.0 

μg/m3and to retain the 24-hour standard level at 35 μg/m3. In the Administrator’s judgment, 

such a suite of primary PM2.5 standards and the rationale supporting such levels could 

reasonably be judged to reflect alternative approaches to the appropriate consideration of the 

strength of the available evidence and other information and their associated uncertainties and 

the advice of CASAC.

The Administrator recognized that the final suite of standards selected from within the 

proposed range of annual standard levels, or the broader range of annual standard levels on 

which public comment was solicited, must be clearly responsive to the issues raised by the D.C. 

Circuit’s remand of the 2006 primary annual PM2.5 standard. Furthermore, at the time of the 

proposal, she recognized that the final suite of standards will reflect her ultimate judgment in 

the final rulemaking as to the suite of primary PM2.5 standards that would be requisite to 

protect the public health with an adequate margin of safety from effects associated with fine 

particle exposures. The final judgment to be made by the Administrator will appropriately 

consider the requirement for a standard that is neither more nor less stringent than necessary 

and will recognize that the CAA does not require that primary standards be set at a zero-risk 

level, but rather at a level that reduces risk sufficiently so as to protect public health with an 

adequate margin of safety. 

At the time of the proposal, having reached her provisional judgment to propose 

revising the annual standard level from 15.0 to within a range of 12.0 to 13.0 μg/m3and to 
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propose retaining the 24-hour standard level at 35 μg/m3, the Administrator solicited public 

comment on this range of levels and on approaches to considering the available evidence and 

information that would support the choice of levels within this range. The Administrator also 

solicited public comment on alternative annual standard levels down to 11 μg/m3and on the 

combination of annual and 24-hour standards that commenters may believe is appropriate, 

along with the approaches and rationales used to support such levels. In addition, given the 

importance the evidence from epidemiologic studies played in considering the appropriate 

annual and 24-hour levels, the Administrator solicited public comment on issues related to 

translating epidemiological evidence into standards, including approaches for addressing the 

uncertainties and limitations associated with this evidence.

c. Comments on Standard Levels

This section addresses comments that relate to consideration of the appropriate levels 

of the primary annual and 24-hour PM2.5 standards, including comments on the general 

approach used by the EPA to translate the available scientific information into standard levels 

and how specific PM2.5 exposure studies should be considered as a basis for the standard levels. 

These comments on standard levels expand upon the more general comments that either 

supported or opposed any change to the current suite of primary PM2.5 standards, which are 

addressed above in section III.D.2.104 As explained there, one group of commenters generally 

opposed any change to the current primary PM2.5 standards and more specifically disagreed 

with the basis for the EPA’s proposal to revise the annual standard level. Another group of 

 

104 Specific comments on the forms of the annual and 24-hour standards are addressed in 

section III.E.3.a and III.E.3.b, respectively.
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commenters supported revising the current suite of primary PM2.5 standards to provide 

increased public health protection. Some commenters in this second group argued that both 

the annual and 24-hour standard levels should be lowered while other commenters in this 

group agreed with the EPA’s proposal to retain the level of the 24-hour standard in conjunction 

with revising the level of the annual standard. While generally supporting the EPA’s proposal to 

lower the level of the annual standard, many commenters in this group disagreed that a level 

within the EPA’s proposed range was adequately protective and supported a level of 11 μg/m3

or below.

i. Annual Standard Level

The group of commenters opposed to any change to the current suite of primary PM2.5

standards generally raised questions regarding the underlying scientific evidence, including the 

causal determinations reached in the Integrated Science Assessment, and focused strongly on 

the uncertainties they saw in the scientific evidence as a basis for their conclusion that no 

changes to the current standard levels were warranted. In commenting on the proposed 

standard levels, these commenters typically relied on the arguments summarized and 

addressed above in section III.D.2 as to why they believed it was inappropriate for the EPA to 

make any revisions to the suite of primary PM2.5 standards. That is, they asserted that the EPA’s 

causal determinations were not adequately supported by the underlying scientific information; 

the biological plausibility of health effects observed in epidemiological studies has not been 

demonstrated in controlled human exposure and toxicological studies; uncertainties in the 

underlying health science are as great or greater than in 2006; there is no evidence of greater 
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risk since the last review to justify tightening the current annual PM2.5 standard; and “new” 

studies not included in the Integrated Science Assessment continue to increase uncertainty 

about possible health risks associated with exposure to PM2.5.

With regard to the level of the annual standard, these commenters strongly disagreed 

with the Agency’s proposed decision to revise the level to within a range of 12 to 13 μg/m3and 

argued that the current standard level of 15 μg/m3should be retained. For example, UARG, API, 

and other commenters in this group raised a number of issues that they asserted called into 

question the EPA’s interpretation of the epidemiological evidence to support revising the 

annual standard level. These commenters raised specific questions related to the general 

approach used by the EPA to translate the air quality and other information from specific 

epidemiological studies into standard levels, including: (1) the EPA’s approach for using 

composite monitor air quality distributions reported in epidemiological studies to select a 

standard level that would be compared to measurements at the monitor recording the highest 

value in an area to determine compliance with the standard; (2) the appropriate exposure 

period for effects observed in long-term exposure mortality studies; and (3) the use of the 

EPA’s analysis of distributions of underlying population-level data (i.e., health event and study 

population data) for those epidemiological studies for which such information was available. 

These commenters also raised questions regarding the EPA’s consideration of specific scientific 

evidence as a basis for setting a standard level, including: (4) evidence of respiratory morbidity 

effects in long-term exposure studies and (5) more limited evidence of health effects which 

have been categorized in the Integrated Science Assessment as suggestive of a causal 
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relationship (i.e., developmental and reproductive outcomes). These comments are discussed 

in turn below.

(1) Some commenters in this group argued that one reason why they believe there is no 

basis for setting a standard level below 15 μg/m3is that the air quality metric from 

epidemiological studies that the EPA relied on in the proposal is not the same metric that will 

be compared to the level of the standard to determine compliance with the standard. That is, 

commenters noted that the long-term mean PM2.5 concentrations that the EPA considered, 

shown in Figure 4 above, are composite monitor mean concentrations (i.e., concentrations 

averaged across multiple monitors within areas with more than one monitor), whereas the 

PM2.5 concentrations that will be compared to the level of the standard are maximum monitor

concentrations (i.e., the concentration measured by the monitor within an area reporting the 

highest concentration). This comment was presented most specifically in UARG’s comments 

(UARG, 2012, Attachment 1, pp. 2 to 6), which raised two overarching issues as discussed 

below.

First, the commenter noted that the EPA’s approach of considering composite monitor 

mean PM2.5 concentrations in selecting a standard level, and then comparing the maximum 

monitor mean PM2.5 concentration in each area to the standard level when the standard is 

implemented, was characterized in the proposal as inherently having the potential to build in a 

margin of safety (UARG, 2012, Attachment 1, p. 4, citing 77 FR 38905). The commenter asserted 

that the Administrator is ignoring this distinction between composite and maximum monitor 

concentrations, and that this approach creates an unwarranted case for lowering the standard 
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level, since in the commenter’s view, it would result in a margin of safety that would be 

arbitrary, not based on evidence, and unquantified (UARG, 2012, Attachment 1, p. 4). In 

support of this view, the commenter asserted that there is a significant difference between 

composite monitor mean PM2.5 concentrations and maximum monitor mean PM2.5

concentrations. The commenter asserted that the maximum monitor value will always be 

higher than the composite monitor value (except in areas that contain only a single monitor), 

such that when an area just attains the NAAQS, that area’s composite monitor long-term mean 

PM2.5 concentration will be lower than the level of the standard (UARG, 2012, Attachment 1, p. 

3).

Second, the commenter asserted that a more “reasoned and consistent approach would 

be to decide on a mean composite monitor PM2.5 level that should be achieved and then 

identify the maximum monitor level that would result in that composite value” (UARG, 2012, 

Attachment 1, p. 4). The commenter conducted an analysis of maximum monitor versus 

composite monitor annual mean PM2.5 concentrations using monitoring data105 from 2006 to 

2008 and presented results averaged across areas within two groups (i.e., those with design 

values106 above the current standard level and those with design values just below the current 

standard level) to illustrate their suggested alternative approach. The commenter interpreted 

this analysis as showing that the composite monitor long-term mean PM2.5 concentrations from 

the subset of the epidemiological studies shown in Figure 4 (of the proposal and above) that 

 

105 The commenter indicated that this analysis was based on monitoring data for every core 

based statistical area (CBSA) in the EPA’s Air Quality System (AQS) database.

106 The design value is the air quality statistic that is compared to the level of the NAAQS to 

determine the attainment status of a given area.
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the commenter considered to be an appropriate focus for this analysis would be achieved 

across the U.S. if the current annual NAAQS of 15 μg/m3is retained and attained. The 

commenter considered the subset of epidemiological studies that included only long-term 

exposures studies of effects for which the evidence is categorized as causal or likely causal, but 

did not consider short-term exposure studies. On this basis, the commenter asserted that 

attaining the current annual PM2.5 standard would result in composite monitor long-term mean 

concentrations in all areas that would be generally within or below the range of the composite 

monitor long-term mean concentrations from such studies and, as a result, there is no reason 

to lower the level of the current annual NAAQS.

In considering the first issue related to the EPA’s approach, the EPA notes that in 

proposing to revise both the form and level of the annual standard, the Administrator clearly 

took into account the distinction between the composite monitor long-term mean PM2.5

concentrations from the epidemiological studies, considered as a basis for selecting an annual 

standard level, and maximum monitor long-term mean PM2.5 concentrations. In deciding to 

focus on the composite monitor long-term mean concentrations in selecting the standard level, 

and on the maximum monitor concentrations in selecting the form of the standard (i.e., 

consistent with proposing to eliminate the option for spatial averaging across monitors within 

an area when implementing the standard107), the Administrator reasonably considered the 

distinction between these metrics in a manner that was consistent with advice from CASAC 

(Samet et al., 2010d, pp. 2 to 3).

 

107 As discussed above in section III.E.3.a.
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As noted above in section III.A.3, the EPA recognizes that a statistical metric (e.g., the 

mean of a distribution) based on maximum monitor concentrations may be identical to or 

above the same statistical metric based on composite monitor concentrations. More 

specifically, many areas have only one monitor, in which case the composite and maximum 

monitor concentrations are identical. Based on the most recent data from the EPA’s AQS from 

2009 to 2011 in the 331 CBSAs in which valid PM2.5 data are available, as discussed in Frank 

(2012a, Table 5), there were 208 such areas (with design values ranging up to about 15 μg/m3). 

Frank (2012a) also observed that other areas have multiple monitors with composite and 

maximum monitor mean PM2.5 concentrations that were the same or relatively close, with 57 

areas in which the maximum monitor mean concentration was no more than 0.5 μg/m3higher 

than the composite monitor mean concentration and 56 areas in which the difference was 

between 0.6 and 2 μg/m3. Further, there were only a few other areas in which the maximum 

monitor mean concentration was appreciably higher than the composite monitor mean 

concentration, such as areas in which some monitors may be separately impacted by local 

sources. There were only 10 such areas in the country in which the maximum monitor mean 

concentration was between 2 to 6 μg/m3higher than the composite monitor concentration 

(Frank, 2012a, Table 4).108 Thus, the EPA does not agree that there is a significant difference 

between composite monitor mean PM2.5 concentrations and maximum monitor mean PM2.5

concentrations in the large majority of areas across the country.

 

108 The average difference between the maximum and composite design value among the 123 

CBSAs with two or more monitors is 0.8 μg/m3and the median difference is 0.6 μg/m3. The 

25th and 75th percentiles are 0.3 and 1.0 μg/m3, respectively (Frank, 2012a, p. 4). 
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In proposing to revise the form of the annual PM2.5 standard, as discussed above in 

section III.E.3.a, the EPA noted that when an annual PM2.5 standard was first set in 1997, the 

form of the standard included the option for averaging across measurements at appropriate 

monitoring sites within an area, generally consistent with the composite monitor approach

used in epidemiological studies, with some constraints intended to ensure that spatial 

averaging would not result in inequities in the level of protection for communities within large 

metropolitan areas. In the last review the EPA tightened the constraints on spatial averaging, 

and in this review has eliminated the option altogether, on the basis of analyses in each review 

that showed that such constraints may be inadequate to avoid substantially greater exposures

for people living in locations around the monitors recording the highest PM2.5 concentrations in 

some areas, potentially resulting in disproportionate impacts on at-risk populations of persons 

with lower SES levels as well as minorities. In light of these analyses, and consistent with the 

Administrator’s decision to revise the form of the annual PM2.5 standard by eliminating the 

option for spatial averaging, the EPA continues to conclude that a standard level based on 

consideration of long-term mean concentrations from composite monitors, and applied at each 

monitor within an area including the monitor measuring the highest concentration, is the 

appropriate approach to use in setting a standard that will protect public health, including the 

health of at-risk populations, with an adequate margin of safety, as required by the CAA.

The EPA acknowledges that at proposal, the Agency characterized the approach of using 

maximum monitor concentrations to determine compliance with the standard, while selecting 

the standard level based on consideration of composite monitor concentrations, as one that 
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inherently had the potential to build in a margin of safety (77 FR 38905), and CASAC reiterated 

that view in supporting the EPA’s approach (Samet, 2010d, p. 3). Nonetheless, in light of the 

discussion above, the EPA more specifically recognizes that this approach does not build in any 

margin of safety in the large number of areas across the country with only one monitor. 

Further, based on the analyses done to inform consideration of the form of the standard 

(Schmidt, 2011, Analysis A), the EPA concludes that this approach does not provide a margin of 

safety for the at-risk populations that live around the monitor measuring the highest 

concentration, such as in those few areas in which the maximum monitor concentration is 

appreciably higher than the composite monitor concentration. Rather, this approach properly 

treats those at-risk populations the same way it does the broader populations that live in areas 

with only one monitor, by providing the same degree of protection for those at-risk populations 

that would otherwise be disproportionately impacted as it does for the broader populations in 

other areas, While the EPA recognizes that this approach can result in some additional margin 

of safety for the subset of areas with multiple monitors in which at-risk populations may not be 

disproportionately represented in areas around the maximum monitor, which may be the case 

in areas with relatively small differences between the maximum and composite monitor 

concentrations, the EPA notes that this margin would be relatively small in such areas.

Based on the above considerations, the EPA does not agree that the Agency’s approach

of using maximum monitor concentrations to determine compliance with the standard, while 

selecting the standard level based on consideration of composite monitor concentrations 

creates an unwarranted case for lowering the standard level based on a margin of safety that 
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would be arbitrary, not based on evidence, or lack quantification. The EPA recognizes that 

setting a standard to protect public health, including the health of at-risk populations, with an 

adequate margin of safety, depends upon selecting a standard level sufficiently below where

the EPA has found the strongest evidence of health effects so as to provide such protection, 

and that the EPA’s approach regarding consideration of composite and maximum monitor 

concentrations is intended to, and does, serve to address this requirement as part of and not 

separate from the selection of an appropriate standard level based on the health effects 

evidence.

In considering the second issue related to the commenter’s suggested alternative 

approach, the EPA strongly disagrees with the commenter’s view that a more “reasoned and 

consistent approach would be to decide on a mean composite monitor PM2.5 level that should 

be achieved and then identify the maximum monitor level that would result in that composite 

value” (UARG, 2012, Attachment 1, p. 4). As discussed above, the EPA notes that for areas with 

only one monitor, or with multiple monitors that measure concentrations that are very close in 

magnitude, the maximum monitor level that would limit the composite monitor PM2.5 level to 

be no greater than the level that should be achieved to protect public health with an adequate 

margin of safety, would essentially be the same as that composite monitor level. Further, as 

discussed above, even for areas in which the maximum monitor concentration is appreciably 

higher than other monitor concentrations within the same area, public health would not be 

protected with an adequate margin of safety if the disproportionately higher exposures of atrisk, susceptible populations around the monitor measuring the highest concentration were in 
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essence averaged away with measurements from monitors in other locations within large 

urban areas. Further, the commenter’s suggested approach would be based on annual average 

PM2.5 concentrations that have been measured over some past time period. Such an approach 

would reflect the air quality that existed in the past, but it would not necessarily provide 

appropriate constraints on the range of concentrations that would be allowed by such a 

standard in the future, when relationships between maximum and composite monitor 

concentrations in areas across the country may be different. For these reasons, the EPA 

fundamentally rejects the commenter’s suggested approach because it would result in a 

standard that is inconsistent with EPA’s reasons for concluding that a standard of 15ug/m3

does not protect public health, including providing protection for at-risk populations, with an 

adequate margin of safety in areas across the country.

