1

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 27711 

April 7, 2011

MEMORANDUM

SUBJECT: PM2.5 Distributional Statistical Analyses 

FROM: Pradeep Rajan, OAR/OAQPS/HEID /s/ 

Mark Schmidt, OAR/OAQPS/AQAD /s/ 

Beth Hassett-Sipple, OAR/OAQPS/HEID /s/ 

TO: PM NAAQS Review Docket EPA-HQ-OAR-2007-0492

Overview

 

This memorandum documents the analyses of population-level data from epidemiological 

studies conducted by EPA staff for the review of the particulate matter (PM) national ambient air 

quality standards (NAAQS). The purpose of these analyses was to employ distributional 

statistics to inform staff conclusions in the Policy Assessment on a range of alternative standard 

levels that is appropriate to consider in identifying a primary annual PM2.5 standard that provides 

appropriate protection for health effects associated with both long- and short-term PM2.5

exposures.1

 

In reaching staff conclusions on the range of annual standard levels appropriate to 

consider, we expanded our general approach for identifying alternative standard levels beyond 

the consideration of a single statistical metric (i.e., mean) from an epidemiological study, to the 

extent such information was available. In so doing, we employed distributional statistics to 

identify the broader range of PM2.5 concentrations that were most influential in generating health 

effect estimates in epidemiological studies as well as to characterize where the bulk of the data 

exist. Thus, we considered the range of PM2.5 concentrations where the data (i.e., populationlevel) from epidemiological studies were most dense, and at what range below the long-term 

mean PM2.5 concentrations where there is a comparative lack of data and our confidence in the 

associations is appreciably less. We recognize there is no one percentile value within a given 

distribution that is the most appropriate or “correct” way to characterize where our confidence in 

the associations becomes appreciably lower. Consequently, we have determined that the range 

from the 25th to 10th percentiles is a reasonable range to consider as a region where we have 

appreciably less confidence in the associations observed in the epidemiological studies. 

 

1

 US EPA (2011). Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality 

Standards. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. EPA 452/R-11-003. April 2011. 
2

In considering distributional statistics from epidemiological studies, we recognized that 

there are two types of study population-level metrics that are useful to consider in identifying the 

PM2.5 concentrations most influential in generating the health effect estimates reported in the 

epidemiological studies. The most relevant information to consider is 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 event data can be 

restricted, and consistent with advice from CASAC on the second draft Policy Assessment we 

also considered the number of study participants within each study area as a reasonable surrogate 

for health event data.2,3 

In the absence of health event and study population data, we also considered the 

distribution of PM2.5 concentrations across study areas as representative of the PM2.5

concentrations likely experienced by study participants, which are integral to the generation of 

effect estimates. This approach is particularly relevant for identifying the range of PM2.5

concentrations most influential in generating the effect estimates in short-term exposure studies 

(e.g., time-series studies), but, as noted by CASAC, is more “complex” for long-term PM2.5

exposure studies (Samet, 2010, p. 2). In short-term exposure studies, a similar number of events 

are likely occurring on a daily basis and, thus, information on the PM2.5 air quality distributions 

provides a better approximation of a study participant’s likely exposure in relation to the 

observed health effects as compared to long-term exposure studies where individual study 

participant’s exposures change over the course of the study follow-up period (Samet, 2010, p. 2). 

Specific analyses conducted, including tasks, assumptions, caveats, and processing 

methodologies are described in more details below. In summary, these analyses address the 

following: 

o Analysis 1 – Distributions of air quality and associated health event and population data 

from selected long-term epidemiological studies 

o Analysis 2 – Distributions of air quality and associated health event and population data 

from selected short-term epidemiological studies 

General Data Processing 

These analyses built on previous air quality analyses described in earlier EPA staff 

memoranda (Schmidt et al., 2010; Hassett-Sipple, et al., 2010).4

 For the current analyses, we 

 

2

 Samet J. (2010, p. 2). Letter from Dr. Jonathan M. Samet, Chair, Clean Air Scientific Advisory Committee to the 

Honorable Lisa P. Jackson, Administrator, US EPA. CASAC Review of Policy Assessment for the Review of the 

PM NAAQS – Second External Review Draft (June 2010). September 10, 2010. Available at: 

http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/CCF9F4C0500C500F8525779D

0073C593/$File/EPA-CASAC-10-015-unsigned.pdf. 

3

 A limitation of applying population data as a substitute for the number of the health events occurring in a study 

area is the recognition that baseline incidence rates of health events will vary across study areas. 

