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

### 957 

# Research

Epidemiological studies have provided evi

dence of an association between short-term and long-term exposure to fine particulate

matter [ ≤ 2.5 μ m in aerodynamic diameter (PM 2.5 ) ] and risk for premature mortality and excess morbidity (Pope and Dockery 2006). These studies have focused on the risk associ

ated with the total mass of particles, without considering the heterogeneity in their chemi

cal composition. The U.S. National Research Council Committee on Research Priorities for Airborne Particulate Matter identified the assessment of PM characteristics associ

ated 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 com

ponents (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 epidemi

ologic studies examining exposures at ambi

ent 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 differen

tial 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 con

trolling 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 PM 2.5 component data from the U.S. Environmental Protection Agency ( EPA ) allows us to examine at a population level the heterogeneity in toxicity of PM 2.5 components. In previous work we identified seven components of PM 2.5 that contribute most of the PM 2.5 total mass or that covary closely with PM 2.5 total mass (Bell

et al. 2007). We also reported evidence of an association between PM 2.5 total mass and hospital admissions in the Medicare popula

tion 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 ( PM 10–2.5 ) ] with hospital admissions for 108 U.S. counties (Peng et al. 2008). For the present study , we
use d time-series approaches and estimate d the association between the seven PM 2.5 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 admis

sions for the period 2000

– 2006 from bill

ing 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 Dominici 1

1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; 2 School of Forestry and Environmental Studies, Yale University, New Haven, Connecticut, USA; 3 Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; 4 Department of Preventive Medicine, University of Southern California, Los Angeles, California, USA

B ackground : Population-based studies have estimated health risks of short-term exposure to fine particles using mass of PM 2.5 (particulate matter ≤ 2.5 μ m in aerodynamic diameter) as the indica

tor. Evidence regarding the toxicity of the chemical components of the PM 2.5 mixture is limited. O b

jective : In this study we investigated the association between hospital admission for cardiovascular disease (CVD) and respiratory disease and the chemical components of PM 2.5 in the United States. M ethods : We used a national database comprising daily data for 2000

– 2006 on emergency hospi

tal admissions for cardiovascular and respiratory outcomes, ambient levels of major PM 2.5 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 esti

mated the associations between daily levels of PM 2.5 components and risk of hospital admissions in 119 U.S. urban communities for 12 million Medicare enrollees ( ≥ 65 years of age ). R

esults : 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. C onclusions : Ambient levels of EC and OCM , which are generated primarily from vehicle emis

sions, diesel, and wood burning, were associated with the largest risks of emergency hospitalization across the major chemical constituents of PM 2.5 . K

ey words : cardiovascular disease, chemical components, hospital admission, particulate matter, PM 2.5 , 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 ser

vice, disease classification using International Classification of Diseases
, 9th Revision ( ICD

- 9 ) codes ( Centers for Disease Control and Prevention 2008), age, and county of resi

dence. 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 hospi

tal admissions record. We excluded other clas

sifications, 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 PM 2.5 component data compared with the PM 2.5 total mass data, to obtain suffi

cient statistical power we collapsed the data into two broad categories of hospital admis

sions: a ) CVD, which include s 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 cal

culated the daily counts of hospitalizations by summing the hospital admissions for each dis

ease of interest recorded as a primary diagno

sis. To calculate daily hospitalization rates, we constructed a parallel time series of the num

bers 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 mea

sure more than 50 PM 2.5 chemical compo

nents, in addition to total mass. The STN includes > 50 national air monitoring stations (NAMS) and > 200 state and local air moni

toring 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 con

struction of the database can be found else

where (Bell et al. 2007). We also used PM 2.5 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 PM 2.5 . These requirements ensured that we would have enough data in a particular location to estimate an association between PM 2.5 components and hospital admissions. The study population consisted of 12 million Medicare enrollees living in 119 urban coun

ties in the United States ( Figure 1 ). We limited our analysis to the compo

nents making up a large fraction of the total PM 2.5 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 PM 2.5 mass, whereas all other com

ponents individually contributed < 1%

. We computed countywide averages for each of these components and for PM 2.5 total mass by averaging the daily values from all moni

tors in a county. We adjusted organic car

bon measurements for field blanks to estimate OCM . We used a standard approach such that OCM = k ( OC m – OC b )
, where OCM represents organic carbon matter, OC m rep resents measured organic carbon, OC b repre sents organic carbon for blank filters, and k is the adjustment factor to account for non carbon 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 PM 2.5 indicators (four scenarios total) provided comparable estimates of the short-term associations of PM 2.5 with cardiovascular and respiratory admissions: PM 2.5 (1), PM 2.5 measured by the national PM 2.5 monitoring network for the period 1999