More specifically, in further considering the commenter’s analysis of design values 

based on maximum versus composite monitor annual mean PM2.5 concentrations using 

monitoring data from 2006 to 2008 which they assert supports retaining the current standard 

level of 15 μg/m3, the EPA finds flaws with the numerical results and the scope of the analysis, 

as well as flaws in the commenter’s translation of the analysis results into the basis for selecting 

an annual standard level.

In considering the commenter’s analysis, the EPA notes that the analysis compared 

maximum versus composite monitor annual mean PM2.5 concentrations, averaged over 3 years, 

for two groups of areas: (1) areas with design values that exceed the current annual standard 

level (i.e., greater than 15.0 μg/m3) and (2) areas with design values that are just attaining the 
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current annual standard (i.e., between 14.5 and 15.0 μg/m3).109 The commenter indicated that 

they used the full body of PM2.5 monitoring data from the EPA’s AQS database (UARG, 2012, 

Attachment 1, p. 4), In attempting to reproduce the commenter’s results, the EPA repeated the 

calculations using only valid air quality data (i.e., data that meet data completeness and 

monitor siting criteria) from the AQS database for the same time period (Frank, 2012a).110

Based on this corrected analysis, the EPA finds that the composite monitor concentrations 

averaged across the areas within each group are somewhat higher than those calculated by the 

commenter, and the average differences between the maximum and composite monitor 

concentrations are somewhat smaller (Frank, 2012a, Table 3).111 Notably, the difference 

between the maximum and composite monitor average concentrations for the second group of 

areas is substantially reduced in the corrected analysis, such that the difference (averaged 

 

109For the first group of areas (which included 33 areas), this analysis calculated an average 

across the areas of maximum monitor annual mean PM2.5 concentrations, averaged over 3 

years, of 17.2 μg/m3compared to an average of composite monitor concentrations of 14.3 

μg/m3. For the second group of areas (which included 11 areas), this analysis calculated an 

average across the areas of maximum monitor annual mean concentrations, averaged over 3 

years, of 14.8 μg/m3compared to an average of composite monitor concentrations of 13.6 

μg/m3(UARG, 2012, Attachment 1, Table 1).

110 The EPA notes that the Frank (2012a) analysis is similar to an earlier EPA staff analysis 

(Hassett-Sipple et al., 2010), which used air quality data from EPA’s AQS database to 

compare maximum versus composite monitor long-term mean PM2.5 concentrations across 

the study areas in six selected multi-city epidemiological studies.

111 The EPA’s analysis was intended to repeat the commenter’s analysis, but using only valid air 

quality data (from 2006 to 2008). For the first group of areas (which included 21 areas with 

valid data), the EPA’s analysis calculated an average across the areas of maximum monitor 

annual mean concentrations, averaged of 3 years, of 16.8 μg/m3compared to an average of 

composite monitor concentrations of 14.8 μg/m3. For the second group of areas (which 

included 10 areas with valid data), the EPA’s analysis calculated an average across the areas 

of maximum monitor annual mean concentrations, averaged over 3 years, of 14.8 μg/m3

compared to an average of composite monitor concentrations of 14.2 μg/m3(Frank, 2012a, 

Table 3).
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across the 10 areas with valid data in the second group) is approximately 0.5 μg/m3, not 1.2 

μg/m3as in the commenter’s analysis. In addition, the commenter’s analysis compared the 

average of the composite monitors to the average of the maximum monitors for each subset of 

areas. This comparison of averages across all the areas in each subset masks the fact that the 

large majority of areas across the country have only one monitor, with the composite monitor 

and maximum monitor values the same for such areas, and many other areas have a maximum 

monitor value that is close to the composite monitor value. As discussed above, these 

circumstances have a major impact on the protection that would be achieved by the approach 

suggested by the commenter.

With regard to the scope of the commenter’s analysis, the EPA finds that by limiting the 

scope to a small subset of areas with design values above or just below the current annual 

standard level of 15 μg/m3, the analysis ignores the large number of areas across the country 

with lower design values that are relevant to consider in light of the epidemiological evidence 

of serious health effects at lower concentrations, well below the level of the current standard.

In translating the analysis results into the basis for selecting an annual standard level, 

the commenter’s translation is premised on the view that the “natural focal point” for setting 

an annual PM2.5 standard level should be somewhere within the range of the long-term mean 

PM2.5 concentrations from the subset of epidemiological studies that included only long-term 

exposure studies of effects for which the evidence is categorized as causal or likely causal, but 

not for effects categorized as suggestive of causality, nor did it include short-term exposure 

studies (which are included in Figure 4 of the proposal notice and above). Such a view is not 
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consistent with setting a standard that would provide sufficient protection from the serious 

health effects reported even in the limited subset of studies considered by the commenter, 

including protecting public health with an adequate margin of safety. As discussed below, the 

EPA does not agree with the commenter’s view as to the appropriate focal point for selecting 

the level of an annual PM2.5 standard, or with the limited set of studies considered by the 

commenter as a basis for selecting the level of the annual PM2.5 standard.

Regarding an appropriate focal point for selecting the level of the annual standard, as 

discussed in the proposal and as advised by CASAC, the EPA has focused on PM2.5

concentrations somewhat below the lowest long-term mean concentrations from each of the 

key studies of both long- and short-term exposures of effects for which the evidence is causal 

or likely causal, as considered by the EPA (i.e., the first two sets of studies shown in Figure 4). If

the level of the annual standard was set just somewhere within the range of the long-term 

mean concentrations from the various long-term exposure studies, then one or more of the 

studies would have a long-term mean concentration below the selected level of the standard. 

Absent some reason to ignore or discount these studies, which the commenter does not 

provide (and of which the EPA is unaware), setting such a standard would allow that level of air 

quality, where the evidence of health effects is strongest, and its associated risk of PM2.5-

related mortality and/or morbidity effects to continue. Selecting such a standard level could not 

be considered sufficient to protect the public health with an adequate margin of safety. 

Further, focusing on just the long-term mean PM2.5 concentrations in the key 

epidemiological studies – even the lowest long-term mean concentration from the set of key 
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studies -- is not appropriate. Concentrations at and around the long-term mean concentrations 

represent the part of the air quality distribution where the data in any given study are most 

concentrated and, thus, where the confidence in the magnitude and significance of an 

association in such study is strongest. However, the evidence of an association with adverse 

health effects in the studies is not limited to the PM2.5 concentrations just at and around the 

long-term mean, but rather extends more broadly to a lower part of the distribution, 

recognizing that no discernible population-level threshold for any such effects can be identified 

based on the available evidence. This broader region of the distribution of PM2.5 concentrations

should be considered to the extent relevant information is available, recognizing that the 

degree of confidence in the association identified in a study would become lower as one moves 

below concentrations at and around the long-term mean concentration in any given study. The 

commenter’s approach ignores this fundamental consideration.

Regarding the set of studies that is appropriate to inform the selection of the level of 

the annual PM2.5 standard, the EPA finds that limiting consideration only to the long-term 

exposure studies, as this commenter suggests, would be tantamount to ignoring the short-term 

exposure studies,

112 which provide some of the strongest evidence from the entire body of 

 

112 The commenter suggests that the EPA should not place significant reliance on the long-term 

mean concentrations from short-term exposure studies because “[T]he short‐term studies 

did not use the annual average of PM2.5 to develop their associations; they used the daily 

24‐hour averages of PM2.5. Thus, short‐term studies do not provide a natural indicator for 

the appropriate level of an annual standard....” (UARG, 2012, Attachment 1, p. 3). The EPA 

finds this argument unpersuasive. Quite simply, effects were observed in these studies with 

an air quality distribution that can meaningfully be characterized by these long-term mean 

concentrations. Indeed, in remanding the 2006 standard, the D.C. Circuit discussed at length 

the interrelationship of the long- and short-term standards and studies, and remanded the 
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epidemiological studies. Thus, selecting an annual standard level using the limited set of studies

suggested by the commenter would fail to provide a degree of protection that would be

sufficient to protect public health with an adequate margin of safety.

For all the reasons discussed above, the EPA finds the commenter’s concerns with the 

EPA’s approach to considering composite and maximum monitor PM2.5 concentrations in 

selecting the level of the annual PM2.5 standard to be without merit. Further, the EPA finds no 

support in the commenter’s analysis for their suggested alternative approach.

(2) With respect to the appropriate exposure period for mortality effects observed in 

long-term exposure studies, some commenters in this group generally expressed views 

consistent with comments from UARG that argued that these studies “are most likely detecting 

health risk from earlier, higher PM2.5 levels and misattributing those risks to more recent, lower 

PM2.5 levels” (UARG, 2012, Attachment 1, p 7). Further, this commenter asserted that “there is 

no knowledge or evidence indicating whether premature deaths are the result of PM2.5

exposures in the most recent year; or due to physical damages incurred from PM2.5 exposures 

much earlier in life (with the impact on lifespan only emerging later in life); or due to total 

accumulated PM2.5 exposure over many years.” Id. Further, the commenter asserted that the 

long-term exposure studies of mortality are central to the EPA’s basis for proposing to set a 

lower annual standard level, since most of the estimated benefits associated with a lower 

 

2006 standard to the EPA, in part, for ignoring those relationships without adequate 

explanation. American Farm Bureau Federation v. EPA. 559 F. 3d at 522-24.
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annual PM2.5 standard are based on reductions in mortality related to long-term exposures to 

PM2.5.

As an initial matter, the EPA has recognized the challenge in distinguishing between 

PM2.5-associated effects due to past and recent long-term exposures, and in identifying the

relevant latency period for long-term exposure to PM and resultant health effects (U.S. EPA, 

2009a, section 7.6.4; 77 FR 38941/1). While the EPA has acknowledged that there remain 

important uncertainties related to characterizing the most relevant exposure periods in longterm exposure studies, the assertion that there is “no knowledge or evidence” that helps to 

inform this issue is not correct, as discussed below.

Both in the last review and in the current review, the EPA has assessed studies that used 

different air quality periods for estimating long-term exposure and tested associations with 

mortality for the different exposure periods (U.S. EPA, 2004, section 8.2.3.5; U.S. EPA 2009a, 

section 7.6.4). In this review, the Integrated Science Assessment discussed studies available 

since the last review that have assessed the relationship between long-term exposure to PM2.5

and mortality to explore the issue of the latency period between exposure to PM2.5 and death 

(U.S. EPA, 2009a, section 7.6.4).

Notably, in a recent analysis of the extended Harvard Six Cities Study, Schwartz et al. 

(2008) used model averaging (i.e., multiple models were averaged and weighted by probability 

of accuracy) to assess exposure periods prospectively (77 FR 38907/1-2). The exposure periods 

were estimated across a range of unconstrained distributed lag models (i.e., same year, one 

year prior, two years prior to death). In comparing lags, the authors reported that the effects of 
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changes in exposure to PM2.5 on mortality were strongest within a 2-year period prior to death 

(U.S. EPA, 2009a, p. 7-92, Figure 7-9). Similarly, a large multi-city study of the elderly found that 

the mortality risk associated with long-term exposure to PM10 reported cumulative effects that 

extended over the years that deaths were observed in the study population (i.e., the follow-up 

period) and for the 3-year period prior to death (Zanobetti et al., 2008). 

Further, in a study of two locations that experienced an abrupt decline in PM2.5

concentrations (i.e., Utah Steel Strike, coal ban in Ireland), Röösli et al. (2005) reported that 

approximately 75 percent of health benefits were observed in the first 5 years (U.S. EPA, 2009a,

Table 7-9). Schwartz et al. (2008) and Puett et al. (2008) found, in a comparison of exposure 

periods ranging from 1 month to 48 months prior to death, that exposure to PM10 24 months 

prior to death exhibited the strongest association, and the weakest association was reported 

for exposure in the time period of 1 month prior to death. 

Overall, the EPA notes that the available evidence for determining the exposure period

that is causally related to the mortality effects of long-term PM2.5 exposures, as discussed 

above, cannot specifically disentangle the effects observed in long-term exposure studies 

associated with more recent air quality measurements from effects that may have been 

associated with earlier, and most likely higher, PM2.5 exposures. While the evidence suggests

that a latency period of up to five years would account for the majority of deaths, it does not 

provide a basis for concluding that it is solely recent PM2.5 concentrations that account for the 

mortality risk observed in such studies. Nonetheless, the more recent air quality data does well 

at explaining the relationships observed between long-term exposures to PM2.5 and mortality, 
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with the strongest association observed in the two years prior to death. Further, the EPA 

recognizes that there is no discernible population-level threshold below which effects would 

not occur, such that it is reasonable to consider that health effects may occur over the full 

range of concentrations observed in the epidemiological studies, including the lower 

concentrations in the latter years.

In light of this evidence and these considerations, the EPA concludes that it is 

appropriate to consider air quality concentrations that are generally contemporaneous with the 

collection of health event data (i.e., collected over the same time period) as being causally 

associated with at least some proportion of the deaths assessed in a long-term exposure study. 

This would include long-term mean PM2.5 concentrations from most of the key long-term 

exposure studies of effects with causal or likely causal evidence shown in Figure 4 above, which 

reported long-term mean PM2.5 concentrations ranging from 13.6 μg/m3to 14.3 μg/m3. These 

studies include studies of mortality by Eftim et al. (2008), which separately analyzed the ACS 

and Harvard Six City sites, Zeger et al. (2008), and Lipfert et al. (2006), as well as studies of 

morbidity endpoints by Goss et al. (2004), McConnell et al. (2003) and Gauderman et al. (2004), 

and Dockery et al. (1996) and Razienne et al. (1996). The EPA acknowledges that uncertainty in 

the relevant exposure period is most notable in two other long-term exposure studies of 

mortality. The Miller et al. (2007) reported a long-term mean PM2.5 concentration for a 1-year 

exposure period that post-dated the follow-up period in which health event data were collected 

by two years. Also, the Krewski et al. (2009) study reported a long-term mean PM2.5

concentration for an exposure period that included only the last two years of the 18-year 
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follow-up period. Based on these considerations, the EPA does not now consider it appropriate 

to put weight on the reported long-term mean concentrations from these two studies for the 

purpose of translating the information from the long-term mortality studies into a basis for 

selecting the level of the annual PM2.5 standard.113

In addition, the EPA acknowledges that exposure periods that extend at least a couple 

years prior to the follow-up period in which health event data were collected would likely more 

fully capture the PM-related deaths in such studies. To explore how much higher the long-term 

mean PM2.5 concentrations would likely have been had air quality data prior to the follow-up 

years of the studies been included, the EPA conducted a sensitivity analysis of long-term mean 

PM2.5 concentrations (Schmidt, 2012a) particularly considering studies that only included 

deaths from a relatively recent follow-up period. As examples of such studies, this analysis 

considered the Eftim et al. (2008) study of mortality in the ACS sites and the Harvard Six Cities 

sites, as well as sites in the eastern region in the Zeger et al. (2008) study. Using data from the 

EPA’s AQS database, the analysis added the two years of air quality data just prior to the followup period in each study, which was 2000 to 2002 in Eftim et al. (2008) and 2000 to 2005 in 

Zeger et al. (2008). The analysis then calculated the extended long-term mean PM2.5

concentration for each study. As discussed in Schmidt (2012a), in each case the long-term mean 

PM2.5 concentration averaged over the extended exposure period was less than 0.4 μg/m3

higher than the long-term mean PM2.5 concentration averaged over the follow-up period. The 

 

113 Nonetheless, the EPA notes that the Krewski et al. (2009) and Miller et al. (2007) studies 

provide strong evidence of mortality and cardiovascular-related effects associated with longterm PM2.5 exposures to inform causality determinations reached in the Integrated Science 

Assessment (U.S. EPA, 2009a, sections 7.2.11 and 7.6).
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EPA finds it reasonable to conclude that such a relatively small difference in long-term mean 

PM2.5 concentrations would likely apply for other long-term exposure studies that used similarly 

recent follow-up periods as well (e.g., Goss et al., 2004; Lipfert et al., 2006).

