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

Research

Many multicity studies have shown that ambient particulate air pollution, generally measured as particulate matter with aerodynamic 

diameter ≤ 10 μm (PM10), is associated with 

increased risk of death for broadly defined 

cardiovascular or respiratory causes, using 

time series analysis (Dominici et al. 2000; 

Katsouyanni et al. 1997; Schwartz 1994, 

2000) or case–crossover analysis (Schwartz 

2004; Zeka et al. 2005).

It is generally thought that fine particles 

(those with an aerodynamic diameter of 

≤ 2.5 μm; PM2.5) are more harmful to health 

than larger particles (PM10) (Cifuentes et al. 

2000; Schwartz et al. 1996), although some 

studies have shown stronger effects with coarse 

particles (particles 2.5–10 μm in aerodynamic 

diameter) (Ostro et al. 2000). 

The literature on the association between 

fine particles (PM2.5) and mortality is relatively sparse, because of two main issues:

The U.S. Environmental Protection Agency 

(EPA) began PM2.5 monitoring in 1999, and 

mortality data from the National Center for 

Health Statistics (NCHS) were not available 

nationwide after the year 2000. Nevertheless, 

several multicity studies have been performed. 

Using early PM2.5 monitoring data from 

the Harvard Six City Study, Schwartz et al. 

(1996) reported a strong association between 

2-day average PM2.5 and daily deaths, but 

little association with coarse particles. A study 

of eight Canadian cities similarly found associations with fine but not coarse particles 

(Burnett et al. 2000). Dominici et al. (2007) 

examined PM2.5‒mortality associations using 

national data in the United States, but only 

for the years 1999‒2000. 

Franklin and co-authors in two papers 

(Franklin et al. 2007, 2008) addressed this 

issue a different way. They used mortality data up to year 2000 from the NCHS, 

whereas they obtained mortality data directly 

from state health departments for the years 

2001‒2005 to examine the mortality effects 

of PM2.5 for an extended period of time. They 

were limited by their ability to obtain mortality state data, however. 

Ostro et al. (2006) examined the associations between PM2.5 and daily mortality for 

the years 1999‒2002 in nine California counties. They also found significant associations 

with PM2.5. 

With a common effort of the U.S. EPA–

funded PM research centers and the help of 

the U.S. EPA, we were able to obtain mortality data from 2001 through 2005 from 

each state in the country (except Hawaii and 

Idaho) through NCHS. As a result, national 

data for an extended period are available for 

the first time. 

Based on the associations found in the 

previous studies and the new mortality data 

available, we hypothesized that PM2.5 is 

associated with increased risk of deaths in a 

national study. Because coarse particles are not 

currently regulated by the U.S. EPA, we also 

test the hypothesis that PM coarse (PM with 

aerodynamic diameter > 2.5 and < 10 μm) 

are associated with mortality. We therefore 

conducted a multicity time series study of the 

acute effect of PM2.5 and PM coarse on the 

increased risk of death for all causes, all cardiovascular disease (CVD), myocardial infarction

(MI), stroke, and respiratory mortality.

Materials and Methods

Health data. The NCHS provided researchers 

with national data sets of mortality records, 

including date of death. Certain locations were 

excluded from the data, either because the 

population of the county was low, or because 

the state was not willing to cooperate with 

the project. The analysis was conducted at 

the county level, because this was the smallest 

resolution available for all mortality data; the 

name of the major city within each county was 

used as an identifier rather than the county 

name. The mortality data used provided nonconfidential information on decedents including state of death, county of death, age, sex, 

date of death and primary cause of death. 

We excluded those individuals who 

died in a state different from their state of 

residence. Only those individuals who died 

of nonaccidental causes were examined 

[i.e., International Statistical Classification 

of Diseases, 10th Revision (ICD-10; World 

Health Organization 2007) codes S00 

through U99 were excluded]. 

Specific causes were derived from the 

ICD­10 code for the underlying cause of death: 

respiratory disease (ICD­10: J00 through 

J99), CVD (ICD-10: I01 through I59), MI 

(ICD­10: I21 through I22), stroke (ICD­10: 

I60 through I69), and all-cause mortality 

(TOT; ICD­10: A00 through R99). 

For all nonaccidental deaths and for 

each specific cause, we created daily counts 

Address correspondence to A. Zanobetti, Department 

of Environmental Health, Exposure Epidemiology 

and Risk Program, Harvard School of Public Health, 

401 Park Dr., Landmark Center, Suite 415, Boston, 

MA 02215 USA. Telephone: (617) 384-8751. Fax: 

(617) 384-8745. E-mail: azanobet@hsph.harvard.edu

Supplemental Material is available online at http://

www.ehponline.org/members/2009/0800108/suppl.pdf

This research has been funded in part by National 

Institute of Environmental Health Sciences grant 

ES0002 and in part by the U.S. Environmental 

Protection Agency (EPA) through STAR grant 

RD832416 to Harvard University. 

