Environmental Health Perspectives • volume 117 | number 6 | June 2009 957

Research

Epidemiological studies have provided evidence of an association between short-term 

and long-term exposure to fine particulate 

matter [≤ 2.5 μm in aerodynamic diameter 

(PM2.5)] and risk for premature mortality and 

excess morbidity (Pope and Dockery 2006). 

These studies have focused on the risk associated with the total mass of particles, without 

considering the heterogeneity in their chemical composition. The U.S. National Research 

Council Committee on Research Priorities 

for Airborne Particulate Matter identified 

the assessment of PM characteristics associated with toxicity as a crucial research gap 

(National Research Council 2004), and many 

researchers have called for studies on possible 

differential toxicity of PM based on its components (Nel 2005). Toxicologic studies have 

linked different size ranges and constituents of 

PM with various health responses, but few of 

these results have been replicated by epidemiologic studies examining exposures at ambient concentrations (Schlesinger et al. 2006). 

Given the complex mixture of PM in ambient 

air, characterizing the biological mechanisms 

through which PM harms human health is 

challenging, especially because multiple 

mechanisms are likely to be operative across 

the range of health outcomes associated with 

exposure to PM. Currently, many hypotheses 

have been advanced (Brook et al. 2004; Pope 

and Dockery 2006; Schlesinger et al. 2006),

but understanding of mechanisms is not yet 

sufficient to integrate what is known about 

the characteristics of PM in ambient air with 

the epidemiologic and toxicologic findings 

(Kaufman 2007).

Limited understanding of the differential toxicity of various components of the 

PM mixture also hinders control programs 

intended to reduce the public health burden 

of morbidity and mortality from airborne 

PM. Present measures to reduce the health 

effects of ambient PM depend largely on controlling the emission sources that contribute 

the most to PM mass. Promulgating more 

targeted air quality standards or guidelines 

incorporating PM chemical components, 

other characteristics, or specific sources of 

PM (e.g., traffic vs. electrical utilities) requires 

a more complete scientific foundation than is 

currently available.

The availability of PM2.5 component data 

from the U.S. Environmental Protection 

Agency (EPA) allows us to examine at a 

population level the heterogeneity in toxicity 

of PM2.5 components. In previous work we 

identified seven components of PM2.5 that 

contribute most of the PM2.5 total mass or 

that covary closely with PM2.5 total mass (Bell 

et al. 2007). We also reported evidence of an 

association between PM2.5 total mass and 

hospital admissions in the Medicare population for the period 1999–2005 for 204 U.S.

counties (Dominici et al. 2006; Peng et al. 

2008) and examined the associations of the 

coarse fraction of PM [> 2.5 and ≤ 10 μm 

in aerodynamic diameter (PM10–2.5)] with 

hospital admissions for 108 U.S. counties 

(Peng et al. 2008). For the present study, we 

used time-series approaches and estimated the 

association between the seven PM2.5 chemical 

components and risk of hospital admissions 

among Medicare enrollees for cardiovascular 

disease (CVD) and respiratory disease over 

the period 2000–2006.

Methods

We obtained daily counts of hospital admissions for the period 2000–2006 from billing claims of enrollees in the U.S. Medicare 

Address correspondence to R.D. Peng, Department 

of Biostatistics, Johns Hopkins Bloomberg School of 

Public Health, 615 North Wolfe St., Baltimore, MD 

21205 USA. Telephone: (410) 955-2468. Fax: (410) 

955-0958. E-mail: rpeng@jhsph.edu

Supplemental Material is available online at http://

www.ehponline.org/members/2009/0800185/suppl.pdf

We thank C. Gerczak for editorial input and 

K. Ebisu for help with the components database.

Funding for F.D., R.D.P, M.L.B., A.S.G., 

A.M., S.L.Z., and J.M.S. was provided by the U.S. 

Environmental Protection Agency (EPA) (RD83241701); for F.D., S.L.Z., A.S.G., and R.D.P., 

by the National Institute for Environmental Health 

Sciences (NIEHS) (ES012054-03) and by the 

NIEHS Center in Urban Environmental Health 

(P30 ES 03819); and for M.L.B., by the Health 

Effects Institute through Walter A. Rosenblith 

New Investigator Award 4720-RFA04-2/04-16 and 

NIEHS Outstanding New Environmental Scientist 

award R01-ES015028. 

Although the research described in this study has 

been funded wholly or in part by the U.S. EPA 

through grant agreement RD-83241701 to Johns 

Hopkins University, it has not been subjected to the 

agency’s required peer and policy review and does 

not necessarily reflect the views of the agency, and 

no official endorsement should be inferred. 

The authors declare they have no competing 

financial interests.

Received 12 September 2008; accepted 10 February 

2009.