– 2006; PM 2.5 (1a)
, PM 2.5 (1) for the period 2000

– 2006 and including only days with available measurements for all the seven PM 2.5 components from the STN; PM 2.5 (2), PM 2.5 measured by the STN for the period 2000–2006 and including only days with available measurements for all the seven PM 2.5 components from the STN; and PM 2.5 (3)
, PM 2.5 estimated as the sum of the seven larg

est components of PM 2.5 mass for the period 2000–2006. Significant differences between these estimates would raise uncertainty as to the recorded values of PM 2.5 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 PM 2.5 was available in all 119 counties. We estimated the within-county mon

itor-to-monitor correlation for each of the seven PM 2.5 components to obtain a measure of the spatial homogeneity of each compo

nent. For this calculation we used a subset of 12 counties that had more than one moni

tor ( 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

c orrelations only if at least 90 paired observa

tions were available between two monitors. We also estimated the median within-county correlations between the seven PM 2.5 compo nents, and the three measures of PM 2.5 total **Figure 1.** U.S. counties with populations larger than 150,000 for which sufficient hospital admissions and PM 2.5 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 statis

tical models to estimate county-specific and national average associations between daily variation in the seven PM 2.5 chemical compo

nents 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 poten

tial confounders such as weather, day of theweek, 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 use d 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 change d 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 concentra

tions because o ur previous work with PM 2.5 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 chem

ical component data from the STN prohib

ited the use of distributed lag models where all lags can be examined simultaneously. For estimating the health effects of the PM 2.5 components, we employed singlepollutant and multi pollutant models. In single-pollutant models, we included each PM 2.5 component in the regression model individually, without adjusting for any other chemical component (the model does adjust for other time-varying factors). In multi pol

lutant models, we included PM 2.5 compo nents simultaneously to obtain estimates of the regression coefficients for each com

ponent adjusted for the other components. Ammonium was excluded from models that included sulfate and nitrate because of the high correlation among the se three compo

nents. We included ammonium in a separate multi pollutant 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 aBayesian hierarchical model. In the singlepollutant models, we combined the log-rela

tive risks separately for each pollutant using TLNise two-level normal independent sam

pling 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 sig

nificance 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 statisti

cally significant (Dominici et al. 2006; Peng

et al. 2008). We evaluated whether the relative risks of each PM 2.5 component in a multi pollut

ant model were equal. In this analysis
, the risks represent the percent increase in admis

sions associated with a 1-μ g/m 3 increase in each PM 2.5 component in a multi pollutant 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 sta

tistical software
, version 2.7.0 (R Foundation for Statistical Computing, Vienna, Austria). 

### Results Table 1 summarizes median, interquartile range ( IQR ) , within-county monitor-to-mon

itor correlations
, and percent contribution for each of the seven components of the PM 2.5 total mass. We also calculated these statistics for the three indicators of PM 2.5 total mass. The IQRs calculated here are median s 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 PM 2.5 chemi

cal 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 cor

responding IQRs were 10.4 and 6.4. The PM 2.5 components that constitute a large fraction of PM 2.5 mass had high correla

tion 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 cor

relations between the daily concentrations of county-specific time series for pairs of com

ponents. 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 sul

fate 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 PM 2.5 total mass, for 119 U.S. counties. Within-county PM 2.5 component Median (μg/m 3 ) IQR (μg/m 3 ) correlation (min–max) Percent PM 2.5 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

