Particulate matter (PM) air pollution is

ubiquitous in the urban environment, representing a heterogeneous mix of solid and liquid particles generated by many different

sources. Several recent multicity time-series

studies have demonstrated associations

between daily mortality and fine PM [i.e.,

particles < 2.5 μm in aerodynamic diameter

(PM2.5)] (e.g., Laden et al. 2000; Ostro et al.

2006). There is little information, however,

about the relative effects of PM2.5 constituents. The National Research Council

(NRC) recently highlighted the importance

of investigating characteristics and constituents of particles that contribute to their

toxicity (NRC 2004). Differential toxicity

can have important implications for both the

establishment of ambient air quality standards and for more targeted PM control

strategies. Specifically, focusing regulations on

the most toxic PM2.5 constituents could protect public health at a lower total cost.

Previous time-series analyses indicate that,

of the sources of PM, motor vehicle exhaust

usually has the strongest associations with allcause or cardiovascular mortality (Janssen et al.

2002; Laden et al. 2000; Mar et al. 2000).

Epidemiologic examinations of specific constituents of PM2.5 also indicate that elemental

and organic carbon (EC and OC) and several

transition metals are associated with mortality

(Burnett et al. 2000; Mar et al. 2000). In

California, the ambient particle chemistry, size

distributions, and temporal patterns of exposure are different from those in other parts of

the United States and Canada (Blanchard

2003). In previous work, we demonstrated

associations of daily PM2.5 mass concentrations with total mortality and with several

mortality subcategories in nine heavily populated California counties (Ostro et al. 2006).

In 2000, the U.S. Environmental Protection

Agency (EPA) and the California Air Resources

Board (CARB) embarked on a program to systematically collect data on constituents of

PM2.5 throughout much of California, providing an opportunity to examine daily measurements of these data in relation to mortality. 

In this article, we report the results of our

analysis of PM2.5 components and mortality

in six counties. For comparison, we also examined associations with PM2.5 in a larger data

set that includes nine California counties. The

use of multiple cities in our analysis enhances

statistical power, reduces the likelihood of

spurious results from a single city, and incorporates a broader range of relevant geographic

and population characteristics such as climate,

background health status, demographics, and

economic status. 

Data and Methods

Mortality data. We obtained data on daily

mortality for all California residents from the

California Department of Health Services,

Center for Health Statistics (CDHS), for the

period for which data on PM2.5 components

were collected: 1 January 2000 through 31

December 2003 (CDHS 1999–2003). We

also collected mortality data from 1999 to

support additional analyses of PM2.5 (CDHS

1999). A death was included only when it

occurred in the decedent’s county of residence. Daily counts of total deaths (minus

accidents and homicides) were aggregated for

all ages. In addition, we determined daily total

mortality counts for those > 65 years of age

and for deaths from respiratory disease

[International Classification of Diseases, 10th

Revision (ICD10; World Health Organization

1993) codes J00–J98] and cardiovascular disease (codes I00–I99). 

Pollutant and meteorologic data. We

obtained PM2.5 speciation data for the 4-year

period 2000 through 2003 from the CARB

(CARB 2004). The speciation monitors were

part of the State and Local Air Monitoring

Stations network, and were filter-based Met

One Speciation Air Sampling Systems (Met

One Instruments Inc., Grants Pass, OR). We

included only counties with ≥ 180 days of

observations with PM2.5 species data to ensure

sufficient statistical power. Thus, our study of

PM2.5 components was limited to deaths

occurring in six California counties, which

included approximately 8.7 million people, or

25% of the state’s population. Each of the six

counties had two monitors measuring PM2.5

components and mass. In three counties

(Fresno, Kern, and Riverside), the two monitors were located within four meters of each

Environmental Health Perspectives • VOLUME 115 | NUMBER 1 | January 2007 13

Research

Address correspondence to B. Ostro, California

Office of Environmental Health Hazard Assessment,

16th Floor, 1515 Clay St., Oakland, CA 94612

USA. Telephone: (510) 622-3157. Fax: (510) 622-

3210. E-mail: Bostro@oehha.ca.gov

Supplemental Material is available online at

http://www.ehponline.org/docs/2006/9281/suppl.pdf

We thank the anonymous reviewers for their helpful comments, J. Kim, and B. Malig for his editorial

assistance. The opinions expressed in this article are

solely those of the authors and do not represent the

policy or position of the State of California or the

California Environmental Protection Agency. 

The authors declare they have no competing

financial interests.

Received 21 April 2006; accepted 29 August 2006.

The Effects of Components of Fine Particulate Air Pollution on Mortality in

California: Results from CALFINE

Bart Ostro,1 Wen-Ying Feng,2 Rachel Broadwin,1 Shelley Green,1 and Michael Lipsett3

1California Office of Environmental Health Hazard Assessment, Oakland, California, USA; 2Graduate Group in Biostatistics,

University of California, Davis, California, USA; 3Department of Epidemiology and Biostatistics, University of California,

San Francisco, California, USA

OBJECTIVE: Several epidemiologic studies provide evidence of an association between daily mortality

and particulate matter < 2.5 μm in diameter (PM2.5). Little is known, however, about the relative

effects of PM2.5 constituents. We examined associations between 19 PM2.5 components and daily

mortality in six California counties. 

DESIGN: We obtained daily data from 2000 to 2003 on mortality and PM2.5 mass and components,

including elemental and organic carbon (EC and OC), nitrates, sulfates, and various metals. We

examined associations of PM2.5 and its constituents with daily counts of several mortality categories: all-cause, cardiovascular, respiratory, and mortality age > 65 years. Poisson regressions incorporating natural splines were used to control for time-varying covariates. Effect estimates were

determined for each component in each county and then combined using a random-effects model. 