4

 Schmidt M; Hassett-Sipple B; Rajan P (2010). PM2.5 Air Quality Analyses. Memorandum to PM NAAQS review 

docket EPA-HQ-OAR-2007-0492-0131. July 22, 2010; Hassett-Sipple, B, Schmidt, M, and Rajan, P. (2010) 

Analyses of PM2.5 Data for the PM NAAQS Review. Memorandum to the PM NAAQS review docket. Docket ID 

number EPA-HQ-OAR-2007-0492-0077. March 29, 2010. Available: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.html. 
3

requested population-level data from investigators of the six multi-city U.S. studies included in 

Analysis 2 of Schmidt et al. (2010). As described in Hassett-Sipple et al. (2010) these six studies 

were selected because they considered multiple locations representing varying regions across 

several seasons that provide evidence on the influence of climate and particle mixtures on health 

effects associated with long- or short-term PM2.5 exposures. In addition, these multi-city studies 

considered relatively more recent air quality conditions (1999 to 2005).5 

Specifically, the population-level data for these analyses originated from the authors of 

the following four multi-city long- and short-term PM2.5 exposure epidemiological studies6

: 

Long-term PM2.5 exposure studies:

 ACS Reanalysis II 

o Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi Y, Turner MC, Pope AC III, 

Thurston G, Calle EE, Thun MJ. (2009). Extended Follow-Up and Spatial Analysis of the 

American Cancer Society Study Linking Particulate Air Pollution and Mortality. HEI 

Research Report 140, Health Effects Institute, Boston, MA. Available: 

http://pubs.healtheffects.org/view.php?id=315. 

o Burnett R (2010). Personal communication with Dr. Rick Burnett: Fw: Urgent Request 

for Information from your 2009 study. September 13, 2010. Document ID EPA-HQOAR-2007-0492-0304. 

 Women’s Health Initiative (WHI) 

o Miller KA, Siscovick DS, Sheppard L, Shepherd K, Sullivan JH, Anderson GL, Kaufman 

JD (2007). Long-term exposure to air pollution and incidence of cardiovascular events in 

women. N Engl J Med, 356: 447-458. 

o Miller K (2010). Personal communication with Kristin Miller: Re: Urgent Request for 

Information from your 2007 study. August 24, 2010. Document ID EPA-HQ-OAR2007-0492-0306. 

Short-term PM2.5 exposure studies:

 Medicare Air Pollution Study (MCAPS) 

o Bell ML, Ebisu K, Peng RD, Walker J, Samet JM, Zeger SL, Dominic F (2008). Seasonal 

and regional short-term effects of fine particles on hospital admissions in 202 U.S. 

counties, 1999-2005. Am J Epidemiol, 168: 1301-1310. 

 

5 The network of PM2.5 FRMs reporting to the AQS has been operational since 1999. 6 We requested but did not receive population-level data from the investigators of the following 2 studies: Eftim 

SE; Samet JM; Janes H; McDermott A; Dominici F (2008). Fine Particulate Matter and Mortality: A Comparison of 

the Six Cities and American Cancer Society Cohorts With a Medicare Cohort. Epidemiology, 19: 209- 216., and 

Dominici F; Peng RD; Bell ML; Pham L; McDermott A; Zeger SL; Samet JL (2006). Fine particulate air pollution 

and hospital admission for cardiovascular and respiratory diseases. JAMA, 295: 1127-1134. 
4

o Bell M (2010). Personal communication with Dr. Michelle Bell: Re: Urgent Request for 

Information from your 2008 study. August 30, 2010. Document ID EPA-HQ-OAR2007-0492-0305. 

 112-Cities Mortality Study 

o Zanobetti A, Schwartz J (2009). The effect of fine and coarse particulate air pollution on 

mortality: A national analysis. Environ Health Perspect, 117: 1-40. 

o Zanobetti A (2010). Personal communication with Dr. Antonella Zanobetti; email to 

Pradeep Rajan, US EPA, OAQPS. September 15, 2010. Document ID EPA-HQ-OAR2007-0492-0308, EPA-HQ-OAR-2007-0492-0308.1. 