PM 2.5 (1) 11.79 9.38 0.85 (0.83–0.95) PM 2.5 (2) 12.20 9.51 0.86 (0.73–0.99) PM 2.5 (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 PM 2.5 (2) and PM 2.5 (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 PM 2.5 (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 concen

trations 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 PM 2.5 from the STN were generally consis

tent with previous findings, given the statisti

cal uncertainties. Figure 2 shows the national average estimates and 95% posterior inter

vals (PI s ) of the percent increases in emer

gency admissions for CVD (Figure 2A) and respiratory diseases (Figure 2B) associated with PM 2.5 using three different indicators. Estimates obtained by restricting the data to days with available data from the STN (sce

narios 1a, 2, and 3) have wider P Is because of the sparser sampling rate of the STN moni

tors. 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 asso

ciation between PM 2.5 and hospital admis

sions on the same day. Under scenario 1a, a 10-μg/m 3 increase in PM 2.5 is associ

ated 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 admis

sions for CVD. For respiratory outcomes, the evidence of an association between PM 2.5 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 sce nario 2, a 0.31 % (95% PI
, – 0.38 to 1.01 % ) increase for a 10-μg/m 3 increase in PM 2.5. Although the point estimates are very similar, the uncertainty reflected in the PI s is much greater when restricted to days with measure

ments on the PM 2.5 components from the STN (scenarios 1a, 2, 3). For both outcomes, lag 0 was specifically selected because previ

ous work on PM 2.5 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 hos

pital admissions for CVD and respiratory dis

eases per IQR increase in each of the PM 2.5 components, obtained from single- pollutant ( top row s ) and multi pollutant models ( bot

tom row s ). Figure 3 A 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 multi pol

lutant models (Figure 3B) , most risk estimates were reduced, unlike in the single-pollutant models. However, statistically significant asso

ciations 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 statisti

cally 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 admis

sions. In multi pollutant models, we found statistically significant associations, with IQR increases in OCM at lag 0 and lag 2 associ

ated 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 PM 2.5 component in a multi pollutant 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 hier

archical model used here also provides a pos

terior 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 aver

age EC coefficient for lag 0 concentration was larger than the average of the other five coeffi

cients in the multi pollutant 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 multi site time series study to identify any major components of PM 2.5 that were strongly associated with risk for hos

pitalization 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 emis

sions, 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 mod

els we found evidence that the risk of cardio

vascular 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 PM 2.5 (1) PM 2.5 (2) PM 2.5 (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 PM 2.5 (1) 0.76 0.39 0.68 0.46 0.22 0.07 0.85 PM 2.5 (2) 0.73 0.39 0.68 0.47 0.26 0.09 0.82 0.91 PM 2.5 (3) 0.75 0.48 0.78 0.55 0.20 0.13 0.88 0.92 0.91

PM 2.5 (1), PM 2.5 (2), and PM 2.5 (3) indicate different PM 2.5 scenarios.

**Figure 2**

**.** National average estimates and 95% PI s of the percent increases in emergency admissions for ( A ) CVD and ( B ) respiratory disease associated with PM 2.5 at lag 0 under the four scenarios: PM 2.5 (1), PM 2.5 (1a), PM 2.5 (2), and PM 2.5 (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 1 a 2 3 1 1a 2 3**Percent increase in admissions for a 10-μg/m** **3 increase in PM** **2. 5** **Percent increase in admissions for a 10-μg/m** **3 increase in PM** **2. 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 com

ponents of PM 2.5 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 hospital

ization for CVD and respiratory disease. These results, based on a large human popu

lation and using real-world concentrations of PM components, provide a much more complete picture of the health effects of PM chemical components than previously avail

able. Second, the findings for EC and OCM, together with the weak evidence of harmful effects from the other components, substan

tially narrow the potential range of inves

tigation and will aid the design of targeted mechanistic studies to test for parallel differ

ential toxicity of these components in bioas

says 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 sub populations (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 emis

sion sources, any individual particle compo

nent 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 inves

tigated the potential toxicity of the PM 2.5 chemical components on local or regional scales. In several of these studies, PM com

ponents 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 diver

sity 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 out

comes 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/m 3 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 statis