RESULTS: PM2.5 mass and several constituents were associated with multiple mortality categories,

especially cardiovascular deaths. For example, for a 3-day lag, the latter increased by 1.6, 2.1, 1.6,

and 1.5% for PM2.5, EC, OC, and nitrates based on interquartile ranges of 14.6, 0.8, 4.6, and

5.5 μg/m3, respectively. Stronger associations were observed between mortality and additional

pollutants, including sulfates and several metals, during the cool season. 

CONCLUSION: This multicounty analysis adds to the growing body of evidence linking PM2.5 with

mortality and indicates that excess risks may vary among specific PM2.5 components. Therefore, the

use of regression coefficients based on PM2.5 mass may underestimate associations with some PM2.5

components. Also, our findings support the hypothesis that combustion-associated pollutants are

particularly important in California. 

KEY WORDS: EC, fine particles, mortality, nitrates, OC, particulate matter, PM2.5, species. Environ

Health Perspect 114:13–19 (2007). doi:10.1289/ehp.9281 available via http://dx.doi.org/ [Online

29 August 2006]
other in the cities of Fresno, Bakersfield, and

Rubidoux, respectively. In the other counties

(Sacramento, San Diego, and Santa Clara) the

monitors were not co-located. Fresno, Kern,

Riverside, and Sacramento Counties reported

data every third day, whereas San Diego and

Santa Clara Counties reported data every sixth

day. For the speciation analyses, the number

of observation days available ranged from 243

(San Diego County) to 395 (Sacramento

County). The following constituents of PM2.5

were measured as 24-hr averages: EC, OC,

nitrates (NO3), sulfates (SO4), aluminum,

bromine, calcium, chlorine, copper, iron,

potassium, manganese, nickel, lead, sulfur, silicon, titanium, vanadium, and zinc. These

PM2.5 components represent multiple sources

of PM2.5, including gasoline combustion,

diesel exhaust, wood smoke, crustal material,

and secondary pollutants, among others. 

We also analyzed PM2.5 mass using a

larger data set from 1999 through 2003 using

all available monitors (including those that did

not collect species data) for nine California

counties—the same six counties as above plus

Contra Costa, Los Angeles, and Orange

Counties. The nonspeciated network data

were obtained from the CARB (2004). PM2.5

monitors were filter-based samplers (model

RAAS2.5-300; Thermo Andersen, Smyrna,

GA). From the nonspeciated network, six

counties had only one monitor each collecting

daily PM2.5 data, whereas Los Angeles, San

Diego, and Santa Clara Counties had three,

three, and two monitors, respectively. 

To allow adjustment for the effect of

weather on mortality, we collected daily average temperature and humidity data at meteorologic stations in each of the counties.

Hourly temperature data were obtained from

the National Oceanic and Atmospheric

Administration (NOAA) National Climatic

Data Center (NCDC 2004). All daily mortality, pollutant, and meteorologic data were

converted into a SAS database (SAS Institute

Inc., Cary, NC) and merged by date. 

Methods. Counts of daily mortality are

non-negative discrete integers representing rare

events; such data typically follow a Poisson distribution. Therefore, we used Poisson regression, conditional on the explanatory variables.

In the basic analytic approach, we used similar

model specifications for each city, including

smoothers for time trend and weather using

natural splines. The natural spline model is a

parametric approach that fits cubic functions

joined at knots, which are typically placed

evenly throughout the distribution of the variable of concern, such as time. The number of

knots used determines the overall smoothness

of the fit. Previous analysis has indicated that

different spline models generate relatively similar results, although increasing the number of

knots generally tends to decrease the estimated

effect of pollution [Health Effects Institute

(HEI) 2003; Ostro et al. 2006]. 

The basic regression model included the

following time-varying covariates: day of week,

smoothing splines of one-day lags of average

temperature and humidity [each with 3 degrees

of freedom (df)], and a smoothing spline of

time with 4 df per year of data. We chose 4 df

a priori because this number has been found to

control well for seasonal and secular patterns

(HEI 2003; Ostro et al. 2006). However, we

conducted additional sensitivity analyses to

evaluate the impact of alternative df for the

smooth of time. In our primary analysis for

each pollutant, we examined single-day lags of

0–3 days. Because the species data were only

available every third or sixth day, multiday

exposure averages could not be constructed. To

facilitate comparisons of PM2.5 with its components, PM2.5c was created. PM2.5c was limited to values of PM2.5 mass measured at

monitors that also measured PM2.5 components on the same days. Therefore, PM2.5c

measurements included data from six counties

from 2000 through 2003. To maximize the

PM2.5 measurements and our statistical power,

we also developed an extended metric for

PM2.5 (PM2.5ext) that used both PM2.5c and

any other available measurements of PM2.5

from 1999 through 2003 for nine (rather than

the original six) California counties. 

Regression models were run for each

county, and the results were combined in a

meta-analysis using a random-effects model

(Der Simonian and Laird 1986), although

results were fairly similar using a fixed-effects

model. 

To obtain a daily county pollutant concentration while accounting for missing data,

we used the same process as that reported by

Wong et al. (2001). For each species, the daily

average was developed using the following

method: a) calculating the mean value for each

monitor across the study period; b) subtracting each monitor’s mean concentration from

the nonmissing daily values for that monitor

(i.e., centered data); c) calculating the daily

mean of the available centered data across all

monitors in a given county; and d) by day,

adding back the grand mean (the mean of all

unadjusted daily values of all of the monitors).

On days when no data for a given pollutant

were available from any monitor in the

county, that day was recorded as missing; no

data were imputed. Results generated using

this data set involved a tradeoff between the

increased sample size and statistical power and

the potential effects on measurement error

introduced through the use of multiple monitors in different parts of a given county. 