We requested that investigators from the three mortality studies noted above (Krewski et 

al., 2009; Miller et al., 2007; Zanobetti and Schwartz 2009) provide us with the total number of 

all-cause, non-accidental deaths and the total number of cohort members for each of the study 

areas (i.e., MSA, cities). In addition, we requested that investigators for one morbidity study 

noted above (Bell et al., 2008) provide us with the number of cardiovascular-related and 

respiratory-related hospitalizations and the total number of cohort members for each of the 202 

study areas (i.e., counties). 

To maintain continuity and consistency, the same air quality database included in prior 

analyses was utilized for the current analyses. The air quality data for this project originated 

from EPA’s Air Quality System (AQS) database, the official repository of NAAQS-comparable 

ambient measurements. Data were extracted and processed as described in Hassett-Sipple et al. 

(2010). 

In computing PM2.5 concentrations corresponding with health event and population 

distributions for the four epidemiological studies noted above, the number of health events and 

study participants were combined for those study areas with identical PM2.5 concentrations. A 

description of study-area specific PM2.5 concentrations can be found in Attachment A of HassettSipple et al. (2010). Additional data processing details are described in the analysis-specific 

descriptions below. 

Analysis 1 – Distributions of air quality and associated health event and population data from 

selected long-term epidemiological studies 

In the analysis documented here, we present distributions of health event (i.e., deaths, 

hospitalizations) and population (i.e. study participants) data from Krewski et al. (2009) and 

distributions of population data from Miller et al. (2007). Investigators provided us with MSAspecific counts of deaths and study participants for Krewski et al. (2009), and MSA-specific 

counts of study participants for Miller et al. (2007). These two long-term PM2.5 exposure 

epidemiological studies provided a continuous measure of the density of the data from multi-city 

studies as a function of the annual mean concentrations [14.0 μg/m3 (Krewski et al., 2009); 12.9 
5

μg/m3

 (update of Miller et al. (2007) PM2.5 data included in Curl (2009)7] across the cities in 

each study. Specifically, we considered the distributions of air quality data as well as 

distributions of event and study population data across air quality concentrations. 

In the absence of event data we considered the distribution of population data as a 

surrogate for the occurrence of health events across study areas. Specifically, our analysis of the 

distributions of health events and population data from Krewski et al. (2009) examined the 

reliability of utilizing study population data as a surrogate for event data. Table 1 includes the 

years of air quality data, total study areas, and the annual PM2.5 concentrations computed from 

the site-level 24-hour average PM2.5 concentrations that correspond with study areas contributing 

to the 10th, 25th, and 50th percentiles of the health event and study population data. Since the 

10th, 25th, and 50th percentiles for the health events and study population distributions are nearly 

identical for Krewski et al. (2009), we conclude that the distribution of population data can be a 

useful surrogate for event data, and in particular for Miller et al. (2007), for which we did not 

have health event data. 

In Figure 1, the cumulative variable (i.e., frequency of deaths, study population) for 

Krewski et al. (2009) and Miller et al. (2009) is plotted on the y-axis as a percentage of the total 

study variable (i.e., as a percentage of the total study area-period count of health events or study 

population). The x-axis represents the annual mean concentration in units of μg/m3

. 

Analysis 2 – Distributions of air quality and associated health event and population data from 

selected short-term epidemiological studies 

This analysis built on previous air quality analyses described in Schmidt et al. (2010). In 

the analysis documented here, we present distributions of health events (i.e., deaths, 

hospitalizations) and population (i.e. study participants) data from two short-term PM2.5 exposure 

studies (Zanobetti and Schwartz, 2009; Bell et al., 2008). The long-term mean PM2.5

concentrations for Zanobetti and Schwartz (2009) and Bell et al. (2008) were 13.2 μg/m3 and 

12.9 μg/m3, respectively. 

Table 1. Summary Results for Select Long-and Short-term PM2.5 Exposure Studies 

Cite

Years of 

Air 

Quality 

Data 

Study 

Areas Population-Level 

Data

PM2.5 (μg/m3)

a

Distributional 

Statistics 

10th 25th 50th

Krewski et al. (2009) 1999-

2000 

116 US 

MSAs 

Deaths 10.2 12.0 14.4 

Study Population 10.2 12.1 14.4 

 

7

 Personal communication with Cynthia Curl, MESA Air Project Manager, University of Washington; email to Beth 

Hassett-Sipple, US EPA, OAQPS regarding request for PM air quality data. August 10, 2009. Document ID EPAHQ-ORD-2007-0517-0113. 
6

Miller et al. (2007) 2000 36 

MSAs Study Population 9.7 11.2 12.8 

Zanobetti & Schwartz 

(2009) 

1999-

2005 

99 US 

citiesb 

Deaths 10.3 12.5 14.5 

Study Population 10.3 12.5 14.5 

Bell et al. (2008) 1999-

2005 

202 US 

countie

s 

CV-related HA 9.8 11.5 13.5 

Respiratory-related 

HA 9.8 11.4 13.5 

Study Population 9.6 11.2 13.3 

a

Annual mean PM2.5 concentration corresponding with study areas contributing to the 10th, 25th, 

and 50th percentiles of the distribution of study population-level data. 

b

AQS data were available for 99 of 112 study areas included in Zanobetti and Schwartz (2009). 

