It is generally thought that fine particles (those with an aerodynamic diameter of ≤ 2.5 μm; PM_2.5) 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 (PM_2.5) and mortality is relatively sparse, because of two main issues: The U.S. Environmental Protection Agency (EPA) began PM_2.5 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 PM_2.5 monitoring data from the Harvard Six City Study, Schwartz et al. (1996) reported a strong association between 2-day average PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 and daily mortality for the years 1999–2002 in nine California counties. They also found significant associations with PM_2.5.

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 PM_2.5 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 PM_2.5 and PM coarse on the increased risk of death for all causes, all cardiovascular disease (CVD), myocardial infarction (MI), stroke, and respiratory mortality.

The cities with higher levels of PM_2.5 were in California, with maximum concentrations of PM_2.5 > 100 μg/m³, whereas the cities with the lower maximum levels were in Oklahoma. The median PM_2.5 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 PM_2.5 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 PM_2.5 concentrations. High levels of PM_2.5 (red) are in California and in the industrial Midwest.

Table 1 shows the percent increase in mortality for a 10-μg/m³ increase in PM_2.5 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 PM_2.5, 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 PM_2.5. 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 PM_2.5.

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 PM_2.5, 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 city-specific 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 PM_2.5 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 PM_2.5 and PM coarse by region. The number of cities varied in each region and by pollutant. For PM_2.5 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 PM_2.5 and PM coarse. The effects of PM_2.5 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 PM_2.5); 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.

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 PM_2.5: Mild temperatures, which were associated with greater indoor penetration, were associated with higher PM_2.5 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 PM_2.5, 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 PM_2.5 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 PM_2.5 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 PM_2.5. 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 stroke-related mortality with a 10-μg/m³ increase in previous day PM_2.5. 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 PM_2.5 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 mortality. Again, these are generally comparable figures. In contrast, Dominici et al. (2007) examined the years 1999–2000 and reported a PM_2.5 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 PM_2.5 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 PM_2.5 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).