Several sensitivity analyses were conducted. First, we examined the potential measurement error created by combining data for

each county from multiple monitors with differing numbers of missing values. We created

a data set limited to the single monitor within

each county with the most (and at least 180)

observations for PM2.5 mass and its components. As a second series of sensitivity analyses,

we examined the effects of alternative

smoothers of time, using either 3 or 6 df for

time trend, as opposed to 4 df in the basic

model. Third, we examined the effect of alternative specifications of temperature and

humidity, using unlagged values for these

covariates, as opposed to the 1-day lag used in

the basic model. Finally, we stratified the data

set by warm (April–September) and cool

(October–March) periods to examine potential

seasonal influences. 

All final results were calculated using R

(version 2.1.1; R Development Core Team

2004) for the single-county analyses and Stata

(StataCorp 2003) for the meta-analyses. To

compare relative impacts based on observed

concentrations, the results are presented as the

excess risk [i.e., (RR-1) × 100] in daily mortality for the interquartile range (IQR) of the

pollutants. The full set of results, including

the percent change in mortality per microgram per cubic meter for each component, is

available online in the Supplemental Material

(http://www.ehponline.org/docs/2006/9281/

suppl.pdf). 

Results

Table 1 provides descriptive statistics for mortality categories, air quality, and meteorologic

Ostro et al.

14 VOLUME 115 | NUMBER 1 | January 2007 • Environmental Health Perspectives

Table 1. Mean daily deaths by mortality category and air quality and meteorologic data, by county,

2000–2003.

Contra Los San Santa

Costa Fresno Kern Angeles Orange Riverside Sacramento Diego Clara

Mortality category

All causes 15.7 13.4 11.4 146.7 40.2 28.8 22.0 49.5 21.3

Cardiovascular disease 6.4 5.8 5.1 66.3 17.8 13.2 9.3 20.3 8.7

Respiratory disease 1.8 1.5 1.5 15.1 4.3 3.4 2.6 5.5 2.4

Age > 65 years 12.2 10.1 8.4 108.6 31.8 22.8 16.3 38.8 16.4

County characteristics

Population (1,000s) 949 799 662 9,519 2,846 1,545 1,223 2,814 1,683

Mean PM2.5 (μg/m3) 12.8 17.5 19.5 20.8 21.5 27.1 12.6 15.3 13.9

Mean temperature (°F) 60.1 64.2 65.7 63.8 63.6 65.5 61.8 61.9 59.5

Mean relative humidity 64.4 56.5 58.2 58.8 71.8 62.6 66.1 75.8 68.3

Days in species analysis 0 355 281 0 0 279 395 243 317
data from six counties with species data, as

well as the other three counties included in the

analysis of PM2.5 mass concentrations only.

Mean daily mortality varied from 147 in Los

Angeles County to 11 in Kern County. Mean

daily PM2.5 concentrations over the study

period averaged around 19 μg/m3, and ranged

from 13 μg/m3 in Sacramento and Contra

Costa Counties to 27 μg/m3 in Riverside

County, exceeding the U.S. EPA annual average PM2.5 standard of 15 μg/m3 in six counties, and the California annual average

standard of 12 μg/m3 in all nine counties.

Table 2 summarizes the data on PM2.5 and its

components for the full study period and for

the cooler seasons (October–March). Over the

four years, there were a total of approximately

1,870 observations across the six counties for

most of the species. The largest contributors to

PM2.5 were EC (5%), OC (37%), NO3

(28%), and SO4 (10%). Table 3 provides the

correlations among the species and PM2.5.

Moderate to high correlations (r = 0.4–0.6)

were found between PM2.5 and EC, OC,

NO3, Br, K, and Zn. More modest correlations (r = 0.2–0.4) were observed between

PM2.5 and SO4, Ca, Cu, Fe, Pb, S, Ti, and V. 

Table 4 provides a summary of the basic

meta-analytic results for alternative single-day

lags of pollutant concentrations. The results

suggest many associations between the pollutants and the mortality end points. Among the

pollutants from the speciation network, the

strongest associations were observed for PM2.5

mass, EC, NO3, Cl, Cu, Fe, K, Ti, V, and Zn.

Adding observations to PM2.5 mass by using

data from the nonspeciation counties (so that

all nine counties were included) enhanced the

statistical power and resulted in observable

associations with all four of the mortality categories. When the results by mortality end

points were examined, several patterns

emerged. All-cause mortality was associated

most strongly with Cu and PM2.5ext, with

weaker associations also observed with NO3

and Cl. Cardiovascular mortality was associated most strongly with EC, K, Zn and PM2.5

with more modest associations observed with

OC, NO3, Fe, and Ti. Respiratory mortality

was associated with Cu and Ti, with weaker

associations with V, Zn, and PM2.5ext. Finally,

for mortality among those > 65 years of age,

significant associations were observed with

PM2.5, NO3, Cl, K, and Zn. 

Figure 1 summarizes the quantitative metaanalytic results for all-cause and cardiovascular

mortality using single-day lags of selected pollutants (the full set of results is available in the

Supplemental Material: http://www.ehponline.org/docs/2006/9281/suppl.pdf). Unlike

many time-series studies with continuous daily

data, not all lags refer to the same outcome

days. Specifically, for PM data collected every

third day, lags 0 and 3 will generally refer to

the same days (and numbers of deaths per day)

except at the ends of the time series. However,

for those same PM data, lags 1 and 2 refer to

different days with different numbers of

deaths. Although this phenomenon holds true

for other studies using nondaily PM data, the

number of observations used in this analysis is

small relative to those in most published studies of PM and mortality. Therefore, the results

Components of fine particles and mortality in California

Environmental Health Perspectives • VOLUME 115 | NUMBER 1 | January 2007 15

Table 2. Descriptive statistics for PM2.5 and species in California counties, 2000–2003. 