Emergency Admissions for Cardiovascular and Respiratory Diseases and 

the Chemical Composition of Fine Particle Air Pollution

Roger D. Peng,1 Michelle L. Bell,2 Alison S. Geyh,3 Aidan McDermott,1 Scott L. Zeger,1 Jonathan M. Samet,4 and 

Francesca Dominici1

1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; 2School of Forestry and 

Environmental Studies, Yale University, New Haven, Connecticut, USA; 3Department of Environmental Health Sciences, Johns Hopkins 

Bloomberg School of Public Health, Baltimore, Maryland, USA; 4Department of Preventive Medicine, University of Southern California, 

Los Angeles, California, USA

Background: Population-based studies have estimated health risks of short-term exposure to fine 

particles using mass of PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter) as the indicator. Evidence regarding the toxicity of the chemical components of the PM2.5 mixture is limited.

Objective: In this study we investigated the association between hospital admission for cardiovascular 

disease (CVD) and respiratory disease and the chemical components of PM2.5 in the United States.

Methods: We used a national database comprising daily data for 2000–2006 on emergency hospital admissions for cardiovascular and respiratory outcomes, ambient levels of major PM2.5 chemical 

components [sulfate, nitrate, silicon, elemental carbon (EC), organic carbon matter (OCM), and 

sodium and ammonium ions], and weather. Using Bayesian hierarchical statistical models, we estimated the associations between daily levels of PM2.5 components and risk of hospital admissions in 

119 U.S. urban communities for 12 million Medicare enrollees (≥ 65 years of age). 

Results: In multiple-pollutant models that adjust for the levels of other pollutants, an interquartile 

range (IQR) increase in EC was associated with a 0.80% [95% posterior interval (PI), 0.34–1.27%] 

increase in risk of same-day cardiovascular admissions, and an IQR increase in OCM was associated 

with a 1.01% (95% PI, 0.04–1.98%) increase in risk of respiratory admissions on the same day. 

Other components were not associated with cardiovascular or respiratory hospital admissions in 

multiple-pollutant models.

Conclusions: Ambient levels of EC and OCM, which are generated primarily from vehicle emissions, diesel, and wood burning, were associated with the largest risks of emergency hospitalization 

across the major chemical constituents of PM2.5.

Key words: cardiovascular disease, chemical components, hospital admission, particulate matter, 

PM2.5, respiratory disease, Speciation Trends Network. Environ Health Perspect 117:957–963 

(2009). doi:10.1289/ehp.0800185 available via http://dx.doi.org/ [Online 11 February 2009]
Peng et al.

958 volume 117 | number 6 | June 2009 • Environmental Health Perspectives

system. Because the Medicare data analyzed 

for this study did not include individual 

identifiers, we did not obtain consent from 

individuals. This study was reviewed and 

exempted by the Institutional Review Board 

at the Johns Hopkins Bloomberg School of 

Public Health.

Each billing claim contains the date of service, disease classification using International 

Classification of Diseases, 9th Revision (ICD-9)

codes (Centers for Disease Control and 

Prevention 2008), age, and county of residence. We considered two broad classes of 

outcomes based on ICD-9 codes: urgent or 

emergency cardiovascular admissions and 

urgent or emergency respiratory admissions. 

The classification of “urgent” and “emergency” 

is designated directly on each Medicare hospital admissions record. We excluded other classifications, such as “elective.” A recent study 

(Dominici et al. 2006) considered a number 

of different cardiovascular and respiratory 

outcomes. Because of the sparser sampling of 

the PM2.5 component data compared with 

the PM2.5 total mass data, to obtain sufficient statistical power we collapsed the data 

into two broad categories of hospital admissions: a) CVD, which includes heart failure 

(ICD-9 code 428), heart rhythm disturbances 

(426–427), cerebrovascular events (430–438), 

ischemic heart disease (410–414, 429), and 

peripheral vascular disease (440–448); and 

b) respiratory diseases, which include chronic 

obstructive pulmonary disease (490–492) and 

respiratory infection (464–466, 480–487). 

We excluded admissions for injuries and for 

external causes (800–849). By collapsing these 

health outcomes, we increased statistical power 

and obtained more stable estimates of risk at 

the cost of some specificity of the outcome.

We analyzed each outcome (respiratory or 

cardiovascular admissions) separately. We calculated the daily counts of hospitalizations by 

summing the hospital admissions for each disease of interest recorded as a primary diagnosis. To calculate daily hospitalization rates, we 

constructed a parallel time series of the numbers of individuals enrolled in Medicare that 

were at risk in each county on each day. We 

based the location of each hospital admission 

on the county of residence of the enrollee.

The U.S. EPA established the PM 

Speciation Trends Network (STN) to measure more than 50 PM2.5 chemical components, in addition to total mass. The STN 

includes > 50 national air monitoring stations 

(NAMS) and > 200 state and local air monitoring stations (SLAMS) (U.S. EPA 1999). 

Air pollution concentrations were typically 

measured on a 1-in-3–day schedule in the 

NAMS and on a 1-in-6–day schedule in 

the SLAMS. We removed suspect data and 

extreme values from the original monitor 

records; monitors with very little data were 

omitted altogether. Full details of the construction of the database can be found elsewhere (Bell et al. 2007). We also used PM2.5

total mass measurements from the U.S. EPA’s 

Air Quality System as in our previous analyses 

(Dominici et al. 2006). Of the 187 counties 

described in the Bell et al. (2007) analysis, we 

restricted the present analysis to counties with 

general populations larger than 150,000 and 

with at least 100 observations on components 

of PM2.5. These requirements ensured that 

we would have enough data in a particular 

location to estimate an association between 

PM2.5 components and hospital admissions. 

