BioMed Central

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Environmental Health

Research Open Access

Fine particulate air pollution and its components in association with 

cause-specific emergency admissions

Antonella Zanobetti*1, Meredith Franklin2, Petros Koutrakis1 and 

Joel Schwartz1

Address: 1Department of Environmental Health, School of Public Health, Harvard University, Boston MA, USA and 2Department of Statistics, 

University of Chicago, Chicago IL, USA

Email: Antonella Zanobetti* - azanobet@hsph.harvard.edu; Meredith Franklin - mfranklin@post.harvard.edu; 

Petros Koutrakis - petros@hsph.harvard.edu; Joel Schwartz - joel@hsph.harvard.edu

* Corresponding author 

Abstract

Background: Although the association between exposure to particulate matter and health is well

established, there remains uncertainty as to whether certain chemical components are more

harmful than others. We explored whether the association between cause-specific hospital

admissions and PM2.5 was modified by PM2.5 chemical composition.

Methods: We estimated the association between daily PM2.5 and emergency hospital admissions

for cardiac causes (CVD), myocardial infarction (MI), congestive heart failure (CHF), respiratory

disease, and diabetes in 26 US communities, for the years 2000-2003. Using meta-regression, we

examined how this association was modified by season- and community-specific PM2.5 composition,

controlling for seasonal temperature as a surrogate for ventilation.

Results: For a 10 μg/m3 increase in 2-day averaged PM2.5 concentration we found an increase of

1.89% (95% CI: 1.34- 2.45) in CVD, 2.25% (95% CI: 1.10- 3.42) in MI, 1.85% (95% CI: 1.19- 2.51) in

CHF, 2.74% (95% CI: 1.30- 4.2) in diabetes, and 2.07% (95% CI: 1.20- 2.95) in respiratory

admissions. The association between PM2.5 and CVD admissions was significantly modified when

the mass was high in Br, Cr, Ni, and Na+, while mass high in As, Cr, Mn, OC, Ni, and Na+ modified

MI, and mass high in As, OC, and SO4

2- modified diabetes admissions. For these species, an

interquartile range increase in their relative proportion was associated with a 1-2% additional

increase in daily admissions per 10 μg/m3 increase in mass.

Conclusions: We found that PM2.5 mass higher in Ni, As, and Cr, as well as Br and OC significantly

increased its effect on hospital admissions. This result suggests that particles from industrial

combustion sources and traffic may, on average, have greater toxicity.

Background

Many studies have shown that ambient particulate air pollution (PM), generally measured as particles with aerodynamic diameter less then 10 micrometers (PM10), is

associated with increased risk of hospital admissions for

broadly defined cardiovascular or respiratory causes [1-6].

Published: 21 December 2009

Environmental Health 2009, 8:58 doi:10.1186/1476-069X-8-58

Received: 6 July 2009

Accepted: 21 December 2009

This article is available from: http://www.ehjournal.net/content/8/1/58

© 2009 Zanobetti et al; licensee BioMed Central Ltd. 

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), 

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Environmental Health 2009, 8:58 http://www.ehjournal.net/content/8/1/58

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Similar relationships have been reported in locations

reflecting a wide range of particle composition, and concentrations of gaseous co-pollutants [7-11].

Diabetics are more susceptible to particles and previous

panel studies and time series analyses have shown this

[12-14].

While previous studies have primarily used ambient PM10

as an exposure metric, PM2.5 (particles with aerodynamic

diameter less then 2.5 micrometers) have become a

greater health and regulatory concern due to epidemiologic studies suggesting that PM2.5 might have greater toxicity than larger particles [15,16].

Nevertheless, as the US Environmental Protection Agency

(US EPA) did not begin monitoring PM2.5 concentrations

until 1999, the literature on the association between

PM2.5 and mortality [16-21] is relatively sparse. There

have been even fewer studies examining the association of

PM2.5 with hospital admissions [22-25].

Fine particles consist of a large number of compounds

and their composition varies spatially and temporally. Its

components can be associated with specific sources, such

as elemental carbon with traffic, nickel with oil burning

and selenium with coal burning power plants. While the

evidence for the health effects of fine particles has been

growing, there is uncertainty as to which components of

these particles are most harmful. Moreover, a better

understanding of the relative toxicity of particles with differing chemical composition will in turn lead to more targeted emission control strategies and regulations.

One main limitation in examining PM2.5 components is

the irregularity of the PM2.5 speciation data available from

the EPA Speciation Trends Network (STN) monitoring

sites. The STN was established in 2000 and monitors only

report data for every third or sixth day, thus limiting the

statistical power necessary to detect associations between

individual species and health events. The lack of daily

sampling also prevents the examination of time lags and

effect of multi-day exposures.

Consequently, there have been few studies to date examining the health effects associated with PM2.5 components. One study [26] examined the associations between

19 PM2.5 components and daily mortality in six California

counties, and found that PM2.5 mass and several constituents (EC, OC, NO3

-

, Fe, K, Ti) were associated especially

with cardiovascular deaths at various lags. Another study

[27] used the elemental composition of PM2.5 to investigate the effect of traffic, residual oil and power plant emissions on daily mortality in six US cities. Their results

indicate that combustion particles in the fine particles

from mobile and coal combustion sources were associated with increased mortality.

The most recent study [28] examined the differential

effects of PM2.5 species on mortality in 25 U.S. communities. This study differs from the two previous studies, in

that rather than directly using the observed particle component concentrations in their main model, they overcame the issue of limited statistical power and inability to

examine more than one day exposure due to poor temporal coverage of data reported from the STN sites by using

a hierarchical approach. The authors first determined the

association between PM2.5 mass and mortality, and then

in a second stage of the analysis, a meta-regression was

used to examine how the pooled association was modified by community and season particle composition. They

found evidence that Ni (predominantly from oil combustion) as well as Sulfate and As (from coal burning power

plants) increased the mortality risk associated with PM2.5.

In this study we applied the method of Franklin and coauthors to examine the association between cause-specific

hospital admissions and PM2.5 in 26 U.S. communities,

and explore whether PM2.5 chemical composition played

a role in its toxicity.

Methods

Air Pollution and Meteorological Data

The PM2.5 mass and species concentration data were

obtained online from the EPA Technology Transfer Network Air Quality System [29].

We selected the same cities studied by Franklin et al [30],

but also included Chicago, IL, as we had MEDICARE and

sufficient speciation data between 2000 and 2003. These

cities were originally chosen due to availability of daily

PM2.5 data. For most of these cities, the metropolitan

county encompassed the city and much of its suburbs, but

we used multiple counties for Boston (Suffolk, Norfolk,

and Middlesex), and Minneapolis-St. Paul (Ramsey and

Hennepin). Henceforth we refer to the analyzed geographical areas as communities.

The STN monitors operate on a 24 hour schedule and collect particles on Teflon, nylon or quartz filters which are

analyzed for trace elements using X-ray fluorescence, for

ions using ion chromatography and for organic and elemental carbon using thermal-optical analysis.

