Faculty Bibliography

Although the association between exposure to ambient fine particulate matter with aerodynamic diameter < 2.5 microm (PM2.5) and human mortality is well established, the most responsible particle types/sources are not yet certain. In May 2003, the U.S. Environmental Protection Agency's Particulate Matter Centers Program sponsored the Workshop on the Source Apportionment of PM Health Effects. The goal was to evaluate the consistency of the various source apportionment methods in assessing source contributions to daily PM2.5 mass-mortality associations. Seven research institutions, using varying methods, participated in the estimation of source apportionments of PM2.5 mass samples collected in Washington, DC, and Phoenix, Arizona, USA. Apportionments were evaluated for their respective associations with mortality using Poisson regressions, allowing a comparative assessment of the extent to which variations in the apportionments contributed to variability in the source-specific mortality results. The various research groups generally identified the same major source types, each with similar elemental makeups. Intergroup correlation analyses indicated that soil-, sulfate-, residual oil-, and salt-associated mass were most unambiguously identified by various methods, whereas vegetative burning and traffic were less consistent. Aggregate source-specific mortality relative risk (RR) estimate confidence intervals overlapped each other, but the sulfate-related PM2.5 component was most consistently significant across analyses in these cities. Analyses indicated that source types were a significant predictor of RR, whereas apportionment group differences were not. Variations in the source apportionments added only some 15% to the mortality regression uncertainties. These results provide supportive evidence that existing PM2.5 source apportionment methods can be used to derive reliable insights into the source components that contribute to PM2.5 health effects

BACKGROUND: The assessment of air pollution exposure using only community average concentrations may lead to measurement error that lowers estimates of the health burden attributable to poor air quality. To test this hypothesis, we modeled the association between air pollution and mortality using small-area exposure measures in Los Angeles, California. METHODS: Data on 22,905 subjects were extracted from the American Cancer Society cohort for the period 1982-2000 (5,856 deaths). Pollution exposures were interpolated from 23 fine particle (PM2.5) and 42 ozone (O3) fixed-site monitors. Proximity to expressways was tested as a measure of traffic pollution. We assessed associations in standard and spatial multilevel Cox regression models. RESULTS: After controlling for 44 individual covariates, all-cause mortality had a relative risk (RR) of 1.17 (95% confidence interval=1.05-1.30) for an increase of 10 mug/m PM2.5 and a RR of 1.11 (0.99-1.25) with maximal control for both individual and contextual confounders. The RRs for mortality resulting from ischemic heart disease and lung cancer deaths were elevated, in the range of 1.24-1.6, depending on the model used. These PM results were robust to adjustments for O3 and expressway exposure. CONCLUSION: Our results suggest the chronic health effects associated with within-city gradients in exposure to PM2.5 may be even larger than previously reported across metropolitan areas. We observed effects nearly 3 times greater than in models relying on comparisons between communities. We also found specificity in cause of death, with PM2.5 associated more strongly with ischemic heart disease than with cardiopulmonary or all-cause mortality.

This article provides an overview of previous analysis and reanalysis of the American Cancer Society (ACS) cohort, along with an indication of current ongoing analyses of the cohort with additional follow-up information through to 2000. Results of the first analysis conducted by Pope et al. (1995) showed that higher average sulfate levels were associated with increased mortality, particularly from cardiopulmonary disease. A reanalysis of the ACS cohort, undertaken by Krewski et al. (2000), found the original risk estimates for fine-particle and sulfate air pollution to be highly robust against alternative statistical techniques and spatial modeling approaches. A detailed investigation of covariate effects found a significant modifying effect of education with risk of mortality associated with fine particles declining with increasing educational attainment. Pope et al. (2002) subsequently reported results of a subsequent study using an additional 10 yr of follow-up of the ACS cohort. This updated analysis included gaseous copollutant and new fine-particle measurements, more comprehensive information on occupational exposures, dietary variables, and the most recent developments in statistical modeling integrating random effects and nonparametric spatial smoothing into the Cox proportional hazards model. Robust associations between ambient fine particulate air pollution and elevated risks of cardiopulmonary and lung cancer mortality were clearly evident, providing the strongest evidence to date that long-term exposure to fine particles is an important health risk. Current ongoing analysis using the extended follow-up information will explore the role of ecologic, economic, and, demographic covariates in the particulate air pollution and mortality association. This analysis will also provide insight into the role of spatial autocorrelation at multiple geographic scales, and whether critical instances in time of exposure to fine particles influence the risk of mortality from cardiopulmonary and lung cancer. Information on the influence of covariates at multiple scales and of critical exposure time windows can assist policymakers in establishing timelines for regulatory interventions that maximize population health benefits.

Numerous studies have reported short-term associations between ambient air pollution concentrations and mortality and morbidity. Particulate matter (PM) was often implicated as the most significant predictor of the health outcomes among the various air pollutants. However, a question remains as to the potential role played by the relative error of exposure estimation associated with each pollutant in defining their relative strengths of association. While most of the recent studies on PM exposure measurements have focused on the temporal correlation between personal exposures and the concentrations observed at ambient air quality monitors (within a few miles from the subjects), there have been few studies that systematically evaluated spatial uniformity of temporal correlation of air pollution within the scale of a city (several tens of miles) for which mortality or morbidity outcomes are aggregated in time-series studies. In this study, spatial uniformity of temporal correlation was examined by computing monitor-to-monitor correlation using available multiple monitors for PM(10) and gaseous criteria pollutants (NO(2), SO(2), CO, and O(3)) in the nationwide data between 1988 and 1997. For each monitor, the median of temporal correlation with other monitors within the Air Quality Control Region (AQCR) was computed. The resulting median monitor-to-monitor correlation was modeled as a function of qualitative site characteristics (i.e., land-use, location-setting, and monitoring-objective) and quantitative information (median separation distance, longitude/latitude or regional indicators) for each pollutant. Generalized additive models (GAM) were used to fit the smooth function of the separation distance and regional variation. The intercepts of the models across pollutants showed the overall rankings in monitor-to-monitor correlation on the average to be: O(3), NO(2), and PM(10), (r approximately 0.6 to 0.8)>CO (r<0.6)>SO(2) (r<0.5). Both the separation distance and regional variation were important predictors of the correlation. For PM(10), for example, the correlation for the monitors along the East Coast was higher by approximately 0.2 than western regions. The qualitative monitor characteristics were often significant predictors of the variation in correlation, but their impacts were not substantial in magnitude for most categories. These results suggest that the apparent regional heterogeneity in PM effect estimates, as well as the differences in the significance of health outcome associations across pollutants, may in part be contributed to by the differences in monitor-to-monitor correlations by region and across pollutants.Journal of Exposure Analysis and Environmental Epidemiology advance online publication, 16 June 2004; doi:10.1038/sj.jea.7500386