Research Limitations of Remotely Sensed Aerosol as a Spatial Proxy for Fine Particulate Matter Christopher J. Paciorek1 and Yang Liu 2 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 centrations for use in chronic health studies. Bdnuorgkca : 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. o Bjectivs : We evaluated the degree to which AOD can help predict long-term average PM2.5 con- 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. r esult : We found only limited spatial associations of AOD from three satellite retrievals with daily and yearly PM2.5. hTe 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. c onclusi : hTese 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. words k ey : aerosol optical depth, air pollution, geographic information system, predictive mod- eling, remote sensing, satellite, spatial smoothing, spatiotemporal modeling. Environ Health Perspect 117:904–909 (2009). doi:10.1289/ehp.0800360 available via http://dx.doi.org/ [Online 21 February 2009] Epidemiologic studies provide evidence that chronic exposure to particulate matter (PM) is related to increased mortality and morbid- ity (Dockery et al. 1993; Miller et al. 2007; Pope et al. 2002). Studies of the chronic health effects of PM rely on spatial hetero- geneity in PM concentrations to identify the effects. Spatial statistical modeling combined with land use regression can improve esti- mation of concentrations at fine scales by using land use and meteorologic information (Paciorek et al. 2009; Yanosky et al. 2008), but efforts still suffer from the spatial sparsity of the monitoring network. Remote sensing holds promise for add- ing spatial information for exposure estima- tion, particularly in suburban and rural areas far from monitors (e.g., Figure 1). Satellite- derived aerosol optical depth (AOD) is corre- lated with ground-level PM with aerodynamic diameter ≤ 2.5 µm (PM2.5) (Engel-Cox et al. 2004; Koelemeijer et al. 2006; Liu et al. 2005, 2007; Paciorek et al. 2008; Pelletier et al. 2007; Wang and Christopher 2003). These correlations occur despite the vertical mismatch between total column aerosol, as measured by AOD, and ground-level PM2.5 the level of interest for health studies, and the temporal mismatch between 24-hr average PM2.5 and daytime (often single snapshot) AOD. These results and success in using AOD 904 to document pollution episodes (Al-Saadi et al. 2005; Wang and Christopher 2003) have led to excitement about using AOD as a proxy, standing in for PM2.5, or in combination with ground measurements to better predict PM2.5. Our attention focuses on improving empirical prediction, rather than physical explanation, of the spatial patterns of PM2.5. Most studies of the AOD–PM2.5 asso- ciation focus on temporal (longitudinal) correlations or do not distinguish spatial (cross-sectional) from temporal correlations, but for chronic exposure, estimating spatial heterogeneity is critical. Correlations of long- term averages using matched daily (or hourly) values (e.g., van Donkelaar et al. 2006) do not take into account the large number of missing retrievals, because of orbit patterns, cloud cover, and surface reflectivity, that may seriously compromise the association between available AOD and long-term average PM2.5 concentrations. Finally, but critically, sim- ple correlations do not tell us whether AOD improves predictions within a statistical model that already uses information on mete- orology, land use, and regional variation, and we are not aware of any such analysis of the use of AOD for exposure estimation. Here we report both raw empirical results and statistical modeling of the rela- tionship between AOD and PM2.5 and the ability of AOD retrievals to improve predic- tions of ground-level PM2.5 in the eastern United States, focusing on the mid-Atlantic region. We take a public health perspective, in which good estimates of PM2.5 concen- trations are needed over an entire specified spatial region and time period as an input for epidemiologic analysis. We first show posi- tive, but moderate and variable, correlations at various temporal scales. Correlations do not improve when looking at longer-term averages over all the days in a period of time. We introduce a statistical model that treats AOD as proxy data for PM2.5, estimating a PM2.5 prediction surface that reflects both the PM2.5 and AOD data. This model shows high sensitivity to assumptions about the structure of the discrepancy between AOD and PM2.5. The results suggest there are systematic, spa- tially correlated differences between AOD and PM2.5 and that AOD should be disre- garded in predicting PM2.5. We confirm this using a simpler model with PM2.5 data as the gold standard, regressing PM2.5 on AOD and numerous other predictors, showing no gain in predictive power from the use of AOD in an already successful prediction model. 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 Received 3 November 2008; accepted 20 February financial interests. 2009. ovlume 117 | number 6 | June 2009 • Environmental Health Perspectives