The EPA maintains multiple PM2.5 mass sites, but typically

only one PM2.5 speciation site within a county. In order to

use all the available PM2.5 monitoring sites, the 24-hour

integrated mass concentrations were averaged over the

county using a method previously described [31]. Briefly,

we computed local daily mean concentrations using an
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algorithm that accounts for the different monitor-specific

means and variances. However, before averaging, any

monitor that was not well correlated with the others (r <

0.8 for two or more monitor pairs within a community)

was excluded as it likely measured a local pollution source

and would not represent the general population exposure

over the entire county. The number of monitors across the

counties varied between 1 and 4.

Based on results from previous epidemiological studies

[26-33] we focused on the species with different sources

and toxicological background. In particular we focus on

the paper of Franklin et al [30] who also screened the STN

data for inconsistencies based on the percentage of data

below the minimum detection limit and with quality control flags. We therefore examined the following species:

Arsenic (As), Aluminium (Al), Bromine (Br), Chromium

(Cr), Iron (Fe), Lead (Pb), Manganese (Mn), Nickel (Ni),

Potassium (K), Silicon (Si), Vanadium (V), Zinc (Zn),

ions nitrate (NO3

-

), Sulfate (SO4

2-), ammonium (NH4+),

sodium (Na+), elemental carbon (EC) and organic carbon

(OC).

For all available observations we computed the ratio

between each species and PM2.5 mass and then took averages by season across all years to obtain season- and community-specific long-term mean seasonal concentration

ratios.

Meteorological data including daily mean temperature

and dew point temperature from the predominant

weather station in each community were acquired from

the National Climatic Data Center [34].

Health Data

We extracted data on emergency hospital admissions from

the Health Care Financing Administration (MEDICARE)

billing records for the years 2000-2003. The MEDICARE

system provides hospital coverage for all US citizens aged

65 and over.

Based on evidence from previous studies we chose to

examine causes of admissions which have been associated

with particulate matter and added diabetes as it is related

to CVD complications. We defined cases as persons

admitted from the emergency room with a primary discharge diagnosis of: myocardial infarction (MI, International Classification of Disease ninth revision (ICD-9):

410), diabetes (ICD-9: 250), congestive heart failure

(CHF, ICD-9:428), cardiac disease (CVD, ICD-9:390-

429), and all respiratory disease (RESP, ICD-9:460-519).

Statistical Methods

We applied a time series analysis using Poisson regression

to examine the association between daily counts of causespecific admissions and daily PM2.5 mass concentrations

in each community. In each community, the analysis was

stratified by season, since the composition of particles varies seasonally, due in part to different source contributions at different times of the year. In each community, we

controlled for season and long term trend with a natural

cubic regression spline with 1.5 degrees of freedom (d.f.)

for each season and year (corresponding to six d.f. per

year); day of the week using indicator variables; and threeday averaged temperature and dew point temperature

with a natural cubic spline with three d.f..

The effect estimates were expressed as a percent increase in

hospital admission with a 10 μg/m3 increase in PM2.5

mass concentration averaged over the day before and the

day of admission.

In the second stage of the analysis, we combined the Poisson regression effect estimates using random effects metaanalysis [35] to obtain an overall effect across all the communities. The season and community specific long-term

mean seasonal concentration ratios, which reflect particle

composition and thus the relative contribution of different sources to the PM2.5 mass, were then used in a metaregression to quantify to what extent the association

between PM2.5 mass and admissions was modified by particle composition. This involved regressing the community and season-specific Poisson estimates (four for each

community for a total of 104 coefficients) against the

community and season-specific mean concentration

ratios; we first included one element at the time and then

we included those species that were significant effect modifiers in that first stage in a multivariate model. In the

meta-regression, the variance was composed of the sum of

estimated variance from the first stage, and a random variance-covariance matrix component reflecting heterogeneity over and above what can be explained by the

modifier variables, as previously described by Franklin et

al [28] and Zanobetti et al [36].

Franklin et al [30] also found that the association between

PM2.5 mass and mortality was modified by the seasonal

average temperature and used it as a surrogate to explain

ventilation of ambient air to the indoor environment

[37]. They showed an inverted U-shape relationship with

the PM2.5 - mortality effect estimates and temperature

indicating that at extremes of temperature, when windows

and doors are closed resulting in reduced ventilation, the

effect of ambient PM2.5 on mortality was smaller than at

moderate temperatures. We assumed that this phenomenon held true for hospital admissions and thus the metaregression for each outcome included a linear and quadratic term to control for mean temperature.

We used the I2 statistic to assess the proportion of total

variation in effect estimates that was due to between-community heterogeneity [38]. The I2 statistic, I2 = [Q/(k - 1)]
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- 1/[Q/(k - 1)] where k is the number of communities, is a

generalization of the Χ2 or Q test for heterogeneity and

expresses the proportion of variance explained. When Q/

(k - 1) is below 1 there is no heterogeneous variability in

the estimates.

Finally, as there is evidence that socioeconomic status

(SES) plays a role in the health effects of particles (Finkelstein 2003; Levy 2000), we examined community-specific

parameters including: median household income, percent

of population below poverty line, percent of adult population having graduated high school, and percent of all

households having the head of the household 65 years of

age or older and below poverty level in 1999, obtained

from the US Census Bureau [39]. The community-specific

prevalence of central air conditioning (AC), obtained

from the American Housing Survey [40], was also examined to address the potential for any residual heterogeneity associated with ventilation/particle penetration not

sufficiently accounted for with the quadratic term for seasonally averaged temperature. Each parameter was

included separately in the meta-regression along with

temperature and each species proportion. Although SES

parameters and central AC prevalence did not vary seasonally, both were included in the seasonal meta-regression.

We used SAS 9.1 [41] for data management, and R 2.7.2

[42] for regression modelling.

Results

Table 1 shows the mean number and standard deviation

of daily hospital admissions in each community by cause,

together with the distribution of the 2-day moving average of PM2.5. Over all 26 communities we examined

685,716 CVD, 121,652 MI, 238,587 CHF, 46,192 diabetes, and 261,449 respiratory related admissions.

When looking across all seasons (Table 1), El Paso, TX,

Minneapolis, MN, and Seattle, WA, had the lowest PM2.5

concentrations, while Riverside, Bakersfield, and Los

Angeles, CA had the highest concentrations. The lowest

mean concentration of PM2.5 mass, when looking by season (not shown), was observed in Sacramento in spring

(6.1 μg/m3), well below the National Ambient Air Quality

Standard (NAAQS) of 15 μg/m3, while the highest springtime concentration was in Riverside (24 μg/m3). The highTable 1: Community specific mean and standard deviation for the analyzed causes of hospital admissions for citizen 65 years and older 

during the years 2000-2003, and for PM2.5 averaged over two days.