All-year Cooler months (Oct–Mar)

Mean IQR 95th % Below Mean IQR

Pollutants Obsa (μg/m3) (μg/m3) percentile detection Obsa (μg/m3) (μg/m3)

PM2.5cb 1,878 19.28 14.63 46.91 0.00 844 24.60 21.47

EC 1,879 0.966 0.795 2.57 0.05 842 1.319 1.135

OC 1,879 7.129 4.592 15.91 0.00 842 9.192 6.124

NO3 1,817 5.415 5.524 17.46 0.00 831 7.294 7.985

SO4 1,817 1.908 1.530 5.18 0.00 831 1.483 1.233

Al 1,870 0.044 0.051 0.14 5.45 845 0.036 0.042

Br 1,870 0.004 0.004 0.01 1.76 845 0.005 0.004

Ca 1,870 0.080 0.064 0.20 0.05 845 0.081 0.066

Cl 1,870 0.094 0.069 0.41 2.09 845 0.105 0.083

Cu 1,870 0.007 0.007 0.34 1.34 845 0.008 0.008

Fe 1,870 0.124 0.099 0.26 0.00 845 0.138 0.117

K 1,870 0.117 0.081 0.01 0.00 845 0.135 0.112

Mn 1,870 0.003 0.003 0.01 3.21 845 0.003 0.003

Ni 1,870 0.005 0.003 0.01 2.51 845 0.005 0.003

Pb 1,870 0.004 0.004 1.70 2.83 845 0.005 0.005

S 1,870 0.648 0.499 0.43 0.00 845 0.522 0.434

Si 1,870 0.168 0.151 0.04 0.05 845 0.147 0.132

Ti 1,870 0.009 0.008 47.69 0.32 845 0.008 0.008

V 1,870 0.002 0.003 46.91 5.56 845 0.002 0.002

Zn 1,870 0.012 0.011 2.57 1.82 845 0.018 0.014

PM2.5extc 11,494 18.6 15.1 15.9 0.00 5,777 22.40 19.8

aTotal number of observations (Obs) for analysis across all of the counties. bIncludes six counties with species data,

2000–2003. cIncludes all nine counties in Table 1, 1999–2003. 

Table 3. Longitudinal correlations of PM2.5 and its components.

PM2.5ca EC OC NO3 SO4 Al Br Ca Cl Cu Fe K Mn Ni Pb S Si Ti V Zn PM2.5extb

PM2.5ca 1

EC 0.53 1

OC 0.62 0.61 1

NO3 0.65 0.41 0.44 1

SO4 0.32 0.05 0.12 0.35 1

Al 0.02 0.05 0.05 –0.06 0.07 1

Br 0.54 0.41 0.45 0.45 0.36 0.08 1

Ca 0.23 0.27 0.17 0.11 0.19 0.29 0.31 1

Cl 0.15 0.10 0.08 0.08 –0.07 –0.08 0.05 –0.02 1

Cu 0.23 0.29 0.26 0.15 0.10 0.11 0.26 0.23 –0.04 1

Fe 0.38 0.48 0.39 0.23 0.16 0.32 0.41 0.61 –0.01 0.32 1

K 0.52 0.48 0.57 0.34 0.09 0.13 0.41 0.27 0.13 0.26 0.41 1

Mn 0.21 0.24 0.18 0.14 0.13 0.32 0.21 0.28 0.04 0.11 0.39 0.20 1

Ni 0.11 0.08 0.02 0.10 0.16 0.07 0.10 0.21 0.03 0.04 0.20 0.03 0.13 1

Pb 0.27 0.28 0.27 0.22 0.06 0.03 0.23 0.10 0.12 0.11 0.19 0.23 0.13 0.04 1

S 0.35 0.07 0.14 0.36 0.85 0.08 0.38 0.19 –0.05 0.12 0.18 0.12 0.13 0.15 0.08 1

Si 0.16 0.19 0.15 0.05 0.20 0.44 0.28 0.62 –0.15 0.23 0.59 0.25 0.30 0.14 0.06 0.21 1

Ti 0.24 0.28 0.23 0.12 0.19 0.37 0.30 0.54 –0.06 0.26 0.60 0.30 0.33 0.18 0.11 0.20 0.57 1

V 0.20 0.09 0.12 0.21 0.31 0.08 0.21 0.15 0.08 0.06 0.16 0.08 0.15 0.11 0.09 0.31 0.12 0.20 1

Zn 0.51 0.53 0.50 0.45 0.11 –0.01 0.37 0.20 0.15 0.23 0.37 0.45 0.23 0.03 0.31 0.15 0.14 0.20 0.17 1

PM2.5extb 0.85 0.52 0.60 0.64 0.30 0.01 0.52 0.21 0.18 0.24 0.36 0.48 0.20 0.08 0.27 0.34 0.13 0.22 0.23 0.52 1

aIncludes six counties with species data, 2000–2003. bIncludes all nine counties in Table 1, 1999–2003.
are somewhat sensitive to the specified lag;

however, the findings suggest many associations between the pollutants and mortality end

points. For example, for a 3-day lag, cardiovascular mortality increased by 1.6% [95% confidence interval (CI), 0–3.1] for PM2.5, 2.1%

(95% CI, 0.3–3.9) for EC, 1.6% (95% CI,

–0.1 to 3.2) for OC, 1.5% (95% CI, –0.2 to

3.3) for nitrates and 2.2% (95% CI, 0.3–4.2)

for Zn for IQRs of 14.6, 0.8, 4.6, 5.5, and

0.01 μg/m3, respectively. Most CIs are large

due to the relatively low numbers of observations. In comparing the beta coefficients, the

percent change in cardiovascular mortality per

microgram per cubic meter was much greater

for many of the components relative to PM2.5

mass (see Supplemental Material: http://www.

ehponline.org/docs/2006/9281/suppl.pdf).