Based on the above considerations, the EPA concludes that it is appropriate to consider 

the available air quality information from the long-term exposure studies, while taking into 

account the uncertainties in the relevant long-term exposure periods in weighing the 

information from these studies. The EPA recognizes that considering such information in 

selecting an appropriate annual standard level has the potential to build in some margin of 

safety. The EPA further concludes that it is appropriate to consider the air quality information 

from the set of long-term exposure studies discussed above in the context of the broader array 

of epidemiological studies that inform the EPA’s consideration of the level of the annual PM2.5

standard.

The EPA also notes that while the long-term exposure studies are an important 

component of the epidemiological evidence that informs the Agency’s consideration of the 

level of the annual standard, they do not provide the only relevant information, nor are they 

the set of studies for which the relevant long-term mean PM2.5 concentrations are the lowest. 

As discussed in the proposal, the EPA also considers the long-term mean PM2.5 concentrations 

from the short-term mortality and morbidity studies as providing important information in 

considering the level of the annual standard. As discussed above, a large proportion of the 

aggregate risk associated with short-term exposures results from the large number of days 

during which the 24-hour average concentrations are in the low- to mid-range of the 
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concentrations observed in the studies. Thus, setting the level of the annual standard based on 

long-term mean concentrations, as well as the distribution of concentrations below the mean, 

in the short-term exposure studies is the most effective and efficient way to reduce total PM2.5-

related risk from the broad array of mortality and morbidity effects associated with short-term 

exposures.

Further, the EPA notes that the relevant exposure period for the short-term exposure 

studies is the period contemporaneous with the collection of health event data, and that this 

exposure period is not subject to the uncertainties discussed above related to the long-term 

exposure studies. Recognizing that the long-term mean PM2.5 concentrations from several of 

the multi-city short-term exposure studies shown in Figure 4 are below the long-term mean 

PM2.5 concentrations from the long-term exposure studies (with the exception of Miller et al., 

2007).114 it is reasonable that in selecting the level of the annual standard primary 

consideration should be given to the information from this set of short-term exposure studies. 

There is no reasonable basis to discount the long-term mean concentrations of the short-term 

exposure studies for purposes of setting the level of the annual standard. Thus, the commenter 

is incorrect in asserting that the long-term exposure studies, not the short-term exposure 

studies, would be central in the Administrator’s decision on the level of the annual standard. 

The standard is ultimately intended to protect not just against the single type of effect that 

 

114 As noted above, the EPA is not placing weight on the reported long-term mean 

concentrations from the Miller et al. (2007) study for the purpose of translating the 

information from the long-term mortality studies into a basis for selecting the level of the 

annual PM2.5 standard.
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contributes the most to quantitative estimates of risk to public health, but rather to the broad 

array of effects, including mortality and morbidity effects from long- and short-term exposures 

across the range of at-risk populations impacted by PM2.5-related effects.

(3) With regard to the EPA’s analysis of distributions of underlying population-level data 

(i.e., health event and study population data) and corresponding air quality data from each 

study area in certain key multi-city epidemiological studies (Rajan et al., 2011), some 

commenters in this group raised a number of issues related to this analysis (API, 2012, 

Attachment 1 pp. 5 to 6; McClennan, 2012, pp.2 to 4). Some commenters noted the limited 

number of studies for which health event and study population data were available, and 

questioned whether these distributions would apply to other studies. Commenters expressed 

concerns that this analysis had not been formally reviewed by CASAC and was not published in 

the peer-review literature. Based on such concerns, some commenters asserted that the EPA 

should not consider this information as a basis for selecting a standard level.

As an initial matter, as discussed in section III.E.4.b above, the EPA agrees with CASAC’s 

advice that it is appropriate to consider additional data beyond the mean PM2.5 concentrations 

in key multi-city studies to help inform selection of the level of the annual PM2.5 standard. As 

both the EPA and CASAC recognize, in the absence of a discernible threshold, health effects 

may occur over the full range of concentrations observed in the epidemiological studies. 

Nonetheless, the EPA recognizes that confidence in the magnitude and significance of an 

association is highest at and around the long-term mean PM2.5 concentrations reported in the 

studies and the degree of confidence becomes lower at lower concentrations within any given 
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study. Following CASAC’s advice (Samet, 2010d, p.2), the EPA used additional population-level 

and air quality data made available by study authors to conduct an analysis of the distributions 

of such data, to help inform consideration of how the degree of confidence in the magnitude 

and significance of observed associations varies across the range of long-term mean PM2.5

concentrations in study areas within key multi-city epidemiological studies. In the EPA’s view, 

such consideration is important in selecting a level for an annual standard that will protect 

public health with an adequate margin of safety.

With regard to the number of multi-city studies for which an analysis of the distributions 

of population-level data across the study areas and the corresponding annual mean PM2.5

concentrations was done, the EPA noted at proposal that data for such an analysis were made 

available from study authors for four studies, including two long-term exposure studies and two 

short-term exposure studies.115 The EPA recognized that access to health event data can be 

restricted due to confidentiality issues, such that it is not reasonable to expect that such 

information could be made available from all studies. In considering the information from these 

four studies, the EPA has further taken into consideration uncertainties discussed in response 

to the above comment related to the appropriate exposure period for long-term exposure 

studies. Based on these considerations, as noted above, the EPA concludes that such 

uncertainties are an important factor in evaluating the usefulness of the air quality information 

from the two long-term exposure studies in this analysis (Krewski et al., 2009; Miller et al., 

 

115 Health event data and study population data were available from two short-term exposure 

studies (Bell et al. 2008; Zanobetti and Schwartz, 2009) and one long-term exposure study 

(Krewski et al., 2009). Only study population data were available from another long-term 

exposure study (Miller et al., 2007).
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2007) and that it would not be appropriate to place weight on the distributional analysis of 

health event and air quality data from these two studies specifically for the purpose of 

translating the information from the long-term mortality studies into a basis for selecting the

level of the annual PM2.5 standard. Such uncertainties are not relevant to the short-term 

exposure studies, and thus, the Agency focuses on the two short-term exposure studies in this 

analysis (Bell et al., 2008; Zanobetti and Schwartz, (2009).

In focusing on these two short-term exposure studies, the EPA first notes that these 

studies are key multi-city studies that reported positive and statistically significant associations 

between mortality and cardiovascular-related hospital admissions across a large number of 

areas throughout the U.S. (112 U.S. cities in Zanobetti and Schwartz, 2009; 202 U.S. counties in 

Bell et al., 2008) using relatively recent air quality and health event data (i.e., 1999 through 

2005 in both studies). The EPA considers this to be a modest but important data set to use for 

this distributional analysis to help inform consideration of how much below the long-term 

mean PM2.5 concentrations in key multi-city long- and short-term exposure studies the annual 

PM2.5 standard level should be set. While the EPA acknowledges that having such data available 

from more studies would have been useful, the Agency finds the information from this limited 

set of studies to be an important consideration in selecting an annual standard level, consistent 

with CASAC advice to consider such information.

In considering the results of this distributional analysis, as discussed more fully in the 

Response to Comments document, the EPA considers PM2.5 concentrations between the 25th

and 10th percentiles of the distribution of health events to be a reasonable range for providing a 
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general frame of reference for that part of the distribution in which confidence in the 

magnitude and significance of the association may be appreciably lower than confidence at and 

around the long-term mean concentration. For the two short-term exposure studies included in 

this analysis, the EPA notes that the PM2.5 concentrations corresponding to the 25th percentiles 

of the distributions of health events were 12.5 μg/m3and 11.5 μg/m3, respectively, for 

Zanobetti and Schwartz (2009) and for Bell et al. (2008), with the 10th percentiles being lower 

by approximately 2 μg/m3in each study (Rajan et al., 2011, Table 1). In considering this 

information, the EPA recognizes, however, that there is no clear dividing line or single 

percentile within a given distribution (including both above and below the 25th percentile) 

provided by the scientific evidence that is most appropriate or ‘correct’ to use to characterize 

where the degree of confidence in the associations warrants setting the annual standard level. 

The decision as to the appropriate standard level below the long-term mean concentrations of 

the key studies is largely a public health policy judgment to be made by the Administrator, 

taking into account all of the evidence and its related uncertainties, as discussed in section 

III.E.4.d below.

In response to concerns that this analysis was not reviewed by CASAC nor published in 

the peer-reviewed literature, the EPA notes that this analysis was conducted to directly respond 

to advice from CASAC, as discussed in section III.E.4.b.1 above, in conjunction with their review 

of the Policy Assessment. The EPA notes that the same type of distributional analysis was 

presented in the second draft Policy Assessment based on air quality data, as well as 

population-weighted air quality data, rather than health event or study population data. In 
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considering that distributional information, CASAC urged that the EPA redo the analysis using 

health event or study population data, which is exactly what the EPA did and presented in the 

final Policy Assessment. The EPA provided CASAC with the final Policy Assessment and 

communicated how the final staff conclusions reflected consideration of its advice and that 

those staff conclusions were based in part on the specific distributional analysis that CASAC had 

urged the EPA to conduct (Wegman, 2011, Attachment p. 2). CASAC did not choose to provide 

any additional comments or advice after receiving the final Policy Assessment. The EPA 

considers this distributional analysis to be the product of the peer review conducted by CASAC 

of the Policy Assessment, and thus does not agree with commenters’ characterization that the 

analysis lacked appropriate peer review. The EPA’s final analysis was based on the comments 

provided by CASAC, the peer review committee established pursuant to the CAA, on the draft 

analysis, such that the final analysis stems directly from CASAC’s advice and the EPA’s response 

to its comments.

Based on the above considerations, the EPA continues to conclude that its analysis of 

distributions of health event and air quality data from two key multi-city epidemiological 

studies provides important information related to understanding the associations between 

health events observed in each city (e.g., deaths, hospitalizations) and the corresponding longterm mean PM2.5 concentrations observed in the studies. While recognizing that this is a 

relatively modest data set, the EPA further concludes that such information can appropriately 

help to inform the selection of the level of an annual standard that will protect public health 
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with an adequate margin of safety from these types of health effects which are causally related 

to long- and short-term exposures to PM2.5.

(4) Some commenters in this group asserted there were limitations in the long-term 

exposure studies of morbidity, including studies evaluating respiratory effects in children. For 

example, one commenter (UARG, 2012, p. 12, Attachment 1, pp. 14 to 16) asserted there were 

serious limitations in the long-term exposure studies of respiratory morbidity in each of the 

studies considered by the EPA (including McConnell et al., 2003; Gauderman et al., 2004; 

Dockery et al., 1996; Raizenne et al., 1996; and Goss et al., 2004) and argued that this evidence 

provides only a “weak association” with PM2.5 exposures. This commenter asserted that many 

of these long-term exposure studies evaluating respiratory effects were considered at the time 

the EPA reaffirmed the current annual standard level of 15 μg/m3in 2006, that the 

Administrator in the last review determined that the information they provided “was too 

limited to serve as the basis for setting a level of a national standard,” and that they should be 

given little weight in setting the level of the annual standard in this review (UARG, 2012, 

Attachment 1, p. 14). 

More specifically, this commenter asserted that the McConnell et al. (2003) and 

Gauderman et al. (2004) studies reported mixed results for associations with PM2.5 and stronger 

associations with NO2 (API, 2012, Attachment 1, pp. 14 to 15). Similarly, this commenter argued 

that the Dockery et al. (1996) and Raizenne et al. (1996) studies showed stronger associations 

with acidity than with fine particles (measured as PM2.1). Id. pp. 15 to 16. With regard to the 

cystic fibrosis study, this commenter noted that the association between pulmonary 
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exacerbations and PM2.5 in this study was no longer statistically significant when the model 

adjusted for each individual’s baseline lung function. The commenters referred to the data on 

lung function as an “important explanatory variable,” and suggested that the EPA should rely 

on results from the model that included individual baseline lung function information. Id. p. 16.

For the reasons discussed below and in more detail in the Response to Comments document, 

the EPA disagrees with the commenters’ interpretation of these studies.

As an initial matter, the EPA notes that three of these studies (McConnell et al., 2003; 

Dockery et al., 1996; Raizenne et al., 1996) as well as the initial studies from the Southern 

California Children’s Health Study (Peters et al., 1999; McConnell et al., 1999’ Gauderman et al., 

2000, 2002; Avol et al., 2001) were discussed and considered in the 2004 Air Quality Criteria 

Document (U.S. EPA, 2004) and, thus, considered within the air quality criteria supporting the 

EPA’s final decisions in the review completed in 2006. Two additional studies (Gauderman et 

al., 2004; Goss et al., 2004) were discussed and considered in the provisional science 

assessment conducted for the last review (U.S. EPA, 2006a). The EPA concluded that “new” 

studies considered in the provisional assessment completed in 2006 did not materially change 

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

Criteria Document (71 FR 61148 to 61149, October 17, 2006). All of these studies were 

considered in the Integrated Science Assessment that informs the current review (U.S. EPA, 

2009a).

With regard to the Southern California Children’s Health Study, extended analyses 

considered in the Integrated Science Assessment provided evidence that clinically important 
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deficits in lung function116 associated with long-term exposure to PM2.5 persist into early 

adulthood (U.S. EPA, 2009a, p. 7-27; Gauderman et al., 2004). These effects remained positive 

in copollutant models.117 Additional analyses of the Southern California Children’s Health Study 

cohort reported an association between long-term PM2.5 exposure and bronchitic symptoms 

(U.S. EPA, 2009a, p. 7-23 to 7-24; McConnell et al., 2003, long-term mean concentration of 13.8 

μg/m3) that remained positive in co-pollutant models, with the PM2.5 effect estimates 

increasing in magnitude in some models and decreasing in others, and a strong modifying effect 

of PM2.5 on the association between lung function and asthma incidence (U.S. EPA, 2009a, 7-24; 

Islam et al., 2007). The outcomes observed in the more recent reports from the Southern 

California Children’s Health Study, including evaluation of a broader range of endpoints and 

longer follow-up periods, were larger in magnitude and more precise than reported in the initial

version of the study. Supporting these results were new longitudinal cohort studies conducted 

by other researchers in varying locations using different methods (U.S. EPA, 2009a, section 

7.3.9.1). The EPA, therefore, disagrees with the commenters that the studies by McConnell et 

al. (2003) and Gauderman et al. (2004) are flawed and should not be used in the PM NAAQS 

review process.

 

116 Clinical significance was defined as an FEV1 below 80 percent of the predicted value, a 

criterion commonly used in clinical settings to identify persons at increased risk for adverse 

respiratory conditions (U.S. EPA, 2009a, p. 7-29 to 7-30). The primary NAAQS for sulfur dioxide 

(SO2) also included this interpretation for FEV1 (75 FR 35525, June 22, 2010).

117 Gauderman et al. (2004) clearly stated throughout their analysis that NO2 was one 

component of a highly correlated mixture that contains PM2.5. Gauderman et al. (2004) did 

not present the results from copollutants models but stated “two-pollutant models for any 

pair of pollutants did not provide a significantly better fit to the data than the corresponding 

single-pollutant models.”
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The 24-City study118 by Dockery et al. (1996) (long-term mean concentration of 14.5 

μg/m3) was considered in the current as well as two previous reviews (U.S. EPA, 2009a; U.S.

EPA, 2004; U.S. EPA, 1996). This study observed that PM, specifically “particle strong acidity” 

and sulfate particles (indicators of fine particles), were associated with reports of bronchitis in 

the previous year. Similarly, the magnitude of the associations between bronchitis and PM10

and PM2.1 were similar to those for acidic aerosols and sulfate particles, though the confidence 

intervals for the PM10 and PM2.1 associations were slightly wider and the associations were not 

statistically significant. Acid aerosols, sulfate, and fine particles are formed in secondary 

reactions of the emissions from incomplete combustion and these pollutants have similar 

regional and temporal distributions. As noted by the study authors, “the strong correlations of 

several pollutants in this study, especially particle strong acidity with sulfate (r=0.90) and PM2.1

(r=0.82), make it difficult to distinguish the agent of interest” (Dockery et al., 1996, p. 505). 

