Estimating long-term PM10-2.5 concentrations in six US cities using satellite-based aerosol optical depth data. (1st March 2022)

• Record Type: Journal Article
• Authors: Pedde, Meredith; Kloog, Itai; Szpiro, Adam; Dorman, Michael; Larson, Timothy V.; Adar, Sara D.
• Is Part Of: Atmospheric environment. Volume 272(2022)
• Publication Date: 2022-03-01
• Subjects: PM10-2.5 -- Coarse PM -- Aerosol optical depth (AOD) -- Air pollution -- Spatial prediction model -- Satellite; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2022.118945
• Languages: English
• ISSNs: 1352-2310

Abstract

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 km 2 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 R 2 ranging from 0.50 to 0.97. Only in Winston-Salem did the model have very little predictive ability (R 2 : 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 R 2 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. Highlights: We predicted PM10-2.5 using satellite-based daily AOD data. Long-term PM10-2.5 predictions correlated well with measurements in 5 study areas. Predictions performed better spatially than nearest-monitor and IDW alternatives.