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Title: Satellite-Based Daily PM 2.5 Estimates During Fire Seasons in Colorado

Abstract

The western United States has experienced increasing wildfire activities, which have negative effects on human health. Epidemiological studies on fine particulate matter (PM2.5) from wildfires are limited by the lack of accurate high‐resolution PM2.5 exposure data over fire days. Satellite‐based aerosol optical depth (AOD) data can provide additional information in ground PM2.5 concentrations and has been widely used in previous studies. However, the low background concentration, complex terrain, and large wildfire sources add to the challenge of estimating PM2.5 concentrations in the western United States. In this study, we applied a Bayesian ensemble model that combined information from the 1 km resolution AOD products derived from the Multi‐angle Implementation of Atmospheric Correction (MAIAC) algorithm, Community Multiscale Air Quality (CMAQ) model simulations, and ground measurements to predict daily PM2.5 concentrations over fire seasons (April to September) in Colorado for 2011–2014. Our model had a 10‐fold cross‐validated R2 of 0.66 and root‐mean‐squared error of 2.00 μg/m3, outperformed the multistage model, especially on the fire days. Elevated PM2.5 concentrations over large fire events were successfully captured. The modeling technique demonstrated in this study could support future short‐term and long‐term epidemiological studies of wildfire PM2.5.

Authors:
ORCiD logo [1];  [2]; ORCiD logo [3]; ORCiD logo [4];  [1]; ORCiD logo [5];  [1];  [2]; ORCiD logo [1]
  1. Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta GA USA
  2. Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta GA USA
  3. NOAA Air Resources Laboratory, College Park MD USA; Center for Spatial Information Science and Systems, George Mason University, Fairfax VA USA; Cooperative Institute for Climate and Satellites, University of Maryland, College Park MD USA
  4. Department of Civil and Environmental Engineering, University of Tennessee, Knoxville TN USA; Climate Change Science Institute and Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge TN USA
  5. NOAA Air Resources Laboratory, College Park MD USA
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); UT-Battelle LLC/ORNL, Oak Ridge, TN (Unted States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1565672
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article
Journal Name:
Journal of Geophysical Research: Atmospheres
Additional Journal Information:
Journal Volume: 123; Journal Issue: 15; Journal ID: ISSN 2169-897X
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
Meteorology & Atmospheric Sciences

Citation Formats

Geng, Guannan, Murray, Nancy L., Tong, Daniel, Fu, Joshua S., Hu, Xuefei, Lee, Pius, Meng, Xia, Chang, Howard H., and Liu, Yang. Satellite-Based Daily PM 2.5 Estimates During Fire Seasons in Colorado. United States: N. p., 2018. Web. doi:10.1029/2018jd028573.
Geng, Guannan, Murray, Nancy L., Tong, Daniel, Fu, Joshua S., Hu, Xuefei, Lee, Pius, Meng, Xia, Chang, Howard H., & Liu, Yang. Satellite-Based Daily PM 2.5 Estimates During Fire Seasons in Colorado. United States. doi:10.1029/2018jd028573.
Geng, Guannan, Murray, Nancy L., Tong, Daniel, Fu, Joshua S., Hu, Xuefei, Lee, Pius, Meng, Xia, Chang, Howard H., and Liu, Yang. Fri . "Satellite-Based Daily PM 2.5 Estimates During Fire Seasons in Colorado". United States. doi:10.1029/2018jd028573.
@article{osti_1565672,
title = {Satellite-Based Daily PM 2.5 Estimates During Fire Seasons in Colorado},
author = {Geng, Guannan and Murray, Nancy L. and Tong, Daniel and Fu, Joshua S. and Hu, Xuefei and Lee, Pius and Meng, Xia and Chang, Howard H. and Liu, Yang},
abstractNote = {The western United States has experienced increasing wildfire activities, which have negative effects on human health. Epidemiological studies on fine particulate matter (PM2.5) from wildfires are limited by the lack of accurate high‐resolution PM2.5 exposure data over fire days. Satellite‐based aerosol optical depth (AOD) data can provide additional information in ground PM2.5 concentrations and has been widely used in previous studies. However, the low background concentration, complex terrain, and large wildfire sources add to the challenge of estimating PM2.5 concentrations in the western United States. In this study, we applied a Bayesian ensemble model that combined information from the 1 km resolution AOD products derived from the Multi‐angle Implementation of Atmospheric Correction (MAIAC) algorithm, Community Multiscale Air Quality (CMAQ) model simulations, and ground measurements to predict daily PM2.5 concentrations over fire seasons (April to September) in Colorado for 2011–2014. Our model had a 10‐fold cross‐validated R2 of 0.66 and root‐mean‐squared error of 2.00 μg/m3, outperformed the multistage model, especially on the fire days. Elevated PM2.5 concentrations over large fire events were successfully captured. The modeling technique demonstrated in this study could support future short‐term and long‐term epidemiological studies of wildfire PM2.5.},
doi = {10.1029/2018jd028573},
journal = {Journal of Geophysical Research: Atmospheres},
issn = {2169-897X},
number = 15,
volume = 123,
place = {United States},
year = {2018},
month = {8}
}

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