CHF CVD MI Diabetes Respiratory PM2.5

City mean std mean std mean std mean std mean std mean std

Akron, OH 4.0 2.2 11.4 3.9 1.9 1.4 0.6 0.7 8.2 3.6 16.2 7.5

Bakersfield, CA 1.1 1.0 3.0 1.7 0.6 0.8 0.2 0.5 1.2 1.1 21.0 17.1

Boston, MA 13.2 4.1 36.3 7.5 6.2 2.6 2.1 1.5 13.9 4.6 13.4 5.9

Chicago, IL 29.2 7.0 81.2 14.0 13.7 3.9 6.0 2.7 27.6 7.1 16.1 7.4

Cleveland, OH 10.0 3.6 27.6 6.5 4.4 2.1 1.6 1.3 9.9 3.8 16.9 8.0

Columbus, OH 4.4 2.3 12.1 3.9 1.9 1.4 0.6 0.8 4.6 2.4 16.6 7.5

Dallas, TX 6.0 2.7 17.1 4.9 3.2 1.8 1.2 1.1 6.5 3.0 12.8 5.6

Dayton, OH 3.1 1.8 8.6 3.1 1.4 1.2 0.5 0.7 4.0 2.2 16.2 7.5

Detroit, MI 14.2 4.3 42.4 8.1 7.4 2.8 3.0 1.7 13.8 4.4 16.2 8.1

El Paso, TX 2.7 1.8 8.7 3.4 1.3 1.2 1.3 1.2 6.5 3.5 10.2 4.8

Erie, PA 1.3 1.2 4.0 2.1 0.7 0.9 0.2 0.4 1.2 1.1 13.3 7.2

Fresno, CA 2.3 1.6 7.0 2.8 1.5 1.2 0.5 0.7 2.1 1.6 20.0 15.6

Harrisburg, PA 1.2 1.2 3.7 2.0 0.8 0.9 0.2 0.4 1.0 1.0 15.6 8.3

Houston, TX 8.8 3.4 23.9 6.3 3.9 2.1 1.9 1.5 9.0 3.7 12.8 5.2

Kansas City, MO 2.2 1.5 6.5 2.8 1.2 1.1 0.3 0.6 2.5 1.7 12.7 5.7

Los Angeles, CA 12.5 4.2 40.5 8.0 7.6 2.9 3.0 1.8 16.2 4.8 20.7 11.2

Minneapolis, MN 3.8 2.0 13.2 3.9 2.8 1.7 0.7 0.8 4.7 2.3 10.6 5.9

Philadelphia, PA 10.1 3.6 24.7 6.4 3.3 1.9 2.1 1.4 9.6 3.6 15.0 8.0

Pittsburgh, PA 9.3 3.4 24.0 6.2 3.9 2.1 1.4 1.2 9.5 3.6 15.5 7.8

Port Arthutr, TX 1.4 1.2 4.2 2.1 0.7 0.8 0.3 0.6 1.5 1.3 11.4 5.5

Riverside, CA 1.6 1.2 5.5 2.4 1.2 1.1 0.3 0.6 1.9 1.4 27.4 15.5

Sacramento, CA 1.1 1.1 3.7 2.0 0.9 0.9 0.2 0.5 1.5 1.3 12.5 10.2

San Diego, CA 3.0 1.8 10.0 3.5 2.3 1.6 0.6 0.8 3.8 2.1 15.2 8.4

Seattle, WA 3.0 1.8 9.8 3.2 2.2 1.5 0.4 0.6 4.3 2.3 10.0 5.6

St. Louis, MO 6.6 2.6 18.8 4.6 3.9 2.0 1.1 1.1 7.3 3.1 15.0 6.8

Toledo, OH 2.4 1.5 6.7 2.7 1.3 1.2 0.4 0.6 2.5 1.7 14.9 7.3

Overall 6.1 2.5 17.5 4.5 3.1 1.7 1.2 1.0 6.7 2.8 15.3 8.2
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est winter values were observed in Bakersfield and Fresno,

CA, which were 29.5 and 29.9 μg/m3, respectively.

Species to PM2.5 mass proportions averaged over all communities are presented in Table 2, while community and

season-specific proportions of six selected species are

illustrated in Figure 1. Notably, organic carbon was higher

in all Californian communities in all seasons compared to

other communities; while Ni was higher in Sacramento,

Philadelphia, Minneapolis, and Boston in winter and in

Harrisburg and Fresno in spring.

The meta-analysis results are shown in Table 3. Across all

the seasons, for a 10 μg/m3 increase in two-day averaged

PM2.5 concentration we found a 1.89% increase (95% CI:

1.34-2.45) in CVD admissions, a 2.25% increase (95% CI:

1.10-3.42) in MI admissions, a 1.85% increase (95% CI:

1.19-2.51) in CHF admissions, a 2.74% increase (95% CI:

1.30-4.2) in diabetes admissions, and a 2.07% increase

(95% CI: 1.20-2.95) in respiratory admissions. Seasonally, the percent increase in each cause of admission was

found to be highest in the spring, while it was generally

Community and season-specific pr Organic Carbon, Sodium, and Nickel Figure 1 oportions of species to PM2.5 mass for 6 selected species: Arsenic, Bromine, Chromium, 

Community and season-specific proportions of species to PM2.5 mass for 6 selected species: Arsenic, Bromine, 

Chromium, Organic Carbon, Sodium, and Nickel. The seasons are: "black triangle" = winter; "black square" = spring; 

"star" = summer; "black circle" = autumn.

Proportion of species to mass

Community

Akron

Bakersfield

Boston

Chicago

Cleveland

Columbus

Dallas

Dayton

Detroit

El paso

Harrisburg

Fresno

Erie

Houston

Kansas City

Los Angeles

Minneapolis

Philadelphi

Pittsburgh

Port Arthur

Riverside

Sacramento

San Diego

Seattle

Saint Louis

Toledo

0e+001e−042e−043e−044e−04

Arsenic

0e+001e−042e−043e−044e−045e−046e−04

Bromine

0e+002e−044e−046e−048e−04

Chromium

Akron

Bakersfield

Boston

Chicago

Cleveland

Columbus

Dallas

Dayton

Detroit

El paso

Harrisburg

Fresno

Erie

Houston

Kansas City

Los Angeles

Minneapolis

Philadelphi

Pittsburgh

Port Arthur

Riverside

Sacramento

San Diego

Seattle

Saint Louis

Toledo

0e+002e−044e−046e−048e−04

Nickel

0.000.010.020.030.040.050.06

Sodium

0.00.20.40.60.81.0

Organic Carbon
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lower in summer and autumn, except for diabetes which

was high in autumn as well.

We found significant though moderate heterogeneity

among the community-specific effect estimates across all

seasons (I2-statistic p < 0.05) for CVD and MI admissions,

where 33% (CVD) and 24% (MI) of the total variability,

as reported by the I2 statistic, was attributable to betweencommunity differences (as opposed to stochastic variation). For CHF, diabetes and respiratory admissions no

significant heterogeneity among the community-specific

effect estimates was found. We report the effect modification for all the causes analyzed even though cardiac effects

were the only ones that displayed significant heterogeneity.

