| Limitations of Remotely Sensed Aerosol as a Spatial Proxy
for Fine Particulate Matter Christopher J. Paciorek1 and Yang Liu2 1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA; 2Department of Environmental and Occupational Health, Emory University Rollins School of Public Health, Atlanta, Georgia, USA Abstract
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. Key words: aerosol optical depth, air pollution, geographic information system, predictive modeling, remote sensing, satellite, spatial smoothing, spatiotemporal modeling. Environ Health Perspect 117:904909 (2009) . doi:10.1289/ehp.0800360 available via http://dx.doi.org/ [Online 21 February 2009]
Address correspondence to C. Paciorek, Department of Biostatistics, Harvard School of Public Health, 655 Huntington Ave., Boston, MA 02115 USA. Telephone: (617) 432-4912. Fax: (617) 432-5619. E-mail: paciorek@alumni.cmu.edu Supplemental Material is available online at http://www.ehponline.org/members/2009/0800360/suppl.pdf We thank S. Kondragunta and the National Oceanic and Atmospheric Administration (NOAA) for access to the Geostationary Operational Environmental Satellite aerosol/smoke product aerosol optical depth retrievals, and S. Melly, L. Ryan, C. Stanier, H. Suh, and J. Yanosky. North American Regional Reanalysis data were provided by the NOAA/Office of Oceanic and Atmospheric Research/Earth System Research Laboratory Physical Science Division, Boulder, Colorado (http://www.cdc.noaa.gov) . Research described in this study was conducted under contract to the Health Effects Institute (HEI) , an organization jointly funded by the U.S. Environmental Protection Agency (EPA) (Assistance Award No. R-82811201) and certain motor vehicle and engine manufacturers. The contents of this article do not necessarily reflect the views of HEI or its sponsors, nor do they necessarily reflect the views and policies of the U.S. EPA or motor vehicle and engine manufacturers. The authors declare they have no competing financial interests. Received 3 November 2008 ; accepted 20 February 2009.
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