The study population consisted of 12 million 

Medicare enrollees living in 119 urban counties in the United States (Figure 1).

We limited our analysis to the components making up a large fraction of the total 

PM2.5 mass or covarying with total mass (Bell 

et al. 2007): sulfate, nitrate, silicon, elemental 

carbon (EC), organic carbon matter (OCM), 

sodium ion, and ammonium ion. These seven 

components, in aggregate, constituted 83% of 

the total PM2.5 mass, whereas all other components individually contributed < 1%. We 

computed countywide averages for each of 

these components and for PM2.5 total mass 

by averaging the daily values from all monitors in a county. We adjusted organic carbon measurements for field blanks to estimate 

OCM. We used a standard approach such 

that OCM = k(OCm – OCb), where OCM 

represents organic carbon matter, OCm represents measured organic carbon, OCb represents organic carbon for blank filters, and k

is the adjustment factor to account for noncarbon organic matter. We applied a k value 

of 1.4, as in a previous analysis (Bell et al. 

2007). We obtained temperature and dewpoint temperature data from the National 

Climatic Data Center on the Earth-Info CD 

database (EarthInfo 2006).

As a check on the consistency of the 

chemical component data, we first assessed 

whether three different PM2.5 indicators (four 

scenarios total) provided comparable estimates 

of the short-term associations of PM2.5 with 

cardiovascular and respiratory admissions:

PM2.5 (1), PM2.5 measured by the national 

PM2.5 monitoring network for the period 

1999–2006; PM2.5 (1a), PM2.5 (1) for the 

period 2000–2006 and including only days 

with available measurements for all the seven 

PM2.5 components from the STN; PM2.5 (2), 

PM2.5 measured by the STN for the period 

2000–2006 and including only days with 

available measurements for all the seven PM2.5

components from the STN; and PM2.5 (3),

PM2.5 estimated as the sum of the seven largest components of PM2.5 mass for the period 

2000–2006. Significant differences between 

these estimates would raise uncertainty as to 

the recorded values of PM2.5 total mass and 

its components. The estimates obtained under 

the scenarios 1a, 2, and 3 use data on the 

same subset of days. Each of these measures of 

PM2.5 was available in all 119 counties.

We estimated the within-county monitor-to-monitor correlation for each of the 

seven PM2.5 components to obtain a measure 

of the spatial homogeneity of each component. For this calculation we used a subset of 

12 counties that had more than one monitor (27 monitors total): Jefferson, Alabama; 

Washington, DC; Cook, Illinois; Jefferson, 

Kentucky; Wayne, Michigan; Bronx, 

New York; Cuyahoga, Ohio; Allegheny, 

Pennsylvania; Philadelphia, Pennsylvania; 

Providence, Rhode Island; King, Washington; 

and Kanawha, West Virginia. We computed 

correlations only if at least 90 paired observations were available between two monitors. 

We also estimated the median within-county 

correlations between the seven PM2.5 components, and the three measures of PM2.5 total 

Figure 1. U.S. counties with populations larger than 150,000 for which sufficient hospital admissions and 

PM2.5 chemical component data were available, 2000–2006 (119 total).
Hospital admissions and components of fine PM

Environmental Health Perspectives • volume 117 | number 6 | June 2009 959

mass by a) estimating the correlations between 

time series data for each pair of air pollutants 

within each county and b) taking the median 

of the estimated correlations across the 119 

counties. As a separate measure of spatial 

homogeneity, we calculated, for each of the 

seven components and using all monitors, 

the distance at which the correlation between 

pairs of monitors was 0.5 on average.

We applied Bayesian hierarchical statistical models to estimate county-specific and 

national average associations between daily 

variation in the seven PM2.5 chemical components and daily variation in hospital admissions 

rates. This approach was originally developed 

for the National Morbidity, Mortality, and Air 

Pollution Study (Bell et al. 2004; Samet et al. 

2000) and subsequently extended (Dominici 

et al. 2006) to provide a consistent and unified 

methodology for analyzing data from multiple 

locations. We fit log-linear Poisson regression 

models with overdispersion to county-specific 

time-series data on hospital admissions and 

chemical components, accounting for potential confounders such as weather, day of the

week, unobserved seasonal factors, and longterm trends. In each county-specific regression 

model, we included an indicator for the day

of the week, a smooth function of time with 

8 degrees of freedom (df) per calendar year to 

control for seasonality and long-term trends, a 

smooth function of current-day temperature 

(6 df), a smooth function of the 3-day running 

mean temperature (6 df), a smooth function 

of current-day dew-point temperature (3 df), 

and a smooth function of the 3-day running 

mean dew-point temperature (3 df). For all of 

the smooth functions we used a natural spline 

basis. We conducted a sensitivity analysis with 

respect to the smooth function of time to 

determine the degree to which risk estimates 

changed with varying levels of adjustment for 

smooth unmeasured confounders. Although

other information about Medicare enrollees 

is available, such as sex and race, we excluded 

these factors from all models because they do 

not vary over time and should not play a role 

in our time series analysis.