For example, the risk per unit of EC, OC,

NO3, K, and Zn were several times higher

than that of PM2.5 mass.

Table 5 and Figure 2 summarize the cool

season–specific results. During the cooler

months, there are more associations between

the pollutants and mortality than when the

entire year is included in the analysis. Except

for Al, Br, and Ni, almost all of the pollutants

were associated with all-cause and cardiovascular mortality, and with daily deaths among

those > 65 years of age. In contrast, during the

summer months there were few associations,

except for K with cardiovascular and respiratory deaths, and Al, Cl, Cu, Pb, Ti, and Zn

with respiratory mortality (data not shown).

Additional sensitivity analyses indicated that

the species results were insensitive to treatment

of missing values, alternative df used for the

smoothers of time and weather, and different

lags for the weather terms in the model specifications (data not shown).

Discussion 

In this time-series analysis of PM in California,

ambient concentrations of several constituents

of fine particles were associated with daily mortality. Specifically, the data suggest consistent

associations with EC, OC, NO3, Cu, K, Ti,

and Zn, as well as with PM2.5 mass. Stronger

associations were observed with mortality for

cardiovascular disease and among those > 65

years of age. For cardiovascular mortality, risks

associated with the IQRs of EC and Zn were

particularly elevated. Comparison of the pollution regression coefficients indicated that, in

general, EC and many of the other species that

contribute significantly to PM2.5 mass, including OC, NO3, and Zn, all demonstrated

higher excess risks than PM2.5 mass. Although

this observation may be partly the result of stochastic variability, the associations with mortality were all the more striking given the

relatively small number of days with species

data in each county (range 243–395), because

most time-series studies have > 1,000 days of

Ostro et al.

16 VOLUME 115 | NUMBER 1 | January 2007 • Environmental Health Perspectives

Table 4. Summary of statistically significant associations between mortality and alternative pollutant lags

(numbers in the table indicate whether single lags of 0–3 days were statistically significant).

All-cause Cardiovascular Respiratory Age > 65 years

PM2.5ca — 1*, 3* — 3**

EC — 3** — —

OC — 3* — —

NO3 0* 3* — 0**

SO4 ————

Al — — — —

Br — — — —

Ca — — — —

Cl 1* — — 1**

Cu 1** — 3** —

Fe — 2* — —

K — 2** — 2*

Mn — — — —

Ni — — — —

Pb — — — —

S — — — —

Si — — — —

Ti — 1*, 2* 3** —

V — — 1* —

Zn — 3** 1* 1**, 3**

PM2.5extb 0**, 1* 0**, 1** 1*, 2* 0**, 1**

The regression model includes time (4 df/year), 1-day lags of temperature and humidity (3 df), day of week, and pollutant. 

aIncludes six counties with species data, 2000–2003. bIncludes all nine counties in Table 1, 1999–2003. *p < 0.10; **p < 0.05. 

Figure 1. Excess risk [mean (95% CI)] of mortality per IQR of concentrations. (A) All-cause mortality.

(B) Cardiovascular mortality. 

*p < 0.10. **p < 0.05.

A

B

3.00

2.00

1.00

0

–1.00

–2.00

Species and lag day

Percent excess risk per IQR

PM2.5c

0 1 2 3

EC

0 1 2 3

OC

0 1 2 3

SO4

0 1 2 3

K

0 1 2 3

Zn

0 1 2 3

NO3

0 1 2 3

*

Cu

0 1 2 3

**

PM2.5ext

0 1 2 3

**

*

5.00

4.00

3.00

2.00

1.00

0

–1.00

–2.00

–3.00

Species and lag day

Percent excess risk per IQR

SO4

0 1 2 3

NO3

0 1 2 3

*

Cu

0 1 2 3

PM2.5ext

0 1 2 3

** **

PM2.5c

0 1 2 3

* *

EC

0 1 2 3

**

OC

0 1 2 3

*

K

0 1 2 3

**

Zn

0 1 2 3

**
Components of fine particles and mortality in California

Environmental Health Perspectives • VOLUME 115 | NUMBER 1 | January 2007 17

data (HEI 2003). Increasing the sample size

increased the strength of the PM2.5 associations with mortality. With few exceptions,

these results were relatively insensitive to alternative treatment of missing values, different

smoothers of time, and different lag specifications for meteorologic covariates. Results were

somewhat sensitive, however, to the lag day

examined. More of the associations were with a

1-day lag, which is fairly consistent with many

previous time-series studies of PM < 10 μm in

aerodynamic diameter (PM10) and PM2.5

(HEI 2003). Although there is increasing evidence linking PM exposures with cardiovascular pathophysiology (Brook et al. 2004), there

is little to justify a priori an appropriate lag

structure for the vast majority of PM2.5 constituents. In this analysis, it is unclear whether

the associations of mortality with different lags

were caused by a) different mechanisms; b) different mortality reference days for lags 1 and 2

versus lags 0 and 3 because the exposure data

were not collected on a daily basis (see

“Methods”); or c) stochastic variability due to

the relatively low number of observations. 