This research has not been subjected to the U.S. 

EPA’s required peer and policy review and therefore 

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 19 August 2008; accepted 13 February 2009.

The Effect of Fine and Coarse Particulate Air Pollution on Mortality: 

A National Analysis

Antonella Zanobetti and Joel Schwartz

Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA

Background: Although many studies have examined the effects of air pollution on mortality, data 

limitations have resulted in fewer studies of both particulate matter with an aerodynamic diameter 

of ≤ 2.5 μm (PM2.5; fine particles) and of coarse particles (particles with an aerodynamic diameter 

> 2.5 and < 10 μm; PM coarse). We conducted a national, multicity time-series study of the acute 

effect of PM2.5 and PM coarse on the increased risk of death for all causes, cardiovascular disease

(CVD), myocardial infarction (MI), stroke, and respiratory mortality for the years 1999‒2005.

Method: We applied a city- and season-specific Poisson regression in 112 U.S. cities to examine the 

association of mean (day of death and previous day) PM2.5 and PM coarse with daily deaths. We combined the city-specific estimates using a random effects approach, in total, by season and by region.

Results: We found a 0.98% increase [95% confidence interval (CI), 0.75‒1.22] in total mortality, 

a 0.85% increase (95% CI, 0.46‒1.24) in CVD, a 1.18% increase (95% CI, 0.48‒1.89) in MI, a 

1.78% increase (95% CI, 0.96‒2.62) in stroke, and a 1.68% increase (95% CI, 1.04‒2.33) in respiratory deaths for a 10-μg/m3 increase in 2-day averaged PM2.5. The effects were higher in spring. 

For PM coarse, we found significant but smaller increases for all causes analyzed. 

Conclusions: We conclude that our analysis showed an increased risk of mortality for all and 

specific causes associated with PM2.5, and the risks are higher than what was previously observed for 

PM10. In addition, coarse particles are also associated with more deaths. 

Key words: cardiovascular diseases, fine particulate matter, mortality, PM coarse, respiratory disease, season, time series. Environ Health Perspect 117:898–903 (2009). doi:10.1289/

ehp.0800108 available via http://dx.doi.org/ [Online 13 February 2009]
Fine and coarse air pollution and mortality

Environmental Health Perspectives • volume 117 | number 6 | June 2009 899

of deaths in each of the examined counties. 

This work was done under an exemption from 

Human Subjects Committee of the Harvard 

School of Public Health.

Environmental data. We obtained data 

on PM2.5 and PM10 from the U.S. EPA Air 

Quality System Technology Transfer Network 

(U.S. EPA 2008), which provides daily PM2.5

concentrations from the U.S. EPA National 

and State Local Ambient Monitoring stations. 

In most cities, the analysis was conducted on a county level, because the city lies 

within a single county. However, we used 

multiple counties for Minneapolis–St. Paul, 

Minnnesota (Ramsey and Hennepin), Boston, 

Massachusetts (Middlesex Norfolk, Suffolk), 

Birmingham, Alabama (Blount, Jefferson, 

Shelby, St. Clair, Walker), Atlanta, Georgia 

(Cobb, De Kalb, Fulton, Gwinnett), Miami, 

Florida (Dade, Miami–Dade), Baltimore, 

Maryland (Baltimore City, Baltimore 

County), St. Louis, Missouri (Jefferson, 

Madison, St. Louis, St. Louis City, St. Clair), 

New York City, New York (Kings, New 

York City, Queens, Richmond), Steubenville 

(Jefferson, OH; Brooke, Hancock, WV) and 

Youngstown, Ohio (Mahoning, Columbiana), 

San Antonio, Texas (Bexar, Comal), Norfolk, 

Virginia (Newport News city, Norfolk city, 

Virginia Beach city), Kansas City, Missouri 

and Kansas (Jackson, Clay, MO; Johnson, 

KS), and Washington, DC (Arlington, VA;

Washington, DC), where the city’s population 

extends beyond the boundaries of one county. 

When more than one monitor was available in one county, the 24-hr integrated mass 

concentrations were averaged over the county 

using a method previously described (Schwartz 

2000; Zanobetti et al. 2000). Briefly, we first 

excluded any monitor that was not well correlated with the others (r < 0.8 for two or 

more monitor pairs within a county), because

it likely measured a local pollution source and 

would not represent the general population 

exposure over the entire community. We then 

computed the annual mean for each monitor 

and year and subtracted that mean from the 

daily values of that monitor. We then standardized these daily deviances by dividing by 

the standard deviation for that monitor. The 

daily standardized deviations for each monitor 

on each day were averaged, producing a daily 

averaged standardized deviation. We finally 

multiplied this by the standard deviation of 

all of the monitor readings for the entire year 

and added back in the annual average of all 

of the monitors. This process automatically 

compensates for missing data in some monitors on individual days by preventing that 

missingness from contributing to false variations in the daily value. This process has been 

reported previously (Schwartz 2000) and used 

extensively in previous publications (O’Neill 

et al. 2003; Wellenius et al. 2006; Zanobetti 

and Schwartz 2005; Zanobetti et al. 2000; 

Zeka et al. 2005).