For the exposure concentrations, we 

examined 0-, 1-, and 2-day lag concentrations because our previous work with PM2.5

total mass and hospital admissions showed 

little evidence of a strong association with 

admissions at a lag of ≥ 3 days (Dominici 

et al. 2006). We examined each lag separately 

because the 1-in-6–day sampling of the chemical component data from the STN prohibited the use of distributed lag models where 

all lags can be examined simultaneously.

For estimating the health effects of the 

PM2.5 components, we employed singlepollutant and multipollutant models. In 

single-pollutant models, we included each 

PM2.5 component in the regression model 

individually, without adjusting for any other 

chemical component (the model does adjust 

for other time-varying factors). In multipollutant models, we included PM2.5 components simultaneously to obtain estimates 

of the regression coefficients for each component adjusted for the other components. 

Ammonium was excluded from models that 

included sulfate and nitrate because of the 

high correlation among these three components. We included ammonium in a separate 

multipollutant model that did not include 

sulfate or nitrate but included the remaining 

four components.

We combined the county-specific risk 

estimates to form a national average using a 

Bayesian hierarchical model. In the singlepollutant models, we combined the log-relative risks separately for each pollutant using 

TLNise two-level normal independent sampling estimation software (Everson and Morris 

2000). For the multiple-pollutant models, 

the risks were treated as a vector for each 

county and combined using a multivariate 

normal hierarchical model. We used Markov 

chain Monte Carlo methods to obtain the 

posterior distribution of the national average 

component effects. We assessed statistical significance by the posterior probability that the 

national average relative risk for a component 

was greater than zero. Values of the posterior 

probability > 0.95 were considered statistically significant (Dominici et al. 2006; Peng 

et al. 2008).

We evaluated whether the relative risks 

of each PM2.5 component in a multipollutant model were equal. In this analysis, the 

risks represent the percent increase in admissions associated with a 1-μg/m3 increase in 

each PM2.5 component in a multipollutant 

model. We assessed the evidence against equal 

component risks using a chi-square statistic 

applied to the national average estimates. We 

also estimated the posterior probability that 

the coefficient for a particular component was 

greater than the mean of the coefficients for 

the other components.

For statistical calculations we used R statistical software, version 2.7.0 (R Foundation 

for Statistical Computing, Vienna, Austria).

Results

Table 1 summarizes median, interquartile 

range (IQR), within-county monitor-to-monitor correlations, and percent contribution for 

each of the seven components of the PM2.5

total mass. We also calculated these statistics 

for the three indicators of PM2.5 total mass. 

The IQRs calculated here are medians of the 

within-county IQRs for the 119 counties, 

which indicate typical daily variation for each 

chemical component. This study used a total 

of 134 monitors measuring PM2.5 chemical components in 119 counties. Counties 

included in the study ranged in size from 

23 square miles (New York County, NY) to 

9,203 square miles (Maricopa County, AZ),

with a median of 581 square miles in area. 

The median daily rates of admission per 

100,000 for CVD and respiratory disease

were 19.1 and 6.9, respectively, and the corresponding IQRs were 10.4 and 6.4.

The PM2.5 components that constitute a 

large fraction of PM2.5 mass had high correlation within counties. The distance at which 

the correlation between pairs of monitors was 

0.5 on average was 285 miles for sulfate, 302 

miles for nitrate, 188 miles for silicon, 109 

miles for EC, 209 miles for OCM, 9 miles for 

sodium ion, and 210 miles for ammonium.

Table 2 summarizes the estimated correlations between the daily concentrations of 

county-specific time series for pairs of components. Although in the atmosphere the 

particle composition includes compounds 

such as ammonium sulfate and ammonium 

nitrate, the measurement strategy of the STN 

measures ammonium, sulfate, and nitrate 

separately. Therefore, concentrations of sulfate and ammonium as well as of nitrate and 

Table 1. Median and IQR values, within-county monitor-to-monitor correlations, and percent contribution 

for each of the seven components to the PM2.5 total mass, for 119 U.S. counties. 