We found stronger and more frequent

associations between mortality and PM2.5

components during the cooler months, when

most (but not all) components have higher

concentrations. For example, the warm and

cool season averages for PM2.5 were 14 and

24.6 μg/m3, respectively. For EC, OC, and

NO3, the cool season averages were roughly

twice those of the warm season. These differences represent seasonal variation in sources

(e.g., residential wood combustion), particle

chemistry and meteorology. For example,

Lipsett et al. (1997) reported that during the

winter in Santa Clara County, residential

wood combustion accounts for as much as

45% of PM10. Moreover, in the winter

months the inversion layers and vertical mixing depths throughout much of the state tend

to be much shallower than in warmer

months. In addition, the generally mild climate in California in the cooler months may

mean that more windows are open, resulting

in greater indoor penetration of outdoor pollutants relative to the summer months when

air conditioner use is more common.

Becker et al. (2005) reported seasonal variation in the toxicity of PM, based on in vitro

analysis of markers of inflammation and

oxidative stress, which they hypothesized

could be explained by temporal differences in

particle composition. In our analysis, evidence

of seasonally different effects for a specific

PM2.5 species suggests that differences in both

composition and exposure patterns may be

important. Our findings differ somewhat with

the time-series mortality analysis of PM10 in

100 U.S. cities by Peng et al. (2005). That

study reported stronger effects during the

summer months, based on observations from

Table 5. Summary of statistically significant associations between mortality and alternative pollutant lags

during October–March, 2000–2003 (numbers in the table indicate whether single lags of 0–3 days were

statistically significant).

All-cause Cardiovascular Respiratory Age > 65 years

PM2.5ca 3** 3** — 3**

EC 3* — — 3*

OC 3* 3* — 3*

NO3 — 3* — 0*

SO4 0**, 3* 0*, 3** — 0**

Al — — — —

Br — — — —

Ca 1**, 2*, 3* — — 3*

Cl 1** 1** — 1**

Cu 1* 1** — —

Fe 2*, 3** 3** — 3**

K 3** — — 3**

Mn 2**, 3* 3** — —

Ni — — — —

Pb 3** — — 3*

S 0*, 3** 3* — 0**,3*

Si 1*, 3** — — 3**

Ti 1**, 2*, 3** 1** — 3**

V 1** 3** — 1**

Zn 3** 3** 1* 3**

PM2.5extb 0**, 1**, 2**, 3** 0**, 1**, 2**, 3** 0**, 1**, 2**, 3** 0**, 1**, 2**, 3**

The regression model includes time (4 df/year), 1-day lags of temperature and humidity (3 df), day of week, and pollutant. 

aIncludes six counties with species data, 2000–2003. bIncludes all nine counties in Table 1, 1999–2003. *p < 0.10; **p < 0.05.

Figure 2. Excess risk [mean (95% CI)] of mortality per IQR of concentrations for the cooler months

(October–March). (A) All-cause mortality. (B) Cardiovascular mortality. 

*p < 0.10; **p < 0.05.

A

B

Species and lag day

Percent excess risk per IQR

NO3

0 1 2 3

Cu

0 1 2 3

*

7.00

6.00

5.00

4.00

3.00

2.00

1.00

0

–1.00

–2.00

–3.00

–4.00

Species and lag day

Percent excess risk per IQR

EC

0 1 2 3

K

0 1 2 3

NO3

0 1 2 3

*

PM2.5c

0 1 2 3

**

OC

0 1 2 3

*

Zn

0 1 2 3

**

5.00

4.00

3.00

2.00

1.00

0

–1.00

–2.00

–3.00

PM2.5c

0 1 2 3

**

EC

0 1 2 3

*

OC

0 1 2 3

*

SO4

0 1 2 3

** *

K

0 1 2 3

**

Zn

0 1 2 3

**

PM2.5ext

0 1 2 3

**

**

****

SO4

0 1 2 3

*

**

Cu

0 1 2 3

**

PM2.5ext

0 1 2 3

** ******
cities primarily in the Northeast. Their base

case region–specific analysis showed a modest

warm season effect for the Northwest (which

included Northern California) but no seasonspecific effect for Southern California, the

only region that did not show a larger effect in

summer in their analysis. However, the latter

results appear to be sensitive to the df in the

smooth of time and the PM10 lag used. For

example, if 3 or 5 df for time smooth or an

unlagged PM10 was specified, the effects were

larger in the nonsummer months, a result consistent with our findings. In comparison, our

results were not affected by use of alternative

df for the smooth of time, but were sensitive

to the specified lag of pollution. 

A few previous studies have examined the

associations between some species of PM and

daily mortality. For example, Fairley (2003)

examined the impacts of NO3, SO4, and coefficient of haze (COH) in Santa Clara County.

The latter is highly correlated with EC, and is

likely to be a good marker of particulate pollution from motor vehicles, especially diesel

exhaust, and from wood smoke. All three

PM2.5 constituents were associated with allcause mortality, whereas NO3 was also associated with cardiovascular mortality. These

findings were consistent with those of Hoek

(2003) in the Netherlands, where associations

with mortality were reported for SO4, NO3,

and black smoke. In a study in Buffalo, New

York, Gwynn et al. (2000) reported associations of COH, SO4, and hydrogen ion (a

measure of aerosol acidity) with total mortality.

Ito (2003) failed to find associations of mortality with SO4 or hydrogen ion in Detroit,

Michigan, although only limited data for these

pollutants were available. In their study of the

eight largest Canadian cities, Burnett et al.