tically 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 simi

larly indicative: the Six Cities Study analysis by Laden et al. (2000) and a study of daily emer

gency room admissions in Boston (Zanobetti and Schwartz 2006). Lanki et al. (2006) exam

ined five source indicators in data for Helsinki, Finland
, and found that in multi pollutant models, only EC was associated with ST seg

ment depressions in elderly nonsmoking per sons 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 cardio

vascular mortality increased by 1.6% (95% CI
,

– 0.1 to 3.2) per 4.6 μg/m 3 increase in OCM with a 3-day lag but did not find strong associa

tions with respiratory mortality. An association of OCM with cardiovascular emergency depart

ment visits was also found in Atlanta (Metzger

et al. 2004; Tolbert et al. 2000). EC and OCM were somewhat corre

lated on average (correlation = 0.64)
, and any reduction in effect size of either component when moving from the single- pollutant to the multi pollutant 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 multi pollutant 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 pollut

ants with measured and unmeasured potential confounders
, such as season and weather.

**Figure 3**

**.** National average estimates and 95% PI s for the percent increase in hospital admissions for CVD ( A ) and respiratory disease ( B ) per IQR increase in each of the seven PM 2.5 components in 119 U.S. coun

ties during 2000

– 2006 : single- pollutant model (S ; top row ) and multi pollutant model (M ; bottom row ). The multi pollutant 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) Amm onium (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 admission** **s 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 ) A mmonium (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 associ

ated with cardiovascular admissions in singlepollutant models, although this association lost statistical significance when adjusted for other pollutants in the multi pollutant model. The large effect observed in the single- pollut

ant model may result from another PM com

ponent that is correlated with ammonium ion. In addition, relatively little coherent evi

dence 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 car

diovascular admissions and EC reported in this article is consistent with recent toxi

cologic 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 respira

tory 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 epidemio

logic studies have examined adverse health outcomes such as inflammation, oxidative
stress, and alterations in cardiovascular func

tion associated with particles (Schlesinger

et al. 2006). In a study of young healthy adults, exposure to the OCM and EC com ponents 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 combina

tion of instrument and laboratory error could affect our results because of biased measure

ments of the PM 2.5 components. In particu

lar, there is considerable uncertainty regarding the measurement of OCM because results depend on laboratory techniques and opera

tional 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 con

sistency between the laboratories for most PM 2.5 components ( U.S. EPA 2005). Exposure measurement error, which occurs when the monitor measurement of concentration is not representative of the expo

sure experienced by the study population, is also of concern. Our data and previous studies show that although PM 2.5 total mass is rela

tively homogeneous within counties (Dominici

et al. 2006), components with smaller contri

butions 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 expo

sure. 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 qualita

tive 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 PM 2.5 chemical com

ponents dictated by the U.S. EPA’s monitor

ing schedule for the STN and other networks. Although we were able to estimate statistically significant short-term effects of single-lag con

centrations, 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 limita

tion to all researchers conducting national-level epidemiologic investigations of PM compo

nents. 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 PM 2.5 components. Another limitation on the interpretation of our results is that our definitions of cardio

vascular 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 ben

efit of our approach is the increased power to detect associations between chemical compo

nents and hospital admissions. Previous work (Dominici et al. 2006) examined the associa

tion between PM 2.5 total mass and more spe

cific 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 PM 2.5 components is challenging because of their multiple and shared sources. We addressed this challenge by fitting multi pollutant models to the larg

est 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 (PM 2.5 ) as opposed to the coarse fraction (PM 10–2.5 ). Our recent findings that PM 2.5 is more injurious than

coarse PM (Peng et al. 2008) is therefore consistent with the results reported here. Th e 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 fac

tors, is not uniform throughout the coun

try. 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 bio

logical mechanism of injury from PM remains challenging, opportunities for developing tar

geted interventions for reducing ambient lev

els 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 bur

den 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 oth

ers, provides a substantial evidence base upon which further research into more refined air quality control strategies can be conducted.

