Development and evaluation of alternative approaches for exposure assessment of multiple air pollutants in Atlanta, Georgia

作者：Dionisio Kathie L^*; Isakov Vlad; Baxter Lisa K; Sarnat Jeremy A; Sarnat Stefanie Ebelt; Burke Janet; Rosenbaum Arlene; Graham Stephen E; Cook Rich; Mulholland James; Oezkaynak Halul

来源：Journal of Exposure Science and Environmental Epidemiology, 2013, 23(6): 581-592.

DOI：10.1038/jes.2013.59

• spatiotemporal
• multipollutant
• exposure models
• air quality models
• exposure assessment

Measurements from central site (CS) monitors are often used as estimates of exposure in air pollution epidemiological studies. As these measurements are typically limited in their spatiotemporal resolution, true exposure variability within a population is often obscured, leading to potential measurement errors. To fully examine this limitation, we developed a set of alternative daily exposure metrics for each of the 169 ZIP codes in the Atlanta, GA, metropolitan area, from 1999 to 2002, for PM2.5 and its components (elemental carbon (EC), SO4), O-3, carbon monoxide (CO), and nitrogen oxides (NOx). Metrics were applied in a study investigating the respiratory health effects of these pollutants. The metrics included: (i) CS measurements (one CS per pollutant); (ii) air quality model results for regional background pollution; (iii) local-scale AERMOD air quality model results; (iv) hybrid air quality model estimates (a combination of (ii) and (iii)); and (iv) population exposure model predictions (SHEDS and APEX). Differences in estimated spatial and temporal variability were compared by exposure metric and pollutant. Comparisons showed that: (i) both hybrid and exposure model estimates exhibited high spatial variability for traffic-related pollutants (CO, NOx, and EC), but little spatial variability among ZIP code centroids for regional pollutants (PM2.5, SO4, and O-3); (ii) for all pollutants except NOx, temporal variability was consistent across metrics; (iii) daily hybrid-to-exposure model correlations were strong (r>0.82) for all pollutants, suggesting that when temporal variability of pollutant concentrations is of main interest in an epidemiological application, the use of estimates from either model may yield similar results; (iv) exposure models incorporating infiltration parameters, time-location-activity budgets, and other exposure factors affect the magnitude and spatiotemporal distribution of exposure, especially for local pollutants. The results of this analysis can inform the development of more appropriate exposure metrics for future epidemiological studies of the short-term effects of particulate and gaseous ambient pollutant exposure in a community.

• 出版日期2013-12