To be included in our study, we required 

that at least 265 days of data in at least 1 year 

be available. We found 112 cities with at 

least 265 days of monitoring of PM2.5 per 

year and at least 300 days of mortality data 

per year from NCHS. They represented a 

geographic distribution across the country 

[Figure 1; Table 1 in Supplemental Material 

(available online at http://www.ehponline.

org/members/2009/0800108/suppl.pdf)]. 

PM coarse was estimated by differencing the 

countywide averages of PM10 and PM2.5. 

PM coarse was available for fewer locations, 

because less monitoring of PM10 is currently 

being done. We had 47 locations that met 

our criteria for PM coarse. 

We obtained local meteorologic data from 

the U.S. Surface Airways and Airways Solar 

Radiation hourly data (National Environmental 

Satellite, Data and Information 2003).

Because climate may effect exposure to 

particles (e.g., because ventilation varies by 

climate) and may also modify particle characteristics (e.g., because climate affects secondary 

particle formation), we divided the United 

States into regions based on the Köppen climate classification (Kottek 2006; Kottek et al. 

2006), which is one of the most widely used 

climate classification systems. The Köppen climate classification scheme divides the climates 

into five main groups and several types and 

subtypes. We used the following classification: region 1: humid subtropical climates and 

maritime temperate climates, which includes 

Florida, Louisiana, Texas, Georgia, Alabama, 

Mississippi, Arkansas, Oklahoma, Kansas, 

Missouri, Tennessee, South Carolina, North 

Carolina, Virginia, West Virginia, Kentucky; 

region 2: warm summer, continental climates, including North Dakota, Minnesota, 

Wisconsin, Michigan, Pennsylvania, 

New York, Connecticut, Rhode Island, 

Massachusetts, Vermont, New Hamphsire, 

Maine; region 3: hot summer, continental 

climates with South Dakota, Nebraska, Iowa, 

Illinois, Indiana, Ohio; region 4: dry climates, 

New Mexico, Arizona, Nevada; region 5: dry 

climates together with continental climate with 

Montana, Idaho, Wyoming, Utah, Colorado; 

region 6: Mediterranean climates, which 

includes California, Oregon, Washington. 

Analytical strategy. We investigated the 

association between PM2.5 and PM coarse 

concentrations averaged over the day of death 

and day before death and mortality with a 

time series analysis. The analysis was stratified 

by season because the composition of particles varies seasonally (partly due to different 

source contributions at different times of the 

year) and because the penetration of outdoor 

particles indoors varies seasonally.

We first applied season- and city-specific 

Poisson regression models, controlling for 

long-term trend and seasonality with a natural 

cubic regression spline with 1.5 degrees of 

freedom for each season for each year; day 

of the week using indicator variables; and for 

weather using a natural cubic spline with three 

degrees of freedom for the same-day temperature and for the previous-day temperature. 

We first applied single-pollutant models 

and then fit a model including both PM2.5

and PM coarse. To test the hypothesis that 

coarse particles may affect mortality with a 

Figure 1. Map of the 112 U.S. cities included in the study. Symbol size represents the population; color 

represents PM2.5 concentrations.

City 2000 population Mean PM2.5

2,391–50,000

50,001–200,000

200,001–500,000

500,001–8,008,278

6.67–11.25

11.26–13.22

13.23–15.06

15.07–24.89

Climate region
Zanobetti and Schwartz

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

longer lag, making the choice of lags 0 and 1 

less appropriate, we also fit a distributed lag 

model for 4 days, from the same day and up 

to 3 days earlier for coarse particles.

The city-specific results were then combined with a random effects meta-analysis 

(Berkey et al. 1998). To be conservative, we 

report the results incorporating a random 

effect, whether or not there was a significant 

heterogeneity. 

The pooled analysis was done for each 

outcome separately, with a total of 448 cityand season-specific coefficients for PM2.5

and 188 for PM coarse. In the two-pollutant 

model, the meta-analysis was done for each 

pollutant separately.

