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

• Type of Material: Article
• Authors: Guannan Geng, Emory University; Nancy L. Murray, Emory University; Daniel Tong, NOAA Air Resources Laboratory; Joshua S. Fu, University of Tennessee; Xuefei Hu, Emory University; Pius Lee, NOAA Air Resources Laboratory; Xia Meng, Emory University; Howard Chang, Emory University; Yang Liu, Emory University
• Language: English
• Date: 2018-08-16
• Publisher: American Geophysical Union (AGU)
• Publication Version: Final Published Version
• Copyright Statement: ©2018. American Geophysical Union. All Rights Reserved.
• Final Published Version (URL): http://dx.doi.org/10.1029/2018JD028573
• Title of Journal or Parent Work: Journal of Geophysical Research: Atmospheres
• ISSN: 2169-897X
• Volume: 123
• Issue: 15
• Start Page: 8159
• End Page: 8171
• Grant/Funding Information: D. Tong and P. Lee acknowledge support from NASA Applied Sciences Program (grant NNX16AQ19G) and NOAA National Air Quality Forecast Capability Program. Additional support is from the National Science Foundation through TeraGrid (TG-ATM110009 and UT-TENN0006).; This publication was developed under assistance agreement no. 83586901 awarded by the U.S. Environmental Protection Agency (PI: Y. Liu).; The work of G. Geng, Y. Liu, H. Chang, and X. Meng was partially supported by the NASA Applied Sciences Program (grant NNX16AQ28G, PI: Y. Liu).; The Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory supported by the Office of Science ofthe U.S. Department of Energy (DEAC05–00OR22725) was used for the air pollution model simulations.; This work is also supported by the National Center for Advancing Transnational Sciences of the National Institutes of Health under award UL1TR000454 and the National Institute of Environmental Health Sciences under award R01ES027892.
• Keywords: RESPONSES; URBAN; MODEL DESCRIPTION; PREDICTION; Physical Sciences; WILDFIRE; Meteorology & Atmospheric Sciences; AIR-QUALITY; POLLUTION; FINE PARTICULATE MATTER; Science & Technology; BIOMASS BURNING EMISSIONS; AEROSOL OPTICAL-THICKNESS
• Research Categories: Geophysics; Biology, Biostatistics; Engineering, Civil

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.