Overall, Dockery et al. (1996) (and, similarly, Raizenne et al., 1996) observed similar 

associations between respiratory health effects and acid aerosols, sulfate, PM10 and PM2.1

concentrations. The commenters noted that the associations with particle acidity were 

sensitive to the inclusion of the six Canadian sites. The EPA notes that none of these Canadian 

cities were in the “sulfate belt” where particle strong acidity was highest. Thus, the change in 

the effect estimate when the six Canadian cities were excluded from the analysis is likely due to 

the lower prevalence of bronchitis and the lower concentrations of acid aerosols in these cities, 

 

118 The 24-City study conducted by Dockery et al. (1996) included 18 sites in the U.S. and 6 sites 

in Canada. The Raizenne et al. (1996) study considered 22 of these 24 study areas. Athens, 

OH and South Brunswick, NJ were not included in this study. 
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and not due to some difference in susceptibility to bronchitis between the U.S. and Canadian 

populations that is not due to air pollution, as suggested by the commenters (UARG, 2012, 

Attachment 1, p. 15). In fact, contrary to the statements made by the commenters, the authors 

did not observe any subgroups that appeared to be markedly more susceptible to the risk of 

bronchitis. 

The Goss et al. (2004) study considered a U.S. cohort of cystic fibrosis patients and 

provided evidence of association between long-term PM2.5 exposures and exacerbations of 

respiratory symptoms resulting in hospital admissions or use of home intravenous antibiotics 

(U.S. EPA, 2009a, p. 7-25; long-term mean concentration of 13.7 μg/m3). The commenters 

noted that the association between pulmonary exacerbations and PM2.5 in this study was no 

longer statistically significant when the model adjusted for each individual’s baseline lung 

function. The commenters referred to the data on lung function as an “important explanatory 

variable,” and suggested that the EPA should rely on results from the model that included 

individual baseline lung function information. The EPA disagrees with the commenters’ 

interpretation of this study. The Agency concludes it is unlikely that lung function is a potential 

confounder or an important explanatory variable in this study. In fact, the authors noted that 

“it is more likely that lung function decline may be intimately associated with chronic exposure 

to air pollutants and may be part of the causal pathway in worsening prognosis in CF [cystic 

fibrosis]; in support of this explanation, we found both cross-sectional and longitudinal strong 

inverse relationships between FEV1 and PM levels” (Goss et al., 2004, p. 819). The EPA notes 

that adjusting for a variable that is on the causal pathway can lead to overadjustment bias, 
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which is likely to attenuate the association (Schisterman et al. 2009); this is likely what was 

observed by the authors. Thus, the EPA continues to believe it is appropriate to focus on the 

results reported in Goss et al. (2004) that did not include individual baseline lung function in the 

model.

In addition, the EPA disagrees with commenters’ reliance solely on statistical 

significance when interpreting the study results from individual study results and the collective 

evidence across studies. As discussed in section III.D.2 above, statistical significance of 

individual study findings has played an important role in the EPA’s evaluation of the study’s 

results and the EPA has placed greater emphasis on studies reporting statistically significant 

results. However, in the broader evaluation of the evidence from many epidemiological studies, 

and subsequently during the process of forming causality determinations in the Integrated 

Science Assessment by integrating evidence from across epidemiological, controlled human 

exposure, and toxicological studies, the EPA has emphasized the pattern of results across 

epidemiological studies and whether the effects observed were coherent across the scientific 

disciplines for drawing conclusions on the relationship between PM2.5 and different health 

outcomes. 

As noted in section III.B.1.a of the proposal, with regard to respiratory effects, the 

Integrated Science Assessment concluded that extended analyses of studies available in the last 

review as well as new epidemiological studies conducted in the U.S. and abroad provided 

stronger evidence of respiratory-related morbidity associated with long-term PM2.5 exposure 

(77 FR 38918). The strongest evidence for respiratory-related effects available in this review 
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was from epidemiological studies that evaluated decrements in lung function growth in 

children and increased respiratory symptoms and disease incidence in adults (U.S. EPA, 2009a, 

sections 2.3.1.2, 7.3.1.1, and 7.3.2.1).

In considering the collective evidence from epidemiological, toxicological, and 

controlled human exposure studies, including the studies discussed above, the EPA recognizes 

that the Integrated Science Assessment concluded that a causal relationship is likely to exist 

between long-term PM2.5 exposures and respiratory effects (U.S. EPA, 2009a, p. 2-12, pp. 7-42 

to 7-43). CASAC concurred with this causality determination (Samet, 2009f, p.9).

The commenter’s assertion that the EPA should adhere to its assessment of these 

studies as it did in the review completed in 2006 is significantly mistaken. Most obviously, the 

EPA’s final decision in the last review was held to be deficient by the D.C. Circuit in remanding 

the 2006 primary annual PM2.5 standard. As discussed in section III.A.2 above, the D.C. Circuit 

specifically held that the EPA did not provide a reasonable explanation of why certain morbidity 

studies, including an earlier study from the Southern California Children’s Health Study 

(Gauderman et al., 2000, long-term mean PM2.5 concentration approximately 15 μg/m3) and 

the 24-Cities Study (Raizenne et al., 1996, long-term mean concentrations approximately 14.5 

μg/m3) did not warrant a more stringent annual PM2.5 standard when the long-term mean PM2.5

concentrations reported in those studies were at or lower than the level of the annual 

standard. American Farm Bureau Federation v. EPA. 559 F. 3d at 525. Indeed, the court found 

that, viewed together, the Gauderman et al. (2000) and Raizenne et al., (1996) studies “are 

related and together indicate a significant public health risk...On this record, therefore, it 
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appears the EPA too hastily discounted the Gauderman and 24-Cities studies as lacking in 

significance.” Id.

In this review, the EPA recognizes a significant amount of evidence beyond these two 

studies that expands our understanding of respiratory effects associated with long-term PM2.5

exposures. This body of scientific evidence includes an extended and new analyses from the 

Southern California Children’s Health Study (Gauderman et al., 2004; Islam et al., 2007; 

Stanojevic et al., 2008) as well as additional studies that examined these health effects (Kim et 

al., 2004; Goss et al., 2004). Thus, even more so than in the last review, the evidence indicates a 

“significant public health risk” to children from long-term PM2.5 exposures at concentrations 

below the level of the current annual standard. A standard that does not reflect appropriate 

consideration of this evidence would not be requisite to protect public health with an adequate 

margin of safety.

(5) With regard to the use of studies of health effects for which the EPA finds the 

evidence to be “suggestive” of a causal relationship, some commenters argued that such 

studies “do not merit any weight in the setting of the annual NAAQS” (e.g., UARG, 2012, 

Appendix 1, p. 3).

The EPA disagrees with the commenter’s view that studies of health effects for which 

the evidence is suggestive of a causal relationship, rather than studies of health effects for 

which the evidence supports a causal or likely causal relationship, merit no weight at all in 

setting the NAAQS. To place no weight at all on such evidence would in essence treat such 

evidence as though it had been categorized as “not likely to be a causal relationship.” To do so 
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would ignore the important distinctions in the nature of the evidence supporting these 

different causality determinations in the Integrated Science Assessment. It would also ignore 

the CAA requirement that primary standards are to be set to provide protection with an 

adequate margin of safety, including providing protection for at-risk populations. Thus, ignoring 

this information in making decisions on the appropriate standard level would not be 

appropriate.119 Nonetheless, in considering studies of health effects for which the evidence is 

suggestive of a causal relationship, the EPA does believe that it is appropriate to place less 

weight on such studies than on studies of health effects for which there is evidence of a causal 

or likely causal relationship.

A second group of commenters supported revising the suite of primary PM2.5 standards 

to provide increased public health protection. These commenters found the available scientific 

information and technical analyses to be stronger and more compelling than in the last review. 

These commenters generally placed substantial weight on CASAC advice and on the EPA staff 

analyses presented in the final Policy Assessment, which concluded that the evidence most 

strongly supported an annual standard level within a range of 11 to 12 μg/m3(U.S. EPA, 2011a, 

p. 2-206). While some of these commenters felt that the level should be set within the 

 

119 As discussed in section II.A above, the requirement that primary standards provide an 

adequate margin of safety was intended to address uncertainties associated with 

inconclusive scientific and technical information available at the time of standard setting. I 

was also intended to provide a reasonable degree of protection against hazards that 

research has not yet identified. This certainly encompasses consideration of effects for which 

there is evidence suggestive of a causal relationship.
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proposed range (12 to 13 μg/m3), most of these commenters advocated a level of 11 μg/m3.

120

For example, ALA et al., asserted:

The EPA’s proposed PM2.5 standards, while a step in the right direction are insufficient 

to protect public health, including the health of susceptible populations, with an 

adequate margin of safety as required by the Clean Air Act...we will discuss the 

enormous gap in public health protection afforded by an annual standard of 13 μg/m3, 

at the upper end of the proposed range, compared to the more protective 11 μg/m3, as 

advocated by our organizations (ALA et al., 2012, p. 6). 

In general, these commenters expressed the view that given the strength of the 

available scientific evidence, the serious nature of the health effects associated with PM2.5

exposures, the large size of the at-risk populations, the risks associated with long- and shortterm PM2.5 exposures, and the important uncertainties inherently present in the evidence, the 

EPA should follow a highly precautionary policy response by selecting an annual standard level 

that incorporates a large margin of safety. 

More specifically, these commenters offered a range of comments related to the 

general approach used by the EPA to select standard levels, including: (1) the EPA’s approach 

for setting a generally controlling annual standard; (2) the importance of the greatly expanded 

and stronger overall scientific data base; (3) consideration of the distributional statistical 

analysis conducted by the EPA and other approaches for translating the air quality information 

from specific epidemiological studies into standard levels; and (4) the significance of the PM2.5-

related public health impacts, especially potential impacts on at-risk populations, including 

 

120 As discussed in section III.E.4.c.ii, many of these commenters also supported lowering the 

level of the primary 24-hour PM2.5 standard.
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children, in reaching judgments on setting standards that provide protection with an adequate 

margin of safety. These comments are discussed in turn below.

(1) Some of these commenters disagreed with the EPA’s approach for setting a 

“generally controlling” annual standard in conjunction with a 24-hour standard providing 

supplemental protection particularly for areas with high peak-to-mean ratios. These 

commenters argued this approach would lead to “regional inequities” as demonstrated in the 

EPA’s analyses contained in Appendix C of the Policy Assessment (ALA et al., pp. 26 to 27). 

Specifically, these commenters argued:

There is no basis in the Clean Air Act for such a determination. The Clean Air Act 

requires only that the NAAQS achieve public health protection with an adequate margin 

of safety. It is well-documented that both long- and short-term exposures to PM2.5 have 

serious and sometimes irreversible health impacts. There is no health protection reason 

to argue that one standard should be “controlling” as a matter of policy without regard 

to the health consequences of such a policy. To adopt such a policy ignores the 

obligation to provide equal protection under the law to all Americans because it would 

result in uneven protection from air pollution in different localities and regions of the 

country (ALA et al., 2012, p. 26).

The EPA believes these commenters misunderstood the basis for the EPA’s policy goal of 

setting a “generally controlling” annual standard. This approach relates exclusively to setting 

standards that will provide requisite protection against effects associated with both long- and 

short-term PM2.5 exposures. It does so by lowering the overall air quality distributions across an 

area, recognizing that changes in PM2.5 air quality designed to meet an annual standard would 

likely result not only in lower annual mean PM2.5 concentrations but also in fewer and lower 

peak 24-hour PM2.5 concentrations. As discussed in section III.A.3 in the proposal and above, 

the EPA recognizes that there are various ways to combine the two primary PM2.5 standards to 
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achieve an appropriate degree of public health protection. Furthermore, the extent to which 

these two standards are interrelated in any given area depends in large part on the relative 

levels of the standards, the peak-to-mean ratios that characterize air quality patterns in an 

area, and whether changes in air quality designed to meet a given suite of standards are likely 

to be of a more regional or more localized nature. 

In focusing on an approach of setting a generally controlling annual standard, the EPA’s 

intent is in fact to avoid the potential “regional inequities” that are of concern to the 

commenters. The EPA judges that the most appropriate way to set standards that provide more 

consistent public health protection is by using the approach of setting a generally controlling 

annual standard. This judgment builds upon information presented in the Policy Assessment as 

discussed in section III.A.3 above. More specifically, the Policy Assessment recognized that the 

short-term exposure studies primarily evaluated daily variations in health effects with 

monitor(s) that measured the variation in daily PM2.5 concentrations over the course of several 

years. The strength of the associations observed in these epidemiological studies was 

demonstrably in the numerous “typical” days within the air quality distribution, not in the peak 

days (U.S. EPA, 2011a, p. 2-9). In addition, the quantitative risk assessments conducted for this 

and previous reviews demonstrated the same point, that is, much, if not most, of the aggregate 

risk associated with short-term PM2.5 exposures results from the large number of days during 

which the 24-hour average concentrations are in the low-to mid-range, below the peak 24-hour 

concentrations (U.S. EPA, 2011a, section 2.2.2; U.S. EPA, 2010a, section 3.1.2.2). In addition, 
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there was no evidence suggesting that risks associated with long-term exposures were likely to 

be disproportionately driven by peak 24-hour concentrations.121

For these reasons, the Policy Assessment concluded that strategies that focused 

primarily on reducing peak days were less likely to achieve reductions in the PM2.5

concentrations that were most strongly associated with the observed health effects. 

Furthermore, the Policy Assessment concluded that an approach that focused on reducing peak 

exposures would most likely result in more uneven public health protection across the U.S. by 

either providing inadequate protection in some areas or overprotecting in other areas (U.S.

EPA, 2011a, p. 2-9; U.S. EPA, 2010a, section 5.2.3). This is because reductions based on control 

of peak days are less likely to control the bulk of the air quality distribution. 

As a result, the EPA believes an approach that focuses on a generally controlling annual 

standard would likely reduce aggregate risks associated with both long- and short-term 

exposures more consistently than a generally controlling 24-hour standard and, therefore, 

would be the most effective and efficient way to reduce total PM2-5-related population risk. The 

CASAC agreed with this approach and considered it was “appropriate to return to the strategy 

used in 1997 that considers the annual and the short-term standards together, with the annual 

standard as the controlling standard, and the short-term standard supplementing the 

protection afforded by the annual standard” (Samet, 2010d, p. 1). For the reasons discussed 

above, the EPA disagrees with the comments that this approach will result in the concerns 

 

121 In confirmation, a number of studies have presented analyses excluding higher PM 

concentration days and reported a limited effect on the magnitude of the effect estimates or 

statistical significance of the association (e.g., Dominici, 2006b; Schwartz et al., 1996; Pope 

and Dockery, 1992).
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raised by the commenters; rather the EPA concludes that this approach will help to address 

these concerns.

(2) Many of these commenters asserted that the currently available scientific 

information is greatly expanded and stronger compared to the last review. Some of these 

commenters highlighted the availability of multiple, multi-city long- and short-term exposure 

studies providing “repeated, consistent evidence of effects below the current annual standard 

level” (ALA et al., 2012, pp. 39 to 49) and, more specifically, “significant evidence of harm with 

strong confidence well below EPA’s proposed annual standard range of 12-13 μg/m3” (AHA et 

al., 2012, pp. 10 to 12). 

The EPA recognizes that in setting standards that are requisite to protect public health 

with an adequate margin of safety, the Administrator must weigh the various types of available 

scientific information in reaching public health policy judgments that neither overstate nor 

understate the strength and limitations of this information or the appropriate inferences to be 

drawn from the available science. 