As we also found a similar inverted U-shaped association

as shown in Franklin [28] between the effect estimates

and season and city mean temperature, we adjusted for

temperature with both a linear and square term in the

meta-regression. Results of effect modification by speciesto-mass proportions, adjusted for temperature, are shown

in Table 4. For each species and cause of admission we

present the P-value for the effect modification, and the

percent increase for an IQR increase in the proportions

with the 95% confidence interval (CI). We only show the

results with a P-value < 0.07.

We found that some species, Fe, NH4

+, NO3-

, Si, Zn, EC,

and Pb did not result in any effect modification of the

association between PM2.5 and hospital admissions.

Table 2: Distribution of the community and season averaged species - to-PM2.5 mass proportions for selected species, across all 

communities.

Min. 1st Qu. Median Mean 3rd Qu. Max.

Winter

As 0.00005 0.00007 0.00009 0.00010 0.00013 0.00017

Br 0.00013 0.00021 0.00025 0.00027 0.00030 0.00057

Cr 0.00005 0.00008 0.00013 0.00017 0.00019 0.00073

EC 0.02800 0.04440 0.05921 0.06179 0.07297 0.11720

Mn 0.00007 0.00012 0.00020 0.00025 0.00029 0.00076

Na+ 0.00428 0.00916 0.01234 0.01326 0.01784 0.02206

Ni 0.00002 0.00007 0.00012 0.00018 0.00020 0.00066

OC 0.15770 0.22330 0.26060 0.28270 0.30940 0.60360

SO4

2- 0.05094 0.08475 0.18840 0.17280 0.23510 0.28830

Spring

As 0.00004 0.00009 0.00011 0.00012 0.00013 0.00020

Br 0.00013 0.00027 0.00030 0.00033 0.00034 0.00110

Cr 0.00004 0.00009 0.00013 0.00015 0.00020 0.00035

EC 0.02337 0.04498 0.05780 0.06021 0.06758 0.14310

Mn 0.00008 0.00016 0.00021 0.00037 0.00035 0.00260

Na+ 0.00471 0.01003 0.01325 0.01783 0.02300 0.05077

Ni 0.00002 0.00007 0.00015 0.00020 0.00019 0.00084

OC 0.09634 0.19490 0.24540 0.27130 0.28280 0.99030

SO4

2- 0.12100 0.20270 0.25690 0.27600 0.30840 0.93540

Summer

As 0.00004 0.00007 0.00010 0.00010 0.00013 0.00022

Br 0.00009 0.00018 0.00020 0.00022 0.00026 0.00043

Cr 0.00003 0.00007 0.00012 0.00012 0.00016 0.00021

EC 0.02345 0.03834 0.04898 0.04779 0.05715 0.08223

Mn 0.00010 0.00014 0.00018 0.00025 0.00029 0.00094

Na+ 0.00315 0.00801 0.00952 0.01502 0.01838 0.04950

Ni 0.00002 0.00006 0.00010 0.00015 0.00017 0.00086

OC 0.13800 0.22310 0.25400 0.26160 0.29090 0.46250

SO4

2- 0.17060 0.23030 0.29560 0.28970 0.33640 0.41870

Autumn

As 0.00005 0.00008 0.00011 0.00012 0.00017 0.00024

Br 0.00017 0.00022 0.00027 0.00027 0.00029 0.00038

Cr 0.00004 0.00008 0.00014 0.00015 0.00019 0.00048

EC 0.03039 0.05546 0.06364 0.06337 0.07394 0.09023

Mn 0.00011 0.00017 0.00024 0.00030 0.00032 0.00088

Na+ 0.00388 0.00727 0.01119 0.01131 0.01398 0.02762

Ni 0.00001 0.00006 0.00009 0.00013 0.00016 0.00043

OC 0.15760 0.24590 0.26240 0.27620 0.29270 0.47500

SO4

2- 0.08987 0.16770 0.24910 0.22490 0.27560 0.33880
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Although single species are markers for more complex

particle chemistry, for clarity of presentation we divided

the species in the following groups to represent the general categories for which these elements are markers.

Industrial combustion sources: Ni, V, As, Cr, SO4

2-; Soil

and road dust: Al; Traffic: Br, OC, EC; Sea salt, street salt:

Na+; Soil and industrial sources: Mn; Wood burning and

soil: K.

The association between mass and CVD admissions was

significantly (p = 0.05) modified by Br, Ni, Na+, V and Al.

Specifically, an IQR increase in the proportion of bromine

in PM2.5 mass was associated with an additional 0.81%

increase in CVD admissions (95% CI: 0.2-1.4). Similarly

there was an additional 0.7% (95% CI: 0.01-1.44)

increase for an IQR increase in V, a 0.9% increase (95%

CI: 0.5-1.4) for an interquartile increase in Ni, and a

0.87% increase (95% CI: 0.4-1.4) for an IQR increase in

Na+ (Table 4). The association between PM2.5 and MI was

modified by As, Cr, Mn, OC, Ni, K, and Na+.

Including a combination of species proportions that were

statistically significant in Table 4 and performing a backward elimination, in multi-species models we found that

for CVD the combination of Br and Ni remained statistically significant (P <= 0.05), with an additional 0.57%

increase in CVD for an IQR increase in Br and an additional 0.80% increase for an IQR increase in Ni. When we

examined MI, only Ni remained significant.

Table 3: Estimated percent increase in hospital admissions for a 

10 μg/m3 increase in 2-day averaged PM2.5 by cause of admission 

and season

% 95% CI

Cardiovascular disease

All seasons 1.89 1.34 2.45

Winter 2.60 1.60 3.60

Spring 3.38 2.47 4.30

Summer 0.13 -0.78 1.06

Autumn 1.49 0.49 2.50

Myocardial Infarction

All seasons 2.25 1.10 3.42

Winter 2.10 0.42 3.81

Spring 4.50 1.42 7.68

Summer 2.09 -1.29 5.60

Autumn 0.68 -0.85 2.24

Congestive heart failure

All seasons 1.85 1.19 2.51

Winter 2.90 1.63 4.19

Spring 4.14 2.61 5.68

Summer 0.11 -1.23 1.46

Autumn 0.80 -0.40 2.01

Diabetes

All seasons 2.74 1.30 4.20

Winter -0.52 -3.20 2.24

Spring 5.43 1.97 9.02

Summer 1.85 -1.02 4.80

Autumn 4.78 2.16 7.46

Respiratory disease

All seasons 2.07 1.20 2.95

Winter 1.79 0.47 3.12

Spring 4.34 2.19 6.54

Summer 1.26 -0.60 3.16

Autumn 1.52 -0.06 3.13

Table 4: Modification of the PM2.5 mass association across 26 US 

by PM2.5 composition.

