Satellite-Based Daily PM2.5 Estimates During Fire Seasons in Colorado

被引:38
作者
Geng, Guannan [1 ]
Murray, Nancy L. [2 ]
Tong, Daniel [3 ,4 ,5 ]
Fu, Joshua S. [6 ,7 ,8 ]
Hu, Xuefei [1 ]
Lee, Pius [3 ]
Meng, Xia [1 ]
Chang, Howard H. [2 ]
Liu, Yang [1 ]
机构
[1] Emory Univ, Rollins Sch Publ Hlth, Dept Environm Hlth, Atlanta, GA 30322 USA
[2] Emory Univ, Rollins Sch Publ Hlth, Dept Biostat & Bioinformat, Atlanta, GA 30322 USA
[3] NOAA, Air Resources Lab, College Pk, MD USA
[4] George Mason Univ, Ctr Spatial Informat Sci & Syst, Fairfax, VA 22030 USA
[5] Univ Maryland, Cooperat Inst Climate & Satellites, College Pk, MD 20742 USA
[6] Univ Tennessee, Dept Civil & Environm Engn, Knoxville, TN USA
[7] Oak Ridge Natl Lab, Climate Change Sci Inst, Oak Ridge, TN USA
[8] Oak Ridge Natl Lab, Computat Sci & Engn Div, Oak Ridge, TN USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
FINE PARTICULATE MATTER; AEROSOL OPTICAL DEPTH; BIOMASS BURNING EMISSIONS; AIR-POLLUTION; MODEL DESCRIPTION; WILDFIRE; URBAN; MODIS; PREDICTION; RESPONSES;
D O I
10.1029/2018JD028573
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
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 1km 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 R-2 of 0.66 and root-mean-squared error of 2.00g/m(3), 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. Key Points
引用
收藏
页码:8159 / 8171
页数:13
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