**R efe**  **r ences** 

Anderson HR, Bremner SA, Atkinson RW, Harrison RM, Walters S. 2001. Particulate matter and daily mortality and hospital admissions in the West Midlands conurbation of the United Kingdom: associations with fine and coarse particles, black smoke and sulphate. Occup Environ Med 58:504–510. Bell ML, Dominici F, Ebisu K, Zeger SL, Samet JM. 2007. Spatial and temporal variation in PM 2.5 chemical composition in the United States for health effects studies. Environ Health Perspect 115:989–995. Bell ML, McDermott A, Zeger SL, Samet JM, Dominici F. 2004. Ozone and short-term mortality in 95 US urban communi

ties, 1987–2000. JAMA 292:2372–2378. Brook RD, Franklin B, Cascio W, Hong Y, Howard G, Lipsett M, et al. 2004. Air pollution and cardiovascular disease: a statement for healthcare professionals from the Expert Panel on Population and Prevention Science of the American Heart Association. Circulation 109:2655–2671. Burnett RT, Cakmak S, Brook JR, Krewski D. 1997. The role of particulate size and chemistry in the association betweenHospital admissions and components of fine PM

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

### 963 summertime ambient air pollution and hospitalization for cardiorespiratory diseases. Environ Health Perspect 105:614–620. Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM. 2006. Measurement Error in Nonlinear Models. A Modern Perspective. Boca Raton, FL:Chapman & Hall/CRC. Centers for Disease Control and Prevention . 2008. International Classification of Diseases, Ninth Revision. Available: http:// www.cdc.gov/nchs/icd9.htm [accessed 30 January 2009 ]

. Chuang KJ, Chan CC, Su TC, Lin LY, Lee CT. 2007. Associations between particulate sulfate and organic carbon exposures and heart rate variability in patients with or at risk for car

diovascular diseases. J Occup Environ Med 49:610–617. Dominici F, Peng RD, Bell ML, Pham L, McDermott A, Zeger SL, et al. 2006. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA 295:1127–1134. Dominici F, Peng RD, Ebisu K, Zeger SL, Samet JM, Bell ML. 2007. Does the effect of PM 10 on mortality depend on PM nickel and vanadium content? A reanalysis of the NMMAPS data. Environ Health Perspect 115:1701–1703. EarthInfo Inc. 2006. NCDC Summary of The Day. Available: http://www.earthinfo.com/databases/sd.htm [accessed 7 May 2009]. Everson P, Morris C. 2000. Inference for multivariate normal hierarchical models. J R Stat Soc Series B Stat Methodol 62:399–412. Health Effects Institute. 1995. Diesel Exhaust: A Critical Analysis of Emissions, Exposure, and Health Effects. A Special Report of the Institute’s Diesel Working Group. Cambridge, MA :Health Effects Institute

. Kaufman JD. 2007. Air pollution and mortality: are we closer to understanding the how? Am J Respir Crit Care Med 176:325–326. Kendall M, Brown L, Trought K. 2004. Molecular adsorption at particle surfaces: a PM toxicity mediation mechanism. Inhal Toxicol 16 (s uppl 1 ) :99–105:99–105. Kim E, Hopke PK, Qin Y. 2005. Estimation of organic carbon blank values and error structures of the speciation trends network data for source apportionment. J Air Waste Manag Assoc 55:1190–1199. Laden F, Neas LM, Dockery DW, Schwartz J. 2000. Association of fine particulate matter from different sources with daily mortality in six U.S. cities. Environ Health Perspect 108:941–947. Lanki T, de Hartog JJ, Heinrich J, Hoek G, Janssen NA, Peters A, et al. 2006. Can we identify sources of fine par

ticles responsible for exercise-induced ischemia on days with elevated air pollution? The ULTRA study. Environ Health Perspect 114:655–660. Lippmann M, Ito K, Hwang JS, Maciejczyk P, Chen LC. 2006. Cardiovascular effects of nickel in ambient air. Environ Health Perspect 114:1662–1669. Metzger KB, Tolbert PE, Klein M, Peel JL, Flanders WD, Todd K, et al. 2004. Ambient air pollution and cardiovascular emer

