Part 1 of 2: 2010 March 22: CA: The Effects of Components of Fine Particulate Air Pollution on Mortality in California: Results from CALFINE

2010 March 22: CA: The Effects of Components of Fine Particulate Air Pollution on Mortality in California: Results from CALFINE

Bart Ostro1, Wen-Ying Feng2, Rachel Broadwin1, Shelley Green1, Michael Lipsett3

1 California Office of Environmental Health Hazard Assessment, Oakland, California, USA, 2 Graduate Group in Biostatistics, University of California, Davis, California, USA, 3 Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA

Abstract Top

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.

Keywords: EC, fine particles, mortality, nitrates, OC, particulate matter, PM2.5, species.

Citation: Ostro B, Feng W-Y, Broadwin R, Green S, Lipsett M 2007. The Effects of Components of Fine Particulate Air Pollution on Mortality in California: Results from CALFINE. Environ Health Perspect 115:13-19. doi:10.1289/ehp.9281

Received: 21 April 2006; Accepted: 29 August 2006; Online: 29 August 2006

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.

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 all-cause 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 Top

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 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 Top

Table 1 provides descriptive statistics for mortality categories, air quality, and meteorologic 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 meta-analytic 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 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 Top

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 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.

 

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