P-value for modifier % 95% CI IQR

Cardiovascular disease

Traffic

Br 0.01 0.81 0.23 1.40 0.00010

Sea salt, street salt

Na+ < 0.01 0.87 0.35 1.39 0.00945

Industrial combustion sources

Ni < 0.01 0.90 0.46 1.35 0.00012

V 0.05 0.73 0.01 1.44 0.00017

Soil and road dust

Al 0.05 0.53 0.00 1.07 0.00193

Myocardial Infarction

Traffic

OC 0.03 1.03 0.13 1.94 0.07060

Industrial combustion sources

Ni 0.04 1.13 0.04 2.22 0.00012

As < 0.01 2.35 0.84 3.85 0.00006

Cr 0.05 1.34 0.00 2.68 0.00010

Sea salt, street salt

Na+ 0.03 1.42 0.16 2.68 0.00945

Soil and industrial sources

Mn 0.03 0.99 0.14 1.85 0.00018

Wood burning and soil

K 0.05 1.61 0.01 3.22 0.00270

Diabetes

Traffic

OC < 0.01 -2.42 -3.79 -1.06 0.07060

EC 0.02 -2.12 -3.84 -0.39 0.02469

Industrial combustion sources

As 0.04 2.16 0.11 4.21 0.00006

SO4

2- 0.01 2.91 0.92 4.89 0.11610

Congestive heart failure

Soil and road dust

Al 0.07 0.77 -0.07 1.61 0.00193

Industrial combustion sources

Ni 0.07 0.64 -0.04 1.32 0.00012

Respiratory disease

Sea salt, street salt

Na+ 0.06 0.94 -0.02 1.91 0.00945

Industrial combustion sources

Ni 0.06 0.75 -0.03 1.53 0.00012

Results are expressed as % increase in cause-specific hospital 

admission per 10 μg/m3 increase in PM2.5 for an IQR increase in the 

Species to PM2.5 mass proportions
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In a sensitivity analysis, changing the weather specification in the first stage to linear showed the same results as

using cubic regression splines.

Central air conditioning (AC) and the SES variables were

not statistically significant in the meta-regression and thus

did not explain any residual heterogeneity in the effect

estimates over and above what temperature and the species could explain. Moreover, the magnitude of the modification by the species proportions did not change

drastically with the inclusion of AC or the SES parameters;

they were only slightly increased or reduced.

Because Na+ and Ni were associated with both CVD and

MI admissions, we examined their concentrations more

carefully and found that levels were consistently higher in

California. For Na+, this may be attributable to the contribution of marine aerosol from the Pacific Ocean. To support this we present the relationship between Na+

concentrations and wind direction for the Los Angeles

site. As shown by the wind rose (Figure 2), higher levels

were observed when air masses passed over the sea (coming from SW). As a comparison, a similar wind rose was

examined for EC, typically associated with local traffic

(Figure 2), but no wind pattern was observed. Interestingly, the wind roses for Ni and V were similar to Na+, suggesting that wind coming over the ocean also carries

elements often associated with ship emissions (not

shown).

Discussion

In this multi-community study we found a significant

association between PM2.5 mass and both respiratory and

cardiac hospital admissions. These effects were strongest

in spring and were significantly modified by certain chemical components of the mass. The rationale for using species-to-mass concentration ratios in the second stage was

that in the first stage the admission risk was estimated per

unit of the total PM2.5 mass, which encompasses all measured species, and therefore effect modification by the species was best expressed on a per unit of PM2.5 mass basis.

One study [24] reported associations between hospital

admissions and PM2.5, but the authors examined different

categories of admissions than us, except for heart failure.

They reported an association of 1.28% (95% CI, 0.78%-

1.78%) increase in risk of heart failure per 10 μg/m3

increase in same-day PM2.5, which is comparable to our

estimate of 1.85% (95% CI: 1.19-2.51) increase in CHF

for the same and previous day average of PM2.5. Similar

results have been found by Peng [43], Metzger [44], and

by Halonen [45]. Bell and co-authors [23] found higher

effects in winter; they used a two-stage Bayesian hierarchical model which included two interaction terms allowing

both the exposure and the temporal trend to differ by season. Instead we stratified by season, allowing a more specific control for season and trend within each community.

Another difference is that the authors included cities with

systematically missing data, which may have several

implications in modelling the time series.

Two recent studies [22] examined the toxicity of PM2.5

chemical composition on hospital admissions. Bell et al

[22] used the method we introduced in Franklin 2008 and

used here; in the second stage however, they didn't take

into account indoor infiltration. We had previously demonstrated [28] that mean seasonal temperature was an

important predictor of the effect of PM2.5 in that city and

season, and since species concentrations can vary with

temperature, an important confounder of assessing the

role of species. We controlled for this in the analyses in

this paper. Bell examined a larger number of communities

and utilized same-day (lag 0) PM2.5 concentrations versus

the mean of lags 0 and 1 in our study. They found associations with Ni, EC and V while we did not see any effects

for EC. Peng et al [46] used time-series analysis of the

measured (one in three or one in six day) PM2.5 species,

and found significant effects at lag 0 for EC, OC and

ammonium, but not for Ni. Some of these differences may

be due to confounding by ventilation patterns.

Our findings have several important parallels with Franklin and co-authors [28]. For instance, across all admission

types, we found that the elements emitted primarily

through industrial combustion, namely Ni and As, displayed the greatest and most consistent effect modification. We also found moderate modification by Al, which

was shown to be a strong modifier in the previous study.

The primary differences were that we found that Br and

OC, species often associated with traffic, and Na+, an ion

often associated with marine aerosol, were effect modifiers of several causes of admissions.

We also found a significant effect of PM2.5 on admissions

for diabetes, which, despite the smaller daily counts,

shows the strongest association in all season, spring, and

autumn. As previous studies reported, diabetics might be

particularly susceptible to particles [12-14]. We found that

that SO4

2- and As were significant effect modifiers, associated with higher rates of diabetes admissions, whereas OC

was associated with lower rates. This finding are consistent with that of O'Neill et al [13], who reported that SO4

2-

and particles were associated with impaired flow mediated dilation in diabetic subjects and suggests a continuing concern for coal-derived particles.

As we found that only cardiac effects displayed significant

heterogeneity in the PM2.5-admission effect estimates, we

focus our discussion on CVD and MI.
Environmental Health 2009, 8:58 http://www.ehjournal.net/content/8/1/58

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Cardiac diseases

We found that the association between PM2.5 and CVD

was modified by species related to traffic (Br), ship emissions (Ni and V), marine or street salt (Na+), and soil and

road dust (Al).

General population exposure to Ni is likely from smelters

and oil combustion including domestic heating and ship

emissions as it is a constituent of Residual Oil Fly Ash

(ROFA). Toxicological studies of ROFA have suggested

that its ability to produce cell and tissue injury as well as

stimulate inflammatory response is due to its high transition metal content. Gao et al [47] found that ROFA Ni

content played a very important role in mediating an

inflammatory response in human lung cells and that it

produced significantly greater effects in comparison to

other particle types. ROFA has been associated with

increased oxidative stress [48], increased susceptibility to

bacterial infections [49,50], and altered heart rate, blood

pressure, and electrocardiograms [51].