Investigators provided us with county- or city-specific counts of cardiovascular-related 

and respiratory-related hospitalizations (Bell et al., 2008) and deaths (Zanobetti and Schwartz, 

2009). In addition, investigators for the Bell et al. (2008) study provided us with county-specific 

counts of study participants. For the Zanobetti and Schwartz (2009) study populations were 

estimated for each study by summing the county populations (per 2000 Census) for the cities 

where sites had valid contributing data for the area-period air quality metric estimates. 

Table 1 includes for these two studies the years of air quality data, total study areas, and 

the annual PM2.5 concentrations computed from the site-level 24-hour average PM2.5

concentrations that correspond with study areas contributing to the 10th, 25th, and 50th percentiles 

of the health event and population data. We note that as in the case of Analysis 1, the 10th, 25th, 

and 50th percentiles for the health events and population distributions are nearly identical for 

Zanobetti and Schwartz (2009) and Bell et al. (2008), and thus provide further support for 

utilizing the distribution of population data as a surrogate for event data. 

In Figure 2, the cumulative variable (i.e., frequency of deaths, cardiovascular and hospital 

admissions, study population) is plotted on the y-axis as a percentage of the total study variable 

(i.e., as a percentage of the total study area-period count of health events or study population). 

The x-axis represents the annual mean concentration in units of μg/m3. 
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Percentage of Total Data (i.e., health event or study population)

Corresponding Annual Mean (μg/m3)

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Health Event Data 

Krewski et al., 2009 (ACS) - deaths (116 study areas)

Population Data

Krewski et al., 2009 (ACS)

Miller (WHI, 36 study areas)

Figure 1. Distribution of Population-Level Data and Corresponding PM2.5 Concentrations 

for Selected Long-term PM2.5 Epidemiological Studies

 In the absence of health event and study population data, the distribution of PM2.5

concentrations across study areas could be used to represent the PM2.5 concentrations likely 

experienced by study participants. However, we were unable to determine from the 

methodologies published for these studies how the air quality data were statistically weighted to 

account for variations in the availability of daily PM2.5 measurements by study area. 

Consequently, we are unable to consider this type of information as part of our effort to identify 

the broader range of PM2.5 concentrations that were most influential in generating the health 

effect estimates in epidemiological studies. 

Figure 3 includes the distribution of health event data for Krewski et al. (2009), Zanobetti 

and Schwartz (2009), and Bell et al. (2008), respectively, and corresponding PM2.5

concentrations. We also include the distribution of study participants for Miller et al. (2007) and 

corresponding PM2.5 concentrations. 
8

Corresponding Annual Mean (μg/m3)

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Figure 2. Distribution of Population-Level Data and Corresponding PM2.5 Concentrations 

for Selected Short-term PM2.5 Epidemiological Studies

Percentage of Total Data (i.e., health event or study population)

Health Event Data 

 Bell et al., 2008 (MCAPS) - cardiovascular-related 

hospitalizations (202 study areas)

Bell et al., 2008 (MCAPS) - respiratory-related hospitalizations

Zanobetti and Schwartz, 2009 – mortality (99 study areas)

Population Data

Bell et al., 2008 (MCAPS)

Zanobetti and Schwartz, 2009
9

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Percentage of Total Data (i.e., health event or study population)

Corresponding Annual Mean (μg/m3)

Figure 3. Distribution of Population-Level Data and Corresponding PM2.5 Concentrations 

for Selected Multi-City Epidemiological Studies

Health Event Data 

• Krewski et al., 2009 (ACS) - deaths (116 study areas)

Zanobetti & Schwartz, 2009 - deaths (99 study areas)

 Bell et al., 2008 (MCAPS) - cardiovascular-related 

hospitalizations (202 study areas)

Population Data

♦ Miller et al., 2007 (WHI, 36 study areas)