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Satellite-based PM concentrations and their application to COPD in Cleveland, OH.

Kumar N, Liang D, Comellas A, Chu AD, Abrams T. Satellite-based PM concentrations and their application to COPD in Cleveland, OH. Journal of exposure science & environmental epidemiology. 2013 Nov 1; 23(6):637-46.

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A hybrid approach is proposed to estimate exposure to fine particulate matter (PM(2.5)) at a given location and time. This approach builds on satellite-based aerosol optical depth (AOD), air pollution data from sparsely distributed Environmental Protection Agency (EPA) sites and local time-space Kriging, an optimal interpolation technique. Given the daily global coverage of AOD data, we can develop daily estimate of air quality at any given location and time. This can assure unprecedented spatial coverage, needed for air quality surveillance and management and epidemiological studies. In this paper, we developed an empirical relationship between the 2 km AOD and PM(2.5) data from EPA sites. Extrapolating this relationship to the study domain resulted in 2.3 million predictions of PM(2.5) between 2000 and 2009 in Cleveland Metropolitan Statistical Area (MSA). We have developed local time-space Kriging to compute exposure at a given location and time using the predicted PM(2.5). Daily estimates of PM(2.5) were developed for Cleveland MSA between 2000 and 2009 at 2.5 km spatial resolution; 1.7 million ( 79.8%) of 2.13 million predictions required for multiyear and geographic domain were robust. In the epidemiological application of the hybrid approach, admissions for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) was examined with respect to time-space lagged PM(2.5) exposure. Our analysis suggests that the risk of AECOPD increases 2.3% with a unit increase in PM(2.5) exposure within 9 days and 0.05 ( 5 km) distance lags. In the aggregated analysis, the exposed groups (who experienced exposure to PM(2.5) > 15.4 g/m(3)) were 54% more likely to be admitted for AECOPD than the reference group. The hybrid approach offers greater spatiotemporal coverage and reliable characterization of ambient concentration than conventional in situ monitoring-based approaches. Thus, this approach can potentially reduce exposure misclassification errors in the conventional air pollution epidemiology studies.

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