GH016-08
A Geospatial Analysis of Wildland and Agricultural Burning PM2.5 Air Pollution for Human Health Studies in California

Tuesday, 15 December 2020: 07:21
Virtual
Patricia Koman, University of Michigan School of Public Health, Environmental Health Sciences, Ann Arbor, MI, United States, Michael Billmire, Michigan Technological University, Research Institute, Houghton, MI, United States, Kirk R Baker, Environmental Protection Agency Research Triangle Park, Research Triangle Park, NC, United States, Frank J Anderson, University of Michigan, School of Medicine, Obstetrics and Gynecology, Ann Arbor, MI, United States, Sue Anne Bell, University of Michigan Ann Arbor, School of Nursing, Ann Arbor, MI, United States, Brian J Thelen, Michigan Technological University, Michigan Tech Research Institute, Ann Arbor, MI, United States, Leyi Wang, University of Michigan Ann Arbor, School of Public Health, Biostatistics, Ann Arbor, MI, United States, Nancy H F French, Michigan Technological University, Houghton, MI, United States and Julie M Carter, University of Michigan Ann Arbor, Environmental Health Sciences, Ann Arbor, MI, United States
Abstract:
Wildland fire smoke exposure affects a broad proportion of the global and U.S. population and is increasing due to climate change, settlement patterns and fire seclusion. Exposure to fire-specific PM2.5 Significant public health questions surrounding its effects remain, including the impact on cardiovascular disease and maternal health. Using atmospheric chemical transport modeling, we examined general air quality with and without wildland fire smoke PM2.5. The 24-hour average concentration of PM2.5 from all sources in 12-km gridded output from all sources in California (2007–2018) was 5.16 μg/m3. The average concentration of fire-PM2.5 in California by year was 1.61 μg/m3 (~28% of total PM2.5). The contribution of fire-source PM2.5 ranged from 6.8% to 49.4%. The fire-PM2.5 daily mean was estimated at 4.40 μg/m3 in a high fire year (2008). We define a “smokewave” as 2 or more consecutive days with modeled levels above 35 μg/m3. Based on the model-derived fire-PM2.5 data, 99% of California’s population lived in a county that experienced at least one episode of high smoke exposure (i.e., smokewave) from 2007–2018. Eighty percent of the population lived in a county that experienced at least one smokewave per year over the same period. Photochemical model predictions of wildfire impacts on daily average PM2.5 carbon (organic and elemental) compared to rural monitors in California compared well for most years but tended to over-estimate wildfire impacts for 2008 (2.0 µg/m3 bias) and 2013 (1.6 µg/m3 bias) while underestimating for 2009 (−2.1 µg/m3 bias). The modeling system isolated wildfire and PM2.5 from other sources at monitored and unmonitored locations, which is important for understanding population exposure in health studies. Further work is needed to refine model predictions of wildland fire impacts on air quality in order to increase confidence in the model for future assessments. Atmospheric modeling can be a useful tool to assess broad geographic scale exposure for epidemiologic studies and to examine scenario-based health impacts.
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