Within-county

PM2.5 component Median (μg/m3) IQR (μg/m3) correlation (min–max) Percent PM2.5 mass

Sulfate 2.62 3.06 0.94 (0.77–0.99) 26

Nitrate 0.97 1.64 0.92 (0.78–0.99) 12

Silicon 0.07 0.07 0.63 (0.22–0.95) 1

EC 0.58 0.40 0.68 (0.42–0.85) 5

OCM 3.50 3.18 0.77 (0.53–0.93) 28

Sodium ion 0.09 0.11 0.43 (0.07–0.79) 1

Ammonium 1.18 1.35 0.91 (0.68–0.98) 11

Total mass

PM2.5 (1) 11.79 9.38 0.85 (0.83–0.95)

PM2.5 (2) 12.20 9.51 0.86 (0.73–0.99)

PM2.5 (3) 10.40 8.09 0.86 (0.71–0.94)

Abbreviations: max, maximum; min, minimum. Values are medians of the within-county values across all counties for the 

given statistic. Median (min–max) of the within-county monitor-to-monitor correlations of the seven components and 

of PM2.5 (2) and PM2.5 (3) were calculated using 27 monitors from 12 counties and only monitor pairs with more than 90 

paired observations. Median and IQR (25th–75th percentiles) of the within-county monitor-to-monitor correlations of 

PM2.5 (1) using 401 monitors from 96 counties.
Peng et al.

960 volume 117 | number 6 | June 2009 • Environmental Health Perspectives

ammonium are highly correlated, with mean 

correlation coefficients of 0.83 and 0.52, 

respectively. The correlation between concentrations of ammonium and nitrate is close to 

0.95 in some counties on the West Coast.

We found that national average estimates 

obtained using the different measures of 

PM2.5 from the STN were generally consistent with previous findings, given the statistical uncertainties. Figure 2 shows the national 

average estimates and 95% posterior intervals (PIs) of the percent increases in emergency admissions for CVD (Figure 2A) and 

respiratory diseases (Figure 2B) associated 

with PM2.5 using three different indicators. 

Estimates obtained by restricting the data to 

days with available data from the STN (scenarios 1a, 2, and 3) have wider PIs because of 

the sparser sampling rate of the STN monitors. For scenario 1, on average, 1,896 days 

had observations for each county. For the 

remaining scenarios, on average, 410 days had 

observations for each county.

For the cardiovascular outcome, all four 

estimates show strong evidence of an association between PM2.5 and hospital admissions on the same day. Under scenario 1a, 

a 10-μg/m3 increase in PM2.5 is associated with a 0.64% (95% PI, 0.12–1.15%) 

increase, and under scenario 2, a 0.68% (95% 

PI, 0.26–1.10%) increase in hospital admissions for CVD. For respiratory outcomes, 

the evidence of an association between PM2.5

and hospital admissions was not as strong:

under scenario 1a, a 0.44% (95% PI, –0.36 to 

1.23%) increase in admissions, and under scenario 2, a 0.31% (95% PI, –0.38 to 1.01%) 

increase for a 10-μg/m3 increase in PM2.5.

Although the point estimates are very similar, 

the uncertainty reflected in the PIs is much 

greater when restricted to days with measurements on the PM2.5 components from the 

STN (scenarios 1a, 2, 3). For both outcomes, 

lag 0 was specifically selected because previous work on PM2.5 total mass and hospital 

admissions showed that lag 0 exposure had the 

strongest association with cardiovascular and 

respiratory outcomes (Dominici et al. 2006).

Figure 3 shows national average estimates 

and 95% PIs for the percent increase in hospital admissions for CVD and respiratory diseases per IQR increase in each of the PM2.5

components, obtained from single-pollutant 

(top rows) and multipollutant models (bottom rows). Figure 3A indicates a positive and 

statistically significant association between 

CVD admissions and increases in nitrate, 

EC, OCM, and ammonium at lag 0 (same 

day) in single-pollutant models. We estimated 

that IQR increases in nitrate, EC, OCM, and 

ammonium were associated with 0.46% (95% 

PI, 0.17–0.75%), 0.72% (95% PI, 0.43–

1.01%), 0.66 (95%% PI, 0.29–1.02%), and 

0.68% (95% PI, 0.31–1.06%) increases in 

CVD admissions, respectively. In multipollutant models (Figure 3B), most risk estimates 

were reduced, unlike in the single-pollutant 

models. However, statistically significant associations remained, with an IQR increase in 

EC at lag 0 associated with a 0.80% (95% PI,

0.34–1.27%) increase in CVD admissions and 

an IQR increase in OCM at lag 1 associated 

with a 0.63% (95% PI, 0.06–1.19%) increase.

The association between respiratory 

admissions and OCM at lag 0 was statistically significant in single-pollutant models 

(Figure 3B). An IQR increase in OCM at 

lag 0 was associated with a 0.82% (95% PI,

0.22–1.44%) increase in respiratory admissions. In multipollutant models, we found 

statistically significant associations, with IQR 

increases in OCM at lag 0 and lag 2 associated with 1.01% (95% PI, 0.04–1.98%) and 

1.07% (95% PI, 0.12–2.04%) increases in 

respiratory admissions, respectively.

We evaluated whether the health risks for 

each PM2.5 component in a multipollutant 

model were all equal. We found statistically 

significant heterogeneity in the toxicity of the 

components at lag 0 concentration for both 

the CVD and respiratory outcomes, indicating 

different degrees of toxicity. The Bayesian hierarchical model used here also provides a posterior probability distribution (given all of the 

observed data) for each of the risk parameters 

of interest. With this posterior distribution, we 

can calculate the posterior probability that a 

given estimate is larger than the average of the 

other five coefficients. For the CVD outcome, 

the posterior probability that the national average EC coefficient for lag 0 concentration was 

larger than the average of the other five coefficients in the multipollutant model was 0.99.