In general, the EPA agrees with these commenters’ views that the currently available 

scientific evidence is stronger “because of its breadth and the substantiation of previously 

observed health effects” (77 FR 38906/2) and provides “greater confidence in the reported 

associations than in the last review” (77 FR 38940/1). The EPA also agrees with the 

commenters’ position that it is appropriate to consider the regions within the broader air 

quality distributions where we have the strongest confidence in the associations reported in 

epidemiological studies in setting the level of the annual standard. However, as discussed in 
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section III.E.4.d below, in weighing the available evidence and technical analyses, as well as the 

associated uncertainties and limitations in that information, the EPA disagrees with the 

commenters’ views regarding the extent to which the available scientific information provides 

support for considering an annual standard level below the proposed range (i.e., below 12 to 13 

μg/m3). In particular, the EPA disagrees with the degree to which these commenters place more 

weight on the relatively more uncertain evidence that is suggestive of a causal relationship 

(e.g., low birth weight). Consistent with CASAC advice (Samet, 2010d, p. 1), the Agency 

concludes it is appropriate and reasonable to place the greatest emphasis on health effects for 

which the Integrated Science Assessment concluded there is evidence of a causal or likely 

causal relationship and to place less weight on the health effects that provide evidence that is 

only suggestive of a causal relationship.. 

(3) With regard to using the air quality information from epidemiological studies to 

inform decisions on standard levels, commenters in this group generally supported the EPA’s 

efforts to explore different statistical metrics from epidemiological studies to inform the 

Administrator’s decisions. These commenters argued that by considering different analytic 

measures -- either concentrations one standard deviation below the long-term means reported 

in the epidemiological studies or the EPA’s distributional statistical analysis of population-level 

data that extends the approach used in previous PM NAAQS reviews to consider information 

beyond a single statistical metric -- “the annual standard must be significantly lower than EPA 

has proposed” (ALA et al., 2012, pp. 50 to 61). Furthermore, with regard to characterizing the 

PM2.5 air quality at which associations have been observed, some of these commenters 
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highlighted CASAC’s recommendation that “[f]urther consideration should be given to using the 

10th percentile as a level for assessing various scenarios of levels for the PM NAAQS” (Samet, 

2010c, p. 11) (ALA et al., 2012, p. 55). Other commenters urged that the EPA extend the 

distributional analysis to include additional studies. For example, CHPAC urged the EPA to also 

conduct distributional analysis for children’s health studies to better inform standards that 

would protect both children and adults from adverse health outcomes (CHPAC, 2012, p. 3)

The EPA agrees with these commenters’ views that it is appropriate to take into account 

different statistical metrics from epidemiological studies to inform the decisions on standard 

levels that are appropriate to consider in setting a standard that will protect public health with 

an adequate margin of safety. In the development of the Policy Assessment, the EPA staff 

explored various approaches for using information from epidemiological studies in setting the

standards. The general approach used in the final Policy Assessment, discussed in sections 

III.A.3 and III.E.4.a above, reflects consideration of CASAC advice (Samet, 2010c, d) and public 

comments on multiple drafts of the Policy Assessment.

With regard to using the distributional statistical analysis to characterize the confidence 

in the associations, the EPA emphasizes that there is no clear dividing line provided by the 

scientific evidence, and that choosing how far below the long-term mean concentrations from 

the epidemiological studies is appropriate to identify a standard level that will provide 

protection for the public health with an adequate margin of safety is largely a public health 

policy judgment. The EPA considers the region from approximately the 25th to 10th percentiles 

to be a reasonable range for providing a general frame of reference as to the part of the 
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distribution over which our confidence in the magnitude and significance of the associations 

observed in epidemiological studies is appreciably lower. Based on these considerations, the 

EPA concludes that it is not appropriate to place as much confidence in the magnitude and 

significance of the associations over the lower percentiles of the distributions in each study as 

at and around the long-term mean concentrations. Thus, the EPA disagrees with the 

commenters’ views that this analysis compels placing more emphasis on the lower part of this 

range in selecting a level for an annual standard that will protect public health with an 

adequate margin of safety. The EPA recognizes that this information comes primarily from two 

short-term exposure studies, a relatively modest data set. In light of the limited nature of this 

information, and in recognition of more general uncertainties inherent in the epidemiological 

evidence, the Administrator deems it reasonable not to place more emphasis on concentrations 

in the lower part of this range, as discussed below in section III.E.4.d.

With regard to the scope of the distributional statistical analysis, the EPA requested 

additional population-level data from the study authors for a group of six multi-city studies for 

which previous air quality analyses had been conducted (Hassett-Sipple et al., 2010; Schmidt et 

al., 2010, Analysis 2). These six studies were originally selected because they considered 

multiple locations representing varying geographic regions across multiple years. Thus, these 

studies provided evidence on the influence of different particle mixtures on health effects 

associated with long- and short-term PM2.5 exposures. In addition, these multi-city studies 

considered relatively more recent health events and air quality conditions (1999 to 2005). As 

discussed in section III.E.4.b.i above, the EPA received and analyzed population-level data for 
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four of the six studies (Rajan et al., 2011). Three of these four studies (Krewski et al., 2009; Bell 

et al., 2008; Zanobetti and Schwartz, 2009) served as the basis for the concentration-response 

functions used to develop the core risk estimates (U.S. EPA, 2010a, section 3.3.3). While, the 

EPA agrees that it would be useful to have such data from more studies, the Agency believes 

that the additional data that was requested and received from study authors provide useful 

information to help inform the Administrator’s selection of the annual standard level. 

(4) Many commenters in this group highlighted PM2.5-related impacts on at-risk 

populations, including potential impacts on children, older adults, persons with pre-existing 

heart and lung disease, and low-income populations, to support their views that the annual 

standard should be revised to a level of 11 μg/m3or lower (CHPAC, 2012; AHA et al., 2012; ALA, 

2012, pp. 29 to 38; Rom et al., 2012; Air Alliance Houston, et al, 2012). These commenters 

urged the EPA to adopt a policy approach that placed less weight on the remaining 

uncertainties and limitations in the evidence and placed more emphasis on margin of safety 

considerations, including providing protection against effects for which there is more limited 

scientific evidence. For example, CHPAC urged the EPA “to place the same weight on studies 

examining impacts on children’s health as that of adult studies. ... The fact that there may be 

stronger evidence from adult studies does not mean that standards based on adult studies will 

be protective for children and consequently will meet the standard requisite to protect public 

health with an adequate margin of safety” (CHPAC, 2012 p. 3). Furthermore, with regard to the 

EPA’s approach for weighing uncertainties, some of these commenters stated that “we find no 

justification in the preamble for an annual standard level as high as 13 μg/m3, other than the 
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vague assertion that uncertainties increase at lower concentrations. Further, the final proposal 

completely failed to address the Policy Assessment recommendations that if 13 μg/m3 was 

proposed, the 24-hour standard should be strengthened as well” (ALA et al., p. 7).

The EPA has carefully evaluated and considered evidence of effects in at-risk 

populations. With regard to effects classified as having evidence of a causal or likely causal 

relationship with long- or short-term PM2.5 exposures (i.e., premature mortality, cardiovascular 

effects, and respiratory effects), the Agency takes note that it considered the full range of 

studies evaluating these effects, including studies of at-risk populations, to inform its review of 

the primary PM2.5 standards. Specific multi-city studies summarized in Figures 1, 2, and 3 above 

highlight evidence of effects observed in two different lifestages – children and older adults –

that have been identified as at-risk populations. Thus, the EPA places as much weight on studies 

that explored effects in children for which the evidence is causal or likely causal in nature as on 

studies of such effects in adults, including older adults. .As discussed above in responses to 

commenters supporting the retention of the current standards, in setting the standard, the EPA 

has focused on considering PM2.5 concentrations somewhat below the lowest long-term mean 

concentrations from each of the key studies of both long- and short-term exposures of effects 

for which the evidence supports a causal or likely causal relationship (i.e., the first two sets of 

studies shown in Figure 4). Absent some reason to ignore or discount these studies, which the 

commenter does not provide (and of which the EPA is unaware), the EPA considers the 

available evidence of effects in children as well as other at-risk populations.
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With respect to the EPA’s consideration of more limited studies providing evidence 

suggestive of a causal relationship (e.g., developmental and reproductive effects), as noted 

above in responding to comments from the first group of commenters, the Agency agrees that 

it is important to place some weight on this body of evidence in setting standards that provide 

protection for at-risk populations, as required by the CAA. However, the Agency does not agree

that the same weight must be placed on this information as on the body of scientific 

information for which there is evidence of a causal or likely causal relationship. To do so would 

ignore the difference in the breadth and strength of the evidence supporting the different 

causality determinations reached in the Integrated Science Assessment. 

With regard to weighing the uncertainties and limitations remaining in the evidence and 

technical analyses, as discussed in section II.A above, the EPA recognizes that in setting a 

primary NAAQS that provides an adequate margin of safety, the Administrator must consider a 

number of factors including the nature and severity of the health effects involved, the size of 

sensitive population(s) at risk, and the kind and degree of the uncertainties that remain. As 

discussed in section III.E.4.d below, the Agency agrees with these commenters that, in weighing 

the available evidence and technical analyses including the uncertainties and limitations in this 

scientific information, there is no justification for setting a primary PM2.5 annual standard level 

as high as 13 μg/m3.

Finally, some commenters in both groups also identified “new” studies that were not 

included in the Integrated Science Assessment as providing further support for their views on 

the level of the annual standard. As discussed in section II.B.3 above, the EPA completed a 
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provisional review and assessment of “new” studies published since the close of the Integrated 

Science Assessment, including “new” studies submitted by commenters (U.S. EPA, 2012a). The 

provisional assessment found that the “new” studies expand the scientific information 

considered in the Integrated Science Assessment and provide important insights on the 

relationship between PM2.5 exposure and health effects of PM (U.S. EPA, 2012b). However, the 

EPA notes that the provisional assessment found that the “new” science did not materially 

change the conclusions reached in the Integrated Science Assessment. The EPA notes that, as in 

past NAAQS reviews, the Agency is basing the final decisions in this review on the studies and 

related information included in the Integrated Science Assessment that have undergone CASAC 

and public review, and will consider newly published studies for purposes of decision making in 

the next PM NAAQS review.

ii. 24-Hour Standard Level

With respect to the level of the 24-hour standard, the EPA received comments on the 

proposal from two distinct groups of commenters. One group that included virtually all 

commenters representing industry associations, businesses, and many States agreed with the 

Agency’s proposed decision to retain the level of the 24-hour PM2.5 standard. The other group 

of commenters included many medical groups, numerous physicians and academic researchers,

many public health organizations, some State and local agencies, five State Attorneys General, 

and a large number of individual commenters. These commenters disagreed with the Agency’s 

proposed decision and argued that EPA should lower the level of the 24-hour standard to 30 or 
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25 μg/m3. Comments from these groups on the level of the 24-hour PM2.5 standard are 

addressed below and in the Response to Comments Document.

As noted above, of the public commenters who addressed the level of the 24-hour PM2.5

standard, all industry commenters and most State and local commenters supported the 

proposed decision to retain the current level of 35 g/m3. In many cases, these groups agreed 

with the rationale supporting the Administrator’s proposed decision to retain the current 24-

hour PM2.5 standard, including her emphasis on the annual standard as the generally controlling 

standard with the 24-hour standard providing supplementary protection, and her conclusion 

that multi-city, short-term exposure studies provide the strongest data set for informing 

decisions on the appropriate 24-hour standard level. Many of these commenters agreed with 

the Administrator’s view that the single-city, short-term studies provided a much more limited 

data set (e.g., limited statistical power, limited exposure data) and more equivocal results (e.g., 

mixed results within the same study area), making them an unsuitable basis for setting the level 

of the 24-hour standard. 

While these commenters agreed with the EPA’s proposed decision to retain the current 

24-hour PM2.5 standard, some did not agree with the EPA’s approach to considering the 

evidence from short-term multi-city studies. For example, a commenter representing UARG 

pointed out that the 98th percentile concentrations reported in the proposal for multi-city 

studies reflect the averages of 98th percentile concentrations across the cities included in those 

studies (UARG, 2012; Attachment 1; p. 25). This commenter contended that such averaged 98th

percentile PM2.5 concentrations do not provide information that can appropriately inform a 
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decision on the adequacy of the public health protection provided by the current or alternative 

24-hour standards. 

While the EPA agrees that there is uncertainty in linking effects reported in multi-city 

studies to specific air quality concentrations (U.S. EPA, 2011, section 2.3.4.1), the EPA disagrees 

with this commenter’s view that such uncertainty precludes the use of averaged 98th percentile 

PM2.5 concentrations to inform a decision on the appropriateness of the protection provided by 

the 24-hour PM2.5 standard. In particular, the EPA notes that averaged 98th percentile 

concentrations do provide information on the extent to which study cities contributing to 

reported associations would likely have met or violated the current 24-hour PM2.5 standard 

during the study period. As evidence of this, the EPA notes the three multi-city studies 

specifically highlighted by this commenter as having averaged 98th percentile 24-hour PM2.5

concentrations below 35 g/m3(Dominici et al., 2006; Bell et al., 2008; Zanobetti and Schwartz, 

2009). Based on the 98th percentiles of 24-hour PM2.5 concentrations in the individual cities 

evaluated in these studies, the EPA notes that the majority of these study cities would likely 

have met the current standard during the study periods (Hassett-Sipple et al., 2010). Therefore, 

regardless of whether the averaged 98th percentile concentrations or the 98th percentile 

concentrations in each city are considered, these studies provide evidence for associations 

between short-term PM2.5 and mortality or morbidity across a large number of U.S. cities, the 

majority of which would likely have met the current 24-hour PM2.5 standard during study 

periods. In their review of the PM Policy Assessment, CASAC endorsed the conclusions drawn 

from analyses of averaged 98th percentile 24-hour PM2.5 concentrations, and the EPA continues 
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to conclude that this type of information can appropriately inform the Administrator’s decision 

on the current 24-hour PM2.5 standard.122

Another group of commenters argued that the 24-hour standard level should be 

lowered. Many of these commenters supported setting the level of the 24-hour PM2.5 standard 

at either 25 or 30 g/m3. In support of their position, the ALA et. al., AHA et al., five state 

Attorneys General, and a number of additional groups pointed to 98th percentile PM2.5

concentrations in locations of multi-city and single-city epidemiological studies. For example, 

the ALA and others pointed to multi-city studies by Dominici et al. (2006), Zanobetti and 

Schwartz (2009), Burnett et al. (2000), and Bell et al. (2008) as providing evidence for 

associations with mortality and morbidity in study locations with averaged (i.e., averaged across 

cities) 98th percentile 24-hour PM2.5 concentrations below 35 g/m3. These commenters also 

pointed to several single-city and panel studies reporting associations between short-term 

PM2.5 and mortality or morbidity in locations with relatively low 24-hour PM2.5 concentrations. 

Because some of these multi- and single-city studies have reported associations with health 

effects in locations with 98th percentile PM2.5 concentrations below 35 g/m3, commenters 

maintained that the current 24-hour PM2.5 standard (i.e., with its level of 35 g/m3) does not 

provide an appropriate degree of protection in all areas.

 

122This is not to say that the EPA’s decision on whether to revise the 24-hour PM2.5 standard 

should be based on or only be informed by considerations of whether studies reported 

associations with mortality or morbidity in areas with averaged 98th percentile PM2.5

concentrations less than 35 mg/m3. As discussed below, in reaching a decision in this final 

notice on the most appropriate approach to strengthen the suite of PM2.5 standards, the 

Administrator considers the degree of public health protection provided by the combination 

of the annual and 24-hour standards together. 
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In further support of their position that the level of the current 24-hour standard should 

be lowered, these commenters pointed out the variability across the U.S. in ratios of 24-hour to 

annual PM2.5 concentrations. They noted that some locations, including parts of the 

northwestern U.S., experience relatively low annual PM2.5 concentrations but can experience 

relatively high 24-hour concentrations at certain times of the year. In order to provide 

protection against effects associated with short-term PM2.5 exposures, especially in locations 

with high ratios of 24-hour to annual PM2.5 concentrations, these commenters advocated 

setting a lower level for the 24-hour standard. 

The EPA agrees with these commenters that it is appropriate to maintain a 24-hour 

PM2.5 standard in order to supplement the protection provided by the revised annual standard, 

particularly in locations with relatively high ratios of 24-hour to annual PM2.5 concentrations. 