Lippmann [33] exposed atherosclerotic prone mice to

concentrated air particles over a six month period. During

periods when Ni was especially high, there was a pronounced acute change in heart rate and heart rate variability in those animals. The authors also examined

associations between PM components and mortality in

the National Mortality and Morbidity Air Pollution Study

(NMMAPS), and found that daily mortality rates in the 60

cities with recent speciation data were significantly associated with average Ni and V, but not with other measured

species.

Franklin and coworkers [28] reported that the effect of PM

mass on daily deaths was higher in communities and seasons with particles higher in Ni content, providing additional support for our findings.

Not much literature exists on the effects of particles rich in

Br, Cr, Mn, As and Na+. Metzger [44] found that CVD visits in Atlanta were associated with NO2, CO, PM2.5, OC,

EC, and oxygenated hydrocarbons, whereas we did not see

effect modification by EC or OC in this analysis. EC and

OC are often highly correlated with PM2.5 mass, making it

difficult to distinguish an EC or OC effect from a mass

effect.

When looking at mortality outcomes, in a time series

analysis in six California counties, Ostro and co-authors

[26] found that PM2.5 mass and several constituents, OC,

NO3

-

, Fe, K, and Ti were associated with cardiovascular

deaths.

Franklin et al [28] found that Al was a strong modifier of

the PM2.5-mortality effect, yet in this study it only had a

moderate modification effect for CVD and CHF. Their

results were presented for non-accidental mortality and

were not specific to cardiovascular causes. Nevertheless,

plausible biological mechanisms of an inflammatory

response have been found. Soil and road dust containing

Al and Si have been linked with cardio-pulmonary

responses in canines [52] and Becker [53] found that a factor containing particle Al was significantly related to an

inflammatory response in human epithelial cells.

Myocardial infarctions

We found that species related to traffic (OC) and several

industrial combustion sources (Ni, As, Cr) were modifiers

leading to increased MI admissions. As noted above, As is

a good marker for coal combustion.

Several studies have examined particle components and

MI. Two studies found support for the hypothesis that

exposure to traffic-related air pollution increases the risk

of acute MI [54,55]. Similarly, a study on repolarization

changes and variations in markers of inflammation in

association with ambient PM in a panel of male coronary

artery disease patients, found that traffic-related and combustion-generated particles had stronger adverse health

impact with regard to cardiac effects, and that particles

from different sources induce an acute phase response in

these patients [56].

Limitations

As noted above a key limitation of this study, and all others using the US EPA's speciation network, is the ability for

Los Angeles: wind rose of the relationship between Na+ concentrations in ng/m3 Figure 2 and wind direction

Los Angeles: wind rose of the relationship between 

Na+ concentrations in ng/m3 and wind direction.
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one monitor to adequately represent concentrations of

species which are more highly spatially variable. For

instance, Ito et al [57] found that correlations between

concentrations of As, EC and Ni were moderate to low

between closely located STN monitors in the New York

City area. Thus, in general, for spatially variable species, a

greater amount of measurement error could be present

when using a single monitor to represent exposure over an

entire community. Therefore some components will suffer from greater measurement error due to greater spatial

variability. Similarly, while in most locations PM2.5 is

quite homogeneous over the spatial scale of a community

this is not true everywhere. For example, while in Philadelphia the spatial variability in PM2.5 is small [58], there

can be large variations in PM2.5 concentrations in Los

Angeles [59]. Another limitation of this study is the use of

diabetes admissions which are more problematic to interpret because these are usually related to complications of

the disease, or other cardiovascular complications.

While the use of seasonal ratios of elements to PM2.5 as

modifiers of the effect of mass on mortality allows us to

gain power by using the daily PM2.5 mass concentrations,

it has some limitations. First, while some of the variation

in the ratios is across cities, and some across seasons

within cities, other variation is across days within season,

and this source of variation is not captured. We believe

this day to day variation around the mean for the season

and city is mostly Berkson error with respect to predicting

a city and season specific PM2.5 slope, and hence reduces

power more than inducing bias. Second, because the variation in the ratios includes variations across cities, there

is the possibility of cross-sectional confounding. In contrast to cohort studies, where the outcome is death, and

the confounders are other predictors of mortality, our

meta-regression is different. The outcome is PM2.5 slope,

and hence factors that are predictive of mortality (socioeconomic status, diet, etc) that vary across location are

unlikely to be confounders. The confounders will be other

things besides elemental composition that might predict

differences in PM2.5 slopes. We believe the most important one is infiltration rates, and have used mean temperature by season and city as a surrogate for that. Obviously,

it is not a perfect one. Other potential confounders might

be differences in individual susceptibility. However,

because we have variation within city across season, we

believe this approach problem is partially mitigated.

Conclusions

Our study shows that some chemical species significantly

modify the association between PM2.5 and cause-specific

hospital admissions. This important finding illustrates

that mass alone is not a sufficient metric to use when evaluating health effects of PM exposure.

One recommendation for decision-makers is that daily

speciation data is needed. It is difficult to analyze the

effect of the PM2.5 mass composition with the data available only one day in three or six. Furthermore, future

research aimed to address the issue of the effects of the elements on, for example, cardiovascular endpoints, could

focus more on study such as chamber or panel study, and

toxicological animal study.

More work is needed in order to better understand the

biological mechanisms of PM components, and to better

direct regulation of particles and sources producing pollution high in these chemical species.

List of Abbreviations

Al: Aluminium; As: Arsenic; Br: Bromine; CHF: congestive

heart failure; CI: Confidence Interval; Cr: Chromium;

CVD: cardiac causes; EC: elemental carbon; Fe: Iron; K:

Potassium; MI: myocardial infarction; Mn: Manganese;

Na+: sodium; NH4

+: ammonium; Ni: Nickel; NO3-

: ions

nitrate; OC: organic carbon; Pb: Lead; PM: particulate pollution; PM2.5: particles with an aerodynamic diameter of

less than 2.5 μm; Si: Silicon; SO4

2-: Sulfate; V: Vanadium;

Zn: Zinc.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

AZ participated in the design of the study, prepared the

datasets, performed the statistical analysis, and drafted the

manuscript. MF participated in the design of the study,

prepared the datasets, and helped writing the manuscript.

JS participated in the design of the study, and helped writing the manuscript, revising it critically for important

intellectual content. PK participated in the design of the

study, revised the manuscript critically for important

intellectual content.

All authors read and approved the final manuscript.

Acknowledgements

This study was funded in part by NIEHS (Grant number 00002), and in part 

by the United States Environmental Protection Agency through STAR grant 

RD832416 to Harvard University. It has not been subjected to the Agency's 

required peer and policy review and therefore does not necessarily reflect 

the views of the Agency and no official endorsement should be inferred.

