Remote sensing is of particular interest in developing countries with little monitoring (e.g., Kumar et al. 2008), but our results sug- gest that spatial patterns seen in AOD may poorly reflect spatial patterns in ground-level PM2.5. Without evidence of strong correlations over space, as opposed to purely temporal cor- relations, use of AOD to determine spatial het- erogeneity in PM2.5 may be misleading. Given our focus on a region of moderate size, it is possible AOD would be more helpful for larger regions, although daily spatial correlations over the eastern United States are relatively weak (Table 1), and previous work shows at best moderate long-term correlations over the United States (Rush et al. 2004). AOD might be helpful for estimating temporal heterogene- ity, but missing AOD is a major problem. One might ask whether AOD is useful under specific conditions or in specific loca- tions, such as for pollution episodes (e.g., Wu et al. 2006). It is not clear how important such episodes are for long-term average PM2.5 prediction or how to include such informa- tion only under the circumstances in which it is predictive of PM2.5. To the extent to which AOD is useful in some but not all circum- stances, the practical challenge is the need of epidemiologists for exposure estimates with- out gaps in space or time, often over large domains and long periods of time. Systematic discrepancies such as those in the satellite AOD proxy for PM2.5 can easily be misleading because the spatial structure seen in the proxy leads one to think that the patterns reflect real patterns in the process of interest. In this setting, the evidence suggests that much of this structure does not represent true structure in PM2.5. Such systematic dis- crepancies arise in other contexts (Campbell 1996, p. 378; Robinson 2004, pp. 91–92). It seems likely that deterministic model out- put used to estimate atmospheric processes, including pollution, such as the widely used Community Multiscale Air Quality model, contain systematic errors that induce corre- lated errors in model output, because of either errors in inputs or aspects of the system under study that are not captured by the model. Some avenues for potential improve- ment in using remote-sensing information to predict PM2.5 hold promise. First, Liu et al. (2005) and van Donkelaar et al. (2006) report improvements in relationships of AOD and PM2.5 when adjusting for the vertical mismatch based on vertical profile informa- tion from an atmospheric chemistry model. However, even after such adjustment, AOD missingness continues to be a problem, and this strategy requires expensive and time-con- suming long-term model runs. MISR retrieval information, because it provides reflectivity at multiple angles, and ground light detec- tion and ranging (LIDAR) (Engel-Cox et al. 2006) might also prove helpful in distinguish- ing ground-level aerosol from aerosol aloft, but spatial coverage is limited. Second, AOD retrieval algorithms aim to accurately estimate AOD, with comparisons with ground-based observations of AOD from the aerosol robotic network (AERONET). Instead, a tailored approach that modifies AOD retrieval algo- rithms to directly derive a proxy for ground- level PM2.5 may improve upon the current algorithms. Improved characterization of spa- tial patterns in surface reflectivity and par- ticle composition may be a critical avenue for retrieval algorithm improvement, potentially via statistical approaches that are informed by ground-level PM2.5 data. 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