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 PM 1 0 . 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 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 season-specific 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 all-cause 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 down-wardly 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.
Figures and Tables Top
Excess risk [mean (95% CI)] of mortality per IQR of concentrations. (A) All-cause mortality. (B) Cardiovascular mortality.
*p < 0.10. **p < 0.05.
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.
Mean daily deaths by mortality category and air quality and meteorologic data, by county, 2000–2003.
Descriptive statistics for PM2.5 and species in California counties, 2000–2003.
Longitudinal correlations of PM2.5 and its components.
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).
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).
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