Sensitivity analysis indicated that results 

were not sensitive to changes in df for the 

smooth functions of time (ranging from 2 to 

14 df per year), temperature, or dew-point 

temperature.

Discussion

We conducted a multisite time series study to 

identify any major components of PM2.5 that 

were strongly associated with risk for hospitalization and found evidence of differing 

health risks across the components. We found 

that ambient levels of EC and OCM, which 

are generated primarily from vehicle emissions, diesel engines, and wood burning, were 

associated with the largest risk of emergency 

hospital admissions for CVD and respiratory 

disease in both single- and multiple-pollutant 

models. Furthermore, in multipollutant models we found evidence that the risk of cardiovascular admission associated with a same-day 

EC concentration was larger than the risks 

associated with any other component.

Our results add to those previously 

reported in several important ways. First, we 

Table 2. Median (across counties) correlation between daily concentrations of time series for pairs of 

pollutants.

Sulfate Nitrate OCM EC Silicon Sodium ion Ammonium PM2.5 (1) PM2.5 (2) PM2.5 (3)

Sulfate 

Nitrate 0.09 

OCM 0.38 0.25 

EC 0.18 0.33 0.64 

Silicon 0.18 –0.04 0.22 0.14 

Sodium ion 0.10 0.12 0.10 0.03 0.10 

Ammonium 0.83 0.52 0.45 0.33 0.11 0.04 

PM2.5 (1) 0.76 0.39 0.68 0.46 0.22 0.07 0.85 

PM2.5 (2) 0.73 0.39 0.68 0.47 0.26 0.09 0.82 0.91 

PM2.5 (3) 0.75 0.48 0.78 0.55 0.20 0.13 0.88 0.92 0.91

PM2.5 (1), PM2.5 (2), and PM2.5 (3) indicate different PM2.5 scenarios.

Figure 2. National average estimates and 95% PIs of the percent increases in emergency admissions for 

(A) CVD and (B) respiratory disease associated with PM2.5 at lag 0 under the four scenarios: PM2.5 (1), 

PM2.5 (1a), PM2.5 (2), and PM2.5 (3). The estimates obtained under scenarios 1a, 2, and 3 use data on the 

same exact subset of days. 

1.5

1.0

0.5

0.0

–0.5

1.5

1.0

0.5

0.0

–0.5

1 1a 2 3 1 1a 2 3

Percent increase in admissions for a

10-μg/m3 increase in PM2.5

Percent increase in admissions for a

10-μg/m3 increase in PM2.5

A B
Hospital admissions and components of fine PM

Environmental Health Perspectives • volume 117 | number 6 | June 2009 961

used the largest database on chemical components of PM2.5 available on a national 

scale and found that ambient levels of EC 

and OCM were the most toxic components 

with respect to risk of emergency hospitalization for CVD and respiratory disease. 

These results, based on a large human population and using real-world concentrations 

of PM components, provide a much more 

complete picture of the health effects of PM 

chemical components than previously available. Second, the findings for EC and OCM, 

together with the weak evidence of harmful 

effects from the other components, substantially narrow the potential range of investigation and will aid the design of targeted 

mechanistic studies to test for parallel differential toxicity of these components in bioassays and to identify mechanisms of injury by 

these particular PM components. Such work 

may further lead to indications of appropriate 

biomarkers and the identification of sensitive 

subpopulations (Nel 2005). Third, this study 

indicates sources of PM air pollution that 

could be targeted as part of a comprehensive 

air quality control strategy. Because ambient 

particles are produced by numerous emission sources, any individual particle component identified as having an association with 

a health outcome may be acting as a marker 

for other components or a set of components 

with similar sources. For example, EC results 

from combustion of fossil fuels, including 

transportation sources such as diesel, but also 

from the combustion of biomass and coal and 

from industry (Health Effects Institute 1995). 

OCM also comes from vehicles, as well as 

from coal and oil combustion, industry, and 

vegetative burning. Finally, the statistical tools 

we have developed for this study represent a 

reproducible methodology that can be applied 

to other similar databases for studying the 

health effects of complex mixtures.

A few epidemiologic studies have investigated the potential toxicity of the PM2.5

chemical components on local or regional 

scales. In several of these studies, PM components have been examined as independent 

predictors, whereas one study used factor 

analysis of the Six Cities Study data to trace 

components back to their sources (Laden et al. 

2000). Overall, the epidemiologic evidence 

linking particular PM components to health 

risks is mixed (Anderson et al. 2001; Burnett 

et al. 1997; Chuang et al. 2007; Dominici 

et al. 2007; Laden et al. 2000; Lippmann 

et al. 2006; Metzger et al. 2004; Ostro et al. 

2007, 2008; Sarnat et al. 2006; Tolbert 

et al. 2000; Zanobetti and Schwartz 2006). 

Differences in findings may reflect the diversity of the study locations, health outcomes, 

or the analytic methods.