(2000) examined the impact of 47 separate

constituents of PM2.5. Within the fine fraction,

SO4, Zn, Ni, and Fe were all associated with

mortality, as was COH. NO3, EC, and OC

were not measured in the Canadian study. Mar

et al. (2000) reported associations between

mortality in Phoenix, Arizona, and EC, OC,

and K. Finally, in analyses of emergency department visits, Metzger et al. (2004) reported

associations of both EC and OC with visits for

any cardiovascular disease. Several other studies

also examined source-oriented combinations of

pollutants. For example, Laden et al. (2000)

examined PM2.5 data from the Harvard Six

Cities study and categorized the pollutants as

motor vehicle exhaust (using Pb as a marker),

coal combustion (using selenium), or soil and

crustal material (using Si). Generally, both the

motor vehicle and coal factors were associated

with mortality, with the strongest effect from

the former. The crustal material indicator was

not associated with mortality. In our full-year

analysis, we also found no association with Si;

however, associations with mortality were

observed during the cooler months. Factor

analysis of multiple elements conducted by

Mar et al. (2000) in Phoenix suggested associations between cardiovascular mortality and factors relating to three sources: motor vehicle

exhaust and resuspended road dust; vegetative

burning; and regional SO4. 

Our findings are generally consistent with

these previous findings—primary and secondary products of fuel combustion (EC, OC,

NO3, as well as SO4 in the winter) and other

measures of mobile source-related emissions

(Cu, Ti, and Zn) exhibit the strongest and

most consistent associations with mortality.

However, we have not undertaken a formal

source apportionment analysis in this paper.

Although EC and OC are generally considered markers of fossil fuel combustion, residential wood combustion may also make

significant contributions to both of these as

well (Lewis et al. 2003; Maykut et al. 2003). A

variety of vehicular-associated sources, including fuel combustion, may contribute to particulate metal emissions, including Cu, Ti, Zn,

and V (Schauer et al. 2006). For instance,

brake wear and lube oil emissions may represent important sources of fine particulate Cu

and Zn, though the latter may also be associated with tire dust (Lough et al. 2005; Schauer

et al. 2006). Although potassium is a crustal

element, it is generally considered a reasonable

marker for vegetative burning, including residential wood combustion (Maykut et al.

2003; Watson et al. 2001). 

The use of multiple cities increased the statistical power and reduced the likelihood that

these results were due to factors associated with

one geographic location. The association with

mortality of any single substance, however,

may be a result, at least in part, of its own toxicity or of exposures to other substances with

which it is highly correlated. In future work,

we will examine the impact of specific sources

through use of source profiles for the six

California counties based on chemical mass

balance models (Thurston et al. 2005).

It is important to note the limitations of

our data. First, the use of a single location for

monitoring PM2.5 components in several of

the counties is likely to lead to random measurement error and the potential for downwardly biased effect estimates. Second, because

every-day monitoring was not available, we

were unable to estimate the impact of cumulative exposures, which tend to generate larger

effect estimates than that of a single-day lag

(Schwartz 2000). Third, given the numbers of

pollutants and end points examined and the

relatively low number of observations, it is

possible that some of the results may have

occurred by chance. Finally, there may be differential measurement error among the components both with respect to spatial variability

and indoor/outdoor penetration. For example,

Janssen et al. (2005) analyzed the longitudinal

correlation of personal and outdoor concentrations of several PM2.5 species in Helsinki,

Finland, and Amsterdam, the Netherlands.

Correlations were high (0.7–0.9) for EC

(using an absorption coefficient) and Zn;

moderate (0.55–0.7) for PM2.5, Fe, and K;

and low (0.25–0.55) for Ca, Cl, Cu, and Si.

The authors suggest that these differences

could be a result of both ambient spatial variability and the influence of indoor generation

of pollutants. 

Our findings add to the growing body of

evidence linking PM2.5 with mortality and

indicate that excess risks may vary with the

specific PM2.5 constituent. The results also

support the hypothesis that pollution from

motor vehicles and other sources of combustion, including residential wood burning, may

be of particular concern. Finally, the use of

regression coefficients based only on PM2.5

mass may underestimate the effects of some of

its specific components. 

REFERENCES

Becker S, Dailey LA, Soukup JM, Grambow SC, Devlin RB,

Huang Y-CT. 2005. Seasonal variations in air pollution

particle–induced inflammatory mediator release and

oxidative stress. Environ Health Perspect 113:1032–1038.

Blanchard C. 2003. Spatial and temporal characterization of

particulate matter. In: Particulate Matter Science for

Policy Makers: A NARSTO Assessment (McMurry PH,

Shepherd MF, Vickery JS, eds). Cambridge, UK:Cambridge

University Press, 191–231.

Brook RD, Franklin B, Cascio W, Hong Y, Howard G, Lipsett M,

et al. 2004. Air pollution and cardiovascular disease: a

statement for healthcare professionals from the Expert

Panel on Population and Prevention Science of the

American Heart Association. Circulation 109:2655–2671. 

Burnett RT, Brook JR, Dann T, Delocla C, Philips O, Calmak S,

et al. 2000. Association between particulate- and gasphase components of urban air pollution and daily mortality in eight Canadian cities. Inhal Toxicol 12(suppl 4):15–39.

CARB (California Air Resources Board). 2004. Air Quality Data

Section, Planning and Technical Support Division.

Sacramento:California Air Resources Board. 

CDHS (California Department of Health Services). 1999–2003.

Death Statistical Master Files, 1999–2003. Sacramento,

CA:Center for Health Statistics. 

Der Simonian R, Laird N. 1986. Meta-analysis in clinical trials.

Control Clin Trials 7:177–188.

Fairley D. 2003. Mortality and air pollution for Santa Clara

County, California, 1989–1996. In: Revised Analyses of

Time-Series Studies of Air Pollution and Health. Special

Report. Boston:Health Effects Institute, 97–106. Available:

http://www.healtheffects.org/Pubs/TimeSeries.pdf

[accessed 16 February 2006].

