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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

Abstract

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 km 2 spatial resolution across the eastern United States (east of 90° W) with the aid of 1 × 1 km 2 MAIAC aerosol optical depth (AOD) data, 36 × 36 km 2 WRF-Chem output, 1 × 1 km 2 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 (r 2 = 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 (r 2 = 0.66) is the most important contributor to the skill of the model. MAIAC AOD is also a strong contributormore » (r 2 = 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 km 2) without sacrificing the temporal resolution (daily) or spatial coverage (>2,000,000 km 2).« less

Authors:
 [1];  [2];  [3];  [3];  [3];  [1];  [1]
  1. Argonne National Lab. (ANL), Argonne, IL (United States). Energy Systems Division; Univ. of Chicago, IL (United States). Consortium for Advanced Science and Engineering
  2. University Space Research Association, Greenbelt, MD (United States); NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
  3. North Carolina State Univ., Raleigh, NC (United States). Dept. of Marine, Earth, and Atmospheric Sciences
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States); North Carolina State Univ., Raleigh, NC (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); USEPA; National Science Foundation (NSF)
OSTI Identifier:
1490424
Grant/Contract Number:  
AC02-06CH11357; RD835871; ACI-1053575
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Atmospheric Environment (1994)
Additional Journal Information:
Journal Volume: 199; Journal ID: ISSN 1352-2310
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES; 47 OTHER INSTRUMENTATION

Citation Formats

Goldberg, Daniel L., Gupta, Pawan, Wang, Kai, Jena, Chinmay, Zhang, Yang, Lu, Zifeng, and Streets, David G.. Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States. United States: N. p., 2018. Web. doi:10.1016/j.atmosenv.2018.11.049.
Goldberg, Daniel L., Gupta, Pawan, Wang, Kai, Jena, Chinmay, Zhang, Yang, Lu, Zifeng, & Streets, David G.. Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States. United States. doi:10.1016/j.atmosenv.2018.11.049.
Goldberg, Daniel L., Gupta, Pawan, Wang, Kai, Jena, Chinmay, Zhang, Yang, Lu, Zifeng, and Streets, David G.. Tue . "Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States". United States. doi:10.1016/j.atmosenv.2018.11.049.
@article{osti_1490424,
title = {Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States},
author = {Goldberg, Daniel L. and Gupta, Pawan and Wang, Kai and Jena, Chinmay and Zhang, Yang and Lu, Zifeng and Streets, David G.},
abstractNote = {To link short-term exposures of air pollutants to health outcomes, scientists must use high temporal and spatial resolution estimates of PM2.5 concentrations. We develop a daily PM2.5 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 PM2.5 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 PM2.5 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 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 × 1 km2) without sacrificing the temporal resolution (daily) or spatial coverage (>2,000,000 km2).},
doi = {10.1016/j.atmosenv.2018.11.049},
journal = {Atmospheric Environment (1994)},
issn = {1352-2310},
number = ,
volume = 199,
place = {United States},
year = {2018},
month = {11}
}

Journal Article:
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