In our national study, cardiovascular 

admissions were most strongly associated with 

EC. Several other single-city or regional studies 

found associations between cardiovascular outcomes and EC. Ostro et al. (2007) found that 

risk of daily cardiovascular mortality increased 

by 2.1% (95% CI, 0.3–3.9) for a 0.8-μg/m3

increase in EC at a 3-day lag in a study of six 

California counties and, in a separate study, 

found that this association was stronger among 

people without a high school education (Ostro 

et al. 2008). Metzger et al. (2004) found statistically significant associations between EC and 

cardiovascular emergency department visits in 

Atlanta, Georgia, in single-pollutant models, 

as did Tolbert et al. (2000). These findings for 

EC point to a role for transportation sources 

in causing adverse health effects (Sarnat et al. 

2008). Two source-directed analyses are similarly indicative: the Six Cities Study analysis by 

Laden et al. (2000) and a study of daily emergency room admissions in Boston (Zanobetti 

and Schwartz 2006). Lanki et al. (2006) examined five source indicators in data for Helsinki, 

Finland, and found that in multipollutant 

models, only EC was associated with ST segment depressions in elderly nonsmoking persons with stable coronary heart disease.

We also found evidence for association of 

OCM with respiratory admissions. In California, 

Ostro et al. (2007) found that the risk of cardiovascular mortality increased by 1.6% (95% CI,

–0.1 to 3.2) per 4.6 μg/m3 increase in OCM 

with a 3-day lag but did not find strong associations with respiratory mortality. An association 

of OCM with cardiovascular emergency department visits was also found in Atlanta (Metzger 

et al. 2004; Tolbert et al. 2000).

EC and OCM were somewhat correlated on average (correlation = 0.64), and any 

reduction in effect size of either component 

when moving from the single-pollutant to the 

multipollutant model could be a result of this 

correlation. However, as with all results from 

multiple regression models, it is difficult to 

identify a single reason for changes in the risk 

estimates between the single-pollutant and 

multipollutant models. Correlation between 

pollutants in the model is one possibility, 

although not all of the pollutants examined 

here were highly correlated with each other. 

Another factor is the correlation of the pollutants with measured and unmeasured potential 

confounders, such as season and weather.

Figure 3. National average estimates and 95% PIs for the percent increase in hospital admissions for CVD 

(A) and respiratory disease (B) per IQR increase in each of the seven PM2.5 components in 119 U.S. counties during 2000–2006: single-pollutant model (S; top row) and multipollutant model (M; bottom row). The 

multipollutant model (M*) for ammonium excludes sulfate and nitrate.

1.0

0.5

0.0

–0.5

1.0

0.5

0.0

–0.5

0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2

0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2

1.0

0.5

0.0

–0.5

1.0

0.5

0.0

–0.5

Lag (days)

Percent increase in admissions

per IQR increase

Sulfate (S) Nitrate (S) Silicon (S) EC (S) OCM (S) Sodium ion (S) Ammonium (S)

Sulfate (M) Nitrate (M) Silicon (M) EC (M) OCM (M) Sodium ion (M) Ammonium (M*)

2

1

0

–1

0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2

0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2

Lag (days)

Percent increase in admissions

per IQR increase

Sulfate (S) Nitrate (S) Silicon (S) EC (S) OCM (S) Sodium ion (S) Ammonium (S)

Sulfate (M) Nitrate (M) Silicon (M) EC (M) OCM (M) Sodium ion (M) Ammonium (M*)

2

1

0

–1

2

1

0

–1

2

1

0

–1

B

A
Peng et al.

962 volume 117 | number 6 | June 2009 • Environmental Health Perspectives

Ammonium ion was also strongly associated with cardiovascular admissions in singlepollutant models, although this association 

lost statistical significance when adjusted for 

other pollutants in the multipollutant model. 

The large effect observed in the single-pollutant model may result from another PM component that is correlated with ammonium 

ion. In addition, relatively little coherent evidence supports a role for ammonium ion and 

other secondary inorganic aerosols in causing 

adverse health effects (Schlesinger et al. 2006).

The evidence of increased risk of cardiovascular admissions and EC reported in 

this article is consistent with recent toxicologic findings. Biological responses have 

been shown to vary across characteristics 

and components of PM (Obot et al. 2004). 

Mechanisms by which inhaled particles may 

adversely affect cardiovascular and respiratory health have been proposed (Kendall 

et al. 2004; O’Neill et al. 2007), but specific 

mechanisms that might contribute to greater 

toxicity of particles with higher EC or OCM 

levels have not yet been identified (Pope and 

Dockery 2006). Toxicologic and epidemiologic studies have examined adverse health 

outcomes such as inflammation, oxidative 

stress, and alterations in cardiovascular function associated with particles (Schlesinger 

et al. 2006). In a study of young healthy 

adults, exposure to the OCM and EC components of concentrated ambient particles was 

linked with decreased brachial artery diameter 

(Urch et al. 2004). Associations have also been 

found between carbon black exposure and 

altered heart rate variability (Tankersley et al. 