However, in highlighting 98th percentile PM2.5 concentrations in study locations without also 

considering the impact of a revised annual standard on short-term concentrations, these 

commenters ignore the fact that many areas would be expected to experience decreasing 

short- and long-term PM2.5 concentrations in response to a revised annual standard. 

In considering the specific multi-city studies highlighted by public commenters who 

advocated a more stringent 24-hour standard, the EPA notes that such studies have reported 

consistently positive and statistically significant associations with short-term PM2.5 exposures in 

locations with averaged 98th percentile PM2.5 concentrations ranging from 45.8 to 34.2 μg/m3

and long-term mean PM2.5 concentrations ranging from 13.4 to 12.9 (Burnett and Goldberg, 

2003; Burnett et al., 2004; Dominici et al., 2006a; Bell et al., 2008; Franklin et al., 2008; 
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Zanobetti and Schwartz, 2009).123 The EPA notes that to the extent air quality distributions are 

reduced to meet the current 24-hour standard with its level of 35 g/m3and/or the revised 

annual standard with its level of 12 g/m3, additional protection would be anticipated against 

the effects reported in these short-term, multi-city studies. Put another way, to attain an 

annual standard with a level below the long-term means in the locations of these short-term 

studies (as EPA is adopting here), the overall air quality distributions in the majority of study 

cities will necessarily be reduced, resulting in lower daily PM2.5 ambient concentrations. We 

therefore expect that the revised annual standard will result in 98th percentile PM2.5

concentrations in these cities that are lower than those measured in the studies, and that the 

overall distributions of PM2.5 concentrations will be lower than those reported to be associated 

with health effects. Thus, even for effects reported in multi-city studies with averaged 98th

percentile concentrations below 35 μg/m3, additional protection from the risks associated with 

short-term exposures is anticipated from the revised annual standard, without revising the 24-

hour standard, because long-term average PM2.5 concentrations in multi-city study locations 

 

123Commenters also highlighted associations with short-term PM2.5 concentrations reported in 

sub-analyses restricted to days with 24-

3

(Dominici, 

2006b). These sub-analyses were not included in the original publication by Dominici et al. 

(2006a). Authors provided results of sub-analyses for the Administrator’s consideration in a 

letter to the docket following publication of the proposed rule in January 2006 (personal 

communication with Dr. Francesca Dominici, 2006b). As noted in section III.A.3, these subanalyses are part of the basis for the conclusion that there is no evidence suggesting that 

risks associated with long-term exposures are likely to be disproportionately driven by peak 

24-hour concentrations. Because the sub-analyses did not present long-term average PM2.5

concentrations, it is not clear whether they reflected PM2.5 air quality that would have been 

allowed by the revised annual PM2.5 standard being established in this notice. 
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were above the level of the revised annual standard (i.e., 12 μg/m3).124 As discussed above, 

reducing the annual standard is the most efficient way to reduce the risks from short-term 

exposures identified in these studies, as the bulk of the risk comes from the large number of 

days across the bulk of the air quality distribution, not the relatively small number of days with 

peak concentrations. 

In considering the single-city studies highlighted by public commenters who advocated a 

more stringent 24-hour standard, the EPA first notes that, overall, these single-city studies 

reported mixed results. Specifically, some studies reported positive and statistically significant 

associations with PM2.5, some studies reported positive but non-significant associations, and 

several studies reported negative associations or a mix of positive and negative associations 

with PM2.5. In light of these inconsistent results, the proposal noted that the overall body of 

evidence from single-city studies is mixed, particularly in locations with 98th percentiles of 24-

hour concentrations below 35 μg/m3. Therefore, although some single-city studies reported

effects at appreciably lower PM2.5 concentrations than short-term multi-city studies, the 

uncertainties and limitations associated with the single-city studies were noted to be greater. In 

light of these greater uncertainties and limitations, the Administrator concluded in the proposal 

that she had less confidence in using these studies as a basis for setting the level of the

standard (77 FR 38943). 

 

124It is also the case that additional protection is anticipated in locations with 98th percentile 24-

hour PM2.5 concentrations above 35 μg/m3, even if long-term concentrations are below 12 

μg/m3. As noted in the proposal and in the Policy Assessment (U.S. EPA, 2011, Figure 2-10), 

parts of the northwestern U.S. are more likely than other parts of the country to violate the 

24-hour standard and meet the revised annual standard. 
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Given the considerations and conclusions noted above, in the proposal the 

Administrator concluded that the short-term multi-city studies provide the strongest evidence 

to inform decisions on the level of the 24-hour standard. Further, she viewed single-city, shortterm exposure studies as a much more limited data set providing mixed results, and she had 

less confidence in using these studies as a basis for setting the level of a 24-hour standard (77 

FR 38942).. In highlighting specific single-city studies, public health, environmental, and State 

and local commenters appear to have selectively focused on studies reporting associations with 

PM2.5 and to have overlooked studies that reported more equivocal results (e.g., Ostro et al., 

2003; Rabinovitch et al., 2004; Slaughter et al., 2005; Villeneuve et al., 2006) (U.S. EPA, 2011, 

Figure 2-9). As such, these commenters have not presented new information that causes the 

EPA to reconsider its decision to emphasize multi-city studies over single-city studies when 

identifying the appropriate level of the 24-hour PM2.5 standard.

In further considering the single-city studies highlighted by public commenters, the EPA 

notes that some commenters advocating for a lower level for the 24-hour PM2.5 standard also 

discussed short-term studies that have been published since the close of the Integrated Science 

Assessment. These recent studies were conducted in single cities or in small panels of 

volunteers. As in prior NAAQS reviews and as discussed above in more detail (section II.B.3), the 

EPA is basing its decisions in this review on studies and related information assessed in the 

Integrated Science Assessment. The studies assessed in the Integrated Science Assessment, and 

the conclusions based on those studies, have undergone extensive critical review by the EPA, 

CASAC, and the public. The rigor of that review makes the studies assessed in the Integrated 
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Science Assessment, and the conclusions based on those studies, the most reliable source of 

scientific information on which to base decisions on the NAAQS. 

However, as discussed above (section II.B.3), the EPA recognizes that ‘‘new studies” may 

sometimes be of such significance that it is appropriate to delay a decision on revision of a 

NAAQS and to supplement the pertinent air quality criteria so the studies can be taken into 

account. In the present case, the EPA’s provisional consideration of ‘‘new studies” concludes 

that, taken in context, the “new” information and findings do not materially change any of the 

broad scientific conclusions made in the air quality criteria regarding the health effects of PM2.5

(U.S. EPA, 2012a). 

For this reason, reopening the air quality criteria review would not be warranted, even if 

there were time to do so under the court order governing the schedule for completing this 

review. Accordingly, the EPA is basing its final decisions in this review on the studies and related 

information included in the PM Integrated Science Assessment (i.e., the air quality criteria) that 

has undergone CASAC and public review. The EPA will consider the “new studies” in the next 

periodic review of the PM NAAQS, which will provide an opportunity to fully assess these 

studies through a more rigorous review process involving the EPA, CASAC, and the public. 

Some public health, medical, and environmental commenters also criticized the EPA’s 

interpretation of PM2.5 risk results. These commenters presented risk estimates for 

combinations of annual and 24-hour standards using more recent air quality data than that 

used in the EPA’s Risk Assessment (U.S. EPA, 2010a). Based on these additional risk analyses, 

the ALA and other commenters contended that public health benefits could continue to 
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increase as annual and 24-hour standard levels decrease below 13 g/m3and 35 g/m3, 

respectively. 

The EPA agrees with commenters that important public health benefits are expected as 

a result of revising the level of the annual standard to 12 g/m3, as is done in this notice, rather 

than 13 g/m3. The Agency also acknowledges that estimated PM2.5-associated health risks 

continue to decrease with annual standard levels below 12 g/m3and/or with 24-hour 

standard levels below 35 g/m3. However, the EPA disagrees with the commenters’ views 

regarding the extent to which risk estimates support setting standard levels below 12 μg/m3

(annual standard) and 35 g/m3(24-hour standard).125

The CAA charges the Administrator with setting NAAQS that are “requisite” (i.e., neither 

more nor less stringent than necessary) to protect public health with an adequate margin of 

safety. In setting such standards the Administrator must weigh the available scientific evidence 

and information, including associated uncertainties and limitations. As described above, in 

reaching her proposed decisions on the PM2.5 standards that would provide “requisite” 

protection, the Administrator carefully considered the available scientific evidence and risk 

information, making public health policy judgments that, in her view, neither overstated nor 

understated the strengths and limitations of that evidence and information. In contrast, as 

discussed more fully above, public health, medical, and environmental commenters who 

recommended levels below 35 g/m3for the 24-hour PM2.5 standard have not provided new 

 

125This section focuses on the 24-hour standard. Section III.E.4.c.i above also discusses these 

commenters’ recommendations within the context of the annual PM2.5 standard. 
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information or analyses to suggest that such standard levels are appropriate, given the 

uncertainties and limitations in the available health evidence, particularly uncertainties in 

studies conducted in locations with 98th percentile 24-hour PM2.5 concentrations below 35 

g/m3and long-term average concentrations below 12 g/m3. 

d. Administrator’s Final Conclusions on the Primary PM2.5 Standard Levels

In reaching her conclusions regarding appropriate standard levels, the Administrator has 

considered the epidemiological and other scientific evidence, estimates of risk reductions 

associated with just meeting alternative annual and/or 24-hour standards, air quality analyses, 

related limitations and uncertainties, the advice of CASAC, and extensive public comments on 

the proposal. After careful consideration of all of these, the Administrator has decided to revise 

the level of the primary annual PM2.5 standard from 15.0 μg/m3to 12.0 μg/m3and to retain the 

level of the primary 24-hour standard at 35 μg/m3.

As an initial matter, the Administrator agrees with the approach supported by CASAC 

and discussed in the Policy Assessment as summarized in sections III.A.3 and III.E.4.a above, of 

considering the annual and 24-hour standards together in determining the protection afforded 

against mortality and morbidity effects associated with both long- and short-term exposures to 

PM2.5. This approach is consistent with the approach taken in the review completed in 1997, in 

contrast to the approach used in the review completed in 2006 where each standard was 

considered independently of the other (i.e., only data from long-term exposure studies were 
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used to inform the level of the annual standard and only data from short-term exposure studies 

were used to inform the level of the 24-hour standard).126

Based on the evidence and quantitative risk assessment, the Administrator concludes 

that it is appropriate to set an annual standard that is generally controlling, which will lower the 

broad distribution of 24-hour average concentrations in an area as well as the annual average 

concentration, so as to provide protection from both long- and short-term PM2.5 exposures. In 

conjunction with this, it is appropriate to set a 24-hour standard focused on providing 

supplemental protection, particularly for areas with high peak-to-mean ratios of 24-hour 

concentrations, possibly associated with strong local or seasonal sources, and for PM2.5-related 

effects that may be associated with shorter-than daily exposure periods. The Administrator 

concludes this approach will reduce aggregate risks associated with both long- and short-term 

exposures more consistently than a generally controlling 24-hour standard and is the most 

effective and efficient way to reduce total PM2-5-related population risk and to protect public 

health with an adequate margin of safety. 

In selecting the level of the annual PM2.5 standard, based on the characterization and 

assessment of the epidemiological and other studies presented and assessed in the Integrated 

Science Assessment and the Policy Assessment, the Administrator recognizes the substantial 

increase in the number and diversity of studies available in this review. This expanded body of 

evidence includes extended analyses of the seminal studies of long-term PM2.5 exposures (i.e., 

ACS and Harvard Six Cities studies) as well as important new long-term exposure studies (as 

 

126

 See 71 FR 61148 and 61168, October 17, 2006.
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summarized in Figures 1 and 2). Collectively, the Administrator notes that these studies, along 

with evidence available in the last review, provide consistent and stronger evidence than 

previously observed of an association between long-term PM2.5 exposures and premature 

mortality in areas with lower long-term ambient concentrations than previously observed, with 

the strongest evidence related to cardiovascular-related mortality. The Administrator also 

recognizes the availability of stronger evidence of morbidity effects associated with long-term 

PM2.5 exposures, including evidence of respiratory effects such as decreased lung function 

growth, from the extended analyses for the Southern California Children’s Health Study and 

evidence of cardiovascular effects from the WHI study. Furthermore, the Administrator 

recognizes new U.S. multi-city studies that greatly expand and reinforce our understanding of 

mortality and morbidity effects associated with short-term PM2.5 exposures, providing stronger 

evidence of associations in areas with ambient concentrations similar to those previously 

observed in short-term exposure studies considered in the previous review (as summarized in 

Figure 3). 

The Administrator recognizes the strength of the scientific evidence for evaluating 

health effects associated with fine particles, noting that the newly available scientific evidence 

builds upon the previous scientific data base to provide evidence of generally robust 

associations and a basis for greater confidence in the reported associations than in the last 

review. She notes the conclusion of the Integrated Science Assessment that this body of 

evidence supports a causal relationship between long- and short-term PM2.5 exposures and 

mortality and cardiovascular effects and a likely causal relationship between long- and short-
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term PM2.5 exposures and respiratory effects. In addition, the Administrator notes additional, 

but more limited evidence, for a broader range of health endpoints including evidence 

suggestive of a causal relationship for developmental and reproductive effects as well as for 

carcinogenic effects.

Based on information discussed and presented in the Integrated Science Assessment, 

the Administrator recognizes that health effects may occur over the full range of concentrations 

observed in the epidemiological studies of both long-term and short-term exposures, since no 

discernible population-level threshold for any such effects can be identified based on the 

currently available evidence (U.S. EPA, 2009a, section 2.4.3). To inform her decisions on an 

appropriate level for the annual standard that will protect public health with an adequate 

margin of safety, in the absence of any discernible population-level thresholds, the 

Administrator judges that it is appropriate to consider the relative degree of confidence in the 

magnitude and significance of the associations observed in epidemiological studies across the 

range of long-term PM2.5 concentrations in such studies. Further, she recognizes, in taking note 

of CASAC advice and the distributional statistics analysis discussed in the Policy Assessment and 

in section III.E.4.a above, that there is significantly greater confidence in the magnitude and 

significance of observed associations for the part of the air quality distribution corresponding to 

where the bulk of the health events evaluated in each study have been observed, generally at 

and around the long-term mean concentrations. Conversely, she also recognizes that there is 

significantly diminished confidence in the magnitude and significance of observed associations 

in the lower part of the air quality distribution corresponding to where a relatively small 
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proportion of the health events were observed. Further, the Administrator recognizes that the 

long-term mean concentrations, or any other specific point in the air quality distribution of 

each study, do not represent a “bright line” at and above which effects have been observed and 

below which effects have not been observed. 

In considering the long-term mean concentrations reported in epidemiological studies, 

the Administrator recognizes that in selecting a level of the annual standard that will protect 

public health with an adequate margin of safety, it is not sufficient to focus on a concentration 

generally somewhere within the range of long-term mean concentrations from the key longterm and short-term exposure studies that reported lower concentrations than had been 

observed in earlier reviews. These key studies provide information for various types of serious 

health endpoints (including mortality and morbidity effects), different study populations (which 

may include at-risk populations such as children and older adults), and different air quality 

distributions that are specific to each study. A level somewhere within the range of long-term 

mean concentrations of the full set of key studies would be higher than the long-term mean of 

at least one of the studies being considered and therefore would not provide a sufficient 

degree of protection against the health effects observed in that study. Absent some reasoned 

basis to place less weight on the evidence in the epidemiological study with the lowest longterm mean concentration among these key studies, this approach would not be consistent with 

the requirement to set a standard that will protect public health with an adequate margin of 

safety.127 Thus, the Administrator recognizes it is important to protect against the serious 

 

127 See American Farm Bureau Federation v. EPA, 559 F. 3d 512, 525-26 (D.C. Cir. 2009).
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effects observed in each of these studies so as to protect public health with an adequate 

margin of safety. In so doing, she looks to identify the study with the lowest long-term mean 

concentration within the full set of key studies to help inform her decision of the appropriate 

standard level which will provide protection for the broad array of health outcomes observed in 

all of the studies, including effects observed in at-risk populations.