gency department visits. Epidemiology 15:46–56. National Research Council. 2004. Research Priorities for Airborne Particulate Matter: IV. Continuing Research Progress. Committee on Research Priorities for Airborne Particulate Matter. Washington, DC : National Academies Press. Nel A. 2005. Atmosphere. Air pollution-related illness: effects of particles. Science 308:804–806. Obot CJ, Morandi MT, Hamilton RF, Holian A. 2004. A compari

son of murine and human alveolar macrophage responses to urban particulate matter. Inhal Toxicol 16:69–76. O’Neill MS, Veves A, Sarnat JA, Zanobetti A, Gold DR, Economides PA, et al. 2007. Air pollution and inflammation in type 2 diabetes: a mechanism for susceptibility. Occup Environ Med 64:373–379. Ostro B, Feng WY, Broadwin R, Green S, Lipsett M. 2007. The effects of components of fine particulate air pollution on mortality in California : results from CALFINE. Environ Health Perspect 115:13–19. Ostro B, Feng WY, Broadwin R, Malig B, Green S, Lipsett M. 2008. The impact of components of fine particulate matter on cardiovascular mortality in susceptible subpopulations. Occup Environ Med 65:750–756. Peng RD, Chang HH, Bell ML, McDermott A, Zeger SL, Samet JM, et al. 2008. Coarse particulate matter air pol

lution and hospital admissions for cardiovascular and respiratory diseases among Medicare patients. JAMA 299:2172–2179. Peters A, Dockery DW, Muller JE, Mittleman MA. 2001. Increased particulate air pollution and the triggering of myocardial infarction. Circulation 103:2810

– 2815. Pope CA III, Dockery DW. 2006. Health effects of fine particulate air pollution: lines that connect. J Air Waste Manag Assoc 56:709–742. Pope CA III, Muhlestein JB, May HT, Renlund DG, Anderson JL, Horne BD. 2006. Ischemic heart disease events triggered by short-term exposure to fine particulate air pollution. Circulation 114:2443– 244 8. Samet JM, Dominici F, Curriero FC, Coursac I, Zeger SL. 2000. Fine particulate air pollution and mortality in 20 U.S. cities, 1987–1994. N Engl J Med 343:1742–1749. Sarnat JA, Marmur A, Klein M, Kim E, Russell AG, Sarnat SE, et al. 2008. Fine particle sources and cardiorespiratory morbidity: an application of chemical mass balance and factor analytical source-apportionment methods. Environ Health Perspect 116:459–466. Sarnat SE, Suh HH, Coull BA, Schwartz J, Stone PH, Gold DR. 2006. Ambient particulate air pollution and cardiac arrhyth

mia in a panel of older adults in Steubenville, Ohio. Occup Environ Med 63:700–706. Schlesinger RB, Kunzli N, Hidy GM, Gotschi T, Jerrett M. 2006. The health relevance of ambient particulate matter char

acteristics: coherence of toxicological and epidemiologi

cal inferences. Inhal Toxicol 18:95–125. Tankersley CG, Campen M, Bierman A, Flanders SE, Broman KW, Rabold R. 2004. Particle effects on heart-rate regulation in senescent mice. Inhal Toxicol 16:381–390. Tolbert PE, Klein M, Metzger KB, Peel J, Flanders WD, Todd K, et al. 2000. Interim results of the study of particulates and health in Atlanta (SOPHIA). J Expo Anal Environ Epidemiol 10:446–460. Urch B, Brook JR, Wasserstein D, Brook RD, Rajagopalan S, Corey P, et al. 2004. Relative contributions of PM 2.5 chemi

cal constituents to acute arterial vasoconstriction in humans. Inhal Toxicol 16:345–352. U.S. EPA (U.S. Environmental Protection Agency). 1999. Air Quality Index. Available: http://airnow.gov/ [accessed 12 September 2008]. U.S. EPA . 2005. Technical Memorandum: Experimental Intercomparison of Speciation Laboratories. Montgomery, AL : U.S. Environmental Protection Agency National Air Radiation Environmental Laboratory. Zanobetti A, Schwartz J. 2006. Air pollution and emergency admissions in Boston, MA. J Epidemiol Community Health 60:890–895.