2004). The larger effect estimated for sameday EC is consistent with the literature on 

the biological mechanisms of CVD associated 

with short-term exposure to PM (Brook et al. 

2004). Direct effects of PM can be associated 

with rapid cardiovascular responses such as 

increased myocardial infarctions and ischemia 

(e.g., Peters et al. 2001; Pope et al. 2006).

Measurement error due to a combination of instrument and laboratory error could 

affect our results because of biased measurements of the PM2.5 components. In particular, there is considerable uncertainty regarding 

the measurement of OCM because results 

depend on laboratory techniques and operational protocols (Kim et al. 2005). The U.S. 

EPA has conducted a quality assurance study 

of laboratories handling STN filter samples 

and the results indicate that, although there 

was some variability between laboratories 

(particularly for lower mass components, such 

as aluminum and sodium, and for specific 

subfractions of OCM), there was general consistency between the laboratories for most 

PM2.5 components (U.S. EPA 2005).

Exposure measurement error, which 

occurs when the monitor measurement of 

concentration is not representative of the exposure experienced by the study population, is 

also of concern. Our data and previous studies 

show that although PM2.5 total mass is relatively homogeneous within counties (Dominici 

et al. 2006), components with smaller contributions to total mass had lower within-county 

correlations. We would therefore expect that 

the countywide average for such components 

would be a poorer surrogate for personal exposure. To assess the potential effect of exposure 

measurement error for EC and OCM, we fit 

a separate measurement error model (Carroll 

et al. 2006) to adjust for spatial variability of 

each component [for details of the statistical 

models, see Supplemental Material (http://

www.ehponline.org/members/2009/0800185/

suppl.pdf)]. The national average associations 

of EC and OCM with cardiovascular and 

respiratory outcomes did not show qualitative differences. Another potential reason for 

exposure misclassification in this analysis is 

our restriction to more populous counties, 

which generally have substantial geographic 

areas. However, smaller cities (i.e., with 

< 150,000 residents) would likely produce risk 

estimates with greater uncertainties because 

of the smaller numbers of events. Hence,

these estimates would be down-weighted 

when combined in our Bayesian hierarchical 

model and would likely contribute little to the 

overall results.

One limitation of this study is the 

1-in-6–day sampling of PM2.5 chemical components dictated by the U.S. EPA’s monitoring schedule for the STN and other networks. 

Although we were able to estimate statistically 

significant short-term effects of single-lag concentrations, we were not able to fit distributed 

lag models that estimate cumulative short-term 

effects spread over multiple days. However, at 

this point the nature of this data set is a limitation to all researchers conducting national-level 

epidemiologic investigations of PM components. Although the national average estimates 

for a single lag might be an underestimate of 

the cumulative effect over a few days from the 

exposure, the single-lag effects estimated here 

nevertheless indicate a significant health risk 

from PM2.5 components.

Another limitation on the interpretation 

of our results is that our definitions of cardiovascular and respiratory admissions, which 

both include multiple ICD-9 diagnostic 

codes, reduce the specificity of the estimated 

associations compared with more narrowly 

defined disease categories. However, the benefit of our approach is the increased power to 

detect associations between chemical components and hospital admissions. Previous work 

(Dominici et al. 2006) examined the association between PM2.5 total mass and more specific CVD and respiratory disease categories 

and found that there was a strong association 

with all categories. Here, as in many studies, 

we had to strike a balance between power and 

specificity of the outcome.

Estimating effects of PM2.5 components 

is challenging because of their multiple and 

shared sources. We addressed this challenge 

by fitting multipollutant models to the largest national dataset available for the United 

States. EC and OCM, which we found to be 

the most injurious, are both found primarily 

in the fine fraction PM (PM2.5) as opposed 

to the coarse fraction (PM10–2.5). Our recent 

findings that PM2.5 is more injurious than 

coarse PM (Peng et al. 2008) is therefore 

consistent with the results reported here. The 

monitoring networks used in this study (the 

STN) are sited in or near urban communities,

so the ambient exposures measured reflect the 

pollutant mix found in those communities. 

In addition, although we have made use of 

the U.S. EPA’s publically available national 

data on PM components, the geographic 

distribution of the monitors, determined by 

the U.S. EPA according to a variety of factors, is not uniform throughout the country. Hence, the sample of counties used in 

this analysis contains many counties from the 

industrial Midwest and Northeast regions of 

the country.

Although understanding the precise biological mechanism of injury from PM remains 

challenging, opportunities for developing targeted interventions for reducing ambient levels of PM air pollution are already apparent. 

Our results for EC and OCM suggest that the 

control strategies targeting their sources could 

be effective at reducing the public health burden attributable to PM. Given the U.S. EPA’s 

approach in the past to setting National 

Ambient Air Quality Standards, the evidence 

requirements will likely be high before any 

move toward standards or guidelines based 

on composition or sources can be made. This 

study, along with the related work of others, provides a substantial evidence base upon 

which further research into more refined air 

quality control strategies can be conducted.

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