Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States

Goldberg, Daniel L.; Gupta, Pawan; Wang, Kai; Jena, Chinmay; Zhang, Yang; Lu, Zifeng; Streets, David G.

doi:10.1016/j.atmosenv.2018.11.049

Title: Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM_2.5 concentrations at 1 km resolution in the Eastern United States

Journal Article · Tue Nov 20 00:00:00 EST 2018 · Atmospheric Environment (1994)

DOI:https://doi.org/10.1016/j.atmosenv.2018.11.049· OSTI ID:1490424

Goldberg, Daniel L. ^[1]; Gupta, Pawan ^[2]; Wang, Kai ^[3]; Jena, Chinmay ^[3]; Zhang, Yang ^[3]; Lu, Zifeng ^[1]; Streets, David G. ^[1]

Argonne National Lab. (ANL), Argonne, IL (United States). Energy Systems Division; Univ. of Chicago, IL (United States). Consortium for Advanced Science and Engineering
University Space Research Association, Greenbelt, MD (United States); NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
North Carolina State Univ., Raleigh, NC (United States). Dept. of Marine, Earth, and Atmospheric Sciences

To link short-term exposures of air pollutants to health outcomes, scientists must use high temporal and spatial resolution estimates of PM_2.5 concentrations. We develop a daily PM_2.5 product at 1 × 1 km² spatial resolution across the eastern United States (east of 90° W) with the aid of 1 × 1 km² MAIAC aerosol optical depth (AOD) data, 36 × 36 km² WRF-Chem output, 1 × 1 km² land-use type from the National Land Cover Database, and 0.125° × 0.125° ERA-Interim re-analysis 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 PM_2.5 from the EPA Air Quality System (AQS) monitoring network. The model generates a high-fidelity estimate (r² = 0.75 using a 10-fold random cross-validation) of daily PM_2.5 throughout the eastern United States. Of the inputs to the statistical model, WRF-Chem output (r² = 0.66) is the most important contributor to the skill of the model. MAIAC AOD is also a strong contributor (r² = 0.52). Daily PM_2.5 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 PM_2.5 in a computationally efficient manner that is constrained to ground monitors, satellite data, and chemical transport model output at high spatial resolution (1 × 1 km²) without sacrificing the temporal resolution (daily) or spatial coverage (>2,000,000 km²).

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Research Organization:: Argonne National Lab. (ANL), Argonne, IL (United States); North Carolina State University, Raleigh, NC (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); USEPA; National Science Foundation (NSF)

Grant/Contract Number:: AC02-06CH11357; RD835871; ACI-1053575

OSTI ID:: 1490424

Journal Information:: Atmospheric Environment (1994), Vol. 199; ISSN 1352-2310

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 51 works

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