We used the I2 statistic to assess the proportion of total variation in effect estimates 

that was attributed to between-city heterogeneity (Higgins and Thompson 2002). The I2

statistic is a generalization of the C2 or Q test 

for heterogeneity and expresses the proportion 

of variance explained. We used the following 

formula: I2 = [Q/(k‒1)] ‒ 1/[Q/(k‒1)], where 

Q is the Q-test for heterogeneity and k is the 

number of community. If Q/(k‒1) is < 1, 

then the I2 is null and indicates that no variability is attributable to heterogeneity (zero in 

the tables).

We analyzed the data using R version 

2.7.2 (R Development Core Team 2008). The 

effect estimates were expressed as a percent 

increase in mortality for a 10-μg/m3 increase 

in PM2.5 mass or PM coarse concentration. 

Results

In the 112 cities during the study period 

1999‒2005, there were 5,609,349 total 

deaths, 1,787,078 for CVD, 397,894 for MI, 

330,613 for stroke, and 547,660 for respiratory disease. The biggest cities are Los Angeles, 

California; New York City, New York; and 

Chicago, Illinois.

The cities with higher levels of PM2.5 were 

in California, with maximum concentrations 

of PM2.5 > 100 μg/m3, whereas the cities with 

the lower maximum levels were in Oklahoma. 

The median PM2.5 ranged from 5.6 μg/m3

in Albuquerque, New Mexico, followed by 

Eugene and Bend, Oregon, with 6 μg/m3, 

whereas the highest median concentrations 

were 21.5 μg/m3 in Rubidoux, California,

and 17.4 μg/m3 in Los Angeles.

Table 1 in Supplemental Material (available online at http://www.ehponline.org/

members/2009/0800108/suppl.pdf) presents 

the following for each of the 112 cities: the 

years of study, the daily mean concentration 

levels for PM2.5 and PM coarse, and the daily 

mean number of death by cause.

Figure 1 shows the location of the 112 

U.S. cities included in the study; the symbol 

size represents the population in each city, 

and the color represents the PM2.5 concentrations. High levels of PM2.5 (red) are in 

California and in the industrial Midwest. 

Table 1 shows the percent increase in 

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

for the mean of lags 0 and 1 (henceforth 

mean01), for the mean01 by season.

We found significant associations with all 

the analyzed causes of death and PM2.5, with 

the highest effect for stroke with a 1.78% 

increase [95% confidence interval (CI),

0.96‒2.62], and respiratory mortality with 

a 1.68% increase (95% CI, 1.04‒2.33) for a 

10-μg/m3 increase in the mean01 of PM2.5. 

When looking at the results by season, the highest effects are in spring, with > 2% increases.

Table 2 shows the percent increase in 

mortality for a 10-μg/m3 increase in PM 

coarse across the 47 cities for the sum of 

the 4 days distributed lag model and by season. We found significant associations with 

total mortality, stroke, CVD, and respiratory mortality, for which we had the largest 

effect: a 1.2% increase (95% CI, 0.4‒1.9) for 

a 10-μg/m3 increase in PM coarse. The effect 

sizes per unit of mass for PM coarse are about 

half those for PM2.5.

When we examined the distributed lag for 

PM coarse (Figure 2), we found little evidence 

that the effects were at longer lags. Figure 2 

reports the percent increase in cause-specific 

mortality at each lag from the distributed lag 

model, combined across the 47 cities. There 

were indications of some effect at lag 2, but 

the sum of the distributed lag was not higher 

than the results for mean01. 

When looking at the heterogeneity by season, we found no significant heterogeneity 

in general in summer (Table 1), whereas significant heterogeneity was seen in spring and 

autumn. For all-cause mortality and PM2.5, 

32% in spring and 15% in autumn of the 

total variability in city-specific coefficients was 

attributable to between-community differences (as opposed to stochastic variation). For 

PM coarse, we only found significant cityspecific heterogeneity for all-cause and respiratory mortality. Again, the highest percentage 

of explained total variability was in spring.

Table 3 shows the results from the multipollutant model, which included both 

PM2.5 and PM coarse. There were only minor 

changes in the effect size estimates for either 

pollutant, and both remained significant for 

all-cause mortality, although some of the 

cause-specific results had increased CIs that 

included no effect. 

Table 4 shows the effects for PM2.5 and 

PM coarse by region. The number of cities 

Table 1. Combined results across 112 cities of the 

mortality PM2.5 association for the mean01 and for 

the mean01 by season. 