References

1. Atkinson RW, Anderson HR, Sunyer J, Ayres J, Baccini M, Vonk JM,

Boumghar A, Forastiere F, Forsberg B, Touloumi G, Schwartz J,

Katsouyanni K: Acute effects of particulate air pollution on respiratory admissions: results from APHEA 2 project. Air Pollution and Health: a European Approach. American journal of

respiratory and critical care medicine 2001, 164:1860-1866.

2. Le Tertre A, Medina S, Samoli E, Forsberg B, Michelozzi P, Boumghar

A, Vonk JM, Bellini A, Atkinson R, Ayres JG, Sunyer J, Schwartz J,

Katsouyanni K: Short-term effects of particulate air pollution
Environmental Health 2009, 8:58 http://www.ehjournal.net/content/8/1/58

Page 11 of 12

(page number not for citation purposes)

on cardiovascular diseases in eight European cities. Journal of

epidemiology and community health 2002, 56:773-779.

3. Burnett RT, Smith-Doiron M, Stieb D, Cakmak S, Brook JR: Effects

of particulate and gaseous air pollution on cardiorespiratory

hospitalizations. Archives of environmental health 1999, 54:130-139.

4. Schwartz J: Air pollution and hospital admissions for heart disease in eight U.S. counties. Epidemiology 1999, 10:17-22.

5. Schwartz J, Morris R: Air pollution and hospital admissions for

cardiovascular disease in Detroit, Michigan. American journal of

epidemiology 1995, 142:23-35.

6. Sunyer J, Anto JM, Murillo C, Saez M: Effects of urban air pollution

on emergency room admissions for chronic obstructive pulmonary disease. American journal of epidemiology 1991,

134:277-286;.

7. Goldberg M, Burnett RT, Brook J, Bailar JC, Valois MF, Vincent R:

Associations between daily cause-specific mortality and concentrations of ground-level ozone in Montreal Quebec. American Journal of Epidemiology 2001, 154:817-826.

8. Koken PJ, Piver WT, Ye F, Elixhauser A, Olsen LM, Portier CJ: Temperature, air pollution and hospitalization for cardiovascular

diseases among elderly people in Denver. Environmental Health

Perspectives 2003, 111:1312-1317.

9. Linn WS, Szlachcic Y, Gong H Jr, Kinney PL, Berhane KT: Air pollution and daily hospital admissions in metropolitan Los Angeles. Environmental health perspectives 2000, 108:427-434.

10. Sunyer J, Ballester F, Tertre AL, Atkinson R, Ayres JG, Forastiere F,

Forsberg B, Vonk JM, Bisanti L, Tenias JM, Medina S, Schwartz J,

Katsouyanni K: The association of daily sulfur dioxide air pollution levels with hospital admissions for cardiovascular diseases in Europe (The Aphea-II study). European heart journal

2003, 24:752-760.

11. Zmirou D, Schwartz J, Saez M, Zanobetti A, Wojtyniak B, Touloumi

G, Spix C, Ponce de Leon A, Le Moullec Y, Bacharova L, Schouten J,

Ponka A, Katsouyanni K: Time-series analysis of air pollution

and cause-specific mortality. Epidemiology 1998, 9:495-503.

12. Bateson TF, Schwartz J: Who is sensitive to the effects of particulate air pollution on mortality? A case-crossover analysis of

effect modifiers. Epidemiology 2004, 15:143-149.

13. O' Neill MS, Veves A, Zanobetti A, Sarnat JA, Gold DR, Economides

PA, Horton ES, Schwartz J: Diabetes enhances vulnerability to

particulate air pollution-associated impairment in vascular

reactivity and endothelial function. Circulation 2005,

111:2913-2920.

14. Zanobetti A, Schwartz J: Cardiovascular damage by airborne

particles: are diabetics more susceptible? Epidemiology 2002,

13:588-592.

15. Cifuentes LA, Vega J, Kopfer K, Lave LB: Effect of the fine fraction

of particulate matter versus the coarse mass and other pollutants on daily mortality in Santiago Chile. Journal of the Air &

Waste Management Association 2000, 50:1287-1298.

16. Schwartz J, Dockery DW, Neas LM: Is daily mortality associated

specifically with fine particles? Journal of the Air & Waste Management Association 1996, 46:927-939.

17. Borja-Aburto VH, Castillejos M, Gold DR, Bierzwinski S, Loomis D:

Mortality and ambient fine particles in southwest Mexico

City 1993-1995. Environmental health perspectives 1998,

106:849-855.

18. Franklin M, Zeka A, Schwartz J: Association between PM2.5 and

all-cause and specific-cause mortality in 27 US communities.

Journal of exposure science & environmental epidemiology 2007,

17:279-287.

19. Ostro B, Broadwin R, Green S, Feng WY, Lipsett M: Fine particulate air pollution and mortality in nine California counties:

results from CALFINE. Environmental health perspectives 2006,

114:29-33.

20. Wichmann HE, Spix C, Tuch T, Wolke G, Peters A, Heinrich J, Kreyling WG, Heyder J: Daily mortality and fine and ultrafine particles in Erfurt, Germany part I: role of particle number and

particle mass. Research report (Health Effects Institute) 2000:5-86.

21. Zanobetti A, Schwartz J: Air pollution and emergency admissions in Boston, MA. Journal of epidemiology and community health

2006, 60:890-895.

22. Bell ML, Ebisu K, Peng RD, Samet JM, Dominici F: Hospital Admissions and Chemical Composition of Fine Particle Air Pollution. American journal of respiratory and critical care medicine 2009.

23. Bell ML, Ebisu K, Peng RD, Walker J, Samet JM, Zeger SL, Dominici F:

Seasonal and regional short-term effects of fine particles on

hospital admissions in 202 US counties, 1999-2005. American

journal of epidemiology 2008, 168:1301-1310.

24. Dominici F, Peng RD, Bell ML, Pham L, McDermott A, Zeger SL,

Samet JM: Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. Jama 2006,

295:1127-1134.

25. Fung KY, Khan S, Krewski D, Chen Y: Association between air

pollution and multiple respiratory hospitalizations among

the elderly in Vancouver, Canada. Inhalation toxicology 2006,

18:1005-1011.

26. Ostro B, Feng WY, Broadwin R, Green S, Lipsett M: The effects of

components of fine particulate air pollution on mortality in

california: results from CALFINE. Environmental health perspectives 2007, 115:13-19.

27. Laden F, Neas LM, Dockery DW, Schwartz J: Association of fine

particulate matter from different sources with daily mortality in six U.S. cities. Environmental health perspectives 2000,

108:941-947.

28. Franklin M, Koutrakis P, Schwartz P: The role of particle composition on the association between PM2.5 and mortality. Epidemiology 2008, 19:680-689.

29. Nehls GJ, Akland GG: Procedures for handling aerometric

data. J Air Pollution Control Assoc 1973, 23:180-184.

30. Franklin M, Schwartz J: The Impact of Secondary Particles on

the Association between Ambient Ozone and Mortality.

Environmental health perspectives 2008, 116:453-458.

31. Schwartz J: Assessing confounding, effect modification, and

thresholds in the association between ambient particles and

daily deaths. Environmental health perspectives 2000, 108:563-568.