Gwynn RC, Burnett RT, Thurston GD. 2000. A time-series analysis of acidic particulate matter and daily mortality and

morbidity in the Buffalo, New York, region. Environ Health

Perspect 108:125–133.

HEI. 2003. Revised analyses of the National Morbidity, Mortality,

and Air Pollution Study (NMMAPS), Part II. In: Revised

Analyses of Time-Series Studies of Air Pollution and Health.

Special Report. Boston:Health Effects Institute, 9–72.

Available: http://www.healtheffects.org/Pubs/TimeSeries.pdf

[accessed 16 February 2006]. 

Hoek G. 2003. Daily mortality and air pollution in the Netherlands.

In: Revised Analyses of Time-Series Studies of Air Pollution

and Health. Special Report. Boston:Health Effects Institute,

133–142. Available: http://www.healtheffects.org/Pubs/

TimeSeries.pdf [accessed 2 February 2006].

Ito K. 2003. Associations of particulate matter components with

daily mortality and morbidity in Detroit, Michigan. In:

Revised Analyses of Time-Series Studies of Air Pollution

Ostro et al.

18 VOLUME 115 | NUMBER 1 | January 2007 • Environmental Health Perspectives
Components of fine particles and mortality in California

Environmental Health Perspectives • VOLUME 115 | NUMBER 1 | January 2007 19

and Health. Special Report. Boston:Health Effects

Institute, 143–156. Available: http://www.healtheffects.org/

Pubs/TimeSeries.pdf [accessed 16 February 2006].

Janssen NAH, Lanki T, Hoek G, Vallius M, de Hartog JJ,

Van Grieken R, et al. 2005. Associations between ambient,

personal, and indoor exposure to fine particulate matter

constituents in Dutch and Finnish panels of cardiovascular

patients. Occup Environ Med 62:868–877.

Janssen NAH, Schwartz J, Zanobetti A, Suh H. 2002. Air conditioning and source-specific particles as modifiers of the

effect of PM10 on hospital admissions for heart and lung

disease. Environ Health Perspect 101:43–49.

Laden F, Neas LM, Dockery DW, Schwartz J. 2000. Association of

fine particulate matter from different sources with daily mortality in six U.S. cities. Environ Health Perspect 108:941–947.

Lewis CW, Norris GA, Conner TL, Henry RC. 2003. Source

apportionment of Phoenix PM2.5 aerosol with the Unmix

receptor model. J Air Waste Manag Assoc 53:325–338.

Lipsett M, Hurley S, Ostro B. 1997. Air pollution and emergency

room visits for asthma in Santa Clara County, California.

Environ Health Perspect 105:216–222.

Lough GC, Schauer JJ, Park JS, Shafer MM, Deminter JT,

Weinstein JP. 2005. Emissions of metals associated with

motor vehicle roadways. Environ Sci Technol 39:826–836. 

Mar TF, Norris GA, Koenig JQ, Larson TV. 2000. Associations

between air pollution and mortality in Phoenix, 1995–1997.

Environ Health Perspect 108:347–353.

Maykut NN, Lewtas J, Kim E, Larson TV. 2003. Source apportionment of PM2.5 at an urban IMPROVE site in Seattle,

Washington. Environ Sci Technol 15:37:5135–5142.

Metzger KB, Tolbert PE, Klein M, Peel JL, Franders WD,

Mulholland JA, et al. 2004. Ambient air pollution and cardiovascular emergency department visits. Epidemiol 15:46–56.

NCDC (National Climatic Data Center). 2004. Climate

Resources. Available: http://www.ncdc.noaa.gov/oa/

climate/climateresources.html [accessed 4 April 2005]. 

NRC (National Research Council). 2004. Research Priorities for

Airborne Particulate Matter. IV. Continuing Research

Progress. Washington, DC:National Academies Press. 

Ostro B, Broadwin R, Green S, Feng W-Y, Lipsett M. 2006. Fine

particulate air pollution and mortality in nine California

counties: results from CALFINE. Environ Health Perspect

114:29–33. 

Peng RD, Dominici R, Pastor-Barriuso R, Zeger SL, Samet JM .

2005. Seasonal analysis of air pollution and mortality in 100

U.S. cities. Am J Epidemiol 161:585–594.

R Development Core Team. 2004. R: A Language and Environment

for Statistical Computing. Vienna, Austria:R Foundation for

Statistical Computing. Available: http:/www.R-project.org

[accessed 6 September 2005].

Schauer JJ, Lough GC, Shafer MM, Christensen W, Arndt MF,

DeMinter JT, Park J-S. 2006. Characterization of Metals

Emitted from Motor Vehicles. Report 133. Boston:Health

Effects Institute. Available: http://healtheffects.org/

Pubs/Schauer.pdf [accessed 19 March 2006].

Schwartz J. 2000. The distributed lag between air pollution and

daily deaths. Epidemiology 11:320–326.

StataCorp. 2003. Stata version 8. College Station, TX:StataCorp.

Thurston GD, Ito K, Mar T, Christensen WF, Eatough DJ, Henry

RC, et al. 2005. Workgroup report: workshop on source

apportionment of particulate matter health effects—intercomparison of results and implications. Environ Health

Perspect 113:1768–1774.

Watson JG, Chow JC, Houck JE. 2001. PM2.5 chemical source

profiles for vehicle exhaust, vegetative burning, geological

material, and coal burning in Northwestern Colorado during 1995. Chemosphere 43:1141–1151.

World Health Organization. 1993. International Classification of

Diseases, 10th Revision. Geneva:World Health Organization.

Wong CM, Ma S, Hedley AJ, Lam TH. 2001. Effect of air pollution

on daily mortality in Hong Kong. Environ Health Perspect

109:335–340.