Estimating long-term PM10-2.5 concentrations in six US cities using satellite-based aerosol optical depth data

Abstract

A major challenge in assessing the health risks of PM10-2.5 is the limited ground-level measurement data from which to estimate exposure. This is especially problematic for studying long-term PM10-2.5 health effects since PM10-2.5 is more spatially variable than PM2.5 or PM10, particularly in urban areas. Fortunately, Aerosol Optical Depth (AOD) data from satellites offer opportunities to assess PM10-2.5 more broadly. Our project leverages measurements from NASA's Terra satellite to estimate long-term PM10-2.5 concentrations in six US urban areas (Los Angeles, CA; Chicago, IL; St. Paul, MN; Baltimore, MD; New York, NY; Winston-Salem, NC) for 2000–2012. We calibrated AOD (1 km2 resolution) with EPA monitored PM10 and PM2.5 levels daily using an area-specific mixed-modeling approach with land-use regression. We then used spatial smoothing in generalized additive mixed-models to predict daily PM10 and PM2.5 when AOD was missing. PM10-2.5 was estimated after taking the difference of spatially matched PM10 and PM2.5 daily predictions. Model performance for our long-term average predictions was evaluated using leave-one-station-out cross-validation and compared to alternative, nearest-monitor and inverse distance weighting (IDW) approaches. Final long-term PM10-2.5 predictions were well correlated with measured levels estimated from collocated PM2.5 and PM10 sites in five of the six areas, with spatial CV R2 ranging from 0.50 to 0.97. Only in Winston-Salem did the model have very little predictive ability (R2: 0.34). All spatial predictions performed better than the nearest-monitor and IDW alternatives. In contrast, our final PM10-2.5 predictions had poor temporal performance, with mean monitor-level CV R2 ranging from 0.15 to 0.42. Given the superior performance of our spatial predictions compared to nearest-monitor and IDW alternatives and the high costs of field sampling, our results show the potential for combining AOD data with land-use regression to estimate long-term PM10-2.5 concentrations in localized areas.