Further, consistent with the general approach summarized in section III.E.4.a above and 

supported by CASAC as discussed in section III.E.4.b.ii above, the Administrator recognizes that 

it is appropriate to consider a level for an annual standard that is not just at but rather is 

somewhat below the long-term mean PM2.5 concentrations reported in each of the key longand short-term exposure studies. In so doing, she focuses especially on multi-city studies that 

evaluated health endpoints for which the associations are causal or likely causal (i.e., mortality 

and cardiovascular and respiratory effects associated with both long- and short-term PM2.5

exposures). As discussed above, the importance of considering a level somewhat below the 

lowest long-term mean concentrations in this set of key studies is to establish a standard that 

would be protective against the observed effects in all of the studies, and that takes into 

account the relative degree of confidence in the magnitude and significance of observed 

associations across the air quality distributions in these studies. 

The Administrator recognizes that there is no clear way to identify how much below the 

long-term mean concentrations of key studies to set a standard that would provide requisite 

protection with an adequate margin of safety. She therefore must use her judgment to weigh 

the available scientific and technical information, and associated uncertainties, to reach a final 
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decision on the appropriate standard level. In considering the information in Figures 1 - 4 for 

effects classified as having evidence of a causal or likely causal relationship with long- or shortterm PM2.5 exposures, she observes a cluster of short-term exposure studies with long-term 

mean concentrations within a range of 13.4 μg/m3down to 12.8 μg/m3(Dominici et al., 2006; 

Burnett and Goldberg, 2003; Zanobetti and Schwartz, 2009; Bell et al., 2008; Burnett et al., 

2004). She also observes a cluster of long-term exposure studies with long-term mean 

concentrations within a range of 14.5 μg/m3to 13.6 μg/m3(Dockery et al., 1996; Lipfert et al., 

2006; Zeger et al., 2008; McConnell et al., 2003; Goss et al., 2004; Eftim et al., 2008). For the 

reasons discussed in response to public comments in section III.E.4.c above, the Administrator 

is less influenced by the long-term mean PM2.5 concentrations from the Miller et al. (2007) and 

Krewski et al. (2009) studies with reported long-term mean PM2.5 concentrations of 12.9 and 

14.0 μg/m3, respectively. In each case, the most relevant exposure periods would likely have 

had higher mean PM2.5 concentrations than those reported in the studies.128 Thus, the 

Administrator considers the long-term mean PM2.5 concentrations from these two studies to be 

a highly uncertain basis for informing her selection of the annual standard level.129

To help guide her judgment of the appropriate level below the long-term mean 

concentrations in the epidemiological studies at which to set the standard, the Administrator 

 

128 In the case of Miller et al. (2007), the mean concentration is based on a single year of air 

quality data which post-dated by two years the period for which the health events data were 

collected. In the case of Krewski et al. (2009),the air quality data were based on the last two 

years of the 18-year period for which the health event data were collected. 

129 Nonetheless, as noted above, the EPA notes that the Krewski et al. (2009) and Miller et al. 

(2007) studies provide strong evidence of mortality and cardiovascular-related effects 

associated with long-term PM2.5 exposures to inform causality determinations reached in the 

Integrated Science Assessment (U.S. EPA, 2009a, sections 7.2.11 and 7.6). 
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considered additional information from epidemiological studies concerning the broader 

distribution of PM2.5 concentrations which correspond to the health events observed in these 

studies (e.g., deaths, hospitalizations). The Administrator observes that the development and 

use of this information in considering standard levels is consistent with CASAC’s advice, as 

discussed in section III.E.4.b.ii above, to focus on understanding the concentrations that were 

most influential in generating the health effect estimates in individual studies (Samet, 2010d, p. 

2). 

In considering this additional population-level information, the Administrator recognizes 

that, in general, the confidence in the magnitude and significance of an association identified in 

a study is strongest at and around the long-term mean concentration for the air quality 

distribution, as this represents the part of the distribution in which the data in any given study 

are generally most concentrated. She also recognizes that the degree of confidence decreases 

as one moves towards the lower part of the distribution. Consistent with the approach used in 

the Policy Assessment, the Administrator believes that the range from approximately the 25th

to 10th percentiles is a reasonable range for providing a general frame of reference as to the 

part of the distribution in which her confidence in the associations observed in epidemiological 

studies is appreciably lower. However, as noted above, it is important to emphasize that there 

is no clear dividing line or single percentile within a given distribution provided by the scientific 

evidence that is most appropriate or ‘correct’ to use to characterize where the degree of 

confidence in the associations warrants setting the annual standard level. The decision of the 

appropriate standard level below the long-term mean concentrations of the key studies, which 
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in conjunction with the other elements of the standard would protect public health with an 

adequate margin of safety, is largely a public health policy judgment, taking into account all of 

the evidence and its related uncertainties. 

As discussed in section III.E.4.b, the Administrator takes note of additional populationlevel data that were made available to the EPA by study authors.130 In considering this 

information, the Administrator particularly focuses on the analysis of the distributions of the 

health event data for each area within these studies and the corresponding air quality data for 

the two short-term exposure studies (Zanobetti and Schwartz, 2009; Bell et al., 2008). These 

short-term exposure studies evaluate the relationship between daily changes (one or more 

days) in PM2.5 concentrations and daily changes in health events (e.g., deaths, hospitalizations), 

such that the air quality concentrations that comprise the most relevant exposure periods in 

these studies are contemporaneous with the health event data. In addition, these studies 

considered more recent air quality data, representing generally lower PM2.5 concentrations, in a 

large number of study areas across the U.S. Thus, such studies provide the most useful evidence 

for an analysis evaluating the distribution of health event data and the corresponding long-term 

mean PM2.5 concentrations across the areas included in each multi-city study. 

 

130 As summarized in section III.E.4.a, population-level data were provided to the EPA for four 

studies. These four studies represent some of the strongest evidence showing associations 

between health effects and PM2.5 within the overall body of scientific evidence and include 

three studies (Krewski et al., 2009; Bell et al., 2008; and Zanobetti and Schwartz, 2009) that 

were used as the basis for concentration-response functions in the quantitative risk 

assessment (U.S. EPA, 2010a, section 3.3.3). The Administrator recognizes that the additional 

population-level data available for these four multi-city studies represents a more limited 

data set compared to the set of long-term mean PM2.5 concentrations which were available 

in the published literature for all studies considered in the Integrated Science Assessment. 
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The Administrator also considered the additional population-level data that were made 

available to EPA for two long-term exposure studies (Krewski et al., 2009; Miller et al., 2007). 

She recognizes that in long-term exposure studies investigators follow a specific group of study 

participants (i.e., cohort) over time and across urban study areas, and evaluate how PM2.5

concentrations averaged over a period of years are associated with specific health endpoints 

(e.g., deaths) across cities. As discussed in response to public comments in section III.E.4.c, 

disentangling the effects observed in long-term exposure studies associated with more recent 

air quality measurements from effects that may have been associated with earlier, and most 

likely higher, PM2.5 exposures introduces some uncertainty with regard to understanding the 

appropriate exposure window associated with the observed effects. This is in contrast to the 

short-term exposure studies where the relevant exposure period is contemporaneous to the 

period for which the health data were collected. In light of these considerations, as noted 

above, the Administrator considers the analysis of air quality concentrations that correspond to 

the distribution of population-level data in these two studies to be a highly uncertain basis for 

informing her selection of the annual standard level. 

Based on the above considerations, the Administrator views the additional populationlevel data for the two short-term exposure studies as appropriate to help inform her judgment 

of how much below the long-term mean concentrations to set the level of the annual standard.

The Administrator notes that the long-term mean PM2.5 concentrations corresponding with 

study areas contributing to the 25th percentiles of the distribution of deaths and cardiovascularrelated hospitalizations in these two short-term exposure studies were 12.5 μg/m3

and 11.5
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μg/m3, respectively, for Zanobetti and Schwartz (2009) and for Bell et al. (2008), with the 10th

percentiles being lower by approximately 2 μg/m3in each study..

The Administrator recognizes, as summarized in section III.B above and discussed more 

fully in section III.B.2 of the proposal, that important uncertainties remain in the evidence and 

information considered in this review of the primary fine particle standards. These 

uncertainties are generally related to understanding the relative toxicity of the different 

components in the fine particle mixture, the role of PM2.5 in the complex ambient mixture, 

exposure measurement errors, and the nature and magnitude of estimated risks related to 

increasingly lower ambient PM2.5 concentrations. Furthermore, the Administrator notes that 

epidemiological studies have reported heterogeneity in responses both within and between 

cities and geographic regions across the U.S. She recognizes that this heterogeneity may be 

attributed, in part, to differences in fine particle composition in different regions and cities.131

With regard to evidence for reproductive and developmental effects identified as being 

suggestive of a causal relationship with long-term PM2.5 exposures, the Administrator 

recognizes that there are a number of limitations associated with this body of evidence 

including: the limited number of studies evaluating such effects; uncertainties related to 

identifying the relevant exposure time periods of concern; and limited toxicological evidence 

providing little information on the mode of action(s) or biological plausibility for an association 

 

131 Nonetheless, as explained in section III.E.1, the currently available evidence is not sufficient 

to support replacing or supplementing the PM2.5 indicator with any other indicator defined in 

terms of a specific fine particle component or group of components associated with any 

source categories of fine particles. Furthermore, the evidence is not sufficient to support 

eliminating any component or group of components associated with any source categories 

of fine particles from the mix of fine particles included in the PM2.5 indicator. 
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between long-term PM2.5 exposures and adverse birth outcomes. Nonetheless, the 

Administrator believes that this more limited body of evidence provides some support for 

considering that serious effects may be occurring in a susceptible population at concentrations 

lower than those associated with effects classified as having a causal or likely causal 

relationship with long-term PM2.5 exposures (i.e., mortality, cardiovascular, and respiratory 

effects).

Overall, the Administrator believes that the available evidence interpreted in light of the 

remaining uncertainties, as summarized above and discussed more fully in the Integrated 

Science Assessment and the Policy Assessment, provides increased confidence relative to 

information available in the last review and provides a strong basis for informing her final 

decisions in the current review. The Administrator is mindful that considering what standards 

are requisite to protect public health with an adequate margin of safety requires public health 

policy judgments that neither overstate nor understate the strength and limitations of the 

evidence or the appropriate inferences to be drawn from the evidence. In considering how to 

translate the available information into appropriate standard levels, the Administrator weighs 

the available scientific information and associated uncertainties and limitations. For the 

purpose of determining what annual standard level is appropriate the Administrator recognizes 

that there is no single factor or criterion that comprises the ‘‘correct’’ approach to weighing the 

various types of available evidence and information.

In considering this information, the Administrator notes the advice of CASAC that “there 

are significant public health consequences at the current levels of the standards that justify 
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consideration of lowering the PM2.5 NAAQS further” (Samet, 2010c, p. 12). In addition, she 

recognizes that CASAC concluded, “although there is increasing uncertainty at lower levels, 

there is no evidence of a threshold (i.e., a level below which there is no risk for adverse 

effects)” (Samet, 2010, p.ii) and that the final decisions on standard levels must reflect a 

judgment of the available scientific information with respect to her interpretation of the CAA’s 

requirement to set primary standards that provide requisite protection to public health with an 

adequate margin of safety (Samet, 2010d, p. 4). The Administrator recognizes CASAC’s advice 

that the currently available scientific information provided support for considering an annual 

standard level within a range of 13 to 11 μg/m3and a 24-hour standard level within a range of 

35 to 30 μg/m3. In considering how the annual and 24-hour standards work together to provide 

appropriate public health protection, the Administrator observes that CASAC did not express 

support for any specific levels or combinations of standards within these ranges. She also notes 

that CASAC encouraged the EPA staff to consider additional data from epidemiological studies 

to help quantify the characterization of the PM2.5 concentrations that were most influential in 

generating the health effect estimates in these studies (Samet, 2010d, p. 2). 

In response to CASAC’s advice, the Administrator recognizes that the EPA staff acquired 

additional data from authors of key epidemiological studies and analyzed these data to 

characterize the distribution of PM2.5 concentrations in relation to health events data to better 

understand the degree of confidence in the associations observed in the studies as discussed 

above. The Administrator recognizes that the final Policy Assessment included consideration of 

these additional analyses in reaching final staff conclusions with regard to the broadest range of 
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alternative standard levels supported by the science. She takes note that the final Policy 

Assessment concluded that while alternative standard levels within the range of 13 to 11 μg/m3

were appropriate to consider, the evidence most strongly supported consideration of an annual 

standard level in the range of 12 to 11 μg/m3. The Administrator is aware that, in transmitting 

the final Policy Assessment to CASAC, the Agency notified CASAC that the final staff conclusions 

reflected consideration of CASAC’s advice and that those staff conclusions were based, in part, 

on the specific distributional analysis that CASAC had urged the EPA to conduct (Wegman, 

2011). Thus, CASAC had an opportunity to comment on the final Policy Assessment, but chose 

not to provide any additional comments or advice after receiving it. 

In selecting the annual standard level, the Administrator has considered many factors 

including the nature and severity of the health effects involved, the strength of the overall body 

of scientific evidence as considered in reaching causality determinations, the size of the at-risk 

populations, and the estimated public health impacts. She has also considered the kind and 

degree of the uncertainties that remain in the available scientific information. She recognizes 

that the association between PM2.5 and serious health effects is well established, including at 

concentrations below those allowed by the current standard. Further, she recognizes the CAA 

requirement that requires primary standards to provide an adequate margin of safety was 

intended to address uncertainties associated with inconclusive scientific and technical 

information as well as to provide a reasonable degree of protection against hazards that 

research has not yet identified. In considering the currently available evidence, as summarized 

and discussed more broadly above, the information on risk, CASAC advice, the conclusions of 
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the Policy Assessment, and public comments on the proposal, the Administrator strongly 

believes that a lower annual standard level is needed to protect public health with an adequate 

margin of safety.

In reaching her final decision on the appropriate annual standard level to set, the 

Administrator is mindful that the CAA does not require that primary standards be set at a zerorisk level, but rather at a level that reduces risk sufficiently so as to protect public health, 

including the health of at-risk populations, with an adequate margin of safety. On balance, the 

Administrator concludes that an annual standard level of 12 μg/m3 would be requisite to 

protect the public health with an adequate margin of safety from effects associated with longand short-term PM2.5 exposures, while still recognizing that uncertainties remain in the 

scientific information. 

In the Administrator’s judgment, an annual standard of 12 μg/m3appropriately reflects 

placing greatest weight on evidence of effects for which the Integrated Science Assessment 

determined there is a causal or likely causal relationship with long- and short-term PM2.5

exposures. An annual standard level of 12 μg/m3is below the long-term mean PM2.5

concentrations reported in each of the key multi-city, long- and short-term exposures studies 

providing evidence of an array of serious health effects (e.g., premature mortality, increased 

hospitalization for cardiovascular and respiratory effects). As noted above, the importance of 

considering a level somewhat below the lowest long-term mean concentration in the full set of 

studies considered is to set a standard that would provide appropriate protection against the 

observed effects in all such studies. 
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In reaching her decision, the Administrator has taken into account that at and around 

the mean PM2.5 concentration in any given study represents a part of the air quality distribution 

in which the health event data in that study are generally most concentrated. Furthermore, in 

identifying an appropriate annual standard level below the long-term mean concentrations, she 

recognizes that there is no evidence to support the existence of any discernible threshold, and, 

therefore, she has a high degree of confidence that the observed effects are associated with 

concentrations not just at but extending somewhat below the long-term mean concentration. 

