Publications

Goldberg, DL; Gupta, P; Wang, K; Jena, C; Zhang, Y; Lu, ZF; Streets, DG (2019). Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States. ATMOSPHERIC ENVIRONMENT, 199, 443-452.

Abstract
To link short-term exposures of air pollutants to health outcomes, scientists must use high temporal and spatial resolution estimates of PM2.5 concentrations. In this work, we develop a daily PM2.5 product at 1 x 1 km(2) spatial resolution across the eastern United States (east of 90 degrees W) with the aid of 1 x 1 km(2) MAIAC aerosol optical depth (AOD) data, 36 x 36 km(2) WRF-Chem output, 1 x 1 km(2) land-use type from the National Land Cover Database, and 0.125 degrees x 0.125 degrees ERA-Interim reanalysis meteorology. A gap-filling technique is applied to MAIAC AOD data to construct robust daily estimates of AOD when the satellite data are missing (e.g., areas obstructed by clouds or snow cover). The input data are incorporated into a multiple-linear regression model trained to surface observations of PM2.5 from the EPA Air Quality System (AQS) monitoring network. The model generates a high-fidelity estimate (r(2) = 0.75 using a 10-fold random cross-validation) of daily PM2.5 throughout the eastern United States. Of the inputs to the statistical model, WRF-Chem output (r(2) = 0.66) is the most important contributor to the skill of the model. MAIAC AOD is also a strong contributor (r(2) = 0.52). Daily PM2.5 output from our statistical model can be easily integrated into county-level epidemiological studies. The novelty of this project is that we are able to simulate PM2.5 in a computationally efficient manner that is constrained to ground monitors, satellite data, and chemical transport model output at high spatial resolution (1 x 1 km(2)) without sacrificing the temporal resolution (daily) or spatial coverage ( > 2,000,000 km(2)).

DOI:
10.1016/j.atmosenv.2018.11.049

ISSN:
1352-2310