 Percent (95% CI) I

2

All-cause mortality

Overall 0.98 (0.75 to 1.22) 0.21*

Winter 0.56 (0.17 to 0.94) 0.14

Spring 2.57 (1.96 to 3.19) 0.32*

Summer 0.25 (–0.13 to 0.63) 0

Autumn 0.95 (0.56 to 1.34) 0.15*

CVD

Overall 0.85 (0.46 to 1.24) 0.17*

Winter 0.70 (0.04 to 1.36) 0.11

Spring 2.18 (1.22 to 3.15) 0.22*

Summer –0.03 (–0.75 to 0.69) 0.04

Autumn 0.92 (0.17 to 1.68) 0.22*

MI 

Overall 1.18 (0.48 to 1.89) 0.05

Winter 1.29 (–0.14 to 2.75) 0.16*

Spring 2.12 (0.53 to 3.74) 0.03

Summer –0.03 (–1.46 to 1.42) 0

Autumn 1.24 (0.12 to 2.36) 0

Stroke

Overall 1.78 (0.96 to 2.62) 0.10*

Winter 1.93 (0.34 to 3.54) 0.18*

Spring 2.04 (–0.02 to 4.13) 0.15

Summer 1.64 (0.05 to 3.26) 0

Autumn 1.69 (0.06 to 3.35) 0.19*

Respiratory

Overall 1.68 (1.04 to 2.33) 0.10*

Winter 0.86 (–0.16 to 1.88) 0.06

Spring 4.62 (3.08 to 6.18) 0.10

Summer 0.78 (–0.49 to 2.06) 0

Autumn 1.45 (0.19 to 2.72) 0.14

Values are percent increase (95% CI) for 10-μg/m3 increase 

in PM2.5. I2 statistics and significance level (*p < 0.05) for 

heterogeneity. Mean01 is the mean of lags 0 and 1 (overall 

and by season).

Table 2. Combined results across 47 cities of the 

mortality PM coarse association for the mean01 

and for the mean01 by season. 

Percent (95%CI) I

2

All-cause mortality 

Overall 0.46 (0.21 to 0.71) 0.26*

Sum distributed lag 0.31 (0.00 to 0.63) 0

Winter –0.14 (–0.48 to 0.20)

Spring 1.01 (0.47 to 1.57) 0.41*

Summer 0.57 (–0.03 to 1.18) 0.29*

Autumn 0.44 (–0.02 to 0.89) 0.25*

CVD

Overall 0.32 (0.00 to 0.64) 0.05

Winter –0.47 (–1.04 to 0.10) 0

Spring 0.95 (0.01 to 1.90) 0.41*

Summer 1.00 (0.34 to 1.67) 0

Autumn 0.16 (–0.36 to 0.68) 0

MI

Overall –0.12 (–0.80 to 0.56) 0.05

Winter –1.23 (–2.38 to –0.07) 0

Spring 0.60 (–1.06 to 2.28) 0.22

Summer 0.87 (–0.51 to 2.27) 0

Autumn –0.23 (–1.40 to 0.95) 0.02

Stroke

Overall 0.84 (0.07 to 1.62) 0.06

Winter 0.38 (–1.32 to 2.12) 0.13

Spring 0.74 (–0.71 to 2.21) 0.07

Summer –0.02 (–1.87 to 1.87) 0.07

Autumn 1.54 (0.34 to 2.76) 0

Respiratory 

Overall 1.16 (0.43 to 1.89) 0.19*

Winter 0.64 (–0.86 to 2.17) 0.27*

Spring 2.56 (0.99 to 4.16) 0.33*

Summer –0.05 (–1.34 to 1.25) 0.004

Autumn 1.18 (0.10 to 2.26) 0.04

Values are percent increase (95% CI) for 10-μg/m3 increase 

in PM2.5. I2 statistics and significance level (*p < 0.05) for 

heterogeneity. Mean01 is the mean of lags 0 and 1 (overall 

and by season).
Fine and coarse air pollution and mortality

Environmental Health Perspectives • volume 117 | number 6 | June 2009 901

varied in each region and by pollutant. For 

PM2.5 in the six regions, we have 47, 28, 

17, 2, 3, and 15 cities, respectively. For PM 

coarse, we have 15, 11, 11, 2, 3, and 5 cities. 

The regions with dry climates and together 

with continental climate are the regions with 

the lower number of cities because they are 

not very populated, but they have the same 

number of cities for both PM2.5 and PM 

coarse. The effects of PM2.5 on all-cause

mortality are similar for all regions except 

for the last (Mediterranean), which include 

California, Oregon, and Washington. The 

results were more varied for the specific causes 

of death, but the precision of the estimates 

was also less. There was a consistent trend for 

lower effects in the Mediterranean region for 

each cause as well. 

In contrast, the pattern was different for 

coarse particles. First, there was considerably more variation in general in the all-cause

mortality effects by region. Not only was the 

Mediterranean region different (as for PM2.5); 

there was no effect in the dry region as well. 

In addition, the effect size in the dry continental region was double that in the humid 

subtropical region for all-cause mortality and 

triple for CVD deaths. 

The percentage of explained total variability differed between regions and between the 

two pollutants, with significant heterogeneity in the “warm summer, continental,” “hot 

summer, continental,” and “Mediterranean”

regions when looking at all-cause mortality 

and PM2.5; for PM coarse significant heterogeneity was found in “warm summer, continental” and “dry” regions.

