Estimating PM2.5 concentrations from wildland fires for health impact assessments

Authors: Xiangyu Jiang*, University at Buffalo, Eun-Hye Yoo, University at Buffalo
Topics: Spatial Analysis & Modeling, Medical and Health Geography
Keywords: wildland fire, PM2.5, downscaler, CMAQ, health impact assessments
Session Type: Paper
Day: 4/6/2019
Start / End Time: 5:00 PM / 6:40 PM
Room: Truman, Marriott, Mezzanine Level
Presentation File: No File Uploaded

Wildfire has become a frequent and dreadful threat across the U.S. in recent years due to climate change. Increasing number of studies reported that wildfire contributed to poor air quality and had adverse impacts on public health. While some epidemiological studies have demonstrated the association between wildfire-related air pollution, such as fine particulate matter (PM2.5), and increased rates of hospital admissions and mortality, these findings are inconsistent from the challenges in the accurate estimation of air pollution concentrations specific to wildland fires. In the present study, we aim to investigate their causal associations by predicting daily air pollution concentrations from wildland fires and estimating adverse health outcomes during wildland fire periods. To illustrate our point, we modeled wildfire-related PM2.5 concentrations using Community Multiscale Air Quality (CMAQ) v5.2 over the northeastern US for the year 2014. We ran the CMAQ model with and without fire emissions, respectively, and calculated the difference between the two simulations as the air pollution contributed by wildland fires. Additionally, we calibrated wildland fire-specific PM2.5 and downscale it to finer spatial resolution using a Bayesian downscaler model. Lastly, we quantified excess mortality and hospital admissions attributable to exposure to wildfire-related PM2.5 using the Benefits Mapping and Analysis Program (BenMAP).

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