摘要：Background Recent research highlights the promise of remotely sensed aerosol optical depth (AOD) as a proxy for ground-level particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5). Particular interest lies in estimating spatial heterogeneity using AOD, with important application to estimating pollution exposure for public health purposes. Given the correlations reported between AOD and PM2.5, it is tempting to interpret the spatial patterns in AOD as reflecting patterns in PM2.5. Objectives We evaluated the degree to which AOD can help predict long-term average PM2.5 concentrations for use in chronic health studies. Methods We calculated correlations of AOD and PM2.5 at various temporal aggregations in the eastern United States in 2004 and used statistical models to assess the relationship between AOD and PM2.5 and the potential for improving predictions of PM2.5 in a subregion, the mid-Atlantic. Results We found only limited spatial associations of AOD from three satellite retrievals with daily and yearly PM2.5. The statistical modeling shows that monthly average AOD poorly reflects spatial patterns in PM2.5 because of systematic, spatially correlated discrepancies between AOD and PM2.5. Furthermore, when we included AOD as a predictor of monthly PM2.5 in a statistical prediction model, AOD provided little additional information in a model that already accounts for land use, emission sources, meteorology, and regional variability. Conclusions These results suggest caution in using spatial variation in currently available AOD to stand in for spatial variation in ground-level PM2.5 in epidemiologic analyses and indicate that when PM2.5 monitoring is available, careful statistical modeling outperforms the use of AOD.
关键词：aerosol optical depth; air pollution; geographic information system; predictive modeling; remote sensing; satellite; spatial smoothing; spatiotemporal modeling
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