Discussion

In this national, multicity study we found a 

significant association between fine particulate 

air pollution and the risk of mortality for all 

causes, MI, CVD, stroke, and respiratory disease. We also found a significant association 

of coarse PM with daily deaths. Both effects 

were little changed after controlling for the 

other pollutant. There were several other features of our results worth noting.

These associations were higher during 

spring, which is consistent with the findings 

of the effects of PM10 on mortality by Zeka 

et al. (2006) but at variance with the findings 

of the National Morbidity, Mortality, and Air 

Pollution Study for PM10 (Peng et al. 2005),

which found stronger effect during summer. 

It is also consistent with the report of Franklin 

et al. (2008), who showed that mean temperature in a given city had an inverted U-shaped 

association with the season-specific coefficient 

for PM2.5: Mild temperatures, which were 

associated with greater indoor penetration, 

were associated with higher PM2.5 effects, 

whereas both hot and cold temperatures were 

associated with lower effects. In our study, the 

higher effect sizes in the spring may reflect the 

same pattern. 

In contrast to the PM10 association, where 

substantial regional differences have been 

reported, our analysis showed most climatic 

regions had very similar effect size estimates for 

PM2.5, except for the Mediterranean climatic 

region. However, the coarse particle effects 

varied much more, and the pattern of which 

regions were higher and lower differed between 

fine and coarse mode particles. This suggests 

that there are regional variations in the toxicity 

of coarse particles that require further study. It 

may be possible that coarse particles are coated 

with different substances in different regions, 

for example. In some previous studies, PM10

showed substantially lower effects and more 

regional heterogeneity than we see for PM2.5

and PM coarse. However, if the relative toxicity of PM fine and PM coarse varies differently 

by region, models fit with PM10 as the exposure index, which is essentially the sum of the 

two, may be effectively inducing measurement 

error in the exposure variable, which likely contributes to a downward bias in effect size, compared with treating them separately. Similarly, 

the different regional variability in effect size 

for fine and coarse mass may result in greater 

regional variability of PM10 coefficients.

One possible explanation for the lower 

effect in the Mediterranean region, which 

includes California, is more measurement 

error due to the extremely large counties in 

California, where people may live far away 

from the monitors. Moreover, in California 

there is substantial within-counties gradient 

in particle concentrations, as shown by Jerrett

et al. (2005).

The magnitude of the PM2.5 risk estimates 

reported in our study are similar to the estimates from the two studies of Franklin and 

co-authors. In the first paper, Franklin et al.

(2007) analyzed 27 cities that had PM2.5. The 

results from the case–crossover analysis for the 

previous day were a 1.21% (95% CI, 0.29 

to 2.14%) increase in all-cause mortality, a 

1.78% (95% CI, 0.20 to 3.36%) increase in 

respiratory-related mortality, and a 1.03% 

(95% CI, 0.02 to 2.04%) increase in strokerelated mortality with a 10-μg/m3 increase in 

previous day PM2.5. These results are generally comparable but slightly higher than ours, 

although we report results for a 2-day average.

In the second paper, Franklin et al. (2008) 

reported the association from the time series 

analysis by season for the 2-day averaged 

PM2.5 concentrations. They found a 0.74% 

(95% CI, 0.41 to 1.07%) increase in total 

mortality; a 0.47% (95% CI, 0.02 to 0.92%) 

increase in CVD; a 0.67% (95% CI, ‒0.21 

to 1.07%) in stroke; and a 1.01% (95% 

CI ‒0.03 to 1.57%) increase in respiratory 

Figure 2. Percent increase in cause-specific mortality for the 4 days distributed lag model, combined in 

47 cities for PM coarse. Error bars represent 95% CIs of the estimates. 

1.5

1.0

0.5

0.0

–0.5

–1.0

0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3

All-cause CVD MI Stroke Respiratory

Lag day

Percent increase

Table 3. Percent increase (95% CI) in mortality for 10-μg/m3 increase in PM coarse and PM2.5 for the 

mean01 across the 47 cities; two-pollutant model and single-pollutant model results for PM2.5 in 47 cities.

PM2.5 PM coarse

All-cause mortality 0.77 (0.43 to 1.12) 0.47 (0.21 to 0.73)

0.94 (0.65 to 1.22)

CVD 0.61 (0.05 to 1.17) 0.29 (–0.04 to 0.61)

0.97 (0.51 to 1.43)

MI 0.75 (–0.12 to 1.63) 0.04 (–0.72 to 0.81)

1.18 (0.43 to 1.93)

Stroke 0.82 (–0.21 to 1.86) 0.71 (0.02 to 1.41)

1.96 (0.88 to 3.07)

Respiratory 1.63 (0.69 to 2.59) 1.14 (0.43 to 1.85)

 1.92 (1.08 to 2.78)

Two-pollutant models are shaded. Mean01 is the mean of lags 0 and 1.
Zanobetti and Schwartz

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

mortality. Again, these are generally comparable figures. In contrast, Dominici et al. 