To further inform her judgment in setting the annual standard level so as to protect public 

health with an adequate margin of safety, the Administrator has placed weight on additional 

population-level information available from a subset of these epidemiological studies, 

consistent with CASAC advice. In particular, she has drawn from two short-term exposure 

studies, which provide the most relevant information for evaluating the distribution of health 

events and corresponding long-term PM2.5 concentrations. As explained above, this helps 

inform her judgment as to the degree of confidence in the observed associations in the 

epidemiological studies. In this regard, the Administrator generally judges the region around 

the 25th percentile as a reasonable part of the distribution to help guide her decision on the 

appropriate standard level. Since this evidence comes primarily from two studies, a relatively 

modest data set, the Administrator deems it reasonable not to draw further inferences from air 

quality and health event data in the lower part of the distribution for the purpose of setting a 

standard level. The Administrator notes that the long-term mean PM2.5 concentrations around 

the 25th percentile of the distributions of deaths and cardiovascular-related hospitalizations 
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were approximately around 12 μg/m3in these two studies. The Administrator views this 

information as helpful in guiding her determination as to where her confidence in the 

magnitude and significance of the associations is reduced to such a degree that a standard set 

at a lower level would not be warranted to provide requisite protection that is neither more nor 

less than needed to provide an adequate margin of safety.

The Administrator also recognizes that a level of 12 μg/m3places some weight on 

studies which provide evidence of reproductive and developmental effects (e.g., infant 

mortality, low birth weight). These studies were identified in the Integrated Science Assessment 

as having evidence suggestive of a causal relationship with long-term PM2.5 concentrations. A 

level of 12 μg/m3is approximately the same level as the lowest long-term mean concentration 

reported in such studies (Figures 2 and 4; 11.9 μg/m3for Bell et al., 2007).132 While the 

Administrator acknowledges that this evidence is limited, she believes it is appropriate to place 

some weight on these studies in order to set a standard that provides protection with an 

adequate margin of safety, including providing protection for at-risk populations, as required by 

the CAA. Due to the limited nature of this evidence, she has determined it is not necessary to 

set a standard below the lowest long-term mean concentration in these studies. 

 

132 With respect to cancer, mutagenic, and genotoxic effects, the Administrator observes that 

the PM2.5 concentrations reported in studies evaluating these effects generally included 

ambient concentrations that are equal to or greater than ambient concentrations observed 

in studies that reported mortality and cardiovascular and respiratory effects (U.S. EPA, 

2009a, section 7.5). Therefore, the Administrator concludes that in selecting alternative 

standard levels that provide protection from mortality and cardiovascular and respiratory 

effects, it is reasonable to anticipate that protection will also be provided for carcinogenic 

effects.
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In reflecting on extensive public comments received on the proposal as discussed in 

section III.E.4.c above, the Administrator recognizes that some commenters have offered 

different evaluations of the evidence and other information available in this review and would 

make different judgments about the weight to place on the relative strengths and limitations of 

the scientific information and about how such information could be used in making public 

health policy decisions on the annual standard level. One group of such commenters who 

supported a higher annual standard level (e.g., above 13 μg/m3) would place greater weight on 

the remaining uncertainties in the evidence as a basis for supporting a higher standard than the 

Administrator judges to be appropriate. Such an approach is based on these commenters’ 

judgment that the uncertainties remaining in the evidence are too great to warrant setting an

annual standard below the current level. The Administrator does not agree. 

As an initial matter, an annual standard level of 13 μg/m3or higher would be above the 

long-term mean concentrations reported in two well-conducted, multi-city short-term exposure 

studies reporting positive and statistically significant associations of serious effects (Burnett et 

al., 2004 and Bell et al., 2008). These important studies are fully consistent with the pattern of 

evidence presented by the large body of evidence in this review. As the Administrator 

recognized in the proposal, and as advised by CASAC, the appropriate focus for selecting the 

level of the annual PM2.5 standard is on concentrations somewhat below the lowest long-term 

mean concentrations from the set of key studies of both long-term and short-term PM2.5

exposures considered by the EPA (i.e., as shown in Figure 4). Thus, a standard level set at 13 

μg/m3or higher would clearly not provide protection for the effects observed in the full set 
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epidemiological studies and, therefore, this standard level could not be judged to be requisite 

with an adequate margin of safety.133

In addition, as noted above, in recognizing that there is no evidence to support the 

existence of a discernible threshold below which an effect would not occur, the Administrator is 

mindful that effects occur around and below the long-term mean concentrations reported in 

both the short-term and long-term the epidemiological studies. A standard level of 13 μg/m3or 

higher would not appropriately take into account evidence from the two well-conducted, multicity, short-term exposure studies reporting serious effects with long-term mean concentrations 

below 13 μg/m3noted above (Burnett et al, 2004; Bell et al., 2008). Such a standard level would 

also not appropriately take into account additional population-level data from a limited number 

of epidemiological studies. This approach would ignore CASAC’s advice to consider such 

information in order to better understand the concentrations over which there is a high degree 

of confidence regarding the magnitude and significance of the associations observed in 

individual epidemiological studies and where there is appreciably less confidence. 

Furthermore, a standard level of 13 μg/m3or higher would not appropriately take into 

account the more limited evidence of effects in some at-risk populations (e.g., low birth 

weight). In the Administrator’s view, a standard set at this level would not provide protection 

 

133 The Administrator is mindful that, in reviewing the 2006 final PM NAAQS decisions, the D.C. 

Circuit Court of Appeals concluded that the EPA failed to adequately explain why that annual 

standard provided requisite protection from effects associated with both long- and shortterm exposures or from morbidity effects in children and other at-risk populations when 

long-term means of important short-term studies were below the level the Administrator 

selected for the annual standard. See American Farm Bureau v. EPA. 559 F. 3d 512, 524-26. 

There is no reasonable basis to discount these two studies for purposes of setting the level of 

the annual standard.
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with an adequate margin of safety, including providing protection for at-risk populations. The 

Administrator is mindful that the CAA requirement that primary standards provide an adequate 

margin of safety, discussed in section II.A above, was intended to address uncertainties 

associated with inconclusive scientific and technical information available at the time of 

standard setting as well as to provide a reasonable degree of protection against hazards that 

research has not yet identified. 

In light of the entire body of evidence as discussed above, the Administrator judges that 

an annual standard level set above 12 μg/m3 would not be sufficient to protect public health 

with an adequate margin of safety from the serious health effects associated with long- and 

short-term exposure to PM2.5.

The Administrator also recognizes that a second group of commenters supported a 

lower annual standard level (e.g., no higher than 11 μg/m3). Such a standard level would reflect 

placing essentially as much weight on the relatively more limited data providing evidence 

suggestive of a causal relationship for effects observed in some at-risk populations (e.g., low 

birth weight) as on more certain evidence of effects classified as having a causal or likely causal 

relationship with PM2.5 exposures. In the Administrator’s view, while it is important to place 

some weight on such suggestive evidence, it would not be appropriate to place as much weight 

on it as the commenters would do. 

An annual standard level of 11 μg/m3 would also reflect these commenters’ judgment 

that it is appropriate to focus on a lower part of the distributions of health event data from the 

small number of epidemiological studies for which this information was made available than 
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the Administrator believes is warranted. In the Administrator’s view, using this type of 

information to set a standard level of 11 μg/m3or below would assume too high a degree of 

confidence in the magnitude and significance of the associations observed in the lower part of 

the distributions of health events observed in these studies. Given the uncertainties in the 

evidence and the limited set of studies for which the EPA has information on the distribution of 

health event data and corresponding air quality data, the Administrator believes it is not 

appropriate to focus on the lower part of the distributions of health events data. 

On balance, the Administrator finds that the available evidence interpreted in light of 

the remaining uncertainties does not justify a standard level set below 12 μg/m3as necessary 

to protect public health with an adequate margin of safety. 

After carefully considering the above considerations and the public comments 

summarized in section III.E.4.c above, the Administrator has decided to set the level of the 

primary annual PM2.5 standard at 12 μg/m3. In her judgment, a standard set at this level 

provides the requisite degree of public health protection, including the health of at-risk 

populations, with an adequate margin of safety and is neither more nor less stringent than 

necessary for this purpose.

As discussed above, the Administrator concludes that an approach that focuses on 

setting a generally controlling annual standard is the most effective and efficient way to reduce 

total population risk associated with both long- and short-term PM2.5 exposures. Such an 

approach would result in more uniform protection across the U.S. than the alternative of 
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setting the levels of the 24-hour and annual standard such that the 24-hour standard would 

generally be the controlling standard in areas across the country (see section III.A.3). 

The Administrator recognizes that potential air quality changes associated with meeting 

an annual standard level of 12.μg/m3 will result in lowering risks associated with both long- and 

short-term PM2.5 exposures by lowering the overall air quality distribution. However, the 

Administrator recognizes that such an annual standard alone would not be expected to offer 

sufficient protection with an adequate margin of safety against the effects of short-term PM2.5

exposures in all parts of the country. As a result, in conjunction with an annual standard level of 

12 μg/m3, the Administrator concludes that it is appropriate to continue to provide 

supplemental protection by means of a 24-hour standard set at the appropriate level, 

particularly for areas with high peak-to-mean ratios possibly associated with strong local or 

seasonal sources and for areas with PM2.5-related effects that may be associated with shorterthan-daily exposure periods. 

In selecting the level of a 24-hour standard meant to provide such supplemental 

protection, the Administrator relies upon evidence and air quality information from key shortterm exposure studies. In considering these studies, the Administrator notes that to the extent 

air quality distributions in the study areas considered are reduced to meet the current 24-hour 

standard (at a level of 35 μg/m3) or to meet the revised annual standard discussed above (at a 

level of 12 μg/m3), additional protection would be anticipated against the effects observed in 

these studies. In light of this, when selecting the appropriate level for the 24-hour standard, the 

Administrator considers both the 98th percentiles of 24-hour PM2.5 concentrations and the long-
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term mean PM2.5 concentrations in the locations of the short-term exposure studies. She notes 

that such consideration of both short- and long-term PM2.5 concentrations can inform her 

decision on the extent to which a given 24-hour standard, in combination with the revised 

annual standard established in this notice, would provide protection against the health effects 

reported in short-term studies. 

As discussed in section III.E.4.a above, the Administrator concludes that multi-city shortterm exposure studies provide the strongest data set for informing her decisions on 

appropriate 24-hour standard levels. With regard to the limited number of single-city studies 

that reported positive and statistically significant associations for a range of health endpoints 

related to short-term PM2.5 concentrations in areas that would likely have met the current suite 

of PM2.5 standards, the Administrator recognizes that many of these studies had significant 

limitations (e.g., limited statistical power, limited exposure data) or equivocal results (mixed 

results within the same study area) that make them unsuitable to form the basis for setting the 

level of a 24-hour standard. 

With regard to multi-city studies that evaluated effects associated with short-term PM2.5

exposures, the Administrator observes an overall pattern of positive and statistically significant 

associations in studies with 98th percentile 24-hour values averaged across study areas within 

the range of 45.8 to 34.2 μg/m3(Burnett et al., 2004; Zanobetti and Schwartz, 2009; Bell et al., 

2008; Dominici et al., 2006a, Burnett and Goldberg, 2003; Franklin et al., 2008). The 

Administrator notes that, to the extent air quality distributions are reduced to reflect just 

meeting the current 24-hour standard, additional protection would be provided for the effects 
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observed in the three multi-city studies with 98th percentile values greater than 35 μg/m3

(Burnett et al., 2004; Burnett and Goldberg, 2003; Franklin et al., 2008). In the three additional 

multi-city studies with 98th percentile values below 35 μg/m3, specifically 98th percentile 

concentrations of 34.2, 34.3, and 34.8 μg/m3, the Administrator notes that these studies 

reported long-term mean PM2.5 concentrations of 12.9, 13.2, and 13.4 μg/m3, respectively (Bell 

et al., 2008; Zanobetti and Schwartz, 2009; Dominici et al., 2006a). In revising the level of the 

annual standard to 12 μg/m3, as discussed above, the Administrator recognizes that additional 

protection would be provided for the short-term effects observed in these multi-city studies 

such that revision to the 24-hour standard would not be warranted. That is, by lowering the 

level of the annual standard to 12 μg/m3, the 98th percentile of the distribution would be 

lowered as well such that additional protection from effects associated with short-term 

exposures would be afforded. Therefore, the epidemiological evidence supports a conclusion 

that it is appropriate to retain the level of the 24-hour standard at 35 μg/m3, in conjunction 

with a revised annual standard level of 12 μg/m3. 

In addition to considering the epidemiological evidence, the Administrator also has 

taken into account air quality information based on county-level 24-hour and annual design 

values to understand the implications of revising the annual standard level from 15 to 12 μg/m3

in conjunction with retaining the 24-hour standard level at 35 μg/m3. She has considered this 

information to evaluate the public health protection provided by the two standards in 

combination and to evaluate the most appropriate means of developing a suite of standards 

providing requisite public health protection with an adequate margin of safety. 
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In considering the air quality information, the Administrator observes that a suite of 

PM2.5 standards that includes an annual standard level of 12 μg/m3and a 24-hour standard 

level of 35 μg/m3 would result in the annual standard as the generally controlling standard in 

most regions across the country, except for certain areas in the Northwest, where the annual 

mean PM2.5 concentrations have historically been low but where relatively high 24-hour 

concentrations occur, often related to seasonal wood smoke emissions (U.S. EPA, 2011a, pp. 2-

89 to 2-91, Figure 2-10). In fact, these are the type of areas for which the supplemental 

protection afforded by the 24-hour standard is intended, such that the two standards together 

provide the requisite degree of protection. The Administrator concludes the current 24-hour 

standard at a level of 35 μg/m3, in conjunction with a revised annual standard level of 12 μg/m3, 

will provide appropriate protection from effects observed in studies in such areas in which the 

long-term mean concentrations were below 12 μg/m3and the 98th percentile 24-hour 

concentrations were above 35 μg/m3(e.g., areas in the Northwest U.S.). 

After carefully taking the public comments and above considerations into account, the 

Administrator has decided to retain the current level of the primary PM2.5 24-hour standard at 

35 μg/m3in conjunction with revising the annual standard level from 15.0 μg/m3to 12.0 

μg/m3.

134 In the Administrator’s judgment, this suite of primary PM2.5 standards and the 

rationale supporting these levels appropriately reflects consideration of the strength of the 

available evidence and other information and its associated uncertainties as well as the advice 

 

134As noted in section II.B.1, Table 1 and section III.E.4.a above, the annual standard level is 

defined to one decimal place. Throughout this section, the annual standard levels discussed 

have been denoted as integer values (e.g., 12 μg/m3) for simplicity.
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of CASAC and consideration of public comments. In the Administrator’s judgment, this suite of 

primary PM2.5 standards is sufficient but not more protective than necessary to protect the 

public health, including at-risk populations, with an adequate margin of safety from effects 

associated with long- and short-term exposures to fine particles. This suite of standards will 

provide significant protection from serious health effects including premature mortality and 

cardiovascular and respiratory morbidity effects that are causally or likely causally related to 

long- and short-term PM2.5 exposures. These standards will also provide an appropriate degree 

of protection against other health effects for which there is more limited evidence of effects 

and causality, such as reproductive and developmental effects. This judgment by the 

Administrator appropriately considers the requirement for a standard that is requisite to 

protect public health but is neither more nor less stringent than necessary.135

D. Administrator’s Final Decisions on Primary PM2.5 Standards

For the reasons discussed above, and taking into account the information and 

assessments presented in the Integrated Science Assessment, Risk Assessment, and Policy 

Assessment, the advice and recommendations of CASAC, and public comments to date, the 

Administrator revises the current suite of primary PM2.5 standards. Specifically, the 

Administrator revises: (1) the level of the primary annual PM2.5 standard to 12.0 μg/m3and (2) 

the form of the primary annual PM2.5 standard to one based on the highest appropriate areawide monitor in an area, with no option for spatial averaging. In conjunction with revising the 

 

135 The Administrator also judges that this suite of standards addresses the issues raised by the 

D.C. Circuit’s remand of the 2006 primary annual PM2.5 standard by appropriately revising 

that standard.
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primary annual PM2.5 standard to provide protection from effects associated with long- and 

short-term PM2.5 exposures, the Administrator retains the level of 35 μg/m3and the 98th

percentile form of the primary 24-hour PM2.5 standard to continue to provide supplemental 

protection for areas with high peak PM2.5 concentrations. The Administrator is not revising the 

current PM2.5 indicator or the annual and 24-hour averaging times for the primary PM2.5

standards. The Administrator concludes that this suite of standards would be requisite to 

protect public health with an adequate margin of safety against health effects potentially 

associated with long- and short-term PM2.5 exposures.