32. Bell ML, Dominici F, Ebisu K, Zeger SL, Samet JM: Spatial and temporal variation in PM(2.5) chemical composition in the

United States for health effects studies. Environmental health

perspectives 2007, 115:989-995.

33. Lippmann M, Ito K, Hwang JS, Maciejczyk P, Chen LC: Cardiovascular effects of nickel in ambient air. Environmental health perspectives 2006, 114:1662-1669.

34. National Environmental Satellite, Data, and Information Service: TD3280 U.S. Surface Airways and Airways Solar Radiation

Hourly. Washington, D.C., U.S. Department of Commerce; 2003. 

35. Berkey CS, Hoaglin DC, Mosteller F, Colditz GA: A random-effects

regression model for meta-analysis. Statistics in medicine 1995,

14:395-411.

36. Zanobetti A, Schwartz J, Dockery DW: Airborne particles are a

risk factor for hospital admissions for heart and lung disease.

Environmental health perspectives 2000, 108:1071-1077.

37. Koutrakis P, Sax SN, Sarnat JA, Coull B, Demokritou P, Oyola P, Garcia J, Gramsch E: Analysis of PM10, PM2.5, and PM2 5-10 concentrations in Santiago, Chile, from 1989 to 2001. Journal of

the Air & Waste Management Association (1995) 2005, 55:342-351.

38. Higgins JP, Thompson SG: Quantifying heterogeneity in a metaanalysis. Statistics in medicine 2002, 21:1539-1558.

39. US Census Bureau: 1990 Decennial Census of Population and

Housing Characteristics. Washington, DC: Department of Housing and Urban Development; 2000. 

40. US Census Bureau: American Housing Survey for the United

States: 2007. 2007 [http://www.census.gov/hhes/www/housing/ahs/

ahs07/ahs07.html]. Washington, DC: U.S. Department of Commerce/

U.S. Department of Housing and Urban Development

41. SAS: SAS Software Release 9.1. 9.1th edition. Cary NC: SAS Institute; 2006. 

42. R: A language and environment for statistical computing.

2.7.2 edition. Vienna, Austria: R Foundation for Statistical Computing;

2008. 

43. Peng RD, Chang HH, Bell ML, McDermott A, Zeger SL, Samet JM,

Dominici F: Coarse particulate matter air pollution and hospital admissions for cardiovascular and respiratory diseases

among Medicare patients. Jama 2008, 299:2172-2179.

44. Metzger KB, Tolbert PE, Klein M, Peel JL, Flanders WD, Todd K, Mulholland JA, Ryan PB, Frumkin H: Ambient air pollution and cardiovascular emergency department visits. Epidemiology 2004,

15:46-56.

45. Halonen JI, Lanki T, Yli-Tuomi T, Tiittanen P, Kulmala M, Pekkanen J:

Particulate air pollution and acute cardiorespiratory hospi-
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Environmental Health 2009, 8:58 http://www.ehjournal.net/content/8/1/58

Page 12 of 12

(page number not for citation purposes)

tal admissions and mortality among the elderly. Epidemiology

2009, 20:143-153.

46. Peng RD, Bell ML, Geyh AS, McDermott A, Zeger SL, Samet JM,

Dominici F: Emergency admissions for cardiovascular and respiratory diseases and the chemical composition of fine particle air pollution. Environmental health perspectives 2009,

117:957-963.

47. Gao F, Barchowsky A, Nemec AA, Fabisiak JP: Microbial stimulation by Mycoplasma fermentans synergistically amplifies IL6 release by human lung fibroblasts in response to residual

oil fly ash (ROFA) and nickel. Toxicol Sci 2004, 81:467-479.

48. Roberts ES, Malstrom SE, Dreher KL: In situ pulmonary localization of air pollution particle-induced oxidative stress. Journal

of toxicology and environmental health 2007, 70:1929-1935.

49. Roberts JR, Taylor MD, Castranova V, Clarke RW, Antonini JM: Soluble metals associated with residual oil fly ash increase morbidity and lung injury after bacterial infection in rats. Journal

of toxicology and environmental health 2004, 67:251-263.

50. Roberts JR, Young SH, Castranova V, Antonini JM: Soluble metals

in residual oil fly ash alter innate and adaptive pulmonary

immune responses to bacterial infection in rats. Toxicology and

applied pharmacology 2007, 221:306-319.

51. Wichers LB, Nolan JP, Winsett DW, Ledbetter AD, Kodavanti UP,

Schladweiler MC, Costa DL, Watkinson WP: Effects of instilled

combustion-derived particles in spontaneously hypertensive

rats. Part I: Cardiovascular responses. Inhalation toxicology 2004,

16:391-405.

52. Clarke RW, Coull B, Reinisch U, Catalano P, Killingsworth CR, Koutrakis P, Kavouras I, Murthy GG, Lawrence J, Lovett E, Wolfson JM,

Verrier RL, Godleski JJ: Inhaled concentrated ambient particles

are associated with hematologic and bronchoalveolar lavage

changes in canines. Environmental health perspectives 2000,

108:1179-1187.

53. Becker S, Dailey LA, Soukup JM, Grambow SC, Devlin RB, Huang YC:

Seasonal variations in air pollution particle-induced inflammatory mediator release and oxidative stress. Environmental

health perspectives 2005, 113:1032-1038.

54. Peters A, von Klot S, Heier M, Trentinaglia I, Hormann A, Wichmann

HE, Lowel H: Exposure to traffic and the onset of myocardial

infarction. The New England journal of medicine 2004,

351:1721-1730.

55. Lanki T, Pekkanen J, Aalto P, Elosua R, Berglind N, D' Ippoliti D, Kulmala M, Nyberg F, Peters A, Picciotto S, Salomaa V, Sunyer J, Tiittanen

P, von Klot S, Forastiere F: Associations of traffic-related air pollutants with hospitalisation for first acute myocardial infarction. The HEAPSS study. Occupational and environmental medicine

2006, 63:844-851.

56. Yue W, Schneider A, Stolzel M, Ruckerl R, Cyrys J, Pan X, Zareba W,

Koenig W, Wichmann HE, Peters A: Ambient source-specific

particles are associated with prolonged repolarization and

increased levels of inflammation in male coronary artery disease patients. Mutation research 2007, 621:50-60.

57. Ito K, Xue N, Thurston G: Spatial variation of PM2.5 chemical

species and source-apportioned mass concentrations in New

York City. Atmospheric Environment 2004, 38:5269-5282.

58. Burton RM, Suh HH, Koutrakis P: Spatial variation in particulate

concentrations within metropolitan Philadelphia. Environmental Science and Technology 1996, 30:400-407.

59. Jerrett M, Burnett RT, Ma R, Pope CA, Krewski D, Newbold KB,

Thurston G, Shi Y, Finkelstein N, Calle EE, Thun MJ: Spatial analysis

of air pollution and mortality in Los Angeles. Epidemiology

2005, 16:727-736.