(2007) examined the years 1999‒2000 and 

reported a PM2.5 effect at lag 1 day of 0.29% 

(posterior interval, 0.01, 0.57) per 10-μg/m3

increase for all causes. This is lower than our 

estimate but was done using data for 2 years 

only. One other possible explanation for the 

difference is that Dominici et al. (2007) estimated an effect for PM2.5 in a single model 

for all seasons. In contrast, our analysis and 

the Franklin time-series analysis fit separate 

regressions in each city for each season. This 

allows for seasonal differences in the effect of 

day of the week terms and of the splines for 

temperature. Hence, it allows, for example, 

for different effects of a hot day in May versus August. In contrast, Dominici et al. used 

many more degrees of freedom to control 

for weather variables, but fit those curves for 

the entire year. Thus, control for confounding was different. In addition, we fit different 

effect size estimates for PM2.5 for each season. Although we averaged those estimates to 

obtain a yearly average effect, because that is 

relevant to a pollutant with an annual average 

standard, we began with different effects by 

season, which were significant. In contrast, 

the Dominici analysis effectively assumes 

the same size effect in each season. If that 

assumption is false, it could result in a type of 

measurement error such as attenuation in the 

effect size estimate, even for the annual average effect, because seasonal changes in toxicity 

of 1 μg/m3 of particles can be considered as 

a seasonal variation in measurement error [in 

the use of the same exposure metric, when the 

true exposure (toxicity weighted) is different]. 

Moreover, the differences in the effect sizes 

could be explained by differences in exposure 

metrics, where we also used the mean of lags 

0 and 1 instead of lag 1 alone as exposure 

metric.

There is considerable toxicologic support for these findings. Animal experiments 

indicate that reactive oxygen species, which 

have established relevance in the pathogenesis 

of CVD and aging (Dhalla et al. 2000), are 

affected by particles, which represents one 

pathway for their cardiovascular and lung 

effects (Brook et al. 2004; Gurgueira et al. 

2002; Nel 2005; Rhoden et al. 2004). Diesel 

particles increase oxidative stress in endothelial tissue, inducing the production of heme 

oxygenase-1, a rapid response part of the 

body’s defense system against oxidative stress 

(Furuyama et al. 2006). The viability of cell 

cultures of microvascular endothelial cells was 

impaired by diesel particles, with an accompanying large increase in induction of heme 

oxygenase-1 (Hirano et al. 2003). 

The implication of oxidative stress mechanisms has epidemiologic support as well. A 

recent report showed that subjects who were 

GSTM1 null or had the long variant of 

HMOX-1 had enhanced effects of particles 

on heart rate variability, including a three-way 

interaction (Chahine et al. 2007). Rossner 

et al. (2008a, 2008b) examined bus drivers in 

Prague and reported increased levels of indicators of oxidative stress such as F-2 isoprostane 

and 8-hydroxydeoxyguanosine (8-OHdG) in 

drivers compared with controls. Particles, and 

particularly the metals on particles, have also 

been associated with an increased production 

of 8-OHdG (Chuang et al. 2007; Kim et al. 

2004; Knaapen et al. 2002; Prahalad et al. 

2000, 2001), including specifically exposure 

to traffic pollution (Chuang et al. 2003; 

Lai et al. 2005; Ma and Ma 2002; Tokiwa 

et al. 1999). 8-OHdG was also elevated in 

urban children compared with rural children 

(Tondel et al. 2005). 

Other mechanisms have also been implicated. Particles have been shown to increase 

sICAM-1 (soluble intercellular adhesion 

molecule-1) and sVCAM-1 (soluble vascular 

adhesion molecule 1) in diabetics (O’Neill 

et al. 2007), a finding confirmed in a controlled human exposure chamber study (Salvi 

et al. 1999). Both animal and controlled 

human exposure studies have demonstrated 

that ambient particles can increase prothrombotic (clot-forming) activity and even induce 

thrombosis in acute exposures (Mills et al. 

2007; Mutlu et al. 2007; Nemmar et al. 

2003). In a relevant epidemiologic study, 

Baccarelli et al. (2007, 2008) reported an association of airborne particles with decreased 

clotting time as well as the risk of deep vein 

thrombosis. This is consistent with the results 

of a controlled exposure study to diesel particles, which reported increased ST depression (a sign of ischemic heart disease) and 

alterations in fibrinolytic capacity (the ability 

to break up clots that have formed in blood 

vessels) (Mills et al. 2007).

Conclusions

In summary, there is a strong association 

of both fine and coarse particles with daily 

deaths. These associations are biologically 

plausible and, at the mean concentrations 

in the United States, suggest tens of thousands of early deaths per year, which could be 

avoided by reducing particle concentrations. 

Because coarse particles are not currently regulated by U.S. EPA and many power plants 

Table 4. Percent increase (95% CI) in mortality for 10-μg/m3 increase in the mean01 PM2.5 and PM coarse, 

combined by regions.

 PM2.5 PM coarse

 Percent (95% CI) I

2 Percent (95% CI) I2

All-cause mortality

Humid, subtropical, and maritime 1.02 (0.65 to 1.38) 0.06 0.53 (0.18 to 0.88) 0.03

Warm summer, continental 1.19 (0.73 to 1.64) 0.18* 0.77 (0.14 to 1.40) 0.37*

Hot summer, continental 1.14 (0.55 to 1.73) 0.27* 0.87 (0.47 to 1.26) 0

Dry 1.18 (–0.70 to 3.10) 0 –0.02 (–1.11 to 1.10) 0.57*

Dry, continental 1.26 (–0.21 to 2.76) 0.11 1.11 (0.11 to 2.11) 0

Mediterranean 0.50 (0.00 to 1.01) 0.40* –0.46 (–0.95 to 0.02) 0.24

CVD

Humid, subtropical, and maritime 0.78 (0.05 to 1.51) 0.17* 0.21 (–0.34 to 0.77) 0

Warm summer, continental 1.43 (0.67 to 2.19) 0.15 0.88 (–0.13 to 1.90) 0.34*

Hot summer, continental 0.43 (–0.53 to 1.40) 0.17 0.71 (0.03 to 1.39) 0

Dry 3.11 (–0.02 to 6.33) 0 0.28 (–1.29 to 1.88) 0.33

Dry, continental 1.67 (–0.75 to 4.16) 0 0.70 (–1.26 to 2.69) 0

Mediterranean 0.16 (–0.46 to 0.79) 0.12 –0.15 (–0.87 to 0.58) 0.16

MI

Humid, subtropical, and maritime 0.97 (–0.29 to 2.26) 0 0.82 (–0.88 to 2.56) 0.22*

Warm summer, continental 1.50 (0.05 to 2.97) 0.07 –0.45 (–1.73 to 0.84) 0

Hot summer, continental 0.64 (–0.96 to 2.28) 0 0.40 (–1.05 to 1.87) 0

Dry 4.25 (–2.38 to 11.33) 0 –2.16 (–4.82 to 0.57) 0

Dry, continental 0.60 (–7.42 to 9.32) 0.17 5.09 (0.31 to 10.09) 0.12

Mediterranean 1.85 (–0.66 to 4.41) 0.34* –1.18 (–2.28 to –0.08) 0

Stroke 

Humid, subtropical, and maritime 2.94 (1.59 to 4.32) 0 1.62 (0.37 to 2.89) 0

Warm summer, continental 1.85 (0.04 to 3.69) 0.13 1.03 (–1.41 to 3.53) 0.26*

Hot summer, continental 0.77 (–1.77 to 3.38) 0.23* 0.86 (–1.23 to 3.01) 0.22

Dry 1.82 (–6.98 to 11.45) 0.17 0.34 (–2.43 to 3.18) 0.01

Dry, continental 2.49 (–2.32 to 7.53) 0 0.32 (–3.75 to 4.56) 0

Mediterranean 0.95 (–0.66 to 2.59) 0.23* 0.07 (–1.14 to 1.31) 0

Respiratory 

Humid, subtropical, and maritime 0.91 (–0.25 to 2.08) 0.03 2.16 (1.14 to 3.20) 0

Warm summer, continental 2.12 (0.89 to 3.36) 0.04 1.67 (0.03 to 3.33) 0.16

Hot summer, continental 3.36 (1.95 to 4.79) 0 2.09 (0.80 to 3.40) 0

Dry 5.81 (–0.04 to 12.00) 0.22 –2.55 (–5.39 to 0.37) 0.49*

Dry, continental –0.31 (–5.89 to 5.61) 0.26 1.81 (–1.93 to 5.70) 0.32

Mediterranean 1.06 (–0.36 to 2.50) 0.38* –0.68 (–1.78 to 0.43) 0.08

I

2

 statistics and significance level (*p < 0.05) for heterogeneity. Mean01 is the mean of lags 0 and 1.
Fine and coarse air pollution and mortality

Environmental Health Perspectives • volume 117 | number 6 | June 2009 903

and pre-2007 diesel engines are grandfathered 

from having to retrofit controls to reduce fine 

particles, considerable public health improvement may be possible. 

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